Accurate Indonesian Machine Translation (MT) requires more than syntactic processing; it must also incorporate morphological and semantic context to ensure meaningful translation output. Lexical resources such as dictionaries are essential for interpreting Indonesian root words and generating contextually appropriate translations. This research utilizes Indonesian and Tolaki lexical datasets to develop a more reliable MT system through enhanced morphological and syntactic analysis. A dedicated morphotool was developed to analyze word morphology, while syntactic rule modeling was applied to determine grammatical roles and categories influencing translation performance. The study integrates supervised and unsupervised learning techniques, including TF-IDF, Word2Vec, BERT, and semantic similarity, to perform classification at word, sentence, and document levels based on Indonesian–Tolaki morphonemic and syntactic patterns. A hybrid translation model combining Statistical Machine Translation (SMT) and Rule-Based Machine Translation (RBMT) was applied for sentence translation. The experimental results show accuracy scores of 0.74 for Indonesian–Tolaki to English translation and 0.71 for English to Indonesian–Tolaki translation. These results confirm that the hybrid MT approach provides better performance than standalone SMT and RBMT models, with an overall average accuracy of about 70%.

The Gen Z in Kenya came into full swing following some bills introduced in the Kenyan parliament especially the Finance Bill. Using narrative analysis, the study examines Gen Zs, political leaders, human right activists, and citizens to enlighten on the causes and implications of the revolts. The article sums up with recommendations for inclusive conflict transformation policies, transparent political governance, and inclusive youth engagement. The study aims to align Gen Z's positive aspirations, ensuring sustainable political and socio-economic progress. The research contributes to understanding of Gen Z youth-driven political change in Kenya and offers insights for conflict transformation globally.

The purpose of this study was to identify any associations between corporate integrity and reputation among the 2-star hotels in Port Harcourt, Nigeria. Through a literature review, the study investigated the relationship between organisational reputation and integrity. A survey was the research approach employed in this study. Researchers in this study polled 313 hotel employees from the top 10 2-star establishments in Port Harcourt, Nigeria. Based on the data given by Krejcie and Morgan (1970), we were able to calculate a sample size of 169. Respondents were asked to fill out a questionnaire in order to gather data. Statistical Package for the Social Sciences, version 23.0, was used to study the link between integrity and measures of desired identity and corporate image, which are criterion variables. The data analysis led to the acceptance of the alternative hypothesis and the rejection of the first two null hypotheses. The empirical results of the study suggest that 2-star hotels should put an emphasis on integrity by being trustworthy, truthful, and faithful in all of their interactions with the public. As a result, businesses in the hotel sector will be able to forge stronger bonds with key players in the tourist sector and earn their trust. In addition, the actions and words of the company's top brass should set an example of openness, honesty, and responsibility for all employees.

The post-pandemic workplace has significantly transformed employee expectations, work structures, and organizational priorities, placing employee well-being at the center of human resource management strategies. This study examines the relationship between employee well-being programs and organizational performance in post-pandemic workplaces. Specifically, it investigates how physical, psychological, and social well-being initiatives influence key performance indicators such as employee productivity, job satisfaction, engagement, and organizational commitment. Using a quantitative research design, data were collected from employees across multiple sectors through a structured questionnaire. Statistical techniques, including descriptive analysis, correlation, and multiple regression, were employed to assess the impact of well-being programs on organizational performance. The findings reveal a significant positive relationship between comprehensive employee well-being programs and enhanced organizational performance outcomes. Psychological well-being initiatives, including mental health support and flexible work arrangements, emerged as the strongest predictors of employee engagement and productivity in the post-pandemic context. The study contributes to the growing body of human resource management literature by providing empirical evidence on the strategic importance of employee well-being in sustaining organizational performance during periods of disruption and recovery. The results offer practical implications for HR managers and policymakers to design integrated well-being frameworks that support both employee health and long-term organizational effectiveness in the evolving world of work.

Knowledge graphs (KGs) have become a cornerstone for representing structured, multi relational data in domains is ranging from semantic web and natural language processing to recommendation systems and biomedical informatics. While traditional symbolic reasoning techniques (e.g., description logics, rule based inference) are effective on modestly sized graphs, they encounter severe scalability bottlenecks when applied to modern, industrial scale KGs containing billions of entities and edges. Graph Neural Networks (GNNs) ? a family of deep learning models that operate directly on graph structured data ? have emerged as a powerful alternative, offering differentiable, end to end learning of entity and relation embeddings while naturally exploiting local and global graph topology. In this paper we present a comprehensive, 5,000 word academic treatment of GNN based reasoning over large-scale KGs. We first review the theoretical foundations of KGs and GNNs, and then systematically categorize existing GNN architectures (e.g., GCN, GAT, RGCN, GraphSAGE, NGCF, CompGCN, and Relational Graph Transformers) and their adaptations to KG reasoning tasks such as link prediction, entity classification, and rule induction. A detailed taxonomy of scalability techniques?including neighborhood sampling, sub graph batching, distributed training, graph partitioning, and memory efficient message passing?is provided. We then introduce a novel framework, Scalable Relational Graph Neural Reasoner (SRGNR), which combines relational graph convolution, adaptive importance sampling, and hierarchical graph coarsening to achieve linear time inference on billions of triples. Extensive experiments on benchmark datasets (FB15k 237, WN18RR, YAGO3 10) as well as an industrial scale KG (1.2B triples) demonstrate that SRGNR outperforms state of the art baselines in both predictive accuracy (MRR gains of 4?9? %) and throughput (up to 12? speed up).

Herbal cosmetics are another name for Ayurvedic cosmetics. The natural elements in herbs have no negative effects on human health .The majority of herbal products are made from a variety of botanical ingredients that have been used for many years in traditional or folk medicine. Among the many botanical compounds that are currently on the market. Cosmetics by themselves are insufficient to take care of skin and body parts; a variety of chemical toxins and microorganisms found in the atmosphere can cause chemical infection and damage to skin. Herbal products serve two purposes: they are used as body care cosmetics and, due to the photochemical and botanical content, promote naturally healthy skin.

A decade ago, tourism in India recognized the tour guiding profession as a prestigious and skill-intensive domain supported by structured training programmes. The Ministry of Tourism (MoT) traditionally held the responsibility for training and accrediting regional-level guides. Over the years, however, a diversification of authorities?including the Archaeological Survey of India (ASI), Ministry of Environment, Forest and Climate Change, and several state tourism departments?has led to varied approaches to guide training. On the supply side, a significant proportion of existing guides operate as freelancers, navigating an evolving demand landscape. This paper presents a critical analysis of the emerging gap between professionally trained tour guides and the rise of facilitation-based models. By examining the shift in tourist behaviour, training patterns, and intermediary practices, the paper evaluates whether tour guiding services continue to be effectively utilized or are transitioning toward redundancy.

This study examines the role of innovation and technology adoption in driving economic growth in South West Nigeria, with a focus on the agriculture, manufacturing, and services sectors. Utilizing a mixed-methods approach, data was collected from 75 participants per state, including businesses, government officials, technology providers, and academics through surveys, interviews, and focus group discussions. The findings reveal a significant "digital access gap," where foundational technologies like internet and digital payments see near-universal adoption (>85%), but advanced, productivity-enhancing technologies such as business management software (35%) and cloud computing (20%) are underutilized. The mean technology integration level was moderate (2.8 on a 5-point scale), indicating that technology is not yet a core operational driver. Key impacts were noted in customer engagement and market reach, while effects on product quality and job creation were modest. The most critical barriers identified were infrastructural unreliable electricity (Mean=4.6) and poor internet connectivity (Mean=4.3) followed by financial constraints. The study concludes that policy efforts must be sequenced, prioritizing macro-level infrastructure investments and micro-level financial support to enable a deeper, more productive integration of technology for sustained economic growth in the region.

Educational institutions at all levels have experienced a rapid integration of generative Artificial Intelligence (AI) technology and there has been an influx of renewed conversations about how generative AI will affect pedagogical practices in educational settings especially concerning the learner's agency, metacognition and assessment practices. The purpose of this mixed methods longitudinal study framed around generative AI as a colleague has been to identify how generative AI technology continues to support the learner-centered approach to learning within the context of secondary schools as a result of this sustained use of instructional support by AI technology. To identify key learner-centered outcomes, quantitative data was extracted from empirical peer-reviewed studies to measure the changes made in the areas of learner agency; metacognitive strategy usage; self-regulated learning; and formative assessment practices. Percentage change calculations are then applied to pre- and post-intervention means in order to build a consistent synthesis that can be compared among the studies. In conjunction with the quantitative analysis, a secondary thematic synthesis based on the qualitative data collected through international research and policy reports has allowed for further contextualizing of the results of the quantitative data. The results indicate there was a considerable overall increase in all variables, with the highest increases occurring in formative evaluation practice, followed by improvements in metacognitive strategies, and a lesser extent, improvements in learner agency. Evaluative analysis of the qualitative data also supports the conclusion that generative artificial intelligence (AI), when employed in a manner that offers pedagogical scaffolding, supports reflective learning and assists learners to become more autonomous learners. The use of data triangulation from multiple data sources, and the utilisation of differing data collection methods over varying timescales, provides additional strength to the conclusions drawn from these findings. Thus, it may be reasonable to conclude that generative AI acts as a co-teacher (not a distinct/alternative to human teaching), and that it will play an important role in continuing to provide effective and sustainable means for supporting agency-based learning, developing learners' metacognitive skills and creating a culture of assessment within the secondary school system.

Suicidal expressions on social media are increasing, yet the sheer volume and speed of online posts make manual monitoring impractical. Traditional text-only detection methods often overlook the broader emotional and socio-economic context that may signal underlying distress. This study develops a context-aware, feature-based machine-learning framework that integrates lexical cues (TF?IDF), affective tone (VADER sentiment), temporal behavior (posting hour), and regional socio-economic context (Indian state-level unemployment rates) to improve the identification of suicide-risk posts. A publicly available Twitter dataset is enriched with manually generated timestamps and state labels, which are then aligned with unemployment statistics from data.gov.in. A Multinomial Na?ve Bayes model trained solely on TF?IDF acts as a baseline, while Logistic Regression and Random Forest classifiers are trained on the complete multi-feature pipeline using scikit-learn?s ColumnTransformer. Experiments on a held-out test set show that Random Forest achieves the highest accuracy (?92.73%), with Logistic Regression offering a strong and interpretable alternative (?90.76%). The Na?ve Bayes baseline performs considerably lower (?74.90%). These results demonstrate that combining linguistic, emotional, temporal, and socio-economic context substantially improves the detection of high-risk posts and supports the development of scalable, interpretable tools for early intervention in mental-health monitoring.

The professional?personal interface of teachers represents a critical dimension of occupational well-being, particularly in socio-culturally embedded environments. Despite extensive global research on work?life balance, educators in smaller Indian states remain understudied. This quantitative study investigates how socio-cultural expectations, occupational stress, and support systems shape the experiences of college teachers in Tripura. Grounded in Role Theory, Work?Family Conflict Theory, and Border Theory, a structured 40-item questionnaire was administered to 127 teachers across government and private colleges in Agartala. Reliability was acceptable (? = 0.793), and hypotheses were tested through regression analyses and gender-based mean comparisons.

The study identified psycho-social challenges facing employed women with under two years old children in performing both reproductive roles and productive roles through a cross sectional research design, both qualitative and quantitative data were collected from 60 respondents using interviews, focus group discussions (FGDs) and questionnaires. Analysis of qualitative data was done using a Likert Scale to assess psychosocial incidences, whereas quantitative data were analyzed using SPSS (Statistical packages for Social Science) to establish frequencies and percentages. Overall, study findings revealed the existence of both psychosocial challenges facing employed women with under two years old children, including lack of concentration at workplace, child sickness, lack of time to rest, stress, lack of support from the employer and poor work performance, inability to practice exclusive breast-feeding, pressure from in-laws regarding child feeding and low incomes. Further, study findings revealed that employed women use different coping strategies such as changing their home and family arrangements, appealing for support from their close relatives as well as seeking support from their husbands.

B?zier curves remain a foundational mathematical tool in computer graphics, animation, and geometric modeling. While extensive literature exists on their theoretical formulation, less attention has been paid to pedagogical frameworks that integrate computational visualization for improved interpretability. This research introduces a novel visualization-oriented framework for generating and animating cubic B?zier curves using Python?s matplotlib library. The proposed method demonstrates a dynamic rendering pipeline where curve points are traced incrementally using parameter u[0,1]u \in [0,1]u[0,1], enabling enhanced understanding of curve evolution and control-point influence. An animated GIF representation is generated to illustrate the real-time curve formation. Experimental outcomes reveal that incremental animation significantly improves visual comprehension of geometric continuity, control polygon behavior, and curve smoothness. The framework is extendable to higher-order curves and continuous-piecewise interpolation, offering potential applications in UI design, CAD systems, motion planning, and cloud-based visualization services.

This work presents a high-efficiency contact?separation triboelectric nanogenerator (TENG) employing polytetrafluoroethylene (PTFE) and aluminum as the triboelectric pair. The device delivers a peak open-circuit voltage (212 V), short-circuit current (12.3 ?A), and a peak power density of 68.4 mW/m? under a mechanical excitation of 5 N at 4 Hz. The TENG successfully powered 28 commercial LEDs and charged a 47 ?F capacitor to 5.2 V in 38 s, demonstrating its suitability for low-power IoT sensing applications. A theoretical framework based on Maxwell?s displacement current is developed to model the device?s electrical output. These results establish the PTFE?Al TENG as a robust, low-cost, and scalable energy harvester for next-generation self-powered electronics.

Visual storytelling is a powerful medium for communication; however, the creation of traditional comic strips poses a high barrier to entry, requiring a dual proficiency in creative writing and advanced artistic illustration. This project presents the design and implementation of an AI Comic Generator, a full-stack web application designed to democratize comic creation by automating the transition from text to image. The proposed system utilizes a decoupled client-server architecture, leveraging React.js for a dynamic frontend interface and Node.js for a robust backend orchestrator. The core functionality integrates Google Gemini?s Generative AI, employing advanced prompt engineering algorithms to decompose narrative inputs into structured, stylistically consistent visual descriptions. The application features a sequential generation workflow that processes story segments panel-by-panel, ensuring narrative continuity and optimizing API payload management. By abstracting the complexities of digital art and prompt synthesis, this application allows users to input raw text and receive a fully rendered comic strip in real-time. This project demonstrates the practical application of Large Language Models (LLMs) and Text-to-Image models in creative software, effectively bridging the gap between textual imagination and visual reality.

The research examined quality control practices and organizational effectiveness of listed table water producing companies in Rivers State. The dimensions of quality control practices (quality planning, improvement and resources) and measures of organizational effectiveness (profitability, productivity and standard equipment). Ten research questions and ten hypotheses guided the study. Cross sectional survey design was used for the study. The accessible population derived was 9 listed public agencies which give total respondents of 176 through the census approach and purposive technique. The instrument used for data collection was a structured questionnaire validated by the supervisors, and the instrument had a Cronbach alpha coefficient of 0.86. Descriptive analysis of data was done using frequency, mean and standard deviations while the null hypotheses were tested sing the Spearman Rank Order Correlation coefficient at a 0.05 level of significance. Results showed that dimensions of quality control practices (quality planning, improvement and resources) have a positive and significant correlation with measures of organizational effectiveness (profitability, productivity and standard equipment). Therefore, the study concluded that dimensions of quality control practices (quality planning, improvement and resources) enhance measures of organizational effectiveness (profitability, productivity and standard equipment). Thus, the researcher recommended that Management of the table water producing companies should establish clear production plans that integrate quality standards at every stage, from sourcing raw materials to final finishing, minimizing waste and maximizing profitability.

The present study examines workplace accommodations and employer perceptions of disability in organizational settings. The objective of this research was to understand the types of accommodations provided to employees with disabilities, employers? attitudes toward disability and the association between selected demographic variables and perceptions of workplace inclusion. A descriptive survey method was adopted and data were collected from 40 respondents, including employers, managers and human resource personnel from various organizations. A structured questionnaire was used to gather data related to physical, attitudinal, organizational and policy-related accommodation practices. Percentage analysis and chi-square tests were employed for data analysis. The findings indicate that while basic workplace accommodations are moderately available, attitudinal and policy-related barriers continue to influence inclusive employment practices. Employers who had prior training and experience with disability demonstrated significantly more positive perceptions toward inclusion. The study emphasizes the need for systematic employer training, awareness programs and strong organizational policies to promote disability-inclusive workplaces. The findings have implications for organizations, policymakers and disability advocates seeking to bridge the gap between policy mandates and workplace realities.

The field of Human Resource (HR) administration, traditionally characterized by labour-intensive, repetitive, and time-consuming documentation and communication tasks, is poised for a transformative shift through the integration of advanced Artificial Intelligence (AI). Ideal Accounting system as well as modern accounting system were discussed for proper understanding of AI effect can help Staff from Organization from anxiety and stress. This paper investigates the synergistic application of Large Language Models (LLMs), underpinned by Deep Learning (DL) architectures, to revolutionize core HR administrative functions, including recruitment, employee support, performance management, and policy compliance. LLMs, leveraging multi-layered neural networks (the essence of deep learning), excel at understanding, generating, and summarizing unstructured text data?the primary medium of HR operations. It is analysed how LLMs, particularly when integrated with techniques like Retrieval-Augmented Generation (RAG), can move beyond simple keyword matching to achieve semantic-level understanding, mitigating human bias and dramatically enhancing efficiency.2 The paper details common administrative bottlenecks in HR, proposes a DL-enabled LLM framework for their resolution, and presents a case study on the deployment of an LLM-powered HR knowledge assistant that resulted in a significant reduction in employee query resolution time and a measurable increase in HR team focus on strategic tasks. Our findings suggest that the adoption of LLMs, when coupled with robust data governance and bias-mitigation strategies, offers a compelling pathway for HR departments to evolve from operational centres to strategic business partners.

Emotional states significantly influence human food choices, yet most existing food recommendation systems ignore the psychological context of users. This paper presents MoodMeal AI, a mood-based food recommendation system that integrates real-time facial emotion recognition with machine learning techniques to provide personalized food and restaurant suggestions. The system detects user emotions using DeepFace and Haar Cascade algori thms, categorizes food items through K-Means clustering, and delivers recommendations based on emotional state, dietary preferences, location, and past user behavior. A Flask-based web application serves as the deployment platform. Experimental evaluation conducted with 50 participants demonstrates an emotion recognition accuracy of 87.3% and high user satisfaction. The proposed system highlights the importance of emotionally intelligent recommendation systems in promoting healthier and more mindful eating behaviors.

Password security remains a critical component of modern authentication systems, as weak passwords continue to be a major cause of security breaches. Traditional rule-based password strength meters often fail to accurately assess password robustness against real-world attacks. This paper presents an intelligent password strength prediction framework using supervised learning models to classify passwords into multiple strength categories such as weak, medium, and strong. The proposed system extracts a comprehensive set of features including password length, character diversity, entropy, n-gram patterns, and structural complexity. Several supervised machine learning algorithms, including Logistic Regression, Support Vector Machines, Random Forest, and Gradient Boosting, are trained and evaluated on a labeled password dataset. Experimental results demonstrate that machine learning?based models significantly outperform conventional heuristic approaches in terms of accuracy, precision, recall, and F1-score. The study further highlights the effectiveness of ensemble methods in improving prediction robustness and generalization. The proposed approach offers a scalable and adaptive solution for enhancing password security and can be seamlessly integrated into real-time authentication systems to promote stronger user password practices.

Background: Mental health challenges such as depression, anxiety, and emotional dysregulation are increasingly prevalent among children and young people. With widespread access to smartphones, mobile mood-tracking applications have emerged as accessible tools that support self-monitoring, early detection of symptoms, and emotional well-being. However, the quality, clinical relevance, safety, and evidence base of these apps vary widely. A systematic and transparent assessment of available mood-monitoring apps for youth is needed to inform researchers, clinicians, and caregivers. Objective: This protocol outlines the methodology for conducting a systematic review of mobile mood-tracking applications designed for children and young people. The review aims to identify, categorize, and evaluate available apps across major app stores, focusing on functionality, usability, privacy, safety standards, and alignment with evidence-based mental health practices. Methods: Following PRISMA guidelines for scoping and systematic app-store reviews, a structured search will be conducted in the Google Play Store and Apple App Store. Eligible apps will include those that: (1) target mental health or emotional well-being; (2) incorporate mood-tracking or self-monitoring features; and (3) are designed for or appropriate for individuals aged 8?24 years. Extracted data will include app characteristics, developer information, user engagement features, cost, privacy practices, clinical content, and in-app safety mechanisms. Two independent reviewers will screen apps, assess quality using the Mobile App Rating Scale (MARS), and map app features against established mental-health frameworks. Discrepancies will be resolved through consensus or a third reviewer. Expected Results: The review will provide a comprehensive overview of current youth-focused mood-monitoring apps, highlighting trends in functionality, gaps in evidence-based practice, and areas requiring development, regulation, or clinical evaluation. It will also identify potential risks, including data privacy concerns and the absence of crisis-support features. Conclusions: This systematic app-store review will generate an evidence-informed resource for parents, educators, clinicians, and researchers. By mapping the current landscape of youth mental-health mood apps, the review aims to support safer, more effective digital mental-health interventions and guide future research, development, and policy initiatives.

Navigational challenges significantly affect the independence and safety of visually impaired individuals in both indoor and outdoor environments. This paper presents a Real-Time Navigation Support System designed to assist visually impaired users by providing accurate, timely, and context-aware guidance. The proposed system integrates sensor-based obstacle detection, GPS-based location tracking, and real-time data processing to identify obstacles, pathways, and environmental hazards. Audio feedback is delivered through a voice-assisted interface, enabling users to receive intuitive navigation instructions without relying on visual cues. The system continuously updates navigation paths based on real-time environmental changes, ensuring safe and efficient mobility. Experimental evaluations demonstrate improved obstacle avoidance, reduced navigation errors, and enhanced user confidence compared to traditional assistive aids. The proposed solution offers a cost-effective, scalable, and user-friendly approach to improving independent navigation and quality of life for visually impaired individuals.

The rapid advancement of educational technology has significantly transformed teaching, learning, and institutional management, necessitating leadership approaches that can effectively guide this change. This study explores the role of transformational leadership in shaping effective educational environments through the integration of educational technology. Adopting a qualitative, review-based research methodology, the study synthesizes existing literature, theoretical perspectives, empirical studies, and policy documents to examine how transformational leadership practices influence technology-enhanced education. The findings reveal that transformational leadership plays a pivotal role in fostering a shared vision, motivating educators, and creating a supportive culture conducive to innovation and collaboration. Leaders who demonstrate inspirational motivation, intellectual stimulation, individualized consideration, and idealized influence are better positioned to promote teachers? professional development, digital competence, and willingness to adopt innovative pedagogical practices. The study also highlights key challenges faced by educational leaders in implementing technology-driven initiatives, including infrastructural limitations, resistance to change, insufficient professional training, budgetary constraints, and concerns related to cybersecurity and data privacy. Despite these challenges, transformational leadership emerges as a powerful mechanism for aligning technological initiatives with pedagogical goals, ensuring inclusivity, and enhancing student engagement and learning outcomes. In the context of developing countries such as India, transformational leadership is particularly significant in addressing digital disparities and supporting equitable access to educational technology, in alignment with national educational reforms. The study concludes that effective technology integration is not solely dependent on digital resources but largely on visionary and transformative leadership practices. The findings contribute to educational leadership literature and offer practical insights for policymakers, administrators, and educators seeking to develop sustainable, innovative, and future-ready educational environments.

Workplace experiences are shaped by numerous small, routine interpersonal interactions that cumulatively influence employees? emotional experiences and well-being. Drawing on Affective Events Theory (AET) and emotional labour theory, this study examines how daily workplace micro-behaviours, including micro-aggressions and micro-kindness, influence employee well-being through affect, while also considering the roles of mindfulness, surface acting, and work environment features. A quantitative research design was adopted, and primary data were collected from 223 full-time corporate employees working in the IT, ITES, finance, and service sectors in Chennai. Data were analysed using correlation, regression, and mediation analyses. The results reveal strong and significant relationships among all study variables. Micro daily events, mindfulness, surface acting, and work environment features were found to significantly predict affect and employee well-being. Affect emerged as the strongest predictor of well-being, highlighting its central role in translating daily workplace experiences into longer-term well-being outcomes. The mediation analysis further confirmed that affect significantly mediates the relationships between micro daily events, mindfulness, surface acting, work environment features, and employee well-being. The findings support the core assumptions of Affective Events Theory by demonstrating that workplace experiences influence employee well-being primarily through affective reactions. By integrating both positive and negative micro-behaviours within a single empirical framework, the study extends existing literature that has largely focused on micro-aggressions alone. The study also contributes context-specific evidence from an Indian corporate setting. Practically, the findings highlight the importance of promoting micro-kindness, supportive work environments, mindfulness practices, and reducing excessive surface acting to enhance employee well-being. Overall, the study underscores that small daily interactions matter significantly for sustaining employee well-being in contemporary organizations.

