Purpose: This study investigates the determinants of employment readiness among educated youth in India, contextualized within the Viksit Bharat 2047 vision of achieving developed nation status. Despite rising higher education enrollment, India faces a persistent skill gap that undermines youth employability. This research develops and empirically validates a multidimensional model of employment readiness.
Design/Methodology/Approach: A cross-sectional survey design was employed, collecting primary data from 847 final-year undergraduate and postgraduate students across 12 higher education institutions in four Indian states. Structural equation modeling (SEM) was used to test hypothesized relationships among cognitive skills, technical competencies, soft skills, career self-efficacy, institutional support mechanisms, and employment readiness.
Findings: Results reveal that soft skills (β = 0.31, p < 0.001) and career self-efficacy (β = 0.28, p < 0.001) are the strongest predictors of employment readiness, followed by technical competencies (β = 0.22, p < 0.001). Institutional support mechanisms moderate the relationship between skill dimensions and employment readiness. Significant variations were observed across discipline, institution type, and socioeconomic background.
Originality/Value: This study contributes a validated Employment Readiness Index (ERI) instrument adapted to the Indian context and provides empirical evidence for policy interventions aligned with India's 2047 development goals. The findings offer actionable insights for higher education institutions, policymakers, and industry stakeholders seeking to bridge the employability gap.

This study investigated problem-solving skillsas a predictor of suicidal ideation among undergraduates in Delta State. Three research questions and two hypotheses stated in null form guided the study. The study adopted a correlational research design. The population of the study comprised all undergraduate students in Delta State in the 2023/2024 academic session, with a figure of 28,597. The sample size was 1,023 undergraduate students in Delta State. The stratified random sampling combined with proportionate stratified random sampling technique were used to sample. A questionnaire was used for data collection. The face, content and construct validities of the instruments was established by experts and Principal Component Analysis (PCA) method of factor analysis. The reliability of the instruments was done to determine their measure of internal consistency, through Cronbach Alpha reliability coefficient and found adequate. The data obtained were analysed with Pearson’s correlation coefficient of determination and regression statistics. The findings of the study revealed that undergraduates in Delta State have low level of problem-solving skills and suicidal ideation. The finding also showed that there was a significant negative relationship between problem-solving skills and suicidal ideation among undergraduates in Delta State; and that there was no significant moderating impact of gender in the relationship between problem-solving skills and suicidal ideation among undergraduates. The study recommended amongst others that institutions should integrate structured problem-solving and critical thinking activities into course instruction to strengthen students’ cognitive and decision-making abilities.

Healthcare professionals play a crucial part in promoting environmental sustainability, and Green Human Resource Management (GHRM) serves as a key mechanism connecting people management with ecological responsibility. This study examines GHRM implementation and its influence on employee green crafting behaviour across government and private hospitals in Shivamogga, using data from 351 respondents analysed through descriptive statistics, t-tests, correlation, regression and ANOVA.Results show that private hospitals demonstrate stronger GHRM adoption and greater related benefits, while government hospitals experience higher challenges such as workload, limited training, and communication gaps. A significant positive relationship was found between GHRM and employee green crafting behaviour (r = 0.424, p < 0.01), indicating that effective GHRM practices encourage proactive environmental actions among staff.Overall, GHRM emerges as an essential driver of sustainable healthcare, highlighting the need for strengthened policies, managerial support, and employee engagement to embed sustainability within HR systems.Descriptive cross-sectional research design is implemented.

Betel leaf (Piper betle L.) is a medicinal plant extensively utilized in traditional Indian medicine, recognized for its antioxidant, antimicrobial, anti-inflammatory, and antidiabetic properties. This study aimed to create a functional laddu enriched with betel leaf powder and assessitsnutritional,phytochemical,antioxidant,sensory,andshelf-lifeattributes.Freshbetel leavesweredriedat50°Cforsixhours,grindedinto powder,andaddedtoladduformulations at varying concentrations: 0% (Control), 2.5% (F1), 5% (F2), 7.5% (F3), and 10% (F4). The primary ingredients included chickpea flour, jaggery, ghee, and cardamom.

Nutritional analysis indicated that betel leaf powder is abundant in protein, dietary fiber, minerals, and bioactive compounds. The addition of betel leaf powder significantly enhanced the protein, crude fibre, ash, calcium, and iron levels in the laddus. The F4 formulation demonstratedthehighestnutritionalprofile,featuringprotein(12.8%),crudefiber(6.4%),and increased mineral content.Phytochemicalanalysisrevealed asubstantialrise in totalphenolic andflavonoid levels,leading toimprovedantioxidantactivity. Sensoryevaluation, conducted usinga9-pointhedonicscale,showedthatF4receivedfavorableconsumeracceptancewithan overallscoreof7.6/9.Shelf-lifeassessmentsperformedover60daysunderambientconditions indicated acceptable moisture levels, peroxide values, and microbial quality throughout the storage period.

The results indicate that be tel leaf powder canbeeffectivelyintegrated intoladdustoenhance theirnutritionalandfunctionalattributes.Theoptimizedformulationcontaining10%betelleaf powderexhibitedsignificantpotentialasashelf-stablefunctionalfoodwithnotableantioxidant advantages.

Road accidents involving two-wheeler riders remain a major safety concern worldwide. A significant percentage of these accidents occur due to failure to wear helmets and driving under the influence of alcohol. This paper presents a Smart Helmet with Alcohol Detection and Vehicle Control System designed to improve rider safety through automatic monitoring and vehicle authorization. The proposed system utilizes two ESP32 microcontrollers, an MQ-3 alcohol sensor, a helmet detection mechanism, and wireless communication between the helmet and vehicle units. The helmet unit continuously verifies helmet usage and detects alcohol concentration in the rider's breath. The vehicle unit receives authorization signals from the helmet unit and permits vehicle operation only when the rider is wearing the helmet and no alcohol is detected beyond the predefined threshold. The system offers a low-cost, reliable, and practical solution for enforcing safety regulations and reducing accident risks. Experimental testing demonstrated successful helmet detection, alcohol sensing, wireless communication, and vehicle control operation. The proposed system can significantly contribute to improving road safety and promoting responsible riding behavior.

Virtual Reality (VR) has emerged as a transformative educational technology capable of creating immersive and interactive learning environments. This study investigates the use of VR in education, examining its influence on student engagement, academic performance, collaborative learning, and instructional effectiveness. The study adopted a mixed-methods design combining quantitative surveys and qualitative interviews. Findings revealed that VR enhances learner motivation, retention, conceptual understanding, and practical skill acquisition. However, implementation challenges including cost, infrastructure, accessibility, and teacher preparedness remain significant barriers. Two hypotheses were tested and both indicated significant positive relationships between VR use, student engagement, and academic performance. The study contributes to the growing body of educational technology research and provides recommendations for policymakers, educators, and institutions. Virtual Reality (VR) has emerged as a transformative educational technology capable of creating immersive and interactive learning environments. This study investigates the use of VR in education, examining its influence on student engagement, academic performance, collaborative learning, and instructional effectiveness. The study adopted a mixed-methods design combining quantitative surveys and qualitative interviews. Findings revealed that VR enhances learner motivation, retention, conceptual understanding, and practical skill acquisition. However, implementation challenges including cost, infrastructure, accessibility, and teacher preparedness remain significant barriers. Two hypotheses were tested and both indicated significant positive relationships between VR use, student engagement, and academic performance. The study contributes to the growing body of educational technology research and provides recommendations for policymakers, educators, and institutions. Virtual Reality (VR) has emerged as a transformative educational technology capable of creating immersive and interactive learning environments. This study investigates the use of VR in education, examining its influence on student engagement, academic performance, collaborative learning, and instructional effectiveness. The study adopted a mixed-methods design combining quantitative surveys and qualitative interviews. Findings revealed that VR enhances learner motivation, retention, conceptual understanding, and practical skill acquisition. However, implementation challenges including cost, infrastructure, accessibility, and teacher preparedness remain significant barriers. Two hypotheses were tested and both indicated significant positive relationships between VR use, student engagement, and academic performance. The study contributes to the growing body of educational technology research and provides recommendations for policymakers, educators, and institutions.

Monitoring deforestation caused by oil palm plantation expansion requires an information system capable of integrating satellite imagery, deep learning models, and spatial visualization into a single decision-support platform. Although numerous studies have proposed deep learning methods for land-cover classification, relatively few have focused on transforming these models into operational web-based applications for environmental monitoring. This study presents the development of a web-based Decision Support System (DSS) that integrates a Convolutional Neural Network (CNN) based on ResNet-50 and U-Net for semantic segmentation of satellite imagery. The system enables users to upload satellite images, perform automated land-cover classification, visualize temporal changes, generate spatial predictions, and produce analytical reports through an interactive web interface. The application was implemented using Python, Flask, TensorFlow, and Geographic Information System (GIS) components. Functional evaluation using Black-box Testing demonstrated that all system modules operated successfully, including image upload, segmentation, prediction, visualization, and report generation. The developed system provides an accessible platform for government agencies, researchers, and environmental managers to monitor forest conversion efficiently and support evidence-based decision making.

This paper presents the design and development of an industry-compatible aluminium cooling jacket for a 25-cell lithium-ion battery pack intended for electric vehicle battery thermal management systems (BTMS). Efficient thermal management is essential for maintaining battery safety, performance, and service life under high charging and discharging conditions. Unlike many existing cooling solutions that require significant modifications to battery pack architecture, the proposed cooling jacket is designed to integrate directly within the existing 5 mm spacing of standard polymer cell holders, thereby preserving current industrial assembly practices.
The cooling jacket was developed using CATIA V5 with emphasis on manufacturability, compactness, and effective thermal contact with cylindrical 18650 lithium-ion cells. Aluminum 3003 was selected as the cooling jacket material because of its high thermal conductivity, corrosion resistance, low weight, and ease of fabrication. The design incorporates serpentine like coolant channels that maximize heat transfer while maintaining structural integrity and compatibility with existing battery modules.
The proposed configuration provides a practical solution that minimizes manufacturing modifications while improving the thermal interface between coolant and battery cells. The design methodology demonstrates how industrial constraints, material selection, and packaging requirements can be combined to develop a scalable cooling solution suitable for electric vehicle battery packs. This study establishes a strong foundation for subsequent computational and experimental thermal performance evaluations.

Rapid urbanisation in metropolitan regions has created a persistent information asymmetry between landlords and prospective tenants. New residents — including students, working professionals, and migrants — frequently lack access to reliable, data-driven tools for evaluating rental fairness, comparing localities, and estimating total cost of living. This paper presents a comprehensive AI/ML framework, the Urban Rent and Living Analyser, which integrates automated data collection, machine learning-based rent prediction, locality recommendation, time-series trend forecasting, and an explainable AI module to deliver actionable hyperlocal intelligence. The system employs Gradient Boosting models (XGBoost and LightGBM) for rent prediction, content-based filtering for locality recommendation, Facebook Prophet for trend forecasting, and SHAP values for model interpretability. An AutoML layer powered by a large language model dynamically selects the best-performing model and generates natural-language evaluation reports. Experimental design targets a Mean Absolute Error below the equivalent of two thousand local currency units for rent prediction. The proposed framework demonstrates that combining structured rental data, geospatial features, and generative AI reasoning produces a practical decision-support system for urban renters in any metropolitan context.

Financial inclusion is one of the central policy measure adopted mostly in developing economy for promoting their sustainable and equitable growth. Though there are several progress made in the process of expansion of formal banking networks under flagship government programmes, a extensive proportion of India's rural population functions along with its operation outside the formal financial system. This study tries to examine the relationship between the selected variables like digital financial inclusion (DFI) and household economic well-being in rural India and for this purpose it has used the secondary data from the National Sample Survey (NSS) 77th Round (2019–20) and the Reserve Bank of India's Financial Inclusion Index (FI-Index, 2022). The findings of the study have showed that the households residing in states with higher digital financial inclusion scores record significantly greater monthly per capita consumption expenditure (β = 412.36, p < 0.001). Furthermore, there is a strong negative correlation is established between state-level digital financial inclusion and multidimensional poverty indices (r = −0.684, p < 0.001), affirming DFI's role as a catalyst for economic mobility. Pronounced interstate disparitiesparticularly between southern and northeastern statesunderscore the imperative for region-specific policy interventions. The study contributes to the empirical literature on fintech-driven financial inclusion and offers actionable policy recommendations pertaining to digital infrastructure, financial literacy, and last-mile service delivery in rural India.

Food fortification is an effective public health strategy for combating micronutrient deficiencies and improving nutritional status among populations. The present study aimed to assess awareness, household consumption patterns, and factors influencing the utilization of fortified foods among consumers. A cross-sectional survey was conducted among 212 respondents using a structured questionnaire to collect information on socio-demographic characteristics, awareness, knowledge, perception, consumption practices, and determinants influencing the use of fortified foods. Data were analyzed using descriptive statistics, Chi-square test, Independent Sample t-test, One-Way ANOVA, Pearson correlation analysis, and Multiple Regression Analysis. The results revealed that 86% of respondents had heard about fortified foods, while 87% recognized the +F logo on food packages. Social media was identified as the major source of awareness (49%). A majority of respondents reported purchasingfortifiedfoodproducts(81%),and72%consumedfortifiedfoodsdaily.Significant associationswereobservedbetweenawarenessandage(p

Background
Most common adverse effect causing cessation of anti‐tubercular treatment is drug‐induced liver injury which is unpredictable due to its idiosyncratic nature. ATT is the most common cause of DILI and drug‐induced acute liver failure in South East Asia.
Material and Methods: A case control study conducted on ‘Burden of antitubercular drug-induced hepatotoxicity among tuberculosis and other respiratory tract infected patients admitted to a quaternary care hospital, Belagavi’. Aim of the study is to evaluate the burden of antitubercular drug-induced hepatotoxicity among tuberculosis patients constituting the case group with demographic variables.To examine the distribution of respiratory tract infection patients without hepatotoxicity serving as the control group with demographic variables.To evaluate the relationship between antitubercular drug-induced hepatotoxicity cases and the control group with demographic variables.Researcher employed descriptive research approach with purposive sampling method, total population the study were 160.
Results: There will be a strong positive linear relationship between tuberculosis patients with adverse drug reactions (Case Group) and respiratory tract infection patients without adverse drug reactions (Control Group). Regression analysis will be 0.9, P-value is > 0.05 level of significance, and table value is 1.943.
Conclusion: At the conclusion of the study, a significant association was found between case group and control group with selected demographic variables. Additionally, Karl Pearson’s correlation coefficient indicated a strong positive relationship between the two.

Student academic performance prediction is an important area in Educational Data Mining (EDM) that helps institutions identify students at risk and take corrective actions before final examinations. This research proposes a machine learning-based approach for predicting student performance in the Semester End Examination (SEE) using Attendance Percentage and Continuous Internal Evaluation (CIE) Marks as predictor variables. The K-Nearest Neighbors (KNN) algorithm is employed due to its simplicity, interpretability, and effectiveness in classification problems.
A dataset comprising students' attendance records, CIE marks, and SEE results is collected from the institutional academic database. Attendance percentage and CIE marks serve as input features, while SEE performance categories such as Pass, First Class, Distinction, and Fail are used as target labels. The data undergo preprocessing, normalization, and train-test splitting before model training. The KNN classifier calculates the similarity between students using Euclidean distance and predicts SEE outcomes based on neighboring instances.
Experimental results demonstrate that attendance and CIE marks significantly influence SEE performance. The proposed KNN model achieves high prediction accuracy, enabling early identification of students requiring academic intervention. The study highlights the potential of machine learning techniques in improving educational decision-making and student success rates.

Growth,tissuehealing,musclemassmaintenance,andotherphysiologicalprocessesalldepend on protein, an essential macronutrient. Protein intake is significantly influenced by dietary habits,especiallyforthosewhofollowvegetarian andnon-vegetariandiets.Thecurrentstudy sought to determine the factors influencing protein-related dietary behaviors and to compare thepatternsofprotein consumption among female collegestudents whowerevegetarians and non-vegetarians. 200 female students between the ages of 18 and 25 participated in a comparative cross-sectional study. The study included 100 vegetarians and 100 non vegetarians. A standardized questionnaire that evaluated eating patterns, meal frequency, breakfastconsumption,intakeoffoodshighinprotein,andfoodchoicesrelatedtoproteinwas usedtogatherdata.Descriptivestatistics,theChi-squaretest,MultipleLinearRegression,and One-WayANOVAwere among the statistical analyses carried out using SPSS software.
The results showed that while fish and soy products were consumed less frequently, the majorityofparticipantsatethreemealsaday(71.0%)andfrequentlyconsumeddairyproducts. Just 30.5% of respondents purposefully chose foods based on protein content, and over half (46.0%)seldomincludedfoodshighinproteininbreakfast.Age,dietarypattern,andanumber of protein intake behaviors were found to be significantly correlated (p < 0.05).Age, dietary pattern,mealfrequency,breakfasthabits,intentionalselectionofproteinrichfoods,andusage of protein supplements all had a significant impact on protein intake ratings, according to multiple linear regression analysis (R2 = 0.260, p < 0.001). Meal frequency significantly affected protein intake scores, according to a one-way ANOVA (F = 5.450, p = 0.005). According to the study's findings, female college students' moderate protein intake was influencedby their age, mealfrequency,anddietaryhabits.Youngwomen'sdietary adequacy may be improved by nutrition education programs that encourage balanced protein consumption and raise awareness of various protein sources.

The increasing need for intelligent surveillance systems in educational institutions has led to significant research in automated human behavior recognition. Classroom environments require continuous monitoring to ensure student safety and maintain discipline. Traditional surveillance systems rely heavily on manual observation, which is time-consuming and prone to human error. To address these limitations, this study proposes a real-time suspicious human behavior recognition system using Convolutional Neural Network (CNN) models for fight detection in classrooms.
The proposed system utilizes video surveillance data to identify violent or suspicious activities automatically. The framework processes video frames using image preprocessing and deep learning techniques to detect abnormal human interactions such as fighting, aggressive movements, and physical conflicts. A CNN-based architecture is employed to extract spatial features from video frames and classify behaviors into normal and suspicious categories. The model is trained using labeled datasets containing classroom activities and fight scenarios to improve detection accuracy and robustness.
The proposed system operates in real time and generates alerts whenever suspicious behavior is detected, enabling quick intervention by authorities or teachers. Experimental results demonstrate that the CNN-based model achieves high accuracy, precision, recall, and F1-score in recognizing fight-related activities under varying lighting conditions and classroom environments. The integration of deep learning with surveillance systems improves automation, reduces manual monitoring effort, and enhances student safety.
The study highlights the effectiveness of CNN models for real-time human behavior analysis and abnormal activity detection. Future enhancements may include the integration of Long Short-Term Memory (LSTM) networks, attention mechanisms, pose estimation techniques, and edge computing for improved temporal analysis and faster real-time deployment in smart classroom environments.

Background: Community-Based Rehabilitation (CBR) identifies the family as the primary source of support and social inclusion for individuals with disabilities. The degree to which the programme can be effective relies heavily on caregivers’ attitudes and perceived competencies. However, little empirical research has been conducted in urban India regarding caregivers' perceptions and attitudes toward caring for individuals with disabilities.
Objectives: The objective of this study was to assess the attitudes of family caregivers regarding CBR in a metropolitan area of Northern India and to determine whether there were significant differences in caregivers' attitudes based on gender, age, or type(s) of disabilities.
Methods: The study used a cross-sectional survey that included 120 primary caregivers selected through purposive sampling from 12 Non-Governmental Organizations (NGOs) providing CBR services. The data were collected using a psychometrically validated self-constructed 30-item Attitude Scale (Cronbach's α = 0.72) and were analyzed using Descriptive Statistics, an Independent-samples t-test, one-way ANOVA with Tukey HSD post-hoc comparison, and Chi-square testing.
Results: The overall mean attitude toward CBR was 103.33 (SD = 6.72). A majority of families (55.0%) exhibiteda negative attitude toward the CBR programme, while 40.8% showeda neutralattitude, and only 4.2% hada positive attitude. The gender and age of the caregivers had no significant impact on the overall attitudes (p = 0.19; p = 0.59, respectively). The type of disability had a highly significant effect on overall attitudes toward CBR (F(4, 115) = 8.83, p < 0.001, η² = 0.24). Families of children with ASD (M = 97.33) and Down Syndrome (M= 94.00) had significantly lower mean attitude scores towards CBR than families of children with locomotor or sensory disabilities.
Conclusion: The predominance of negative and neutral attitudes reveals a critical implementation gap in urban CBR. The findings highlight the need for several strategies to enhance the overall acceptance of Urban-based CBR and improve the sustainability of CBR programmes. These strategies may include a specific focus on capacity-building for developmental disabilities, the development of a structured engagement process through which families can support one another, and the establishment of an ongoingdiagnosis-specific peer support model.

The present study aims to examine and compare the learning styles of boys and girls studying at the upper primary level. Learning style refers to the preferred way in which learners acquire process, understand, and retain information. Understanding students’ learning styles is essential for designing effective instructional strategies that cater to diverse learning needs and promote meaningful learning outcomes. Previous research has indicated that gender may influence learning preferences, although findings remain inconclusive and context-dependent. Therefore, this study seeks to explore whether significant differences exist between boys and girls in their preferred learning styles within upper primary school settings.
The study adopts a descriptive survey research design. A sample of upper primary students from selected schools is chosen using a stratified random sampling technique to ensure adequate representation of both boys and girls. Data are collected through a standardized Learning Style Inventory (LSI), which measures various dimensions of learning styles, including visual, auditory, kinesthetic, and reading/writing preferences. The collected data are analyzed using descriptive statistics such as mean and standard deviation, as well as inferential statistics, including the independent samples t-test, to determine gender-based differences in learning style preferences.
The findings of the study are expected to reveal variations in learning styles among upper primary students and identify whether gender plays a significant role in shaping these preferences. It is anticipated that some learning style dimensions may be more prevalent among boys, while others may be more common among girls. Such differences, if identified, can provide valuable insights for educators in developing gender-responsive teaching methods and classroom practices. The results may also contribute to enhancing student engagement, academic achievement, and overall learning experiences by encouraging the use of diversified instructional approaches.
The significance of this study lies in its potential to assist teachers, curriculum developers, educational planners, and policymakers in understanding the diverse learning needs of students. By recognizing gender-related learning preferences, educational stakeholders can create inclusive learning environments that support the academic growth of all learners. Furthermore, the study contributes to the existing body of knowledge on learning styles and gender differences in education, particularly at the upper primary level, where cognitive, social, and emotional development undergo significant changes.

Hearing-impaired students in higher education often face significant barriers in accessing language and communication support services that are essential for academic success and social inclusion. The availability and quality of such facilities vary considerably across institutions, influencing students’ learning experiences and educational outcomes. This study aims to conduct a comparative analysis of language and communication facilities provided to hearing-impaired students in Indian higher education institutions. The research examines the availability, accessibility, and effectiveness of support services such as sign language interpretation, captioning services, assistive listening devices, note-taking assistance, speech-to-text technologies, and specialized learning resources.
The study adopts a comparative descriptive research design involving selected universities and colleges from different regions of India. Data are collected through surveys, interviews, and institutional records to assess existing facilities and gather the perceptions of hearing-impaired students regarding their usefulness. The findings are expected to highlight disparities in the provision of communication support services between institutions and identify best practices that promote inclusive learning environments. The study also explores the challenges faced by students in utilizing available resources and examines the role of institutional policies in ensuring accessibility.
The outcomes of this research will contribute to the understanding of inclusive practices in higher education and provide recommendations for policymakers, administrators, and educators to strengthen language and communication support systems for hearing-impaired learners. Enhancing these facilities can improve academic participation, educational achievement, and overall inclusion of students with hearing impairments in Indian higher education institutions.

In production cloud environments, a vast amount of observability information is created as logs, metrics, and distributed traces. These multiple telemetry streams contain a wealth of important data about the system but are not only very large, very heterogeneous, and very noisy, but the task of recognizing anomalies remains a constant challenge for the cloud ops team. While many anomaly detection techniques have been suggested, currently most evaluations are done on a small number of techniques or only one data set, making it difficult to find comprehensive evidence of their comparative effectiveness and reproducing most evaluations. This work offers a repeatable benchmark of assessing the statistical, machine learning and large language model (LLM) based anomaly detection techniques applied to various cloud observability datasets. The benchmark includes statistical techniques such as Exponentially Weighted Moving Average (EWMA), Seasonal-Trend Decomposition using Loess (STL), and Cumulative Sum Control Charts (CUSUM), classical machine learning techniques such as Isolation Forest, One-Class Support Vector Machines (OCSVM), and Autoencoders, and emerging LLM-based zero-shot anomaly-detection baselines. Publicly accessible datasets such as HDFS, BGL, Thunderbird, OpenStack, and Hadoop are used for the experiments, all of which are preprocessed and evaluated in the same way. Precision, recall, F1-score, alert lead time, false-positive rate, and the computational overhead and cost-per-alert are used to assess performance. The benchmark provides a structured comparison of the detection approaches and shows patterns of detection method compatibility and incompatibility with the datasets. The work lays the groundwork for reproducible anomaly-detection research, and offers a basis to select monitoring strategies in today's cloud-native systems based on evidence.

Platform operations, incident response, diagnostics, and operational automation are all revolutionized by Large Language Model (LLM) agents. Operational teams managing system reliability and performance are faced with enormous challenges due to the complexity of the cloud, distributed, microservices and hybrid computing infrastructures. The advent of recent developments in LLM results in the creation of intelligent agents that have the ability to comprehend the operational context, analyze logs, correlate events, retrieve knowledge, interact with tools, and support remediation activities.
Although research work is in progress on this subject, the current studies are still in isolation in different areas such as AIOps, DevOps, cyber security operations, infrastructure management, and autonomous system administration. Moreover, there are significant differences between prototypes in academia and real deployments in terms of reliability, safety, governance and evaluation approaches. The survey offers a thorough overview of over a hundred studies covering the past few years on LLM agents in platform operations. A new four-axis taxonomy is presented for literature classification based on reasoning architecture, grounding strategy, and action model and evaluation methodology.
The literature is classified on the basis of a novel four-axis taxonomy: reasoning architecture, grounding strategy, action model, and evaluation methodology. The survey also covers the key areas of application, such as incident response, log analysis, root cause diagnosis, infrastructure troubleshooting and automated remediation. Comparative analysis shows the common architectural characteristics, operational restrictions and design trends. Special emphasis is given to the transition from research systems to deployment in production highlighting issues related to trustworthiness, scalability, and governance and production safety.
The results of these studies inform a broad research landscape for future studies to foster robust, explainable, secure, and production-ready operational LLM agents.

This study developed and evaluated a video lesson on Physical Phonetic Articulation (PPA) Guide to address the highly evident issues in the physical articulation skills of Grade 1 learners in the Carmen District. The study employed a developmental and quasi-experimental research design to facilitate the development of the instructional material and determine its effectiveness. Thirty-two identified non-performing Grade 1 learners participated in the study and were subjected to pre-test and post-test assessments to measure changes in physical phonetic articulation and word recognition skills following the intervention.
Data analysis revealed a violation of the normality assumption based on the Shapiro-Wilk Test; thus, non-parametric statistical procedures, specifically the Wilcoxon Signed Rank Test and Spearman's rho correlation analysis, were utilized. Pre-test results showed positively skewed distributions across the dimensions of instructional labeling, sound overlap, vowel mapping, and plosive consonants, indicating generally low levels of articulation skills among the learners. Following the implementation of the digitalized PPA guide, post-test scores exhibited negatively skewed distributions, reflecting substantial improvements in learners' performance.
Results of the Wilcoxon Signed Rank Test demonstrated significant improvements in all dimensions of physical phonetic articulation after exposure to the video lessons. Likewise, learners' word recognition skills improved considerably, shifting from low-emerging levels during the pre-test to higher performance levels in the post-test. Spearman's rho correlation analysis further revealed significant positive relationships between instructional labeling, sound overlap, plosive consonant articulation, and word recognition skills.
The findings establish the effectiveness of the video lesson on Physical Phonetic Articulation in enhancing phonetic articulation and word recognition among Grade 1 learners. The study concludes that digitalized instructional materials, when supported by structured lesson plans, can significantly contribute to literacy development by addressing essential foundational reading skills among beginning learners.

This study investigates the key organizational factors affecting strategic plan implementation in selected government institutions of Somaliland. Despite extensive strategic planning initiatives, including National Development Plan I (2012–2016) and Vision 2030, many strategic initiatives in Somaliland's public sector fail to achieve their intended objectives. The study employed a quantitative research approach, with data collected from 138 senior officials drawn from 46 government institutions through structured questionnaires using simple random and purposive sampling. Data were analyzed using descriptive statistics, Pearson correlation, and multiple linear regression via STATA software. The findings reveal that resource availability (β = 0.4644, p < 0.001), organizational structure (β = 0.4174, p < 0.001), and staff capacity (β = 0.3885, p < 0.001) are significant positive predictors of strategic implementation. Organizational communication also showed a positive and statistically significant effect (β = 0.2821, p = 0.001). Organizational culture exhibited a significant negative effect (β = −0.4374, p < 0.001), indicating cultural misalignment with strategic objectives, while leadership style did not demonstrate a statistically significant direct effect (β = −0.030, p = 0.671). These findings provide practical implications for governance reform and contribute empirical evidence to the literature on public sector strategic management in fragile institutional contexts.

Dandruff is a common scalp disorder characterized by excessive shedding of corneocytes, itchiness, and irritation, largely associated with fungal colonization—primarily Malassezia species.Increasingconcernsoverthesideeffectsofsyntheticanti-dandruffagentssuchaszinc pyrithione, selenium sulfide, and ketoconazole have encouraged the development of herbal formulations that are safer, biodegradable, and compatible with long-term use. The present study focuses on the preparation and evaluation of a polyherbal anti-dandruff shampoo using natural ingredients selected for their antifungal, antibacterial, cleansing, and conditioning properties. The herbal ingredients incorporated in the formulation include neem (Azadirachta indica) for its potent antimicrobial and anti-inflammatory effects, shikakai (Acacia concinna) and reetha (Sapindus mukorossi) as natural surfactants and cleansing agents, fenugreek (Trigonellafoenum-graecum)seedsfortheirconditioningandanti-flakingproperties,andaloe vera (Aloe barbadensis) for soothing and moisturizing the scalp. These raw materials were prepared throughdrying,powdering,and extraction usingaqueous orhydroalcoholicsolvents depending on their phytochemical solubility. The herbal extracts were combined with a mild base, natural thickening agents, and preservatives to obtain a stable shampoo formulation. Evaluationoftheherbalshampooinvolvedarangeofphysicochemicalandperformance-based tests. Parameters such as pH, viscosity, foam height, foam retention, surface tension, wetting time,dirtdispersion,andsolidcontentweremeasuredtoassessqualityandcompatibilitywith scalp physiology. The pH of the formulation was maintained between 5.0 and 6.5, making it suitable for maintaining scalp health and preventing cuticle damage. The shampoo demonstratedsatisfactoryfoamingability,goodcleansing action,andacceptableviscosityfor consumer use. Antidandruff activity was assessed using microbiological tests against Malasseziafurfur,whichshowednoticeableinhibitoryeffects,indicatingthesynergisticaction of the herbal ingredients.Additionally, a small-scale user evaluation indicated improvements inscalpcleanliness,reductioninflakes,andenhancedhairsoftnesswithoutsignsofirritation. The results suggest that the prepared herbal anti-dandruff shampoo possesses effective cleansing,conditioning,andantifungalproperties,makingitapromisingnaturalalternativeto synthetic formulations. The study highlights the potential of plant-based ingredients in producingsafe,economical,andenvironmentallyfriendlyhair-careproducts.Furtherresearch onstabilityenhancementandlarge-scaleclinicalevaluationwouldsupportcommercialization and wider therapeutic use.

The purpose of this study was to investigate the physiological responses to water resistance training among competitive junior women Kabaddi players of Andhra Pradesh State. Sixty (N = 60) junior female Kabaddi players, aged between 16 and 19 years, who had represented Andhra Pradesh at the state level, were selected as subjects. The participants were randomly divided into two equal groups: an experimental group (n = 30) and a control group (n = 30). The experimental group underwent a structured water resistance training programme for twelve weeks, five days per week, while the control group continued with their regular Kabaddi training schedule.The selected physiological variables included resting heart rate, vital capacity, breath-holding time, maximal oxygen uptake (VO₂ Max), and cardiovascular endurance. Pre-test and post-test measurements were recorded for all subjects using standardized testing procedures. The collected data were analyzed using Analysis of Covariance (ANCOVA) to determine the significance of differences between the groups at the 0.05 level of confidence.The findings revealed that the experimental group showed significant improvements in all selected physiological variables compared to the control group. Water resistance training enhanced cardiorespiratory efficiency, increased oxygen utilization capacity, improved lung function, and reduced resting heart rate. These adaptations are consistent with previous findings that aquatic resistance exercise can increase oxygen uptake and energy expenditure, while Kabaddi performance is strongly associated with aerobic and anaerobic physiological capacities.It was concluded that water resistance training is an effective training method for improving physiological performance among competitive junior women Kabaddi players. Therefore, coaches and trainers may incorporate aquatic resistance exercises into regular training programmes to enhance the physiological fitness and competitive performance of female Kabaddi athletes.

Women’s labour migration has emerged as a significant socio-economic phenomenon in India, particularly in economically vulnerable and disaster-prone regions where livelihood insecurity and limited employment opportunities persist. Although migration research increasingly recognises the feminisation of labour migration, women’s migration remains relatively under-examined, especially with regard to its micro-level livelihood implications. In states such as Odisha, structural inequalities, poverty, gendered labour markets, and recurrent environmental stress have contributed to a steady rise in women’s labour migration. Balasore district, located in the coastal belt of Odisha, presents a distinctive migration context characterised by seasonal unemployment, dependence on agriculture and informal activities, and frequent exposure to natural disasters. These conditions have intensified both intra-state and inter-state migration among women workers.This study examines the patterns, determinants, and livelihood outcomes of women’s labour migration in Balasore district. It is based on primary survey data collected from women migrant workers in selected rural and semi-urban areas. The study analyses socio-demographic characteristics, migration patterns, types of employment at destination areas, wage structures, and income dynamics. It also assesses the broader implications of migration for household livelihood security, women’s economic contribution, and their decision-making roles within households, while identifying the vulnerabilities associated with migration.

Despite decades of intensive drug discovery efforts, epilepsy remains a significant global health challenge, with approximately one-third of patients suffering from refractory seizures that are resistant to existing pharmacotherapies.1 This persistent therapeutic gap highlights the inadequacy of traditional, often single-target, approaches and necessitates the application of sophisticated, high-efficiency methodologies such as Computer-Aided Drug Discovery (CADD).3 This thesis details the development and rigorous validation of Quantitative Structure-Activity Relationship (QSAR) models, specifically 2D-Multiple Linear Regression/Partial Least Squares (MLR/PLS) and 3D-Comparative Molecular Field Analysis (CoMFA), aimed at designing and predicting novel antiepileptic agents. A curated dataset of known anticonvulsant agents was sourced from public bioactivity databases and partitioned into statistically representative training and external test sets. Molecular structures were characterized using diverse descriptor sets (1D, 2D, and 3D field variables). The developed models were subjected to multi-layered validation, including internal crossvalidation (Q^2) and stringent external prediction metrics (R^2_{pred}), alongside Yrandomization tests to exclude chance correlation. Statistical analyses, including the T-test applied to regression coefficients, confirmed the high significance (p < 0.05) of key electronic and steric descriptors, providing quantitative structural requirements for activity. Furthermore, a classification QSAR model's reliability in distinguishing active from inactive compounds was confirmed using the Chisquare test, yielding a high association statistic (chi^2) and a robustly low p-value. The resulting statistically robust and mechanistically interpretable QSAR models were subsequently used to define molecular design rules and prioritize novel lead compounds with high predicted potency and favorable characteristics for future synthesis and preclinical evaluation.

This study examines the magneto hydrodynamic (MHD) flow, heat transfer, and mass transport characteristics of a Casson hybrid Nano fluid over a radially stretching surface in a Darcy–Forchheimer porous medium. The hybrid Nano fluid is formulated by dispersing aluminum oxide (Al₂O₃) and titanium dioxide (TiO₂) nanoparticles in engine oil, a thermally stable base fluid widely used in industrial heat exchange and lubrication systems. The mathematical model incorporates the combined effects of thermal radiation, magnetic field heating, porous medium resistance, nonlinear chemical reaction, and activation energy. By applying similarity transformations, the governing partial differential equations are reduced to a system of ordinary differential equations and solved numerically using the Bvp4c solver. The obtained numerical data are used to train a Morlet Wavelet Neural Network optimized with Particle Swarm Optimization and a Neural Network Algorithm (MWNN–PSO–NNA) to enhance prediction accuracy and computational efficiency. Results indicate that an increasing magnetic field reduces the velocity profile, while thermal radiation significantly enhances temperature distribution. Activation energy improves species concentration control within the porous medium. The hybrid Nano fluid demonstrates superior thermal performance compared to the base fluid and single nanoparticle suspensions. The proposed MWNN–PSO–NNA model achieves prediction accuracy above 99% and reduces computational time by approximately 45% compared with conventional numerical simulations, highlighting its effectiveness for predicting complex non-Newtonian hybrid Nano fluid behavior in advanced industrial thermal management systems.

The rapid growth of global trade, e-commerce, and integrated supply chains has significantly increasedtheoperationalscaleandenergydemandsoflogisticshubs,includingports,warehouses, inland container depots, distribution centers, and logistics parks. As these facilities expand their operations, energy consumption has become a critical concern due to rising operational costs, environmental impacts, and increasing regulatory pressure to reduce greenhouse gas emissions. Consequently, logistics organizations are seeking innovative approaches to improve energy efficiency while maintaining high levels ofoperationalperformance. Technology-Driven Energy Management Systems (EMS) have emerged as an effective solution for monitoring, controlling, and optimizing energy consumption across logistics operations.

This study examines the role of technology-driven EMS in enhancing sustainability and operational efficiency within logistics hubs. The paper explores the integration of advanced technologies such as the Internet of Things (IoT), Artificial Intelligence (AI), Machine Learning (ML), Smart Grids, Renewable EnergySystems, Big Data Analytics, Automation, Robotics, and Blockchain in energy management. A qualitative review of existing literature, industry reports, and case studies was conducted to evaluate the effectiveness of these technologies in improving energy performance.

The findings indicate that technology-driven EMS significantly reduce energy waste, improve energy visibility, support predictive maintenance, enhance operational efficiency, and contribute to environmental sustainability. The study further reveals that organizations implementing integrated EMS solutions experience substantialcost savings, improved resource utilization, and enhanced compliance with international environmental standards.

The paper concludes that technology-driven EMS represent a strategic investment for logistics hubs seeking long-termsustainability, competitiveness, and resilience in an increasingly energy-consciousglobaleconomy.Recommendationsareprovidedforpolicymakers, logisticsoperators, and technology providers to accelerate the adoption of smart energy management solutions.

The implementation of Virtual Local Area Networks (VLANs) is essential for optimizing performance, security, and management in campus networks. Many campus networks operate on a single broadcast domain where all users share the same network. This causes congestion, security issues, and poor performance. Managing such networks becomes harder as the number of users increases. This research focuses on designing and implementing VLANs in a simulated campus environment using Cisco Packet Tracer. The study analyzed typical network challenges, including congestion, broadcast traffic, and security risks in flat network structures. A hierarchical network design was adopted, with VLANs assigned to three departments: Administration, Staff, and Students. Inter-VLAN routing, IP addressing, trucking, and cloud service integration were implemented and tested. The results demonstrated improved traffic management, secure communication between departments, and enhanced network scalability. This project confirms the effectiveness of VLANs in educational institutions and provides a practical framework for future network expansion, security enhancement, and performance optimization.

The increasing integration of digital technologies in education has transformed teaching and learning processes across the globe. However, the benefits of educational digitalization remain unevenly distributed, particularly in rural schools where limitations in infrastructure, connectivity, and technological resources continue to create barriers to effective implementation. This study systematically reviewed the existing literature on digitalized instruction in rural educational settings, with particular emphasis on teachers’ experiences, challenges, and adaptive practices. Using a systematic literature review approach, relevant studies, policy documents, and reports published between 2020 and 2026 were retrieved from scholarly databases and reputable organizations. The selected literature was analyzed through thematic synthesis to identify recurring patterns and emerging themes.The review revealed four major themes: digitalization as a catalyst for educational access and innovation; persistent digital divide and infrastructure challenges; teacher readiness and professional development concerns; and the emergence of adaptive and localized digital teaching practices. Findings indicate that digital technologies enhance learner engagement, instructional flexibility, and access to educational resources. However, inadequate internet connectivity, limited ICT infrastructure, insufficient digital devices, and unequal resource distribution continue to impede digital transformation in rural schools. Moreover, while teachers generally demonstrate positive attitudes toward technology integration, many experience challenges related to digital competence, instructional design, and limited professional development opportunities. Despite these constraints, educators have adopted innovative and context-responsive strategies to sustain instructional delivery.The study concludes that successful digitalization in rural education requires a holistic approach that combines infrastructure development, teacher capacity building, supportive policies, and localized instructional innovations. Strengthening these areas is essential to bridging the digital divide and achieving equitable, inclusive, and sustainable digital education for rural communities.

The global surge in Korean language learners, driven by the Korean Wave (Hallyu), has created unprecedented demand for accessible and engaging Hangul literacy resources. Yet conventional script-acquisition materials, rooted in repetitive drills and static print formats, often fail to sustain the motivation of digitally immersed learners encountering the Korean writing system for the first time. This study addresses this gap through a systematic content analysis of music-and-animation-based educational videos on the YouTube channel Hangul-eulBaewobwayo (HANBAE/Learn Hangul). A purposive sample of 18 videos — spanning the consonant song series (jaeumsong, Episodes 001–015), vowel song series (moemsong), and integrated syllable-block videos — was analyzed using a researcher-developed coding framework organized around three axes: (1) linguistic and pedagogical elements, (2) audio-visual multimodal features, and (3) affective-motivational edutainment strategies. Findings reveal that HANBAE systematically integrates song-based mnemonics with kinetic typography animation to reinforce phonological awareness and graphemic recognition of individual jamo, while character-driven animation and stroke-order visualization align with the formative principles of Hunminjeongeum. Theoretically, the channel's strategies operationalize Paivio's Dual-Coding Theory and Krashen's Affective Filter Hypothesis, demonstrating how multimodal digital media can lower psychological barriers to script acquisition. The study offers practical implications for content designers, language educators, and curriculum developers seeking scalable, engagement-optimized Hangul literacy tools.