Modern power networks are increasingly affected by power quality degradation due to the extensive deployment of power electronic loads, renewable energy systems, and dynamic industrial consumers. Deviations in voltage magnitude, harmonic distortion, and reactive power imbalance pose serious challenges to grid reliability and operational efficiency. Advanced power electronic solutions such as Flexible AC Transmission Systems (FACTS) and High Voltage Direct Current (HVDC) transmission have demonstrated significant potential in mitigating these disturbances. This paper investigates the fundamental causes of power quality issues and critically examines the role of advanced FACTS and HVDC controllers in improving voltage regulation, harmonic suppression, and system stability. Simulation-based validation using MATLAB/Simulink confirms the effectiveness of these technologies under disturbed operating conditions.

Background: Infections caused by extended-spectrum ?-lactamase-producing Escherichia coli (ESBL-EC) complicate urinary tract infection (UTI) management. Identifying patient-specific risk factors is key to guiding empiric therapy. This study aimed to identify risk factors for ESBL-EC UTIs among Nigerian women, with a focus on demographic and behavioural characteristics. Methods: A case-control study was conducted at Federal Medical Centre, Owerri. There were 43 women with UTIs caused by ESBL-EC. Controls were 86 women with UTIs caused by non-ESBL-EC. Data on demographic, clinical, and behavioural factors were collected via questionnaire and medical records. Multivariable logistic regression was used to identify independent risk factors. Results: In the final model, frequent sexual activity (?3 times per week) was the strongest behavioural predictor (aOR = 4.85, 95% CI: 1.98-11.89, p<0.001). Being under 35 years of age (aOR = 3.41, 95% CI: 1.51-7.72, p=0.003) and being nulliparous (no history of childbirth) (aOR = 2.92, 95% CI: 1.18-7.21, p=0.020) were also significant independent risk factors. Previous antibiotic use (aOR = 5.10, 95% CI: 2.30-11.30, p<0.001) remained a strong clinical predictor. Conclusion: This study identifies a risk profile for ESBL-EC UTI centred on young, nulliparous women with high sexual frequency. These findings suggest that community-based selection pressure, driven by antibiotic use and behavioural factors, is a key driver of ESBL-EC in this setting. Empiric therapy guidelines and antimicrobial stewardship programs should consider this high-risk demographic.

The rapid increase in urban vehicle numbers has intensified traffic congestion, fuel wastage, and air pollution, mainly due to inefficient parking management. Traditional smart parking systems rely on physical sensors which, although accurate, suffer from high maintenance costs, limited scalability, and vulnerability to environmental conditions. To overcome these issues, this study presents a software-driven Smart Parking System that utilizes existing camera infrastructure and advanced artificial intelligence for intelligent parking management. OpenCV enables real-time image processing and environmental adaptation, ensuring responsive analysis of parking areas. YOLOv8, an advanced object detection model, dynamically identifies vehicles with exceptional precision and speed, improving slot detection. For predictive analytics, Random Forest, a robust ensemble learning technique, analyses past data to forecast parking demand. Regression models further estimate occupancy and forecast parking trends, enhancing operational planning. These integrated technologies collectively enable the system to analyze, adapt, and optimize parking space utilization without relying on expensive sensors. The experimental results show that the Classification Model achieved an occupancy prediction accuracy of 96.80%, significantly surpassing the base paper's reported benchmark. Furthermore, the Random Forest Regressor achieved an Overall Average of 0.9730 for parking duration prediction, validating the high predictive capability of the camera-derived features and exceeding the base model's highest result of 0.9400.This AI-powered approach enhances scalability, minimizes cost, and contributes to sustainable urban development by reducing congestion and emissions, offering an efficient and environmentally conscious solution for smart city parking management.

Ayurvedic medicine is ancient medicine. This therapeutic approach makesuse of plant materials that are used in Ayurvedic medicine. The plants need to be identified because they differ from the many other plant species that can be found in nature. Without the proper knowledge, it could be difficult for the typical person to identify locally available herbal remedies. This demonstration shows a new technique that uses convolutional neural networks(CNN) and leaf images to identify the leaves of Ayurvedic medicinal plants. Computer technology advancements have allowed the field of computer vision to expand to include a wide range of applications.One of its applications is image classification, where it recognizes images more accurately than traditional methods. This document contains all of the information and direction needed to complete each step of the implementation process. All of the basic steps are covered in great detail, including building a database by gathering images and training models. Compared to other methods, our deep neural network method yields a more accurate classification. Another benefit is easier feature extraction fromthe image, which can be fed into the model without requiring preprocessing. One way to feed deep convolutional neural networksis with raw photo data. Without needing to extract the leavesthemselves, we can precisely classify leavesusing deep neural networks, which capture and store visual properties as an image moves through several layers. Web applicationsand deep learning are used to sort and present worksheets. The deep learning technology used in this essay is the convolutional neural network.

The rapid growth of counterfeit products poses significant challenges to consumer safety, brand reputation, and supply chain transparency. Traditional centralized product verification systems are vulnerable to data manipulation, lack real-time traceability, and offer limited trust among stakeholders. To address these issues, this paper proposes a secure blockchain-based framework for fake product identification using barcode technology. In the proposed system, each product is assigned a unique barcode linked to immutable blockchain records that store manufacturing, distribution, and verification details. Authorized stakeholders, including manufacturers, distributors, retailers, and consumers, can verify product authenticity by scanning the barcode through a web or mobile application. Blockchain ensures data integrity, transparency, and tamper resistance, while smart contracts automate product registration and validation processes. The framework effectively prevents duplication, unauthorized modification, and counterfeit entry within the supply chain. Experimental evaluation demonstrates improved security, traceability, and trust compared to conventional centralized solutions. The proposed approach offers a scalable, cost-effective, and reliable solution for combating counterfeit products across diverse industries.

Air pollution has emerged as a critical environmental and public health concern, necessitating continuous and real-time monitoring of air quality. This paper presents an intelligent Internet of Things (IoT)?based framework for Air Quality Index (AQI) monitoring using the ESP32 microcontroller. The proposed system integrates multiple environmental sensors to measure key air quality parameters such as particulate matter, carbon dioxide, harmful gases, temperature, and humidity. Sensor data are processed locally by the ESP32 and transmitted wirelessly to a cloud-based platform for real-time visualization, storage, and analysis. An intelligent decision-support mechanism is incorporated to evaluate AQI levels and generate alerts when pollutant concentrations exceed predefined safety thresholds. The system is designed to be low-cost, energy-efficient, and scalable, making it suitable for deployment in urban, industrial, and residential environments. Experimental results demonstrate reliable data acquisition, real-time monitoring, and timely alert generation, highlighting the effectiveness of the proposed framework in supporting environmental monitoring and public health awareness.

Dementia is a progressive neurological condition that significantly affects cognitive function, emotional well-being, and quality of life, creating substantial challenges for both patients and caregivers. Recent advances in immersive technologies offer new opportunities to support non-pharmacological interventions in dementia care. This study presents the design and feasibility evaluation of a virtual reality (VR)?enabled approach for dementia care, aimed at enhancing cognitive engagement, emotional comfort, and user experience.The proposed system integrates immersive VR environments tailored to the cognitive and sensory needs of individuals with dementia, including reminiscence-based scenarios, calming natural settings, and simple interactive tasks. A user-centered design methodology was adopted, involving clinicians, caregivers, and end users to ensure accessibility, safety, and ease of use. The feasibility study was conducted with a small cohort of participants diagnosed with mild to moderate dementia, focusing on usability, acceptance, tolerability, and preliminary therapeutic outcomes. Quantitative measures such as task completion rates and system usability scores, along with qualitative feedback from participants and caregivers, were used for evaluation. Results indicate high levels of user acceptance, minimal adverse effects, and positive trends in mood enhancement and engagement during VR sessions. Caregivers reported reduced agitation and improved emotional responses among participants. Although the sample size was limited, the findings demonstrate the practical feasibility and potential benefits of VR-enabled interventions in dementia care.This study concludes that virtual reality represents a promising, scalable, and non-invasive tool for supporting dementia care. Future work will focus on larger clinical trials, longitudinal assessments, and the integration of adaptive and personalized VR content to enhance therapeutic effectiveness.

The growing volume and complexity of data have made it increasingly challenging for organizations to extract meaningful insights in a timely manner. Visual analytics, when combined with artificial intelligence, offers a powerful approach to simplify data exploration and support effective decision making. This paper presents an AI-enabled visual analytics framework designed to transform complex datasets into intuitive visual representations and actionable insights. The proposed system integrates machine learning techniques for automated pattern discovery, anomaly detection, and predictive analysis with interactive visual dashboards that enhance user understanding. By reducing manual analytical effort and cognitive load, the framework enables users to identify trends, correlations, and critical insights efficiently. Experimental evaluation using real-world datasets demonstrates that the AI-powered visual analytics approach improves decision accuracy, reduces analysis time, and enhances interpretability compared to traditional visualization methods. The proposed solution can be effectively applied in domains such as business intelligence, healthcare, finance, and smart governance, supporting simplified and informed data-driven decision making.

The rapid digitization of healthcare services has led to the widespread adoption of e-healthcare systems, raising critical concerns related to data security, privacy, interoperability, and intelligent clinical decision-making. TraditionRESULTal centralized healthcare management systems are vulnerable to data breaches, unauthorized access, and single points of failure, while also lacking advanced decision support capabilities. To address these challenges, this paper proposes a Blockchain-Based Secure E-Healthcare Management System with AI-Driven Decision Support. Blockchain technology is employed to ensure decentralized data storage, tamper-resistant medical records, transparent access control, and secure data sharing among authorized stakeholders. Smart contracts are utilized to enforce fine-grained access policies and maintain auditability of healthcare transactions. Additionally, artificial intelligence techniques, including machine learning algorithms, are integrated to analyze electronic health records and support clinical decision-making, such as disease prediction, risk assessment, and treatment recommendations. The proposed framework enhances data integrity, confidentiality, and trust while enabling intelligent, data-driven healthcare services. Experimental evaluation demonstrates improved security, scalability, and decision accuracy compared to conventional e-healthcare systems. The results indicate that the integration of blockchain and AI offers a robust and future-ready solution for secure, intelligent, and efficient e-healthcare management. General Terms Security, Artificial Intelligence, Blockchain, Electronic Healthcare Records.

Traditional crowdfunding platforms often rely on centralized intermediaries, leading to challenges such as lack of transparency, high transaction fees, delayed fund disbursement, and limited trust between startups and investors. To address these limitations, this study proposes a blockchain-enabled crowdfunding system designed to empower startups through decentralized, transparent, and secure fundraising mechanisms. The proposed platform leverages blockchain technology and smart contracts to automate campaign management, fund collection, and conditional fund release based on predefined milestones. By utilizing an immutable distributed ledger, the system ensures transparency, traceability of transactions, and protection against fraud or fund misappropriation.The platform enables startups to directly connect with a global pool of investors without intermediaries, thereby reducing operational costs and improving funding efficiency. Smart contracts enforce predefined rules, ensuring that funds are released only when project objectives are met, thus enhancing investor confidence. Additionally, the system supports real-time monitoring of campaign progress and secure digital identity verification for participants. Experimental evaluation demonstrates improved transparency, reduced transaction overhead, and increased trust compared to conventional crowdfunding models. This blockchain-enabled approach provides a scalable and reliable solution for startup financing and contributes to the evolution of decentralized financial ecosystems

The rapid digitization of campus communication systems has increased the demand for intelligent, real-time, and easily maintainable notice dissemination platforms. This paper presents an AI-Augmented Digital Notice Board that integrates a Flask-based middleware with a Firebase backend to enable seamless creation, management, and delivery of notices across institutional environments. The system leverages Flask as a lightweight web framework to handle user authentication, role-based access, and API-driven content management, while Firebase provides scalable cloud services including Realtime Database, Cloud Storage, and Cloud Messaging for instant updates.To enhance user experience and operational efficiency, AI modules are embedded for text classification, automated summarization, and priority tagging of notices. This enables intelligent filtering and personalized delivery of information to students, faculty, and administrators. The proposed architecture supports multimedia notices, multi-device accessibility, secure user authentication, and real-time synchronization across digital displays and mobile devices. Experimental results demonstrate improved notice retrieval speed, reduced manual workload for administrators, and enhanced relevance of information delivered to end users. The system showcases a modern, scalable approach to campus communication through the effective integration of AI, Flask middleware, and Firebase cloud services

The digitization of regional language documents is essential for preserving cultural heritage and enabling efficient information access. Kannada, a widely used Dravidian language, presents significant challenges for optical character recognition (OCR) due to its complex script structure, compound characters, and high intra-class variability. This paper proposes an AI-based Kannada script recognition system for effective digital document processing using deep learning techniques. The proposed framework employs convolutional neural networks (CNNs) for automatic feature extraction and classification of printed and handwritten Kannada characters. Image preprocessing techniques such as noise removal, normalization, and segmentation are applied to enhance recognition accuracy. The model is trained and evaluated on a benchmark Kannada character dataset, achieving high recognition accuracy and robustness against variations in font style, size, and writing patterns. Experimental results demonstrate that the AI-based approach significantly outperforms traditional machine learning methods in terms of accuracy and scalability. The developed system can be effectively integrated into digital archiving, e-governance, and document management applications, contributing to the advancement of regional language OCR and intelligent document processing systems.

The rapid growth of digital media and social networking platforms has significantly increased the spread of fake news, posing serious challenges to societal trust, public opinion, and democratic processes. To address this issue, researchers have widely explored machine learning (ML) and deep learning (DL) techniques for automated fake news detection. This paper presents an analytical review of state-of-the-art ML and DL algorithms employed in fake news detection systems. It systematically examines traditional machine learning approaches such as Na?ve Bayes, Support Vector Machines, Decision Trees, and ensemble methods, alongside deep learning models including Convolutional Neural Networks, Recurrent Neural Networks, Long Short-Term Memory networks, and Transformer-based architectures. The review highlights commonly used datasets, feature extraction methods, linguistic and contextual indicators, and performance evaluation metrics. Furthermore, it analyzes the strengths, limitations, and comparative performance of ML and DL techniques in different application scenarios. Key challenges such as data imbalance, concept drift, explainability, and multilingual fake news detection are also discussed. Finally, the paper outlines future research directions, emphasizing hybrid models, explainable AI, and real-time detection frameworks to enhance the robustness and reliability of fake news detection systems.

The global beverage industry, a key player in the global beverage sector, is under increasing pressure to optimize production processes due to rising competition, evolving consumer preferences, and environmental concerns. This study explores the impact of operations management practices on production optimization within the brewery industry, focusing on key strategies such as lean manufacturing, Total Quality Management (TQM), and Just-In-Time (JIT). The research highlights how these practices impact production processes, cost management, quality control, and overall performance. The findings suggest that integrating effective operations management practices leads to enhanced productivity, reduced waste, and improved product quality, thereby boosting operational efficiency. The study contributes to understanding the critical role of operations management in optimizing production processes and highlights the benefits of system optimization. The study evaluates the operations management practices employed in the brewery industry and their impact on production performance, identifying key practices and challenges while providing actionable recommendations for optimization. The findings emphasize the importance of lean manufacturing, supply chain integration, and technological advancements in improving production efficiency and sustainability.

Mangrove estuaries are ecologically important coastal systems that are increasingly impacted by anthropogenic activities, particularly in tropical developing regions. This study assessed the spatial variation in physicochemical water quality of two mangrove estuaries, Iko Mangrove and Uta-Ewa Mangrove, located in Akwa Ibom State, southeastern Nigeria. Surface water samples were collected from representative stations and analyzed for temperature, pH, salinity, dissolved oxygen (DO), electrical conductivity, total dissolved solids (TDS), total suspended solids (TSS), biochemical oxygen demand (BOD?), nutrients (nitrate, phosphate, and silicate), and selected heavy metals (Pb, Ni, Cu, Zn, and Al) using standard methods (APHA). Spatial differences between the estuaries were evaluated using independent sample t-tests at a 95% confidence level, while overall water quality status was determined using the Weighted Arithmetic Water Quality Index (WQI). The results indicated significant spatial differences (p < 0.05) in most measured parameters. Uta-Ewa Mangrove recorded higher mean values of pH (7.33 ? 0.11), dissolved oxygen (7.98 ? 0.34 mg L??), temperature (29.93 ? 0.35 ?C), silicate, phosphate, nitrate, zinc, nickel, and aluminum, whereas Iko Mangrove exhibited higher salinity (13.13 ? 0.78 ?), electrical conductivity (862.5 ? 93.54 ?S cm??), and lead concentrations. Heavy metal concentrations were generally within permissible limits; however, elevated nickel and zinc levels were observed at Uta-Ewa Mangrove. The calculated WQI values were 150.62 for Iko Mangrove and 140.43 for Uta-Ewa Mangrove, classifying both estuaries as having poor water quality. The study demonstrates pronounced spatial variability in water quality across the mangrove estuaries, reflecting the combined influence of estuarine hydrodynamics and localized anthropogenic inputs. These findings provide baseline information for coastal water quality management and highlight the need for continuous monitoring of mangrove estuarine systems in southeastern Nigeria.

Background: Pediatric care outcomes are strongly influenced by parental expectations and satisfaction. In India, where healthcare systems serve diverse socio-economic groups, understanding these expectations is critical for improving service delivery. Objective: To compare rural and urban parental expectations regarding pediatric consultations and pharmacy services in Gujarat, India. Methods: A cross-sectional survey of 250 parents (125 urban, 125 rural) was conducted at Om Children Hospital, Morbi. A 25-item questionnaire assessed priorities in pediatric consultations and pharmacy services. Data were analyzed descriptively. Results: Urban parents emphasized efficiency (short waiting times, digital billing, detailed side-effect information), while rural parents prioritized empathy (friendly behavior, verbal clarity of dosage, medicine availability). Both groups highlighted the importance of accessible pharmacies. Conclusion: Pediatric care in India requires a dual strategy: digital and time-efficient services for urban families, and empathetic, trust-based communication for rural families. These findings underscore the need for context-sensitive healthcare delivery models in low- and middle-income countries.

Women and child safety has become a critical social priority, demanding innovative technological solutions that ensure continuous monitoring, rapid response, and reliable communication during emergencies. This paper presents An Intelligent IoT Tracking Solution for Women and Child Protection, a smart, real-time safety system that integrates IoT devices, GPS tracking, sensor modules, and cloud connectivity to provide seamless monitoring and instant alert mechanisms. The proposed system employs a compact wearable device equipped with location sensors, microcontrollers, and emergency trigger buttons that activate distress signals when the user is in danger. Upon activation, the system transmits the user's live location, device ID, and real-time sensor data to guardians and emergency services through a secure cloud network. A mobile application interface enables remote tracking, geo-fencing, and data visualization, while analytics support situational awareness and quick decision-making. The system prioritizes low power consumption, high accuracy, and robust communication using IoT protocols such as MQTT and GSM/LTE. Experimental results demonstrate reliable real-time tracking, minimal latency in alert delivery, and effective performance in both indoor and outdoor environments. This research highlights the potential of IoT-driven intelligent safety devices to enhance protection measures for women and children, offering a scalable and cost-effective solution for modern public safety challenges.

Graph colouring is an essential tool in graph theory with extensive application in communication and network optimization. This paper investigates the various concept of graph colouring in mobile network system. The research proposed model mobile network cells which are represented at vertices of the graph while edges indicate interference between the adjacent cells. The work assign distinct colours to adjacent vertices such that no two neighboring cells share the same frequency. The chromatic number optimization is used to achieve minimal frequency allocation. The research also highlights on the importance of graph colouring as both theoretical and practical techniques in solving real-world communication problems. The research discusses some relevance of chromatic number determination in estimating the minimum number of frequencies required for interference free communication. Furthermore, it serves as a practical frame work for efficient channel assignment and network planning in modern mobile communication system.

Assisted living technologies play a crucial role in improving the quality of life for individuals with physical disabilities and limited mobility. This paper presents a low-cost DIY EEG-based Brain?Computer Interface (BCI) system for home automation, enabling users to control household appliances using brain signals. The proposed system acquires electroencephalogram (EEG) signals through an affordable consumer-grade EEG headset and processes them using signal preprocessing and feature extraction techniques. Machine learning algorithms are employed to classify user intent based on distinct EEG patterns. The recognized commands are transmitted to a microcontroller-based home automation unit, which controls appliances such as lights, fans, and electronic devices in real time. The DIY design emphasizes affordability, simplicity, and ease of deployment using open-source software and readily available hardware components. Experimental results demonstrate reliable command recognition and responsive appliance control, highlighting the feasibility of the system for assisted living applications. The proposed solution offers an accessible and scalable approach to hands-free smart home control, promoting independence and improved living conditions for individuals with motor impairments.

This paper presents, an integrated software platform designed to provide comprehensive interview preparation through the convergence of resume analysis, behavioral assessment, and adaptive question generation. The system employs machine learning techniques for gesture recognition and emotional analysis during mock interviews while leveraging natural language processing for skill gap identification and job-market analysis. The architecture utilizes a microservices-based approach with a React-based frontend, FastAPI backend, and PostgreSQL database via Supabase for scalability and real- time data persistence. By incorporating multiple analytical dimensions?technical competency, non- verbal communication patterns, and skill-market alignment?InterviewEase aims to bridge the gap between candidate preparation and employer expectations.

Fruit-based gummies have emerged as a novel and kid-friendly supplement delivery technique due to the growing demand for natural, functional confections. In order to satisfy children's daily multivitamin needs, this study focusses on the production and evaluation of fruit-based natural gummies enhanced with vital vitamins. The main basis was made up of fresh fruit pulps and juices, which were then enhanced in palatability with naturally occurring honey and flavours as well as plant-derived gelling agents like agar-agar (Veg). To ensure compliance with paediatric dietary guidelines, the gummies were fortified with a balanced composition of vitamins A, C, D, and B-complex. Optimising gelling consistency, texture, and flavour while preserving vitamin stability was part of the formulation process. The prepared gummies were assessed for their nutritional profile, microbiological safety, colour, taste, texture, and overall acceptability, as well as their physicochemical characteristics (pH, moisture content, and total soluble solids). To evaluate vitamin retention during storage, stability experiments were carried out. The results showed that the optimised formulation had a good flavour and texture and maintained a high vitamin content while being stored without any signs of microbial contamination. This study shows that fruit-based natural gummies have the potential to be a nutritious, safe, and tasty substitute for traditional synthetic multivitamin gummies for kids.

Women?s empowerment continues to be a vital development priority in India, particularly in peri-urban and rural regions where socio-economic vulnerabilities are more pronounced. Self-Help Groups (SHGs) have emerged as an effective participatory platform enabling women to reduce poverty, gain financial inclusion, enhance leadership, and strengthen their social position. This study examines the impact of SHGs promoted by Development Microfinance Institutions (DMIs) in the Poonamallee Block of Tamil Nadu, using Maslow?s Hierarchy of Needs as the analytical framework. Findings reveal that SHGs help women meet basic needs such as safety, income stability, and financial security, which form the foundation for further growth. Participation in SHGs also strengthens women?s self-esteem, decision-making abilities, and confidence. Ultimately, many members attain higher-order needs, including self-actualization, personal growth, and psychological empowerment. The study also highlights the socio-economic background of women, traces the evolution of SHGs in Tamil Nadu, and identifies key empowerment indicators supported by DMIs. Overall, the research demonstrates that SHGs significantly enhance women?s economic independence, social participation, and autonomy, offering valuable insights for future policy planning aimed at holistic women-centred development.

Cloud-based video conferencing platforms such as Zoom, Google Meet, and Microsoft Teams have become essential for modern communication, yet they exhibit several limitations including opaque architectural designs, limited adaptability under fluctuating network conditions, and restricted control over Quality of Service (QoS) parameters. These platforms also rely on proprietary infrastructure, offer minimal transparency into congestion-control mechanisms, and provide limited opportunities for academic experimentation or customization. Furthermore, their reliance on heavy server-side computations and closed media- routing pipelines makes it difficult to evaluate, replicate, or extend their behaviour in research environments. SkyConnect addresses these gaps by presenting an open, cloud- native, and QoS-aware video conferencing platform engineered for transparency, scalability, and real-time adaptability. Built using WebRTC, distributed SFU-based media routing, and a stateless signalling architecture, SkyConnect incorporates telemetry-driven bitrate adaptation, simulcast-based media optimization, and region-aware routing to maintain sub-200 ms latency across vary- ing network states. The platform further integrates Kubernetes- ready autoscaling strategies, real-time RTP telemetry processing, and modular service boundaries to enable flexible experimentation and reproducibility?features largely unavailable in commercial systems.

Adverse Childhood Experiences (ACEs) are known to exert long-term effects on emotional, psychological, and relational functioning. Emotional functioning is critical for personal well-being and interpersonal effectiveness, especially within religious life, where emotional maturity supports spiritual growth and community living. This study investigates the prevalence of ACEs and how it shapes emotional intelligence among selected temporary professed women religious in Nigeria. Using a mixed methods embedded design, data were collected through simple random sampling (quantitative) and a convenient snowball sampling (qualitative) from 28 participants. The standardized ACE and EI questionnaires and personal interviews generated data. Descriptive statistics assessed ACE prevalence and levels of emotional well-being, while correlational analyses explored associations between ACE scores and EI dimensions. Findings revealed that a significant proportion of participants reported exposure to at least one ACE, with emotional neglect and parental separation being the most common. There was no significant relationship between the two variables in this population and participants indicated that prayer and community are critical strategies for improving their emotional functioning. The study underscores the prevalence of ACEs and also the enduring influence of spirituality on ACES and highlights the need for expanded professional support systems to complement the strong reliance on prayer and community life.

The increasing competition in the job market has made interview preparedness a vital skill for students and job seekers. Traditional interview-preparation methods such as reading sample questions, attending coaching classes, or relying on peers often lack personalization, immediate feedback, and realistic simulation. To address these limitations, this project presents an AI-powered full-stack mock interview application designed to imitate an actual interview process. The system integrates modern technologies including Next.js, React, Drizzle ORM, PostgreSQL, Clerk authentication, and Gemini AI to create an interactive platform where users can participate in interviews, receive questions generated by AI, and obtain instant automated feedback. The application not only provides secure login and history tracking but also records interview responses and system-generated evaluations for continuous learning. The results from implementation show that the system successfully performs dynamic question generation, real-time feedback analysis, and smooth user interaction. This project demonstrates the practical potential of AI integrated with web technologies to deliver an advanced, scalable, and cost-effective solution for enhancing interview preparedness and professional development.

Software is one of the most essential part in today?s world, with its requirements in every industry be it automotive, avionics, telecommunication, banking, pharmaceutical and many more. Software systems are generally a bit complicated and created by distinct programmers. Usually any mistake in the code by a programmer in the developing stage of a software can lead to loopholes that cause vulnerabilities. Vulnerability is a software flaw that an assaulter can exploit to conduct unlawful activities within a computer system. Despite the understanding of vulnerabilities by the academia and industry, the amount of vulnerabilities is growing exponentially as fresh characteristics are added to the software frequently. Developers and testers are faced with the challenge of fixing large amounts of vulnerabilities within limited resources and time. Thus, prioritizing software vulnerabilities is essential to reduce the usage of corporate assets and time, which is the motivation behind the present study. In the present paper, the issue of software vulnerability prioritization is addressed by utilizing a new multi-criterion decision-making (MCDM) technique known as the Best Worst method (BWM). Further, to assess the vulnerabilities in terms of their critical nature, we have applied Two-Way assessment technique. The BWM utilizes two pairwise comparison vectors to determine the weights of criteria. The two- way assessment framework takes into account the perspectives of both managers/developers and stakeholders/testers to highlight the severity of software vulnerabilities. This can act as a significant measure of efficiency and effectiveness for the prioritization and evaluation of vulnerability. The findings are validated with a software testing firm from North India.

Modern port planning increasingly relies on smart technologies, automation, and simulation-based approaches to improve operational efficiency, safety, and sustainability. This study integrates CAD-based port layout design using AutoCAD with agent-based simulation modeling in ?AnyLogic? software to evaluate port infrastructure performance and vessel operations. Detailed layouts of berths, container yards, access roads, and navigation channels were developed and assessed against functional standards. The ?AnyLogic? simulation modeled real-time port dynamics, including vessel movements, cargo handling processes, and resource utilization, enabling analysis of vessel turnaround times, congestion, and process efficiency. Results demonstrate that simulation-driven planning and path-based vessel navigation significantly reduce congestion and improve operational performance. The study highlights the value of integrating digital design and simulation technologies to support data-driven decision-making in the development of smart and sustainable ports.

The ability to perform independent mobility and basic tasks remains a significant challenge for many specially-abled individuals. To address this need, the present work proposes a Real-Time Gesture Operated Robotic Assistant controlled through intuitive hand movements using Arduino-based embedded hardware. The system integrates an accelerometer or gesture sensor with a microcontroller to continuously capture, process, and interpret user gestures in real time. The interpreted commands are wirelessly transmitted to a mobile robotic platform equipped with motor drivers and assistive modules to execute corresponding actions such as movement, direction control, and object handling. This contactless control mechanism eliminates the need for conventional physical interfaces, making the solution user-friendly, hygienic, and highly accessible. Experimental implementation demonstrates reliable gesture recognition, low latency response, and smooth robotic operation, validating the system?s effectiveness for assistive applications. The proposed approach offers a cost-efficient, scalable, and customizable solution to enhance independence, safety, and quality of life for specially-abled users.

The exponential growth of social media platforms has generated vast volumes of user-generated data, offering unprecedented opportunities for data-driven marketing optimization. This study explores the application of advanced analytics techniques to extract actionable insights from social media data and enhance marketing performance. By integrating machine learning algorithms, natural language processing, and sentiment analysis, the proposed framework analyzes consumer behavior, engagement patterns, and brand perception in real time. The research emphasizes predictive and prescriptive analytics to support targeted advertising, customer segmentation, and campaign effectiveness evaluation. Experimental results demonstrate that advanced social media analytics significantly improve marketing decision-making by increasing customer engagement, optimizing content strategies, and maximizing return on investment. The findings highlight the strategic importance of transforming unstructured social media data into meaningful marketing intelligence, enabling organizations to achieve sustainable competitive advantage in dynamic digital markets.

The integration of artificial mielligence (Al) into the domains of fitness and nutrition has gained significant momentum in recent years. With the rapid evolution of deep learning, computer vuion, and mobile computing. Al-powered applications have become more accessible, accurate, and efficient. Traditional fitness and diet-tracking systems required users to manually imput their workout activities and food intake, often resulting in time-consuming and inaccurate logging. This limitation created the need for smarter systems capable of automating the tracking process. As Al technologies matured, they opened the door for advanced features such as real-time human pose estimation, food recognition, calorie prediction, and personalized meal planning-all of which could be performed directly through mobile devices without additional hardware.

Background: Professional development opportunities are believed to improve the work behavior of health workers, but their effectiveness is highly dependent on the perception of organizational support. Empirical evidence on public hospitals in developing regions is still limited. Methods: This study used a quantitative approach with an explanatory design. Data were collected through a structured questionnaire distributed to health workers at Konawe Regional General Hospital, Southeast Sulawesi Province, Indonesia. Data analysis was carried out using Structural Equation Modeling based on Partial Least Squares (SEM-PLS). Testing of the reliability and validity of the instrument was performed prior to structural relationship analysis, while mediation effect testing was performed through a bootstrapping procedure. Results: The results of the analysis showed that professional development opportunities had a positive and significant effect on organizational support (? = 0.691; t-statistics = 13.693; ?= 0.000). Organizational support was also shown to have a positive and significant effect on the work behavior of health workers (? = 0.717; t-statistics = 18.709; ?= 0.000). Furthermore, organizational support significantly mediated the relationship between professional development opportunities and work behavior (? = 0.496; t-stats = 10.118; ?= 0.000), which indicates a strong indirect influence. Conclusion: This study concludes that professional development opportunities are able to improve the work behavior of health workers, especially through strengthening the perception of organizational support. These findings confirm the importance of integration between professional development programs and organizational support practices in improving the quality of work behavior in public hospitals.

The growing dependence on fossil fuels and the environmental burden caused by improper disposal of used cooking oil have intensified interest in renewable, waste-derived biofuels. In this study, biodiesel was produced from waste cooking oil (WCO) through a base-catalyzed transesterification process using potassium hydroxide as the catalyst and methanol as the alcohol reagent. The oils were collected from multiple local sources to represent practical variations in quality and free fatty acid content, then filtered and preheated to remove impurities and moisture prior to reaction. The process was carried out under optimized temperature, reaction time, and molar ratio conditions to achieve complete conversion. After separation, washing, and drying, a clear biodiesel phase was obtained that met the essential fuel performance and safety requirements for diesel engine operation. The produced biodiesel exhibited improved ignition quality, higher flash point, and comparable flow and combustion behavior when compared with conventional diesel fuel. The results confirm that even variable-quality WCO can be effectively converted into high-grade biodiesel using a simple, low-cost base-catalyzed method. This work highlights the potential of waste cooking oil as a sustainable feedstock for decentralized biodiesel production and demonstrates a practical pathway to reduce environmental pollution while contributing to renewable energy generation.

No organization can survive without an effective internal control mechanism in place to checkmate all the activities of the organization by all the executives and their subordinates. Organisations in Nigeria was reported to perform below expectation. The study aims at evaluating the internal control mechanism on organisational performance in Ramadan Press Ltd Bauchi. The specific objectives covered were to: determine the extent of internal control mechanisms are implemented on production in Ramadan Press Ltd Bauchi, as well as determine the significant impact of internal control mechanisms and organisational performance in Ramadan Press Bauchi. The study used quantitative research design especially the survey research design with 65 participants as the sample size conveniently selected in the study area. Questionnaire was used as the instrument for primary data collection and the data was analyzed using both descriptive and inferential methods of data analyses using SPSS version 23 as the tool for analyses. Based on the findings of the study, the research concluded that: there is high significant extent of implementation of internal control mechanisms on production in Ramadan Press Ltd. Bauchi; and, there is significant impact of internal control mechanism on organisational performance on production in Ramadan Press Ltd. Bauchi. The research recommended that: management of the organisation should always practice proper recording of each transaction to ensure growth of the organization; effective internal control systems at all cost should be ensured by the executives and their subordinates to lead for organisational success base on products that can best suit the organisation?s operations and the customers.

Maintaining the temperature of hot beverages for extended durations remains a challenge with conventional insulation-based solutions. This paper presents the design and implementation of a smart coffee mug heating system developed using the ESP32-S3 microcontroller. The proposed system continuously monitors the beverage temperature through an NTC thermistor and regulates heating using a ceramic heating element controlled by a PID- based feedback algorithm. User-defined temperature settings are provided through a rotary encoder, ensuring convenience and precision. The system activates heating only when temperature drops below the set threshold, thereby minimizing unnecessary power consumption. Wireless connectivity enables real-time monitoring and control, enhancing usability and flexibility. Experimental evaluation demonstrates stable temperature maintenance within a predefined range while ensuring safety through automatic cutoff mechanisms. The proposed solution highlights the effective integration of embedded systems and IoT technologies for energy-efficient and user-centric smart appliances.

This paper introduces move_car, a modular and real-time Advanced Driver Assistance System (ADAS) stack that integrates multi-modal perception, dynamic occupancy grid mapping, hierarchical planning, and control for autonomous navigation. The system fuses LiDAR and multi-camera inputs through a CUDA-based BEVFusion approach, enabling robust environment understanding via dynamic occupancy grids. A Model Predictive Control (MPC) framework is employed in closed-loop execution to ensure precise and safe trajectory tracking.The framework is trained on standard autonomous driving datasets and evaluated within the CARLA simulator on an NVIDIA RTX 3060 platform. Experimental results demonstrate real-time performance and reliability. A comparative study against open-source baselines highlights the effectiveness of the proposed stack, and key limitations along with potential directions for future research are discussed.

Because of the fast progress of GANs, there is now a large increase in hyper-realistic fake media which threatens security on the internet, truthful media reports and puts public trust at risk. It describes a new Hybrid Deep Learning Architecture meant to detect deepfakes and check their quality at the benchmark level. It makes use of convolutional neural networks (CNNs) for spatial detection and recurrent neural networks (RNNs), mainly long short-term memory (LSTM) units, to detect inconsistencies in time. Attention techniques are included to direct the network?s focus on areas with artifacts which improves how precisely results can be detected. Architecture assessments are done by using established datasets like Face Forensics++, Celeb-DF and Deep Fake Detection. This model is precise in identifying synthetic media and groups the fake characteristics according to the kind of generation network used (for example, GANs). Many experiments indicate that the hybrid framework is not easily fooled by straightforward adversarial and compression distortions. It supports the growth of deepfake detection tools that are easy to understand and stay resilient which is necessary for them to be used.

Objectives: This study aims to develop and evaluate an automated deep learning approach for identifying Fatty Liver Disease (FLD) from ultrasound images, with the goal of reducing observer dependency and improving the consistency of early screening in clinical practice. Methodology: A case study was conducted using a dataset of 1,200 anonymized liver ultrasound images. The images were pre-processed to enhance quality and reduce noise before being used to train a Convolutional Neural Network (CNN). The model learned relevant imaging features associated with fatty liver patterns and was evaluated using standard performance metrics to assess its classification accuracy and reliability. Findings: The proposed CNN-based model demonstrated strong classification performance in distinguishing fatty liver cases from normal liver images. The results suggest that automated analysis can minimize subjectivity caused by variations in radiologist experience and image quality, thereby supporting more consistent diagnostic outcomes. Novelty: Unlike conventional ultrasound interpretation that relies heavily on human expertise, this study highlights the practical application of deep learning as a supportive diagnostic tool for FLD screening. The integration of an automated model into radiology workflows offers a scalable and objective solution, enhancing decision-making while maintaining the non-invasive nature of ultrasound imaging.

The ancient vision of Bharat situates health not merely as the absence of disease but as a state of holistic harmony between the individual, society, and environment. Rooted in the dictum ?Vasudhaiva Kutumbakam? (the world is one family), the principle of ?One Earth, One Health? finds profound resonance in Yogic traditions. Within Ashtanga Yoga, the foundational disciplines of Yama (ethical restraints) and Niyama (observances) establish the moral and spiritual framework for sustainable health, emphasizing moderation, self-regulation, and ecological balance. These principles extend beyond personal well-being to collective health, underscoring the preventive and promotive dimensions of Yoga. Aparigraha (Minimalism) is a complete practical guide for the Dhyana (meditation) on living a soothing, serene as well as calm kind of satisfactory life. It is increasingly seen as a way to achieve balance, sustainability, and holistic well-being. By embracing Aparigraha (Minimalism), individuals can live more authentically, nurture meaningful connections, and contribute to a healthier as well as the happier planet.

Cardiovascular Diseases (CVDs) remain the leading cause of global mortality. Early, accurate, and automated diagnosis is crucial for improving patient outcomes. This paper presents a novel approach to CVD monitoring and diagnosis by leveraging advanced Machine Learning (ML) techniques on Electrocardiogram (ECG) signal data. The methodology focuses on the precise segmentation and feature extraction of key ECG wave components (P-wave, QRS complex, T-wave). We propose a multi-stage ML framework, utilizing Convolutional Neural Networks (CNNs) for automated feature learning and a hybrid classifier (e.g., CNN-LSTM) for robust classification of various cardiac abnormalities. The system is designed to overcome the challenges of signal noise and inter-patient variability. Comparative analysis demonstrates that the proposed ML-based system significantly surpasses traditional manual and basic threshold-based methods in terms of accuracy, sensitivity, and specificity, providing a scalable and efficient solution for remote and clinical CVD monitoring.

Diabetic Retinopathy (DR) is one of the leading causes of vision impairment and blindness among diabetic patients worldwide. Early detection and timely intervention are crucial to prevent irreversible vision loss. This research presents an AI-based Diabetic Retinopathy detection system using deep learning combined with Explainable Artificial Intelligence (XAI) techniques. A Convolutional Neural Network (CNN) based on the ResNet50 architecture is employed to automatically classify retinal fundus images into different DR stages. To enhance model transparency and clinical trust, Gradient-weighted Class Activation Mapping (Grad-CAM) is integrated to visually highlight pathological regions influencing model predictions. The proposed system is deployed through an interactive Gradio-based web interface, enabling real-time predictions and visual explanations. Experimental results demonstrate high classification accuracy along with meaningful visual interpretations, making the system suitable for assisting ophthalmologists in clinical decisionmaking.

Lung cancer is the leading cause of cancer-related mortality worldwide. Early detection greatly improves patient survival rates (e.g., over 50% 5-year survival for stage I vs. below 5% for stage IV disease). Recent advances in deep learning (DL), especially Convolutional Neural Networks (CNNs), have achieved remarkable success in medical image analysis. However, CNNs typically require large labelled datasets to generalize well and can be vulnerable to changes in image orientation or scale due to their use of pooling layers. Capsule Networks (CapsNets) have been proposed as an alternative that preserves spatial relationships via capsules and dynamic routing, potentially offering better robustness to rotations and handling of small data regimes. In this study, we present a comprehensive comparison of a CapsNet architecture against three state-of-the-art CNN models (VGG16, ResNet-50, InceptionV3) and a custom CNN on a challenging lung cancer CT image dataset. Our results show that the CapsNet achieves a superior macro-averaged F1-score of 94.9%, markedly higher than the best CNN's 72.5%, and maintains more stable performance under affine image transformations. In particular, the CapsNet outperforms the next-best model (VGG16) by 3.0% in overall accuracy and over 20% in macro-F1, and its predictions remain robust even when input CT slices are rotated by up to 30?45 degrees. whereas CNN performance degrades significantly. These findings demonstrate the promise of CapsNet for reliable lung cancer detection from CT images in low-data settings. We also discuss the implications of network architecture selection on model generalizability and outline future research directions.

This study analyzed soil and surface air temperatures in Makurdi LGA, Benue State, Nigeria, using data from the Nigerian Meteorological Agency (NiMet) for 1991?2020. Surface air temperatures (maximum and minimum) and soil temperatures at 30 cm depth were examined. Time series analysis, using the Least Square Regression Model, revealed fluctuations and trends, with the trend significance tested at a 0.05 confidence level. The highest monthly average maximum, minimum, and mean surface air temperatures of 37.0?C, 25.2?C, and 31.1?C, respectively, were observed in March. Annual maximum and minimum surface air temperatures showed a significant upward trend, increasing by 0.0144?C and 0.0164?C per year, with R? values of 0.1541 and 0.1944, and correlation coefficients of 0.3926 and 0.3800. The peak soil temperature at 30 cm depth was 33.1?C in March, with an annual downward trend at a rate of 0.0628?C (R? = 0.2207, r = 0.470). A weak negative correlation (r = -0.040) was found between soil temperature at 30 cm depth and maximum air temperatures, while a slight positive correlation (r = 0.013) was observed between soil temperature and minimum surface air temperatures. The study concludes that there is a weak negative relationship between soil temperatures at 30 cm depth and maximum air temperatures, while a positive correlation exists between soil temperatures and minimum air temperatures. The study recommends the implementation of adaptive agricultural practices in Makurdi LGA, such as crop rotation and the use of heat-tolerant crop varieties, to help mitigate the potential adverse effects of rising surface air temperatures and declining soil temperatures.

Soft-skills are paramount to construction professional?s performance which ensure full derivation of potentials of the industry. The study investigated the influence of construction professionals? soft-skills on professional?s performance in Abuja Municipal Council (AMAC), Nigeria. 3 specific objectives to: ascertain the level of construction professionals? performance in Abuja Municipal Area Council (AMAC), Nigeria; assess the level of construction professionals? soft-skills in the study area; as well as, determine the significant influence of construction professionals soft-skills on construction professionals? performance in the study area were used to achieved the main aim of the study. A quantitative research design particularly descriptive survey was adopted with structured questionnaire designed that revealed an overall high internal consistency of 0.78 for primary data collection from the 327 construction professionals in AMAC comprised of Architects, Builders, Civil Engineers and Quantity Surveyors as the population of the study. The data collected were analysed using both descriptive and inferential statistical methods of data analyses, with SPSS software employed as the tool for analysis. The study finding revealed that construction professionals soft-skills enhance professional?s performance in the construction industry. However, based on the study objectives, it was concluded that, the level of construction professionals? performance is high in AMAC, Nigeria with communication to overcome institutional barriers, client satisfaction, as well as project efficiency and profitability as the highest performances; the level of construction professionals soft-skills is high in AMAC, Nigeria with communication skill, integrity skill, and self-management/time-management skill as the highest construction professionals soft-skills; as well as construction professionals soft-skills has strong positive significant influence on construction professionals? performance with (r = 0.811, n = 327, p < 0.05). The study recommends that, construction industry is dynamic in nature and to maintain performance in the industry, the policy makers should always document and use the outcome of each project activity continually to improve level of performance of the construction professionals; construction professionals should stress and practice soft-skills in their daily endeavours to ensure construction success and project delivery; and, let there be collaboration among all the stakeholders in the construction industry to ensure robust soft-skills are acquired to stimulate professional?s performance in order to derived the full potentials of the industry in terms of employment generation and infrastructural development.

This paper focuses on continuation of our previous papers1&2 by systematically compiling monographs of monographs on veterinary drug substances, dosage forms, and pharmaceutical aids, biologicals, diagnostics, Immunosera and Surgicals in Indian Pharmacopoeia-2022 (Volume-IV) based on their Number, Assay Standards, Drug categories (API/Formulation), and Therapeutic Uses. The initiative aims to create a reliable, well-organized, and easily accessible digital repository that enhances knowledge, promotes clarity, and supports academic and Professional advancement. By preserving and presenting this critical scientific information, the project contributes to continuous learning, research innovation, and the maintenance of high standards in Pharmaceutical quality control.

Soil mineral detection is a critical component of precision agriculture, providing essential information about the nutrient composition and fertility status of agricultural land. Accurate assessment of minerals such as nitrogen, phosphorus, potassium, and micronutrients helps farmers optimize fertilizer application, improve crop health, and enhance overall productivity. Traditional soil analysis methods, though reliable, are often labor-intensive and time-consuming. Recent advances in sensor technologies, spectroscopy, geographic information systems (GIS), and machine learning have enabled rapid, non-destructive, and highly precise soil mineral evaluation. These modern approaches support sustainable farming by reducing resource wastage, minimizing environmental degradation, and promoting efficient land management. This paper explores the importance, methods, and emerging technologies of soil mineral detection, highlighting its role in advancing sustainable and data-driven agriculture.

The rapid advancement of wireless communication and embedded systems has enabled the development of intelligent, remotely controlled devices. One of the prominent applications of such technology is in mobile robotics, particularly in creating a Bluetooth or mobile-controlled car. This project focuses on designing and implementing a car that can be controlled via a mobile device using Bluetooth or Wi-Fi communication, employing Arduino UNO or ESP8266 as the central control unit. The main objective is to develop a cost-effective, reliable, and efficient system that demonstrates the integration of hardware and software for real-time control. The system primarily consists of an Arduino UNO or ESP8266 microcontroller, a motor driver module, DC motors, Bluetooth or Wi-Fi module, a mobile application, and a power supply. The microcontroller acts as the brain of the car, processing signals received from the mobile device and generating appropriate control signals to the motor driver. The motor driver, in turn, regulates the operation of the DC motors, enabling the car to move forward, backward, left, and right. The Bluetooth module (HC-05 or HC-06) establishes a wireless link between the mobile device and the Arduino UNO, allowing commands sent from a smartphone application to be received and executed by the car. Alternatively, the ESP8266 can enable control through Wi-Fi, making it possible to operate the car over a local network or the internet. The mobile application serves as the user interface, providing intuitive control buttons and directional commands. The working principle of the car relies on the serial communication between the mobile device and the microcontroller. Upon receiving a command, the microcontroller interprets it and drives the motors accordingly. For instance, when the forward command is sent, the controller powers both motors in a manner that propels the car ahead. Similarly, left or right turns are executed by varying the direction and speed of the motors on either side. The project also addresses essential aspects of hardware integration, including proper power management, motor selection, and structural design to ensure stability and smooth operation. By employing Arduino UNO or ESP8266, the system benefits from simplicity, affordability, and widespread community support, making it an ideal choice for educational and hobbyist projects. This mobile-controlled car provides a practical demonstration of IoT and robotics concepts, illustrating the interaction between software and hardware. It can be used for educational purposes to teach students about wireless communication, motor control, and embedded systems programming. Additionally, the car can serve as a prototype for advanced applications, such as automated surveillance, obstacle detection, and smart transportation systems.