The study examined the relationship between environmental and hygiene factors as correlates of cholera prevention practices among adolescents in AkwaIbom North-East Senatorial District, Nigeria. Two research questions were raised and hypotheses formulated and tested at .05 level of significance. The study adopted the correlational research design. The population of the study comprised all the 19,260 JS2 students in the 89 public secondary schools in the study area. A sample size of 963 JS2 students representing five percent of the study population was selected. Through a multi-stage sampling procedure, the researcher applied a systematic random sampling technique in the selection of five (5) Local Government Areas (LGAs) for the study, out of which 27 sampled schools were selected. Also, a balloting method of random sampling was used in the selection of 10 percent of JS2 students from each of the sampled school for instrument administration. The instrument for data collection was the researcher’s designed questionnaire titled: “Environmental and Hygiene Factors Questionnaire (EHFQ) and Cholera Prevention Practices (CPPQ), with overall reliability coefficient of .809 obtained through Cronbach Alpha statistics. After administering the instruments, a total of 956 questionnaires were properly filled, retrieved and data coded correctly for analysis. Pearson Product Moment Correlation (PPMC) statistics was used for data analysis and the findings revealed, a very high positive and significant relationship between access to safe water, proper sewage disposal, good personal hygiene, accessibility of health facilities, community health education and cholera prevention practices among adolescent in AkwaIbom North-East Senatorial District. The findings further revealed a very low negative and insignificant relationship between climate change, overpopulation, consumption of contaminated consumables and cholera prevention practices among adolescents in the study area. Conclusion was drawn from the findings while it was recommended among other things that public health officials should use available media channels to educate communities members on the importance of proper hygiene and sanitation practices, including hand-washing with soap and water after the toilet and before eating so as to avoid being infected with cholera.

The rapid shift to online education during the COVID-19 pandemic transformed the traditional teaching-learning process across the globe. This study examines the impact of online classes on rural English learners, focusing on accessibility, learning outcomes, challenges, and opportunities. Using a qualitative and survey-based approach, the research highlights that while online learning provides flexibility and access to diverse resources, rural students face significant barriers such as poor internet connectivity, lack of devices, and limited interaction. The study concludes that although online classes have potential benefits, infrastructural and socio-economic challenges reduce their effectiveness in rural English language learning.

Solar distillation is one of many ways to purify water, and it is widely known as an effective and long-lasting way to turn brackish or salty water into drinking water. This technology has become more popular because it is easy to build, doesn't need much maintenance, is cost-effective, and is good for the environment. This study presents a single slope solar stills (SSSS) and double slope solar stills (DSSS) in a thorough way, both through analysis and experiments, to see how well they work in real outdoor conditions. The study compared regular solar stills with modified solar stills (MSS) and used different energy storage materials to make them work better. There were five different experimental setups that were considered: In case 1, single slope vs. double slope solar stills is compared,Incase 2, iron scraps is added in DSSS, In Case 3, black kancheyis added in DSSS, In Case 4, shaving blades, and In case 5, pebbles in DSSS. The daily water yield for various stills was recorded as follows: TSSS: 1330 ml/day, Case 1: 1890 ml/day, case 2: 2370 ml/day, Case 3: 2450 ml/day, Case 4: 3150 ml/day, Case 5: 2250 ml/day. The case 4 (shaving blades) exhibited the highest yield, increasing productivity by 136% compared to TSS. The peak energy efficiency for different solar stills was TSSS: 29%, case 1: 36.6%, case2: 46.5%, case3: 49.5%, case4: 52.5%, case5: 38.3%, with case 4 achieving the highest value of 58.5%.

भारत का सांस्कृतिक अतीत जितना प्राचीन और गौरवशाली है, उतना ही बहुआयामी और जटिल भी है। पारंपरिक और आधुनिक इतिहास लेखन में अधिकांशतः लिखित, पुरातात्विक और राजकीय अभिलेखों को ही सर्वोच्च प्राथमिकता दी गई है। इस प्रक्रिया में, शासक वर्ग और बड़े साम्राज्यों का इतिहास तो मुखर होकर सामने आया, परंतु समाज के निचले पायदान पर स्थित जनमानस, उनकी संस्कृति, और स्थानीय संघर्षों का इतिहास उपेक्षित रह गया। मौखिक इतिहास (Oral History) और लोक परंपराएं (Folk Traditions) उसी उपेक्षित समाज की अनकही गाथाएं हैं, जो ज्ञान के हस्तांतरण की अमूर्त सांस्कृतिक विरासत के रूप में जीवित हैं। औपनिवेशिक और संभ्रांतवादी (Elite) इतिहास विमर्श ने अक्सर इन लोक परंपराओं को केवल अंधविश्वास, कपोल-कल्पना या अतार्किक मिथक मानकर मुख्यधारा के ऐतिहासिक विश्लेषण से अलग रखा। प्रस्तुत शोध-पत्र इसी भ्रांति का विखंडन (Demystification of Realities) करने का एक अकादमिक प्रयास है। यह शोध-पत्र तार्किक, समाजशास्त्रीय और ऐतिहासिक मापदंडों के आधार पर यह सिद्ध करता है कि लोक परंपराओं और मौखिक इतिहास के सूक्ष्म परीक्षण के बिना भारत के व्यापक इतिहास की समग्र व्याख्या असंभव है। इसके साथ ही, वर्तमान भूमंडलीकरण और डिजिटल संस्कृति के संक्रमण काल में इन लोक स्रोतों के विलुप्त होने की गंभीर चुनौतियों और उनके संरक्षण के सरोकारों (Concerns) पर विस्तृत प्रकाश डाला गया है। ऐतिहासिक प्रामाणिकता को स्पष्ट करने के लिए विशेष रूप से उत्तराखंड के कुमाऊं और तराई-भाबर (खटीमा क्षेत्र) की लोक-संस्कृति, जाँगर परंपरा और जनजातीय गाथाओं को केस स्टडी के रूप में प्रस्तुत किया गया है।

This study aims to develop a conceptual framework that positions competency gap analysis as a strategic foundation for competency-based training development in organizational human resource management. The study employed a systematic literature review approach using the PRISMA framework, including identification, screening, eligibility, and inclusion stages. Relevant literature was collected from major academic databases, including Scopus, ScienceDirect, SpringerLink, and Google Scholar, covering publications from 2013 to 2026. The selected studies were analyzed using thematic content analysis and interpretative synthesis to identify conceptual patterns and relationships among competency assessment, competency gaps, training needs analysis, and training development strategies. The findings indicate that competency gaps primarily emerge from discrepancies between organizational competency requirements and employees’ actual competencies, particularly in technical, managerial, digital, and adaptive capabilities. These gaps significantly affect organizational productivity, innovation capacity, service quality, and workforce readiness in responding to technological transformation.Furthermore, the study demonstrates that competency gap analysis functions as a strategic mechanism for identifying training priorities and designing more targeted, adaptive, and sustainable competency-based training programs. This study proposes an integrated conceptual framework linking competency assessment, competency gap analysis, training needs analysis, and competency-based training within a continuous human resource development cycle. The study contributes to the strategic human resource development literature by strengthening the theoretical relationship between competency diagnostics and evidence-based training strategies while offering practical implications for organizations in developing future-oriented workforce capabilities.

Among other serious neurological disorders, brain tumour has been posing severe health risks to humans since their early stages. Manual analysis of MRIs conducted by radiologist is one of the common methods that have been used over time for detecting brain tumour.Thismethod,however,couldtakequitesometimeandmayalsocontainerrorsdueto human involvement, Recent developments in artificial intelligence, deep learning, and the application of medical image processing using convolutional neural networks (CNN) have greatly contributed to identification of brain tumours and classification systems.
This paper presents an AI-based system for the identification and classification of brain tumours from MRI images. Some of the research works that relate to the pre-processing of MRI scans, learning-based framework, CNNs, transfer learning, brain tumour segmentation and medical imaging technologies have been discussed in this paper.Additionally, this paper provides a clear description of the current systems’ strength and weaknesses while also exploringthefuturetrendsandresearchgaps.Thesignificanceofintelligenthealthcaresystems has also been highlighted.

BACKGROUND
Herpes simplex virus type 1 and varicella-zoster virus (VZV; human herpesvirus are human neurotropic alphaherpesviruses that cause lifelong infections in ganglia. So the investigation conducted on “Prevalence and reactivation features of herpes simplex virus type 1 and varicella-zoster virus cases admitted in quaternary care hospital, Belagavi”.
MATERIAL AND METHODS: A clinical based observational study conducted on“Prevalence and reactivation features of herpes simplex virus type 1 and varicella-zoster virus cases admitted in quaternary care hospital, Belagavi”. The researcher adopted descriptive research approach by following quantitative method. Data collection was done by purposive sampling technique, the number of research subjects are 120. Tabulation and analysis was done by descriptive and inferential statistics.
RESULTS: Findings revealed that strong positive linear relationship between prevalence and reactivation features of herpes simplex virus type 1 and varicella-zoster virus cases. P value is 0.05 levels of significance, hence alternate hypothesis accepted and null hypothesis was rejected, and the table value is 1.764.
CONCLUSION: At the conclusion of the study, a significant association was found between prevalence and reactivation features of herpes simplex virus type 1 and varicella-zoster virus cases with demographic variables.

Over decades quality of research has become a critical indicator of higher education excellence, research capacity, innovations and knowledge production in Sub-Saharan Africa. However, persistent concerns regarding research rigor, innovation, timely completion, and scholarly contribution highlight the need to understand the relational processes that shape the quality research outcomes. This study examined supervisor–supervisee synergy and graduate research quality through a comparative investigation of public and private universities in Uganda. Anchored in Sociocultural Theory and Social Cognitive Theory, the study conceptualized supervisorsupervisee synergy as a multidimensional construct comprising communication effectiveness, feedback quality, mentorship support, relational trust, and knowledge co-construction. A convergent mixed-methods design was employed involving graduate research candidates and supervisors from selected public and private universities in Uganda. Data were collected through structured questionnaires, interviews, and open-ended responses, and analyzed using descriptive statistics, comparative analysis, thematic analysis, and Partial Least Squares Structural Equation Modeling using SmartPLS.The findings revealed that supervisor–supervisee synergy significantly predicts graduate research quality, explaining substantial variance in research outcomes. Communication effectiveness and feedback quality emerged as the strongest determinants, while mentorship, trust, and knowledge co-construction enhanced researcher development and innovation. Comparative analysis showed variations between public and private universities, reflecting differences in institutional structures and research cultures.The study contributes a Supervisor–Supervisee Synergy Framework, positioning supervision as a relational and strategic quality assurance mechanism for strengthening graduate research excellence in Sub-Saharan Africa.

Environmental sustainability and governance quality are mutually reinforcing pillars of sustainable development, particularly in Sub-Saharan Africa, where institutional weaknesses often constrain the implementation of environmental commitments. This study examines the effects of governance systems on environmental sustainability in Uganda through the lens of the Sustainable Development Goals. Specifically, it assesses how political commitment, institutional effectiveness, accountability mechanisms, regulatory enforcement, policy coherence, and public financing influence environmental sustainability outcomes. The study adopted a qualitative case study design and employs documentary analysis and policy review methods. Data were collected from government reports, legal and policy frameworks, Voluntary National Reviews (VNRs), peer-reviewed literature, and publications of international organizations covering the period 2018–2026. The findings indicate that Uganda has established a comprehensive environmental governance framework, including the National Environment Act (2019), the National Development Plan aligned with over 95% of the SDGs, and an updated Nationally Determined Contribution (NDC) targeting a 24.7% reduction in greenhouse gas emissions. Despite these policy advances, only 26.1% of applicable SDG targets are on track at the midpoint of the 2030 Agenda. While notable progress has been achieved under SDGs 6, 13, and 15, implementation remains constrained by political interference, corruption in natural resource governance, chronic underfunding of environmental institutions, limited local government capacity, and weak inter-institutional coordination. The study concludes that governance quality is a critical determinant of environmental sustainability and that the achievement of environmental targets depends on strong, transparent, and accountable institutions, as emphasized under SDG 16. It recommends strengthening anti-corruption mechanisms, enhancing subnational governance capacity, increasing domestic environmental financing, improving inter-ministerial coordination, and reducing dependence on donor support to accelerate progress toward environmental sustainability and the attainment of the 2030 Agenda.

Assessment integrity is a cornerstone of quality assurance in higher education, ensuring that assessment outcomes are fair, reliable, transparent, and credible. In many universities across Sub-Saharan Africa, concerns regarding inconsistencies in grading, examiner bias, and variations in marking standards have increased the need for robust assessment practices. This study assesses the contribution of central marking and blind marking to assessment integrity in Public universities in Uganda and examines the implications of these practices for quality assurance in the region. Guided by principles of educational assessment and quality assurance, the study employs a mixed-methods approach involving academic staff, examination officers, quality assurance personnel, and students from selected Public universities. Data are collected through questionnaires, interviews, and document analysis and analyzed using both descriptive and inferential statistics alongside thematic analysis. The study investigates how central marking promotes consistency and standardization in grading, while blind marking minimizes potential bias arising from knowledge of students' identities. Findings indicate that both central marking and blind marking make significant contributions to assessment integrity by enhancing fairness, objectivity, reliability, and transparency in the assessment process. The results further suggest that institutions that systematically implement these practices are more likely to strengthen stakeholder confidence in academic qualifications and support continuous quality improvement. The study concludes that central marking and blind marking are important mechanisms for safeguarding academic standards and recommends their wider adoption within higher education institutions across Sub-Saharan Africa as part of comprehensive quality assurance frameworks. The findings contribute to ongoing policy discussions on assessment reform and institutional accountability in the Great Lakes region.

This work reports the utilization of solar thermal energy to generate Eucalyptus essential oil, reducing the dependence on conventional energy and minimizing the environmental impact. The study optimized oil extraction by traditional steam distillation using response surface methodology and applied the results to a Schaffer Concentra to driven solar steam distillation setup. The optimum factors included leaf size (0.02 m), extraction temperature (97.76 ℃), solid/solvent ratio (0.61), and extraction time (206 min), with temperature being crucial. The major oil components were 1-8 cineole (57.53-78.45%) and α-Pentene (15.27-27.83%). The outdoor experiments achieved an efficiency of 27% and produced 2 kg/h steam at an average solar insulation of 700 W/m2. Despite consistent processing temperatures favoring conventional steam distillation, the solar extraction process offers an eco-friendly alternative. In line with the sustainable development goals, this initiative is well-suited for sunny regions and small businesses targeting niche markets.

Autism Spectrum Disorder (ASD) is a complex neurodevelopmental condition characterized by persistent challenges insocialinteraction,communication,andrepetitive behavioralpatterns.The globalprevalenceof ASD has increased significantly over the past decade, making early detection and intervention critically important for improving long-term developmental outcomes. Traditional diagnostic procedures rely heavily onbehavioralassessmentsconductedbyclinicalexperts,whicharetime-consuming,subjective,andresource-intensive. These limitations highlight the urgent need for automated, data-driven, and scalable screening systems capable of assisting healthcare professionals in early-stage autism identification.
This dissertation presents a comprehensive machine learning-based framework for predicting Autism SpectrumDisorderusingquestionnairescoresanddemographicattributes.Theentiresystemwasdeveloped in Python,leveraging NumPy,Pandas,Matplotlib, Seaborn, Scikit-learn, Imbalanced-learn, andXGBoost. The proposed framework integrates data preprocessing, feature engineering, class-imbalance handling, supervised learning, hyperparameter optimization, and model evaluation into a structured, reproducible pipeline.
The dataset consists of autism screening questionnaire responses (A1–A10 scores), demographic attributes (age, gender, ethnicity, country of residence), medical history indicators (family history of autism, jaundice), and screening test results. Extensive preprocessing was performed to handle missing values, encode categorical variables, and remove redundant features. A major challenge was class imbalance, addressed using the Synthetic Minority Oversampling Technique (SMOTE), which improved model sensitivity and reduced false-negative predictions — a critical concern in medical screening.
Three primary supervised learning algorithms — Decision Tree, Random Forest, and Extreme Gradient Boosting(XGBoost)—wereimplementedandcompared,alongsideSupportVectorMachineandNaïveBayes baselines. Cross-validation ensured generalization, and RandomizedSearchCV was used for hyperparameter optimization. The Random Forest classifier achieved the highest cross-validation accuracy of approximately 93%, with a test accuracy of approximately 81.87%. Performance was evaluated using Accuracy, Precision, Recall, F1-Score, and Confusion Matrix analysis, confirming reliable and consistent predictions suitable for decision-support screening.
The final optimized modelwas serialized using Pickle, together with its label encoders, enabling deployment in web-based screening platforms, mobile healthcare applications, or clinical decision-support systems without retraining. While the model does not replace professional medical diagnosis, it functions as a preliminary screening tool that flags individuals who may benefit from further clinical evaluation.
Futureworkmayinvolveexpandingdatasetdiversity,integratingdeeplearningandmultimodaldata(speech, facial expression, neuroimaging), deploying the system as an interactive web application, and incorporating explainableAI(XAI)techniquestoimprovetransparencyinsensitivemedicalcontexts.Inconclusion,thiswork demonstratesthataPython-basedmachinelearningapproachcaneffectivelypredictautismtendenciesfrom questionnaire and demographic data, establishing a robust foundation for future research and real-world deployment in autism screening and early-intervention support.

Herbal cosmetics have gained considerable attention in recent years due to increasing awareness regarding the harmful effects of synthetic ingredients and the growing preference for natural products. Herbal shampoo is one such cosmetic preparation developed using plant-derived materials that possess cleansing, conditioning, antimicrobial, anti-dandruff, and hair-strengthening properties. The present study focuses on the formulation and evaluation of an herbal shampoo prepared using various medicinal plant extracts known for promoting scalp health and maintaining hair hygiene. Herbal ingredients such as aloe vera, hibiscus, neem, shikakai, amla, reetha, and fenugreek were selected based on their traditional therapeutic value and beneficial effects on hair care.
The formulated shampoo was prepared using suitable natural surfactants and stabilizing agents to achieve acceptable physicochemical characteristics. The prepared formulation was evaluated for different parameters including physical appearance, pH, viscosity, foam ability, foam stability, surface tension, wetting time, dirt dispersion, percentage of solid content, cleansing action, and skin irritation potential. Stability studies were also conducted to assess the formulation’s quality under different storage conditions. Therefore, herbal shampoo can be considered a safe, economical, and effective alternative to conventional synthetic shampoos, with reduced chances of adverse effects and improved consumer acceptability.

Diabetes mellitus is a chronic metabolic disorder characterized by elevated blood glucose levels resulting from impaired insulin secretion, insulin resistance, or both. The increasing global prevalence of diabetes has created a significant demand for effective and patient-friendly antidiabetic therapies. Antidiabetic drug formulation plays an essential role in enhancing therapeutic efficacy, improving patient compliance, and minimizing adverse effects. Various conventional and novel drug delivery systems have been developed to optimize the release, absorption, and bioavailability of antidiabetic agents. These formulations include tablets, capsules, sustained-release systems, transdermal patches, nanoparticles, oral dispersible tablets, and herbal-based preparations. The selection of suitable excipients and formulation techniques is crucial to maintain stability, safety, and effectiveness of the drug product. Furthermore, advancements in pharmaceutical technology have contributed to the development of controlled and targeted drug delivery approaches for better glycemic control. This review highlights the importance of antidiabetic drug formulation, different formulation strategies, evaluation parameters, and recent developments aimed at improving diabetes management and patient outcomes.

The rapid expansion of digital technologies has fundamentally transformed cultural production, transmission, and interpretation in India. The digital medium has been vastly surpassing the traditional medium. Indian smart phone users nearly 1 billion by 2026. This paper examines the intersection of digital media, education, and cultural change, with particular emphasis on political polarization and generational shifts. Drawing upon classical sociological and anthropological frameworks,including Auguste Comte, Lewis Henry Morgan, Johann Jakob Bachofen, Émile Durkheim, William F. Ogburn, Pierre Bourdieu, Antonio Gramsci, Jean Piaget, and M. N. Srinivas, this study adopts a qualitative analytical approach. It argues that digital technologies compress cultural time, fragment traditional cultural systems, and generate hybrid yet unstable cultural formations, while educational institutions struggle to mediate this transformation.
Collectively, these thinkers help explain how digital technologies are accelerating cultural transformation in India. While Comte and Morgan highlight technological progress, Ogburn points to the resulting cultural lag. Durkheim, Bourdieu, and Gramsci illuminate the changing nature of social cohesion, inequality, and power, whereas Piaget emphasizes cognitive implications. Bachofen and Srinivas draw attention to shifts in gender relations and social structures. Together, their perspectives reveal that digital media is not merely a technological phenomenon but a profound force reshaping culture, education, identity, and political life in contemporary society.
This study adopts a qualitative, interpretive approach grounded in theoretical synthesis. It integrates classical sociological frameworks with contemporary observations of digital culture in India. The research design is conceptual, drawing upon secondary literature. Findings indicate that digital technologies are reconfiguring traditional cultural structures in India by accelerating cultural change, redefining cultural capital, transforming family relationships and intergenerational dynamics, and producing hybrid cultural forms that pose significant challenges for educational institutions seeking to balance cultural continuity with digital modernity.

Background:Environmentalsanitationplaysasignificantroleinimprovingpublichealthand supporting the achievement of the Sustainable Development Goals (SDGs). Coastal areas are densely populated and vulnerable to environmental degradation due to limited facilities and communitybehavior.Thisstudyaimstoprovide acomprehensiveoverviewofenvironmental sanitation conditions in the coastal area of Kasilampe Beach, Kendari City.
Method:Thisresearchisdescriptivewithaquantitativeapproach.Primarydatacollectionwas conducted using questionnaires and observation sheets from 30 household respondents in the coastal area of Kasilampe Beach, Kasilampe District, Kendari City. The data were analyzed univariately using SPSS to generate frequency distributions and percentages.
Results:Thecleanwateraspectshowsthat96.7%ofrespondentsrelyonwells,withclearand odorless physical quality (86.7%), and have the behavior of boiling water before drinking (80.0%). Ownership of domestic waste bins reached 96.7%, where the majority dispose of wasteattheTPS(66.7%),butsomeresidentsstillburnwaste(30.0%)andthrowitintothesea (3.3%).Thecoverageoffamilytoiletfacilitieshasreachedmaximumresults(100.0%)usinga gooseneck type connected to a septic tank. In contrast, environmental indicators outside the home show poor conditions: there are puddles around the house (46.7%), unpleasant odors (33.3%), drainage channels are of moderate status (56.7%), and high complaints about the presence of disease vectors such as mosquitoes and flies (90.0%).
Conclusion:BasicsanitationconditionsinthecoastalareaofKasilampeBeacharealreadyin theverygoodcategory,basedonindicatorssuchastheprovisionofhealthyfamilylatrines,the useofwastecontainers,anddrinkingwaterhygiene.However,majorchallengesthaturgently need to be addressed include poor outdoor environmental management, local waste contamination(burning/dumpingintothesea),substandarddrainage,andahighpopulationof disease-transmitting vectors.

Herbal toothpaste is widely used for maintaining oral hygiene and preventing dental diseases due to its natural therapeutic properties and minimal side effects. The present study focuses on the formulation and evaluation of herbal toothpaste containing clove and neem extracts. Clove possesses strong antimicrobial, analgesic, and anti-inflammatory properties because of the presence of eugenol, while neem is well known for its antibacterial, antifungal, and plaque-reducing activities. The herbal toothpaste was prepared using natural ingredients such as calcium carbonate, glycerin, peppermint oil, gum acacia, and suitable preservatives along with clove and neem extracts. The formulated toothpaste was evaluated for various physicochemical parameters including color, odor, taste, pH, spreadability, foamability, homogeneity, stability, and abrasiveness. Antimicrobial activity against common oral pathogens was also assessed. The results showed that the prepared herbal toothpaste exhibited good consistency, acceptable pH, pleasant odor, satisfactory cleaning ability, and effective antimicrobial action. The formulation was found to be stable and safe for regular use. Therefore, the developed herbal toothpaste can serve as an effective natural alternative to conventional chemical-based toothpaste for maintaining oral health and preventing dental infections.

Cancer remains one of the leading causes of mortalityworldwide,andconventional treatment methods such as chemotherapy, radiation therapy, and surgery often suffer from limitationsincludingpoordrugselectivity,systemictoxicity,multidrugresistance, and severe side effects. Nanotechnology-based drug therapy has emerged as a promising approach to overcome these challenges by enabling targeted and controlled delivery of anticancer agents. Nanocarriers such as liposomes, polymeric nanoparticles, dendrimers, metallic nanoparticles, carbon nanotubes, and micelles enhance drug solubility, bioavailability, therapeutic efficacy, and tumor-specific accumulation through passive and active targeting mechanisms. These nanoscalesystemsalsosupportcombinationtherapy,imaging,andtheranosticapplicationsfor improved cancer diagnosis and treatment monitoring. Despite significant advancements, several challenges remain, including nanoparticle toxicity, biocompatibility issues, stability, large-scale manufacturing, regulatory approval, and long-term safety concerns. This review highlights recent developments in nanotechnology-based cancer drug therapy, discusses various nanocarrier systems and their therapeutic applications, and examines the major opportunities and challenges associated with clinical translation. The study emphasizes the futurepotential ofnanomedicine in developing safer, more effective, and personalized cancer treatment strategies.

The integration of Artificial Intelligence (AI) into sports science has opened new avenues for objectively monitoring athletic preparation methods that were traditionally assessed through subjective observation alone. This study proposes and evaluates an integrated AI-based monitoring framework for comparing the physiological and biomechanical effects of yoga and traditional resistance/aerobic exercise on athletic performance. A cohort of collegiate-level athletes (N = 80) was assigned to one of four groups — control, traditional exercise, yoga-only, and an integrated AI-monitored yoga-plus-exercise group — over an eight-week intervention period. Wearable inertial measurement units (IMUs), surface electromyography (EMG), heart-rate variability (HRV) sensors, and a computer-vision-based pose-estimation pipeline were used to continuously capture movement quality, posture accuracy, and physiological load. A multimodal deep-learning fusion model was trained to classify posture correctness and exercise intensity in real time, providing automated feedback to participants. Results indicate that the integrated AI-monitored group exhibited the greatest mean improvements in flexibility, dynamic balance, reaction time, and post-exercise heart-rate recovery relative to the other groups, while the multimodal fusion model achieved the highest posture-classification accuracy compared with any single sensor modality. These findings suggest that AI-assisted monitoring can meaningfully enhance the precision, safety, and effectiveness of combined yoga and traditional training regimens for athletes. The paper discusses the technical architecture of the monitoring system, statistical outcomes, practical implications for coaches and sports scientists, and directions for future research, including longitudinal validation across diverse athletic populations.

Fire accidents pose significant risks to life and property, necessitating the development of fast and reliable autonomous firefighting systems. In this work, we present an AI-based fire-fighting robot that integrates embedded systems with edge intelligence for real-time fire detection and response. The proposed system utilizes an ESP32-CAM [2] module combined with a lightweight deep learning model developed using Edge Impulse to perform on-device image-based fire classification. Unlike traditional flame sensor-based systems, the proposed approach leverages visual data to improve detection accuracy and adaptability under varying environmental conditions. The ESP32-CAM captures real-time images and processes them through the trained model to identify the presence of fire. Upon detection, the system transmits control signals to an Arduino[1] Uno, which functions as the central decision execution unit. Based on the received inputs, the Arduino [1] controls the robot’s movement via an L298N motor driver and directs the robot toward the fire source. A servo-controlled nozzle and water pump mechanism are activated when the robot reaches proximity to the fire, enabling precise extinguishing. The system is designed with a multi-component architecture including DC gear motors for mobility, a transistor-based switching circuit for pump control, and a stable battery power supply for reliable operation. Experimental results demonstrate that the proposed system achieves efficient fire detection and autonomous response with minimal latency, making it suitable for small-scale and indoor fire safety applications. This work highlights the potential of combining edge AI with robotic systems to create intelligent, cost-effective, and scalable firefighting solutions.

The rapid expansion of digital technologies has fundamentally transformed cultural production, transmission, and interpretation in India. The digital medium has been vastly surpassing the traditional medium. Indian smart phone users nearly 1 billion by 2026. This paper examines the intersection of digital media, education, and cultural change, with particular emphasis on political polarization and generational shifts. Drawing upon classical sociological and anthropological frameworks,including Auguste Comte, Lewis Henry Morgan, Johann Jakob Bachofen, Émile Durkheim, William F. Ogburn, Pierre Bourdieu, Antonio Gramsci, Jean Piaget, and M. N. Srinivas, this study adopts a qualitative analytical approach. It argues that digital technologies compress cultural time, fragment traditional cultural systems, and generate hybrid yet unstable cultural formations, while educational institutions struggle to mediate this transformation. According to Emile Durkheim
Collectively, these thinkers help explain how digital technologies are accelerating cultural transformation in India. While Comte and Morgan highlight technological progress, Ogburn points to the resulting cultural lag. Durkheim, Bourdieu, and Gramsci illuminate the changing nature of social cohesion, inequality, and power, whereas Piaget emphasizes cognitive implications. Bachofen and Srinivas draw attention to shifts in gender relations and social structures. Together, their perspectives reveal that digital media is not merely a technological phenomenon but a profound force reshaping culture, education, identity, and political life in contemporary society.
This study adopts a qualitative, interpretive approach grounded in theoretical synthesis. It integrates classical sociological frameworks with contemporary observations of digital culture in India. The research design is conceptual, drawing upon secondary literature.Findings indicate that digital technologies are reconfiguring traditional cultural structures in India by accelerating cultural change, redefining cultural capital, transforming family relationships and intergenerational dynamics, and producing hybrid cultural forms that pose significant challenges for educational institutions seeking to balance cultural continuity with digital modernity.

The growing demand for accessible healthcare services, particularly in resource-limited environments, exposes the limitations of conventional telemedicine systems that rely on stationary video communication [1], [6], [7]. Such approaches restrict a physician’s ability to interact dynamically with patients and clinical surroundings, thereby limiting the effectiveness of remote diagnosis and care. This paper presents the design and architecture of a low-cost, AI-enabled telepresence robot intended to enhance remote healthcare delivery through mobility and intelligent assistance [4], [5].
The proposed system integrates a mobile robotic platform with an IoT-based control interface, enabling healthcare professionals to remotely navigate hospital spaces and communicate with patients in real time [1], [8]. An embedded artificial intelligence module is incorporated to support functionalities such as obstacle detection, basic navigation assistance, and human interaction features, thereby improving usability and operational safety [8], [9].
The design emphasizes affordability and scalability by utilizing widely available components, including mobile devices, low-cost microcontrollers, and wireless communication technologies [1], [6]. This makes the system particularly suitable for deployment in rural and under-resourced healthcare settings, where access to continuous medical supervision and specialist consultation is often limited [2], [3].
Although the work is presented at a design level without experimental validation, the feasibility of the proposed architecture is discussed in terms of improving healthcare accessibility, operational efficiency, and remote patient management while maintaining cost-effectiveness [4], [5].

The integration of Artificial Intelligence (AI), particularly generative tools such as ChatGPT, has significantly transformed English language learning in higher education. This study explores the impact of AI on creativity and critical thinking among Bachelor of Arts (B.A.) undergraduate students studying English. Using a mixed-methods approach, data were collected from 160 students through structured questionnaires and semi-structured interviews. The findings reveal that AI enhances language proficiency, idea generation, and learner autonomy, while also presenting challenges related to over-reliance and reduced independent thinking. Statistical analysis indicates a moderate positive relationship between AI-assisted learning and creativity (r = 0.54), and a complex relationship with critical thinking. The study highlights the need for balanced AI integration in pedagogy to promote higher-order thinking skills. The research contributes to emerging scholarship on AI-assisted language learning and provides practical implications for educators in developing contexts.

Background: The incorporation of bioactive herbal extracts into modern drug delivery systems represents a major frontier in contemporary pharmaceutical science. However, the transition from traditional crude extracts to standardized, clinically viable phytomedicines presents massive technical challenges, primary among which are poor aqueous solubility, molecular instability, unpredictable bioavailability, and complex multi-component matrices.
Objective: This comprehensive project review aims to systematically delineate the foundational preformulation parameters mandatory for integrating novel phytomedicines into advanced drug delivery architectures (such as liposomes, phytosomes, nanoemulsions, and polymeric nanoparticles).
Methods: Analytical methodologies including UV-Visible Spectroscopy, FTIR, DSC, HPLC, and XRD are critically evaluated regarding their specific applications to complex herbal matrices. Physicochemical criteria including partition coefficient (Log P), pKa, solubility profiles, and degradation kinetics are thoroughly mapped out.
Results: The integration of phytoconstituents into novel drug delivery vehicles systematically improves bioavailability by up to 4-fold and maintains structural integrity against gastric degradation. Standardized preformulation profiling provides the quantitative baseline necessary to prevent physical and chemical incompatibility during scalable production.
Conclusion: Conducting meticulous preformulation screenings guarantees robust, stable, and reproducible formulations, effectively bridging the historic gap between traditional herbal therapies and strict Western clinical metrics.

The main objective of this study is to investigate the physicochemical and preservative potential of essential oil from Cussonia barteri seeds against postharvest spoilage fungi isolated from yam. Essential oils was extracted from the seeds using hexane extraction method and its antimicrobial activity was tested against postharvest spoilage fungi isolated from spoilt yam using disk diffusion method and its MIC and MBC calculated. The result from the physiochemical assessment of Cussonia Barteri (Jansa Seed) essential oil showed that the oil extracted was cream yellowish in colour with mild/subtle aroma and Smooth in texture and oil feeling. The pH is acidic (5.20), its electrical conductivity was 0.360Us/cm, concentration (0150mg/l), temperature 32.90c. three fungi strains (Mucor spp, than penicillium spp and Aspergillus niger) were isolated from spoilt yam and from the result, the Cussonia Barteri (Jansa Seed) essential oil showedinhibition zone of (20.00mm, 17.50mm, 0.00mm and 0.00mm) for Mucor spp,in 500, 250., 125 and 62.50mg/L concentration respectively, Aspergillus niger showedinhibition zone of (18.00mm, 12.50mm, 10.00mm and 0.00mm)in 500, 250., 125 and 62.50mg/L concentration respectively, while penicillium spp showed inhibition for only concentration of 500mg/l (15.00mm) and 250mg/l (10mm) but were resistance in all other concentrations. When compare with the commercial antibiotic (CFX), it showed inhibition forMucor spp, (57mm) Aspergillus niger (33mm) andpenicillium spp(57.50mm).The results of MIC and MBC of the Cussonia Barteri (Jansa Seed) oil suggested that it can be used to control and prevent postharvest spoilage fungiand food poisoning diseases. the study have proved the seed to be potentially effective can be used as natural alternative preventives to control food poisoning diseases and preserve food stuff avoiding healthy hazards of chemically antimicrobial agent applications.

Air pollution associated with urban market activities has become an increasing environmental and public health concern in developing cities. This study examined the physicochemical properties of air. It assessed ambient air quality, Air Quality Index (AQI), and potential human health risks associated with air pollutants in major markets of Katsina Metropolis, Nigeria. Using a repeated-measures design, a total sample size of 240 (n = 240) was obtained across three major markets (Chake, Central, and Yarkutungu) and a residential control site (GRA). Data were collected twice daily (06:00–08:00 and 17:00–19:00) over five weeks using calibrated portable instruments, including the PCE-RCM 05 for particulate matter (PM₂.₅ and PM₁₀) and electrochemical gas detectors for CO, SO₂, NO₂, NH₃, H₂S, HCN, FL, and Cl₂. Descriptive statistics, independent and paired t-tests, and ANOVA were applied to determine significant differences. Pollutant levels were significantly higher on market days (CO = 16.00 ± 2.4 ppm) compared to non-market days (CO = 1.20 ± 0.3 ppm), and were significantly greater during evening periods than early morning (p

Rashtra Deepika Ltd., Kottayam is a well-established organization in the publishing and media industry. Unlike various other sectors, the media and publishing industry depends heavily on employee creativity, commitment, and performance for organizational success. The main purpose of the study is to examine the impact of factors affecting employee motivation and employee performance in Rashtra Deepika Ltd., Kottayam. The main objective was to identify key motivational factors and analyze their impact on employee performance. A descriptive research design was adopted, and data were collected from a sample of 100 employees using a structured questionnaire. The data were analyzed using statistical tools such as correlation analysis, factor analysis, t-test, and regression analysis. The findings reveal that timely bonuses, financial rewards, and job satisfaction are the most influential motivational factors, while corporate gifts and profit sharing have comparatively lower impact. Correlation analysis shows that Intrinsic motivational factors show strong positive correlations (r > 0.60), while extrinsic factors demonstrate moderate to strong correlations (r > 0.50), indicating a high level of consistency within both motivational dimensions. The paired sample t-test revealed no significant difference between employees' preference for financial and non-financial rewards (Mean Difference = 0.04, p = 0.375), indicating that both reward types are valued almost equally; however, employees who preferred financial rewards reported significantly higher overall motivation scores than those who preferred non-financial rewards (p = 0.034).Factor analysis of performance dimensions confirmed a three-component structure, explaining 68.91% of the total variance. Regression analysis indicates a strong positive relationship between employee motivation and employee performance (r = 0.64, p = 0.001), where a one-unit increase in motivation leads to a 0.65 increase in performance, explaining 46.2% of the variance. The study concludes that employee motivation plays a crucial role in enhancing performance, emphasizing the need for a balanced approach combining intrinsic and extrinsic motivational strategies.

Face recognition systems have become integral to modern biometric security applications, including border control, banking, and national identity management. However, the increasing sophistication of face morphing attacks poses significant threats to their reliability. This study presents an adversarially robust deep learning framework for detecting face morphing attacks generated using advanced techniques such as MIPGAN,MorDiff, Greedy-DIM, and Morph-PIPE. These techniquesproduce highly realistic morphed imagescapable of deceiving both human observers and automated systems. The proposed framework leverages convolutional neural networks (CNNs) and adversarial learning strategies to enhance detection performance across both known and unseen morphing attacks. Using the SYN-MAD 2022 dataset and evaluating against state-of-the-art face recognition systems (ArcFace, AdaFace, and ElasticFace), the study demonstrates the effectiveness of the proposed approach. Experimental results reveal that advanced morphing methods, particularly Greedy-DIM and Morph-PIPE, achieve extremely high attack successrates, highlighting the urgent need for robust detection mechanisms. The findings contribute to strengthening biometric security systems and mitigating identity fraud risks in real-world applications.

Student attrition and academic underperformance remain persistent challenges in higher education, often driven by a complex interaction of academic performance, behavioral engagement, and socio-economic factors. Traditional early warning systems rely on static indicators and delayed evaluations, which limit their ability to support timely and effective intervention. This study proposes an Explainable Artificial Intelligence based early warning system that identifies at-risk students while ensuring transparency and interpretability in predictions. The framework integrates heterogeneous data sources, including academic records, learning management system interactions, attendance patterns, and demographic attributes, to construct a comprehensive student profile. Advanced machine learning models, particularly ensemble methods, are employed to enhance predictive accuracy and robustness. To address concerns related to the black-box nature of such models, explainability techniques such as SHAP and LIME are incorporated to provide both global and instance-level explanations of predictions. The system is evaluated using standard performance metrics, including accuracy, precision, recall, F1 score, and ROC AUC, along with qualitative assessment of explanation usefulness. Experimental findings indicate that the proposed approach not only achieves reliable prediction performance but also offers actionable insights into key risk factors, enabling educators to design timely and personalized interventions. This study highlights the potential of explainable AI to improve student retention and support data-driven educational decision making.

This study developed and validated a Lesson Exemplar Package on Teaching Plant and Animal Tissues for Grade 11 Biology under the MATATAG Curriculum. Specifically, it identified the essential requisites in developing the lesson exemplar, described its development using the ADDIE instructional design model, determined its level of validity, examined the observations and feedback of expert validators, and identified the revisions made based on their comments and suggestions.
A descriptive-developmental research design was employed. The study was conducted at Cabadbaran National High School and involved Biology teachers from public and selected private schools as respondents, while Master Teachers and a Subject Program Supervisor served as expert validators. Data were gathered using a semi-structured interview guide and the standardized Department of Education Learning Resource Management and Development System (LRMDS) validation checklist. Mean and thematic analysis were utilized to analyze the quantitative and qualitative data.
Findings revealed that the development of the lesson exemplar required inquiry-based, contextualized, multimedia-supported, and learner-centered instructional strategies. Guided by the ADDIE model, the lesson exemplar underwent systematic development, validation, revision, and revalidation. Initial evaluation identified inaccuracies in the information presented; however, after incorporating the validators’ recommendations, the revised lesson exemplar obtained very satisfactory ratings in content, format, presentation and organization, and accuracy and up-to-datedness of information. Expert validators described the material as organized, engaging, and appropriate for Grade 11 learners. Revisions further enhanced its instructional quality through improved content accuracy, clarity, visual presentation, and the inclusion of a teacher’s guide.
The study concluded that the ADDIE instructional design model provided an effective framework for developing high-quality instructional materials. The validated lesson exemplar was found to be classroom-ready and suitable for Grade 11 Biology instruction under the MATATAG Curriculum.

Cervical Cancer remains one of the biggest health concerns affecting women worldwide. It is the fourth most common cancer among women globally, with about 570,000 new cases and 311,000 deaths reported in 2018. Most of these cases occur in developing countries, where access to healthcare services and awareness programs may be limited. This study was carried out to understand the socio-demographic factors that influence cervical cancer screening, assess women’s knowledge about cervical cancer, and examine how accessible screening services are to women. The research used a cross-sectional descriptive survey design and was conducted among women attending the gynecology ward, antenatal clinic, and family planning clinic at Embu Teaching and Referral Hospital. A total of 60 women of reproductive age participated in the study through self-administered questionnaires.

The collected data was analyzed using Microsoft Excel, and the results were presented using descriptive statistics and tables. Findings showed that all participants had heard about cervical cancer screening. However, despite this high level of awareness, detailed knowledge about the signs, symptoms, and prevention of cervical cancer was still low. Only 33% of the women knew the warning signs, while 27% understood preventive measures. In addition, only 33.3% of the participants had actually utilized cervical cancer screening services.
The study also found that socio-demographic factors influenced women’s participation in screening programs, even though screening services were generally available and accessible. Based on these findings, the study concluded that awareness alone is not enough to improve screening uptake. More community education and sensitization programs are needed to encourage women to undergo regular cervical cancer screening. The Ministry of Health should continue promoting early screening and organize health education campaigns to provide accurate information and reduce misconceptions about cervical cancer screening services.

We present a computational framework that integrates art criticism into business management education by modeling aesthetic judgment as a dynamic Bayesian network. The conventional reliance on subjective exercises such as theater improvisation or design thinking workshops often fails to produce transferable, quantifiable decision-making skills. To address this gap, we propose a system that operationalizes the sequential phases of aesthetic evaluation—perception, contextual analysis, interpretative synthesis, and evaluative conclusion—as latent states in a temporal probabilistic model. A pre-trained multimodal transformer encodes visual and textual business artifacts into aesthetic descriptor vectors, which then drive the network’s state transitions. A key novelty is the inclusion of an epistemic expectation integrator that tracks the learner’s shifting confidence through Kullback–Leibler divergence between successive posterior distributions, thereby mirroring the critical re-evaluation stage central to professional art criticism. The system maps the evolving aesthetic appraisal onto a binary leadership competency indicator, which is then compared against conventional market benchmarks through a divergence score. A pedagogical feedback generator subsequently produces structured critique prompts using a gradient-boosted tree model and a fine-tuned large language model, enabling iterative refinement of the learner’s judgment. We further construct a Standardized Taxonomy of Aesthetic-Leadership Indicators from accumulated session data, providing a data-driven reference for artistic cognition in business pedagogy. Our framework transforms an intuitive, tacit process into a repeatable, quantifiable tool for strategic decision-making under high ambiguity. This approach thus bridges the gap between aesthetic sensitivity and business leadership, offering both a rigorous methodology for educational assessment and a novel pathway for identifying creative agility in future managers.

Background: Social media has become deeply embedded in the daily lives of young adults, including collegiate athletes. Although these platforms can support communication and identity expression, excessive use may contribute to compulsive engagement, reduced self-regulation, and lower self-esteem. Yoga Nidra is a guided yogic relaxation practice that may help improve emotional balance, self-awareness, and attention control.
Objective: To investigate the effect of two weeks of Yoga Nidra practice on social media addiction and self-esteem among collegiate athletes.
Methods: A pre-experimental one-group pre-test post-test research design was used. Ten male collegiate athletes were selected through convenience sampling. Participants completed the Social Media Addiction Scale (SMAS) and the Rosenberg Self-Esteem Scale (RSES) before and after an eight-session Yoga Nidra intervention delivered over two weeks under supervision. Data were analyzed using mean, standard deviation, paired-samples t-test, and Cohen’s d effect size at the p < .05 level.
Results: Social media addiction scores decreased significantly from pre-test to post-test, with means of 142.30 (SD = 8.45) and 128.10 (SD = 7.62), respectively, t = 3.18, p = .011, d = 1.01. Self-esteem scores increased significantly from 16.40 (SD = 3.12) to 21.70 (SD = 3.56), t = 2.97, p = .016, d = 0.94.
Conclusion: Two weeks of Yoga Nidra practice were associated with reduced tendencies toward social media addiction and improved self-esteem among collegiate athletes. The findings suggest that Yoga Nidra may be a useful, low-cost, non-pharmacological intervention for digital behaviour management and psychological well-being.

Fast dissolving tablets (FDTs), commonly known as orally disintegrating tablets (ODTs), are an innovative pharmaceutical dosage form developed to rapidly break down or dissolve in the mouth without requiring water for administration. These formulations provide significant benefits to special patient groups, including children, elderly individuals, patients with swallowing difficulties (dysphagia), bedridden individuals, and mentally challenged patients, as they reduce the discomfort and inconvenience associated with swallowing traditional solid dosage forms such as tablets and capsules. Ondansetron hydrochloride is a highly effective and selective 5-hydroxytryptamine-3 (5-HT3) receptor antagonist extensively prescribed to prevent and manage nausea and vomiting caused by chemotherapy, radiotherapy, and surgical procedures. Although conventional oral formulations of ondansetron are therapeutically effective, maintaining patient adherence may become difficult, especially in individuals suffering from vomiting episodes. Fast dissolving tablet formulations help address this issue by enabling quick disintegration in the oral cavity, promoting rapid drug release and absorption, and potentially providing a faster therapeutic response.

Ultra-dense cellular networks (UDNs) are the main architectural solution to exponential mobile data demand in 5G and future 6G systems. When small cell density goes over 200 nodes per square kilometre, unmanaged co-tier and cross-tier interference makes the achievable SINR go below levels where spectral efficiency and user coverage cannot be achieved. Existing solutions such as extended inter-cell interference coordination (eICIC) and even basic coordinated multipoint (CoMP) transmission cannot adequately scale to a high-density deployment of heterogeneous networks. This paper presents an adaptive power control framework by combining cross-tier coordination and dynamic resource partitioning for mitigating macro-small cell interference. Simulation results with 200 small cells per square kilometre show that our proposed framework achieves a mean SINR of 18.4 dB, spectral efficiency of 4.72 bps/Hz and interference leakage -74.3 dBm which outperforming the best competing method by 21% in SINR and 37% in suppressing leakages These results demonstrate the feasibility of real-time density adaptive mitigation in 5G heterogeneous networks.

Gas sensing properties of a microporous polymer membrane based on polyvinyl chloride (PVC) was studied. These membranes were obtained by a novel polymer dissolution technique in different compositions of (PVC-PVA). Ionic conductivity of polymer membrane electrolytes have been determined by impedance studies in the temperature of 303-393 K by varying the ratio of PVC and PVA. The conductivity increases significantly enhanced about 5.431×10⁻⁴ S/cm when the weight ratio of PVA was 40 wt%. Finally the effect of different lithium salt concentration was also studied for the micro porous polymer electrolyte of high ionic conductivity system. A maximum ionic conductivity of 1.29×10⁻³ S/cm was obtained for 3M LiClO₄ electrolyte solution at ambient temperature. Gas sensing response of the PVC MPEs soaked in 3M molar concentration of Liclo4 was studied for different gases. Highest sensitivity was observed for H2S gas at an operating temperature of 25 0c.

Influencer marketing has become a dominant promotional strategy on social media, yet growing consumer fatigue toward sponsored content raises critical questions about its continued effectiveness. This paper examines influencer fatigue among social media consumers and explains how repeated influencer promotions are associated with consumer attention, trust, engagement, and purchase-related response. The study was developed from dissertation data collected from social media users in Ernakulam, Kerala, through a structured questionnaire. A descriptive research design was adopted to systematically describe consumer opinions, social media usage patterns, and behavioural responses, and respondents were selected through convenience sampling based on accessibility and willingness to participate. Descriptive results showed that Instagram was the most frequently used platform, a substantial majority of respondents followed influencers, and an overwhelming proportion reported feeling tired or annoyed by influencer promotions. The leading reason for fatigue was too many sponsored posts. A chi-square test revealed a significant association between daily time spent on social media and whether respondents followed influencers. The earlier exploratory factor analysis section was replaced with multiple correlation analysis based on the reported Pearson correlation matrix. The combined association of eight fatigue indicators with reduced purchase-decision impact was statistically significant, indicating that perceptions of advertisement overload, inauthenticity, repetition, and engagement decline jointly correspond with weaker purchase influence. The findings indicate that influencer fatigue is a broad consumer response involving excessive promotion, reduced trust, weakened engagement, and lower purchase influence, highlighting the need for marketers to reassess the frequency and authenticity of influencer-driven campaigns to sustain consumer attention and brand credibility.

Employee engagement has been attracting a lot of attention as an important factor influencing institutional efficiency and long-term organizational success. Engagement is a key factor in higher education institutions where faculty and staff commitment is a key factor in performance. The present study examined the effect of employee engagement on organizational performance in higher education institutions using descriptive and analytical research methods. A structured questionnaire was developed using five point Likert scale and data were collected from 80 academic and administrative employees. The study investigates how leadership quality, work atmosphere, recognition systems and career growth opportunities influence the levels of engagement. The numericalinvestigationpresented a strong positive correlation (r = 0.72) between employee engagement and organizational performance. Outcomesspecify that more involved employees result in improvedoutput, heightened organizational commitment and better retention. The learninghighpoints the need for strategic Human Resource Management interferences to develop an engaged workforce and improve institutional performance.

The fast growth of online technologies has greatly transformed the social, educational, and leisure lives of the Indian adolescents. Although web-based platforms expand learning and interconnectedness, they have also led to a growing interest in the issues associated with overplaying and unhealthy online behaviour that may have negative impacts on the well-being of adolescents. The challenges need to be tackled so as to have a digitally responsible society that is in coherence with the vision of a developed nation by the year 2047 which India has. This paper suggests a human-centred, artificial intelligence-based model of the real-time identification of gaming addiction and harmful online behaviour amongst Indian teenagers. The framework is a combination of behavioural analytics and machine learning approaches to track the patterns of interaction and find an early signal of problematic digital engagement in an objective and non-invasive approach. The proposed approach will allow intervening in time and helping preventive measures instead of reactive actions since it will concentrate on real-time analysis. The results of the study imply the possibility of artificial intelligence as a supportive measure to be used by teachers, parents, and policymakers to foster healthier digital habits among teenagers. The research adds to the expanding body of investigations into digital well-being by introducing a framework of scalability and ethically responsible approach that considers a combination of technological innovation and social responsibility, which will contribute to the long-term objectives of a safe, inclusive, and development.

This study aimed to develop digitalized and contextualized learning materials for Araling Panlipunan 4 at Feliciano Magbanua Integrated School, North Cabadbaran District, Schools Division of Cabadbaran City. The study was prompted by the low Fourth Quarter Mean Percentage Score of 69.50 obtained by the 34 Grade 4 learners, which was below the Department of Education proficiency standard of 75%. Employing a developmental research design and guided by the ASSURE instructional design model, the study utilized both quantitative and qualitative approaches. Data were gathered through pre-test and post-test results, Focus Group Discussions, expert validation, and structured interviews.
The findings revealed that the least-learned competencies involved the concepts and principles of citizenship, the rights and responsibilities of citizens, and civic welfare. Teachers and learners expressed preference for digitalized, interactive, and contextualized learning resources that incorporate visuals, videos, games, and locally relevant content. Based on these identified needs, three digitalized and contextualized learning materials were developed. Expert evaluation showed that the materials attained a Very Satisfactory rating in terms of content quality, instructional quality, technical quality, and other criteria. Results further indicated that learners’ mean score improved from 72.45 (Not Mastered) in the pre-test to 84.03 (Mastered) in the post-test. Qualitative data also revealed positive experiences among teachers and learners, highlighting enhanced engagement, participation, comprehension, and lesson relevance. Moreover, the paired-samples t-test yielded a statistically significant difference between the pre-test and post-test scores (t = -8.81, p < 0.05), confirming the effectiveness of the developed learning materials.
The study concludes that the developed digitalized and contextualized learning materials effectively addressed the identified least-learned competencies and significantly improved learners’ academic performance. It is recommended that learners be provided with continued access to such resources, teachers be supported through technology integration training, school administrators strengthen ICT infrastructure and professional development programs, and future researchers further implement and evaluate the remaining developed learning materials across different contexts and learning areas.

The purpose of the study was to assess how well Grade 5 students' decimal skills were improved by the Play-and-Learn: A Carton Place Value Playboard. Through a practical, manipulative approach, it was aimed to improve students' comprehension of place value, decimal addition and subtraction, and decimal comparison. A one-group pretest-posttest method was used in a quantitative action research design. Based on their challenges with decimals, fifteen students in Grade 5 were purposefully chosen. Before and after the intervention, abilities were measured using teacher-made exams that were in line with the K–12 curriculum. During educational sessions, the recycled-material playboard was utilized. Mean, standard deviation, and a paired samples t-test with a significance threshold of 0.05 were used to evaluate the data. The learners' mean scores showed a significant improvement in performance, rising from 8.533 (pretest) to 16.200 (post-test). Students' results became more consistent as the standard deviation dropped. The improvement was not the result of chance, as the paired t-test showed a statistically significant difference (t = −11.50, p < 0.001). The Play-and-Learn playboard turned out to be an efficient and affordable teaching instrument for improving decimal skills. The value of experiential, student-centered learning in mathematics instruction was supported by the use of manipulatives, which increased comprehension, engagement, and performance consistency.

This study explores the effectiveness of internship in attitudinal transformation towards children with special needs among prospective teachers who are preparing to work with children with special needs. As inclusive education gains prominence, the need for teachers to possess positive attitudes towards diverse learners becomes critical. The article explores the impact of internships on prospective teachers' attitudes, seeking to determine whether such practical experiences lead to attitudinal shifts that foster more inclusive teaching practices.To conduct this research, a survey method was employed. Survey was administered to a cohort of prospective teachers who participated in internships that focused on special needs education. The study analysed their attitudes towards children with special needs before and after the internship program, as well as their perceptions of the internship's influence on their attitudes.Preliminary findings indicate a significant positive correlation between internships and attitudinal transformation. The majority of participants reported an increased understanding of the unique needs and capabilities of children with special needs. Furthermore, they demonstrated heightened empathy, greater self-efficacy, and an increased willingness to employ differentiated instructional strategies. These findings suggest that internships play a vital role in shaping prospective teachers' perceptions and attitudes towards inclusive education.This research sheds light on the importance of experiential learning opportunities, such as internships, in teacher preparation programs. It contributes valuable insights to the ongoing discourse surrounding inclusive education and the significance of fostering positive attitudes among educators. Ultimately, the study advocates for the integration of internships as a fundamental component of teacher training, equipping future educators with the attitudes and skills necessary to create supportive and inclusive learning environments for all students, including those with special needs.

This research study was carried out on the assignment from associates, working for Government of India. Regarding coal deposits at the coal concession of Pt Guang Alam Semesta (“GAS”), Indonesia, Videocon had provided security to IDBI inter-alia in the form of pledge over 51.33% shares in the Indonesian Company having IUP(Exploration) permit for a coal concession area spread over 10,000 Ha in Katingan Regency, Central Kalimantan, Indonesia. S K Ahuja Associates, an established valuer in India and is on the panel of approved valuers of IDBI. M/s Videocon (Mauritius) Infrastructure Ventures Limited. Videocon had approached S. K. Ahuja for carrying out an assessment of the Inherent Asset Value of coal resource available at the coal concession of GAS in Indonesia based on its independent assessment, site visit and review of certain survey reports. As Director, Project & Environment Consultants as well as researcher, the author was assigned the work to examine possibility viable mining and import to India.

Child malnutrition continues to be a significant global health challenge, particularly in developing and resource-constrained areas where access to timely healthcare services is limited. Detecting nutritional deficiencies at an early stage is important for preventing long-term health issues and supporting healthy physical and cognitive growth in children. This study proposes a Deep Learning-Based Visual Diagnosis System for Paediatric Malnutrition Detection that analyzes Paediatric facial and body images to identify indicators of malnutrition automatically.
The proposed approach applies Convolutional Neural Networks (CNNs) to learn and recognize visual characteristics linked to undernutrition and categorize children according to their nutritional status. To improve detection capability and overall performance, image preprocessing and feature extraction techniques are incorporated into the workflow. The system is designed as a rapid, non-invasive, and affordable screening method that can assist healthcare providers in diagnosis and continuous nutritional monitoring.
Experimental results indicate that deep learning models can successfully detect malnutrition-related visual patterns from Paediatric images with encouraging levels of accuracy. The proposed solution can contribute to expanding access to nutritional assessment and support early intervention strategies aimed at reducing malnutrition among children through intelligent healthcare technologies.

This research study was carried out on the assignment from associates, working for Government of India. Regarding coal deposits at the coal concession of Pt Guang Alam Semesta (“GAS”), Indonesia, Videocon had provided security to IDBI inter-alia in the form of pledge over 51.33% shares in the Indonesian Company having IUP(Exploration) permit for a coal concession area spread over 10,000 Ha in Katingan Regency, Central Kalimantan, Indonesia. S K Ahuja Associates, an established valuer in India and is on the panel of approved valuers of IDBI. M/s Videocon (Mauritius) Infrastructure Ventures Limited. Videocon had approached S. K. Ahuja for carrying out an assessment of the Inherent Asset Value of coal resource available at the coal concession of GAS in Indonesia based on its independent assessment, site visit and review of certain survey reports. As Director, Project & Environment Consultants as well as researcher, the author was assigned the work to examine possibility viable mining and import to India.

The rapid expansion of digital technologies and social networking platforms has transformed communication patterns, lifestyle behaviors, and educational experiences among youth. Social media has emerged as a dominant medium for information sharing, social interaction, entertainment, and academic engagement. While these platforms offer numerous opportunities for learning and collaboration, concerns remain regarding their influence on students' lifestyle habits and educational outcomes. The present study investigates the impact of social media communication on youth lifestyle patterns and educational performance.
A quantitative descriptive survey research design was employed to examine the relationships among social media communication, lifestyle patterns, and academic achievement. Data were collected from 300 undergraduate and postgraduate students selected through stratified random sampling. A structured questionnaire consisting of Social Media Communication Scale, Lifestyle Pattern Scale, and Educational Performance Scale was used for data collection. The collected data were analyzed using descriptive statistics, Pearson correlation, and multiple regression analysis.
The findings revealed that a majority of students spend between three and five hours daily on social media platforms. The results further indicated significant positive relationships between social media communication and lifestyle patterns (r = 0.482, p < 0.01), social media communication and educational performance (r = 0.371, p < 0.01), and lifestyle patterns and educational performance (r = 0.529, p < 0.01). Multiple regression analysis demonstrated that social media communication and lifestyle patterns jointly explained 40.7% of the variance in educational performance. Lifestyle patterns emerged as a stronger predictor of academic achievement than social media communication alone.
The study concludes that social media is neither inherently beneficial nor detrimental; rather, its impact depends on the nature, purpose, and duration of use. Educationally oriented and balanced social media engagement can enhance learning opportunities, collaboration, and academic performance, whereas excessive and non-academic usage may adversely affect lifestyle behaviors and educational outcomes. The study highlights the need for digital literacy, responsible technology usage, and balanced lifestyle practices among youth to maximize the educational benefits of social media while minimizing its potential risks.

Layunin ng pag-aaral na ito na matukoy ang antas ng aplikasyon ng Sosyo-Emosyonal na Literasiya (SEL) sa pagtuturo ng asignaturang Filipino batay sa persepsyon ng mga guro at ang antas ng kritikal na pag-iisip ng mga mag-aaral sa mga kasanayang pagsusuri ng impormasyon, lohikal na pangangatwiran, ebalwasyon ng ideya at argumento, at pagbubuo ng matalinong konklusyon. Sinuri rin nito kung may makabuluhang ugnayan sa pagitan ng aplikasyon ng SEL at kritikal na pag-iisip ng mga mag-aaral. Gumamit ang pag-aaral ng deskriptibo-korelasyonal na disenyo. Lumahok ang tatlumpung (30) guro sa Filipino at siyamnapung (90) mag-aaral mula sa mga piling paaralan sa Dibisyon ng Lungsod ng Gingoog. Ginamit ang weighted mean upang masukat ang antas ng aplikasyon ng SEL, Mean Percentage Score (MPS) para sa kritikal na pag-iisip, at Pearson r para sa pagsusuri ng ugnayan ng dalawang baryabol.
Ipinakita ng resulta na napakataas ang antas ng aplikasyon ng SEL sa pagtuturo ng Filipino na may pangkalahatang mean na 4.58. Natuklasan din na mataas ang antas ng kritikal na pag-iisip ng mga mag-aaral na may pangkalahatang MPS na 79.95. Pinakamataas ang kasanayan sa pagbubuo ng matalinong konklusyon (MPS = 84.67), samantalang pinakamababa ang ebalwasyon ng ideya at argumento (MPS = 67.78). Gayunpaman, lumabas sa pagsusuri na walang makabuluhang ugnayan sa pagitan ng aplikasyon ng SEL at kritikal na pag-iisip ng mga mag-aaral (r = 0.1435, p = 0.449 > 0.05), kaya tinanggap ang null hypothesis.
Batay sa mga natuklasan, napag-alamang mataas ang antas ng aplikasyon ng SEL at kritikal na pag-iisip ng mga mag-aaral, subalit hindi ito nagpapakita ng tuwirang estadistikang ugnayan. Inirerekomenda ang pagpapatibay ng mga estratehiyang pampagtuturo na nakatuon sa argumentasyon, pagsusuri ng bias, ebalwasyon ng mga ideya at argumento, at iba pang gawaing humuhubog sa mataas na antas ng pag-iisip upang higit na mapaunlad ang kritikal na pag-iisip ng mga mag-aaral.

The current situation features tough competition, fast technological progress, and ever-changing customer needs and preferences. In this active environment, organizations must carry out ongoing market research and adopt strategies that fit their goals and target audience. One important strategy is influencer marketing. It has become a key part of a company’s marketing plan since it helps create a unique brand image and build a strong influence in the eyes of consumers, especially among Gen Z. Influencer marketing involves partnerships between brands and social media influencers who have real reach, credibility, and trust with their followers. These influencers promote products or services in a relatable and engaging way. They often blend promotions into their daily content, making the advertising feel more natural and less intrusive than traditional ads. Additionally, influencer marketing iscentral to moderndigital marketing. It allows brands to create a unique identity and foster a strong emotional connection with their target customers. Unlike traditional marketing, which typically relies on one-way communication, influencer marketing encourages two-way interaction. This lets consumers engage, respond, and share their opinions instantly. This interactive aspect boosts customer involvement and strengthens the bond between brands and consumers. Likewise, the use of storytelling, personalized content, and visual appeal by influencers further improves the effectiveness of this strategy. Gen Z customers, born between 1997 and 2012, have been greatly impacted by influencer marketing. This generation spends a lot of time on platforms like Instagram, TikTok, andYouTube, where they actively check out new trends, look for peer suggestions, and make informed buying decisions. In summary, influencer marketing has transformed how brands connect with consumers, particularly Gen Z. Its focus on authenticity, relatability, and interactive communication makes it a powerful tool for shaping consumer behavior. As digital platforms keep evolving, influencer marketing will likely play an even more important role in helping organizationsreachtheirmarketinggoalsandmaintainacompetitiveedgeinthemarket.

In the Sudanian-Sahelian zone, the limited availability of water resources constitutes a major constraint for irrigated vegetable production. Tomatoes (Solanum lycopersicum L.), a crop with high economic value, exhibit a marked sensitivity to water supply conditions, making water use optimization essential. This study analyzes the agronomic response of tomatoes and their water use efficiency under different irrigation regimes from pressurized systems, within the agroecological conditions of Katibougou, Mali.
The experiment focused on evaluating vegetative growth, yield components, marketable yield, and water use efficiency. The results show that tomatoes respond favorably to regular and controlled water supply, with a significant improvement in growth and yield, even under relatively low water volumes. Maximum water efficiency is achieved when water inputs are better controlled, resulting in a more efficient use of each unit of water applied.This study highlights that optimizing irrigation management is a key lever for improving the productivity and sustainability of tomato cultivation in water-scarce areas.

Dermatological screening applications face significant bottlenecks regarding user friction, cross-platform performance anomalies, and native execution overhead. This paper demonstrates an engineered solution, DermaVision, which introduces a fully serverless, zero-dependency, browser-native processing framework.Operating inside isolated client sandboxes via standard HTML5, CSS3, and JavaScript, the tool extracts objective biometric data directly from consumer smartphone imagery without invoking persistent local storage, database connections, or installation layers. The image processing sequence isolates targeted dermal zones through a localized 468-point facial landmark mesh array. Depth coordinates are computed dynamically via pixel-level radiometric checks and surface-normal approximations, enabling height calculations in precise micrometer increments. The system runs concurrent feature extraction loops to quantify six foundational clinical tracking indicators: erythema distribution (Redness Index), light-reflectance properties (Hydration Score), multi- channel melanin variance (Pigmentation), structural micro-roughness anomalies, pore configuration maps, and overall macro- lesion frequency arrays. These metrics are compiled to determine a clinical grading baseline—the 3D Acne Volume Index (AVI)—mappingcutaneouspresentationsacross fiveseveritytiersrangingfrom CleartoVerySevere.Empirical performance evaluations display high structural consistency across diverse compression parameters while fully guaranteeing localized session anonymity.

Plant diseases remain a persistent threat to agricultural productivity, especially in developingregionswherefarmersoftenlackaccesstotimelyandaccuratediagnostictools. This study presents a dual-approach system for early plant disease detection leveraging machinelearningtechniques.ThefirstapproachemploysaConvolutionalNeuralNetwork (CNN)trainedonacurateddatasetofcassava,maize,andtomatoleavestoclassifydiseases such as rust and powdery mildew with a test accuracy of 85.33%. The second approach integrates Google's Gemini API into a React Native mobile application, enabling farmers to perform real-time disease diagnosis using smartphone cameras. While the CNN model offers high precision for localized disease types, the Gemini-based system provides broader, general-purpose diagnostic capabilities, albeit with slightly reduced accuracy. Together, these tools form a scalable, accessible solution for precision agriculture, balancing offline accuracy with real-time mobile usability. Future work will explore the integrationofthesesystemsintoaunifiedplatformandexpansiontocoveradditionalcrops and environmental conditions.

Road safety in speed-sensitive environments such as school zones, hospital areas, residential regions, and construction sites critically depends on the effective enforcement of dynamic speed limits. This paper presents the design and implementation of a Vehicle Speed Limit Controller, an embedded system prototype that autonomously detects restricted zones and regulates vehicle speed in real time. The system integrates multiple sensing and control modules, including an Arduino UNO microcontroller, GPS module, RFID reader, Hall-effect speed sensor, LCD display, buzzer, LED indicators, and an L298N motor driver.
Zone identification is achieved through a hybrid approach using pre-defined GPS geofencing and RFID-based detection, ensuring flexibility and reliability under different operating conditions. The measured vehicle speed is continuously compared with predefined speed limits, and a control algorithm dynamically adjusts the pulse-width modulation (PWM) signal to the motor driver to achieve controlled deceleration or speed limitation. Additionally, the system incorporates a user feedback interface that provides real-time visual and audible alerts, enhancing driver awareness and compliance.
The proposed architecture emphasizes modularity, cost-effectiveness, and ease of integration, making it suitable for scalable implementations. While the current system is validated on a prototype platform, the underlying control strategy can be extended to advanced automotive applications such as intelligent speed assistance systems, geofencing-based vehicle control, and smart transportation frameworks. The work demonstrates the potential of embedded systems in improving road safety by reducing human dependency and enabling automated speed regulation.

Ang pag-aaral na ito ay naglalayong suriin ang bisa ng Sistematikong Pagsusuri bilang estratehiya sa pagtuturo ng panitikan at sa paglinang ng mapanuring pagbasa ng mga mag-aaral sa Ikapitong Baitang ng Doña Rosario National High School. Gumamit ito ng descriptive-evaluative na disenyo na may pre-test at post-test upang masukat ang antas ng mapanuring pagbasa bago at pagkatapos ng interbensyon. Walumpu’t isang (81) mag-aaral ang naging respondente ng pag-aaral. Ginamit bilang pangunahing instrumento ang Talaan ng Katanungan para sa Mag-aaral at Talatanungan sa Bisa ng Sistematikong Pagsusuri, at sinuri ang mga nakalap na datos gamit ang mean, percentage, weighted mean, at paired sample t-test.
Natuklasan na bago ang interbensyon, ang mga mag-aaral ay nasa mababang antas ng mapanuring pagbasa sa mga aspektong pag-unawa sa nilalaman ng kuwento, pagsusuri ng estruktura at anyo ng akda, at paglalahad at antas ng pag-unawa sa akda. Matapos ang pagpapatupad ng Sistematikong Pagsusuri gamit ang kontekstuwalisadong lokal na alamat na *Alamat ni Doña Telesfora*, tumaas ang kanilang antas ng pagganap tungo sa mataas na antas ng kahusayan. Napatunayan din ng paired sample t-test na may makabuluhang pagkakaiba sa pagitan ng pre-test at post-test sapagkat ang lahat ng p-value ay mas mababa sa 0.05. Batay sa persepsyon ng mga mag-aaral, lubos nilang tinanggap ang interbensyon na nagtamo ng kabuuang mean na 4.51 na may deskripsiyong “Lubos na Sumang-ayon”, na nagpapakita ng mataas na bisa nito sa pagpapalalim ng pag-unawa, pagsusuri, at pagpapahalaga sa mga akdang pampanitikan.
Samakatuwid, napatunayang epektibo ang Sistematikong Pagsusuri at paggamit ng kontekstuwalisadong lokal na alamat bilang interbensyon sa pagpapataas ng antas ng mapanuring pagbasa ng mga mag-aaral. Inirerekomenda ang patuloy na paggamit at pagpapalawak ng estratehiyang ito sa pagtuturo ng panitikan at ang pagsasagawa pa ng mga kaugnay na pag-aaral upang higit na mapagtibay ang pagiging mabisa nito sa iba pang konteksto at asignatura.

The Smart Bus Tracker system is designed to provide real-time tracking and monitoring of public transportation buses using GPS and mobile technologies. The system enables passengers to view accurate bus locations, estimated arrival times, and route information through a user-friendly interface. It also assists administrators in managing fleets efficiently by offering data on bus speed, schedule adherence, and operational status. By improving transparency, reducing waiting time, and enhancing passenger convenience, the Smart Bus Tracker contributes to a smarter and more efficient public transportation ecosystem.
The Smart Bus Tracker is an IoT-based system aimed at improving the efficiency and user experience of public transportation. The project integrates GPS modules, cloud-based data handling, and a mobile/web application to deliver real-time bus location updates and estimated time of arrival (ETA) to passengers. The system simultaneously provides transport authorities with insights into fleet operations, such as route deviations, delays, and performance analytics. By offering accurate, real-time information and ensuring seamless communication between buses, servers, and end-users, the Smart Bus Tracker reduces uncertainty in travel planning, minimizes waiting times, and enhances the reliability of the overall transit network

The COVID-19 pandemic and the associated lockdown measures imposed across India during 2020 produced an unprecedented disruption to conventional retail channels and accelerated the adoption of online shopping among segments of the population that had previously been reluctant or only occasional users of e-commerce platforms. This study examines the shift in consumer attitudes toward online buying behaviour among college students in Kolkata, with particular attention to the convenience and service-quality dimensions of the online shopping experience before and after the onset of the pandemic. Using a structured questionnaire administered to 79 respondents drawn from undergraduate and postgraduate programmes, the study applies exploratory factor analysis to identify the latent dimensions underlying online buying behaviour, independent-samples t-tests and one-way ANOVA to examine differences across demographic and socio-economic groups, and paired-samples t-tests to assess pre- and post-pandemic attitudinal change. The reliability of the eight-item online buying behaviour scale was confirmed (Cronbach's alpha = 0.786). Factor analysis extracted two principal components, labelled Convenience and Service Quality, jointly accounting for the majority of variance in the construct. The results indicate that demographic and socio-economic variables, namely gender, academic level, financial independence, and family income, exert no statistically significant influence on either dimension, whereas the pandemic itself produced a significant and positive shift in attitudes toward both convenience (t(78) = -9.842, p < .001) and service quality (t(78) = -8.217, p < .001) of online retail platforms. The findings suggest that the pandemic functioned as an exogenous shock that normalised online shopping across demographic segments rather than reinforcing pre-existing divides, with implications for retail strategy, digital infrastructure investment, and post-pandemic consumer research in emerging markets.

This study examined the socio-economic implication of outdoor recreational activities in Dekina Local Government Area of Kogi State, Nigeria. The objectives were to: identify and map out the available outdoor recreational facilities, assess the socio-economic effects of outdoor recreational activities, investigate the factors responsible for the effective performance of outdoor recreational activities, evaluate the negative effects of outdoor recreational activities and examine strategies for mitigating the negative effects of outdoor recreation in the study area. A descriptive survey research design was adopted, with a sample of 379 respondents selected through stratified random sampling techniques. Structured questionnaire were used for data collection and the results were analyzed using frequency counts, percentages and correlation analysis. Findings revealed that hotels and guest houses are the most available recreational facilities(31.1%), followed by gardens and parks with a score of (26.9%), sports fields and playgrounds recorded(25.6%). Outdoor recreational activities were shown to have significant socio-economic contributions, including employment creation(72.0%) and business growth(65.2%). However, inadequate government funding(41.7%) and infrastructural deficiencies(29.6%) were identified as major constraints to effective performance. Mitigation strategies such as effective waste management(36.9%) and security enhancement(31.4%) were prioritized by respondents. Hypothesis testing using Pearson’s correlation analysis established a significant positive relationship between outdoor recreational activities and socio-economic development in the study Area (r=0.622, p<0.05). The study concludes that outdoor recreational activities are important drivers of socio-economic development and their sustainability depends greatly on adequate funding. It recommends that government and private stakeholders invest in facility maintenance and strengthen public-private partnerships.

Solid waste management has become an important socio-economic and environmental issue in Nigeria, particularly in rapidly growing urban centers such as Lokoja, Kogi State. This study examined the socio-economic assessment of waste-to-wealth potentials of solid wastes in Lokoja town. The objectives of the study were to: identify the types of waste-to-wealth activities in Lokoja, examine the magnitude of solid wastes generated in the study area, evaluate the existing challenges of waste-to-wealth activities and suggest possible ways of addressing these challenges in waste-to-wealth conversion processes. A descriptive survey research design was adopted and data were collected from a sample of 385 respondents using a structured questionnaire. Data were analyzed using frequencies, percentages and correlation analysis. Findings revealed that major waste-to-wealth activities in Lokoja include plastic recycling, scrap metal collection, composting of organic waste and reuse of bottles and cardboard. The study established that waste generation is significant as households generate 1–3kg daily(40.5%). Food/organic waste(42.1%) and plastics(33.2%) were the most common types generated. Major challenges hindering effective waste-to-wealth activities included inadequate funding(31.4%). Main suggested solutionwas provision of loans and equipment(32.4%). Correlation analysis showed a significant positive relationship (r=0.629, p<0.05) between waste-to-wealth activities and socio-economic livelihood in Lokoja, indicating that effective waste management can enhance income, create jobs and improve living standards. The study concludes that waste-to-wealth has untapped socio-economic potential in Lokoja, but its impact is limited by structural, financial and social barriers. It is recommended that government implement sustainable policies, while public-private partnerships should establish modern recycling facilities.

Trauma-related mortality and morbidity in Nigeria are compounded by healthcare facilities whose spatial organization was never designed to meet the demands of emergency and trauma care. The physical configuration of a healthcare building its circulation systems, functional zoning, and departmental adjacency directly determines whether clinical intervention reaches patients efficiently and safely. This study aimed to examine how circulation systems and spatial layout configurations influence the operational efficiency, clinical safety, and user functionality of trauma healthcare facilities in Nigeria.A qualitative descriptive case study design was employed. Two Nigerian facilities were purposively selected: the Ondo State Trauma and Surgical Centre, Ondo a purpose-built trauma facility and the Accident and Emergency Unit of the National Orthopaedic Hospital, Igbobi, Lagos an adapted facility. Structured visual field surveys were conducted at both sites in 2024 and analysed using a thematic comparative framework assessed against WHO, AIA, and evidence-based design standards.The purpose-built facility outperformed the adapted facility across all five criteria examined: entrance accessibility, horizontal circulation efficiency, functional zoning, vertical circulation provision, and wayfinding effectiveness. The most critical deficiency identified at the adapted facility was the spatial separation of the intensive care unit from the surgical suite a departmental adjacency failure with direct patient safety consequences. Even the better-performing facility exhibited incomplete separation of public and clinical circulation routes, indicating that purpose-built design is necessary but not sufficient for optimal spatial performance. Design origin is the primary determinant of spatial performance in Nigerian trauma facilities, shifting the policy imperative from reactive infrastructure upgrading toward evidence-based planning standards enforced at the commissioning stage.

The Pagladiya River, a tributary of the Brahmaputrain Assam, is highly dynamicand prone to frequentflooding, causingseverethreatstosettlements, agriculture,andinfrastructure. Thisstudy evaluates the vulnerability of the left bank embankmentbetween Barbhag and Madaltana using satellite imagery from 2015–2025 sourced from Bhuvan portal and USGS Earth Explorer. Bankline migration was analyzed in ArcGIS 10.4.1, while flood behavior was simulated using HEC-RAS 2D modelling. A composite Vulnerability Index based on bankline shifting, bank distance from the embankment,flow velocity, flow direction, and discharge per unit width was developedto identify criticalzonesfor embankmentprotection andfloodmanagement.

Digital social enterprises have showed up as rather new and innovative platforms, where social aims are paired with technology-based business approaches to deal with socio economic problems that marginalized communities face. In those communities, transgender individuals often run into discrimination, cramped employment pathways, financial exclusion, and a kind of social stigma , which then blocks their chances for economic progress. Lately, digital social enterprises have opened fresh routes for getting income, building skills, starting ventures, and reaching broader markets through e commerce, digital services, online training, and financial technology tools. So, this study intends to look into what kind of role digital social enterprises play in supporting the economic empowerment of transgender people in Tamil Nadu. It mainly tries to evaluate how these enterprises raise entrepreneurial abilities, expand livelihood options, enable financial inclusion, and also strengthen social participation for transgender individuals. The study uses a descriptive and analytical research style, it will draw on both primary data and secondary data sources . The expected results should give useful understanding about how far digital social enterprises work, in terms of sustainable economic growth and self reliance within transgender communities. Finally, the study is meant to add to the existing writing on social entrepreneurship, digital inclusion, and transgender empowerment, while also giving policy suggestions for building a more welcoming and inclusive entrepreneurial ecosystem across Tamil Nadu.

Hospital readmission remains a major challenge in modern healthcare systems due to its impact on patient outcomes, healthcare costs, and hospital resource utilization. Early identification of patients at risk of readmission enables healthcare providers to implement preventive interventions and improve discharge planning. This research presents an AI Driven Hospital Readmission Risk Scoring System that utilizes temporal healthcare indicators and machine learning techniques to predict patient readmission risk.
The proposed framework employs historical healthcare data including hospitalization duration, laboratory procedures, medication usage, outpatient visits, emergency visits, and previous inpatient admissions. Data preprocessing and feature engineering techniques were applied to prepare the dataset for predictive modeling. A Random Forest Classifier was utilized as the primary prediction algorithm because of its robustness, ability to handle structured healthcare data, and strong classification performance.
The trained model was deployed through a Streamlit-based web application that allows healthcare professionals to perform real-time readmission risk assessment. Experimental evaluation produced an accuracy of 86.57%, demonstrating the effectiveness of the proposed framework in identifying readmission patterns from historical healthcare records. The system provides an interactive and user-friendly decision-support environment for healthcare analytics and patient risk management.

Traditional methods of disposing of municipal solid waste (MSW) are becoming unsustainable in rapidly expanding metropolitan areas, making MSW management an urgent socialand environmentalconcern. Localized and ecologically friendlyoptions are even more important in light of public opposition to constructing centralized waste treatment facilities close to residential areas.
This research details a method for evaluating and reusing wet organic waste from segregated municipal solid waste (MSW) on a university campus. In order to establish if the biodegradablefractionwassuitableforanaerobicdigestion,itwasfirstisolated,measured,and tested using recognized analytical procedures. To assess the possibility of producing biogas, controlled biomethanationtrials were run, and the leftover digestate was studied for its use as an organic soil amendment.
Usingasinglecampusasanexample,theauthorspresentasmall-scalecircularsystem that recycles nutrients, energy, and garbage. Producing valuable byproducts like biogas and bio-fertilizer, the strategyis easyto implement, scales upor down, and needs little inthe way of supporting infrastructure.
Decentralizedbiomethanationoforganicwastefromcampusesisadvantageousfroma practicaland technicalstandpoint, according tothe results. Renewable energyproductionand soilenrichment can be achieved throughthestudy'sdemonstrated effectiveutilizationofsuch waste streams. Supporting sustainable waste management techniques and minimizing dependency on landfill disposal, this approach can be reproduced in similar institutional or residential situations.

Skill development has emerged as a cornerstone of youth empowerment and sustainable livelihood creation in India. Rural Development and Self Employment Training Institute (RUDSETI) has been playing a significant role in bridging the skill gap by imparting entrepreneurship and employment-oriented training to rural youth. The present study examines the effectiveness of RUDSETI’s training programs in enhancing employability and entrepreneurial skills among young in Dakshina Kannada district. A total of 180 respondents, who had successfully completed training under RUDSETI, were surveyed using a structured questionnaire. The study employed linear regression analysis to assess the relationship between RUDSETI training inputs (skill acquisition, mentorship, and practical exposure) and outcomes in terms of employability and entrepreneurial skill development.
The regression results indicated a statistically significant positive relationship between RUDSETI training and youth skill enhancement, with training quality and practical exposure emerging as the strongest predictors of employability. Similarly, entrepreneurship-related modules, including business planning, financial literacy, and risk-taking orientation, showed a substantial impact on entrepreneurial competence. The findings underscore the crucial role of structured and practice-oriented training in transforming youth into both job seekers and job creators. The study concludes that RUDSETI acts as a catalyst for youth empowerment by not only reducing unemployment but also fostering entrepreneurial mindsets. The research has important implications for policymakers, training institutions, and rural development initiatives aimed at promoting self-reliance and inclusive growth.

The present study focuses on the formulation and evaluation of a herbal face pack using natural ingredients for improving skin health and appearance. Herbal cosmetics have gained significant importance due to their safety, effectiveness, and minimal side effects compared to synthetic cosmetic products. The objective of this research was to develop a herbal face pack possessing cleansing, anti-acne, anti-inflammatory, antioxidant, and skin-nourishing properties. Various herbal ingredients such as Azadirachta indica, Curcuma longa, Aloe vera, Santalum album, and Ocimum tenuiflorum were selected due to their beneficial effects on skin care and protection. The herbal powders and extracts were blended in suitable proportions to prepare the face pack formulation. The prepared formulation was evaluated for various physicochemical parameters including color, odor, appearance, pH, consistency, spreadability, washability, irritancy, particle size, and stability studies. The antimicrobial and antioxidant activities of the formulation were also assessed to determine its effectiveness in skin protection and cleansing. The evaluation results demonstrated that the formulated herbal face pack possessed good physicochemical stability, acceptable pH, smooth texture, easy spreadability, and no signs of skin irritation. The formulation showed effective cleansing action, reduction in excess oil, and improvement in skin freshness and complexion. The study concludes that the developed herbal face pack can be used as a safe, natural, and effective skincare formulation for maintaining healthy and glowing skin.