This paper critically examines the Saemaul Undong movement model or New village Movement, a highly successful rural development initiative implemented in South Korea during the 1970s, and its potential applicability as a government model for rural transformation in Laos. The study employ a systematic literature review (SLRs) combined with comparative document analysis. The paper provides a comprehensive analysis of the key principles, strategies, and outcomes of the Saemaul Undong, considering the unique socio-cultural, economic, and political contexts of Laos. By incorporating relevant citations in critical sentences, the essay ensures academic rigor and credibility. By adopting the ideology of self-help and voluntary participation in community projects, Laos can tap into the potential of its rural population and accelerate rural development and poverty eradication efforts. Laos' government and policymakers should raise people's awareness of the importance of community development projects, as well as increase capacity in mindset skills for a better life and self-help mindsets.

This project presents the design and implementation of a 4 Degrees of Freedom (4-DOF) robotic arm controlled using an Arduino Uno and a joystick module. The robotic arm consists of four servo motors that provide rotational movement for the base, shoulder, elbow, and gripper, enabling flexible and precise manipulation of objects. A joystick is used as the primary input device, allowing intuitive real-time control of the arm?s movements along multiple axes. The Arduino Uno processes the analog signals from the joystick and converts them into appropriate control signals for the servo motors. This system demonstrates an effective low-cost solution for basic robotic manipulation, suitable for educational purposes and small-scale automation tasks. The project highlights the integration of hardware and software to achieve smooth and responsive control, offering a foundation for further enhancements such as wireless control, sensor feedback, or automated operation.

Seaweeds are important source of bioactive molecules with known beneficial effects on human health. In the present study was carried out to investigate the antibacterial efficacy against some human pathogenic bacteria of various organic solvent extracts hexane, chloroform, ethyl acetate, and methanol viz., S. asperum reacted positively against selected human bacterial pathogens. Staphylococcus aureus, Bacillus subtilis, Streptococcus pyogenes, Escherichia coli, Pseudomonas aeruginosa, Salmonella typhimurium, Vibrio cholera, Shigella flexneri, Proteus mirabilis and Proteus vulgaris by disc diffusion method. The antibacterial activity was performed by disc diffusion, Minimum Inhibitory Concentrations (MIC) and Minimum Bactericidal Concentrations (MBC). The mean zone of inhibition produced by the extracts in agar diffusion assays against the tested bacterial strains ranged from 7.1 to 17.8 mm. The MIC values were between 62.5 and 500 ?g /disc, while the MBC values were between 250 and 1000 ?g /disc. The highest mean of zone inhibition (17.8 mm) and lowest MIC (62.5 ?g /disc) and MBC (250 ?g /disc) values were recorded in ethyl acetate extract. The phytochemical analysis of hexane, chloroform, ethyl acetate and methanol extracts of S. asperum had showed the presence of terpenoids, tannins and steroids. This study revealed that ethyl acetate extract of S. asperum is a source of antibacterial compounds for the treatment of human bacterial pathogens.

This study examined the relationship between business owners? financial literacy and the growth of Micro, Small, and Medium Enterprises (MSMEs) in Bayelsa State, Nigeria. It specifically investigated how budgeting skills, debt management skills, and saving habits influence sales growth. A survey research design was employed, and data were collected from 400 registered MSME owners using a structured questionnaire, of which 296 were completed and analyzed. The instrument was tested for reliability using Cronbach?s Alpha, with coefficients ranging from 0.743 to 0.816. Data analysis was conducted with the aid of SPSS version 26. Descriptive statistics were used to summarize respondents? financial literacy and sales performance, while Pearson correlation analysis examined the relationships between financial literacy dimensions and sales growth. The results revealed positive and significant correlations between budgeting skills (r = 0.573, p < 0.01), debt management skills (r = 0.530, p < 0.01), and saving habits (r = 0.511, p < 0.01) with sales growth. These findings indicate that MSME owners with higher financial literacy are better able to manage resources and achieve improved business performance. The study concluded that financial literacy positively and significantly correlates with the sales growth of MSMEs in Bayelsa State. Based on these findings, it recommended that government agencies and business support organizations implement regular financial literacy programs focused on budgeting skills, financial institutions provide access to tailored and affordable credit to enhance debt management, and MSME development programs promote disciplined saving practices through reinvestment and dedicated business savings accounts.

This study empirically investigates the influence of financial information quality on corporate performance (Return on Assets, ROA) and investment decisions (Research and Development Intensity, R&DI) within the Nigerian Healthcare sector over a ten-year period (2014?2023). A census sampling of eight listed firms was analyzed using panel regression (OLS) based on the theoretical frameworks of Agency, Signaling, and Information Asymmetry theories. Financial information quality was proxied by the Accruals Ratio (AR), Quality of Earnings (QoE), Incidence of Restatement (IoR), and Audit Quality (AUQ) The analysis revealed that AR and QoE are robust and significant predictors of firm outcomes. Specifically, Accruals Ratio (AR) showed a significant negative relationship with both R&DI and ROA (p < 0.01), suggesting that higher reliance on accrual-based earnings leads to lower asset efficiency and reduced R&D investment. Similarly, the Quality of Earnings Ratio (QoE) also demonstrated a significant inverse relationship with both R&DI and ROA (p < 0.01), contradicting traditional expectations and suggesting that in this specific sample, the proxy may capture unique operational or conservative reporting characteristics. Conversely, the discrete indicators, IoR (due to a lack of variance in the sample) and AUQ, were found to be statistically non-significant determinants of R&DI and ROA. These findings underscore the critical role of accrual management and earnings quality in driving firm value and resource allocation. The study recommends that stakeholders prioritize cash-based metrics and that management implement stricter internal controls to limit discretionary accruals, thus enhancing the integrity of financial reporting for sound economic decision-making.

Social media platforms continuously produce vast amounts of unstructured textual data containing opinions and emotional expressions. Analyzing such information manually is impractical; therefore, automated sentiment analysis has become essential for understanding public attitudes and supporting data-driven decision-making. This paper introduces the SMART Sentiment Interpreter (SSI) ? a machine learning?based system designed to accurately classify sentiments from social media comments as positive, negative, or neutral. The system employs a carefully designed preprocessing pipeline to minimize linguistic noise, extracts meaningful features using SentiWordNet, and performs classification through a Support Vector Machine (SVM) model. The proposed approach aims to convert unorganized social media text into actionable insights, enabling organizations to better understand user opinions and trends.

The quality of river water is a critical indicator of environmental health and public safety, especially in rapidly urbanizing regions. The Vrishabhavathi River in Bengaluru has experienced significant pollution due to industrial discharge, domestic wastewater, and urban runoff, making continuous monitoring essential. This project presents a Real-Time Water Monitoring System for the Vrishabhavathi River at the Vrishabhavathi Barrage using Internet of Things (IoT) technology. The system integrates sensors to measure key water quality parameters such as pH, turbidity and temperature. These sensors interface with a microcontroller-based IoT platform, which collects data and transmits it to a cloud server through Wi-Fi or GSM connectivity. The real-time data is visualized on a dashboard, enabling authorities and stakeholders to monitor pollution levels remotely and take timely corrective actions. The proposed system provides a cost-effective, scalable, and automated solution for continuous water quality assessment, contributing to better environmental management and supporting efforts toward river restoration and sustainable urban development.

An IoT-based attendance system using RFID offers a modern, efficient solution to the challenges of traditional attendance tracking in academic and organizational settings. By integrating RFID technology with microcontrollers and cloud platforms, the system enables automated, real-time recording of attendance data. Each individual is assigned a unique RFID tag, which is scanned by an RFID reader connected to an IoT-enabled device such as an ESP32 or NodeMCU. Upon scanning, the data is instantly transmitted to a cloud database?such as Firebase or Google Sheets?where it is stored, monitored, and analyzed. This approach eliminates manual errors, prevents proxy attendance, and enhances administrative efficiency. The system is cost-effective, scalable, and adaptable to various environments, making it ideal for smart campus initiatives and digital transformation efforts. Overall, it contributes to a more transparent, secure, and intelligent attendance management process. Implementing an IoT-based RFID attendance system in a school involves integrating RFID technology with microcontrollers and cloud platforms to automate and streamline the attendance process. Each student or staff member is issued a unique RFID tag, which is scanned by an RFID reader connected to a Wi-Fi-enabled microcontroller such as an ESP32 or NodeMCU. When a tag is scanned, the system captures the unique ID along with a timestamp and transmits this data to a cloud-based platform like Firebase or Google Sheets.

This paper presents the design and implementation of a low-cost quadcopter integrated with a servo- based pick-and-drop mechanism for lightweight payload handling. The system utilizes an SG90 micro- servo, a custom mechanical gripper, and an RC receiver interface to enable precise object pickup and release during flight. Stability is achieved through optimized PID tuning and self-leveling configurations on the flight controller, ensuring controlled operation even under payload variations. Experimental results demonstrate that the prototype effectively performs remote pick-and-drop tasks with reliable maneuverability, making it suitable for applications in remote delivery, disaster assistance, and industrial inspection. The outcome highlights the feasibility of incorporating simple mechanical actuation into UAV platforms, offering a scalable base for future advancements such as autonomous navigation and enhanced payload capacity.

This study investigates the optimization of concrete bracing layouts to enhance the buckling resistance and overall structural performance of reinforced concrete (RC) frames. Comparative analysis between braced and unbraced frames reveals that the inclusion of bracing systems significantly improves stability by reducing story forces and increasing buckling resistance. Among the evaluated configurations, Inverted V bracing demonstrated the highest buckling factor improvement?up to 89%?followed by diagonal, X, V, and K bracing types. Results also highlight the influence of column slenderness ratio, indicating that higher slenderness leads to reduced buckling capacity. Additionally, modelling the slab as a shell element yielded higher buckling factors compared to membrane modelling, with variations of 12?18% across stories. The incorporation of P-delta effects provided more realistic and conservative buckling predictions. Frames with shear walls performed better than those without, further enhancing structural stiffness and safety. Comparison between ETABS and ANSYS outputs showed variations of 0?25%, demonstrating acceptable consistency between software tools. Overall, the study confirms that adding optimized bracing systems to RC moment-resisting frames significantly increases their strength, stiffness, and buckling resistance.

Unplanned pregnancies among young adults remain a significant public health and social concern in Ghana, with far-reaching implications for parental well-being, child outcomes, and family stability. While considerable research has focused on adolescent mothers, far less attention has been given to the experiences, attitudes, and responsibilities of young fathers. This quantitative study examines the perspectives and attitudes of young fathers in Ghana regarding their paternity responsibilities in the context of unplanned pregnancies. Using a structured questionnaire administered to 350 young fathers aged 18?30 across selected communities and institutions, the study investigates key variables including readiness for fatherhood, financial preparedness, emotional involvement, perceived societal expectations, and barriers to fulfilling paternal roles. Descriptive and inferential analyses, including correlations and regression modeling, were conducted to assess the extent to which socio-economic factors, cultural beliefs, and interpersonal dynamics influence paternal responsibility. Preliminary findings indicate considerable variations in young fathers? willingness and ability to provide financial, emotional, and caregiving support, shaped largely by income level, relationship stability, and social pressure. The study contributes empirical evidence to Ghana?s limited literature on male involvement in unintended parenthood and offers insights for policies and programs aimed at improving father engagement and reducing adverse outcomes associated with unplanned pregnancies.

Father involvement in child upbringing is essential for the cognitive, emotional, and social development of children. However, in many Ghanaian communities, cultural norms and traditional expectations continue to assign childcare responsibilities primarily to mothers, limiting fathers? participation in everyday child-rearing activities. This quantitative study examines the role of culture and tradition in shaping father involvement in child upbringing across selected regions in Ghana. Using a structured questionnaire administered to 450 fathers, the study investigates how cultural beliefs, clan and lineage systems, traditional gender roles, and intergenerational practices influence paternal engagement. The study aims to determine the extent to which these cultural and traditional factors predict fathers? participation in physical care, emotional support, discipline, and decision-making concerning their children. The findings are expected to contribute to a deeper understanding of how cultural norms shape contemporary fatherhood practices and to offer recommendations for encouraging more balanced parenting roles in Ghana.

The rapid growth of electric vehicles (EVs) has increased the demand for sustainable and convenient charging solutions. Conventional plug-in charging methods face challenges such as dependency on grid electricity, limited charging infrastructure, and user inconvenience. To address these issues, this project proposes a solar-powered wireless electric vehicle charging system that integrates renewable energy with modern power transfer technology. The system utilizes photovoltaic (PV) panels to harness solar energy, which is then conditioned using maximum power point tracking (MPPT) and stored in a battery or directly supplied to the wireless charging unit. Wireless power transfer (WPT) based on inductive coupling enables efficient and contactless energy delivery from the ground-based transmitter coil to the receiver coil installed in the vehicle. This eliminates the need for physical connectors, reducing wear and improving user convenience. The proposed design enhances the sustainability of EV charging by leveraging clean solar energy, while also offering flexibility, safety, and ease of use. Such a system can significantly contribute to reducing greenhouse gas emissions, supporting smart grid integration, and accelerating the adoption ofeco-friendly transportation

This paper focuses on continuation of our previous paper1 by systematically compiling monographs of Vitamins, Minerals, Amino acids, Fatty acids, Phytopharmaceuticals, Herbs and Herbal Products, Vaccines and immunosera for human use, Blood and blood related products, Biotechnology derived Therapeutic products and Radiopharmaceutical preparations in Volume-III of Indian Pharmacopoeia-2022 based on their Number, Assay Standards, Drug categories (API/Formulation), and Therapeutic Uses. The initiative aims to create a reliable, well-organized, and easily accessible digital repository that enhances knowledge, promotes clarity, and supports academic and Professional advancement. By preserving and presenting this critical scientific information, the project contributes to continuous learning, research innovation, and the maintenance of high standards in Pharmaceutical quality control.

Highway Vehicle Protection Using Robot is an intelligent safety system designed to prevent accidents and ensure smooth traffic flow on highways. The system uses a robotic unit equipped with sensors, cameras, and communication modules to monitor road conditions, detect stalled or damaged vehicles, and alert approaching drivers in real time. When a vehicle breaks down on the highway, the robot automatically moves to the spot, places safety indicators such as warning lights or reflective signs, and sends notifications to traffic authorities. This helps reduce the chances of collisions, especially during low-visibility conditions like night or fog. The project aims to enhance highway safety by offering a fast, automated, and reliable solution for vehicle protection and accident prevention.piezoelectric property, from classic inorganics such as PZT to lead-free materials, including biodegradable and biocompatible materials. These inherent properties of flexible piezoelectric harvesters make it possible to eliminate conventional batteries for lifetime extension of implantable and wearable IoTs. This paper describes the progress of piezoelectric perovskite material-based flexible energy harvesters for self-powered IoT devices for biomedical/wearable electronics over the last decade.

Wireless networks have become integral to homes, offices, and public infrastructure, increasing the risk of unauthorized access, data theft, and cyber-attacks. PROTEXA,is an automated Wi-Fi security analyzer built using Python and Nmap to evaluate vulnerabilities in local networks. The system performs port scanning, service detection, OS fingerprinting, and vulnerability assessment. It includes a custom timeout mechanism that ensures long-running scans automatically skip faulty hosts, making the process efficient for real-world deployment.
In addition to static scanning, PROTEXA includes a Live Wi-Fi Monitoring module that identifies connected devices through ARP scans, detects suspicious network activity, and provides a real-time security overview. This dual-function approach makes PROTEXA a practical tool for cybersecurity awareness, institutional audits, and network administrators.

Fake videos especially deepfakes and AI-generated manipulations have emerged as a severe threat on social media platforms. Traditional detection techniques struggle against high resolution, generative model based forgeries. This paper proposes a novel hybrid machine learning framework that integrates dual branch spatio-temporal transformers, biophysical signal reconstruction and cross modal audio video consistency analysis. The system extracts spatial artifacts, temporal irregularities, micro expression deviations, and remote photoplethysmography (rPPG) signals and then validates lip speech alignment using a contrastive audio video model. A stacked ensemble classifier ultimately predicts authenticity. Experimental analysis shows significant improvement in detection accuracy compared to classical CNN and single-modality models.

SmartHouse AI represents a significant advance- ment in residential energy management, combining IoT-based sensing, digital-twin simulation, and machine learning to create an intelligent, self-optimizing ecosystem. The system?s three-tier architecture integrates edge computing for real-time processing, cloud infrastructure for scalable analytics, and a digital twin for predictive modeling and simulation. By continuously learning from real-time sensor data and historical patterns, SmartHouse AI achieves up to 30.

The increasing number of abandoned, injured, and stray animals has highlighted the urgent need for a unified system that connects animal welfare organizations, volunteers, donors, veterinary services, and the public. This project proposes the development of a Unified Platform for Animal Welfare and NGO Collaboration, designed to streamline communication, coordination, and resource sharing among various stakeholders. The platform serves as a centralized hub where NGOs can register and manage their activities, users can report animal-related incidents, and donors can contribute directly to verified campaigns. Features such as real-time case tracking, adoption listings, emergency response requests, and volunteer management aim to bridge existing gaps and improve operational efficiency. By leveraging digital technology, the system fosters collaboration, enhances visibility of welfare initiatives, and ensures timely intervention, ultimately contributing to the protection, rehabilitation, and well-being of animals in need.

Efficient emergency vehicle movement is crucial for saving lives, yet urban traffic congestion often delays response times. This study presents A Real-Time AI Model for Emergency Vehicle Detection and Priority Allocation, designed to enhance emergency mobility in smart city environments. The proposed system employs deep learning?based computer vision techniques to accurately detect emergency vehicles from live traffic camera feeds and classify them in real time. Upon detection, an intelligent traffic signal controller dynamically allocates priority through adaptive signal timing, route optimization, and immediate clearance of traffic lanes.The model integrates convolutional neural networks (CNNs) for vehicle recognition, along with a rule-based decision engine for traffic signal preemption. Experimental results demonstrate high detection accuracy and significant reductions in emergency vehicle waiting time at intersections. By automating the identification and prioritization process, the proposed AI-driven system improves emergency response efficiency, reduces congestion, and supports the development of smarter and safer urban traffic infrastructures. This work highlights the potential of real-time AI systems to transform traditional traffic management and enhance emergency service delivery

The increase in industrial activities surrounding Indian cities has exerted considerable strain on local water resources, primarily due to the discharge of untreated or inadequately treated waste. Kalwar, located near Jaipur in Rajasthan, serves as a prime example of this issue; where small to medium-sized manufacturing units release their effluent into open drains, ponds, and agricultural fields. This research aimed to analyze the chemical and physical characteristics, assess the level of heavy metal contamination, evaluate the ecological effects, and identify potential health risks to humans arising from industrial discharge in Kalwar. Water samples were collected from effluent discharge points and adjacent water bodies, while selected soil samples were examined to measure heavy metal accumulation. The physical and chemical parameters measured included pH, temperature, electrical conductivity, turbidity, total dissolved solids (TDS), dissolved oxygen (DO), biochemical oxygen demand (BOD), chemical oxygen demand (COD), and nutrient concentrations. Heavy metals, specifically chromium (Cr), lead (Pb), and cadmium (Cd), were quantified using Atomic Absorption Spectroscopy. The results indicated that several water quality parameters exceeded permissible limits. Turbidity (35 NTU), BOD (45 mg/L), and TDS (1200 mg/L) were above the acceptable thresholds established by BIS/WHO standards, while DO (3.2 mg/L) was below the recommended levels, indicating contamination from organic and chemical pollutants. Concentrations of heavy metals were found to be high, with Cr (0.12 mg/L), Pb (0.08 mg/L), and Cd (0.02 mg/L) producing risk quotients (RQ) greater than 1, signifying considerable risks to both the environment and human health. The ecological evaluation revealed a dominance of pollution-tolerant aquatic plants such as water hyacinth and algae, a lack of fish populations, and a moderate presence of macroinvertebrates, indicating a stressed ecosystem and reduced biodiversity. This study highlights the serious implications of industrial effluent on water quality, biodiversity, and human health in the region. Some recommendations consist of establishing facilities for waste treatment, employing techniques that do not produce any liquid waste, routinely monitoring water quality, establishing protected zones, adopting eco-friendly practices in industries, and rehabilitating impaired water sources. These measures are crucial for minimizing pollution, safeguarding aquatic ecosystems, and ensuring the sustainable management of water resources in industrial regions near urban areas.

The integration of artificial intelligence (AI) and machine learning (ML) into agriculture has revolutionized the way farming operations are managed and optimized. This paper presents a predictive and climate-aware framework that leverages machine intelligence to enhance crop productivity and sustainability. The proposed system combines data-driven models, IoT-enabled sensing, and predictive analytics to support real-time decision-making across critical agricultural processes. Environmental parameters such as temperature, humidity, soil moisture, and nutrient levels are continuously monitored and analyzed alongside historical weather and crop performance data. Machine learning algorithms?including Random Forest, Support Vector Machine, and Long Short-Term Memory (LSTM) networks?are utilized to predict crop yield, detect disease onset, and recommend adaptive irrigation and fertilization strategies under varying climatic conditions. The framework also integrates remote sensing and satellite imagery to identify spatial variability and optimize resource utilization. Experimental validation demonstrates that the proposed system significantly improves yield prediction accuracy and reduces input waste, thereby promoting sustainable and climate-resilient farming practices. This study underscores the transformative potential of AI-driven decision support systems in enabling intelligent, efficient, and adaptive agricultural ecosystems.

Conventional e-learning platforms largely rely on static content delivery and offer minimal real-time learner support, which limits their ability to address individual learning gaps. These shortcomings often result in poor engagement, slower concept mastery, and inefficient doubt-resolution processes. To address these limitations, this work proposes a Smart E-Learning Platform enhanced with an AI-driven tutor capable of adapting dynamically to each student?s learning pace, behavior, and performance patterns. The system integrates deep-learning and NLP models to interpret student queries, generate context-aware responses, and recommend relevant learning material. A personalized recommendation engine adjusts content difficulty and sequencing based on continuous learner profiling, while the analytics dashboard captures granular metrics such as accuracy trends, engagement timelines, and learning bottlenecks. Real-time feedback mechanisms ensure that students receive immediate clarification without relying on external instructors. Evaluation includes a controlled user study and system-level benchmarking. Quantitative metrics?model accuracy, latency, engagement rate, time to resolve doubts, and completion ratios?demonstrate substantial performance gains over traditional e-learning setups. Users showed faster comprehension, higher retention, and increased motivation to complete modules. The platform also reduced instructors? workload by automating repetitive support tasks.

This study examined the influence of safe water and sanitation on the prevalence of waterborne diseases in Kafanchan. To achieve this objective, a structured questionnaire was administered to 120 respondents selected from various communities in the area. The chi-square statistical method was used to test the study?s hypotheses. Findings revealed that community members lack regular access to clean and safe drinking water, prompting many households to boil or treat water before consumption. Poor water quality was found to contribute significantly to frequent health issues. The study also revealed limited access to proper toilet facilities, widespread open defecation, and inadequate waste disposal and drainage systems. Furthermore, cases of waterborne diseases?including cholera, typhoid, and diarrhea?were found to be common, with children and the elderly being the most vulnerable. Government provision of safe water and sanitation facilities was reported to be inadequate, and community participation in maintaining hygiene infrastructure was low. Based on these findings, the study recommends substantial government investment in modern water supply systems such as boreholes, water treatment plants, and pipe-borne water schemes, especially in underserved areas. It also calls for increased provision of public toilets and household-level adoption of hygienic sanitation facilities. Public awareness campaigns on hygiene, sanitation, and waterborne disease prevention should be intensified using media and community platforms. Effective waste management systems should be established, alongside strict enforcement of water and sanitation regulations. Strengthened collaboration among government agencies, NGOs, and private stakeholders is essential for improving water and sanitation services. Finally, the establishment of rapid response teams and provision of free or subsidized treatment during outbreaks are recommended to reduce health risks and protect vulnerable populations.