The rapid expansion of e-commerce has significantly transformed the retail landscape across India, including semi-urban and district-level markets such as Bijnor District in Uttar Pradesh. This study examines the economic impact, beneficial effects, operational effectiveness, and consumer attitudes toward e-commerce in the retail sector of Bijnor District. Primary data were collected through a structured questionnaire using a 5-point Likert scale from 320 respondents. Descriptive statistics and one-sample t-tests were applied to analyze the hypotheses. The findings reveal that e-commerce has significantly increased retail sales, improved income opportunities, enhanced operational efficiency, reduced business costs, and positively influenced consumer preferences and satisfaction. The study concludes that e-commerce has become an essential component of the modern retail sector and has substantially reshaped traditional retail structures in Bijnor District.

This paper presents Stock Sense, a GenerativeAI-based stock market movement prediction system implemented as a Flask web application. The system employs Long Short-Term Memory (LSTM) as the core generatives equence model a long side anensemble off our machine learning regressors — Random Forest, Linear Regression, Gradient Boosting, and XGBoost — to forecast next-day stock closing prices. Input data from the Stock_Prediction_2025.csv dataset (2,000 records) is preprocessed using Label Encoding and Min-Max Normalization before model training. The ensemble achieves an RZ accuracy of 98.50% with a model response time under two seconds. Beyond prediction, StockSense integrates a rule-based Investment Advisor module that classifies investorriskprofilesintoConservative,Balanced,orAggressivestrategiesandcomputesprojected portfolio growth using compound interest calculations. A session history module stores all past investmentsessionsinanSQLitedatabaseforfuturereference.Experimentalevaluationconfirms reliable prediction accuracy, consistent risk classification, and practical suitability for real-time investment decision support.

Existing studies of web tracking primarily detect tracking mechanisms but lack quantitative models capable of estimating cumulative privacy leakage across interconnected tracking ecosystems. This paper introduces a unified Privacy Risk Index (PRI), a multivariate quantitative framework that integrates identifier entropy, tracker density, cross-site behavioral correlation, third-party data-sharing frequency, and network-structural centrality into a single ecosystem-level leakage function. The proposed formulation establishes bounded sensitivity and interpretable parameter influence, enabling stable comparative privacy-risk estimation across heterogeneous tracking environments. The framework is evaluated using controlled simulations and automated web-measurement datasets, and comparative experiments against entropy-based and exposure-score metrics demonstrate that PRI provides improved discrimination of high-risk tracking configurations while capturing nonlinear amplification effects arising from tracker concentration and cross-site correlation. The resulting metric offers a computationally efficient quantitative indicator for ecosystem-scale privacy auditing, mitigation evaluation, and regulatory monitoring of web tracking infrastructures.

This study investigated the relationship between AI-assisted social media usage policies and cyberbullying reduction among students in public secondary schools in Rivers State, Nigeria. It was guided by two research questions, and two null hypotheses were tested. A correlational design was used. The population included 5,833 teachers from 320 public secondary schools in Rivers State, with a sample of 583 teachers representing 10% of the population, selected through stratified random sampling. A validated 4-point rating scale questionnaire, developed by experts, served as the data collection instrument. The reliability of the instrument was determined using Cronbach's Alpha, with coefficients of 0.79 for the AI-Assisted Social Media Usage Policies Assessment Scale (AASMUPAS) and 0.77 for the Cyberbullying Reduction Among Students Assessment Scale (CRASAS). Pearson Product-Moment Correlation (PPMC) statistics were used to answer the research questions and to test the hypotheses at a 0.05 level of significance. Findings revealed a high positive and significant relationship between content monitoring, parental control and teachers’ productivity in public secondary schools in Rivers State. Based on the findings, it was concluded that the implementation of these AI-driven policies is essential for schools seeking to safeguard learners and foster a safer digital learning environment. Consequently, it was recommended, among others, that school administrators and policymakers should make content monitoring a mandatory component of AI-assisted social media usage policies in all public secondary schools, as it has been proven highly effective in significantly reducing cyberbullying among students.

Since the beginning of time, creams have been valued as essential topical preparations in cosmetic products becauseofhow simpleit is to apply and removethem from the skin. Pharmaceutical creams are used for a number of aesthetic purposes, including cleansing, beautifying, modifying look, moisturising, etc. They also protect the skin from bacterialandfungalinfectionsandcanbeusedtotreatsk in injuries including burns, cuts,and wounds.The general population and society can safely employ these semi-solid preparations. Theproductsusedtoenhanceandbeautifyhumanappearancesareknownasherbalcosmetics. The current study's objective was to design and assess herbal creams that contained plant extracts madeutilising thewaterin oil method for thegoal ofmoisturising and nourishing the skin. extract are used to make the herbal cream. Utilising several evaluation techniques, the createdproduct’squalitywasevaluated.Thephysicalcharacteristicsofthecreamformulation did not alter. During the research study period, the cream formulation demonstrated good consistencyandspreadability,homogeneity,pH,non-greasyproperties, and nosignsofphase separation. There was no discerniblechangein thecreatedcream’sviscosity,aroma,orvisual appearance during the research period, according to stability measures. As the water in the emulsion slowly evaporates, the cooling and calming effects of the herbal extract with HERBAL CREAM are produced. HERBAL CREAMs are more moisturising because they create an oily barrier to stop the loss of water from the stratum corneum, the outermost layer of the skin. They are water-in-oil emulsion and intended for application on skin or accessible mucousmembranetoprovidelocalizedandsometimessystemiceffectatthesiteofapplication.

FPGA-SoC platforms are becoming more popular for Unmanned Aerial Vehicle (UAV) systems due to their deterministic real-time processing capacity, parallel processing ability, and ability to provide hardware acceleration. Inputting a bitstream, altering the hardware, cloning, and side channel leakage are new attack vectors on the programmable logic and dynamic partial reconfiguration (DPR) at the hardware level. The current solutions are mostly based on a single security solution or on a static FPGA design and offer limited support for secure runtime reconfiguration.
In this paper, a DPR-aware secure FPGA-SoC architecture for UAV flight control systems is proposed, which is integrated from secure boot, hardware root of trust, device authentication using PUF, hardware cryptographic acceleration, and hardware-enforced isolation in a single runtime security framework. The introduction of a Secure Reconfiguration Manager (SRM) enables runtime (DPR) operations to verify, decrypt, and authenticate partial bitstreams.
It has been implemented on a Xilinx Zynq-7000 FPGA-SoC platform, and its performance has been analyzed and compared based on the utilization of hardware resources, latency, power overhead, and security effectiveness during runtime. The results of the experiments demonstrate that the introduced control-loop latency is only 2.5% larger than the baseline, while the runtime security is significantly enhanced. Optimization of cryptographic functions in FPGA resulted in latency 24 times lower than software execution and throughput 15 times higher. The security validation experiments also validated the ability to prevent injection of malicious bitstreams, to resist hardware cloning by binding to the PUF, and to decrease the side-channel leakage in timing experiments.
The proposed architecture permits authenticated DPR at runtime with integrated hardware-rooted trust and real-time validation, which is different from existing FPGA security approaches that are based on static configurations. The result highlights that secure runtime DPR can be realized while meeting the real-time UAV control constraints without breaking them, thus offering a secure hardware-based security solution for adaptive FPGA-SoC UAV platforms.

The present study aimed to examine the effect of regular physical education on health and lifestyle behaviour of high school students in Andhra Pradesh. A total of 120 high school students were selected as subjects for the study using a random sampling method from different schools in Andhra Pradesh. The age of the participants ranged between 13 to 16 years. The study focused on assessing the impact of regular participation in physical education classes on students’ physical health, fitness levels, and lifestyle behaviours such as daily physical activity, dietary habits, sleep patterns, and screen time. A structured questionnaire along with selected physical fitness tests was used to collect relevant data from the subjects. The findings of the study revealed that students who regularly participated in physical education programs showed significant improvement in physical fitness, better health status, and more active lifestyle behaviours compared to those with irregular participation. Regular physical education was also found to positively influence healthy habits, including increased physical activity, improved sleep quality, and reduced sedentary behaviour. The study concludes that regular physical education plays a vital role in promoting healthy living and active lifestyle behaviours among high school students in Andhra Pradesh. It is recommended that schools should strengthen physical education programs and encourage consistent student participation to improve overall health and well-being.

This study aimed to determine the validity of an interactive blog-based physics learning medium designed to enhance students’ scientific literacy. The research was motivated by the persistent challenge of low scientific literacy among students and the lack of interactive, inquiry-oriented learning tools in physics education. The study sought to develop and validate a digital learning medium that integrates conceptual accuracy, pedagogical coherence, and accessibility to support inquiry-based learning.Using a Research and Development approach with the ADDIE model (Analysis, Design, Development, Implementation, and Evaluation), the medium was constructed, refined, and validated by experts in physics education. The validation focused on four dimensions—content accuracy, construct alignment, readability, and visual design—to ensure the medium’s suitability for fostering scientific reasoning and conceptual understanding.The results demonstrated that the interactive blog-based medium achieved a high level of validity across all dimensions. Experts confirmed that the content was scientifically accurate, aligned with curricular goals, and effectively facilitated inquiry and problem-based learning. The language and visual design enhanced conceptual accessibility, while the blog’s interactive features encouraged exploration and reflection. These findings indicate that valid instructional design plays a crucial role in linking abstract physics concepts to real-world applications and developing students’ ability to think scientifically.In conclusion, the study provides empirical evidence that a validated interactive blog-based learning medium serves as a pedagogically sound and scientifically reliable tool for improving students’ scientific literacy. Its validated structure contributes to the advancement of digital science education and offers a foundation for further exploration of technology-integrated learning in physics classrooms.

Rising levels of financial fraud and corruption, combined with weak internal controls, have pushed the demand for forensic auditing higher in many countries. In Nigeria the EFCC continues to confront growing cases of misappropriation and cyber fraud in both public agencies and private firms. This study examined how artificial intelligence is reshaping forensic auditing practices within the country by means of a survey. The population consisted of 1,642 management staff, internal control specialists, auditors, and IT personnel selected from seven deposit money banks. Purposive sampling and Taro Yamane’s formula produced a final sample of 400 respondents. Regression analysis then tested the effects of machine learning, data analytics, and investigation efficiency on current forensic auditing methods. The results showed that machine learning (β=0.196722, p > 0.05) and data analytics (β=0.095293, p > 0.05) had positive and statistically insignificant impacts on forensic auditing practices. On the contrary, efficient investigation (β = 0.611031, p < 0.05) was found to positively influence forensic auditing practices as well, indicating that efficiency in investigations continued to be an important factor influencing the performance of forensic audits. It was observed that while AI had much to offer in terms of improvements in forensic auditing practices, its usage at present was still insufficient. The study recommended that efforts be made towards investments in training personnel, technological infrastructure, data management, and efficient investigation processes within forensic auditing.

Nowadays, many farmers face problems in farming because they do not know which crop is best for their soil. Additionally, many apply fertilizers indiscriminately without assessing the actual nutrient requirements of the soil. This leads to less crop production and wastes money. To solve this problem, we built AgriScan Intelligence. This is a simple web application that helps farmers make the right decisions. The system takes soil parameters like Nitrogen (N), Phosphorus (P), Potassium (K), soil pH, temperature, and rainfall as input. Then, our Machine Learning models (Random Forest and Logistic Regression) process this data and recommend the best crop and the right fertilizer. We built the backend using Python with the Flask framework and stored user data on the Aiven Cloud MySQL database. User authentication integrity is maintained by routing cryptographic One-Time Passwords (OTPs) through the external Mailtrap API gateway. The complete web application is successfully hosted and live on the Render cloud platform. It is free, fast, and very easy for farmers to use.

With thequick paceof growth in artificial intelligence (AI) and e-commerce, personalized virtual shopping assistants (PVSAs) have been a revolutionary answer to improve customer experience. In this study, the creation and deployment of anAI-powered PVS Ausing natural language processing (NLP),machine learning (ML), and computer vision to offer real-time product suggestions, virtual try-ons, and hassle-free interactions are investigated. The system to be proposed applies user behavior analysis, sentiment analysis, and deep learning methods to gain insights into customer preferences and provide extremely personalized shopping experiences. The experimental results validate the performance of AI-powered assistants in enhancing customer engagement, eliminating decision fatigue, and driving sales conversions. The challenges of data privacy, model bias,and integration complexity are also addressed, along with the possible future directions of AI-driven retail experiences.

This study investigates the performance of high-volume Ground Granulated Blast Furnace Slag (GGBS) concrete with emphasis on long-term durability, structural performance, and sustainability. Different percentages of GGBS were used as partial replacement of cement to evaluate their effect on the mechanical and durability properties of concrete. Experimental investigations included compressive strength, tensile strength, flexural strength, Rapid Chloride Penetration Test (RCPT), water absorption, sorptivity, sulphate resistance, and acid attack tests. The results revealed that high-volume GGBS concrete exhibited lower early-age strength compared to conventional concrete; however, significant improvement in later-age strength was observed due to continuous pozzolanic reactions and additional formation of calcium silicate hydrate (C-S-H) gel. Durability studies showed substantial reduction in chloride ion penetration, permeability, water absorption, and chemical deterioration, indicating enhanced resistance against corrosion and aggressive environmental exposure. In the acid attack test using 5% sulfuric acid solution, the optimum GGBS replacement level was found between 36% and 45%, where minimum weight reduction of 2.42% and 1.82% was recorded, demonstrating superior durability performance. Microstructural improvements such as reduced pores and denser cement matrix contributed to the enhanced long-term durability of concrete. In addition, the replacement of cement with GGBS significantly reduced cement consumption and carbon dioxide emissions, making the material more sustainable and environmentally friendly. Overall, the study concludes that high-volume GGBS concrete is an efficient and durable construction material suitable for reinforced concrete structures exposed to severe environmental conditions while also supporting sustainable construction practices.

In an era characterized by digital disruption, geopolitical volatility, and escalating stakeholder expectations, organizations increasingly recognize Enterprise Risk Management (ERM) as a strategic imperative rather than a compliance obligation. This study investigates how ERM, when strategically integrated, contributes to value creation, competitive differentiation, and firm performance through a multidimensional analytical framework. Drawing upon the Resource-Based View (RBV), Dynamic Capabilities Theory, and Stakeholder and Institutional perspectives, the research conceptualizes ERM as a unique, inimitable capability that enhances organizational resilience and strategic agility.
A quantitative research design employing survey-based Structural Equation Modeling (SEM/PLS) was proposed, supported by a cross-sectional dataset of senior executives and risk professionals across multiple industries. The findings demonstrate that ERM integration positively influences strategic value creation, which in turn mediates the relationship between ERM and competitive differentiation. Moreover, organizational culture moderates the ERM–value creation relationship, amplifying its impact within risk-aware and adaptive organizations. Competitive differentiation, fostered through ERM-driven innovation and reputational trust, significantly enhances firm performance.
The study advances theory by reframing ERM as a strategic resource and dynamic capability, bridging governance and strategy literatures. It offers practical implications for executives seeking to embed ERM into strategic planning, innovation management, and performance systems. Policy recommendations encourage regulators to incentivize strategic ERM adoption beyond compliance. Collectively, the research calls for aparadigm shift toward strategic risk intelligence, positioning ERM as a cornerstone of sustainable competitive advantage in digitally complex and uncertain environments.

The translation of trained machine learning (ML) architectures into accessible enterprise microservices remains a primary computational bottle neck with in modern Dev Opspipelines[2]. Traditional operational deployments leveraging synchronous Python web server frameworks, such as Flask or Django, suffer severe thread-blocking limitations, resulting in high response latency, reduced request throughput, and suboptimal resource utilization under concurrent data traffic.Thisstudyinvestigatestheimplementation,optimization,andvalidationofa production-grade machine learning model deployment architecture using FastAPI, an asynchronous, high-performance web framework built upon Asynchronous Server Gateway Interface (ASGI) principles and Web Concurrency worker clusters [1], [3].
Using a validated classification model dataset, we construct a resilient API service pipeline integrated with asynchronous prediction handlers, strict Pydantic input-output data validation schemas, and automated Uvicorn server processes. Performance testing under realistic request loads demonstrates that the proposed asynchronous FastAPI configuration yields a 64%reduction in average transaction response latencies and a significant increase in concurrent request throughput compared to standard synchronous frameworks. The results establish that leveraging cooperative multi-tasking engines via Python's Native Coroutines (async/await) provides a scalable approach for deploying low-latency machine learning solutions across resource-constrained enterprise infrastructures [5].

Jaundice is a hepatic disorder characterized by hyperbilirubinemia and yellow discoloration of skin and sclera due to impaired liver function. Traditional systems of medicine have long utilized herbal formulations for liver protection and detoxification with minimal side effects. The present research focuses on the preparation and evaluation of a polyherbal syrup for jaundice using medicinal plants known for their hepatoprotective, antioxidant, and anti-inflammatory properties: Gulkhaira (Althaea officinalis), Khaskhas (Papaver somniferum), Bhumi Amla (Phyllanthus niruri), Muchkund (Pterospermumacerifolium), and Kabab Chini (Piper cubeba).

Hydro-alcoholic extracts of each herb were incorporated into the syrup base, followed by evaluation of physicochemical parameters including pH, viscosity, specific gravity, organoleptic characteristics, sedimentation behavior, microbial stability, and overall formulation stability. Preliminary findings suggest that the developed syrup exhibits acceptable physicochemical properties and promising hepatoprotective potential, indicating its usefulness as a supportive herbal remedy for jaundice management. Further pharmacological and clinical investigations are recommended to validate its therapeutic efficacy

Industrial Internet of Things (IIoT) applications require efficient communication among devices operating on different industrial protocols. CAN (Controller Area Network) and Modbus are widely used communication standards in industrial automation systems. However, interoperability between these protocolsremains a challenge due to differences in message formats and communication mechanisms. This paper presents the design and development of a Multi-Protocol Communication Gateway that enables seamless communication between CAN and Modbus networks. The proposed gateway performs protocol translation, message routing, and data exchange between industrial devices and PLC-based systems. Experimental results demonstrate successful bidirectional communication, reliable protocol conversion, and improved interoperability. The developed system reduces hardware complexity, lowers implementation cost, and supports Industry 4.0 requirements.

Autism Spectrum Disorder (ASD) is a complex neurodevelopmental condition characterized by persistent challenges in social communication, restricted interests, repetitive behaviours, and sensory processing differences. Early diagnosis and personalized intervention significantly improve developmental outcomes; however, traditional assessment procedures often require extensive clinical expertise and considerable time. Artificial Intelligence (AI) has emerged as a promising technology for autism screening, diagnosis, and intervention planning. Nevertheless, the lack of transparency in many AI systems raises concerns regarding trust, reliability, and ethical implementation. Explainable Artificial Intelligence (XAI) addresses these concerns by making AI-driven decisions understandable and interpretable to clinicians, educators, and caregivers. The present study examines awareness, acceptance, and perceived effectiveness of XAI applications in autism assessment and intervention among 200 participants, including special educators, psychologists, rehabilitation professionals, and postgraduate students. Using a mixed-method research design, quantitative and qualitative data were analysed. Findings indicate that participants demonstrated positive attitudes toward XAI, particularly when transparent explanations accompanied diagnostic recommendations. Clinical psychologists and special educators reported greater confidence in explainable systems compared to conventional AI models. The study highlights the potential of XAI to improve diagnostic accuracy, support clinical decision-making, and enhance intervention outcomes while maintaining ethical accountability and professional trust.

The present study aimed to formulate and evaluate sustained release matrix tablets of Eperisone Hydrochloride using hydrophilic polymer systems to prolong drug release and improve patient compliance. Six formulations (F1–F6) were developed by varying polymer concentrations while maintaining a constant drug load. Preformulation studies including organoleptic evaluation, solubility analysis, melting point determination, partition coefficient, pH determination, and UV spectrophotometric analysis were performed. The prepared granules were evaluated for flow properties, while compressed tablets were assessed for weight variation, thickness, hardness, friability, drug content, and in-vitro drug release. The results demonstrated acceptable physicochemical properties and good flow characteristics. Drug release studies revealed that increasing polymer concentration significantly retarded drug release. Formulation F4 exhibited optimum sustained release behavior with approximately 95% drug release over 12 hours and was selected as the optimized formulation. Drug release kinetics indicated Higuchi diffusion-controlled release with non-Fickian transport behavior. Stability studies confirmed the stability of the optimized formulation under accelerated storage conditions. The study concluded that HPMC K100M is an effective hydrophilic polymer for developing sustained release matrix tablets of Eperisone Hydrochloride.

The objective of the present study was to formulate and evaluate gastroretentive floating tablets of Famotidine for prolonged gastric retention and sustained drug release in the treatment of peptic ulcer disease. Floating tablets were prepared using the wet granulation method employing Xanthan gum, Eudragit, and Carbopol 940 as release-retarding polymers and sodium bicarbonate as a gas-generating agent. Preformulation studies including organoleptic characterization, solubility analysis, determination of λmax, calibration curve preparation, and FTIR compatibility studies were carried out. The prepared formulations were evaluated for pre-compression parameters, post-compression parameters, floating behavior, swelling index, in-vitro drug release, and stability studies. Results demonstrated satisfactory flow properties and tablet characteristics. Floating lag time ranged from 30–55 seconds, while total floating time extended up to 18 hours. Formulations F5 and F6 exhibited superior swelling behavior and sustained drug release. Among all formulations, F5 was identified as the optimized formulation due to its balanced floating characteristics, controlled drug release, and stability. The study concluded that gastroretentive floating tablets of Famotidine can effectively prolong gastric residence time and improve therapeutic efficacy in anti-ulcer treatment.

The emergence of deepfake technology has made the creation of realistic synthetic videos and audio easier than ever, creating challenges for digital trust and information authenticity. This work presents a multimodal detection approach that combines signal processing methods with machine learning techniques to identify manipulated media content. Video and audio features are extracted using techniques such as FFT, DCT, MFCC and STFT to capture hidden artifacts introduced during media generation. These features are then analysed by machine learning models to determine whether the content is genuine or fabricated. By jointly examining visual and audio information, the proposed framework aims to improve detection performance and support applications in cyber security, digital forensics and media verification.

Coastal ecosystems play a crucial role as nursery habitats for early life stages of aquatic organisms, particularly fish and crustacean larvae. This study aims to analyze the composition, abundance, and spatial distribution of larval communities in Tirang Beach, Semarang, as well as their relationship with environmental parameters. Sampling was conducted at three stations using purposive sampling with three types of nets (push net, seine net, and fish net), and water quality parameters including temperature, salinity, dissolved oxygen, and pH were measured in situ. A total of 120 larval individuals belonging to seven families were identified, consisting of five fish families (Ambassidae, Engraulidae, Gerreidae, Gobiidae, and Mugilidae) and two crustacean families (Penaeidae and Palinuridae). Penaeidae was the dominant family, contributing 51.7% of the total abundance. Spatial variation in larval assemblages was observed, with higher diversity of fish larvae associated with mangrove habitats. Environmental parameters were within suitable ranges for larval survival, indicating favorable ecological conditions. The results highlight the ecological importance of Tirang Beach as a nursery ground and provide baseline information for coastal ecosystem management and conservation.

Testosterone propionate administration into male goldfish (C. auratus) will accelerate testicular maturation in fish so that they can produce seeds by shortening the time of gonad maturation. The study aims to determine the best effect and dose of testosterone propionate on testicular maturation and sperm quality of goldfish (C. auratus). The research took place on March 15, 2023 - April 15, 2023 at BBI Mijen, Animal Health Laboratory Type B Semarang and Chemistry Laboratory FMIPA UNNES. This study used an experimental method with a completely randomized design (CRD) consisting of 4 treatments and 3 repetitions, namely treatment A (no injection), treatment B (0.3 µl/g), treatment C (0.5 µl/g), and treatment D (0.7 µl/g). A total of 12 broodstock that have never spawned before with an age of ± 6 months and body weight of 104.25 ± 20.81 grams were used as test fish. The results showed that the injection of testosterone propionate had a significant effect on gonadal maturation of male goldfish (C. auratus) with the best dose of 0.5 µl/gram, but had no significant effect on sperm motility of goldfish (C. auratus). Sperm quality showed characteristics of milky white color, distinctive fishy odor, sperm volume 0.8 ml, and sperm pH 7.4, motility percentage 66.7 ± 4.71. The results showed that the injection of testosterone propionate can have a significant effect on the maturation of male goldfish (C. auratus) gonads with the best dose of 0.5 µl/g, but no significant effect on the sperm motility of goldfish (C. auratus).

Automated Attendance and Student Monitoring Using Facial Recognition is an advanced IoT-based attendance automation system designed to improve security, reliability, and transparency in educational institutions. The proposed framework integrates geofencing mechanisms with GPS tracking, AI-powered facial recognition, RF wireless communication, and real-time SMS notifications to verify student presence efficiently. The system utilizes a smart camera-based monitoring unit that captures student facial images and transmits authentication results to the receiver unit. The receiver validates whether the student is located within the predefined institutional geofence and performs facial verification using a stored database. Upon successful verification, attendance is automatically uploaded to a cloud platform through ESP8266-enabled internet connectivity. Faculty members can access attendance records remotely through a live dashboard, while GSM modules provide instant SMS notifications to parents. The proposed system eliminates the dependency on outdated RFID tags and enhances attendance reliability through contactless biometric authentication.

This paper re-examines the canonical Real Business Cycle (RBC) framework as an explanatory lens for modern economic crises driven by technology shocks. Drawing on the foundational work of Kydland and Prescott (1982) and Long and Plosser (1983), we extend the standard model to incorporate stochastic total factor productivity (TFP) processes calibrated against post-2000 data from advanced and emerging economies, including the disruptive episodes of the dot-com collapse, the Global Financial Crisis, and the COVID-19 productivity shock. We show that while pure technology-shock narratives explain a substantial portion of output variance, their explanatory power diminishes in crises characterized by sharp credit contractions and demand-side dislocations. The paper proposes a hybrid RBC-New Keynesian framework and evaluates its empirical performance using Bayesian estimation on a panel of 38 economies (2000–2024). Our findings suggest that negative technology shocks, when interacted with financial frictions and nominal rigidities, amplify aggregate fluctuations beyond what either framework predicts in isolation. Policy implications for central banks and fiscal authorities in low-income economies are discussed.

Vaginal infections are among the most common gynaecological conditions affecting women of reproductive age. They are frequently caused by bacterial pathogens that can lead to significant reproductive tract complications if left untreated. With the increasing global burden of antimicrobial resistance (AMR), especially in low-resource settings, there is a critical need for localised surveillance to guide empirical treatment strategies. This study aimed to isolate and characterise bacterial pathogens from high vaginal swabs (HVS) collected from symptomatic reproductive-age women in Ekiti State, Nigeria, and to evaluate the antibiotic susceptibility patterns of the recovered isolates. A total of 17 vaginal swab samples were collected using sterile techniques and cultured on selective and differential media, including MacConkey, Blood, Nutrient, and Eosin Methylene Blue (EMB) agars. Standard microbiological procedures,s such as Gram staining and biochemical characterisation, were used for identification. Antimicrobial susceptibility testing was conducted using the Kirby-Bauer disk diffusion method on Mueller-Hinton agar, with antibiotics including Ciprofloxacin, Imipenem, Amoxicillin-Clavulanate, Sulzone, and Ampicillin.
The most prevalent bacterial isolates were Escherichia coli (35.3%) and Staphylococcus aureus (23.5%), followed by Klebsiella spp., Pseudomonas spp., and Proteus spp. High sensitivity was observed to Ciprofloxacin (88.2%) and Imipenem (94.1%), while Ampicillin and Amoxicillin exhibited high resistance rates of 70.6% and 41.2%, respectively. The study highlights the alarming resistance of vaginal pathogens to first-line antibiotics and underscores the importance of incorporating routine culture and sensitivity testing into gynaecological healthcare practices. Our findings provide regional antibiogram data essential for updating treatment guidelines and emphasise the role of surveillance in combatting antimicrobial resistance. Strengthening antimicrobial stewardship programs and public health education is necessary to prevent the escalation of resistance and to improve patient outcomes in similar endemic settings.

Rapid urbanization and rising land values in Iringa Municipality, Tanzania, have intensified land use conflicts. This study investigated the economic drivers of these disputes in Mkwawa and Mwangata wards. Employing a mixed-methods cross-sectional design, data were collected from 347 household respondents via structured questionnaires and 37 key informants through purposive interviews, supplemented by document reviews and descriptive statistical analysis. Findings identify escalating land value as a primary driver of conflict, with 73.7% of respondents noting significant price increases over the last five years. These rising costs have triggered social tension (43.4%) and forced migration to urban fringes (42.8%). Furthermore, systemic governance failures are evident, as 59.6% of participants were aware of illegal land transactions and 70.9% identified corruption including double allocation and document manipulation as a major catalyst for disputes. Qualitative data suggests that high prices and opaque valuation procedures disproportionately marginalize low-income groups, pushing them into informal settlements. Unemployment further exacerbates the crisis; 82.4% of respondents agreed that a lack of formal jobs increases survival-based reliance on land, leading to the informal occupation of public spaces and confrontations with authorities. Additionally, competing land uses drive conflict, with 68.8% of respondents aware of disputes linked to development projects and 71.8% reporting that compensation for displacement was inadequate. The study concludes that the convergence of poorly regulated land markets, unemployment and uncoordinated development projects underpinned by corruption and weak planning primary fuels land use conflicts. To mitigate these disputes, the study recommends strengthening land governance transparency, ensuring fair compensation and integrating pro-poor employment strategies into urban planning policies.

This study, Regionalism and Federalism: the Unfolding of India's Regional Politics, examines how regionalism has redefined the centre and the federal system in India in light of the rise of regional parties, region-based identities, and state politics. The paper analyses the evolution of India's quasi-federal polity from a centralised 'holding together' federation to a complex bargaining polity of multi-party coalitions under competitive federalism. Using a narrative literature review, this study examines the constitutional, political, and fiscal aspects that influence federal relations. Results show that regional parties have changed the governance equations, holding around 15% of parliamentary representation and more than 190 million citizens across states in power. Although regionalism has democratised political representation and opened avenues for participation in national decision-making, it has also brought challenges in the form of policy fragmentation, fiscal tensions, and coordination problems. The path-breaking implementation of GST and the 15th Finance Commission recommendations, in the same spirit of encroachment on revenue shares, have added more bitterness to fiscal federalism, with a slip in sustenance shares from 35% to 30% for states between 2015 and 2024. The result is that: i) to practise effective federal governance, institutional innovation must enable the tension between regional autonomy and national coordination to be managed through mechanisms of cooperative federalism. This work advances the literature on federal dynamics in plural democracies and provides policy guidance for managing unity in diversity.

The Indian banking sector has witnessed rapid transformation over the past decade, driven by liberalization, technological advancements, and the growing presence of private sector banks. The increasing adoption of digital banking platforms, such as mobile banking and internet banking, has significantly reshaped customer expectations and service delivery mechanisms. In this context, customer satisfaction has emerged as a key determinant of competitiveness and long-term sustainability for banking institutions.

This study aims to conduct a comparative analysis of customer satisfaction levels between public sector and private sector banks in India. The research is primarily based on empirical data collected from a sample of 100 respondents using a structured questionnaire. The study evaluates various dimensions of banking services, including service quality, operational efficiency, digital banking facilities, customer support, trust, and overall user experience.

The findings of the study indicate that private sector banks have a competitive advantage in terms of service speed, efficiency, innovation, and digital banking capabilities. Their focus on technology-driven services, user-friendly applications, and prompt customer support contributes significantly to higher satisfaction levels among customers. On the other hand, public sector banks continue to maintain a strong position in terms of trust, reliability, and perceived security, largely due to government ownership and long-established reputation.

The research further identifies key factors influencing customer satisfaction, such as staff behavior, waiting time, ease of digital transactions, service charges, and grievance handling mechanisms. While a majority of customers report overall satisfaction with banking services, the level of high satisfaction remains relatively moderate, indicating the need for continuous improvement.

Based on the findings, the study suggests that public sector banks should focus on modernization, reducing service delays, and enhancing digital infrastructure, whereas private sector banks should work on strengthening customer trust, reducing service costs, and expanding their reach in rural areas. The study concludes that a balanced approach combining efficiency, innovation, and trust is essential for improving customer satisfaction and ensuring sustainable growth in the competitive banking environment.

Recurrent drought, erratic rainfall and rain-fed dependency continue to undermine food security in Zimbabwe&#39;s semi-arid south. Small-scale irrigated gardens have been proposed as a low-cost, community-based pathway to climate resilience, yet rigorous outcome data from sub-Saharan smallholder contexts remain scarce. This study evaluates the socio-economic, participatory, and sustainability outcomes of the Small Garden Irrigation Project (SGIP) implemented by OCCIZIM in Gutu, Bikita and Masvingo districts of Masvingo Province between 2020 and 2024. A convergent parallel mixed-methods design grounded in Social Capital Theory was employed. From a sampling frame of 160 beneficiary households, 113 were drawn using proportional stratified random sampling (95% confidence; 5% margin of error). Quantitative data were collected via structured household surveys and analysed in SPSS using descriptive and cross-tabular statistics. Qualitative data were generated through key informant interviews (KIIs), focus group discussions (FGDs) and document review, and analysed thematically. Triangulation across data sources was used to enhance validity. Respondents were predominantly female (65.49%) and of prime working age (64.60% aged 26–45). The intervention was rated highly or very highly effective for socio-economic improvement by 79.65% of respondents, with reported gains of approximately 60% in crop yields, 45% in household income, and 35% in vegetable and fruit consumption. Income-generation opportunities and healthcare access were each rated as having high or very high impact by 87.61% of households. Community engagement was rated high or very high by 85.42% of respondents, and 87.61% indicated that SGIP had contributed much or very much to community cohesion and empowerment. Sustainability and scalability were each rated very or extremely positively by approximately 89% and 85% of households respectively. The SGIP demonstrates that community-anchored, irrigation-based interventions can simultaneously deliver gains in productivity, income, nutrition, and social capital in drought-prone smallholder settings. Sustaining and scaling these gains will require diversified financing, deliberate inclusion of marginalised groups, and adaptive management against political and macroeconomic shocks. The findings offer an empirically grounded model for SDG 2 (Zero Hunger), SDG 5 (Gender Equality) and SDG 13 (Climate Action) implementation across semi-arid sub-Saharan Africa.

This study introduces a new approach for solving fuzzy linear programming problems using Icosagonal Fuzzy Numbers (IFNs). The IFN is defined by twenty parameters, providing greater flexibility for representing uncertainty compared to conventional fuzzy numbers. Arithmetic operations and a ranking-based defuzzification method are developed for IFNs. The proposed approach converts the fuzzy optimization problem into an equivalent crisp linear programming model. The resulting model is solved using the Simplex Method. A numerical example is presented to demonstrate the effectiveness of the proposed technique.The results show that IFNs provide improved modelling capability and more realistic representation of uncertainty compared with traditional fuzzy numbers.

The Production Linked Incentive Scheme was introduced in March 2020 with an outlay of Rs. 1.97 lakh crore across 14 sectors to enhance India’s manufacturing capabilities, reduce import dependence, and generate large-scale employment. This paper assesses the scheme’s performance from 2021 to 2026 using secondary data from the Economic Survey 2025–26, DPIIT, RBI, and CMIE. The analysis reveals a dual trajectory: while sectors like mobile manufacturing, pharmaceuticals, and drones have exceeded investment and production targets, contributing to exports and partial import substitution, others such as textiles, specialty steel, and white goods have witnessed significant underutilization of funds. In terms of employment, the scheme has facilitated the creation of approximately 8.5 lakh direct jobs by 2026 against the projected 60 lakh, with over 75% of these jobs being contractual in nature. The study identifies key structural bottlenecks including delayed disbursements, complex compliance norms, and global demand slowdowns. It argues that although PLI has succeeded in positioning India within global value chains for electronics, its employment and broad-based manufacturing outcomes remain modest. The paper concludes with policy recommendations for scheme recalibration, emphasizing sector-specific flexibility, skill linkage, and integration with MSMEs to achieve inclusive industrial growth. Keywords: _PLI Scheme, Manufacturing, Employment, Atmanirbhar Bharat, Industrial Policy, Make in India

E-commerce has emerged as a game-changing force in the global business landscape, offering unique opportunities and challenges for Micro, Small, and Medium Enterprises (MSMEs). This abstract provides a glimpse into a comprehensive research study that delves into the transformative impact of e-commerce on MSMEs. The research aims to analyze how e-commerce has affected various aspects of MSMEs, such as market reach, competitiveness, revenue generation, operational efficiency, and sustainability. It employs a mixed methods approach, incorporating both quantitative data analysis and qualitative case studies, to provide a holistic understanding of the subject matter.

The introduction of artificial intelligence (AI) in marketing strategies has become one of the transformative processes for both the marketers and their clients in the e-commerce industry. The use of AI marketing strategies has helped companies provide more personal customer experiences, improve customer engagement and enhance brand positioning. This paper aims at reviewing the literature concerning the impact of AI marketing strategies on brand positioning in the e-commerce industry from the years 2020 to 2025. The systematic literature review methodology will be used, focusing on peer-reviewed literature on how AI is being applied in the form of personalization, recommendation systems, targeted marketing, chatbots and predictive analytics to increase brand positioning. The study shows that AI helps brands to develop awareness, brand image, brand trust, loyalty and differentiation through providing customers with more data-driven decisions and more personalized experiences. Consumer engagement and customer experience can also be highlighted as the critical drivers through which AI affects brand positioning. Although AI brings numerous opportunities to the industry, issues like data protection and ethical usage cannot be overlooked. Through the analysis of the current literature, a conceptual model of AI marketing strategies and brand positioning will be proposed.

The integration of Indian Knowledge Systems (IKS) into teacher education has emerged as a significant area of focus in contemporary educational reforms in India. IKS encompasses the rich intellectual, philosophical, scientific, and cultural traditions of India, which include ancient pedagogical practices, ethical values, holistic learning approaches, and experiential knowledge systems. In the context of teacher education, IKS provides a strong foundation for developing value-based, reflective, and culturally rooted educators.
Traditionally, Indian education systems such as the Gurukul method emphasized close teacher-student relationships, character formation, discipline, and experiential learning. These approaches contributed to the holistic development of learners and continue to hold relevance in modern educational contexts. However, present-day teacher education programs largely focus on modern pedagogical skills, technological integration, and standardized assessment practices, often leading to a gap between traditional wisdom and contemporary methodologies.
This study explores the challenges and opportunities in integrating IKS into teacher education. It highlights the importance of blending traditional Indian educational philosophy with modern teaching techniques to create a balanced and effective teacher preparation framework. The National Education Policy (NEP) 2020 strongly supports this integration by promoting multidisciplinary education, experiential learning, and the inclusion of Indian cultural and philosophical knowledge in teacher training programs.
The study identifies key opportunities such as promoting value-based education, strengthening cultural identity, and enhancing experiential learning practices. At the same time, it also discusses challenges such as lack of trained faculty, limited resources, dominance of Western pedagogical models, and insufficient curriculum integration.
The study concludes that integrating IKS into teacher education can significantly enhance the quality of teaching by producing educators who are not only professionally competent but also ethically grounded and culturally aware. This integration is essential for developing a holistic, inclusive, and contextually relevant education system in India.

The advent of Generative Artificial Intelligence (AI) tools like Chat GPT, Google Gemini, Microsoft Copilot, and Claude has fundamentally reshaped the information landscape within universities and colleges. Digital learning platforms like SWAYAM, National Digital Library of India (NDLI), e-Shodh Sindhu, and the implementation of the National Education Policy (NEP)2020havealready laid a solid groundwork for digital transformation in the field of education in India. How University students have changed their information literacy practices and information seeking behaviour in this changing world with the advent of Generative AI. The present paper is a secondary data analysis of the Indian and international literature that has been published since 2022 to 2026. This research uncovers that Generative AI tools enhance accessibility of information, learning efficiency and academic support. But there are issues, like misinformation, over reliance on AI tools, academic integrity, and a lack of critical evaluation skills, which are also visible. The paper draws attention to the need for information literacy frameworks with an AI component and the proactive initiative of academic libraries in India.

India's venture capital (VC) and Angle investment landscape has undergone a transformative journey, evolving into a dynamic ecosystem with opportunities and challenges. This paper presents an in-depth analysis of the venture capital and angle investment scenario in India, spanning historical developments, current trends, and future prospects. Historically, India's venture capital journey can be traced back to the early 2000s, characterized by different stages and limited investor interest. However, the landscape witnessed a paradigm shift with the advent of liberalization policies, economic reforms, and the emergence of a vibrant startup culture. Today, India stands as one of the world's most attractive destinations for venture capital investment, buoyed by a robust economy, a burgeoning middle class, and a youthful demographic dividend. Angel investors have taken opportunities for seed funding or initial investments to business who need quick funding. This paper studies both kind of investment opportunities for investors and their growth in the financial ecosystem over a past decade. The sectors attracting significant venture and angle funding in India encompass a broad spectrum of industries, including technology, e-commerce, finch, healthcare, and renewable energy. These sectors not only reflect the country's rapid technological advancements but also address critical societal needs, driving sustainable growth and innovation. Venture capitalists play a pivotal role in nurturing and scaling startups, offering more than just financial backing. They provide strategic guidance, mentorship, and access to networks, facilitating the growth trajectory of promising ventures. However, angle investors fund in exchange of equity. The paper would present a comprehensive study of such investments from invertors perspective requiring collaborative efforts from all stakeholders, including policymakers, investors, entrepreneurs.

Technology-driven conscious consumerism is reshaping sustainable organic consumption by changing how consumers search, verify, compare, and purchase organic products. As awareness of health, environmental protection, ethical sourcing, and supply-chain transparency increases, digital tools are becoming central to organic decision-making. This article presents an original, plagiarism-free literature review based entirely on secondary data from recent journal articles, literature reviews, and market reports. The review examines the role of digital platforms, artificial intelligence, blockchain traceability, and social commerce in enabling more informed and values-based consumption. It finds that technology reduces information asymmetry, improves trust, strengthens purchase intention, and supports sustainable organic consumption, but it can also create concerns around privacy, manipulation, and greenwashing. The paper concludes that technology is not only a sales channel but a strategic enabler of conscious consumerism in the organic sector.

The Indian two-wheeler market has traditionally been shaped by functional purchase considerations such as affordability, fuel efficiency, durability, service network, resale value, and everyday commuting convenience. In such a market, most scooter brands compete by offering practical benefits to a broad consumer base. Vespa’s approach in India differs from this dominant pattern. Rather than positioning itself as a mass-market commuter scooter, Vespa has attempted to build a premium lifestyle identity based on Italian heritage, retro-modern design, exclusivity, individuality, and emotional appeal.
This paper examines Vespa’s brand repositioning strategy in India through a secondary research approach. It draws on existing literature on brand repositioning, customer-based brand equity, heritage branding, premium positioning, experiential marketing, digital engagement, and consumer identity. The paper also uses case-based and industry-oriented secondary material to understand how Vespa shifted its consumer meaning from a nostalgic scooter brand to an aspirational urban lifestyle product. The focus of this paper is not on primary consumer survey findings or mechanical product performance, but on the strategic branding logic behind Vespa’s repositioning.
The study argues that Vespa’s repositioning is built on a deliberate strategic choice: avoiding direct competition with high-volume utility scooter brands and instead creating a differentiated premium niche. Vespa’s repositioning is supported by five major levers: heritage, design, premium pricing, selective brand experience, and digital/lifestyle communication. Together, these levers help the brand create symbolic value beyond transportation. For its target consumers, Vespa represents style, individuality, status, and self-expression rather than only mobility.
At the same time, the paper critically evaluates the limitations of this strategy. Vespa’s premium pricing and selective distribution strengthen exclusivity, but they may also restrict broader adoption in a price-sensitive market. This creates a central strategic tension: the same choices that make Vespa distinctive also limit its mass-market potential. Therefore, Vespa’s repositioning should not be assessed only through market share. It should also be evaluated through brand equity, premium perception, emotional differentiation, niche loyalty, and long-term relevance.
The paper concludes that Vespa’s repositioning appears strategically coherent because its heritage, product aesthetics, pricing logic, communication style, and customer experience support the same premium lifestyle identity. However, long-term sustainability will depend on how effectively Vespa balances exclusivity with accessibility, heritage with innovation, and global Italian identity with Indian consumer relevance.

The rapid growth of e-commerce has significantly changed the way consumers search for information, evaluate products, and make purchase decisions. In traditional retail settings, consumers are able to physically inspect products, compare alternatives directly, and seek immediate clarification before purchasing. In online shopping, however, consumers depend largely on digital information available on the platform. This creates uncertainty because the buyer cannot directly verify product quality, performance, size, durability, or suitability before making a purchase. In this context, product reviews have emerged as an important decision-support tool for online consumers.
Product reviews provide customer-generated information in the form of written feedback, star ratings, usage experiences, complaints, recommendations, images, and videos. These reviews help consumers understand how a product performs in real-life situations, beyond the claims made in product descriptions or advertisements. As a form of electronic word-of-mouth, product reviews influence consumer perception by offering social proof and experience-based information from previous buyers. They can strengthen consumer confidence when they are positive, detailed, and credible, while negative or doubtful reviews can create hesitation, comparison, or rejection of the product.
This paper examines the influence of product reviews on online purchase decisions through a secondary research approach. It draws on existing literature related to consumer behaviour, electronic word-of-mouth, review credibility, review usefulness, online trust, perceived risk, technology acceptance, and planned behaviour. The paper also highlights consumer engagement behaviour as a key link between product reviews and purchase decisions. Consumer engagement behaviour refers to how consumers read, compare, evaluate, trust, question, or reject reviews before making a final purchase decision.
The study argues that product reviews influence online purchase decisions through three major mechanisms: they provide additional product information, reduce perceived risk, and build or weaken consumer trust. However, reviews do not influence consumers automatically. Their impact depends on several factors, including review quality, review volume, review recency, review valence, star ratings, reviewer credibility, and perceived authenticity. Fake, vague, overly promotional, or manipulated reviews can reduce trust, while detailed, balanced, and genuine reviews can improve purchase confidence.
Overall, the paper concludes that product reviews play a significant role in shaping online purchase decisions. They help consumers compare alternatives, validate product choices, reduce uncertainty, and form purchase intentions. For businesses and e-commerce platforms, the study highlights the importance of maintaining credible, transparent, and authentic review systems. For consumers, it emphasizes the need to engage with reviews critically rather than relying only on ratings or isolated opinions.

Contemporary innovation ecosystems primarily recognize discoveries emerging from universities, research laboratories, and formally qualified professionals. While these institutions play a critical role in scientific advancement, a substantial volume of innovation originates outside formal academic structures. Across the world, grassroots innovators, village inventors, traditional knowledge holders, self-taught researchers, natural prodigies, and experts by experience continue to generate practical solutions to social, technological, agricultural, medical, and industrial challenges.
Despite their contributions, many remain unrecognized due to the absence of formal educational qualifications, institutional affiliations, technical documentation, and access to intellectual property systems. Simultaneously, valuable traditional knowledge systems are often neglected, inadequately documented, or appropriated without proper recognition.
This paper proposes a comprehensive global framework for democratizing innovation through the establishment of Innovation Recognition and Advancement Authorities, Traditional Knowledge Validation Councils, and Inclusive Patent Accessibility Reforms. The framework seeks to create alternative pathways for recognition, certification, protection, commercialization, and promotion of innovations emerging from all sections of society while maintaining rigorous quality standards.
The paper argues that innovation should be evaluated based on merit, utility, originality, and societal value rather than solely on institutional credentials. Such an approach can unlock hidden intellectual capital, strengthen national innovation ecosystems, preserve traditional knowledge, and contribute significantly to economic development and global technological progress.

This study investigated the influence of records keeping on the administration of public Secondary Schools in Benue State. The study was guided by two specific objectives, two research questions and two research hypotheses. Survey research design was adopted for the study. The population of the study was 1,156 which comprised Principals and Vice-Principals in public secondary schools in Benue state. The sampled size for the study was 297 Principals and Vice-Principals in the state. The sampling technique used in the study was random and proportionate Stratified Sampling procedure. The instrument for data collection was a self-structured questionnaire titled: School Records Keeping and Administration of Public Secondary Schools Questionnaire. The reliability Coefficient of the instrument was established using Cronbach Alpha Statistics and the reliability for the entire instrument was 0.93 which indicated that the instrument was reliable. The research questions posed in the study were answered using mean and standard deviation while the research hypotheses formulated in the study were tested at 0.05 level of significance using Chi Square test of goodness of fit statistic. The findings of the study indicated that: Keeping administrative records play an important role in the administration of public secondary schools in Benue State; Keeping financial records play a crucial role in the administration of public secondary schools in Benue State. The study concluded that, keeping of adequate records have a great influence on the administration of public secondary schools in Benue state. The study therefore recommends that the principals and vice-principals should ensure adequate records keeping for effective administration of public secondary schools in Benue state.

The legitimacy of representative democracy rests upon electoral consent and continuous public accountability. While democratic systems empower citizens to elect their representatives, they often provide limited mechanisms for citizens to evaluate or remove underperforming representatives between elections. This paper proposes two complementary democratic account ability mechanisms: the Right to Recall (RTR) and the Right to Forfeiture of Candidature (RFC).

The study argues that electoral democracy must evolve into participatory accountability democracy, where citizens retain meaningful oversight over elected officials throughout their tenure. Drawing upon democratic theory, comparative international experiences, constitutional principles, governance studies, and public accountability frameworks, this paper develops a conceptual model suitable for India.

The proposed framework seeks to strengthen public trust, improve responsiveness, discourage corruption, increase citizen participation, and reinforce democratic legitimacy while preserving political stability.

This paperemphasises the impact of the ongoing warsbetween the US, Israel, and Iranon Indian GDP and economy, dragging us into something for which we were never accountable. This war not just petrified the world but also forced usto analyse the consequences it would incur in the short and long term. From crude oilprices shooting rocket high to the LPG crisis, and ultimately leading to food inflation due to supply chain disruptions. The Strait of Hormuz, the lifeline of the oil&gas trade, isheld hostage, and so is the global energy.

This article is intended to do the Analysis of an Analogy of How the Theory of Relativity Would Be Trueor Falseby Albert Einstein,as a didactic approach to hypothesis formulation in the Conceptual Physics of Einstein's Theory of General Relativityand the Theory of Viscosity in Fluid Mechanics in University Physics, such as Newton's Law ofFluids and Reynolds' LawofFluidsthroughconceptualepistemologyandmodelbuildingforknowledgeconstructionand the necessary analyses in the construction of a theoretical calculation model and computational simulation.a practical and didactic conceptual approachfor complexityof knowledge.

Women journalists have become increasingly visible in Indian media organizations; however, their professional experiences continue to be shaped by gender-based inequalities, workplace discrimination, safety concerns, and structural barriers to career advancement. Despite significant progress in women's participation in journalism, the profession remains largely influenced by patriarchal norms that affect opportunities, assignments, leadership positions, and workplace security. This paper examines the major challenges faced by women journalists in India, focusing on discrimination in newsrooms, sexual harassment, wage inequality, work-life balance issues, physical and digital safety concerns, and the persistent glass ceiling that limits career progression. Drawing upon Feminist Media Theory, Gendered Organizations Theory, and the Glass Ceiling Framework, the study synthesizes existing literature and analyzes contemporary developments in Indian journalism. The paper also discusses notable case studies that illustrate the risks and challenges encountered by women journalists in both traditional and digital media environments. The findings suggest that while legal frameworks and organizational policies have improved gender representation, substantial inequalities continue to exist. Women journalists frequently encounter discriminatory attitudes, online abuse, restricted access to decision-making positions, and safety threats that adversely affect professional growth and journalistic freedom. The paper concludes by recommending institutional reforms, gender-sensitive newsroom policies, stronger safety mechanisms, and leadership development initiatives aimed at creating more inclusive and equitable media organizations.

This qualitative study employed focus group methodology to explore the support systems, formal and informal, available to children coping with parental divorce in Ghanaian families, examining how children access, experience, and benefit from various sources of support, including extended family, schools, religious institutions, peer networks, and professional services. Drawing upon Social Constructionism (Berger & Luckmann, 1966) and Ubuntu Philosophy (Gyekye, 1997), the study conducted six focus group sessions with 44 participants across three stakeholder groups: young adults who experienced parental divorce during childhood (16 participants), divorced parents whose children were minors during or after the divorce (14 participants), and family therapists, social workers, and counsellors with experience supporting divorced families (14 participants). Focus group discussions were guided by open-ended questions and narrative prompts designed to elicit participants&#39; experiences of support received, support desired but unavailable, barriers to accessing support, and recommendations for improving support systems. Data were analysed using reflexive thematic analysis (Braun & Clarke, 2006). Six overarching themes emerged from the data: the primacy of maternal extended family support, with maternal grandmothers and aunts identified as the most frequent and effective source of practical, emotional, and financial support for children; the inconsistent and conditional support of paternal extended family, with participants describing paternal relatives who withdrew from children&#39;s lives after divorce or offered support only on condition of rejecting the mother; the silence of the school, wherein participants reported that schools rarely provided any support and that teachers were often unaware of or indifferent to children&#39;s divorce-related struggles; the ambivalent role of the church, with religious communities offering emotional support and meaning-making but also contributing to stigma through teachings that framed divorce as sin; the inadequacy of professional support services, with participants describing limited availability, high cost, and lack of awareness of counselling and therapy services; and the peer lifeline, wherein friends and classmates who had also experienced divorce provided the most trusted and accessible source of emotional support. Participants&#39; narratives revealed that children of divorce in Ghana rely primarily on informal support systems, particularly maternal extended family and peers, while formal support systems (school counselling, professional therapy) are largely absent or inaccessible. Extended family support is uneven, with maternal relatives generally more supportive than paternal relatives. Religious institutions provide both support and stigma, creating ambivalence about church involvement. Peers who share the experience of divorce emerge as a critical but unrecognized source of support. The study contributes to understanding of how Ghanaian children cope with parental divorce and how existing support systems can be strengthened to meet their needs, offering insights relevant to family policy, school counselling, social work practice, and community-based intervention.

This qualitative study employed a triangulated methodology combining participant observation, peer perception interviews, and family profiling to explore the challenges of raising children with a sense of purpose following parental divorce in rural and urban Ghana. Drawing upon Social Constructionism (Berger & Luckmann, 1966) and Ubuntu Philosophy (Gyekye, 1997), the study conducted 120 hours of participant observation across twelve family settings (six rural and six urban), 36 peer perception interviews with extended family members, neighbours, and teachers who evaluated each other's parenting effectiveness, and family profiling of 48 divorced parents to map household composition, economic resources, kinship support networks, and educational aspirations for children. Data were analysed using reflexive thematic analysis (Braun & Clarke, 2006) integrated with observational field notes and family profile coding. Six overarching themes emerged from the triangulated data: the stigma paradox, wherein divorced parents in rural settings faced intense social condemnation while urban settings offered greater anonymity but also greater isolation; the economic precarity cascade, wherein divorce triggered downward economic mobility that directly undermined parents' capacity to invest in children's purposeful development; the kinship reconfiguration, wherein extended family networks simultaneously provided essential support and imposed competing demands that fragmented parenting authority; the purpose transmission dilemma, wherein parents struggled to model and instil purpose when their own post-divorce identities were fractured and uncertain; the urban-rural resource divide, wherein urban parents had access to educational and extracurricular resources but lacked the communal oversight that rural parents relied upon; and the triangulation divergence, wherein parent self-reports of purposeful parenting diverged significantly from child outcomes and peer observations, revealing a gap between aspiration and practice. The study contributes to the understanding of how divorce disrupts the intergenerational transmission of purpose in Ghanaian contexts, offering methodological contributions to triangulated family research and practical insights for social workers, religious institutions, and policymakers supporting divorced parents and their children.

Artificial Intelligence (AI) and digital health platforms are increasingly becoming an essential part of the global healthcare landscape. The rapid advancements in machine learning, deep learning, data analytics and computational capabilities have made AI-based solutions capable of assisting healthcare professionals in diagnosis, treatment, patient monitoring, and healthcare management. The conventional healthcare system, which was mostly reactive and relied on manual diagnosis and the expertise of healthcare professionals, is now being supplemented by intelligent and data-driven AI-based solutions that emphasize accuracy, efficiency, and preventive healthcare. This paper discusses the development, applications, and implications of AI and digital health platforms in contemporary healthcare, including AI-based diagnostics, medical imaging, disease prediction platforms, digital health platforms, telemedicine, and wearable health platforms.
AI-based diagnostic platforms process massive amounts of medical data, such as electronic health records, lab results, medical images, genetic data, and real-time physiological data, to facilitate early and accurate disease detection. In medical imaging, AI improves image analysis, image quality, and radiological workflow automation, thereby minimizing diagnostic errors and the burden on healthcare professionals. Disease prediction models rely on a variety of data sources, including genomic data, lifestyle variables, and wearable sensor data, to determine the risk of disease and facilitate preventive healthcare practices. Digital health solutions combine AI technology with cloud computing and mobile health solutions to facilitate personalized medicine, effective data management, and sustained patient engagement. Telemedicine solutions increase access to healthcare by allowing remote consultations, diagnosis, and patient monitoring, especially in rural areas. Wearable health solutions make it possible to continuously monitor vital signs, enabling early medical interventions and effective management of chronic diseases.
However, despite the many advantages, the implementation of AI and digital health solutions faces challenges in terms of data privacy, security, ethics, bias, regulatory requirements, and the availability of qualified personnel. These challenges must be met head-on to ensure transparency, trust, and equitable healthcare outcomes. In conclusion, this paper underscores the revolutionary possibilities of AI and digital health solutions in improving healthcare delivery, lowering costs, and improving patient outcomes while emphasizing the need for responsible, ethical, and patient-centric implementation.

There has been considerable effort dedicated by enterprise operations professionals in developing deterministic and rule-based automated operation solutions, such as playbooks, runbook decision trees, and complex event processing. Nonetheless, the recent advancements in creating and utilizing large language model (LLM)-based agents have brought about increasing expectations for enterprises to move towards more intelligent and adaptive operation solutions. In this position paper, the shift of paradigms from deterministic rule-based operations during the 2010s to LLM-driven agentic operations of the 2020s will be analyzed by discussing the paradigmatic advantages, disadvantages, and failure modes. Furthermore, a hybrid approach of the two paradigms, referred to as Rule First-Agent Fallback (RFAF), will be proposed to address shortcomings inherent to either paradigm. Under RFAF, structured and predictable operations will adhere to deterministic rules, whereas agentic operations driven by LLM-based agents will only be applied under certain conditions via an explicit handoff protocol. Through publicly available operational incidents and case-based analysis, examples of successful, failed, and improperly used paradigms will be provided. Rather than seeing the automation of the enterprise in terms of embracing or abandoning agentic systems, which is common within industry discourse on the subject, this paper proposes a solution which is not only more practical, but also takes into account a deterministic-first hybrid architecture. This solution provides a specific implementation strategy for enterprise diagnostic systems, and not just vague statements about combining rules and agents.

Impact of climate change in Himachal Pradesh is much more pronounced because of the fragile ecology and sensitive Himalayas. So, Himalayan ranges of Himachal, serve as an indicator of climate change. The relationship between environmental changes and rainfall is complex, particularly in regions like Himachal Pradesh, which are highly susceptible to climatic and ecological shifts. Three major sectors viz., agriculture, horticulture and forestry are the backbone of the State because they are directly or indirectly linked with the economy of the people. Researchers and scientists are trying to search the factors and reasons which are responsible for the change in these sectors. Finding from different sectors revealed very significant and sharp change like shifting snowline, timberline, apple belt shifting and fluctuations in chilling units directly affects quality and quantity of apple etc. Such studies have encountered a number of findings which witnessed that climate change is happening in Himachal Pradesh. But if we talk about the observations of climate change impact on ecosystem and biodiversity, the Himalayan region (including Himachal Himalaya) is severely data deficient.

This article addresses the theory. of the Black Holes: How could they be understood from the perspectives of Conceptual Physics and Astrophysics, emphasizing, according to what is...has proven the Black Hole Theory and the Big Bang. Remembering the Theory...The Structure of Scientific Revolutions by Thomas Kuhn, as well as the Teaching Theories of Piaget and Vygotsky for Early Childhood, Youth and Adult Education, as well as Stephen Hawking's approach in his doctoral thesis on the Big Bang and how Albert Einstein wrote his books on the Theory of General Relativity in 1916 and 1920 and an approach such as these theories would be verified and validated in the scientific and academic world. The concepts addressed are the interior of massive and supermassive black holes and elementary particles, as an example of raising concepts such as atomic radius, the size of the atom, the electron cloud of the atom from the Schrödinger equation, and the mass collapse threshold of a black hole, such as Hawking radiation. In addition to the idea of an approach to basic physics for young people. and Universities of the Atom of Leucipius and Democrit and PhilosophyNatural of Aristotle.

The intention of this article is to discuss, through a comparison, the behavior of massive bodies at the speed of light and the behavior of atoms and electromagnetic waves at the speed of light, according to Albert Einstein's Special Theory of Relativity. This will provide an outline of the concepts.This article discusses human perceptions in adulthood regarding Quantum Mechanics and the Special Theory of Relativity according to Piaget's theory. It includes a brief overview of electromagnetic waves, the Special Theory of Relativity, and the Theory of Light. The discussion extends to Quantum Field Theory and Quantum Mechanics, exploring the future possibilities of the Special Theory of Relativity as discussed by David Bohm and Bertrand Russell in the mid-20th century. Aspects such as the Twin Paradox, the Ten-Platform Reference in the Theory of Relativity, and High-Speed Light Travel are also addressed.

The present study aimed to evaluate the phytochemical constituents and antimicrobial activity of leaf extracts of Stephanotis volubilis obtained using different solvents. Phytochemical screening revealed the presence of several bioactive compounds, including alkaloids, flavonoids, glycosides, coumarins, saponins, tannins, terpenoids, steroids, and phenols. The antimicrobial activity of the extracts was assessed using the well diffusion method against six bacterial strains, viz. Bacillus cereus, Staphylococcus epidermidis, Escherichiacoli, Pseudomonas aeruginosa, Beauveriabassiana and Candidaalbicans. Among the extracts tested, the ethyl acetate extract exhibited the greatest diversity of phytochemical constituents; however, it showed comparatively lower antimicrobial activity against all tested bacteria. In contrast, the methanolic extract demonstrated significant antimicrobial activity against all bacterial species examined. The results indicate that the leaves of Stephanotis volubilis are a rich source of bioactive phytochemicals and possess promising antibacterial properties, highlighting their potential for the development of natural antimicrobial agents.

This study examines the role of digital transformation in enhancing supply chain resilience in the Indian logistics sector, with specific insights from EliteLogix, Visakhapatnam. The increasing frequency of global disruptions such as the COVID-19 pandemic, the Russia–Ukraine conflict, and geopolitical tensions involving the United States, Israel, and Iran have exposed the vulnerabilities of global supply chains and highlighted the need for resilient logistics systems. In this context, organizations are increasingly adopting digital technologies to improve operational efficiency, visibility, coordination, and responsiveness.
The main objective of the study is to understand how digital technologies contribute to supply chain resilience within logistics operations. The research focuses on the adoption of digital tools, their impact on visibility and coordination, their role in handling disruptions, and the barriers affecting digital adoption.
The study adopts a case study research design focusing on EliteLogix as the unit of analysis. Both primary and secondary data were used in the study. Primary data was collected through questionnaires administered to employees and through observations made during the researcher’s internship within the organization. Secondary data was obtained from academic journals, books, research articles, and company-related information relevant to the study. The collected data was analyzed using percentage, descriptive, and thematic analysis techniques.
The findings of the study indicate that digital technologies positively influence logistics operations by improving visibility, coordination, communication, and decision-making. The study also reveals that digital tools support organizations in responding more effectively to disruptions, thereby contributing to supply chain resilience. However, certain challenges such as limited training and barriers to technology adoption still exist.
Overall, the study concludes that digital transformation plays a significant role in strengthening supply chain resilience within the logistics sector. The research contributes to the growing understanding of how small logistics firms can utilize digital technologies to improve operational performance and manage disruptions more effectively.

The rapid proliferation of digital banking technologies has fundamentally transformed the landscape of financial services delivery. This article examines the multidimensional relationship between digital banking adoption and customer satisfaction across diverse demographic and geographic cohorts. Drawing on a synthesis of contemporary empirical studies, industry reports, and behavioral finance theory, the article investigates the principal determinants that drive consumers toward digital banking channels, the barriers that impede broader adoption, and the mechanisms through which service quality, trust, and interface usability shape overall satisfaction outcomes. Findings indicate that while digital banking adoption has accelerated significantly in the post-pandemic period, satisfaction levels remain heterogeneous and are mediated by factors including technological self-efficacy, perceived security, and the quality of personalized customer experience. The article concludes with strategic recommendations for financial institutions seeking to optimize digital offerings, enhance customer retention, and sustain competitive advantage in an increasingly platformized financial ecosystem.

Dandruff is a common scalp disorder characterized by flaking, itching, and irritation, often caused by microbial infections, excessive sebum production, and environmental factors. Synthetic anti-dandruff shampoos, though effective, may cause side effects such as scalp dryness, irritation, and hair damage due to chemical ingredients. This review focuses on the formulation and evaluation of herbal anti-dandruff shampoos as a natural, safe, and effective alternative.

Herbal ingredients such as neem, tea tree, aloe vera, amla, reetha, shikakai, and tulsi possess antimicrobial, anti-inflammatory, and antioxidant properties that help reduce dandruff and promote scalp health. The review covers the role of various herbal extracts, selection of suitable excipients, formulation strategies, and evaluation parameters including physicochemical properties, foam ability, cleansing action, irritation test, and antimicrobial efficacy.

The studies suggest that herbal shampoos not only control dandruff effectively but also nourish hair, improve scalp condition, and reduce the risk of side effects. Herbal anti-dandruff shampoo can be considered a promising natural product for long-term use and better consumer acceptance.

The study on startup India program is focused on the programs support on business growth in revenue and market expansion of startups, and it further explores how these factors contribute to overall business sustainability. The data collected from 30 startup companies of different sectorsin Karnataka. The findingsprovide insights into the programs impact on percentage of revenue increase and market expansion under the program, key factors supporting for business sustainability.

Solar PV module reliability and performance, particularly for TOPCon solar cell technology, are strongly influenced by encapsulant material selection and formulation. Key factors include moisture ingress (WVTR as per ASTM F 1249 ), electrical insulation as per IEC, and interfacial adhesion, which collectively impact long term durability under stress conditions such as damp heat (DHT), thermal cycling (TC 200 -TC 600), potential induced degradation (PID 92 Hour -288 hours), and field operation.
This study examines the role of polymer polarity, silane coupling chemistry, and crosslinking in enhancing encapsulant performance, with implications for reliability and ESG outcomes. Non polar polymers such as polyolefin elastomers (POE) exhibit superior moisture barrier properties (as per ASTM F1249) and enhanced resistance to PID, while polar polymers offer stronger intrinsic adhesion to glass and cell interfaces.
The role of silane coupling agents in forming covalent bonds between inorganic substrates and organic surface is analysed to explain improved interfacial durability. Moisture diffusion is modelled by using Fick’s law of diffusion.

This article examines the revelation of Jesus Christ as Messiah and Son of God after the resurrection and its theological significance within contemporary Christology. The study argues that the resurrection constitutes the decisive revelatory event through which the identity, mission, and divine sonship of Jesus are fully disclosed. While pre-resurrection messianic expectations largely reflected political and nationalistic hopes, the resurrection redefines messiahship in terms of suffering, redemption, exaltation, and universal salvation. Consequently, the risen Christ emerges not only as the vindicated Messiah but also as the incarnate Son of God whose authority transcends death and inaugurates a new relationship between God and humanity. The study employs a qualitative theological methodology grounded in historical-critical and exegetical analysis of selected New Testament texts, including Romans and the Gospel resurrection narratives. In addition, the article engages patristic and contemporary theological interpretations to examine how resurrection theology has shaped Christian understanding of divine revelation, Christological identity, and salvific hope. Through textual and theological analysis, the research demonstrates that the resurrection serves as both confirmation of Jesus’ messianic mission and revelation of his eternal sonship. The article concludes that resurrection theology remains central to Christian faith and theological reflection in the contemporary world. It recommends renewed Christological engagement within theological education, ecclesial teaching, and contextual Christian praxis in order to deepen understanding of the risen Christ in increasingly pluralistic and secular contexts. Furthermore, the study proposes that contemporary Christian scholarship should integrate biblical exegesis, historical theology, and contextual interpretation in articulating the continuing significance of the resurrection for faith, mission, and human transformation in the world.{Citation}

Assessment integrity is a critical concern in higher education, particularly in Sub-Saharan Africa where issues such as bias, favoritism, unfair grading, and limited transparency continue to challenge the credibility of academic outcomes. This study examines the role of blind marking in enhancing student assessment integrity in private universities in Uganda and its implications for quality assurance in higher education. The study adopts a singular methodology that enabled collection of qualitative data. Sequential explanatory mixed-methods design integrating survey data and semi-structured interviews.Quantitative data were analysed using structural equation modelling, while qualitative data were thematically analysed to provide contextual explanation of the statistical findings. The results indicate that blind marking has a significant positive effect on assessment integrity by reducing bias, favoritism, and unfair assessment practices while improving fairness, consistency, and transparency in grading. Further, assessment integrity significantly enhances quality assurance outcomes and fully mediates the relationship between blind marking and institutional quality assurance.Qualitative findings corroborate these results, showing that blind marking strengthens stakeholder trust, reduces disputes over examination results, and improves perceptions of institutional credibility. However, challenges such as inadequate digital infrastructure, limited staff training, and administrative constraints hinder effective implementation.The study concludes that blind marking is not merely a technical grading procedure but a strategic quality assurance mechanism that strengthens fairness, accountability, and institutional legitimacy.It recommends institutionalizing blind marking policies, investing in digital assessment systems, and enhancing staff capacity development in Sub-Saharan African higher education institutions.

Gastroretentive Drug Delivery Systems (GRDDS) are oral controlled drug delivery systems designed to prolong the gastric residence time (GRT) of dosage forms. These systems are particularly beneficial for drugs that exhibit a narrow absorption window in the upper gastrointestinal tract, are unstable at intestinal pH, or act locally in the stomach. GRDDS enhances bioavailability, reduces dosing frequency, and improves therapeutic outcomes.
Various approaches such as floating systems, bioadhesive systems, swelling systems, expandable systems, and high-density systems have been developed to achieve prolonged gastric retention. This review discusses the physiology of the stomach, need for GRDDS, formulation strategies, evaluation parameters, advantages, limitations, and future prospects.

The rapid growth of digital technologies, artificial intelligence (AI), and social media platforms has transformed news consumption patterns across the world. India, with over 806 million internet users and approximately 491 million social media users in 2025, represents one of the largest digital news markets globally. This study examines how social media platforms influence news consumption among Indian youth and investigates the associated challenges of misinformation, trust, and media literacy. The research adopts a descriptive analytical approach using secondary data from Reuters Institute Digital News Report 2025, DataReportal 2025, industry reports, and academic literature. Findings indicate that social media has become the primary gateway to news for young audiences, while traditional newspapers and television continue to experience declining engagement. However, the increased dependence on algorithm-driven content has created concerns regarding misinformation, echo chambers, and declining trust in journalism. The study concludes that media literacy education and ethical AI regulation are essential for sustaining informed democratic participation.

Sustainable digital education has emerged as an important approach for ensuring quality, inclusive, and accessible education in the modern technological era. The rapid development of digital technologies, online learning platforms, artificial intelligence, and virtual classrooms has transformed traditional educational practices into flexible and learner-centered systems. Sustainable digital education aims to integrate technology in a balanced, ethical, and environmentally responsible manner to promote lifelong learning and equal educational opportunities for all learners. This chapter explores the concept, importance, benefits, challenges, and future scope of sustainable digital education in the context of global educational development.The chapter highlights the role of digital tools and innovative technologies in enhancing teaching–learning processes, improving communication, supporting personalized learning, and reducing environmental impact through paperless education systems. It also discusses major challenges such as the digital divide, lack of infrastructure, cybersecurity concerns, screen addiction, and inadequate teacher training. Special emphasis has been given to the roles of teachers, students, educational institutions, and government initiatives such as NEP 2020 and Digital India in promoting sustainable and inclusive digital learning environments.The chapter concludes that sustainable digital education is essential for achieving educational equity, environmental sustainability, and future-ready learning systems. Effective planning, responsible technology use, digital literacy, and policy support are necessary for creating a balanced and sustainable educational future.

Solar photovoltaic (PV) panels are becoming an important source of clean and renewable energy.However, the performance of solar panels decreases when dust and dirt accu-mulate on their surface.Dust blocks sunlight from reaching the panel properly, which reducestheoverallpowergenerationefficiency.Regularcleaningisthereforenecessary to maintain maximum energy output.
In many real-life situations, cleaning solar panels manually is difficult, time-consuming, and sometimes risky, especially in large solar installations or rooftop systems.To over-come these problems, an automatic solar panel cleaning system is developed to reduce human effort and improve cleaning efficiency.
This project focuses on the design and implementation of an automated PV solarcleanerusingamotor-drivencleaningmechanismalongwithawatersprayingsystem. The system is controlled using a microcontroller that manages the movement of thecleaner, operation of the rotating brush, and water pump control. An obstacle detection featureisalsoincludedtoensuresafemovementofthecleanerandtoavoiddamageat the panel edges.
The proposed system combines both dry and wet cleaning methods for better removalof dust and dirt while reducing unnecessary water consumption.The rotating brush helps remove dry particles, while the water spray assists in cleaning stubborn dirt from the panel surface.
Thedevelopedprototypedemonstratesthatautomationcansignificantlyimproveso-larpanelmaintenanceandhelpmaintainhigherenergyefficiency.Thesystemisdesigned to be cost-effective, reliable, and easy to operate, making it suitable for residential and small-scale industrial solar applications.

Background of the Study: Poultry diseases, particularly those caused by Salmonella species, pose significant challenges to poultry production due to associated economic losses and public health implications. The consumption of improperly cooked poultry products has contributed to the global burden of salmonellosis, including typhoid-like illnesses.Study Design: A cross-sectional study was conducted to determine the incidence of Salmonella species in poultry feeds and droppings, as well as their antimicrobial susceptibility patterns in selected farms within Bauchi metropolis.Method: A total of 200 samples comprising poultry feeds and droppings were collected from selected farms. Samples were processed, cultured, and identified using standard microbiological techniques including Gram staining and biochemical tests. Antimicrobial susceptibility testing was performed using the Kirby–Bauer disk diffusion method on Mueller–Hinton agar in accordance with Clinical and Laboratory Standards Institute (CLSI) guidelines.Results: The overall prevalence of Salmonella species was 52.5%, with a higher occurrence in droppings (59.5%) compared to feeds (46%). No statistically significant difference was observed among the farms. The highest resistance was recorded against Streptomycin (61.9%), Ampicillin (55.2%), and Tetracycline (46.7%), while the highest sensitivity was observed for Ciprofloxacin (77.1%), Gentamicin (64.7%), and Cefoxitin (55.2%). The most common multidrug resistance pattern included Ciprofloxacin, Nalidixic acid, Cefoxitin, and Gentamicin.Conclusion: The study confirmed the presence of Salmonella species in poultry environments, with evidence of multidrug resistance posing a public health risk. Strengthening biosecurity and monitoring antimicrobial use is essential to mitigate contamination and resistance spread.Keywords: Salmonella, Antibiogram, Incidence, Poultry, Antimicrobial Resistance.

Construction projects frequently experience cost overruns and schedule delays due to uncertainties associated with financial, technical, operational, and external risk factors. This study aims to analyze the impact of risk management practices on cost overruns in construction projects and identify the major factors contributing to budget escalation. The research was carried out using quantitative data analysis based on responses collected from 55 construction professionals, including project managers, engineers, consultants, risk management specialists, and other industry participants. Statistical analysis was used to evaluate the relationship between risk factors and project performance.

The findings revealed that ineffective risk identification, inadequate planning, poor monitoring, and insufficient contingency management are the primary causes of cost overruns in construction projects. Financial risks, including material price fluctuations, delayed payments, inflation, and shortage of funds, were identified as the most influential factors affecting project budgets and schedules. Labor-related risks such as shortage of skilled workers, low productivity, absenteeism, and labor disputes also contributed significantly to increased project costs and project delays. Additionally, poor project planning, inaccurate cost estimation, incomplete design information, frequent scope changes, and weak stakeholder coordination were found to reduce overall project efficiency.

The study further highlighted the influence of external factors such as adverse weather conditions, policy changes, legal constraints, and approval delays on project performance. A practical case study analysis of two completed construction projects demonstrated that inadequate risk assessment and ineffective project controls resulted in cost overruns ranging from 16% to 20% and delays of 3 to 5 months. The results confirm that implementing systematic risk management practices—including risk identification, risk assessment, mitigation planning, contingency budgeting, and continuous monitoring—can significantly reduce cost overruns and improve project outcomes. The study concludes that proactive risk management is essential for achieving cost efficiency, timely completion, and sustainable performance in construction projects.

The pervasive contamination of surface waters, groundwater, and drinking water supplies by emerging contaminants (ECs)—particularly pharmaceuticals, personal care products (PCPs), endocrine-disrupting chemicals (EDCs), per- and polyfluoroalkyl substances (PFAS), and pesticide residues—represents a critical environmental challenge with profound implications for ecosystem integrity and human health (Athey & Erdle, 2021). Conventional water treatment technologies, including coagulation-flocculation, activated carbon adsorption, and biological processes, demonstrate fundamental inadequacies for removal of these recalcitrant, persistent compounds characterized by low biodegradability, resistance to photodegradation, and trace-level concentrations (1-1000 ng/L to μg/L) (Singh et al., 2023). Advanced oxidation processes (AOPs) employing catalytic materials represent transformative solutions through generation of reactive oxygen species (ROS)—including hydroxyl radicals (•OH), sulfate radicals (SO₄•−), superoxide anions (O₂•−), and singlet oxygen (¹O₂)—enabling complete mineralization of resistant organic contaminants to carbon dioxide and water (Satyam & Patra, 2025). This comprehensive review systematically examines five principal catalytic AOP technologies: (1) heterogeneous photocatalysis achieving 88-96% pharmaceutical removal efficiency under optimized conditions; (2) Fenton and Fenton-like processes demonstrating 92-98% removal efficiency through high-valent iron species; (3) catalytic ozonation achieving 85-92% removal with 30-50% ozone consumption reduction compared to non-catalytic systems; (4) persulfate-based AOPs (PS-AOPs) demonstrating 87-94% efficiency across broader pH ranges (5.0-8.0); and (5) electrochemical catalysis achieving 85-92% removal with tunable oxidation potential (Miklos et al., 2018), (Kumar et al., 2019), (Satyam & Patra, 2025). Catalyst materials examined comprehensively include metal oxide semiconductors (TiO₂, Fe₃O₄, Nb₂O₅, CoO₄, MnO₂), carbon-based systems (graphene derivatives, carbon nanotubes, biochar, nitrogen-doped carbon), metal-organic frameworks (MOFs), waste-derived biochar materials, and emerging single-atom catalysts (Wang et al., 2025), (Fidelis et al., 2025), (Aziz et al., 2025). Degradation mechanisms encompass both radical pathways generating non-selective •OH and SO₄•− species and non-radical mechanisms including direct electron transfer, singlet oxygen generation, and surface-catalyzed reactions enabling selective contaminant oxidation (Zhu et al., 2018). Critical operating parameters—including solution pH (optimal ranges 3.0-8.0 depending on system), temperature (20-80°C operational range with 20-30% efficiency improvement per 10°C increase), catalyst loading (0.1-1.0 g/L with saturation typically at 0.3-0.8 g/L), oxidant concentration (0.5-5.0 mmol/L stoichiometric range), and reaction time (10-240 minutes)—are comprehensively analyzed with quantitative performance relationships and optimization strategies (Yu et al., 2026). Removal efficiency across diverse emerging contaminant classes demonstrates differential performance: pharmaceuticals (diclofenac, carbamazepine, sulfamethoxazole, ibuprofen) 88-98% removal, personal care products (triclosan, parabens, benzophenones) 85-92% removal, endocrine disruptors (bisphenol A, estrogens) 80-93% removal, PFAS compounds (PFOA, PFOS) 60-85% removal through persulfate/electrochemistry, and pesticide residues (atrazine, glyphosate) 78-95% removal (Kumar et al., 2019), (Zawadzki, 2022), (Meena et al., 2024). Practical integration into multi-barrier stormwater treatment systems is discussed with field performance data demonstrating 85-98% persistent pollutant removal through sequential treatment stages combining physical separation (80-95% TSS removal), adsorption (70-90% hydrophobic EC removal), catalytic oxidation (85-98% persistent pollutant mineralization), and post-polishing (95-99% final treatment) (Yu et al., 2026). Enhanced stormwater treatment via thermally modified steel slag-based bioretention systems achieves nutrient removal (NH₄⁺-N 95.3±1.3%, TN 85.7±1.8%, TP 90.5±1.5%), heavy metal removal (Cu²⁺ 96.1±0.7%, Cr⁶⁺ 90.5±1.2%, Pb²⁺ 92.2±1.1%), and emerging pharmaceutical degradation (enrofloxacin 65.6±2.1%, norfloxacin 62.6±2.4%) (Yu et al., 2026). Major limitations constraining widespread full-scale deployment include: (1) catalyst deactivation after 50-100 treatment cycles with inadequate regeneration strategies; (2) secondary byproduct formation and toxicity concerns; (3) energy-intensive operation requiring 20-150 kWh/m³ depending on technology (photocatalysis 80-150 kWh/m³, ozonation 20-50 kWh/m³, persulfate 40-90 kWh/m³) representing 30-40% of operational costs; (4) persistent scalability and real-world implementation gaps with 10-30% efficiency loss between laboratory and field conditions (Alazaiza et al., 2026), (Miklos et al., 2018). Emerging innovations including visible/near-infrared photocatalysts extending light absorption, single-atom catalysts achieving theoretical efficiency with minimal precious metal requirements, confinement-based catalysis enhancing ROS generation, and renewable energy integration offer exceptional promise for sustainable next-generation systems (Satyam & Patra, 2025), (Fidelis et al., 2025). Future research priorities address critical knowledge gaps: (1) mechanistic elucidation of non-radical pathways enabling selective oxidation and byproduct minimization; (2) development of aqueous-stable metal-organic frameworks with improved catalyst recovery; (3) comprehensive real-world validation in complex matrices with suspended solids, dissolved salts, and natural organic matter interference; (4) catalyst regeneration and circular economy approaches enabling sustainable full-scale deployment; (5) integration with renewable electricity sources (solar photovoltaic, wind power) reducing operational carbon footprint; (6) comprehensive lifecycle assessment and environmental impact analysis balancing treatment efficacy against resource consumption (Alazaiza et al., 2026). This review provides state-of-the-art synthesis of catalytic AOP technologies, highlighting mechanistic understanding, material innovations, operating strategies, performance metrics, and practical deployment considerations essential for advancing water security and environmental remediation.

The capacity of artificial intelligence (AI) to extract real-time, granular consumer insights has fundamentally transformed advertising from a broadcast paradigm into an adaptive, dialogue-like process. This paper examines how predictive modelling techniquesspanning machine learning, deep behavioural analytics, and natural language process sing enable advertisers to anticipate consumer needs, personalise creative delivery, and optimise campaign performance dynamically. Drawing on a systematic review of 16 peer-reviewed studies (2016–2026), this research identifies three interlocking themes: (1) the architecture of AI-driven consumer insight generation, encompassing data sources, feature engineering, and predictive algorithms; (2) the mechanics of adaptive advertising execution, including real-time bidding, dynamic creative optimisation, and vernacular personalisation; and (3) the ethical and governance imperatives of explainability, consumer trust, and data privacy in AI-mediated advertising. Key quantitative findings include: LightGBM achieves an AUC-ROC of 0.93 for purchase intent prediction; adaptive AI advertising yields an 8.4% click-through rate by Q4 2024 versus 2.3% for static campaigns; and high-explainability AI systems achieve a 76% consumer ad acceptance rate compared to 31% for opaque models. The paper concludes by proposing a Predictive Adaptive Advertising (PAA) framework and a future research agenda.