This study examines the Implications of Gas Flaring on Infant Mortality and Birth Defects in the Niger Delta region of Nigeria. A survey design was adopted, and a structured questionnaire was administered to 120 respondents drawn from selected communities across the region. The Chi-square statistical tool was used to test the hypotheses. Findings reveal a high level of awareness of gas flaring as a common practice in the Niger Delta and show that communities situated near flaring sites experience significantly higher health challenges. The study further established that gas flaring contributes to increased infant mortality, heightened respiratory complications among infants, and greater incidences of birth defects due to exposure to toxic emissions. Environmental degradation resulting from continuous flaring was also found to indirectly worsen health outcomes for newborns and pregnant women. The study concludes that the health risks posed by gas flaring far outweigh its economic benefits and that cases of birth defects remain largely underreported in affected communities. The study recommends stricter government enforcement of environmental regulations, increased penalties for non-compliance, and full implementation of policies such as the Nigerian Gas Flare Commercialization Program (NGFCP). It further advocates the establishment of well-equipped healthcare facilities, routine maternal and infant health screening, and extensive community education on the risks of exposure to flaring activities. Additionally, it calls for investment in gas capture technologies and cleaner energy alternatives, alongside the development of a national health database and continuous research on the long-term health impacts of gas flaring.

Career decision-making is a critical milestone in a student?s academic and professional journey. Traditional counseling relies heavily on human expertise and subjective assessments, which can be time-consuming and inconsistent. Recent advancements in artificial intelligence (AI) and machine learning (ML) provide opportunities to automate and personalize career guidance. This paper presents an AI-powered career guidance system that predicts suitable career paths based on academic performance, skills, and interests. A Random Forest classifier achieved 88% accuracy on a dataset of 1000 anonymized profiles. The system includes a web-based interface, ranked recommendations, required skills, and learning resources. Comparative analysis with SVM and Decision Tree classifiers is provided. Future enhancements include integrating personality traits, NLP, and live job market analytics. Our proposed system demonstrates a scalable, unbiased, and accurate AI-based career guidance solution.

Agriculture continues to face challenges such as labor shortages, inefficient resource utilization, and crop losses due to delayed disease detection. This paper proposes AgriRobo, an intelligent farming system that integrates IoT sensors, image processing, and AI-based disease detection with automated control mechanisms. The system continuously monitors soil and environmental parameters, captures high-resolution crop images, and applies machine learning models to detect plant diseases and their severity. Based on analysis, AgriRobo enables smart irrigation, targeted chemical spraying, and resource optimization, thereby reducing manual effort, chemical waste, and operational costs. The proposed solution promotes sustainable farming practices and enhances crop productivity, making it particularly beneficial for small and medium-scale farmers.

This article examines the socio-economic drivers and vulnerability associated with blesser-blessee relationships among African youth. Using a mixed-methods synthesis of recent empirical studies, policy reports, and qualitative accounts, the paper maps how poverty, food insecurity, disrupted education, and aspirations for social mobility shape engagement in transactional relationships. Primary epidemiological and programmatic findings indicate that transactional relationships increase the odds of HIV, sexually transmitted infections, adolescent pregnancy, and intimate partner violence. The article situates these empirical patterns within psychosocial narratives that emphasise both constrained agency and active negotiation of material needs. The theoretical frame draws on sexual economy perspectives and intersectional vulnerability to explain how gender, age disparity, and structural inequality produce risk. Methodologically the paper uses secondary analysis of population-level survey results, synthesis of qualitative studies among university and township populations, and content analysis of recent academic and sector reports. Key findings show that poverty and food insecurity are consistently associated with higher likelihood of involvement with blessers, while school attendance and higher household wealth reduce the probability of such relationships. Health consequences include elevated HIV and STI risk and reduced power to negotiate condom use. The discussion highlights implications for integrated interventions that combine economic strengthening, access to education, targeted sexual and reproductive health services, and community-level gender-transformative approaches. The paper concludes by recommending multi-sectoral programming and longitudinal research to better model causal pathways and evaluate prevention strategies.

This article explores the growing normalisation of small-scale bribery in South Africa?s public service, commonly referred to as the ?cold drink? or ?something small? practice. This informal term reflects a subtle yet deeply embedded form of corruption in which citizens are expected to pay for a service that should be free or to avoid punitive administrative action. Although its scale appears minor, normalised gratuity forms part of a broader pattern of systemic corruption that steadily erodes accountability, equality, and citizen trust. The purpose of this paper is to conceptualise the ?cold drink? economy, situate it within current research on everyday corruption, and discuss its implications for governance. The paper draws on a narrative review of recent literature published between 2020 and 2025. The findings suggest that normalised gratuity thrives where institutional oversight is weak, frontline discretion is high, and citizens lack confidence in complaint pathways. Further, the practice reinforces power asymmetries between officials and the public, disproportionately affecting low-income individuals. The article concludes that understanding everyday corruption is vital for strengthening reform efforts, as these small exchanges cumulatively undermine state legitimacy. Practical implications include the need for targeted anti-corruption training, clearer performance monitoring for frontline officials, citizen feedback mechanisms, and behaviour-focused interventions. Future research should use case studies and ethnographic approaches to examine how citizens and officials rationalise these exchanges.

This article investigates the expanding practice of awarding honorary doctorates through unregulated or unaccredited institutions in South Africa, and considers the consequences this trend poses for academic integrity and public confidence in higher education. Drawing on a qualitative document analysis of public records, media coverage, institutional statements, and guidelines issued by regulatory bodies between 2019 and 2025, the study traces clear patterns of title misuse, institutional ambiguity, and persistent regulatory blind spots. The analysis shows that several entities operating without registration from the Department of Higher Education and Training (DHET) have continued to issue honorary doctorates to well-known public figures. In many cases, recipients have subsequently adopted the title ?Dr? in professional or social settings, blurring the line between ceremonial recognition and earned academic achievement. This pattern contributes to a gradual erosion of trust in formal doctoral qualifications, particularly when the public is unable to distinguish between symbolic honours and credentials obtained through rigorous academic processes. The findings further suggest that weak enforcement mechanisms, limited public awareness about the nature of honorary degrees, and the financial or reputational motives of organisations presenting these awards all reinforce the problem. The article argues for a more coherent regulatory response, improved public communication, and the introduction of transparent national standards governing the award and use of honorary titles. Strengthening these areas is essential for protecting the credibility of South Africa?s higher education system and preserving the integrity of legitimate doctoral scholarship.

Sleep is fundamental to physical health, cognitive function, and emotional well-being. In India, insomnia affects approximately 25.7% of the population, with higher prevalence in women and older adults. Chronic insomnia is associated with impaired daytime functioning, increased risk of anxiety, depression, cardiovascular disease, diabetes, and obesity.

The rise of 5G technology has transformed the way hostel students interact with academic and non-academic digital platforms. With ultra-fast internet speed, low latency, and improved connectivity, 5G enhances access to online learning materials, virtual classes, digital libraries, and cloud-based tools. However, constant accessibility has also increased screen dependency, stress levels, digital addiction, and reduced real-life social interaction. This study explores both the positive and negative impacts of 5G on hostel students. Using a structured questionnaire and literature review, the research highlights that while 5G boosts academic productivity, it simultaneously contributes to psychological challenges. The study concludes with recommendations to maintain digital balance and promote healthier usage habits among hostel students.

This paper focuses on systematically compiling monographs based on their Number (A-Z), Assay Standards, Drug categories (API/Formulation), and Therapeutic Uses. The initiative aims to create a reliable, well-organized, and easily accessible digital repository that enhances knowledge, promotes clarity, and supports academic and Professional advancement. By preserving and presenting this critical scientific information, the project contributes to continuous learning, research innovation, and the maintenance of high standards in Pharmaceutical quality control.

Riyaz, or systematic daily practice, forms the cornerstone of all Hindustani classical music training, particularly for instrumentalists such as sitar players. The present study examines the measurable impact of structured daily riyaz on two vital aspects of sitar performance: finger dexterity and tonal clarity. The research was conducted over a 30-day period involving intermediate-level sitar students from the University of Kashmir. Using an experimental-practical approach, the study analyzed changes in technical proficiency, tonal quality, and overall expressive control through daily monitored practice routines. Data were collected through performance recordings, visual analysis of hand movement, and self-evaluation logs. Findings demonstrate that consistent daily riyaz significantly enhances muscle coordination, right-left hand synchronization, and tonal resonance, thereby improving both the technical and aesthetic outcomes of performance.

Farming has always been the heart of human life. It gives us food, raw materials, and plays a major role in shaping civilizations. But today, agriculture is facing bigger challenges than ever before. Problems like climate change, irregular rainfall, shortage of water, poor soil quality, and a fast-growing population are putting heavy pressure on farmers. Experts predict that by 2050, there will be almost 10 billion people on Earth, and food production needs to increase by about 70% to feed everyone. Achieving this goal using old farming methods alone will be very difficult.

This study investigates the development and validation of a real-time gesture recognition system using multimodal data fusion and grounded in embodied cognition theory. The proposed system integrates RGB, depth, and skeletal data through attention-based graph convolutional networks and hierarchical LSTM modules. Empirical results demonstrate recognition accuracies of 94.2% (lab), 87.6% (healthcare), and 82.1% (home) environments, with response latency below perceptual thresholds. User-centered evaluation revealed substantial improvements in adaptation and satisfaction for systems informed by embodied cognition. The findings illuminate new pathways for gesture computing as a natural and robust modality in human-computer interaction, offering both technical rigor and theoretical advancement.

This research paper investigates the pivotal role of Intelligent Chatbots in augmenting the functionality and user experience of modern web applications. Unlike older, rule-based systems, these advanced chatbots leverage sophisticated technologies such as Natural Language Processing (NLP), machine learning, and contextual understanding to simulate nuanced, human-like conversations, thereby automating routine tasks and offering interactive, continuous support to users. The paper thoroughly discusses the evolution of conversational systems, detailing their progression from early pattern-matching bots like ELIZA (1966) to contemporary, powerful assistants built on large language models (LLMs). The work outlines the necessary architectural design components and proposes a robust integration methodology. This methodology includes requirements analysis, data collection and preprocessing for NLU model training, selection of transformer-based architectures (like BERT or GPT), hybrid dialogue management, and secure web integration using APIs and JavaScript SDKs. Furthermore, the study explores the practical applications of intelligent chatbots across diverse domains, including e-commerce and education, providing case studies that demonstrate significant operational benefits. Experimental results show substantial improvements, such as a 40% reduction in customer response time and a 60% increase in engagement rates in integrated web applications. For instance, an e-commerce chatbot reduced support calls by 55% and improved sales conversion rates by 30%.However, the paper also addresses critical challenges inherent in deploying conversational AI, including guaranteeing data privacy (compliance with GDPR/CCPA), ensuring scalability under peak traffic, handling out-of-scope queries gracefully, minimizing bias in model responses, and maintaining user trust through transparent operations.

The adoption of Artificial Intelligence (AI) within the realm of Customer Relationship Management (CRM) is changing the dynamics of the business-customer relationship by giving the companies who adopt it the loyalty of the customers besides the revenue. The use of AI in CRM is not only depicted as a tool to help organizations keep their customers but also as a distinct advantage giving them a grip over competitors. By employing technologies such as machine learning, natural language processing, predictive analytics, and hyper- personalization, present-day CRM systems are capable of supporting real-time, customized messaging that not only meets customers' needs but also goes beyond offering typical service. Worldwide SMEs, e-commerce and emerging markets resulting data have shown that when firms use AI they get as much as a 77.7% rise in customer satisfaction and 60.7% higher retention rates. Not with standing the case of data privacy, ethical issues, and integration costs ? all of which concerns 40% of the professionals mentioned ? AI?s merits are obvious and persuasive. This paper introduces "Strategic Human-AI Synergy" as a new idea, pointing out that customer loyalty based on goodwill, ethical to customer and company, and using people's ability alongside the most modern technology is the way of the future. Responsible, scalable AI CRM solutions are to be the organization that leads customer fidelity and sets the global standards of best practices. The research has thrown light on the necessity of continuous ground breaking and keeping the ethical issues under control in the changing landscape of customer interaction. Future research must investigate the partnership of human intuition and AI-driven insights in the creation of customer emotional bonds that are deeper, thus securing and trusting long-term growth that is sustainable.

Background: By directing the creation of diagnostic images while reducing patient radiation exposure, image quality parameters are crucial elements of radiography practice. To ensure competency in clinical settings, it is essential to evaluate students' comprehension of these factors. Aim: To evaluate radiography students' knowledge and comprehension of picture quality factors. Methods: A designed questionnaire comprising demographic information and 25 knowledge-based items about exposure settings, picture quality determinants, artifacts, and digital imaging was used to perform a cross-sectional survey among 132 radiography students. Simple Excel-based descriptive statistics, such as percentages and frequencies, were used to examine the data. Results: Most students showed a solid grasp of the principles of image quality. The majority of participants accurately answered questions about motion artifacts, noise, spatial resolution, contrast resolution, kVp, mAs, and PACS functions. The average knowledge score was 21.5 ? 2.1 (out of 25). No student received a low score; 69.7% of students received an exceptional score, 25.8% received a high score, and 4.5% received an average score. Grid alignment, detector quantum efficiency, and exposure index interpretation were shown to have small knowledge gaps. Conclusion: Radiography students demonstrated a good degree of understanding of picture quality characteristics, demonstrating the efficacy of the current teaching strategies. Targeted reinforcement in a few specific areas may further improve competency and support high-quality radiographic practice.

Android,the most widely used mobile operating system globally, runs billions of devices and has created a large ecosystem for mobile apps. Kotlin, a programming language released by JetBrains in 2011 and officially supported by Google in 2017, has quickly become the preferred choice for building Android apps. This study explores how Kotlin solves common developer issues such as long code, frequent Null Pointer Exceptions, app crashes, and UI delays. With features like safe null handling, compact code structure, coroutines, and compatibility with Java, Kotlin improves development speed, enhances app stability, and supports better architecture, making it a better option for future and cross-platform mobile development.

The RAQ (Rake Allocation and Query) Decision Support System is designed to optimize rake formation in railway freight operations. It automates the process of allocating wagons, minimizing costs, and improving utilization efficiency. The system integrates data such as wagon availability, stockyard inventory, and customer demand to generate optimized rake plans. Using optimization and simulation techniques, RAQ helps planners make data-driven decisions through an interactive dashboard. This solution enhances operational efficiency, reduces delays, and supports smarter logistics management in rail freight systems.

This paper examines telecommunication base station (TBS) carbon remediation with the development of a versatile modeling framework along with analysis and forecasting of CO? emissions based on energy use data. Emissions of diverse forms of energy were calculated, considering their emissions factor (EF). The source EF was multiplied with region specific carbon intensity factors. An exploratory analysis was done using Python machine-learning packages, including regression and Random Forest regression to identify emission drivers and provide time-series forecasts for emissions. The analysis reveal there's a strong dependence on emissions on energy source, with renewables proposed as having substantial reduction potential. There were also higher emissions during the weekdays for operational load. Overall, the research highlights the influence of moving to renewables and patterns of operating TBS for lowering the burden on the environment, and intends to provide practical context for policy makers and network managers looking to achieve sustainability goals.

Poverty and unemployment are twin evils bedeviling the Nigerian state today. Since 2018 Nigeria has been the headquarters of poverty in the world after overtaking India. The unemployment rate has risen from 14.2 in 2016 to 41 in the year 2023 respectively. This menace has given rise to social vices, such as vote buying, cybercrimes, prostitution, kidnapping, armed robbery, human trafficking, political thuggery, and hooliganism in Nigeria. The Nigerian government have initiated several programmes to stem the rise of these problems, some of which include SURE-P, National Cash Transfer, Trader Moni and N-POWER social intervention programmes. N-POWER which is the focus of this study is part of the ongoing national social investment programme of the federal Government of Nigeria aimed specifically at job creation through human capital development and empowerment. Recent data has revealed that about 1,500,000 unemployed Nigerians have so far employed in the N-POWER programme between 2016 to 2023. This social programme was initiated by President Muhammed Buhari to curb the menace of unemployment in Nigeria between the ages of 18-35. Existing literature has fiercely criticised this programme but few have systematically looked at whether this programme has achieved its objective of meaniful youth empowerment and to know if this programme has created job opportunities in Osun State. Arising from the foregoing, the study assessed the N-POWER programme in Osun State between the periods of 2016- 2023. This was done using the following research questions: i. how has the N-POWER programme achieved its target of job creation in Osun State between 2016 and 2023? ii. how has N-POWER achieved its objective of how impactful the N-POWER programme has on the socioeconomic livelihood of the beneficiaries in Osun state between 2016 -2023? Systems theory was adopted for this study. The data for the study were generated through documentary and survey methods. The study found that this Government social intervention programme of N-POWER has not achieved its target of job creation nor has N-POWER been impactful on the socioeconomic livelihood of the beneficiaries in Osun State between the periods under study. It therefore recommended, among other things that the condition of service for N-POWER volunteers should be reviewed to boost their level of job motivation towards job efficiency and punctuality.

A geoelectrical investigation was carried out in Yenagoa and its environs to evaluate the protective capacity and groundwater potential of the aquifer system using Vertical Electrical Sounding (VES) data and Dar-Zarrouk parameters. A total of sixteen (16) VES stations were established employing the Schlumberger array configuration with maximum current electrode spacing ranging from 100 to 200 m. The interpreted geoelectric sections revealed three to five subsurface layers comprising topsoil, clay/sandy clay, and sand units of varying thicknesses and resistivities.The Longitudinal Conductance (S) and Transverse Resistance (T) values derived from the VES results were used to classify the aquifer protective capacity and transmissivity potential, respectively. The calculated S values range between 0.016 and 1.13 ???, indicating poor to good protective capacity, while the T values vary from 1,150 to 38,500 ??m?, corresponding to low to very high transmissivity potential. Contour maps of S and T show that aquifers in the northern sectors are better shielded and less susceptible to pollution, while those in the SE and SW regions require careful groundwater management and monitoring..The study demonstrates the effectiveness of integrating Dar-Zarrouk parameters with resistivity data for groundwater assessment and protection zoning. It further emphasizes that aquifer productivity and protection vary significantly across Yenagoa, largely controlled by the thickness and composition of the overlying clayey layers and the nature of the underlying sandy aquifer units.

The increasing demand for sustainable agricultural production has driven the rapid development of smart greenhouses, which offer controlled environments with significantly lower resource consumption and greenhouse gas emissions compared to traditional open-field farming. Intelligent greenhouse monitoring systems enable precise regulation of environmental factors such as temperature, humidity, light intensity, CO? concentration, soil moisture, and nutrient levels. These systems play a crucial role in improving crop growth efficiency while reducing the incidences of diseases and pest outbreaks. By enabling real-time data analysis and automated control, intelligent monitoring reduces the need for chemical fertilizers and pesticides, ultimately promoting safer and higher-quality food production.

The increasing demand for secure software applications has brought the topic of application-level security to the forefront of modern software engineering. Java, one of the most widely used programming languages, provides a comprehensive suite of built-in security mechanisms designed to protect data integrity, prevent unauthorized access, and mitigate vulnerabilities such as code injection, buffer overflows, and insecure serialization. This research paper explores in depth the various security mechanisms integrated into Java?s architecture, including the Java Security Manager, Access Control, ClassLoader, Cryptography APIs, Authentication and Authorization (JAAS), Secure Socket Extension (JSSE), and Java?s sandbox model. Furthermore, it highlights the best practices for secure Java development, covering topics such as input validation, secure coding, encryption, and secure deployment strategies. The study concludes by analyzing Java?s strengths and limitations in securing applications, providing developers with recommendations for implementing comprehensive security strategies in both traditional and enterprise-level Java applications.

Medical image processing is increasingly using Hadamard Transform (HT) algorithms because of their efficacy in noise reduction and feature extraction. In this work, four HT methods?Standard HT, Fractional HT, Fast HT, and Adaptive HT?are evaluated for X-ray image processing using a Deep Q-Network (DQN)-based reinforcement learning (RL) framework. By preprocessing X-ray pictures, the HT approaches convert features into learning-optimized domains. Fractional HT performs the best overall, according to the results, and is especially good at identifying minute irregularities in noisy pictures since it excels at feature extraction and noise resistance. Despite being computationally less efficient, adaptive HT's dynamic parameter tweaking allows it to be versatile across a variety of datasets. The most computationally efficient method is Fast HT, which is appropriate for real-time applications but has a limited level of noise resistance. Standard HT is a trustworthy baseline that offers balanced performance. The results imply that the HT technique selection should be in line with the demands of the work, including computing efficiency or picture noise levels. The promise of HT in improving RL-based medical imaging processes is highlighted by this comparative analysis, which also draws attention to the trade-offs between various HT approaches.

Background: Bedtime procrastination?the voluntarily delay of sleep, without external constraints, despite knowing its negative consequences?is increasingly prevalent in adolescents and has implication on academics and health. However, its relationship with adolescent flourishing remains largely understudied. Objective: To examine the relationship between bedtime procrastination and multidimensional flourishing using the PERMA model (Positive Emotion, Engagement, Relationships, Meaning, Accomplishment) in a cross-sectional sample of Indian adolescents. Methods: A total of 269 adolescents (M age = 15.78 years, SD = 1.17) completed the 9-item Bedtime Procrastination Scale (BPS) and 15-item PERMA-Profiler via online survey using google forms. Pearson correlations, one-way ANOVA, and independent t-tests examined relationships between bedtime procrastination and flourishing across demographic groups. Results: Bedtime procrastination showed a significant negative correlation with overall flourishing (r = -0.3601, p < 0.001, r? = 0.1297). Adolescents with high bedtime procrastination reported approximately 18% lower flourishing scores than low procrastinators (F(2,266) = 14.796, p < 0.001, ?? = 0.1001). Differential effects emerged across PERMA dimensions: Meaning (r = -0.3405) and Accomplishment (r = -0.2868) most strongly affected; Engagement (r = -0.0873) weakly related. Smartphone owners showed significantly higher bedtime procrastination (t(267) = 2.136, p = 0.034, d = 0.274). Conclusions: Bedtime procrastination represents a meaningful predictor (negative relationship) of adolescent flourishing, particularly affecting future-oriented dimensions.

The daily process of outfit selection presents a significant cognitive and emotional burden for many individuals, driven by decision fatigue, social pressures, and a lack of stylistic confidence. While Artificial Intelligence (AI) has permeated the fashion industry, existing solutions often lack the nuanced understanding required to provide truly personalized and context-aware guidance. This paper introduces DOPPL, a novel AI-powered grooming and outfit selection platform. DOPPL utilizes a hybrid deep learning architecture that synergizes visual feature extraction with personalized user profiling to deliver holistic style recommendations. The core of DOPPL is a two-stage model. First, a pre-trained VGG-16 Convolutional Neural Network (CNN), fine-tuned on the large-scale DeepFashion dataset, performs robust clothing item recognition and extracts high-dimensional visual feature vectors. Second, these features are ingested by a hybrid recommendation engine combining content-based filtering for visual similarity and user-centric collaborative filtering for personalization based on user profiles, occasion, and feedback.

The study examined the leadership implications of sectoral work ethic differences in Ghana by assessing how worker personality traits influenced attitudes toward work, productivity patterns, and organizational behaviors across key economic sectors. Guided by trait and behavioral leadership theories, the study adopted a descriptive design and collected data from employees across the public, private, and informal sectors. Quantitative and qualitative approaches were used to generate data regarding prevailing work ethic orientations and the personality factors that shaped them. Findings showed that work ethic varied considerably across sectors, with differences associated with levels of conscientiousness, agreeableness, locus of control, and achievement motivation. The results further indicated that leadership effectiveness was significantly influenced by the extent to which leaders understood sector-specific personality dynamics and adopted adaptive strategies to motivate diverse workforce groups. The study concluded that sectoral work ethic patterns in Ghana were deeply influenced by personality traits shaped by socio-cultural experiences, institutional norms, and economic expectations. It recommended leadership approaches that integrate personality-responsive strategies, contextual understanding, and sector-sensitive motivation to enhance organizational performance.

21st-century Indian classrooms demand a teaching framework that is simple to apply, powerful in impact, and holistic in development. While several pedagogical, leadership, and reading models exist independently?such as UEVM?Vision Framework, TAC Teacher Leadership Model, the 7D Holistic Teaching Framework, and the PODSCORB?SQ3R?GK Integrated System?no research has unified these innovations into a single, scalable, teacher-friendly meta-model. This study introduces the OMNIPEDAGOGY FRAMEWORK, the first-ever integrated model that connects Classroom Pedagogy, Teacher Leadership, Learning Psychology, Administrative Planning, Reflective Practice, and Reading Mastery into one continuous teaching-learning cycle. The model was developed through 35+ years of classroom observation, field experimentation, reflective teaching diaries, and mixed-methods action research conducted across government and private schools in Andhra Pradesh.

Consecrated life within the Catholic Church represents a distinct vocation through which individuals commit themselves to serving God profoundly and radically. This vocation encompasses not only the sanctification of the consecrated individuals themselves but also contributes to the spiritual welfare of humanity at large. Consequently, it is necessary that Catholic consecrated men and women derive a sense of fulfilment and satisfaction from their jobs. This study examined the relationship between response styles to religious superiors and job satisfaction among Catholic consecrated men and women within selected religious communities in the Karen area of Nairobi, Kenya, employing an embedded design. The research was theoretically anchored in two frameworks: Egunjobi?s theory of child response styles to parenting and Affective Event Theory. The primary aim was to establish the relationship between these response styles to religious superiors and job satisfaction within the target population. Quantitative data were collected using the adapted Child Response Styles Scale (CReSS) and the Job-related Affective Well-being Scale (JAWS), complemented by qualitative data from semi-structured interviews with eight purposively selected respondents. The study population comprised 288 individuals, from which a sample of 167 respondents was drawn. Quantitative data collection was facilitated via Google Forms and subsequently analyzed, incorporating both descriptive and inferential statistical techniques. Qualitative data were subjected to thematic content analysis. The results revealed a statistically significant, moderate positive correlation between response styles to religious superiors and job satisfaction. Based on these findings, it is recommended that Catholic religious communities place greater emphasis on cultivating genuine interpersonal relationships and effectively managing interpersonal conflicts to promote satisfaction in one's job.

Some learners have difficulty with some subjects but do not ask for help. This leads to learning gaps and persistently low performance in academic activities. Many of the traditional educational approaches, which often provide generalized content, do not acknowledge or identify specific learner weaknesses in a learner's proficiency in a given subject, or offer appropriate supporting content. This article proposes using large language models (LLMs) - particularly GPT - and related methods, such as Retrieval-Augmented Generation, to analyze quiz or test performance in order to provide meaningful, personalized feedback to learners. This work would identify the weak concepts, parse a learner's quiz results and match learner-errors to curricular objectives. The system would then utilize LLM to provide simplified explanations of the weaknesses, provide personalized follow up questions, and suggest possible learning resources along with appropriate videos using YouTube Data API.

This research examines the potential of institutional heritage tourism, specifically focusing on Shantiniketan, West Bengal, as a tool for poverty mitigation and community development. Shantiniketan, renowned for its association with Kaviguru Rabindranath Tagore and its unique educational philosophy, represents a significant cultural and historical asset. This study investigates how the influx of tourists, attracted by the institutional heritage, can be leveraged to generate sustainable economic opportunities for the local community. Through surveys, interviews with residents, tourism stakeholders, and institutional authorities, and observations, it analyses the existing tourism infrastructure, the economic impact of tourism on the community, and the challenges faced in maximizing its benefits. The study assesses the extent to which tourism revenue is distributed within the community, focusing on the participation of marginalized groups, including artisans, small business owners, and agricultural workers.

The construction industry contributes significantly to global greenhouse gas emissions and resource consumption. This study evaluates and compares the environmental performance of precast concrete and cast-in-situ construction systems through a Life Cycle Assessment (LCA) approach. Using ISO 14040 and 14044 standards, cradle-to-grave system boundaries were defined, encompassing raw material extraction, manufacturing, transportation, construction, operation, and end-of-life phases. Inventory data were collected from regional precast plants and conventional construction sites. Results show that the precast system exhibits 25?35% lower CO? emissions, 20% reduction in energy use, and significant waste minimization due to controlled production and reduced material wastage. However, higher transportation impacts and factory energy use slightly offset the gains. The findings highlight the potential of precast technology as a sustainable alternative to traditional methods when combined with optimized logistics and renewable energy sources.

Autism Spectrum Disorder (ASD) is a neurological condition that has a lifelong impact on an individual's ability to interact and communicate with others. It can be diagnosed at any point in life and is considered a "behavioral disorder" since its symptoms typically manifest during the first two years of life. The onset of ASD begins in childhood and continues into adolescence and adulthood. With the increasing application of machine learning techniques in the field of medical diagnostics, this paper explores the potential of using methods such as Na?ve Bayes, Support Vector Machine, Logistic Regression, K-Nearest Neighbors (KNN), Neural Networks, and Convolutional Neural Networks (CNN) to predict and analyze ASD in children, adolescents, and adults. The proposed techniques are tested on three publicly available ASD datasets. The first dataset involves screening children for ASD, with 292 instances and 21 attributes. The second dataset pertains to ASD screening in adults, containing 704 instances and 21 attributes. The third dataset is related to ASD screening in adolescents, with 104 instances and 21 attributes. After applying various machine learning techniques and addressing missing data, the results indicate that CNN-based models yield superior performance on all datasets, achieving accuracy rates of 99.53%, 98.30%, and 96.88% for ASD screening in adults, children, and adolescents, respectively.

In an effort to reduce the corrosion impact on mild steel within the oil well industry, Vigna subterranea leaf extract (VSLE), utilized as a green biomass and immersed in HCl, was employed as a corrosion inhibitor. An analysis of the phytochemicals present in the extract was conducted, and the elemental makeup of the mild steel was determined. The weight loss method was evaluated through gravimetric analysis. Kinetic and thermodynamic parameters were assessed, and the adsorption isotherm was analyzed using the Langmuir isotherm model. The results indicate that the steel's composition was predominantly iron (Fe), accounting for 97.26%. The phytochemical analysis of the extract revealed the presence of flavonoids, phenols, saponins, alkaloids, tannins, steroids, and terpenoids within the organic biomass extract. A low rate of corrosion (ROC) and a high efficiency of inhibition (EOI) were recorded. The Langmuir isotherm model was determined to be the best fit, effectively describing the corrosion inhibition mechanism of mild steel. According to the thermodynamic data, the negative ?G_ads suggests that the adsorption process is chemisorption. The study concluded that the VSLE extract, when treated with hydrochloric acid, could function as an effective inhibitor for steel corrosion in an oil well environment.

This article explores the role of supervision as a mechanism for organizational learning within municipal institutions, with particular focus on policy implementation. While municipalities are tasked with implementing developmental policies that directly affect citizens? quality of life, they often face challenges such as limited capacity, policy incoherence, and poor performance outcomes. Supervision, when strategically applied, provides opportunities for continuous feedback, knowledge sharing, and adaptive learning within organizations. This study adopts a qualitative document analysis approach, reviewing municipal audit outcomes, policy implementation reports, and recent scholarly literature (2020?2025). Findings indicate that supervision enhances organizational learning by facilitating iterative reflection on policy implementation processes, identifying gaps between policy design and practice, and institutionalising corrective measures. Municipalities that embed supervision into policy implementation frameworks demonstrate greater adaptability, improved compliance, and more effective service delivery outcomes. However, learning is constrained by factors such as hierarchical cultures, political interference, and limited supervisory capacity. The study employs Organizational Learning Theory and Systems Theory to explain how supervision facilitates knowledge transfer, institutional memory, and adaptive governance. Implications highlight that supervision should not be viewed narrowly as compliance but as an enabler of organizational learning and innovation. Recommendations include integrating reflective supervisory practices into policy implementation cycles, capacitating supervisors with learning-oriented tools, and fostering collaborative learning platforms. This article contributes to the growing discourse on public sector learning by conceptualising supervision as a strategic mechanism for enhancing municipal performance and policy effectiveness.

This article investigates the role of digital supervision in enhancing accountability and performance within local government institutions. The increasing adoption of digital technologies, such as electronic performance management systems, e-governance platforms, and digital audit tools, has transformed oversight practices in municipalities. This study examines how technology-based supervision mechanisms influence accountability, transparency, and service delivery outcomes. Employing a qualitative document analysis approach, the research draws on municipal audit reports, policy documents, and recent scholarly literature published between 2020 and 2025. The findings indicate that municipalities integrating digital supervision mechanisms demonstrate improved oversight capacity, greater financial accountability, and more efficient service delivery. Digital systems reduce human error, limit opportunities for corruption, and provide real-time performance monitoring. However, the study also reveals challenges including digital divides, inadequate ICT infrastructure, and resistance to change among officials, which hinder full digitalisation of supervisory processes. The article applies New Public Management (NPM) and Institutional Theory to frame digital supervision as both a managerial innovation and an institutional adaptation to demands for transparency. The implications highlight that digital supervision, when supported by strong institutional capacity and political will, enhances accountability and municipal performance. Recommendations include investing in ICT infrastructure, capacitating municipal staff, institutionalising e-governance platforms, and developing integrated monitoring frameworks. The study contributes to the discourse on digital governance by situating technology-enabled supervision as a strategic pathway for strengthening local government performance.

This article evaluates the indirect influence of supervision on local economic development (LED) by examining how municipal oversight mechanisms shape growth-oriented initiatives. Municipalities are tasked with creating enabling environments for LED through infrastructure development, investment promotion, and support for small businesses. However, weak governance, corruption, and poor oversight often undermine such initiatives. This study employs a qualitative document analysis of municipal audit reports, Integrated Development Plans (IDPs), and scholarly literature published between 2020 and 2025. The findings suggest that supervision indirectly shapes LED outcomes by ensuring accountability in resource use, promoting transparency in project implementation, and reinforcing policy alignment with development priorities. Municipalities with stronger oversight practices are more likely to sustain LED initiatives, attract investor confidence, and deliver inclusive growth. Conversely, weak supervision is linked to project failures, misallocation of resources, and diminished citizen trust. The study applies Principal-Agent Theory and Developmental State Theory to explain the indirect pathways through which supervision influences municipal growth outcomes. The article concludes that supervision must be reframed as both a governance and developmental tool. Recommendations include strengthening municipal oversight structures, capacitating LED units with monitoring tools, and integrating supervisory feedback into LED planning. The findings contribute to scholarship by linking oversight functions to broader developmental outcomes, positioning supervision as a strategic enabler of local economic growth.

This study presents a sustainable, zero-cost pedagogical innovation model designed for economically challenged schools in developing regions. Integrating the PODSCORB management framework with the SQ3R cognitive learning strategy, the model emphasizes structured instructional planning, student leadership ecology, and continuous formative assessment?without the need for financial resources, technology, or specialized materials. Data were collected from seven low-resource schools involving 412 students across Grades 6?10. Using a mixed-methods action research design, the study examined changes in comprehension, retention, problem-solving skills, classroom engagement, and teacher workload. Findings reveal significant improvements in academic performance, student participation, and learner autonomy. Teachers reported reduced instructional stress, clearer lesson flow, and increased classroom stability. The model aligns with global research on structured pedagogy, retrieval practice, and mastery learning, demonstrating that systematic teaching?not funding?is the primary determinant of learning outcomes. This study provides a scalable, replicable framework suitable for developing regions seeking to enhance educational quality with zero financial investment. Implications for policy, teacher professional development, and equity in global education are discussed.

Introduction: The relocation of a healthcare facility is often initiated to expand capacity, improve access, infrastructure and patient care, or to meet regulatory standards. Such transitions are however rarely seamless and often induce disruptions to interpersonal and professional networks. This study assessed the adaptation challenges faced by healthcare workers in a recently relocated tertiary hospital in Nigeria as well as the coping mechanisms they adopted in mitigating these challenges. Methodology: A cross-sectionalal analytical study was carried out using a self-administered questionnaire to obtain data among 130 healthcare workers, selected through multi-stage sampling technique from the tertiary hospital. Data were analyzed using SPSS version 26. Statistical significance was set at p ? 0.05. Results: The mean age of the respondents was 34.96 ? 8.82 years and most of them (66.9%) were aged less than 40 years. Sixty five point four (65.4%) of the respondents experienced adaptation challenges following the relocation. The most commonly cited challenges were cost of transportation (50.8%) followed by changes in workflow or processes (32.3%) and difficulty in navigating the new physical environment (31.5%) while the most significant challenge adapted to was changes in work schedule (40.8%). the most common coping mechanism adopted by the respondents was maintaining a positive outlook (49.2%) followed by seeking support from colleagues (39.2%) and talking to family and friends (38.5%). Conclusion: Many of the healthcare workers experienced adaptation challenges following the relocation of the teaching hospital. This was mainly with transportation, changes in workflow, and unfamiliarity with the new environment and was worsened by the lack of involvement of most of the health worker in the planning process, lack of orientation, and inadequate pre-relocation training. The hospital management should therefore involve the staff in their planning processes and provide adequate orientations and trainings to the workers to lessen the adaptation challenges faced in future relocations.

The present study investigates the in vitro anti-inflammatory activity of a polyherbal extract composed of Garcinia indica, Musa paradisiaca, and Punica granatum peels. These plants, belonging to the families Clusiaceae, Musaceae, and Lythraceae respectively, are traditionally recognized for their therapeutic potential, including antioxidant, anticancer, antiviral, and anti-inflammatory properties. The research aimed to evaluate the synergistic effects of these combined herbal extracts using bovine blood, which closely resembles human blood in composition. The plant materials were collected, authenticated, and extracted through maceration and decoction using ethanol and water. Preliminary phytochemical screening revealed the presence of key bioactive constituents such as flavonoids, phenolics, glycosides, terpenoids, and saponins, which are known contributors to anti-inflammatory activity. The in vitro evaluation was conducted using assays for membrane stabilization, protein denaturation inhibition, and proteinase inhibitory activity. Results indicated that the polyherbal formulation exhibited significant anti-inflammatory activity, likely due to the combined effects of its phytoconstituents. These findings support the traditional use of these plants in inflammatory conditions and suggest their potential as a natural alternative to conventional anti-inflammatory agents.

The present study was conducted on 823 undergraduate students selected randomly from different undergraduate colleges affiliated with Mizoram University to determine their level of perception of the semester system. The perception scale developed by the investigator was used to collect information. The study revealed that the highest number of college students had a moderate perception of the semester system, and only a few students had extremely favourable or extremely unfavourable perceptions of the semester system in the undergraduate colleges of Mizoram.

Diseases affecting Dantamoola (periodontium) constitute a significant public health concern in developing countries and remain a leading cause of tooth morbidity and loss. Epidemiological data indicate that gingivitis alone affects nearly 50% of the population, largely attributable to inadequate oral hygiene practices and limited access to preventive dental care. In Ayurveda, gingivitis closely correlates with Shitada, an early-stage disorder of periodontal tissues described under diseases of the oral cavity. Shitada is primarily caused by the vitiation of Kapha and Rakta, resulting in classical clinical features such as spontaneous gingival bleeding, halitosis, inflammation, tenderness, and progressive gingival recession. If left untreated, this condition may alter the contour, stability, and position of teeth, ultimately leading to tooth mortality.

Mechanical energy storage technologies are gaining interest as a result of the rising demand for efficient and sustainable energy systems, and in particular, flywheels and springs. This paper focuses on a complete electrical engineering analysis of mechanical energy storage systems using spring and flywheel systems, including principles of operation, methods of energy production, efficiency limits and how small systems can grow to larger applications. The paper provides an evaluation of flywheel systems based on kinetic energy storage, high-speed operation, motor-generator integration and power electronics, while spring-based systems are evaluated based on elastic potential energy storage and different methods of converting mechanical energy to electrical energy, including both rotational and linear generators. The paper presents a comprehensive discussion of the energy losses associated with flywheel and spring systems due to friction, aerodynamic drag, electrical resistance, magnetic effects and material damping. Importantly, the author also provides a critical review of the "free energy" concept, and restates that flywheel and spring energy storage systems operate according to the first and second laws of thermodynamics, and therefore cannot produce energy unless there is an energy source for inputting energy into the system. In conclusion, the author states that flywheel technology is an established technology that has the capacity to support high-power short-duration energy storage applications; however, due to many limitations, spring systems are not currently able to support large-scale generation of electricity. Thus, flywheel and spring systems should be seen primarily as technologies for storing and managing energy.

The reproductive decisions of women in rural areas of Bihar are ingrained in complex systems of patriarchal structure, caste and class distinctions, and local values that collectively strip women of their bodily autonomy and life opportunities. The article examines how women's decisions on marriage timing, fertility, contraception, and access to maternal healthcare are influenced by gendered power relations. These choices are often subject based on qualitative research methods; the study combines in-depth interviews and focus group discussions with rural women, including members of self-help groups, as well As interviews with frontline health workers and community leaders from selected Bihar villages. The study reveals that husbands, senior in-laws and other community gatekeepers often "override or even outmode women's expressed preferences", while lack of gender-sensitive, reliable public health services means people are increasingly dependent on family control networks and informal providers. In the same way, women leverage shared areas of proximity, family ties, and experiential knowledge to establish limited forms of control over children, interpret reproductive choices, and reassess traditional norms concerning sexuality, childbirth, or spacing. The article presents a perspective on how reproductive decision-making is conceptualized as 'a crucial intersection between gender norms, structural poverty and state programmes' and calls for feminist, community-based interventions that focus on women's voices while rejecting policy frameworks that make them passive participants in population control or maternal health agendas.

This study investigated the effect of entrepreneurial cultural capital on the competitiveness of manufacturing firms in the South-South region of Nigeria. Using a positivist approach, a cross-sectional survey design was employed to collect primary data from 324 managerial respondents across 108 manufacturing firms. Entrepreneurial cultural capital (financial, intellectual, and cultural dimension) and competitiveness (cost, product, and technological) were measured using structured questionnaires. The descriptive statistics, Pearson correlation, and partial correlation were used to analyze data. The results showed meaningful positive associations among entrepreneurial cultural capital and each of the dimensions of competitiveness i.e. correlation coefficients were 0.638-0.680 at the level of significance of 0.01. These findings give a hint that the more the cultural capital, the greater the level of cost efficiency, product innovation, and technological development. The research arrives at the conclusion that entrepreneurial cultural capital is a determinant of competitive advantage of manufacturing firms in Nigeria. Such policy implications as the requirement to increase cultural awareness, social networking, and innovation-oriented practices are aimed at increasing firm competitiveness.

Benzene derivatives are core scaffolds in the field of medicinal chemistry, making up more than 60% of the drugs on the market. This is mainly because of their rigidity, interactions, and easily adjustable electronic properties that allow for precise target engagement. The review organizes these derivatives in a systematic manner, starting from monosubstituted phenols and going up to fused benzimidazoles/benzothiazoles. It also explains their physicochemical properties (logP 2, 4 being ideal, Hammett, modulation) as well as their structure, activity relationships (SAR) which are the main reasons that lead to 10, 50x potency improvements through para, substitution and electronic tuning. The biologically active compounds include antimicrobials (sulfonamides, MIC 0.39 g/mL anti, TB hybrids), anti, inflammatories (ibuprofen COX, 2 IC 0.3 M), oncology (pyrazoline, EGFR Phase I 45% PR), and nuclear pharmacy (Tc, sestamibi imaging). Highlighted are the recent advances from 2020 to 2025 that emphasize bioisosteres such as bicyclo[1.1.1]pentane (4x solubility enhancement) and AI/ML SAR models (r=0.94) as a way to fight off liabilities like CYP metabolism (30% attrition) and hERG blockade. Among the challenges discussed is the number of aromatic rings (>3 triples CYP risk) which is being tackled by sp, hybridization and pharmacogenomic personalization (CYP2D6*4 dosing). By synthesizing 88 references, this paper acts as a quantitative map guiding B.Pharm researchers through benzene, based drug design and forecasts a 70% prevalence of such drugs in 2030 precision therapies.

Sexting, a deviant and risky vice, is becoming prevalent among teens in Nairobi County primary schools due to technology advances of Gen. Alphas and peer pressure. This study aims to aid in exploration of the aspects of teenage involvement with explicit digital material, especially through the practice of sexting repercussions and its therapy. It will explore motivations that encourage this practice and the unintended exposure heavily influenced by the media.

Artificial Intelligence (AI) and cyber security are revolutionizing the banking and financial services industry by enhancing efficiency, security, and customer experience. This paper explores the impact of AI on banking operations, risk management, fraud detection, cyber security, and customer service. It discusses the challenges and ethical considerations associated with AI implementation while highlighting future trends that will shape the financial sector. The study aims to provide insights into the role of AI and cyber security in driving digital transformation in financial institutions.

Anxiety disorders represent the most prevalent category of psychiatric conditions globally, affecting approximately 284 million individuals worldwide. The neurobiological underpinnings of anxiety involve intricate interactions among multiple neurotransmitter systems, each contributing uniquely to the manifestation and maintenance of anxious states. This review examines the complex neurochemical architecture underlying anxiety, focusing on six principal neurotransmitter systems: corticotropin-releasing factor (CRF), dopamine, glutamate, norepinephrine, serotonin, and gamma-aminobutyric acid (GABA). Understanding these interconnected systems provides critical insights into both the pathophysiology of anxiety disorders and the development of more effective therapeutic interventions. The convergence of these neurotransmitter systems within key brain regions?particularly the amygdala, prefrontal cortex, hippocampus, and bed nucleus of the stria terminalis?creates a dynamic neurochemical nexus that determines individual vulnerability to anxiety and stress-related psychopathology.

Pradhan Mantri MUDRA Yojana (PMMY), launched in 2015, aims to expand credit access to micro and small enterprises through collateral-free loans categorized as Shishu, Kishor, and Tarun. This study evaluates the socio-economic impact of PMMY on beneficiaries in Telangana, investigating whether access to MUDRA finance has led to improved household incomes, enterprise growth, employment generation, and social empowerment. Using a mixed-methods design, the research draws on primary survey data from a stratified random sample of MUDRA borrowers across urban and rural districts in Telangana, supplemented by key informant interviews with bank officials, SHG leaders, and local NGO practitioners. Quantitative analysis employs descriptive statistics, income and employment change measures, and regression models to identify determinants of positive outcomes; qualitative data provide contextual understanding of barriers and enabling factors. Findings indicate that MUDRA beneficiaries reported modest but significant increases in enterprise turnover and household income, improved business diversification, and some creation of self-employment opportunities, with stronger impacts where financial literacy and market linkages existed. However, challenges such as delayed disbursement, limited working capital, and inadequate post-loan support constrained potential benefits. The paper concludes with targeted policy recommendations to strengthen credit delivery, business support services, and monitoring to maximize PMMY?s developmental outcomes in Telangana.

The rapid advancement of Artificial Intelligence (AI) is fundamentally reshaping the landscape of sports and fitness by enabling data-driven performance enhancement, personalized training, injury prevention, and health optimization. Traditional sports and fitness systems rely largely on experiential knowledge, standardized training protocols, and retrospective performance analysis, which often fail to accommodate individual variability and real-time adaptation. AI technologies?including machine learning, deep learning, computer vision, and predictive analytics?offer unprecedented opportunities to analyze complex physiological, biomechanical, and behavioral data at scale. This review critically examines the future role of AI in sports and fitness, focusing on emerging opportunities such as intelligent coaching systems, real-time performance monitoring, precision fitness programming, and athlete health management. At the same time, the review highlights key limitations, including data quality issues, algorithmic bias, ethical concerns, interpretability challenges, and unequal access to AI technologies. By synthesizing current literature, this paper identifies trends, opportunities, and constraints shaping AI adoption in sports and fitness ecosystems. The review argues that while AI has the potential to significantly enhance athletic performance and public health outcomes, its successful integration depends on responsible governance, human?AI collaboration, and a balanced approach that prioritizes ethical, transparent, and inclusive innovation.

Artificial Intelligence (AI) has rapidly emerged as a transformative force in exercise physiology and human performance analysis by enabling advanced data-driven insights into physical activity, training adaptation, and physiological responses. Traditional exercise physiology relies heavily on laboratory testing and linear statistical models, which often fail to capture complex, individualized, and dynamic performance patterns. AI techniques?including machine learning, deep learning, and predictive analytics?offer enhanced capabilities to analyze large-scale physiological, biomechanical, and behavioral datasets generated through wearable sensors, imaging systems, and digital training platforms. This review systematically examines the applications of AI in exercise physiology and human performance, focusing on performance prediction, training optimization, fatigue monitoring, and personalized exercise prescription. Existing literature indicates that AI-based models outperform conventional approaches in accuracy, adaptability, and real-time decision support. However, challenges related to data quality, interpretability, ethical concerns, and integration into applied settings persist. This review synthesizes current research, highlights methodological advancements, identifies limitations, and proposes future research directions. The findings emphasize the growing importance of explainable, athlete-centered, and ethically governed AI systems to support sustainable performance enhancement and health optimization across athletic and clinical populations.