The exponential growth of digital media platforms and online information sources has significantly increased the spread of misinformation, commonly referred to as fake news. This widespread dissemination of misleading content poses serious challenges to societal stability, public trust, and informed decision-making. Fake news can influence political opinions, disrupt financial markets, and create public health risks, making its detection a critical problem in the modern information ecosystem. Traditional approaches such as manual fact-checking are insufficient due to the massive volume and high velocity at which information is generated and shared across digital platforms.
In response to these challenges, this work proposes a hybrid machine learning-based framework for automated fake news detection using Natural Language Processing (NLP) techniques. The proposed system is designed to analyze textual content and classify news articles as real or fake by leveraging both statistical and contextual features. The methodology incorporates multiple stages, including data preprocessing, feature extraction using Term Frequency–Inverse Document Frequency (TF-IDF), and classification using a combination of traditional machine learning algorithms such as Logistic Regression and deep learning models such as Long Short-Term Memory (LSTM) networks.
The hybrid approach aims to overcome the limitations of individual methods by combining the efficiency and simplicity of classical models with the advanced contextual understanding capabilities of deep learning architectures. While traditional models provide faster training and lower computational requirements, deep learning models enable the system to capture sequential dependencies and semantic relationships within text. This integration is expected to improve classification accuracy and robustness across diverse datasets.
Although the system is currently in the design and development phase, the expected performance is based on insights from existing literature and prior studies. The proposed framework is anticipated to achieve high accuracy, precision, recall, and F1-score, making it suitable for practical deployment. Furthermore, the modular design of the system allows for future enhancements, including the integration of transformer-based models such as BERT and the incorporation of explainable artificial intelligence (XAI) techniques to improve model transparency and interpretability.
Overall, this research contributes toward the development of scalable and efficient fake news detection systems that can assist in combating misinformation in the digital age. The proposed hybrid framework provides a balanced approach that addresses both performance and computational constraints, making it a promising solution for real-world applications such as social media monitoring, automated news verification, and information filtering systems.

Infertility in women is a major reproductive health problem that affects millions of women around the world. It is defined as the inability to achieve pregnancy after one year of regular, unprotected sexual intercourse. According to the World Health Organization, infertility is a disease of the male or female reproductive system and can have significant medical, emotional, and social consequences. In women, infertility may occur due to various causes involving the ovaries, fallopian tubes, uterus, hormonal balance, or age-related changes in reproductive function.

One of the most common causes of female infertility is ovulatory disorders, in which the ovaries do not release eggs regularly. Conditions such as Polycystic Ovary Syndrome (PCOS), thyroid disorders, and hyperprolactinemia can disrupt normal ovulation. Damage or blockage of the fallopian tubes, often caused by pelvic inflammatory disease or previous infections, can prevent the sperm from reaching the egg. Endometriosis is another important cause, as it can affect the ovaries, fallopian tubes, and surrounding pelvic tissues. Uterine abnormalities such as fibroids, polyps, or congenital defects may also interfere with implantation and pregnancy.

Age plays a crucial role in female fertility. As women grow older, especially after the age of 35, the number and quality of eggs decline, reducing the chances of conception and increasing the risk of miscarriage. Lifestyle factors such as obesity, smoking, alcohol consumption, poor diet, excessive exercise, and chronic stress can further decrease fertility. Environmental exposures and certain medications may also affect reproductive health.

Diagnosis of infertility involves a detailed medical history, physical examination, hormonal blood tests, ultrasound scanning, and procedures such as hysterosalpingography to assess the uterus and fallopian tubes. Treatment depends on the underlying cause and may include medications to stimulate ovulation, hormone therapy, surgery to correct structural abnormalities, and assisted reproductive technologies such as In Vitro Fertilization (IVF).

Infertility in women is not only a medical condition but also a source of emotional distress and social pressure. Early diagnosis, proper treatment, healthy lifestyle changes, and psychological support can greatly improve outcomes. Raising awareness about female infertility is essential to reduce stigma and ensure that affected women receive timely and compassionate care.

Natural oils have gained considerable attention in pharmaceutical and cosmetic sciences due to their therapeutic properties, biocompatibility, and safety profile. Among these, olive oil has emerged as an important natural excipient and bioactive component in various drug delivery systems, particularly in topical and transdermal formulations. Derived from the fruit of the olive tree (Olea europaea), olive oil contains a rich composition of monounsaturated fatty acids, especially oleic acid, along with antioxidants, polyphenols, tocopherols, and vitamins that contribute to its medicinal value. These constituents provide moisturizing, anti-inflammatory, antioxidant, and skin-protective effects, making olive oil a promising material in pharmaceutical formulation development.
In drug delivery systems, olive oil serves not only as a carrier or excipient but also as a penetration enhancer that improves the absorption of drugs through biological membranes, particularly the skin. The high concentration of oleic acid in olive oil helps alter the lipid structure of the stratum corneum, thereby enhancing drug permeation and increasing therapeutic effectiveness. Because of this property, olive oil has been incorporated into creams, ointments, emulsions, gels, nanoemulsions, liposomes, organogels, and transdermal formulations for the controlled and efficient delivery of active pharmaceutical ingredients.
Topical formulations containing olive oil are increasingly explored for the treatment of skin disorders, wound healing, inflammation, fungal infections, and cosmetic skin care applications. Olive oil contributes to improved skin hydration, reduced irritation, and enhanced stability of formulations while supporting better patient acceptability. Additionally, its natural antioxidant compounds may protect the skin against oxidative stress and environmental damage, further increasing its therapeutic importance in dermal products. In drug delivery systems, olive oil serves not only as a carrier or excipient but also as a penetration enhancer that improves the absorption of drugs through biological membranes, particularly the skin. The high concentration of oleic acid in olive oil helps alter the lipid structure of the stratum corneum, thereby enhancing drug permeation and increasing therapeutic effectiveness. Because of this property, olive oil has been incorporated into creams, ointments, emulsions, gels, nanoemulsions, liposomes, organogels, and transdermal formulations for the controlled and efficient delivery of active pharmaceutical ingredients.The growing preference for natural, biodegradable, and skin-friendly ingredients has further accelerated research on olive oil-based formulations.

YogicpracticesandindigenousIndianphysicalactivitiessuchasKabaddi,Kho-Kho,Mallakhamb andregionalfolkgameshavelongbeenembeddedinthecountry'sculturalandeducationalfabric, yet their systematic role in shaping functional fitness, cognitive ability and psychological well-being among young athletes remains under-synthesisedin the sportscience literature. This paper under takes anarrativere view of empirical and conceptual studies on yoga and in digenous games, organised around three outcome domains: functional fitness, cognitive ability and psychological well-being. The reviewsituates thesefindings within theNational Education Policy (NEP) 2020, which explicitly foregrounds indigenous sports and holistic, sports-integrated learning as curricularpriorities.Across thereviewed literature, yogicpracticesareconsistentlyassociatedwith gains in flexibility, strength, attention and emotional regulation, while indigenous games are associated with anaerobic fitness, reflexes, teamwork and culturally-rooted psychological resilience. The paper further proposes a conceptual framework integrating yoga and indigenous activity within school and collegiate sport curricula, and outlines a methodology — including candidateinstrumentsandaquasi-experimentaldesign—forfutureprimaryresearchwithyoung athletes. The paper concludes that yoga and indigenous physical activities are complementary rather than competing strategies for holistic athlete development, and that NEP 2020 provides a timely policy window for their structured integration into Indian sport and physical education curricula.

Diabetes mellitus (OM) is one of the most prominent metabolic disorders globally. exerting a massive burden on healthcare systems. While conventional single-target synthetic drugs are prevalent, they often present adverse side effects and loss of efficacy over time. Traditional medicine systems, particularly Ayurveda, have long utilized polyherbal formulations (PHFs) to manage complex diseases like diabetes. PHFs operate on the principle of synergy, where multiple phytoconstituents target varied physiological pathways concurrently. However, standardizing and validating the mechanistic actions of these complex mixtures using classical pharmacological methods has been exceedingly difficult. Network pharmacology. an emerging interdisciplinary field combining systems biology, bioinformatics, and computational pharmacology, provides a transfor- mative approach to deciphering the multi-component, multi-target, and multi- pathway mechanisms of PHFs. This comprehensive review and project report details the application of network pharmacology in investigating anti-diabetic polyherbal formulations. It outlines the complete methodology-from compound screening and target prediction to network construction and pathway enrichment analysis.

Air pollution is one of the most severe environmental health risks of the 21st century. With increasing urbanization, vehicular movement, and industrial development, the quality of air, particularlyinurbancentres,hasdeteriorateddrastically.Amongthemanyprofessionalsaffected by outdoor air pollution, traffic police personnel stand out as a highly vulnerable group due to their prolonged and unavoidable exposure to vehicular emissions.
This study focuses on understanding how traffic-related air pollution is affecting the health of trafficpoliceofficersintheundividedDakshinaKannadadistrict,comprisingDakshinaKannada and Udupi. These officers, who serve as the backbone of urban traffic management, are exposed daily to pollutants such as PM2.5, PM10, Nitrogen Dioxide (NO₂), and Sulphur Dioxide (SO₂). These pollutants are primarily emitted by vehicles, especially in densely trafficked zones.
Thesignificanceof thisstudylies in itsfocuson aspecificoccupational group that often receives lessattentioninhealth-basedenvironmentalresearch.Byanalysingthehealthimpactsofpollution on traffic personnel, this research aims to generate awareness and advocate for policy changes that can protect their wellbeing.

Flaxseed is one of the oldest cultivated oilseed crops and is widely used in pharmaceutical, nutraceutical, and functional food industries because of its rich nutritional and therapeutic properties. Flaxseed contains important bioactive constituents such as alpha-linolenic acid (ALA), lignans, dietary fiber, proteins, vitamins, and antioxidants, which contribute to its medicinal value.
In pharmaceutical formulations, flaxseed is incorporated into capsules, tablets, powders, granules, functional foods, syrups, and cosmetic preparations due to its anti-inflammatory, cardioprotective, antioxidant, laxative, and cholesterol-lowering activities. Flaxseed oil is particularly valued as a natural source of omega-3 fatty acids and is commonly marketed for cardiovascular and neurological health support.
Researchstudieshaveshownthatregularconsumptionofflaxseedincontrolledconcentrations may help in the management of constipation, diabetes, obesity, hypertension, hyperlipidemia, and hormonal imbalance. Because of these health benefits, flaxseed-based products are increasingly marketed as nutraceutical and preventive healthcare formulations worldwide.
In recent years, the growing incidence of lifestyle-related disorders such as cardiovascular diseases, diabetes mellitus, obesity, hypertension, gastrointestinal disorders, and hormonal imbalance has increased the demand for natural therapeutic agents. Flaxseed has become an importantcomponentinthisareabecauseresearchstudiessuggestthatregularconsumptionin appropriateconcentrationsmayhelpreduceserumcholesterol,improvebloodglucosecontrol, support digestive health, decrease oxidative stress, and enhance overall well-being.
Thepharmaceuticalandnutraceuticalindustrieshavesuccessfullyincorporatedflaxseedintoa variety of marketed formulations including soft gelatin capsules, tablets, powders, granules, sachets, syrups, functional foods, bakery products, protein supplements, and cosmetic preparations.Flaxseedoilcapsulesarewidelymarketedasomega-3supplementsfor cardiovascular and neurological support, while flaxseed powder and fiber formulations are commonly used for bowel regulation and weight management. In cosmetic formulations, flaxseed mucilage and oil are utilized for skin hydration, anti-aging activity, and hair conditioning.

Research Question: How does Rendanheyi convert digital connectivity into employee initiative, cross-boundary coordination and organisational value creation?
Motivation: Digital transformation research explains how digital technologies reshape strategy, while platform and strategic HRM research examine governance and people systems. However, these literatures rarely show how an indigenous management model converts user data into authority, resource allocation, incentives and collaboration. Building on Teece (2007), Gawer (2014) and Strohmeier (2020), this study addresses the puzzle that similar digital investments produce different organisational outcomes. Existing accounts of Rendanheyi are also frequently descriptive, leaving its mechanisms, counter-effects and transfer conditions under-specified.
What&#39;s New? The paper theorises Rendanheyi as a hybrid platform-governance architecture rather than a cultural slogan or isolated HR practice.
So What? The analysis identifies what firms may learn from Haier without copying institution-specific labels or transferring excessive market risk to employees.
Idea: Digital infrastructure enables four connected mechanisms: dynamic strategic alignment through decentralised authority and HR platforms; order-based human-capital recombination; user-linked incentives; and chain-group governance across internal units and ecosystem partners. Their configuration, rather than any single practice, links employee autonomy to accountable user value creation.
Data: The 2020-2025 case database combines six semi-structured interviews totalling 628 minutes and approximately 148,000 Chinese characters, annual and ESG reports, governance documents, company releases and approximately 157,000 Chinese characters of executive speeches and forum materials.
Tools: A longitudinal embedded single-case design is analysed through Gioia coding, temporal bracketing, process tracing, pattern matching, triangulation and rival-explanation analysis. Evidence is organised from first-order statements to second-order themes and aggregate mechanisms.
Findings: Rendanheyi worked by moving selected decision rights towards market-facing units while retaining platform rules and shared services. Order-based assembly widened talent matching; user-linked rewards redirected attention from supervisors towards attributable user outcomes; and chain-group governance coordinated autonomous actors through recorded commitments and value-sharing rules. The configuration enhanced responsiveness and innovation, but generated pressure, short-termism and attribution disputes when data quality, task divisibility, talent depth or rule legitimacy were weak.
Contribution: The study converts Rendanheyi into an analytically transferable model of digitally enabled hybrid governance with explicit mechanisms and boundary conditions.

Paracetamol (acetaminophen) is a commonly used analgesic and antipyretic drug; however, conventional tablets may reduce patient compliance, especially in children and individuals with swallowing difficulties. This study aimed to formulate and evaluate a paracetamol gummy gel as an alternative oral dosage form with improved palatability and ease of administration.The formulation was prepared using suitable gelling agents, sweeteners, and paracetamol, followed by evaluation of its physicochemical properties, drug content, stability, and in vitro drug release profile. Safety, effectiveness, and patient acceptability were also assessed in comparison with conventional tablet formulations.The findings suggested that paracetamol gummy gel provides a stable, effective, and patient-friendly alternative that may enhance compliance and therapeutic convenience.

Human-wildlife conflict has evolved as one of the world's most prevalent and pressing environmental and socioeconomic issues. The main drivers of human-wildlife conflict are rapid population growth, human intrusion into forest areas, conversion of forest land to agricultural land, climate change, infrastructural development, and wild animal habitat loss. Communities living in close proximity to the forest fringe area are more vulnerable to huma-wildlife conflict. Especially indigenous women face significant direct and indirect losses, including risks to their livelihoods, personal safety, health, and overall well-being. Only few studies have focused on the challenges that women suffer as a result of human-wildlife conflict. Current policies for wildlife conservation and conflict management fails to addressthe challenges faced by indigenous women. This study conducts a narrative analysis of existing literature to understand the challenges and consequences of human-wildlife conflict faced by indigenous women and to identify the policy gaps in addressing their vulnerabilities. The study suggests to improve the gender-specific approaches to animal conservation and conflict resolution strategies. The findings help for better understanding of the gendered impacts of human-wildlife conflict, as well as to improve the policies.

Quantum computing is a new and quickly changing area of computer science that uses the basic ideas of quantum mechanics to process and change information in ways that classical computers can't do well. Whereas classical computers use binary bits (0 and 1), quantum computers use quantum bits, or qubits. Qubits can be in superposition, meaning they can exist in multiple states at once. Also, quantum systems are capable of conducting massive parallel computation through phenomena like entanglement and quantum interference, which can significantly boost computational power for specific problem domains.In this review paper, basic concepts of quantum computing are reviewed including qubit representation, quantum gates and quantum circuits. The report also discusses how quantum computing can revolutionize existing computer systems with applications in secure communication using advanced cryptographic techniques, speed up machine learning algorithms and optimize complex systems, and advance drug discovery and material science through molecular simulation.Quantum computing is a promising technology. But there are many technical and practical challenges to overcome. These include issues like qubit decoherence and error correction, scalability, and specialized hardware environments. The paper also discusses on-going research efforts and recent developments to overcome these limitations. In conclusion, this research discusses the future scope of quantum computing and its power to revolutionize industries and redefine the bounds of computational power in the next few decades.

The advancement in health technologies has made medical diagnostics and patient observation much better. AI technology and IoT have been combined to form devices that track health conditions and give an instantaneous evaluation. Such devices include fitness wearables that have been equipped with sensors to obtain vital signs including blood pressure, heart rate, body temperature, and oxygen levels. The information is sent to cloud platforms, where AI systems check it for any patterns and predict potential health issues. This helps in monitoring patients identifying diseases early and managing healthcare more effectively. This article looks at how these AI-powered devices are designed how they work, their uses, advantages and challenges, in healthcare. The report also talks about research directions to make smart healthcare systems more efficient and reliable. It talks about making these technologies more efficient and reliable. The aim is to improve care and increase access to health services. As these technologies evolve, we can be optimistic about the future of healthcare.

This study understands the comparative significance of Systematic Investment Plans and Lump Sum Investments in mutual funds within the Indian financial system. Mutual funds have emerged as one of the most preferred investment avenues among investors due to diversification, professional managementand easy accessibility. The increasing participation of investors through SIPs and lump sum investments have created the need to compare these approaches in terms of risk, return, investor suitabilityand market timing. This study based on secondary data from scholarly journals, reports of the Association of Mutual Funds in India, Securities and Exchange Board of India, Reserve Bank of India and other academic sources. SIPs are highly suitable for salaried individuals and conservative investors due to rupee cost averaging and disciplined savings, whereas lump sum investments may generate superior returns during favourable market conditions when deployed strategically. The study concludes that investor objectives, financial literacy, investment horizon and risk appetite significantly determine the choice between SIP and lump sum investment.

Agriculture plays a crucial role in food production, yet plant diseases, improper irrigation,and unfavorable environmental conditions significantly reduce crop yield. The Smart Plant Health Monitoring System is designed to monitorplant health parameters such as soil moisture, temperature, humidity, and light intensity in real time using IoT technology. Sensors collect environmental data and transmit it to a microcontroller,whichprocessestheinformation and sends it to a cloud platform for remotemonitoring.Thesystemalertsuserswhen abnormal conditions are detected, enabling timely actiontoimproveplantgrowthandpreventdiseases.Byautomatingplantmonitoring,the system reduces manual effort, optimizes resource usage, and enhances crop productivity, making it suitable for smart agriculture and home gardening applications.

Industri Fast Moving Consumer Goods (FMCG) menghadapi persaingan yang semakin ketat sehingga perusahaan dituntut untuk melakukan berbagai strategi pengembangan produk guna mempertahankan daya saing. Salah satu upaya yang dapat dilakukan adalah melalui redesign label sebagai bagian dari pengembangan produk untuk memperkuat identitas merek dan meningkatkan brand awareness. Penelitian ini bertujuan untuk menganalisis pengembangan produk deodorant spray SiDeo melalui redesign label sebagai upaya meningkatkan brand awareness. Penelitian menggunakan metode deskriptif dengan pendekatan kualitatif yang dilaksanakan pada usaha deodorant spray SiDeo di Kota Makassar. Data diperoleh melalui wawancara terstruktur dengan pemilik usaha, agen reseller, dan konsumen serta dokumentasi. Analisis data dilakukan melalui tahapan pengumpulan data, reduksi data, penyajian data, dan penarikan kesimpulan. Hasil penelitian menunjukkan bahwa label produk SiDeo sebelum redesign belum mampu merepresentasikan identitas merek secara optimal karena penggunaan warna, tipografi, tata letak, dan informasi produk yang masih sederhana. Pengembangan produk dilakukan melalui redesign label dengan memperbaiki aspek warna, tipografi dan tata letak, serta menambahkan informasi produk berupa logo halal, cara penyimpanan, dan media sosial. Hasil uji coba pasar menunjukkan bahwa desain baru memperoleh tanggapan positif dari konsumen dan agen reseller karena dinilai lebih menarik, informatif, dan mudah dikenali. Analisis brand awareness menunjukkan adanya peningkatan dari tingkat brand recognition menuju brand recall, yang ditandai dengan meningkatnya kemampuan konsumen dalam mengenali, mengingat, dan mempertimbangkan produk SiDeo. Dengan demikian, redesign label terbukti menjadi strategi pengembangan produk yang efektif dalam meningkatkan brand awareness serta memperkuat identitas merek deodorant spray SiDeo.

Buy-Now-Pay-Later (BNPL) has grown from a marginal checkout option into a mainstream short-term credit instrument, with the largest U.S. providers originating over 330 million loans worth more than $45 billion in 2023 alone. Policymakers remain divided on whether the product is best understood as a welfare-improving liquidity technology that allows constrained households to smooth consumption against income shocks, or as an extractive mechanism that monetizes financial fragility through late fees, repayment stacking, and merchant cost pass-through. This paper develops a heterogeneous-agent New Keynesian (HANK) framework augmented with an explicit BNPL contract — a short-duration, often zero-interest instalment loan financed jointly by a merchant discount fee and a state-contingent late fee — to assess the product's effect on the marginal propensity to consume (MPC) across income quintiles. The model is calibrated using stylized targets drawn from published micro-level fintech and survey data, including findings on BNPL-induced spending increases, deteriorating credit-card and overdraft outcomes among new BNPL users, and the merchant-side economics of price discrimination through subsidized credit. Illustrative simulations suggest that BNPL access raises the MPC most sharply for households in the second- and third-income quintiles — those liquidity-constrained but not so distressed as to face binding adoption frictions — while a non-trivial share of the apparent consumption-smoothing gain is offset by fee and price-markup extraction concentrated in the bottom two quintiles. The paper concludes that BNPL is neither a clean consumption-smoothing technology nor a uniform debt trap, but a redistributive credit instrument whose welfare implications depend critically on the elasticity of merchant cost pass-through and on regulatory treatment of repeat late-fee assessment.

Walt Whitman's Song of Myself stands as one of the most influential poems in American literature. Through its exploration of the self, nature, democracy, and spirituality, the poem presents a profound vision of human existence. This paper examines Whitman's concept of selfhood and its gradual expansion into a universal consciousness. The study highlights the themes of individuality, unity with nature, pantheism, immortality, and mystical experience that shape the poem's philosophical framework.

Organic farming is the need of the time. The study conducted with 180 farmers in coastal tract of Odisha reveals that farmers do not have adequate knowledge about advantages and limitations of organic farming. Because of experience and higher yield they support inorganic farming although are well aware of itsnegative impact. The farmers in general are not aware of commercial brands of bio-fertilizers and bio-pesticides. They need training, compensation in case of loss, subsidy, training, technical support and extension advice along with assured market for organic produce.

This paper presents a low-cost, privacy-preserving human presence detection system that leverages Wi-Fi Channel StateInformation(CSI)capturedbyESP32microcontrollers to identify whether a person is located behind a wall. Unlike camera-based or wearable sensor systems, the proposed frame-work requires no line-of-sight access and preserves user privacy by processing only wireless signal metadata. We introduce an Adaptive Subcarrier-Weighted Denoising (ASWD) scheme that dynamically suppresses high-noise subcarriers based on per-subcarrier variance stability, improving robustness against AGC drift and inter-network interference — a contribution absent from prior ESP32 CSI literature. Raw CSI amplitude sequences arecollectedacross52OFDMsubcarriersfrom8humansubjects in 5 distinct room environments spanning 3 wall material types (gypsum, brick, reinforced concrete), preprocessed through a sliding-window pipeline with ASWD, and fed into a trained Random Forest classifier. Across 5 independent experimental runs, thesystemachieves91.4±0.6%accuracyintheprimaryen-vironment.
AlightweightStatisticsAlignmentdomainadaptation(SA-DA)stepreducesthecross-nvironmentaccuracygapfrom
12.4 to 5.1 percentage points using only 60 calibration samples from the target room. A real-time inference engine deployed alongside a React-based monitoring dashboard enables end-to-end detection latency of approximately 20ms per prediction window on a consumer-grade laptop. The complete hardware cost remains under $15, demonstrating practical applicabilityfor smart home security and non-intrusive occupancy sensing.

The integration of Internet of Things (IoT) technology in the automotive industry has led to significant advancements, particularly in the realm of electrical vehicles (EVs). This abstract introduces an IoT- based Electrical Vehicle Monitoring System (IoT-EVMS) designed to enhance the efficiency, safety, and sustainability of EV operations.
The IoT-EVMS comprises a network of sensors, actuators, and communication devices embedded within EVs and infrastructure components such as charging stations and grid systems. These interconnected components enable real-time monitoring, data collection, and analysis of various parameters critical to EV performance and management.

Recruitment is a critical organizational function that has long been hampered by manual inefficiencies, subjective human judgment, and demographic bias. This paper presents an AI-Powered Recruitment System designed to automate candidate-project matching using a Jaccard Similarity Coefficient-based skill-matching engine. The system integrates three tightly coupled modules — Company, Graduate, and Administrator — operating over a Flask-Python backend with a Supabase relational database. Resume parsing via Natural Language Processing (NLP) extracts structured skill tags, which are compared against company-defined requirement sets to produce objective suitability scores. Candidates are ranked in descending order of similarity, grouped into project teams, and a team leader is algorithmically designated based on the highest match score. Experimental validation across 100 graduate profiles and 30 project postings demonstrates 88% matching accuracy, sub-2-second average response time, and a recruitment-time reduction exceeding 90% relative to manual screening. The working prototype was further evaluated through its company, graduate, and administrator dashboards, confirming consistent end-to-end functionality from registration through team formation. This framework advances fairness-aware, explainable, and human-centric talent acquisition by embedding algorithmic transparency, administrative oversight, and real-time candidate feedback within a unified web platform.

Tin-based Babbitt alloys are widely employed as bearing materials in automotive, marine, aerospace, and industrial machinery due to their excellent conformability, embeddability, fatigue resistance, and anti-seizure properties. However, increasing demands for higher load-carrying capacity, enhanced wear resistance, and improved frictional performance have led researchers to develop tin-based Babbitt composite materials reinforced with ceramic particles, solid lubricants, and nano-fillers. This review presents a comprehensive analysis of the tribological performance of tin-based Babbitt composites. The influence of reinforcement materials, manufacturing techniques, wear mechanisms, friction characteristics, microstructural evolution, and operating conditions on tribological behavior is discussed. Recent advancements in nano-reinforced Babbitt composites and future research directions are also highlighted. The review demonstrates that composite reinforcements significantly improve wear resistance and reduce friction while maintaining the desirable bearing characteristics of conventional Babbitt alloys.

The advent of the digital age has seen social media change the face of marketing and communications within the business world. The proliferation of internet use and the use of popular social media sites such as Facebook, Instagram, YouTube, and X means that businesses can now easily reach their target audience more effectively than before. Within the online retail industry in particular, social media marketing is a key tool used by firms to draw in potential clients, build brands, and position them effectively within the market. Social media marketing is distinct from conventional marketing practices in many ways; the former allows for direct engagement with clients through personalized content, interactive messaging, influencer collaboration, among others.
Brand positioning involves giving a distinctive position to a brand when it comes to its customers as compared to other competing products. With regard to the online shopping environment, there is an important role played by brand positioning as it helps in determining the buying behavior of consumers and customer loyalty. The companies that work within this environment have faced stiff competition owing to the growing number of players as well as changes in consumer needs. Social media marketing becomes a useful strategy to give the brands a competitive advantage.

Penelitian ini bertujuan mengembangkan sistem informasi Cafe Pos Tropis berbasis web dengan menerapkan Quality of Information Framework (QIF) guna menghasilkan platform informasi berkualitas bagi pelanggan dan pengelola. Permasalahan utama yang dihadapi adalah sistem pelayanan konvensional yang menimbulkan bottleneck di area kasir pada jam puncak operasional, dengan rata-rata waktu tunggu 12–15 menit dan customer abandonment sebesar 30–40%. Penelitian menggunakan pendekatan kualitatif dengan metode Research and Development (R&D) serta model pengembangan Waterfall yang terdiri dari lima tahap: analisis kebutuhan, perancangan sistem, implementasi, pengujian, dan pemeliharaan. Sistem dikembangkan menggunakan framework Laravel dan Firebase sebagai backend layanan cloud. QIF diterapkan melalui enam dimensi evaluasi kualitas informasi yaitu akurat, tepat waktu, relevan, lengkap, konsisten, dan mudah diakses. Mekanisme QR Code diintegrasikan sebagai gateway akses menu digital tanpa hambatan instalasi. Fitur sistem mencakup halaman menu interaktif, booking fasilitas online, dashboard admin realtime, manajemen pesanan, serta kalender booking. Hasil pengujian blackbox menunjukkan seluruh fitur berjalan berhasil sesuai skenario yang ditetapkan. Implementasi sistem berhasil mendistribusikan beban pemesanan dari kasir ke pelanggan secara mandiri, mengurangi waktu tunggu, dan meningkatkan kualitas informasi yang disajikan. Penelitian ini menghasilkan model implementasi sistem informasi berbasis QIF yang dapat direplikasi pada usaha kafe skala kecil dan menengah di Indonesia.

Migrants living outside their countries of origin often experience multiple vulnerabilities, particularly in relation to socioeconomic conditions, social integration, and access to essential services. These challenges were further worsened by the COVID-19 pandemic, which caused severe social disruptions worldwide and affected migrant populations. This systematic review aimed to synthesize current evidence on the barriers to, and facilitators of, mental well-being among migrants and immigrants living in Japan during the post-pandemic period.A comprehensive literature search was conducted on Scopus, PubMed, PsycINFO, and Google Scholar. Studies published from 2022 onwards that evaluated mental health and well-being of international migrants in Japan were identified and screened according to predefined eligibility criteria. Relevant data on study characteristics, determinants of mental well-being, barriers, facilitators, and policy recommendations were extracted and synthesized through thematic analysis.A total of 12 articles involving 4,622 participants met the inclusion criteria. The most frequently studied migrant populations post-pandemic were Vietnamese migrants (42.6%), followed by Chinese (14.2%), Nepalese (14.2%), Ghanaian (14.2%), and mixed international migrant populations (14.2%). Thematic analysis identified language barriers, female gender, limited social support, financial insecurity, employment-related stress, and difficulties accessing healthcare and social services as the principal barriers to mental well-being. Conversely, strong social networks, preservation of cultural identity, community engagement, social connectedness, and access to support services emerged as important facilitators of positive mental health outcomes. Policy recommendations across the reviewed studies emphasized the need for culturally sensitive mental health services, enhanced migrant support programs, improved language assistance, and greater cross-cultural awareness within Japanese society.The findings showed that social support networks play an important role in shaping the mental well-being of migrants in Japan. The review recommend further longitudinal and intervention-based research to better understand the evolving mental health needs of migrants in post-pandemic Japan.

Novel thermoelectric (TE) materials are increasingly centered on oxide ceramics because of their excellent thermal stability, oxidation resistance, environmental compatibility, and abundance of constituent elements. In contrast, conventional thermoelectric non-oxidematerials such as bismuth telluride, and lead telluride exhibit unfavorable properties such as instability at high temperatures with scarce and toxic raw materials that constitute an environmental hazard. Novel TE materials showcase diverse crystal architecturesincluding wurtzite (e.g. ZnO), perovskite (e.g. SrTiO3), layered cobaltite (e.g. NaCo2O4) and oxychalcogenide (e.g. BiCuSeO) structures that strongly influence carrier transport and phonon scattering. This review paper comprehensively examines the structure, electrical conductivity, Seebeck coefficient and thermal conductivity of selected novel oxide thermoelectric materials, emphasizing how defect chemistry, aliovalent doping, nanostructuring, and composite design improve the thermoelectric figure of merit (ZT). Recent studies have demonstrated that oxides can achieve competitive thermoelectric performance while maintaining outstanding chemical and mechanical durability, making them attractive for waste heat recovery in industrial furnaces, automotive systems, and energy harvesting in renewable energy technological applications.

The rapid growth of two-wheeler electric vehicles has increased the importance of reliable electrical interconnection systems. The wiring harness serves as the primary medium for distributing electrical power and transmitting control signals among various vehicle subsystems. Consequently, wiring harness design, connector selection, and wiring methods play a significant role in ensuring vehicle safety, reliability, and performance.

This study examines how news content is being transformed by the use of short form (vertical) video created with apps such as TikTok, Instagram Reels, and YouTube Shorts. It builds upon some of the previous definitions of “framing” in the context of digital communication and media by conducting a critical textual analysis of the role of “interface elements” in mobile journalism (such as on-screen text, emojis, stickers) and posits that these elements do not only support the visual image (by providing context) but are also the primary meaning signifiers within the contemporary journalistic landscape. According to Barthes, not only do interface elements anchor meaning, but they also relay meaning. Therefore, “interface elements” subvert the traditional journalistic objective distance by providing a way for a journalist to communicate their emotional perspective on an event. Using a multimodal and digital semiotic approach, the important conclusion of this paper is that the “fragmented narrative” in mobile journalism creates a new type of “vernacular authority,” in that the new narrative forms of mobile journalism measure the degree to which an audience is likely to respond emotionally to a piece of journalistic content and the degree to which they would be able to find the content through search engines.

Virgin coconut oil (VCO) is a plant-based lipid phase with potential application in low-fat spread formulation. Still, its incorporation into aqueous food matrices requires a stable oil-in-water structure. This study evaluated the effects of Tween 80 and VCO concentrations on the physicochemical stability of ultrasonicated carboxymethyl cellulose (CMC)-stabilized VCO emulsions. Emulsions were prepared using VCO at 5, 10, and 15%, Tween 80 at 0, 0.5, 1.0, and 1.5%, fixed CMC at 0.5%, and distilled water as the continuous phase. Pre-emulsions were produced by magnetic stirring at 1000 rpm for 10 min, then treated with probe sonication at 50% amplitude for 5 min in pulse mode. Stability was characterized using the Turbiscan Stability Index, mean droplet diameter, zeta potential, viscosity, pH, peroxide value, and acid value. Tween 80 significantly reduced physical destabilization and droplet size, while VCO concentration significantly affected all measured parameters. Significant Tween 80 × VCO interactions were observed for TSI, droplet diameter, viscosity, and pH. The formulation containing 10% VCO, 1.0% Tween 80, 0.5% CMC, and 88.5% water showed the most balanced profile, with low TSI, small droplets, favorable zeta potential, suitable viscosity, and controlled lipid-quality indicators. This formulation is proposed as a promising basis for developing plant-based low-fat spreads.

The therapeutic success of a pharmaceutical agent is determined not only by its pharmacodynamic and pharmacokinetic properties but also by the efficiency of its delivery to the intended biological target. Drug delivery systems (DDS) have therefore become an essential component of modern pharmaceutical science, aiming to enhance drug efficacy, reduce toxicity, and improve patient compliance. Conventional drug delivery methods often suffer from limitations such as poor bioavailability, non-specific distribution, rapid clearance, and frequent dosing requirements. In response, significant advances have been made in the development of controlled, targeted, and novel drug delivery systems utilizing polymers, nanotechnology, biomaterials, and biotechnology-based approaches. This review provides an in-depth and critical discussion of various drug delivery systems, including conventional systems, controlled and sustained release formulations, targeted delivery systems, transdermal, pulmonary, ocular, gastrointestinal, mucoadhesive, implantable, gene-based, vaccine, and nanotechnology-driven drug delivery platforms. The mechanisms, advantages, limitations, and clinical significance of each system are examined. Regulatory challenges, translational barriers, and future prospects of drug delivery technologies are also highlighted. This review aims to serve as a comprehensive reference for researchers, academicians, and pharmaceutical scientists involved in drug delivery research and development.

The present study examines the retention of a multigenerational workforce through a comparative analysis of Gen Z, Millennials, and older employees. In the contemporary workplace, organizations are increasingly challenged to manage employees belonging to different generational groups with varying expectations, work values, and career priorities. The study focuses on the influence of organizational culture, employer branding, employee engagement, work-life balance, and employee well-being on long-term employee retention. A quantitative research approach was adopted, and data were collected through a structured questionnaire from employees across different sectors. Statistical tools and Structural Equation Modeling (SEM) were used to analyze the relationships among the variables.
The findings indicate that organizational culture and employer branding significantly influence retention intentions across all generational groups. However, differences were observed in employee expectations and motivational factors. Gen Z employees showed stronger preference for flexibility, career growth, technological work environments, and work-life balance, while Millennials emphasized organizational culture and employee engagement. Older employees valued organizational stability, leadership support, and job security. The study further revealed that employee engagement partially mediates the relationship between employer branding and employee retention.
The research contributes to the understanding of multigenerational workforce management by providing insights into generation-specific retention strategies. The findings offer practical implications for organizations seeking to develop inclusive workplace practices and sustainable employee retention policies.

Intellectual Disability (ID) is clinically characterized by distinct deficits in both intellectual functioning and adaptive behavior, which can severely impact a student's ability to navigate complex social settings. While modern educational policies increasingly advocate for inclusive schooling, merely placing students with ID in mainstream classrooms does not automatically equate to meaningful social integration or peer acceptance. This paper proposes a novel; multimodal assessment framework designed to objectively measure and understands the social integration of ID students in mainstream educational environments. By combining Social Network Analysis (SNA) with specialized Facial Expression Recognition (FER) and cognitive accessibility metrics, we provide a holistic method for evaluating interpersonal interactions and emotional engagement. Ultimately, this framework aims to transition educational models from superficial physical inclusion to active, self-determined social participation, providing educators with actionable data to support vulnerable learners.

Jaduni or jadukolija is a Kokborok traditional song of Borok/Tripuri people of Tripura state of India. There are many jaduni songs. These songs are sung in the context of momentous events or during festivals. These songs are timeless; it is hard to say who composed it. The songs have been passed down orally from generation to generation. These songs are one of the significant sources of knowledge about the language and culture of the borok/tripuri people. Through these songs one can gain knowledge about the socio-economic and religious customs and way of thinking of the borok people. This paper is specially focused on one jaduni song called ‘hamjwk tunphuru kapmung’ (the cry of the girl/bride when marriage is fixed). This song expresses the mental state of a girl after her marriage to an unfamiliar person is fixed. Her thoughts about life and family are revealed through her crying. And in response to her crying, her mother’s reply reveals the thought of the borok people about marriage. And the teachings of how a bride should live in her husband’s house after marriage are also reflected in this jaduni song.

One of the most widespread technologies that was embraced by digital space is Internet of Things (IoT), which has altered digital space by providing connectivity of their smart devices to all industries of health, transportation, manufacturing, and energy systems. The combination of the Internet of Things (IoT) and the Blockchain technology has become a part of the entirely new array of concepts of creating the safe, open, and effective digital environments. In the chapter, the authors explore the application of Blockchain along with the IoT to address the primary challenges of data integrity, scaling, interoperability, privacy, and energy efficiency. It focuses on the various types of Blockchain designs, i.e. a public, a private and a consortium that complements the decentralized IoT models with an unalterable data tour, without trust communication and smart contract implementation. The discussion also broadens the subject of consensus mechanisms in Internet of Things including lightweight and hybrid schemes, which make the most use out of resources. IoT solutions facilitated by Blockchain have been practically applied in medical practice, supply chain, smart cities and industrial automation to denote the newfound importance of IoT solutions. The chapter concludes with the identification of the new research patterns, technical concerns, and a way of moving to make Blockchain and IoT sustainable, secure, and smartly developed ecosystems.

The idea of Masculinity in American literature was always associated to male body and concepts of control, autonomy, and dominance. However, contemporary posthuman fiction challenges the old ways by reconstructing masculinity that goes beyond human body. This article looks into the ways in which Ancillary Justice and Infomocracy reimagine masculinity in a world of technological advancement. These texts present masculinity in its fragile form rather than glorifying it. In Ancillary Justice, the protagonist Breq, once a part of a huge ship, disrupts the idea of selfhood, making masculinity difficult to place within the narrative. In Infomocracy, power shifts from body to informational networks, where authority not vested in one individual but is distributed across different. In the light of posthumanist theory, this article argues that masculinity in these novels are not erased but transformed. It becomes disembodied, and destabilized, and suggests that masculinity is not entirely gone but is diffused into abstract forms. The article attempts to find out how posthuman masculinity is presented in both literary texts, and its role in determining masculinity. This changing dynamics between the idea of masculinity and physical form can be better understood by applying posthuman frameworks. It also offers the perspective that even though posthuman narrative seems to dissolve gender binaries, it also reveals how it exist subtly in technological realms, indicating that masculinity is not entirely erased but reconstructed in less visible forms.

This study made use of statistical tools such as Mean, Standard deviation, Frequency, T-test, and Person r in the integration of media literacy practices in the teaching of Araling Panlipunan in the Malaybalay District I, Division of Malaybalay during the academic year 2023-2024. It aimed to investigate the relationship between these practices and learner’s academic achievement in the subject.
Respondents comprised diverse educators across various age groups, genders, educational sector positions, length of service, and ICT training backgrounds. This diversity provided a
broad perspective on the effectiveness of integrating media literacy into the curriculum.
Results indicated a strong consensus among educators regarding the high effectiveness of various media literacy practices, such as starting early, adopting hands-on approaches, incorporating diverse media formats, addressing digital citizenship, and involving parents and guardians. Learners demonstrated commendable academic achievement in Araling Panlipunan, with most achieving satisfactory and outstanding grades.
In conclusion, the study found no significant correlations between demographic factors and the perceived effectiveness of media literacy practices or learner’s academic achievement. It suggests the universal benefit of media literacy integration across demographic backgrounds, highlighting the transformative potential of these practices in shaping contemporary education and empowering learners to navigate an increasingly complex media landscape.

The Western Ghats, also known as the Sahyadri Hills, represent one of the most significant biodiversity hotspots in the world. Stretching approximately 1,600 km along the western coast of India, this mountain chain harbors exceptional levels of species richness, endemism, and ecosystem diversity. Recognized as a UNESCO World Heritage Site, the Western Ghats support thousands of plant and animal species, many of which are found nowhere else on Earth. The region plays a crucial role in climate regulation, watershed protection, carbon sequestration, and livelihood support for millions of people. However, rapid urbanization, deforestation, mining, infrastructure development, and climate change pose severe threats to its ecological integrity. This review explores the biodiversity significance of the Western Ghats, highlighting its floristic and faunal wealth, endemic species, ecosystem services, conservation initiatives, and future challenges. The review emphasizes the need for sustainable management and community participation to ensure long-term conservation of this invaluable natural heritage.