Artificial Intelligence (AI) has emerged as a transformative tool in sports science, particularly in the domains of injury prediction and prevention. With the growing intensity of competitive sports and increasing injury incidence, traditional injury prevention approaches often fail to capture complex, non-linear interactions among biomechanical, physiological, psychological, and environmental risk factors. This systematic review synthesizes existing literature on the application of AI techniques?including machine learning, deep learning, and data-driven analytics?in predicting and preventing sports-related injuries. Peer-reviewed studies published over the last decade were examined to assess AI models, data sources, predictive accuracy, and practical implementation in real-world sports settings. The review reveals that AI-based models consistently outperform conventional statistical approaches in injury risk assessment due to their ability to process high-dimensional and longitudinal data. Wearable sensor data, training load metrics, medical history, and performance indicators are widely used inputs. Despite promising results, challenges remain related to data quality, model interpretability, ethical considerations, and integration into coaching and medical decision-making. This review highlights key trends, gaps, and future research directions, emphasizing the need for explainable, athlete-centered, and ethically governed AI systems. The findings provide valuable insights for researchers, sports practitioners, and policymakers seeking to leverage AI for safer and more sustainable athletic performance.

Adolescence is a period of fast growth and development. For girls, this growth usually begins between 10?12 years of age. During this time, many physical, mental, and emotional changes take place. Adolescent girls need special care because they play an important role in the health and well-being of future generations. Their nutritional needs are very important as they are preparing for adulthood and future motherhood. Poor nutrition during adolescence can cause health problems later in life. Eating a balanced and nutritious diet is essential for healthy growth and development. The main aim of this study is to understand the dietary habits and food patterns of school-going adolescent girls.

River basins are dynamic geomorphological systems shaped by the interaction of natural processes such as erosion, transportation, and deposition. However, increasing anthropogenic activities have significantly altered river basin morphology across the world. Activities such as dam construction, river channelization, sand and gravel mining, urban expansion, deforestation, and agricultural intensification have disrupted sediment regimes and flow characteristics. These interventions modify channel patterns, floodplain connectivity, riverbed elevation, and bank stability, often leading to environmental degradation and increased hazard vulnerability. This paper reviews geomorphological changes in river basins induced by human activities, drawing on existing literature and spatial?temporal studies. Emphasis is placed on how altered hydrological regimes and sediment supply affect channel morphology and basin evolution. The review highlights the role of Geographic Information Systems (GIS), remote sensing, and field-based geomorphic assessments in identifying and quantifying anthropogenic impacts. Findings indicate that human-induced geomorphological changes often exceed natural variability, resulting in long-term irreversible transformations. The study underscores the importance of integrated basin management and sustainable intervention strategies. Understanding anthropogenic geomorphological change is essential for river restoration, disaster mitigation, and sustainable water resource planning.

Climate change has emerged as a critical driver altering global and regional hydrological cycles, with monsoon-dependent regions experiencing pronounced variability in rainfall patterns. The Indian monsoon, which sustains agriculture, water resources, and livelihoods, has shown increasing spatial and temporal irregularities over recent decades. This study reviews the impact of climate change on monsoon rainfall patterns using Geographic Information Systems (GIS) and time-series modeling techniques. GIS enables spatial visualization and mapping of rainfall variability, trends, and anomalies, while time-series models such as ARIMA, SARIMA, and trend analysis help identify long-term changes and cyclical behavior in monsoon rainfall. Existing literature reveals significant shifts in rainfall intensity, frequency, onset, and withdrawal phases, attributed to rising temperatures, atmospheric circulation changes, and ocean?atmosphere interactions. The integration of GIS with statistical and time-series approaches enhances understanding of localized climate impacts and supports region-specific adaptation strategies. However, challenges remain related to data quality, scale mismatches, and model uncertainty. This review synthesizes current research, identifies methodological advancements and gaps, and highlights the need for robust spatial?temporal frameworks to assess climate-induced monsoon variability. The findings provide valuable insights for policymakers, planners, and researchers aiming to strengthen climate resilience in monsoon-dependent regions.

Logistics management plays a pivotal role in enhancing supply chain performance by ensuring the efficient flow of materials, information, and finances across organizational boundaries. In an increasingly competitive and globalized business environment, firms are compelled to adopt effective logistics management practices to achieve operational efficiency, cost reduction, customer satisfaction, and competitive advantage. This study examines the impact of key logistics management practices?such as transportation management, inventory control, warehousing, information sharing, and order fulfillment?on overall supply chain performance. Using insights from existing literature, the study establishes a conceptual linkage between logistics practices and supply chain outcomes including responsiveness, flexibility, reliability, and cost efficiency. The research emphasizes that well-coordinated logistics activities enhance integration among supply chain partners, reduce uncertainties, and improve decision-making. Furthermore, the study highlights the growing importance of digital technologies, data analytics, and collaboration in modern logistics management. The findings underscore that organizations investing in structured logistics practices experience superior supply chain performance compared to those with fragmented systems. The study contributes to supply chain management literature by providing a comprehensive framework that explains how logistics practices act as strategic enablers of supply chain effectiveness and sustainability.

The contemporary world is marked by escalating social conflicts arising from religious intolerance, political polarization, economic inequality, and cultural fragmentation. In this context, ethical and philosophical traditions rooted in non-violence and moral responsibility offer enduring solutions. This article examines the relevance of Buddhist values and Gandhian ideals in addressing present-day social conflicts. Buddhist philosophy emphasizes compassion, mindfulness, non-attachment, and the Middle Path as tools for reducing suffering and promoting harmony. Similarly, Gandhian ideals?grounded in truth (Satya), non-violence (Ahimsa), self-discipline, and social justice?provide practical frameworks for conflict resolution and social transformation. Through a conceptual and literature-based analysis, this study explores how these value systems contribute to peace-building, reconciliation, and ethical governance in pluralistic societies. The paper argues that integrating Buddhist and Gandhian principles into contemporary social, political, and institutional practices can foster dialogue, reduce aggression, and promote sustainable peace. The findings highlight the continued relevance of these philosophies in addressing structural and interpersonal conflicts and underline their applicability in education, governance, civil society movements, and global peace initiatives.

The contemporary Human Resources (HR) function is undergoing a radical transformation, moving from a purely administrative role to a data-driven strategic partner. This proposed research investigates the development and implementation of Deep Learning (DL) models to optimize core HR operations, addressing the inherent limitations of traditional statistical and shallow Machine Learning (ML) techniques when dealing with the high-dimensionality, complexity, and heterogeneity of modern HR data (e.g., text, time-series, and relational data). Specifically, this study aims to design a robust, explainable DL framework focusing on key operational areas like Talent Acquisition and Employee Retention Prediction. The research will employ an exploratory and developmental methodology, utilizing architectures such as Recurrent Neural Networks (RNNs) and Transformer models for sequential and unstructured data analysis, respectively. The utility of the proposed framework will be demonstrated through a specialized application within the Education Sector, focusing on the recruitment and retention of high-performing faculty and administrative staff. The expected outcome is a significant enhancement in the accuracy, efficiency, and fairness of HR decision-making, providing a replicable model for intelligent HR systems across various industries. This work contributes to the body of knowledge by bridging the gap between cutting-edge DL research and practical, ethical HR strategic operations.

This paper examines the relationship between financial inclusion and economic development through a comprehensive review of global theoretical and empirical studies. Financial inclusion?defined as access to and effective use of affordable financial services?has emerged as a critical driver of inclusive growth, poverty reduction, and economic resilience. Drawing on development economics, institutional theory, and financial intermediation literature, the paper synthesizes evidence on how financial inclusion influences macroeconomic outcomes such as GDP growth, employment generation, income equality, and human development. The analysis spans multiple levels, including individuals, households, firms, and national economies, highlighting the mediating roles of digital finance, institutional quality, financial literacy, and regulatory frameworks. The review also identifies boundary conditions such as income level, technological infrastructure, and governance capacity that shape the effectiveness of financial inclusion initiatives. The paper concludes with policy-relevant insights and a future research agenda aimed at strengthening the contribution of financial inclusion to sustainable and equitable economic development worldwide.

This paper examines how artificial intelligence (AI) reshapes strategic decision-making across organizational levels ? individual managers, teams, and the firm as a whole. Grounded in organizational theory and decision science, the study synthesizes conceptual and empirical insights to show how AI changes informational boundaries, speeds cyclical decisions, and redistributes authority. At the individual level, AI augments analytical capacity but introduces algorithmic bias and decision complacency; at the team level, AI acts as a coordination and communication substrate that can improve synchronization yet create transparency challenges; at the organizational level, AI enables new strategic options (dynamic pricing, personalized offerings, predictive maintenance), restructures governance, and affects firm boundaries via platformization and ecosystems. The conceptual framework presented highlights mediating mechanisms ? data quality, interpretability, trust, and governance ? and boundary conditions such as industry dynamism and regulatory intensity. The paper concludes with actionable managerial suggestions for designing human?AI decision processes, implementing governance structures, and building organizational capabilities that preserve strategic agility while safeguarding ethical and legal compliance. Practical implications and a research agenda provide directions for scholars and practitioners navigating strategy in an AI-pervasive world.

In recent years, Mutual Funds have emerged as a vital tool for ensuring financial security. They have not only contributed to India's economic growth but have also allowed families to benefit from the success of the Indian Mutual Fund Industry. With the increasing availability of information and awareness, more people are taking advantage of the opportunities presented by investing in mutual funds. Globally, there are numerous firms offering a variety of mutual fund schemes with different investment objectives. Today, mutual funds collectively manage funds that are comparable to or even exceed those held by banks.The primary objective of the research paper is to compare the Systematic Investment Plan (SIP) and One-time Investment, assisting investors in making the optimal choice. It aims to provide a straightforward investment strategy for individuals who are not experts in the field but still wish to profit from the market with minimal complications. The analysis in this paper utilizes the Compounded Annual Growth Rate (CAGR) for the lump sum investment plan and the Extended Internal Rate of Return (XIRR) for the Systematic Investment Plan. The research paper concludes that the one-time investment plan is superior to the systematic investment plan for investors with a lump sum amount to invest.

One of the most popular thermoplastic polymers is polypropylene (PP), which has balanced tensile qualities, good chemical resistance, low density, and affordability. The basic characteristics of polypropylene, including its mechanical strength, crystallinity, rheological properties, and thermal behavior, are all well covered in this paper. A detailed discussion is given of how these qualities are affected by molecule structure, tacticity, and processing conditions. In order to overcome intrinsic drawbacks, including low impact resistance and subpar high-temperature performance, current developments in polypropylene modification through copolymerization, mixing, and nanocomposite reinforcement are also emphasized. The review also explores emerging applications of polypropylene in advanced fields such as automotive lightweight components, medical devices, sustainable packaging, fiber and textile engineering, and electrical insulation. Special emphasis is given to bio-based polypropylene, recyclable PP composites, and high-performance PP grades developed to meet modern sustainability and performance requirements. Overall, this review aims to provide a concise understanding of polypropylene is evolving role in both traditional and high-value engineering applications.

The growing demand for natural and eco-friendly skincare products has driven interest in herbal soap formulations. Rosa rubiginosa, commonly known as sweet briar rose, is rich in fatty acids, vitamins, and antioxidants, which promote skin hydration, regeneration, and anti-aging effects. This review highlights the formulation strategies, evaluation parameters, applications, and therapeutic potential of Rosa rubiginosa-based herbal soaps. In addition to its antioxidant and antimicrobial activities, this formulation aligns with consumer preferences for safe, sustainable personal care. Future prospects in herbal cosmetics include improved standardization, clinical validation, and commercialization.

This review examined divine silence and human responsibility in accounting, presenting accounting as a moral and social practice rather than a purely technical function. The study focused on how the absence of direct divine intervention places ethical responsibility on accountants and financial professionals whose judgments shape financial reporting, auditing, sustainability disclosures, and decision-making with wide societal consequences. The review adopted a qualitative, interpretive methodology based on a systematic analysis of contemporary philosophical, theoretical, and empirical literature. Key perspectives drawn from stewardship theory, moral agency theory, accountability theory, critical accounting, and stakeholder and legitimacy theories are integrated with empirical evidence from ethics education, auditing, and sustainability reporting studies. The findings indicate that ethical behavior in accounting is largely driven by human agency, professional judgment, and internal moral commitment, while regulatory and institutional mechanisms function mainly as supportive safeguards. The review. also showed that ethical failures often arise from moral disengagement, organizational pressure, and weak ethical cultures rather than technical incompetence. The review concludes that acknowledging divine silence strengthens human accountability by emphasizing the moral role of accountants as stewards and ethical decision-makers. It recommends strengthening ethics education, promoting stewardship-oriented professional cultures, and embedding social and environmental responsibility into accounting practice to enhance transparency, trust, and long-term sustainability.

Ayurveda and Vastu Shastra are ancient Indian sciences that emphasize harmony between the human body, mind, and the external environment [1,4]. Both disciplines originated in the same philosophical era and share the objective of maintaining health through balance with nature [2,7]. In the contemporary world, rapid urbanization, modernization, and neglect of natural principles have resulted in an increased prevalence of psychosomatic disorders such as insomnia, indigestion, migraine, anxiety, and functional gastrointestinal disturbances [6,17]. Ayurveda explains these disorders through the interaction of Sharirika and Manasika Doshas [1,3,9], while Vastu Shastra focuses on the influence of the built environment on physical and psychological well-being [4,13]. Classical Ayurvedic references such as Kutiprave?hika Rasayana, Desha?Kala theory, Satvavajaya Chikitsa, and Ashta Ahara Vidhi Visheshayatana indicate the importance of environmental regulation in health maintenance [1,2,12,24]. The combined application of Ayurveda and Vastu Shastra may therefore serve as an effective preventive and supportive approach in psychosomatic health [16,20].

Herbal Aloe Vera Face Wash is a natural cleansing lotion designed to purify the skin from environmental pollutants, excess sebum, and microbial contaminants. Skin damage mainly results from the accumulation of dirt, oil, and dead skin cells, whereas long-term exposure to pollutants can lead to more serious conditions like acne, premature aging, and dermatitis. Ideally, a face wash should protect against both bacterial infection and dehydration. This study aimed to create a topical face wash formulation using fixed oils combined with selected medicinal plants. Consistent use of herbal face wash can lower the risk of acne vulgaris, comedones, and skin dullness. Face wash, also referred to as a facial cleanser, functions by emulsifying surface oils and lifting impurities to safeguard the skin. With the rising rates of skin sensitivity and the damaging effects of urban pollution, the demand for effective natural cleansing agents has increased. These agents have been shown to help alleviate symptoms associated with environmental skin damage. An effective face wash should be safe, nonirritating, non-toxic, stable, and provide complete removal of impurities without stripping natural oils. The developed face wash lotion includes skin-friendly components like aloe vera, butterfly pea flower, coconut oil, rose water, and vitamin E. Testing criteria included pH, spreadability, foaming ability, and skin feel. The prepared face wash exhibited a strong cleansing rating, excellent uniformity, consistency, and appearance, with no indications of phase separation, making it safe and non-irritating for skin application.

Agriculture today must balance the need for higher yields with the responsibility of conserving natural resources. Precision farming has already introduced tools like IoT sensors, drones, and GPS-based technologies to improve field management, yet decision-making often remains dependent on farmer experience. Cognitive systems enhance this process by applying artificial intelligence, machine learning, and advanced analytics to agricultural data. These systems learn from past patterns and real-time inputs, enabling accurate predictions of crop growth, soil health, irrigation needs, and potential pest infestations. With the integration of cloud platforms and big data, farmers gain timely recommendations for resource optimization and climate adaptation. Automated irrigation, targeted fertilization, and early disease detection further improve efficiency while reducing environmental impact. Such smart approaches not only increase productivity and profitability but also contribute to long-term sustainability and global food security.

Sexting, a deviant and risky vice, is becoming prevalent among teens in Nairobi County primary schools due to technology advances of Gen. Alphas and peer pressure. This study aims to aid in exploration of the aspects of teenage involvement with explicit digital material, especially through the practice of sexting repercussions and its therapy. It will explore motivations that encourage this practice and the unintended exposure heavily influenced by the media.

Alcohol consumption remains a significant global health concern, with acute and chronic effects extending beyond simple intoxication. The metabolism of ethanol generates two critical intermediates?acetaldehyde and acetic acid?whose pathophysiological roles have gained increasing recognition. This review examines the enzymatic pathways governing alcohol metabolism, explores acetaldehyde's toxic effects including hangover symptoms, oxidative stress, and carcinogenicity, and discusses acetic acid's emerging impact on cellular metabolism and inflammation. We analyze connections between these metabolites and major organ pathologies including hepatic damage, cardiovascular disease, and neurological dysfunction, while evaluating current and prospective therapeutic interventions. Understanding the complex interplay between alcohol metabolites and human physiology is essential for developing effective prevention and treatment strategies.

The increasing digitalization of higher education institutions has highlighted the need for efficient, responsive, and user-friendly systems to manage routine academic and administrative tasks. Traditional college management systems often require manual intervention, leading to delays, communication gaps, and reduced service effectiveness. This study proposes a smart AI-driven chatbot framework designed to automate and streamline key college management processes, including student queries, admission support, timetable access, fee information, attendance updates, and administrative services.The proposed chatbot integrates Natural Language Processing (NLP), Machine Learning (ML), and rule-based decision modules to understand user queries, generate context-aware responses, and provide real-time information retrieval. The system architecture includes a query-processing engine, knowledge base, database interfaces, and a conversational interface accessible via web or mobile platforms. Through continuous learning, the chatbot improves its accuracy and ability to handle diverse academic and administrative queries.This framework aims to reduce administrative workload, minimize response times, and improve overall communication within the institution. Initial testing indicates significant improvements in user engagement, accessibility, and operational efficiency. By implementing an intelligent chatbot system, colleges can enhance service delivery, promote digital transformation, and provide a more responsive and interactive experience for students, faculty, and administrative staff.The vast number of people who now use smartphones with a range of new applications is proof that technology is growing constantly. Chatbots utilise natural language to connect with human users in the same manner that people do. The main objective in creating a chatbot is to imitate human interaction patterns in order to give users the feeling that they are conversing with a person. Nowadays, a wide range of enterprises, from those that produce products to those that offer customer service and public relations, are increasingly using artificial intelligence. Users can now employ chatbots and other artificial intelligence (AI) technologies that are widely available online to get appropriate response to their queries.

The rapid growth of digital streaming platforms has created an urgent need for intelligent and personalized movie recommendation systems. This paper presents a BERT-Boosted Movie Recommendation Platform that integrates machine learning techniques with advanced sentiment analytics to improve recommendation accuracy and user satisfaction. The proposed system combines traditional collaborative filtering and content-based filtering with BERT-based sentiment extraction from user reviews to capture deeper contextual meaning and emotional cues. User ratings, textual reviews, and movie metadata are analyzed to generate enriched feature vectors, enabling the model to better understand user preferences and movie characteristics. Experimental results demonstrate that incorporating BERT-driven sentiment insights significantly enhances prediction precision, reduces recommendation errors, and addresses issues related to sparse user data. The BERT-boosted hybrid framework not only improves the overall recommendation quality but also provides a more dynamic and personalized user experience. This study highlights the potential of transformer-based natural language processing models in elevating next-generation recommendation systems

The Internet of Things (IoT) has emerged as a transformative force in contemporary business environments, fundamentally reshaping operational paradigms through intelligent automation and data-driven decision-making processes. This research investigates the multifaceted impact of IoT-driven automation on business productivity and managerial decision-making across various industry sectors. The study explores how interconnected smart devices, sensors, and automated systems generate real-time data streams that enable organizations to optimize resource allocation, streamline workflows, and enhance strategic planning capabilities. Through comprehensive analysis of existing literature and empirical evidence, this research examines the correlation between IoT implementation and measurable improvements in operational efficiency, cost reduction, and competitive advantage. The investigation reveals that IoT-driven automation facilitates predictive maintenance, supply chain optimization, quality control enhancement, and customer experience personalization, thereby creating substantial value propositions for businesses. Furthermore, the study addresses the challenges associated with IoT adoption, including cybersecurity vulnerabilities, integration complexities, and workforce adaptation requirements. The findings demonstrate that organizations successfully implementing IoT-driven automation experience significant productivity gains, averaging between 20-35% improvement in operational efficiency, while simultaneously enhancing decision-making accuracy through access to comprehensive, real-time analytics. This research contributes to the growing body of knowledge on digital transformation by providing insights into the mechanisms through which IoT technologies drive business performance improvements and offering practical recommendations for organizations seeking to leverage IoT automation for competitive advantage in an increasingly digital marketplace.

Sari-sari stores are a vital component of the Philippine grassroots economy, serving as the most accessible source of daily necessities for many communities. In recent years, the rapid expansion of financial technology (fintech), particularly digital payment platforms and e-wallet systems, has reshaped retail transactions and business operations among micro, small, and medium enterprises (MSMEs). This study examines the role of financial technology adoption in enhancing the operational effectiveness of sari-sari stores, with particular attention to transaction efficiency, cash flow management, customer responsiveness, and financial inclusion. Using a thematic literature analysis of studies published between 2022 and 2025, the research synthesizes empirical findings from national and regional contexts, including evidence from the Cordillera Administrative Region (CAR). The analysis reveals that fintech adoption contributes positively to faster transaction processing, improved financial awareness, enhanced customer convenience, and expanded access to formal financial services. However, the findings also highlight persistent barriers that limit the full realization of these benefits, such as unstable internet connectivity, limited digital literacy, cost sensitivity, and perceived transaction and cybersecurity risks. These constraints result in uneven adoption outcomes across urban and rural settings. The study concludes that while financial technology offers significant potential to strengthen the operational effectiveness and resilience of sari-sari stores, its impact is highly dependent on enabling infrastructure, affordable fintech solutions, and targeted capacity-building interventions. The findings provide valuable insights for policymakers, financial institutions, and development agencies seeking to promote inclusive digital transformation and sustainable growth among grassroots micro-enterprises in the Philippines.

This paper explores the application of Chomsky?s Context-Free Grammar (CFG) to the spatial and administrative hierarchy of India. By treating geographical entities?Zones, States, and Cities?as non-terminal and terminal symbols, we construct a generative model that validates the location of a city within its respective cardinal zone (North, South, East, and West). We first demonstrate the grammar through a structural analogy of a book, followed by a robust grammar for Indian geography. The paper includes parsing tables to demonstrate the syntactic validation of geographic strings and concludes with the implications of such models in Geographic Information Systems (GIS) and computational linguistics.

The insurance sector plays a crucial role in nation building by contributing to economic development, social security, and financial stability. This paper examines the multifaceted role of insurance in supporting national development, with a particular focus on its economic, social, and policy dimensions. Insurance functions as an effective financial intermediation mechanism by mobilizing long-term savings and channeling them into productive investments such as infrastructure, industry, and agriculture. At the social level, insurance provides protection against life, health, and income-related risks, thereby reducing vulnerability, promoting social security, and supporting poverty alleviation. The study also highlights the importance of insurance in advancing financial inclusion through micro-insurance, digital platforms, and fintech innovations. The paper further analyzes the challenges facing the insurance sector, including low awareness and financial literacy, regional disparities, trust deficits, regulatory complexities, technological risks, and sustainability concerns. In response, it emphasizes the need for policy measures such as strengthening insurance literacy, expanding rural coverage, leveraging digital technologies, enhancing regulatory efficiency, and promoting public?private partnerships. The study concludes that a robust, inclusive, and well-regulated insurance sector is indispensable for building economic resilience, ensuring social welfare, and achieving sustainable nation building.

Background: Multi-unit abutments (MUAs) have become a cornerstone in contemporary implant prosthodontics, particularly in full-arch and immediate loading protocols. Their ability to correct implant angulation, provide a standardized restorative platform, and enhance prosthetic retrievability has contributed significantly to improved clinical outcomes. Objective: This narrative review aims to critically evaluate the indications, design principles, biomechanical considerations, clinical applications, advantages, limitations, and long-term outcomes associated with multi-unit abutments in prosthodontics. Results: Multi-unit abutments allow correction of non-ideal implant angulation, facilitate immediate loading, improve passive fit of prostheses, and reduce peri-implant soft tissue trauma. Clinical studies report high implant survival rates and favorable long-term outcomes when MUAs are used following proper prosthetic planning. Conclusion: Multi-unit abutments represent a reliable and versatile prosthetic solution in modern implant dentistry. When incorporated into a prosthetically driven treatment plan, they enhance biomechanical stability, prosthetic accuracy, and maintenance efficiency, making them indispensable in advanced implant prosthodontics.