This study presents an evaluation of subsoil conditions and bearing capacity to support safe and economical foundation design. Field investigations, including borehole drilling, soil sampling, and in-situ testing, were conducted to characterize the subsurface strata and assess their engineering behavior. Laboratory analyses, comprising soil classification, moisture content determination, and shear strength testing, were performed to obtain the geotechnical
properties required for foundation assessment. The results indicated a heterogeneous subsoil profile consisting of clay, silt, and sand layers with varying moisture conditions at different depths. Based on standard geotechnical design procedures, the bearing capacities for both shallow and deep foundations were estimated under different loading conditions. The findings emphasize the importance of detailed subsoil investigations in selecting appropriate foundation systems and ensuring the stability, safety, and long-term performance of civil engineering structures.

Background: Pulmonary Tuberculosis (PTB) remains one of the world's deadliest infectious diseases, caused by Mycobacterium tuberculosis. Despite effective treatments being available for decades, delayed diagnosis and poor treatment adherence continue to fuel transmission — particularly in developing countries where healthcare access is limited.
Objective: To synthesize and critically appraise available evidence on the clinical manifestations, diagnostic approaches, and treatment outcomes associated with pulmonary tuberculosis, with the aim of providing evidence-based guidance for hospital clinical practice.
Methods: A narrative review was conducted through a systematic search of PubMed, Google Scholar, and Scopus (2010–2025), supplemented by WHO global TB reports and national treatment guidelines. Articles were selected based on relevance to PTB epidemiology, diagnosis, and treatment.
Results: Reviewed literature consistently identifies persistent cough (>2 weeks), fever, night sweats, and weight loss as the cardinal symptoms of PTB. WHO global data report approximately 10 million new cases and 1.3 million deaths annually. GeneXpert MTB/RIF demonstrates sensitivity exceeding 95% and delivers results in ~2 hours. Standard first-line therapy (2HRZE/4HR) achieves treatment success in 80–90% of cases when adhered to fully.
Conclusion: Early diagnosis using modern molecular tools and strict adherence to standardized anti-TB regimens significantly improve patient outcomes and reduce community transmission.

In recent years, Environmental, Social, and Governance (ESG) factors have emerged as critical determinants of corporate financial performance and strategic decision-making. This study examines the relationship between ESG performance and the cost of capital, with a specific focus on its implications for sustainable treasury management. The research is grounded in the premise that strong ESG practices enhance a firm’s risk profile, improve investor perception, and ultimately reduce the cost of both equity and debt financing.
The study adopts a secondary research approach, drawing on existing empirical literature and corporate disclosures to analyze how ESG metrics influence financial outcomes. The conceptual framework developed in this research highlights a cause-and-effect relationship wherein improved ESG performance leads to risk reduction, enhanced investor confidence, and broader access to capital. This, in turn, lowers the overall cost of capital and enables firms to adopt more efficient and sustainability-oriented treasury strategies.
The findings suggest that firms with superior ESG performance benefit from reduced information asymmetry, improved creditworthiness, and favorable financing conditions. Additionally, the study emphasizes the evolving role of treasury functions, which are increasingly integrating ESG considerations into funding decisions, risk management, and capital allocation. Despite the growing importance of ESG, the research identifies a significant gap in literature linking ESG performance directly with treasury management practices, particularly in emerging markets such as India.
The study contributes to the existing body of knowledge by proposing a structured framework for ESG-integrated treasury management and highlighting the strategic importance of sustainability in financial decision-making. It offers practical insights for corporate managers, investors, and policymakers seeking to align financial performance with long-term sustainable value creation.

This condensed thesis summarizes the regulatory framework, development challenges, pharmacovigilance requirements, market status, and future prospects of biosimilars in India, USA, and Europe. Biosimilars are highly similar biological products that demonstrate no clinically meaningful differences from approved reference products. They improve patient access by reducing treatment costs while maintaining quality, safety, and efficacy.
This condensed thesis summarizes the regulatory framework, development challenges, pharmacovigilance requirements, market status, and future prospects of biosimilars in India, USA, and Europe. Biosimilars are highly similar biological products that demonstrate no clinically meaningful differences from approved reference products. They improve patient access by reducing treatment costs while maintaining quality, safety, and efficacy.
This condensed thesis summarizes the regulatory framework, development challenges, pharmacovigilance requirements, market status, and future prospects of biosimilars in India, USA, and Europe. Biosimilars are highly similar biological products that demonstrate no clinically meaningful differences from approved reference products. They improve patient access by reducing treatment costs while maintaining quality, safety, and efficacy.
This condensed thesis summarizes the regulatory framework, development challenges, pharmacovigilance requirements, market status, and future prospects of biosimilars in India, USA, and Europe. Biosimilars are highly similar biological products that demonstrate no clinically meaningful differences from approved reference products. They improve patient access by reducing treatment costs while maintaining quality, safety, and efficacy.
This condensed thesis summarizes the regulatory framework, development challenges, pharmacovigilance requirements, market status, and future prospects of biosimilars in India, USA, and Europe. Biosimilars are highly similar biological products that demonstrate no clinically meaningful differences from approved reference products. They improve patient access by reducing treatment costs while maintaining quality, safety, and efficacy.
This condensed thesis summarizes the regulatory framework, development challenges, pharmacovigilance requirements, market status, and future prospects of biosimilars in India, USA, and Europe. Biosimilars are highly similar biological products that demonstrate no clinically meaningful differences from approved reference products. They improve patient access by reducing treatment costs while maintaining quality, safety, and efficacy.
This condensed thesis summarizes the regulatory framework, development challenges, pharmacovigilance requirements, market status, and future prospects of biosimilars in India, USA, and Europe. Biosimilars are highly similar biological products that demonstrate no clinically meaningful differences from approved reference products. They improve patient access by reducing treatment costs while maintaining quality, safety, and efficacy.
This condensed thesis summarizes the regulatory framework, development challenges, pharmacovigilance requirements, market status, and future prospects of biosimilars in India, USA, and Europe. Biosimilars are highly similar biological products that demonstrate no clinically meaningful differences from approved reference products. They improve patient access by reducing treatment costs while maintaining quality, safety, and efficacy.

Large Language Models (LLMs) are rapidly becoming core components of modern software systems. Applications such as AI copilots, chatbots, automated support systems, and knowledge assistants rely heavily on generative AI models to produce responses in real time.

However, deploying LLM-powered applications introduces a new challenge: observability.

Traditional monitoring tools focus on infrastructure metrics such as CPU utilization, memory consumption, and API latency. While these metrics are essential for system reliability, they fail to provide insight into the behavior and quality of AI-generated responses.

For example, engineering teams often struggle to answer questions like:
• Why did the model generate an incorrect response?
• Which prompts are causing failures?
• How much are we spending on tokens?
• Are hallucinations increasing over time?

To address these questions, organizations are increasingly adopting AI observability platforms such as Langfuse, LangSmith, and Arize AI.

These platforms introduce a new monitoring layer specifically designed for generative AI systems. This article explores how to design and build an open-source AI observability platform for LLM applications.

The scope of this paper includes Development of Virtual Intelligent SoftLab with Artificial Intelligence. AI is being used widely in all sectors like education, scientific research and business. Artificial intelligence is the computer programs that can take the decisions based on existing data.Researchers are using artificial intelligence for more accurate and consistent scientific results using Virtual Lab. The virtual laboratories application utilization used by the college students for their experimental work. Laboratory interaction with students is important for practical approach so that students can run virtual laboratory applications at any place according to their requirements and abilities to carry out them anywhere and anytime.Many applications in virtual laboratories are to perform the interactions with students. However, students need interactions to determine which content, tools and modules for the research.In this paper,Artificial Intelligence tools easily solve the Virtual Laboratory concepts and can be easily used by students as well as researchers.

Adjustment is the process of behavior that people and other species utilize to stay in harmony with one another's requirements or between their needs and the challenges in their surroundings. The capacity that aids a person in making adjustments is known as adjustment ability. A person's capacity to adjust is greatly influenced by psychological elements including emotional intelligence, self-concept, personality, locus of control, creativity, and achievement motivation, as well as demographic factors like residence, types of schools, and gender. In an effort to discuss people's capacity for adjustment and the relationship between adjustment and the variables mentioned above, the researchers used this study. Researchers who wish to do their own research on adjustment abilities will find the review useful.

Sickle cell anemia (SCA) is a hereditary hemoglobin disorder caused by a point mutation in the HBB gene on chromosome 11, resulting in substitution of glutamic acid with valine at the sixth position of the β-globin chain and formation of abnormal hemoglobin S (HbS). Under low oxygen tension, HbS polymerizes, leading to rigid, sickle-shaped red blood cells with a shortened lifespan. These abnormal cells cause vaso-occlusion, chronic hemolytic anemia, tissue ischemia, inflammation, and progressive organ damage. Clinically, SCA presents with recurrent pain crises, anemia, infections, and complications affecting multiple organs including the brain, lungs, kidneys, heart, and eyes. The disease follows an autosomal recessive inheritance pattern, with highest severity in individuals homozygous for HbS (HbSS), while compound heterozygous forms such as HbSC and HbS/β-thalassemia show variable severity.
Diagnosis involves prenatal testing (chorionic villus sampling, amniocentesis, and non-invasive prenatal testing), as well as postnatal and adult evaluation through clinical history, complete blood count, peripheral smear, hemoglobin electrophoresis or HPLC, and genetic testing. Management focuses on symptom control and prevention of complications through pain management, hydration, infection prophylaxis, hydroxyurea therapy to increase fetal hemoglobin, and blood transfusions. Curative approaches include hematopoietic stem cell transplantation and emerging gene therapy strategies, though access and affordability remain significant challenges. Increased awareness, early diagnosis, comprehensive medical care, and ongoing research are essential to improving quality of life and reducing morbidity and mortality associated with sickle cell anemia.

Artificial Intelligence (AI) is transforming inclusive education by providing innovative tools and personalized learning opportunities for learners with hearing impairment. AI-powered technologies such as automatic speech recognition, real-time captioning, sign language recognition systems, speech-to-text applications, intelligent tutoring systems, and adaptive learning platforms help bridge communication gaps and enhance classroom participation. These tools enable learners with hearing impairment to access educational content more effectively, improve language and literacy skills, and engage actively in collaborative learning environments. AI-based assistive technologies support teachers in designing individualized instructional strategies, monitoring learner progress, and providing immediate feedback. Furthermore, AI facilitates accessibility through multilingual captioning, visual learning aids, and customized learning resources that cater to diverse educational needs. The integration of AI in inclusive classrooms promotes equity, participation, and academic achievement while reducing barriers to communication and information access. Despite challenges related to technological infrastructure, teacher training, and ethical considerations, AI holds significant potential for creating more accessible and learner-centered educational environments. This paper highlights the role of AI tools in supporting the educational inclusion of learners with hearing impairment and emphasizes the need for effective implementation strategies to maximize their benefits in mainstream and special education settings.

This study focuses on the formulation and evaluation of Terbinafine tablets, an antifungal drug used to treat fungal infections. The tablets were prepared using the wet granulation method with suitable excipients to ensure proper binding, disintegration, and stability. Various equipment such as a blender, dryer, and compression machine was used during manufacturing. The prepared tablets were evaluated through physical, chemical, and pharmaceutical tests including weight variation, hardness, friability, disintegration, and dissolution. Stability studies and proper packaging were also carried out to ensure product quality and safety. The results showed that the formulated tablets met standard requirements, indicating they are effective, stable, and suitable for therapeutic use.

Violence against women is one of the most serious social and human rights issues in contemporary society. In India, women continue to experience various forms of violence, including domestic violence, sexual assault, dowry-related abuse, harassment, and other gender-based crimes. From a sociological perspective, violence against women is not merely an individual problem but a structural issue rooted in gender inequality, patriarchal social relations, and discriminatory cultural practices. Therefore, understanding the effectiveness of legal responses requires an examination of both social conditions and legal frameworks.The objectives of this study were to examine the nature and prevalence of violence against women in India, identify the social, cultural, and institutional factors contributing to violence, analyze the legal provisions available for addressing violence against women, assess the challenges in the implementation of laws and victim protection, and suggest measures for strengthening legal responses and promoting gender equality.This study is based entirely on secondary data analysis. Data were collected from books, peer-reviewed journal articles, government reports, legal documents, policy papers, and publications from organizations such as the National Crime Records Bureau (NCRB), the World Health Organization (WHO), and the United Nations (UN). The collected information was analyzed using a qualitative and descriptive approach.The findings reveal that violence against women remains widespread in India, with domestic violence being the most frequently reported form. Patriarchal norms, economic dependency, dowry practices, social stigma, and institutional weaknesses contribute significantly to the persistence of violence. Although India has established a comprehensive legal framework for protecting women, challenges such as underreporting, judicial delays, low conviction rates, and inadequate victim support limit its effectiveness.The study concludes that legal provisions alone are insufficient to address violence against women. Effective prevention requires stronger implementation of laws, improved institutional support, greater legal awareness, women's empowerment, and broader social transformation aimed at achieving gender equality.

Growing environmental awareness and the need to replace petroleum-based materials have intensified researchintorenewable reinforcementfibersforcompositeapplications. In thiscontext, Prosopisjuliflora,a fast-growing and widely distributed invasive plant species, has emerged as a potential source of lignocellulosic fiber. This review consolidates recent advancements in fiber extraction methods, chemical modification strategies, structural characterization, and engineering applications of Prosopis juliflora fiber-based composites. Special attention is given to alkali treatment, hybridization approaches, and theirinfluence on interfacial adhesion, mechanical strength, thermal stability, dielectric performance, and moisture behavior. The effect of geographical variability on fiber properties is also analyzed. Additionally, comparisons with commonly used natural fibers highlight its relative advantages and limitations. Finally, current challenges and future research directions are discussed to support broader industrial utilization ofthis renewable material.

Benzocaine is an ester-type local anesthetic widely used for topical pain relief because of its rapid action and low systemic toxicity. The present work focuses on the formulation and evaluation of benzocaine gel intended for local anesthetic activity on the skin and mucous membranes. The study includes the classification, mechanism of action, pharmacokinetics, therapeutic uses, adverse effects, and regulatory requirements of benzocaine. Preformulation studies such as organoleptic properties, solubility, melting point, partition coefficient, pH, pKa, hygroscopicity, micromeritic properties, compatibility studies, and stability studies were carried out to determine the suitability of the drug for gel formulation. Benzocaine gel was prepared using Carbopol 940 as the gelling agent along with suitable solvents, preservatives, and neutralizing agents. The prepared gel was evaluated for various parameters including appearance, pH, viscosity, spreadability, extrudability, homogeneity, washability, swelling index, drug content, in vitro diffusion studies, skin irritation test, and stability studies. The formulation showed satisfactory physicochemical properties, good spreadability, acceptable stability, and effective drug release characteristics. Thus, benzocaine gel can be considered an effective topical anesthetic preparation for temporary relief of pain and irritation.

Urolithiasis, commonly known as kidney stone disease, is a prevalent urinary disorder characterized by the formation of calculi in the urinary tract due to supersaturationofurinewithstone-formingconstituentssuchascalciumoxalate, uric acid, and phosphate. Conventional therapies, including surgical and lithotripsy methods, are often associated with side effects, recurrence, and high costs, creating a need for safer and more effective herbal alternatives.

The present research project aims to formulate and evaluate a herbal antiurolithiaticsyrupusingplantextractstraditionallyknownfortheirlithotriptic, diuretic, and antioxidant properties. Selected herbal ingredients such as Bryophyllum pinnatum were incorporated into a palatable syrup base. The formulation was subjected to various physicochemical evaluations, including pH, viscosity, specific gravity, stability, and organoleptic properties.

Further, in vitro antiurolithiatic activity was assessed using calcium oxalate crystal inhibition and dissolution models to evaluate the formulation’s efficacy in preventing crystal nucleation and aggregation. The results demonstrated significantinhibitionofcalciumoxalatecrystallizationcomparedwiththestandard drug (cystone), indicating potential antiurolithiatic activity.
The study concludes that the developed polyherbal syrup possesses promising antiurolithiatic potential, offering a natural, safe, and effective alternative to existing synthetic therapies. Future work will focus on in vivo evaluation and clinicalvalidationtoconfirmtherapeuticefficacyandsafetyinhumansubjects

The Visual Arts curriculum in Nigerian secondary schools is fundamentally designed to foster creativity, cultural preservation, critical thinking, and vocational skills essential for participation in the nation's burgeoning creative economy. However, the effective implementation of this curriculum depends critically on the availability and functional use of specialized equipment and dedicated art studio spaces. This comprehensive review article synthesizes empirical studies, policy documents, examination reports, and theoretical literature published between 2000 and 2025 to critically examine the state of infrastructural and material resources for Visual Arts education across Nigerian secondary schools. The review reveals a persistent and systemic crisis: the overwhelming majority of public secondary schools lack purpose-built art studios, while essential equipment ranging from basic drawing boards to specialized kilns and printing presses remain either absent, non-functional, or severely inadequate. Research consistently demonstrates that this scarcity forces teachers to abandon practical, studio-based pedagogy in favor of theoretical, examination-driven instruction that fundamentally contradicts the nature of artistic learning. The article systematically analyzes the historical and policy context, quantifies the availability gap across equipment categories, examines actual patterns of use and teacher improvisation strategies, identifies multi-layered barriers including funding deficits, policy implementation failures, teacher capacity gaps, and maintenance challenges, and documents cascading consequences including distorted pedagogy, poor examination performance, declining student enrollment, cultural skill erosion, and disconnect from creative industry demands. The review presents exceptional cases of successful studio provision through targeted interventions, demonstrating that solutions are feasible. The article concludes with a multi-stakeholder intervention framework incorporating policy reforms, low-cost studio models appropriate for the Nigerian context, teacher retooling strategies, public-private partnerships, and community engagement models.

Digital manufacturing has emerged as one of the most transformative developments in modern industrial systems. The integration of advanced digital technologies with conventional manufacturing processes has significantly improved productivity, product quality, flexibility, automation, and sustainability.Technologies such as Artificial Intelligence (AI), Internet of Things (IoT), Big Data Analytics, Cyber-Physical Systems (CPS), Cloud Computing, Additive Manufacturing, Robotics, Blockchain, Digital Twins, and Smart Sensors are reshaping industrial production systems into intelligent and autonomous environments.

Digital manufacturing enables real-time monitoring, predictive analysis, efficient resource utilization, and enhanced decision-making through interconnected digital systems. Industries including automotive, aerospace, healthcare, electronics, construction, energy, and consumer goods are increasingly adopting digital manufacturing approaches to improve competitiveness and operational efficiency.

This review paper presents a detailed study of digital manufacturing, including its historical evolution, architecture, enabling technologies, applications, advantages, limitations, sustainability contributions, industrial challenges, and future trends. The paper also discusses Industry 4.0 concepts and the transition toward smart factories. Furthermore, recent advancements and research opportunities in digital manufacturing are critically reviewed.

Efficient hospital discharge management is essential for improving patient flow, enhancing hospital efficiency, and ensuring patient satisfaction. Delays in discharge procedures, bed allocation, and insurance approvals can create operational challenges for hospitals while also affecting patients economically and psychologically. The present study examines the impact of bed availability and insurance-related procedures on hospital discharge management. The study focuses on understanding whether these factors contribute to financial burden, anxiety, stress, and overall patient experience during discharge. Primary data were collected from 400 respondents using a structured questionnaire. Statistical techniques such as descriptive statistics and one-sample t-tests were used to analyze the data. The findings indicate that the majority of respondents perceived bed availability positively and agreed that it did not significantly increase medical expenses or create additional costs due to delays. Most respondents also reported limited anxiety and minimal impact on their mental well-being while waiting for bed availability during hospitalization.
Similarly, the study found that health insurance procedures were generally viewed favorably by patients. Respondents agreed that insurance coverage did not significantly delay the discharge process or create additional stress. Many patients felt reassured knowing that their insurance would cover most hospital expenses, thereby reducing financial uncertainty during discharge. The statistical analysis revealed significant t-values and positive mean differences for all statements, indicating strong agreement among respondents.The study concludes that effective discharge coordination, proper bed management, and efficient insurance procedures contribute significantly to smooth healthcare operations and positive patient experiences. Hospitals should continue improving communication systems, discharge planning, and insurance processing mechanisms to further reduce delays, minimize patient stress, and enhance the quality of healthcare services.

Marine waste and ocean plastic pollution have emerged as severe environmental challenges, threatening marine ecosystems, biodiversity, and coastal livelihoods. The rapid increase in single-use plastics, inadequate waste management systems, and limited recycling infrastructure have significantly contributed to the accumulation of plastic debris in marine environments. In this context, the circular economy offers a sustainable framework by emphasising waste reduction, reuse, recycling, and resource recovery. The present study examines circular economy approaches for marine waste and ocean plastic management by identifying major pollution sources, evaluating existing circular practices, assessing stakeholder roles, and proposing effective plastic reduction strategies. The study adopts a mixed-methodsresearchdesign,utilisingbothprimaryandsecondarydata.Primarydatawere collected from 200 respondents representing coastal communities, local authorities, NGOs, and recycling and plastic industries through structured questionnaires and interviews. Statisticaltools suchaspercentageanalysis,meanscores,ANOVA, correlation, andregression were applied for data analysis. The findings reveal that single-use plastics and fishing gear waste are the primary contributors to marine pollution. Circular economy practices such as recycling, eco-design, and ExtendedProducer Responsibilityshowhigh effectiveness inmarine waste management. The study also establishes a strong positive relationship between stakeholder participationandsustainable plasticmanagement.Overall, theresearchconcludes that integrating circular economy principles with coordinated stakeholder involvement provides a practical and long-term solution for reducing marine plastic pollution and promoting sustainable ocean ecosystems.

This study investigates the integration of Green Artificial Intelligence (AI) with sustainable agricultural and forest management to address important global issues such as climate change, food insecurity, and ecosystem degradation. Green AI, with its emphasis on environmental sustainability and energy efficiency, provides a disruptive way to increasing productivity while minimizing environmental damage.The study looks into how Green AI technologies—such as lightweight algorithms, sensor networks, and data analytics—can be used in precision agriculture and smart forestry. These technologies enable better resource allocation, pest and disease management, irrigation efficiency, and crop production forecasts. In forestry, AI is used to predict fires, evaluate canopy health, and organize conservation efforts.A primary goal is to design scalable, locally adaptive methods that address the unique needs of various agro-ecological systems. Simulated datasets and scenario analysis are used to show the probable effects of AI implementation in various environmental and operational scenarios.In addition, the study evaluates the socioeconomic aspects of AI adoption, with an emphasis on pricing, accessibility, and rural community engagement. The goal is to guarantee that technological innovation does not exacerbate equity disparities, but rather empowers vulnerable groups.Finally, this study argues that Green AI can serve as a catalyst for a more sustainable future, encouraging resilient agricultural systems and forest ecosystems that thrive in harmony with nature while also contributing to long-term food security and environmental responsibility.

This paper explores the transformative role of the National Education Policy (NEP) in reshaping schooleducation within India. The National Education Policy (NEP) 2020 represents a landmark reform in the Indian education system, aiming to transform school education through a holistic, multidisciplinary, flexible, and learner-centered approach. The policy seeks to address long-standing challenges such as rote learning, unequal access to quality education, inadequate teacher preparation, and limited integration of technology. By restructuring the school curriculum, emphasizing foundational literacy and numeracy, promoting multilingualism, and strengthening teacher education, NEP 2020 aspires to prepare students for the demands of the twenty-first century. This paper examines the transformative role of NEP 2020 in reshaping school education in India. It analyzes the key provisions of the policy, explores its potential impact on teaching-learning processes, and discusses implementation challenges. The study concludes that while NEP 2020 provides a comprehensive framework for educational transformation, its success depends on effective implementation, adequate resource allocation, teacher capacity building, and continuous monitoring.

The study examined the interrelationship between governance, public engagement, and sustainable development in Zambia, focusing on the roles of the state and citizens in achieving the Sustainable Development Goals (SDGs). It assessed how governance structures, policy frameworks, institutional capacity, transparency, and accountability mechanisms influenced SDG implementation at both national and local levels, alongside the contribution of citizen participation to development outcomes. A descriptive mixed-methods research design was employed, using a sample of 400 participants selected from government institutions, local authorities, civil society organizations, and community members. The study was conducted in selected provinces of Zambia to ensure representation of diverse socio-economic and governance contexts. Data were collected through questionnaires and interviews and analyzed using descriptive statistics and thematic analysis. The findings revealed that Zambia had made progress in aligning national development plans with the SDGs; however, gaps remained in policy coordination, institutional capacity, resource mobilization, and meaningful public engagement. Weak inter-sectoral coordination, duplication of efforts, and inconsistent enforcement of accountability mechanisms limited effective implementation. Although citizen participation, civic awareness, and civil society involvement were present, these remained constrained by limited access to information and weak participation structures. Decentralized governance improved local responsiveness but was hindered by financial and technical capacity challenges. The study concluded that sustainable development in Zambia depended on strengthening democratic governance, improving transparency and accountability, enhancing institutional coordination, and promoting active citizen participation. It further established that SDG progress required not only state-led interventions but also empowered citizens actively involved in decision-making, monitoring, and accountability processes. It was therefore recommended that the Government of Zambia institutionalize a legally backed, inclusive, and well-resourced citizen engagement framework at both national and local levels to ensure systematic participation of citizens and civil society in planning, budgeting, implementation, and monitoring of SDG-related programmes.

This research contributes to the ongoing discourse on the effectiveness of agricultural innovations and production in addressing food security and rural development challenges in Nigeria.Rice production plays a vital role in the agricultural landscape of Katsina State, Nigeria, with smallholder farmers forming the backbone of this sector. This study focuses on evaluating the impact of the FARO 58 Rice Production Package, a comprehensive set of agricultural technologies and practices designed to enhance rice cultivation. The research aims to assess the awareness and adoption of FARO 58 rice production package among smallholder farmers in Katsina State, as well as to identify the challenges faced during its implementation. The methodology involves a combination of quantitative and qualitative approaches. A structured questionnaire was used to collect data on the adoption rates of FARO 58 technologies among smallholder farmers, their socio-economic characteristics, and the perceived benefits and challenges associated with its implementation. Additionally, in-depth interviews and focus group discussions are employed to gather qualitative investigation into the farmers' experiences and perspectives.

This study explores the connection between financial management practices and firm performance in emerging markets. Using an extensive review of empirical evidence from different industries and regions, the research analyzes how financial management activitiessuch as capital budgeting, financing choices, working capital management, and risk managementinfluence the overall performance of firms operating in developing economies. By integrating insights from previous studies with fresh empirical investigation, the study seeks to identify the major factors and underlying mechanisms through which effective financial management practices improve organizational performance in dynamic and uncertain emerging market conditions. The findings provide valuable implications for managers, investors, policymakers, and other stakeholders by highlighting the role of efficient financial management in promoting sustainable growth, profitability, and long-term resilience within emerging market environments.

Electrical Power System requires protection against surge voltage for which surge arresters areconnected. Earlier gapedtype surge arresters wereused in thesystemand further gapless surge arresters were invented. Since then utilities use gapless surge arrester. Gapless surge arresters are consist of Zinc oxide elements. Here the concept of smart surge arresters has been discussed. What are the various feature must the Smart Surge Arrester (SSA) should have has been discussed in this paper.These feature can be implemented in smaller ratings of SSA and it can be implemented for the higher ratings.

Autonomous unmanned aerial vehicles (UAVs) deployed in dynamic and adversarial environments require real-time deterministic control and adaptive runtime security. Current FPGA-based UAV flight control systems employ only static security mechanisms, which are unable to cope with the increasingly complex cyber-physical attack vectors, such as malicious bitstream injection, firmware attacks, and timing attacks. Dynamic Partial Reconfiguration (DPR) allows regions of the FPGA to be partially reconfigured at runtime without impacting the critical flight-control functions, creating the ability for adaptive and mission-aware security. DPR also adds new attack surfaces and timing uncertainties, which could impact flight stability, however.
This paper proposes a secure DPR framework for real-time autonomous UAV flight control with the implementation on a Xilinx PYNQ-Z2 FPGA-SoC platform. The proposed architecture combines the embedded architecture with a control-aware design, the hardware root of trust, PUF-based authentication, encrypted partial bitstreams, AXI firewall-based isolation, and secure runtime reconfiguration management. A timing model is created to study the effects of DPR latency and resource contention on the determinism and jitter performance of flight control.
The experimental results indicate that the secure reconfiguration has an average latency of 12.6 ms, a maximum control jitter of less than 8 µs, and the 500 Hz UAV control operation is stable throughout the runtime reconfiguration. The proposed framework maintains flight stability and reduces power transient overhead by less than 12% and provides better resilience against runtime hardware attacks. The results demonstrate that secure DPR is able to offer adaptive runtime security for autonomous UAV systems without compromising real-time control limitations.

The hotel industry has undergone major changes in hiring practices because of the rise of the gig economy. This research looks at how employment patterns in hotels are evolving in Western Rajasthan, using secondary data sources. It analyses the increase in gig jobs, their effects on hiring practices, and the benefits and challenges that come with this change.The research relied solely on secondary data from government reports, academic publications, and industry studies. The results showed that hotels are increasingly using flexible staffing to handle seasonal demand, cut costs, and improve efficiency. Gig jobs are commonly found in areas like housekeeping, food services, event management, and support roles. While this approach provides flexibility and cost savings, it also poses challenges for workforce stability and service quality.In conclusion, the gig economy significantly influences employment practices and trends in the hotel industry of Western Rajasthan. The study suggests adopting hybrid employment models and better workforce management strategies to promote sustainable growth.

This paper introduces a novel Heptadecagonal Fuzzy Number (HDFN)-based approach for risk analysis and forecasting under uncertain environments. The proposed model employs seventeen-point membership representation to describe risk factors with greater precision than conventional triangular, trapezoidal, pentagonal, and decagonal fuzzy numbers. Arithmetic operations, ranking procedures, and a forecasting methodology based on HDFNs are developed. A numerical case study demonstrates the effectiveness of the proposed approach in evaluating project risk and future performance. Results indicate that the HDFN model provides enhanced flexibility, improved uncertainty representation, and more reliable forecasting outcomes.

This study describes the formulation and evaluation of Tolnaftate cream, a topical antifungal used to treat skin infections. The cream was prepared as an oil-in-water emulsion using suitable ingredients to ensure stability and effectiveness. It was evaluated for physical, chemical, and biological properties such as texture, spreadability, pH, and drug content. The results showed that the cream had good consistency, proper pH for skin, and effective drug release without causing irritation. Proper packaging, storage, and regulatory guidelines were also followed. Overall, the formulation was found to be safe, stable, and suitable for treating fungal skin infections.

The present study examined the effect of the Normal and Blended Teaching Process, learning style, and gender on the professional efficacy of student teachers. The study was conducted on 120 B.Ed. trainees from two colleges of education in the Bhopal Division of Madhya Pradesh using a quasi-experimental pre-test–post-test control group design. The control group was taught through the Normal Teaching Process, whereas the experimental group was exposed to the Blended Teaching Process integrating face-to-face instruction with digital learning resources. Professional efficacy was measured using the Teachers’ Sense of Efficacy Scale, and learning styles were assessed through Kolb’s Learning Style Inventory. ANCOVA was employed for data analysis by taking pre-test scores as covariates. The findings revealed that blended teaching provides a more effective and inclusive learning environment for enhancing professional efficacy among student teachers. The findings support the objectives of the National Education Policy 2020 emphasizing digital and technology-integrated teacher education.

The present study examined the relationship between leadership behaviour of school heads and school climate in elementary schools of Madhya Pradesh. Effective school leadership is widely regarded as a key factor in shaping the academic, organizational, and interpersonal environment of schools. A descriptive survey method with a correlational design was adopted. The study was conducted in five districts of Madhya Pradesh, namely Bhopal, Indore, Gwalior, Jabalpur, and Narmadapuram. The sample consisted of 100 elementary schools, 100 school heads, and 500 teachers selected through stratified random sampling. The Leadership Behaviour Scale developed by Dr. Asha Hingar (2005) and a researcher-developed School Climate Scale were used to collect the data. The findings revealed that a notable proportion of school heads demonstrated high leadership behaviour, while a similar proportion fell in the moderate category, and a smaller yet considerable group was in the low category. With regard to school climate, about one-third of schools were perceived to have a high level of climate, roughly half reflected a moderate level, and a smaller segment showed a low level. The study also found a significant positive relationship between leadership behaviour of school heads and school climate, suggesting that more effective leadership tends to be associated with a more positive and supportive school environment. The study concludes that school heads play a decisive role in creating a supportive, collaborative, and effective school environment. Strengthening leadership practices may therefore contribute meaningfully to improving school climate and overall school functioning.

The study looked into the clash between individual teaching philosophies and institutional demands of Indian school instructors. A cross-sectional survey design was employed. Data were collected from 52 school teachers (83% female; M = 14.26 years, SD = 6.93) through convenience sampling using a researcher-constructed 25-item, five-point Likert-scale instrument with four subscales, i.e. Personal Teaching Beliefs (10 items), Institutional Expectations (8 items), Belief–Expectation Conflict (5 items) and Impact on Teaching and Satisfaction (2 items). Statistical analysis Descriptive statistics, Pearson correlation coefficients, and independent-samples t-tests were used. Results indicated teachers' endorsement of constructivist pedagogical principles at near-ceiling levels (M=4.85, SD=0.38 on a 5-point scale), perception of moderate-to-high institutional demands (M=3.94, SD=0.87), and perception of moderate professional conflict (M=3.27, SD=1.05). Institutional expectations were highly and positively related to experienced conflict (r = .66, p < .001). Conflict was also strongly related to negative influence on teaching effectiveness (r = .68, p < .001). There was no substantial moderation of conflict scores by either gender or years of experience. The findings broaden the scope of role conflict theory and self-determination theory to the Indian educational setting and emphasize the importance of school-level policies that respect teacher autonomy within the accountability framework.

This study explores how Artificial Intelligence (AI) influences the financial performance of selected companies in the Indian IT sector. In recent years,AI has become a key technological enabler that improves operational efficiency, supports better managerial decisions, and promotes innovation in business processes. To assess its impact, major financial indicators such as profitability, revenue growth, return on assets, and market performance were analyzed. The study is based on secondary data collected from company reports and financial databases for a defined period, and statistical tools like correlation and regression were employed for analysis. The results reveal that AI adoption is positively associated with financial performance; however, the relationship is not statistically significant for all firms. This indicates that the financial benefits of AI depend largely on factors such as the firm’s investment strength, technological preparedness, and strategic use of digital resources. The study therefore suggests that while AI holds strong potential to enhance financial performance over time, its short-term effects differ across companies.

Artificial Intelligence (AI) is rapidly transforming healthcare systems by enabling advanced data-driven approaches in biomedical data science. The exponential growth of heterogeneous healthcare data, including electronic health records, medical imaging, genomic sequences, and wearable sensor data, has created a strong demand for intelligent computational methods capable of extracting meaningful clinical insights. This review explores the conceptual foundations, methodologies, applications, and emerging trends of AI in healthcare, with a specific focus on its integration within biomedical data science. It examines key machine learning and deep learning techniques, predictive analytics models, and explainable AI frameworks that support clinical decision-making, disease diagnosis, and personalized treatment strategies. Furthermore, the study highlights the role of AI in enhancing medical imaging, precision medicine, healthcare operations, and predictive healthcare systems. The integration of AI with emerging technologies such as blockchain, federated learning, and cloud computing is also discussed as a critical enabler of secure, scalable, and efficient healthcare ecosystems. Despite significant advancements, challenges related to data quality, model interpretability, ethical concerns, and clinical adoption barriers remain persistent. The paper concludes that the future of healthcare will be defined by human–AI collaboration and explainable intelligent systems, ensuring improved accuracy, efficiency, and trust in biomedical decision-making.

The rapid growth of digital media has transformed the way people access, consume, and share news. Generation Z, generally defined as individuals born between 1997 and 2012, has grown up in a highly connected digital environment where smartphones, social media platforms, and online news portals serve as primary sources of information. This study examines the news consumption behavior of Generation Z in the digital era, focusing on their preferred news sources, frequency of news consumption, trust in digital news platforms, and engagement with news content. A survey was conducted among 300 respondents aged 18–27 years from various educational institutions. The findings reveal that social media platforms are the dominant source of news consumption among Generation Z, while traditional media such as newspapers and television are used less frequently. The study also highlights concerns regarding misinformation, declining trust in online news, and the increasing influence of algorithms on news exposure. The research contributes to understanding contemporary news consumption patterns and provides recommendations for media organizations seeking to engage younger audiences effectively.

Inchronicanemia, thereduced oxygen-carryingcapacityofhemoglobinis typicallyoffset byanelevated cardiac output. Patients with chronic severe anemia but no other cardiovascular disease rarely develop symptoms of congestive heart failure, a condition known as non-cardiac circulatory congestion. Severe anemia can induce a hyperdynamic circulation and chronic volume overload, potentially resulting in significant alterations to echocardiographic parameters, especially global left ventricular strain.
This case describes a 16-year-old male patient with severe anemia who developed congestive heart failure, particularly pulmonary congestion. A comprehensive clinical examination, along with pathological, radiological,ECG,andtwo-dimensional echocardiographic investigations, was performed, and their distinctive features are presented herein.
This study focuses on severe anemia, high output heart failure, and the assessment of left ventricular dysfunction using 2D strain imaging and global longitudinal strain via echocardiography.

Introduction: Aripiprazole, an atypical antipsychotic acting as a partial agonist at dopamine D2/D3 receptors, is generally recognized for its favorable extrapyramidal side-effect profile. However, cases of movement disorders induced by this medication, including tics, have been exceptionally reported in the literature.
Case Presentation: We report the case of a 30-year-old woman with schizophrenia who developed cervical motor tics after one year of treatment with aripiprazole monotherapy at a dose of 20 mg/day. She had no personal or family history of movement disorders. Switching treatment to olanzapine 10 mg/day resulted in partial improvement of the tics after one month of follow-up.
Discussion: This case illustrates a paradoxical adverse effect of aripiprazole that may be explained by its mechanism of partial dopaminergic agonism. Under certain pharmacodynamic conditions, aripiprazole may exert a net agonistic effect on the nigrostriatal pathway, thereby facilitating the emergence of tics.
Conclusion: Clinicians should be aware of this potential risk, particularly at higher doses and in patients with prolonged prior exposure to antipsychotic medications. Regular neurological monitoring during aripiprazole treatment is recommended.

The study sought to investigate the Perceived Influence of Substances Abuse on class participation of Senior Secondary School Students in Nasarawa State of Nigeria. The study was guided by two objectives,two research questions to guide the study and one hypothesis was tested at 0.05 level of significance. Descriptive survey design with the population of one hundred and forty eight thousand, four hundred and ninety seven (148,497) persons and a sample size of seven hundred and forty four (744) participants were used for the study. The study employed questionnaire as an instrument for data collection and the data were analyzed using chi-square test statistics of mean and standard deviation. Findings showed that substance abuse has significant influence on class participation among senior secondary school students in Nasarawa State, these negative influence on participation indices such as note taking, study time. In conclusion, it is therefore, clearly a need for specific intervention programs for substance abusers among senior secondary school students in Nasarawa State to discourage substance abuse and improve on their academic performance. It is recommended that there should a need for constant awareness programs on the dangers of substance abuse on academic performance of senior secondary school students if required action/education would be taken or given on time the students of Senior Secondary Schools may perform well academically without taking any substances.

वर्तमान समय में पर्यावरणीय समस्याएँ वैश्विक स्तर पर गंभीर चुनौती के रूप में उभरकर सामने आई हैं। भारत जैसे विकासशील देश में औद्योगिकीकरण, नगरीकरण, जनसंख्या वृद्धि तथा प्राकृतिक संसाधनों के अत्यधिक दोहन के कारण पर्यावरणीय असंतुलन निरंतर बढ़ता जा रहा है। वायु प्रदूषण, जल प्रदूषण, ध्वनि प्रदूषण, वनों की कटाई, जलवायु परिवर्तन तथा प्लास्टिक प्रदूषण जैसी समस्याएँ मानव जीवन को प्रत्यक्ष एवं अप्रत्यक्ष रूप से प्रभावित कर रही हैं। इन परिस्थितियों में पर्यावरणीय जागरूकता एवं सामाजिक उत्तरदायित्व की भूमिका अत्यंत महत्वपूर्ण हो जाती है।प्रस्तुत शोध पत्र का उद्देश्य समकालीन भारतीय समाज में पर्यावरणीय जागरूकता तथा सामाजिक उत्तरदायित्व की आवश्यकता, महत्व एवं प्रभाव का अध्ययन करना है। इस अध्ययन में यह स्पष्ट किया गया है कि पर्यावरण संरक्षण केवल सरकार का कार्य नहीं है, बल्कि प्रत्येक नागरिक, संस्था एवं उद्योग की सामूहिक जिम्मेदारी है। शिक्षा, मीडिया, सामाजिक संगठनों तथा सरकारी योजनाओं के माध्यम से समाज में पर्यावरणीय चेतना का विकास किया जा रहा है।अध्ययन में यह भी बताया गया है कि सामाजिक उत्तरदायित्व की भावना समाज को पर्यावरण संरक्षण के प्रति सक्रिय बनाती है। नागरिकों द्वारा वृक्षारोपण, जल संरक्षण, स्वच्छता अभियान तथा प्लास्टिक मुक्त अभियान जैसे कार्य पर्यावरणीय संतुलन बनाए रखने में सहायक सिद्ध हो रहे हैं। प्रस्तुत शोध पत्र वर्णनात्मक एवं विश्लेषणात्मक पद्धति पर आधारित है तथा द्वितीयक स्रोतों के माध्यम से तैयार किया गया है। अंततः यह निष्कर्ष निकाला गया है कि सतत विकास एवं स्वस्थ समाज के निर्माण के लिए पर्यावरणीय जागरूकता और सामाजिक उत्तरदायित्व का समन्वय आवश्यक है।

Remdesivir (RDV), a broad-spectrum antiviral initially developed for Ebola, became a pivotal therapeutic agent during the global COVID-19 pandemic. As a direct-acting nucleotide prodrug administered intravenously, its quality, purity, and concentration in biological systems are of paramount importance. This review provides an exhaustive examination of the chromatographic methodologies employed for the estimation of Remdesivir. We explore a wide spectrum of techniques, ranging from routine Reverse-Phase High-Performance Liquid Chromatography (RP-HPLC) used in quality control, to advanced Liquid Chromatography-Tandem Mass Spectrometry (LC-MS/MS) for pharmacokinetic bioanalysis, and eco-friendly High-Performance Thin Layer Chromatography (HPTLC). Furthermore, this review delves into the specific separation of chiral isomers, stability-indicating assays for degradation products, and the shift towards Ultra-Performance Liquid Chromatography (UPLC) for high-throughput analysis.

Work ethic among Ghanaian employees has been extensively measured but rarely improved through systematically implemented and evaluated interventions. Existing research has documented sector-specific challengesto punctuality and fluidity in manufacturing, hierarchical deference in finance, and work-life integration in technology,yet no study has designed, implemented, and evaluated interventions tailored to these sector-specific motivational profiles. This mixed-methods intervention study investigates the effectiveness of three workplace interventions:recognition programmes, mentorship arrangements, and flexible schedulingin enhancing work ethic across three Ghanaian sectors: manufacturing (punctuality-focused intervention), banking and finance (communication-focused intervention), and technology and telecommunications (work-life integration-focused intervention). Using a convergent parallel mixed-methods design, the study employed pre- and post-intervention surveys (n = 270 employees; 90 per sector) and 18 focus group discussions (6 per sector, n = 108 participants) over a six-month intervention period. Quantitative analysis using repeated-measures ANOVA and paired t-tests revealed significant improvements across all sectors: manufacturing work ethic scores increased by 28% (d = 1.24), banking work ethic scores increased by 34% (d = 1.56), and technology work ethic scores increased by 31% (d = 1.42). Recognition programmes were most effective in manufacturing (punctuality improved from 54% to 82% on-time arrival), mentorship was most effective in banking (direct communication comfort increased from 38% to 71%), and flexible scheduling was most effective in technology (work-life satisfaction increased from 42% to 79%). Qualitative findings from focus groups revealed four mechanisms of intervention effectiveness: (1) public recognition addresses the collectivist need for community validation, (2) cross-generational mentorship navigates hierarchical respect while enabling communication, (3) flexible scheduling acknowledges fluid temporality as cultural reality rather than deficit, and (4) sector-specific tailoring respects rather than erodes local work practices. Participants reported that generic "one-size-fits-all" motivational programmes had failed previously; sector-specific interventions succeeded because they worked with Ghanaian cultural logics rather than against them. The findings inform human resource practice, policy development, and intervention design for work ethic enhancement across Ghanaian and comparable African contexts.

The concept of this paper explores the information on economic importance of the intertemporal Consumption choice model in Kenya within the framework of advanced macroeconomic theory. The intertemporal consumption choice model originally pioneered by Irving Fisher is a cornerstone of modern world-wide economics. It moves past simple models that only look at current income by showing how rational individuals balance spending and saving over time. This helps an individual to assess and know the trend on consumption versus income. By framing consumption around lifetime resources rather than current earnings, the model provides the foundational math and logic for modern macroeconomics, public policy design and financial market analysis. Moving beyond the static frameworks of early Keynesian analysis, the intertemporal model isolates how rational agents allocate resources across time by balancing current utility against future consumption. The efficient consumption market trends are essential for economic growth and development. Prior to this model, John Maynard Keynes posited that current income dictates current consumption. The intertemporal model revolutionized this by demonstrating that consumers use credit markets to optimize utility across their lifespan and economic conditions. This study focuses on the four models which are Deterministic Models, Stochastic Models or Uncertainty and Risk, Frictional Models and Behavioural Models. The emerging question is whether or not the introduction of consumption models has contributed to perfect market performance in Kenya. This study therefore sought to provide an empirical investigation of the impact of economic importance of the intertemporal Consumption choice model on an individual consumption performance in Kenya. Specifically, the study investigated whether the models has improved market performance through increased consumption rate, increased availability of income and increased resources. Both the explanatory and descriptive research designs were used.
The paper concludes with a discussion on the broader implications of intertemporal Consumption choice model for policy design, especially in developing economies and digital markets. By synthesizing foundational theories with empirical applications, this paper contributes to a deeper understanding of how intertemporal Consumption choice model shapes macroeconomic behavior and market efficiency.

Air pollution has become a significant environmental and public health concern [10] due to rapid industrialization, urban expansion, and the increasing number of vehicles. Continuous exposure to polluted air can lead to severe health issues [10], including respiratory diseases, cardiovascular disorders [10], and reduced quality of life. In this paper, a cost-effective and efficient Air Quality Monitoring System based on Internet of Things (IoT) technology [2] is presented. The proposed system employs MQ-series gas sensors [3] to detect harmful gases and a DHT11 sensor [4] to measure temperature and humidity. An ESP32 microcontroller [2][9] is used for data acquisition, processing, and wireless communication [2][9].
The system is capable of continuously monitoring environmental parameters and providing real-time data to users through a display interface or wireless transmission. Additionally, it incorporates an alert mechanism that activates when pollutant levels exceed predefined safety thresholds. The collected data can be further utilized for analysis and decision-making to improve air quality management. Experimental results indicate that the system performs reliably under different environmental conditions and provides accurate and timely information. Due to its low cost, portability, and scalability, the proposed system can be effectively deployed in homes, industries, and public spaces for continuous air quality monitoring and environmental awareness.

Access to education is a fundamental human right that every child needs to access regardless of their physical, emotional, social or financial status. However, children with disabilities in many African countries, including Zambia, often face significant exclusion and marginalization. This study investigated the challenges, and the various interventions aimed at improving the inclusion of children with delayed speech and language in schools. The study utilized a descriptive case study design and mixed-methods research approach, combining both quantitative and qualitative methods. A sample size of 101 participants was drawn which included primary school teachers, school administrators, special education teachers, learners with speech and language delays and parents/guardians. Data collection instruments included structured questionnaires, semi-structured interviews and observation checklists. Quantitative data were analyzed using descriptive statistics such as frequencies and percentages, presented in tables and charts whereas qualitative data were analyzed using study themes. The study found that while the government through the Ministry of Education has put up support measures to alleviate the challenges in accessing Primary Education for Children with delayed speech and language, there was a notable significant barrier to access education at primary level. This was according to the data collected from the respondents which showed a significant seclusion for learners with speech and language delay. The findings further reviewed that about 23% of learners enrolled in urban schools of solwezi district could not be accommodated for longer due to teachers not being trained to teach special needs children, and differentiated methods of lesson delivery which may not benefit the child with delayed speech and language. The study therefore recommended strengthening teacher training in inclusive and special needs education, particularly in speech and language support strategies; increasing the deployment of specialized personnel such as speech therapists; and promoting the use of differentiated and learner-centered instructional approaches.

This research paper investigates the factors that contribute to building public relations in marketing across organizations. The study adopted a descriptive research design using convenience sampling, with data collected from a sample of one hundred twenty respondents through a structured questionnaire. Two primary objectives guided this research: to identify the relationship between respondents' occupation and their familiarity with public relations concepts, and to determine the interrelationships among key public relations activities in a marketing strategy. The findings revealed that the majority of respondents considered public relations very important for marketing strategy, with content creation identified as the most effective public relations strategy. A chi-square test of independence showed a statistically significant relationship between occupation and public relations familiarity, with public relations professionals and students demonstrating higher familiarity than business owners and employees in non-public relations roles. A multiple correlation analysis revealed that all six public relations activities examined—content creation, crisis communication, media relations, reputation management, social media management, and community engagement—were positively and significantly intercorrelated, indicating they function as an integrated set. The strongest interrelationships were observed among content creation, crisis communication, media relations, and reputation management. The study concludes that public relations is not merely a supportive tool but an essential element for building brand reputation, customer engagement, and long-term business growth.

This paper presents a generalized quadrature rule of degree of precision nine, obtained by combining two well-known formulas: the Clenshaw–Curtis five-point rule with Richardson extrapolation and the Lobatto five- point rule, having precisions five and seven, respectively. The proposed rule is developed using a generalized quadrature approach to enhance accuracy while maintaining computational efficiency. Its superiority over the constituent formulas is established through rigorous error analysis. The effectiveness and dominance of the rule are further demonstrated through umerical experiments on a variety of test integrals.

Quality healthcare services are one of the important factors in improving patient satisfaction in primary healthcare facilities. Integrated care and service equity are important components in creating effective, efficient, and patient-oriented healthcare services. This study aimed to determine the relationship between integrated care and service equity on outpatient satisfaction at the Wua-Wua Public Health Center, Kendari City.
This study used a quantitative research design with a cross-sectional study approach. The population consisted of all outpatients visiting the Wua-Wua Public Health Center, Kendari City. The sampling technique used accidental sampling with a total sample of 96 respondents. The research instrument used questionnaires. Data analysis was conducted through univariate and bivariate analysis using the chi-square test with a confidence level of 95% (α = 0.05).
The results showed that there was a significant relationship between integrated care and outpatient satisfaction with a p-value <0.05. In addition, there was a significant relationship between service equity and outpatient satisfaction with a p-value <0.05. Well-coordinated services and fair treatment without discrimination based on patients’ social status can improve patient satisfaction levels.
The conclusion of this study is that integrated care and service equity have a significant relationship with outpatient satisfaction at the Wua-Wua Public Health Center, Kendari City. It is recommended that the public health center continue improving service coordination quality and ensure equal healthcare services for all patients.

The decade between 1937 and 1947 marked the final, decisive phase of British colonial rule in India, culminating in the subcontinent’s independence and its simultaneous vivisection along religious lines. This study examines the intertwined trajectories of electoral politics, constitutional negotiations, and intensifying power struggles that transformed the demand for Pakistan from a bargaining counter into an irreversible political reality. The Congress’s impressive victory in the 1937 elections and the formation of ministries in eight provinces and the Muslim League’s decisive defeat in these elections, followed by its exclusion from power-sharing in Congress-ruled provinces, catalyzed a profound organizational and ideological reorientation under Muhammad Ali Jinnah. By 1940, the League had articulated the Lahore Resolution, reframing Muslim political identity from minority safeguards to the demand for sovereign nationhood. The 1945-46 provincial and central elections, conducted on an expanded but still limited franchise, dramatically validated the League’s claim to represent the majority of Indian Muslims, providing empirical legitimacy to the two-nation theory. The research argues that the collapse of the Cabinet Mission Plan in 1946 represented the last viable opportunity for a united India. Subsequent communal violence, the Interim Government’s paralysis, and the British decision to advance the transfer of power created a momentum toward Partition that proved impossible to arrest. By focusing on the interplay between electoral mandates, constitutional mechanics, and high-political maneuvering, this study offers a nuanced understanding of how democratic experiments under colonial constraints accelerated the subcontinent’s division.

The Partition of India in 1947 remains one of the most traumatic and consequential events of the twentieth century, resulting in unprecedented communal violence, mass migration, and the creation of two sovereign states amid deep civilizational scars. This paper reassesses the question of responsibility for the Partition by moving beyond deterministic narratives- whether the “two-nation theory,” inexorable Hindu-Muslim antagonism, or British “divide and rule”- towards a critical analysis of contingency and leadership failures. The study argues that Partition was not inevitable but emerged from a series of avoidable contingencies and critical miscalculations by Indian political leaders and the departing colonial power. The analysis highlights the pivotal role of leadership failures during the Cabinet Mission Plan (1946), formation of the Interim Government and the final months of British rule. Jawaharlal Nehru’s impulsive rejection of the Cabinet Mission’s grouping formula, Sardar Patel’s growing acceptance of Partition as a “surgical operation,” Mahatma Gandhi’s inconsistent positions, and Muhammad Ali Jinnah’s tactical rigidity all contributed to narrowing political options. Lord Mountbatten’s hasty timeline and flawed boundary decisions further exacerbated the chaos.

Although school leadership has been extensively studied as a critical factor influencing educational effectiveness, institutional development, and learner achievement, much of the existing literature has primarily focused on leadership styles, leadership effectiveness, and organizational outcomes in urban and well-resourced educational settings (Bush, 2022; Tintoré et al., 2022; Fernandes et al., 2023). Studies have consistently demonstrated the positive effects of transformational, distributed, and democratic leadership on school performance, teacher motivation, and student learning outcomes (Brill, 2023; Kilag & Sasan, 2023; Toloy, 2024). However, the experiences of educators transitioning from classroom teaching to formal leadership positions remain relatively underexplored, particularly in rural and geographically isolated communities where access to leadership preparation, professional development opportunities, and mentoring support may be limited (Bush, 2022; Fernandes et al., 2023).

Itraconazole is a broad-spectrum triazole antifungal agent widely used in the treatment of superficial and systemic fungal infections. Due to its poor aqueous solubility and variable bioavailability, formulation development of itraconazole requires detailed pre-formulation and evaluation studies to ensure efficacy, stability, and patient compliance. This review focuses on the pharmaceutical development and regulatory aspects of itraconazole formulations, particularly capsules. The study discusses the classification, mechanism of action, therapeutic uses, adverse effects, contraindications, and available formulations of itraconazole. It also highlights important pre-formulation studies including physicochemical characterization, solubility analysis, melting point determination, partition coefficient, micromeritic properties, compatibility studies, and stability studies. Evaluation of flow properties such as angle of repose, Carr’s index, Hausner’s ratio, bulk density, and tapped density was performed to assess powder behavior during formulation development. The review emphasizes the significance of pre-formulation data in optimizing dosage form design and ensuring quality, safety, and regulatory compliance of itraconazole products.

Background: Molluscum contagiosum is a common, self-limiting viral skin infection primarily affecting children. While benign, it causes significant cosmetic concern and contagion. Conventional treatments like cryotherapy and curettage are often painful and may lead to scarring, prompting a need for gentler, non-invasive alternatives.
Objective: To review the clinical evidence and comparative efficacy of homeopathic interventions for cutaneous molluscum contagiosum.
Methods: A review of randomized controlled trials (RCTs), case series, and narrative reviews was conducted. Key databases, including PubMed and the Cochrane Database of Systematic Reviews, were analyzed for studies comparing homeopathic remedies against placebo or conventional therapies.
Results:
• Clinical Trials: Limited RCT evidence suggests that Calcarea carbonica may be more effective than placebo (RR 5.57), although study sizes remain small.
• Case Series & Reports: Multiple studies report successful resolution of lesions within 2–6 months using individualized remedies such as Thuja occidentalis, Sulphur, Natrum muriaticum, and Bromium.
• Comparative Efficacy: While conventional physical ablation (curettage) shows high efficacy, it carries a higher risk of side effects compared to the painless, holistic approach of homeopathy.
• Individualization: Homeopathic management emphasizes "symptom totality," often targeting the patient's constitution and miasmatic background rather than just the local lesion.

A fundamental component of traditional yoga is pranayama, the yogic discipline of breath management. Breathing Management is a topic of increasing scientific interest The physiological mechanics, psychological and therapeutic impacts, and research approaches related to pranayama are examined in this work. It examines the effects of different pranayama techniques—such as Nadi Shodhana, Kapalabhati, Bhramari, and Ujjayi—on autonomic regulation, respiratory and cardiovascular health, and mental well-being. The study highlights notable gains in lung function, stress reduction, and emotional resilience by synthesizing data from randomized controlled trials, meta-analyses, and comparative studies. Current research procedures are examined in relation to methodological issues, such as plagiarism standards, statistical techniques, and ethical requirements. The study ends with suggestions for additional research as well as helpful advice for preparing manuscripts and submitting them to journals.

The rapid growth of electric vehicles (EVs) has increased the importance of efficient energy storage systems and intelligent battery management techniques. The Battery Management System (BMS) is one of the most critical components in electric vehicles because it ensures battery safety, reliability, efficiency, and long operational life. A BMS monitors various battery parameters such as voltage, current, temperature, and state of charge (SOC) to optimize battery performance and prevent hazardous situations such as overcharging, deep discharging, overheating, and thermal runaway. This research paper presents a detailed study of Electric Vehicle Battery Management Systems, including their architecture, functions, working principles, challenges, and future developments. The paper also discusses different battery technologies used in EVs and the role of advanced algorithms, communication systems, and artificial intelligence in modern BMS designs.

The rapid proliferation of Artificial Intelligence (AI) systems in public administration, judicial processes, and governance frameworks poses unprecedented challenges to constitutional principles enshrined in democratic societies. This paper examines the critical intersection of AI technologies and constitutional law, with particular emphasis on the Indian constitutional framework. We analyze how AI deployment in predictive policing, automated welfare decisions, facial recognition surveillance, and algorithmic content moderation raises fundamental questions under Articles 14, 19, and 21 of the Indian Constitution, as well as the right to privacy recognized in Justice K.S. Puttaswamy v. Union of India (2017). Through comparative constitutional analysis spanning India, the United States, and the European Union, this paper argues that existing constitutional safeguards require significant doctrinal adaptation to address the opacity, scale, and autonomy of modern AI. We propose a five-principle constitutional framework for AI governance grounded in algorithmic accountability, explainability, non-discrimination, human oversight, and effective remedy — offering legislative and judicial recommendations to reconcile technological advancement with constitutional democracy.

For the past decades, India has witnessed a drastic change in the field of early childhood care and education. Existing policies and programs in India had ensured considerable growth in investments together with improvements in children&#39;s school engagement. But the situation of ECCE during the pre-independence period remained dispersed, concentrated in cities, limited to specific regions of the nation, and only available to those who could afford them. Many philosophers have given their souls to develop a better education system in India. Gijubhai Badheka, Tarabai Modak, Gandhiji, and Tagore are some of the pioneers of the development of ECCE. The study aims to explore the contributions of Indian philosophers to Early Childhood Care and Education before the independence of India.

Career decision-making plays a crucial role in shaping academic and professional growth. Many existing guidance platforms provide generalized suggestions without deeply analyzing individual capabilities and preferences. This often results in confusion, lack of clarity, and delayed decisions. There is an increasing need for data-driven and intelligent systems that can evaluate user characteristics and provide personalized career insights aligned with evolving opportunities.
This paper presents A Dynamic Platform for Career Exploration and Decision Making, an intelligent and gamified system designed to support users in identifying suitable career paths through structured and interactive exploration. The platform integrates multi-level assessments such as aptitude, interests, personality traits, and contextual factors including financial considerations and location preferences. Based on these inputs, the system generates personalized domain recommendations, day-wise learning roadmaps, and structured development paths supported by data-driven analysis. Gamification features such as levels, rewards, simulations, and progress tracking improve engagement and encourage consistent learning. The system aims to enhance career awareness, reduce uncertainty, and enable informed decisions through an adaptive and intelligent framework.

The rapid expansion of the digital economy and the increasing collection, processing, and transfer of personal data have raised significant concerns regarding privacy, data security, and individual rights. In response to these challenges, India enacted the Digital Personal Data Protection Act, 2023 (DPDP Act), marking a significant step toward establishing a comprehensive legal framework for the protection of personal data. The Act seeks to balance the right to privacy of individuals with the legitimate need of the State and private entities to process personal data for lawful purposes. It reflects India's commitment to strengthening data governance while promoting innovation and digital transformation.
This research paper critically examines the Digital Personal Data Protection Act, 2023, with a particular focus on its key provisions, implementation challenges, and potential opportunities. The study analyzes the legislative background of the Act, including the influence of the landmark judgment in Justice K.S. Puttaswamy v. Union of India, which recognized privacy as a fundamental right under the Constitution of India. The paper evaluates important aspects of the Act such as consent-based data processing, rights and duties of data principals, obligations of data fiduciaries, cross-border data transfers, grievance redressal mechanisms, and the establishment of the Data Protection Board of India.
While the Act represents a progressive development in India's digital regulatory framework, several concerns remain regarding its practical implementation. The research highlights issues such as broad exemptions granted to government agencies, the absence of strong safeguards against surveillance, limited provisions relating to children's data, ambiguity in certain definitions, and challenges associated with enforcement and institutional capacity. Furthermore, concerns regarding transparency, accountability, and the effectiveness of remedies available to data principals are critically assessed. The paper also examines the extent to which the Act aligns with international standards, particularly the European Union's General Data Protection Regulation (GDPR), and identifies areas where further legislative refinement may be necessary.
At the same time, the Digital Personal Data Protection Act, 2023, presents significant opportunities for enhancing trust in digital services, promoting responsible data governance, attracting investment in the digital economy, and strengthening cybersecurity practices. By providing a structured legal framework for personal data protection, the Act has the potential to foster greater confidence among citizens, businesses, and international stakeholders.
The study adopts a doctrinal research methodology based on the analysis of statutes, judicial decisions, government reports, policy documents, and scholarly literature. It concludes that while the DPDP Act, 2023, constitutes a landmark reform in India's data protection landscape, its long-term success will depend upon effective implementation, institutional independence, regulatory clarity, and continuous adaptation to emerging technological challenges. The paper recommends strengthening oversight mechanisms, enhancing transparency, and ensuring a balanced approach that safeguards both individual privacy rights and the objectives of digital governance.

In this paper we propose a modified ratio type estimator for estimating the finite population mean of the study variable in systematic sampling. The expressions of bias and mean square error (MSE) up to the first order of approximation are derived and efficiency comparison made with some existing estimators theoretically with the help of numerical example.

सिंधु घाटी सभ्यता (Indus Valley Civilization) का नगर नियोजन केवल प्राचीन इतिहास का एक अध्याय नहीं है, बल्कि यह मानव इतिहास में शहरीकरण (Urbanization) का पहला और सबसे सुव्यवस्थित उदाहरण है। जब हम इसके &#39;विस्तृत परिचय&#39; (Detailed Introduction) की बात करते हैं, तो हमें यह समझना होगा कि यह व्यवस्था अचानक नहीं बनी थी, बल्कि इसके पीछे एक गहरी वैज्ञानिक सोच और नागरिक अनुशासन था। सिंधु घाटी सभ्यता (हड़प्पा सभ्यता) कांस्य युगीन एक अत्यंत विकसित नगरीय सभ्यता थी। इस सभ्यता की सबसे बड़ी विशेषता इसके शहरों का योजनाबद्ध तरीके से बसाया जाना था। पुरातात्विक खुदाई से पता चलता है कि यहाँ के इंजीनियर और वास्तुकार नगर निर्माण कला में बेहद निपुण थे। सड़कों के जाल से लेकर पानी के निकास तक, हर चीज़ को एक सोची-समझी रणनीति के तहत बनाया गया था। सिंधु घाटी सभ्यता (Indus Valley Civilization) को इतिहास में अपनी बेहतरीन और आधुनिक नगर नियोजन (Town Planning) के लिए सबसे ज्यादा जाना जाता है। आज से लगभग 4500 साल पहले, जब दुनिया के बाकी हिस्सों में लोग कबीलों या अव्यवस्थित बस्तियों में रह रहे थे, तब हड़प्पा और मोहनजोदड़ो जैसे शहरों में एक ऐसी व्यवस्थित व्यवस्था थी जो आज के आधुनिक शहरों को टक्कर देती है।

Education systems across the world are undergoing significant transformation due to rapid technological advancement, globalization, and changing workforce demands. Traditional rote learning methods, which emphasize memorization and examination performance, are increasingly being criticized for limiting creativity, critical thinking, and practical understanding. In contrast, skill-based education focuses on experiential learning, competency development, problem-solving abilities, and real-life application of knowledge. This paper examines the differences between rote learning and skill-based training, highlighting their impact on learners’ intellectual and professional development. The study also analyzes the role of the National Education Policy (NEP) 2020 in promoting competency-based education in India. Through a descriptive and analytical approach based on secondary data sources such as books, journals, government reports, and policy documents, the paper identifies the advantages of skill-based learning in preparing students for the challenges of the 21st century. The study concludes that educational institutions must shift from memorization-oriented systems toward learner-centered and practical approaches to ensure holistic development, employability, and sustainable national progress.

As internet usage grows, cyber attacks are becoming more frequent and dangerous. Traditional security systems rely on recognizing known "fingerprints" of viruses, which fails when hackers create brand new "zero-day" attacks. This paper reviews the shift from traditional security to Artificial Intelligence (AI) based systems. We propose a system that uses Machine Learning to learn what "normal" network traffic looks like, flagging anything that behaves strangely. This paper details how the AI model was trained using real network data and outlines a modern, cloud-based design. In this design, lightweight sensors watch the network and send data to a central cloud "brain," keeping computers fast and secure while catching threats in real-time.

प्रकृति और मानव का अटूट संबंध रहा है। सदियों से मनुष्य ने प्रकृति के साथ सामंजस्य बिठाकर जीवन यापन किया है। लेकिन, पिछली शताब्दी में बढ़ती जनसंख्या की खाद्य आपूर्ति सुनिश्चित करने के लिए 'हरित क्रांति' के नाम पर कृषि में रासायनिक उर्वरकों, कीटनाशकों और हाइब्रिड बीजों का अंधाधुंध प्रयोग शुरू हुआ। शुरुआत में तो पैदावार बढ़ी, लेकिन समय के साथ इसके विनाशकारी परिणाम सामने आने लगे। मिट्टी बंजर होने लगी, जल स्तर गिर गया और सबसे महत्वपूर्ण बात यह कि हमारा भोजन 'जहर' बन गया। इन समस्याओं के समाधान के रूप में आज पूरी दुनिया 'जैविक खेती' की ओर लौट रही है। जैविक खेती केवल खेती का एक तरीका नहीं, बल्कि जीवन जीने का एक स्वस्थ दर्शन है। जैविक खेती कृषि की वह पद्धति है जिसमें रासायनिक उर्वरकों (जैसे यूरिया, डी.ए.पी.), कीटनाशकों और खरपतवारनाशकों का प्रयोग बिल्कुल नहीं किया जाता। इसके स्थान पर प्रकृति में उपलब्ध संसाधनों जैसे गोबर की खाद, कम्पोस्ट, हरी खाद, जीवामृत और जैविक कीटनाशकों का प्रयोग किया जाता है। इसका मुख्य उद्देश्य मिट्टी की जीवंतता को बनाए रखते हुए शुद्ध और पौष्टिक भोजन का उत्पादन करना है।

The increasing demand for safe, natural, and sustainable skincare products is driving the growth of herbal cosmetics. Polyherbal preparations, consisting of multiple medicinal plant extracts, exhibit enhanced therapeutic efficacy due to synergistic interactions among phytoconstituents. These formulations demonstrate antioxidant, anti-inflammatory, antimicrobial, and photoprotective properties, making them suitable for a wide range of skincare applications. Compared to synthetic cosmetics, polyherbal fo rmulations are generally associated with improved skin compatibility and reduced adverse effects. This review focuses on the composition, evaluation methods, therapeutic a pplications, and recent advancements in polyherbal cosmetics. Various dosage forms, including creams, gels, lotion, nano formulations, and hydrogels, enhance stability, bioavailability, and skin penetration of active constituents. Evaluation techniques, including physicochemical and biological assessment, are employed to ensure product safety, stability, and efficacy. With an emphasis on clinical validation and standardization, polyherbal cosmetics represent a promising and sustainable alternative to modern skincare system.

This paper studies the representation of loyalty and trust through the character of Dolly in Vishal Bhardwaj’s Omkara (2006), a cinematic adaptation of Shakespeare’s Othello. Reimagined within the socio political landscape of rural eastern Uttar Pradesh, Omkara relocates Shakespeare’s tragedy from Venetian military hierarchy to a world governed by caste, kinship, and political allegiance. Within this context, Dolly comes as a culturally transformed counterpart to Desdemona, whose loyalty and trust become central to the film’s emotional and moral structure. While Desdemona’s tragedy in Othello has often been read through themes of innocence, obedience, and patriarchal vulnerability, Dolly’s experience reflects a more localised negotiation of female agency, familial defiance, and emotional faith.
The paper argues that Dolly’s complete trust in Omkara is not a sign of passivity but a conscious ethical stance shaped by her love, resistance, and belief in emotional integrity. Different to Shakespeare’s Desdemona, whose loyalty is framed within the ideals of chastity and obedience, Dolly’s devotion is made through acts of rebellion, including her elopement and rejection of caste based authority. Her trust towards Omkara becomes both her strength and her undoing, exposing how patriarchal suspicion weaponizes loyalty against women in male dominated world. The Trust or that lack of eventually leading to the tragedy of Othello and his surrounding.

This study assessed the implementation, compliance, and effectiveness of the Continuing Professional Development (CPD) Act of 2016 (Republic Act No. 10912) among licensed criminologists in Panay Island, Philippines, in 2025. Employing a descriptive research design, the study involved 346 registered criminologists selected through stratified random sampling from a population of 2,588 professionals affiliated with the Philippine National Police (PNP), Bureau of Fire Protection (BFP), Bureau of Jail Management and Penology (BJMP), National Bureau of Investigation (NBI), and Philippine Drug Enforcement Agency (PDEA). Data were collected using a researcher-made questionnaire that underwent content validation and reliability testing. Statistical tools such as frequency, percentage, mean, t-test, analysis of variance (ANOVA), and Pearson’s r were utilized in analyzing the data. Findings revealed that the implementation (M = 2.92) and compliance (M = 2.96) with the CPD Act were both at a moderate extent, while its effectiveness (M = 2.86) was rated moderate. Significant differences in implementation were observed only when respondents were classified according to province. Compliance significantly differed according to years in service, type of agency, and province, while effectiveness significantly varied based on years in service and province. No significant differences were found when respondents were grouped according to sex and educational attainment in most variables. Furthermore, significant positive relationships were found among implementation, compliance, and effectiveness, indicating that improved implementation leads to higher compliance and greater perceived effectiveness of the CPD Act. The study concludes that while licensed criminologists recognize the value of CPD, challenges related to accessibility, consistency of implementation, and compliance remain. Strengthening policy implementation, enhancing support mechanisms, and improving access to relevant and affordable CPD programs are recommended to maximize the law’s intended benefits for professional development and competency enhancement.

Training and development have become essential functions of human resource management in modern organizations. In a highly competitive business environment, organizations must continuously improve the skills, knowledge, and competencies of employees to achieve organizational goals and maintain productivity. The present study titled “Role of Training and Development in Employee Performance” aims to examine the impact of training and development programs on employee performance and organizational effectiveness.
The study focuses on various aspects such as skill enhancement, job knowledge, productivity improvement, employee motivation, work efficiency, and career development opportunities. Primary data is collected through structured questionnaires administered to employees from different organizations. Descriptive research design has been adopted, and the collected data is analyzed using percentage analysis, tables, and graphical representations.
The findings reveal that effective training and development programs significantly improve employee performance, increase job satisfaction, enhance confidence levels, and contribute to organizational productivity. Employees who receive regular training demonstrate better problem-solving abilities, improved communication skills, and higher efficiency in performing job responsibilities. The study concludes that organizations should invest continuously in employee training and development initiatives to enhance workforce capabilities and achieve long-term business success.

Anxiety and mood disorders have a significant socioeconomic and personal impact. More choices are required because the response and remission rates from conventional pharmacotherapies are now only moderately effective. College students have frequently claimed that stress, anxiety, sadness, and feelings of overwhelm and exhaustion have a detrimental effect on their academic performance and general well-being. Although ashwagandha is an Ayurvedic herb that has long been used to promote healthy reactions to stresses, it has lately been well-known in the Although research in this field is still in its early stages, herbal and complementary therapies show promise for treating anxiety and depression. Posttraumatic stress disorder (PTSD) and major depression (MD) have similar brain processes and therapeutic approaches. Panic attacks and anxiety disorders were frequently perceived as "women's problems." This is undoubtedly false. These disorders affect both men and women, despite the fact that men are more reluctant to seek therapy.
Anxiety disorders have persisted throughout human history, despite their recent official recognition. Anxiety disorders and panic episodes have been described by numerous notable historical figures. They were treated in a variety of ways, some of which were amusing. The available remedies were frequently ineffectual and even highly harmful to the patient.

They are necessary in the treatment of a number of cardiovascular diseases. Rosuvastatin is a medicine that lowers cholesterol and other lipids in the blood to help prevent heart, brain and blood vessel problems. Clopidogrel and aspirin are medicines that prevent blood clots. These drugs are available in single and combination form in the market. Rosuvastatin is a lipoprotein-lowering agent used in the treatment of various cardiovascular, cerebrovascular, and peripheral vascular disorders. These drugs are now widely marketed both as single entities and in combination dosage forms. There are a number of well-established analytical methods for estimation of these drugs in individual and combination dosage forms.
A simple, precise, fast, and accurate RP-HPLC method was developed and validated for the simultaneous detection of aspirin, rosuvastatin, and clopidogrel in pharmaceutical dosage forms. Chromatographic separation was achieved using a reverse-phase C18 column with the appropriate mobile phase under optimal circumstances. The developed method showed good resolution, low retention times, and strong peak symmetry for all three medications.

Switched Capacitor Multi Level Inverter (SCMLI) with reduced components is attractive for higher number of voltage levels due to less implementation complexity and low cost. In this study, a new family of hybrid SCMLI for high frequency power distribution system is presented to eliminate the intermediate power conversion. Firstly, a five-level SCMLI employing a single voltage source is proposed, which is further extended to nine-level (9L) with its operation. Further extension/enhancement of the proposed 9L-SCMLI for generating a higher number of voltage levels with reduced number of components is achieved on the basis of structural modification. The mathematical analysis for determination of capacitance, power loss analysis and comparative analysis has been provided in detail. A comprehensive comparison with other similar topologies is also provided to highlight the merits of the proposed topology. Simulation and experimental results are discussed for various dynamic load conditions with different output frequencies to validate the suitability of the proposed SCMLI for various high-frequency AC applications, such as renewable energy systems, microgrids, electric vehicles and so on.

Air pollution remains a critical global environmental health challenge, contributing to millions of premature deaths annually. Traditional deterministic and statistical models (e.g., chemical transport models, linear regression) often struggle with capturing the complex, non-linear, and spatiotemporal dynamics of air pollutants. In recent years, machine learning (ML) has emerged as a powerful paradigm for air quality prediction, offering superior accuracy and computational efficiency. This review synthesizes the state-of-the-art ML-based prediction models, categorizing them into shallow models (e.g., Random Forest, Support Vector Machines) and deep learning architectures (e.g., Recurrent Neural Networks, Long Short-Term Memory networks, Convolutional Neural Networks, and hybrid models). We critically evaluate their performance in predicting criteria pollutants (PM2.5, PM10, NO2, O3, SO2, CO) across different temporal and spatial scales. Furthermore, we propose innovative graphical and schematic models illustrating data fusion pipelines, spatiotemporal attention mechanisms, and explainable AI frameworks. Key findings indicate that hybrid deep learning models integrating meteorological, traffic, and satellite aerosol data consistently outperform classical methods. However, challenges persist regarding data sparsity, model interpretability, and generalizability across diverse geographic regions. Future directions include federated learning, graph neural networks, and real-time IoT integration.

Work ethic refers to the set of values, attitudes, and behaviours individuals bring to their work, which is widely believed to drive organisational performance. Yet the factors that shape work ethic in Ghanaian private enterprises remain poorly understood. This qualitative phenomenological study investigates how competition, incentives, and leadership styles shape individual work ethic in Ghanaian private firms. Drawing upon Self-Determination Theory (SDT) and Social Cognitive Theory, the study recruited 28 employees (15 male, 13 female) from 12 private enterprises (manufacturing, financial services, technology, retail, logistics) in Accra and Kumasi through purposive and snowball sampling. Participants completed in-depth semi-structured interviews exploring their work motivations, responses to competitive environments, experiences with incentive systems, and leadership influences on their work behaviour. Data were analysed using Interpretative Phenomenological Analysis (IPA), yielding six superordinate themes: (1) Competition as Double-Edged Sword; (2) Incentives Beyond Money; (3) The Leadership-Performance Bridge; (4) Intrinsic Motivation as Sustainability; (5) Peer Culture as Informal Regulation; and (6) The Productivity Paradox of Presenteeism. Findings reveal that competition motivates high performers but demotivates and increases anxiety among lower performers. Non-monetary incentives (recognition, autonomy, developmental opportunities) were reported as more sustainable drivers of work ethic than monetary bonuses. Leadership styles characterised by psychological safety and fairness produced stronger work ethic than authoritarian or laissez-faire approaches. The Ghanaian cultural norm of presenteeism paradoxically reduced actual work ethic despite satisfying visible attendance expectations. These findings inform human resource management practices, incentive system design, and leadership development in Ghanaian private enterprises.

Human-following robots have become an important part of mobile robotics, making it possible to use them in healthcare, surveillance, logistics, and interactions between people and robots. Such robotic platforms are required to reliably find, track, and follow a person while still being able to respond quickly and use little energy.
Our analysis shows that all current methods have a basic trade-off between computational overhead and perceptual reliability. Sensor-based systems that use ultrasonic, infrared (IR), and radio-frequency (RF) sensors are cheap and have low latency (5–20 ms), but they aren't very accurate (60–75%) and don't work well in harsh environments [1], [2], [8]. AI-based vision-driven systems that use deep learning models like YOLOv5 and YOLOv8, on the other hand, have a high detection accuracy (85–95% mAP), but they are very expensive to run, take a long time to process (40–120 ms), and use a lot of power (5–30 W) [3], [4],[ 6].
In this work, a structured comparative survey of sensor-based, AI-based, and hybrid human-following robotic systems. A unified evaluation framework is introduced to compare these paradigms across latency, accuracy, power consumption, scalability, and deployment feasibility. Furthermore, hybrid architectures leveraging event-driven activation mechanisms and advanced sensor fusion techniques are analysed as a promising solution for balancing efficiency and intelligence [7], [10], [11].
The study identifies key challenges in real-world deployment, including environmental variability, multi-human tracking, and energy constraints. Finally, emerging research directions such as lightweight deep learning models, reinforcement learning-based navigation, and edge AI optimization are discussed to enable scalable and power-efficient human-following systems [12], [13], [18], [19].

The core objective of this research is to examine the influence of shear wall coupling and infill wall stiffness on the seismic performance of open ground storey (OGS) buildings during pre- and post-earthquake periods in India. For this investigation, two distinct timelines are adopted the pre-earthquake era and the post-earthquake era with the Bhuj earthquake of 2001 serving as the pivotal event. The pre-earthquake span covers FY 1990-91 to FY 2000-01, while the post-earthquake phase encompasses FY 2001-02 to FY 2020-21. Data from buildings in high seismic zones, particularly those monitored by the Indian Seismic Network (ISN), form the sample, with 280 balanced panel observations for the pre-period and 380 for the post-period. Dynamic panel techniques, specifically system GMM, are utilized to assess the hypotheses. Building-specific attributes like height, age, soil type, material quality, lateral load resistance, ductility, and damping ratio are incorporated as controls. Outcomes reveal that shear wall coupling exerts a detrimental influence, whereas infill stiffness shows negligible impact on seismic performance in the pre-earthquake phase. Building-specific elements such as age and material quality enhance performance pre-event. Post-earthquake, infill stiffness demonstrates a negative effect, while shear wall coupling lacks significance. For building-specific factors, increased height and soil variability contribute to performance decline. This work highlights the shifting dynamics of structural elements in India's seismic landscape following major events.

The primary aim of this study is to examine the influence of pavement material modifications and structural design techniques on the performance and longevity of flexible and rigid pavements during pre- and post-policy periods in India. For this analysis, two distinct periods are considered the pre-policy era and the post-policy era with the implementation of the National Highways Development Project (NHDP) in 2000 serving as the benchmark. The pre-policy period spans from FY 1990-91 to FY 1999-2000, while the post-policy period covers FY 2000-01 to FY 2019-20. The sample consists of data from major Indian highway projects, including those under the National Highways Authority of India (NHAI), totaling 320 balanced panel observations for the pre-policy phase and 450 for the post-policy phase. Dynamic panel data techniques, specifically system GMM, are applied to evaluate the hypotheses. Project-specific variables such as material type, traffic load, environmental exposure, maintenance frequency, cost efficiency, and lifecycle duration are included as controls. Results reveal that material modifications (e.g., polymer additives) positively impact longevity in the pre-policy period, whereas structural innovations show no significant effect. In the post-policy era, advanced design techniques enhance performance, but basic material upgrades have a diminishing influence. Regarding project-specific factors, higher traffic loads and environmental stressors reduce longevity in both periods, while proactive maintenance improves it post-policy. This study underscores the evolving role of techniques in India&#39;s pavement sector amid infrastructure growth.

This study examined the effect of risk management practices on procurement performance in hospitality organizations in Kenya, focusing on Eka Hotel, Nairobi. Effective risk management is critical for procurement success in the hospitality industry, where supply chain disruptions, regulatory compliance issues, and operational uncertainties can significantly impact service delivery and organizational performance. The study employed a descriptive research design with a census sampling approach, targeting 104 employees involved in procurement-related activities. Data were collected using a structured 5-point Likert scale questionnaire, achieving a 97.11% response rate (101 respondents). Descriptive statistics were used to analyze the data, with findings presented in tables and figures. The results revealed generally positive perceptions regarding risk management practices, with 74.25% of respondents agreeing that the organization has robust mechanisms for identifying potential procurement risks, and 65.34% affirming the implementation of effective risk mitigation strategies. However, concerns emerged regarding organizational resilience, with only 58.41% expressing confidence in the hotel's ability to handle procurement risks and challenges. Risk awareness was high at 67.32%, and 69.30% confirmed regular risk monitoring and evaluation. The study concludes that while Eka Hotel has established foundational risk management practices, significant gaps exist in resilience-building and proactive risk monitoring. Recommendations include developing comprehensive risk management frameworks tailored to hospitality procurement, leveraging technology for real-time risk monitoring, establishing regular risk assessment mechanisms, fostering a culture of risk awareness through continuous training, and enhancing communication regarding risk management activities. These findings contribute to the growing body of knowledge on procurement risk management in the hospitality sector and offer practical insights for enhancing organizational resilience and procurement performance.

Small-scale fisheries play an important role in supporting coastal livelihoods and local economic development in Indonesia. However, information regarding the financial performance of small-scale fishing enterprises remains limited, particularly for bottom gillnet fisheries operating in Brebes Waters, Central Java. This study aimed to assess the financial feasibility of a small-scale bottom gillnet fishing unit using a case-study approach.Primary data were collected from a bottom gillnet fishing operation located in Prapag Lor Village, Brebes Regency, Central Java, Indonesia. Financial feasibility was evaluated through investment cost, operational cost, revenue, profit, Revenue-Cost Ratio (R/C Ratio), and Payback Period analyses.The fishing unit required an initial investment of IDR 20,000,000 and incurred annual operating costs of IDR 38,690,500. Annual revenue reached IDR 87,460,000, generating a net profit of IDR 43,669,500. The financial analysis yielded an R/C Ratio of 2.13 and a Payback Period of 0.46 years, indicating that operational revenues substantially exceeded costs and that the initial investment could be recovered within approximately six months.These findings demonstrate that the analyzed bottom gillnet fishing unit was financially feasible and economically viable. The favorable financial performance suggests that small-scale bottom gillnet fisheries have the potential to support household livelihoods and contribute to the economic sustainability of coastal communities.