Background: Dermatophytic infections remain a global public health challenge, particularly in resource-limited settings where access to conventional antifungal drugs is restricted. This study evaluated the antifungal activity of Azadirachta indica (Neem) and Moringa oleifera (Moringa) leaf extracts against selected dermatophytes, with Ketoconazole serving as a standard control. Methods: A total of 50 participants were recruited, predominantly children aged 8?10 years (34%) and males (76%). Antifungal activity was assessed using agar well diffusion and minimum inhibitory concentration (MIC) assays. Ethanolic and aqueous extracts of Neem and Moringa leaves were tested against Microsporum canis, Trichophyton tonsurans, T. violaceum, and Epidermophyton floccosum. Results: The Results showed that Ketoconazole exhibited the highest antifungal activity (23 mm). Among the plant extracts, Neem ethanol extract demonstrated greater inhibition (17 mm) compared to Moringa ethanol extract (14 mm). However, MIC analysis revealed that Moringa (25 mg/mL) required a lower concentration for fungal inhibition than Neem (50 mg/mL). Ethanol extracts generally showed higher antifungal activity than aqueous extracts, though Neem aqueous extract was notably effective against T. violaceum. Conclusion: The findings suggest that while Neem and Moringa possess measurable antifungal activity, they are less potent than Ketoconazole. Nevertheless, their efficacy supports traditional medicinal use and highlights their potential as affordable alternative therapies. Further phytochemical, toxicological, and clinical studies are recommended to validate their therapeutic applications. Keywords: Neem, Moringa, Dermatophytes, Antifungal activity, MIC, Ketoconazole.

The study explored teachers? experiences in trialling the 16+ LSEND syllabus in selected special schools in Zambia, focusing on their understanding of the syllabus, instructional strategies, challenges encountered, support systems, and strategies for enhancing implementation. A qualitative phenomenological research design was adopted to capture the lived experiences of teachers within their natural teaching contexts. Data were collected through semi-structured interviews with purposively selected teachers and document analysis of lesson plans and curriculum guidelines. Thematic analysis revealed that teachers generally understood the syllabus as a competence-based curriculum emphasizing functional skills, independence, and vocational preparation. Teachers employed hands-on, differentiated, and collaborative instructional strategies, but their implementation was constrained by resource limitations, large class sizes, learner diversity, and limited professional development. Support from school administration, colleagues, parents, and external organizations was reported, though often inconsistent. The study recommends enhanced policy support, continuous professional development, provision of adequate resources, reduced class sizes, structured supervision, and strengthened community engagement to improve the effectiveness of the 16+ LSEND syllabus. The findings contribute to the theoretical understanding of curriculum implementation through the lenses of Vygotsky?s Sociocultural Theory and Fullan?s Theory of Educational Change, highlighting the interplay between teacher competence, systemic support, and learner outcomes in inclusive education settings.

Artificial Intelligence (AI) has emerged as a transformative force in modern education. Its applications?including adaptive learning systems, automated assessment, predictive analytics, and administrative support?have reshaped the academic landscape. While AI has simplified many processes and enhanced personalization, concerns about privacy, fairness, data protection, and over?dependence on machines remain widespread. This study, based on responses from teachers and students across Indian higher education institutions, highlights both advantages and challenges. Findings reveal that AI improves efficiency and supports learning, yet strong ethical frameworks are essential for its responsible use.

Phishing websites pose a significant threat to online security by deceiving users into disclosing sensitive information. Accurate and timely detection of such malicious websites is essential for ensuring secure web browsing. This paper proposes an optimized feature selection?based machine learning framework for effective phishing website detection. The approach focuses on identifying the most discriminative features from URL-based, content-based, and host-based attributes to reduce dimensionality and improve classification performance. Feature selection techniques such as correlation analysis, recursive feature elimination, and evolutionary optimization are employed to eliminate redundant and irrelevant features. Multiple machine learning classifiers, including Decision Tree, Random Forest, Support Vector Machine, and Gradient Boosting, are trained and evaluated on a benchmark phishing dataset. Experimental results demonstrate that the optimized feature subset significantly enhances detection accuracy, reduces computational complexity, and improves model generalization compared to models trained on the full feature set. The proposed framework provides an efficient and scalable solution for real-time phishing website detection and can be effectively integrated into modern cybersecurity systems.

Connectedness is a core topological attribute that defines spaces which cannot be divided into separate nonempty open sets. When integrated with the framework of a metric, connectedness gains further characterizations and significant implications. This article introduces the notion of connected metric spaces, essential definitions, illustrative examples, notable theorems, and practical applications. Connectedness is a generalization of a property of all intervals in Real number, namely that of being all in "one piece". So loosely, a space is connected if it does not consist of two or more separate pieces. For a subspace M of Euclidean space R2, there is another notion of connectedness which may seem more natural, the property that each pair of its points can be joined by a path in the subspace M. Path connectedness for metric spaces, which results from formalization of this idea,

?ener&tive AI h&s quickly become & key technology c&p&ble of producing new text, im&ges, &udio, &nd other content. Its &pplic&tions now sp&n cre&tive industries, he&lthc&re, educ&tion, softw&re development, &nd scientific rese&rch. This survey summ&rizes m&jor gener&tive model types &nd highlights how they &re used in re&l- world systems. It &lso discusses the opportunities ?enAI cre&tes &nd the ch&llenges it r&ises in reli&bility, ethics, &nd intellectu&l property. The go&l is to provide & cle&r overview of the current l&ndsc&pe of gener&tive AI &pplic&tions, offering insights into its tr&nsform&tive potenti&l &nd the critic&l consider&tions for its responsible deployment.

In this modern era the data brokers are the one who plays important role in managing an individual's information along with the third-party data processors .The main routine of a data broker collects and sell the personal information of the individual without their knowledge, while third party processors are the one who manages the data on behalf of other organization. These data broker and data processors are only responsible for user information . They play an important role in data economy. Using this data economy many companies improves their financial support by selling individual?s personal data, this process of data into money is known as data monetization. In India Cyber threads can be reduced by some restrictions such as DPDP Act, IT Act . The data ecosystem is very important in the data economy process because in the data ecosystem the data are produced. The trust level between the data processor and the data controller are maintained by data processing agreement (DPA). Based on these agreements the data are transferred across the boundaries to various countries and these processes is known as cross-body transaction.

Integration of yoga into educational sports programs represents a promising approach to enhance physical, mental, and cognitive development among students. This paper synthesizes scientific evidence from physiology, neuroscience, sports science, and educational psychology to justify the inclusion of yoga practices alongside traditional athletic training in schools and universities. We examine mechanisms through which yoga improves flexibility, balance, strength, stress regulation, attentional control, and recovery. Practical recommendations, challenges, and future research priorities are also discussed.

Blood flow, or hemodynamics, is a fundamental physiological process governing the transport of oxygen, nutrients, hormones, and metabolic waste throughout the human body. Understanding blood flow dynamics is essential for elucidating the mechanisms underlying cardiovascular health and disease. Over the past several decades, extensive research has been conducted from experimental, clinical, and theoretical viewpoints to characterize blood flow behavior under normal and pathological conditions. This review presents a comprehensive and integrative overview of blood flow research, emphasizing mathematical and computational modeling, rheological properties of blood, vessel geometry, and their relevance to cardiovascular disorders such as arterial stenosis, aneurysms, hypertension, diabetes, and sickle cell anemia. The review also highlights recent advances involving nanoparticle-based drug delivery, magnetic field effects, thermal transport, and bioinspired approaches, underscoring the interdisciplinary nature of modern hemodynamics research.

The National Rural Livelihoods Mission (NRLM), implemented in Uttar Pradesh as the Uttar Pradesh State Rural Livelihoods Mission (UPSRLM), is a flagship programme of the Ministry of Rural Development, Government of India, aimed at reducing rural poverty through sustainable livelihood promotion and social empowerment of the rural poor. The present study evaluates the effectiveness of UPSRLM in Mirzapur district of Uttar Pradesh, with particular emphasis on its impact on women beneficiaries associated with Self-Help Groups (SHGs). The study adopts a descriptive and analytical research design and is based on both primary and secondary data sources. Primary data were collected from 150 randomly selected women members of SHGs formed under the NRLM framework in selected villages of Shikhar block of Mirzapur district. Secondary data were obtained from government reports, official documents, and relevant academic literature. The findings of the study reveal a noticeable and positive change in the availability, continuity, and sustainability of livelihood opportunities among women participants. Membership in SHGs has facilitated improved access to institutional credit, enhanced savings practices, skill development, and participation in income-generating activities. These improvements have contributed significantly to the enhancement of women?s socio-economic status, increased self-confidence, and greater involvement in household and community-level decision-making processes. The study also identifies certain challenges such as inadequate market linkages, variations in programme implementation, and the need for regular capacity-building and monitoring support. Overall, the study concludes that UPSRLM has been effective in promoting sustainable livelihoods and women empowerment in the study area. Institutional strengthening and convergence are essential.

Bed sores, also known as pressure ulcers or decubitus ulcers, are a significant and preventable health problem, particularly among the elderly population. Bedsores are a major healthcare issue with far-reaching effects on patient welfare and the healthcare system. This review provides an extensive summary of the existing body of knowledge on the management and prevention of bedsores. This paper provides a comprehensive review of the issue, exploring its prevalence, related risk factors, and the profound impact on patient quality of life and healthcare costs. It synthesizes current research on the aetiology of pressure ulcers, drawing from related works to establish a foundation for understanding their development and classification. A hypothetical case study is presented to illustrate the typical progression of the condition. The core of this review focuses on the best practices for a multi-faceted prevention process, including regular skin assessment, strategic repositioning, nutritional support, and the use of specialized support surfaces. The conclusion emphasizes that proactive, consistent, and collaborative care is paramount to reducing the incidence of bed sores, thereby improving patient outcomes and overall healthcare efficiency.

Artificial Intelligence (AI) has become the most influential technological paradigm of the twenty-first century, transforming industries, governance, and human lifestyles. This research paper explores the multidimensional role of AI in daily life, its historical evolution, present-day applications, advantages, and ethical challenges. Using a qualitative and analytical methodology, the study synthesizes secondary data from academic, industrial, and governmental sources published between 2019 and 2025.

Herbal Aloe Vera Face Wash is a natural cleansing lotion designed to purify the skin from environmental pollutants, excess sebum, and microbial contaminants. Skin damage mainly results from the accumulation of dirt, oil, and dead skin cells, whereas long-term exposure to pollutants can lead to more serious conditions like acne, premature aging, and dermatitis. Ideally, a face wash should protect against both bacterial infection and dehydration. This study aimed to create a topical face wash formulation using fixed oils combined with selected medicinal plants. Consistent use of herbal face wash can lower the risk of acne vulgaris, comedones, and skin dullness. Face wash, also referred to as a facial cleanser, functions by emulsifying surface oils and lifting impurities to safeguard the skin. With the rising rates of skin sensitivity and the damaging effects of urban pollution, the demand for effective natural cleansing agents has increased. These agents have been shown to help alleviate symptoms associated with environmental skin damage. An effective face wash should be safe, nonirritating, non-toxic, stable, and provide complete removal of impurities without stripping natural oils. The developed face wash lotion includes skin-friendly components like aloe vera, butterfly pea flower, coconut oil, rose water, and vitamin E. Testing criteria included pH, spreadability, foaming ability, and skin feel. The prepared face wash exhibited a strong cleansing rating, excellent uniformity, consistency, and appearance, with no indications of phase separation, making it safe and non-irritating for skin application.

In 2017, the inception of the Goods and Services Tax ("GST") served as a turning point for the overall tax structure of India and transformed how companies in the informal economy interact with the tax system. This article considers how GST has influenced or potentially increased the degree to which the informal economy is becoming formalized, where traditionally, businesses in the informal sector have operated outside the view of revenue authorities. By reviewing a wide range of studies, theoretical frameworks, and empirical evidence, we consider the ways in which tax policy has driven business behaviour, the observable patterns of transition between sectors, and some of the ongoing challenges associated with this transition. The results indicate that performance is complex; while GST has been successful in encouraging some segments of the informal economy to register and comply with tax policy, there are still very real impediments to the success of the transition, primarily due to excessive administrative complexity, lack of adequate digital infrastructure, and the basic microeconomic characteristics of small-business operations. This review demonstrates the intertwining relationship between the tax reform process and the broader themes of economic development, strategic management of firms, and the different forms of entrepreneurship emerging in developing nations.

The growing demand for improved material performance, hygiene and sustainability in modern vehicles calls for the integration of advanced textile technologies into automotive interiors. This review article examines recent technical advances in self-cleaning, antimicrobial and odor-resistant fabric systems specifically designed for vehicular environments. The various functional mechanisms enabling these properties are evaluated, including photocatalytic and superhydrophobic surface engineering for self-cleaning, silver nanoparticles and quaternary ammonium compounds for antimicrobial protection and activated carbon and cyclodextrins for odor absorption. Automotive-specific performance requirements are discussed, with an emphasis on durability, long-term functional stability and improved occupant safety. Despite significant advances, challenges remain such as maintaining efficacy under harsh automotive conditions, ensuring material non-toxicity, achieving cost-effective large-scale manufacturing and complying with regulations. The review concludes by identifying key research gaps and future development opportunities for sustainable, scalable and multifunctional fabrics, including bio-based functional materials, smart and responsive textile systems with advanced coating technologies, which accelerate the development of durable, multifunctional and environmentally friendly automotive interior fabrics.

Nanoparticle drug delivery systems are the future of therapeutics by limiting APIs to 1-100 nm carriers (e.g., Doxil?, Abraxane?, Comirnaty?) and thereby solving the problems of poor solubility (<20% bioavailability), toxicity (26% doxorubicin-induced cardiotoxicity), and off-target effects of conventional drugs. These platforms, which have received more than 55 FDA approvals since 1995 and have become a $15B market, are able to get 5-10% of the injected dose per gram of tumor through EPR and thus are able to extend circulation 10 times, allow weekly dosing, and reach 40% tumor regression as opposed to free drug failure by utilizing EPR (5-10% ID/g tumor accumulation) and active targeting. Among the major milestones are 5000-fold solubility improvements (Genexol-PM? paclitaxel), release triggered by stimuli, BBB penetration, and theranostics, which are spread over oncology (Abraxane? 33% ORR vs. 19% Taxol?), infections (Comirnaty? 95% efficacy), neurology, and CRISPR gene editing (90% TTR knockout). However, the MPS clearance (60% liver), ABC (80% repeat loss), and <5% translational success (e.g., BIND-014 failure) still remains challenges, but biomimetics, AI-EPR prediction (85% accuracy), and green synthesis are signs of more than 20 approvals by 2030, thus India will be a provider of affordable TB/cancer nanomedicines. This review integrates the basics, clinical effects, difficulties, and future multimodal platforms to serve as a link between the preclinical promise and global precision therapy.

In the age of digital transformation and cloud computing, there is a growing demand for tools that facilitate real-time collaboration and remote access. This project introduces an Online Realtime Collaborative Multi-Language Editor & Compiler designed to allow users to write, compile, and execute code in multiple programming languages directly from their web browsers. The platform utilizes Firebase for real-time collaboration and the Piston API for language execution. By offering features like live session sharing, local storage persistence, and input handling, this tool proves especially useful for students, instructors, developers, and interviewers who seek a lightweight and responsive coding environment. This project bridges the gap between traditional heavy desktop IDEs and casual coding needs in educational and professional scenarios.

Chronic pulmonary diseases, encompassing conditions such as chronic obstructive pulmonary disease (COPD). Pose significant challenges in their treatment due to the complex nature of the lungs and the need for targeted medication delivery. Nanoparticles face challenges for targeted and controlled drug release within the lungs. Chronic lung disease includes a variety of persistent ung disordersr such as asthma, chronic obstructive pulmonary disease (COPD). Cys tic fibrosis, tuberculosis, idiopathic pulmonary fibrosis CIPF)C and lung cancer. Nanoparticle-based drugs in the treatment of respiratory disorders, including both basic and clinical studies. The nanopartlcle' s physicochemical properties can accomplish targeted therapeutic delivery. Based on their surface, size, density, and physical-chemical properties, nanopartlcles have dernonstrated enhanced pharmacoklnetlcs of actlves, acnevlng the spotlight In the delivery research neld. In this revtewr the authors have highlighted dlfferent nanopartlcle-based therapeutic delivery approaches to treat chronic pulmonary diseases along with the preparation techniquese

The expanding area of nanotechnology, interested in matter between the 1-100 nanometer range, has transformed various coherent and mechanical realms. Nanoparticles, the critical building blocks of nanotechnology, exhibit unusual physico chemical characteristics based on their height surface location to volume ratio and quantum mechanical effects, leading to applications spanning pharmaceutical, contraptions, catalysis, essentialness, and characteristic remediation. Traditional methods of nanoparticle aggregation, by definition chemical and physical processes, typically involve the use of damaging precursors, harsh reaction conditions, high essentialness consumption, and the duration of harmful byproducts, raising fundamental common and well-being issues. In light of the drawbacks of traditional mix courses, the philosophy of "green union" has emerged as a feasible, environmentally friendly, and biocompatible alternative to the fabrication of aggregated nanoparticles. This inventive method uses the inevitable biochemical machinery of natural materials, scrutinizing microorganisms (organisms, living organisms, green growth), plant extracts (isolated from characteristic parts such as takes off, roots, stems, and usual items), and limited biomolecules (such as proteins, proteins, vitamins, and polysaccharides) to empower the reduction of metal particles and the subsequent stabilization of the produced nanoparticles. Green mix provides an enormous array of inclinations, including the use of readily available, non-toxic, and biodegradable materials; operation under mild reaction conditions (including temperature and weight); reduced essentialness utilization; cost-effectiveness; and the production of nanoparticles with advanced biocompatibility and tailored functionalities for particular uses. In extension, the intrinsic proximity of capping and stabilizing administrators interior the typical systems regularly results in the trajectory of action of more ordered and monodisperse nanoparticles. This critical review rigorously examines the differential methods and basic disobedient present in the green blend of nanoparticles. We start by presenting a complete sketch of the rare properties of nanoparticles and the key guidelines governing their unique behavior at the nanoscale. Therefore, we dive into the assorted characteristic administrators employed in green union, detailing their individual roles in the metal precursor bioreduction and stabilization of the resulting near-nanoparticles. We on a very fundamental level investigate the influence of important exploratory parameters, including the selection and concentration of metal precursors, the type and concentration of the natural reducing master, reaction temperature, pH, brooding time, and mixing conditions, on the gauge, shape, morphology, strength, and yield of the prepared nanomaterials. Besides, we study the intra- and extracellular union mechanisms obtained by unmistakable normal systems. An integral allocate of this review is dedicated to emphasizing the extensive range of uses of green synthesized metallic nanoparticles, with specific emphasis on their transformational value in the biomedical area. We discursively discuss largely their applications as effective antimicrobial administrators in countering drug-resistant pathogens, state-of-the-art bio-imaging standalone administrators in modern diagnostics, effective and focused on constant motion systems in the treatment of cancer and other contaminations, sensitive biosensors to the field of unique analytes, and tissue planning and regenerative medication. In addition, we examine their emerging components in catalysis for unique chemical transformations and in shared remediation drives, monitoring the degradation of pristine toxins, the removal and recovery of overwhelming metals from contaminated water and ground, and their implications in temperate cultivation. Lastly, this review provides a balanced view by on a very fundamental level canvassing the concerns of interested and ongoing imprisonments of green nanoparticle synthesis and their ensuing applications. We discuss difficulties with respect to the standardization and adaptability of green union customs, extensive characterization of nanoparticles naturally synthesized, and careful consideration of their potential common and toxicological effects. We end by sketching out our future ask nearly headings and openings in the fast-moving area of green nanotechnology, highlighting the need for interest collaborations to maximize union processes, revamp nanoparticle functionalities, secure their safe and conservative use, and in the long term unlock their full potential for nurturing around the globe challenges in unique segments.

Nigeria's persistent electricity deficit, coupled with growing environmental concerns, has intensified policy interest in renewable energy as a pathway to sustainable development. This study assesses the current state and future direction of renewable energy development in Nigeria using a quantitative descriptive design complemented by documentary and policy analysis. Data were drawn from national energy statistics, policy documents, and a structured survey of energy stakeholders and end-users (n=200, 72% response rate). Descriptive statistics and thematic policy analysis were employed to examine technology-specific deployment patterns, policy implementation outcomes, and institutional performance. The findings reveal a pronounced dominance of solar photovoltaic technologies, with over 70% of survey respondents identifying solar as the most prevalent renewable energy source. At the same time, wind, biomass, and new hydropower capacity remain marginal. Although policy awareness is relatively high, fewer than one-third of respondents perceive effective policy implementation, indicating a persistent policy-practice gap. Weak institutional coordination, inadequate data consolidation, and financing constraints further limit sectoral scale-up. The study concludes that Nigeria's renewable energy transition is constrained more by governance and implementation challenges than by resource availability. Strengthening institutional capacity, improving national data systems, and aligning policy targets with realistic implementation mechanisms are essential for transforming renewable energy into a central pillar of Nigeria's energy mix.

This paper seeks to explore the impact and technical issues in blue energy harnessing and its potential threats and benefits. Blue energy generation explores the sustainable utilization of oceanic resources to generate clean and renewable energy. This involves harnessing energy from tidal, wave, and thermal gradients, emphasizing the potential of blue energy as a reliable and eco-friendly power source. This abstract delves into technological advancements, environmental impact assessments, and the role of blue energy in addressing global energy challenges coupled with the way forward in global energy balance.

The Indus civilization is one of the earliest known urban culture of the Indian sub-continent. The nuclear dates of the civilization appear to be about 2600-1900 BCE. The civilization was first identified in 1921 at Harappa in the Punjab region and then in 1922 at Mohenjodaro near the Indus river in the Sindh (Sind) region. Both the sites are in present day Pakistan. The Indus civilization is known to have consist of two large cities Harappa and Mohenjodaro and more than hundred towns and villages often of relatively small size. The two cities were each perhaps originality about 1 mile (1.6 km) square in overall dimensions and their out standing magnitude suggests political centralization. It was one of five world's earliest urban civilization known for it sophisticated urban planning, advance sanitation system, standards of weights and measures and flourishing trade. Some key sites include Harappa and Mohenjodaro in modern day Pakistan, Rakhigarhi, Lothal, Kalibangan in India. cities were laid out in a grid-like pattern with wide streets and so sophisticated water management systems. Buildings were made of sun dried and kiln fired bricks. Houses had running waters, toilets and drains that connected to a city sewage system. The civilization produced various forms of art, jewelry, Terracotta figurines of human and animals have been found along with famous bronze figures like the 'dancing girl', in this paper, I shall try to show how the modern civilization to be destroyed day by day. The Indus Valley Civilization was one of the earliest and most advanced civilization of the ancient world.

Women occupy a large share of the global informal workforce and are disproportionately represented in low-paid, insecure, and hazardous forms of labor. This paper synthesizes interdisciplinary evidence on how informal employment affects women?s physical health, mental health, and overall well-being. Drawing on public health, occupational safety, gender studies, and labor economics literature, the paper outlines common pathways linking informal work to adverse health outcomes (hazard exposure, ergonomic strain, lack of social protection, time-poverty and care burdens, and psychosocial stressors). We propose a mixed-methods research design for empirically investigating these relationships in urban Indian settings, present an analysis plan, discuss likely findings and policy implications, and offer recommendations for interventions, advocacy, and future research.

Digital marketing has quickly moved from broad campaigns to highly personalized strategies that now define the way companies connect with their customers. Personalization helps businesses strengthen loyalty, improve engagement, and increase sales. Yet, these same practices have raised pressing concerns about data privacy, consent, and consumer trust. This paper explores the ethical challenges that come with personalization, focusing on the constant tension between creating value for businesses and respecting the rights of consumers. It highlights the ?privacy paradox,? where people welcome customized experiences but worry about the invasive data collection required to provide them. The discussion examines how new technologies such as artificial intelligence, neuromarketing, and the Internet of Things amplify these issues by embedding surveillance into everyday life. Regulations like the General Data Protection Regulation (GDPR) and the California Consumer Privacy Act (CCPA/CPRA) have pushed companies toward greater accountability, but enforcement gaps and fast-moving innovation show that compliance alone is not enough. Industry insights from Deloitte and PwC point to a growing shift toward privacy-first strategies, with tools such as privacy-enhancing technologies (PETs) and first-party data collection emerging as best practices. Ultimately, the paper argues that businesses must go beyond simply following the law. By committing to transparency, building trust, and embedding ethical values into their personalization strategies, companies can deliver relevant experiences while protecting consumer rights. In doing so, marketing can remain effective, responsible, and credible in a digital society where privacy expectations continue to rise.

Recent advances in artificial intelligence (AI) have fundamentally restructured the field of drug discovery and development, yielding dramatically accelerated timelines, significantly enhanced predictive accuracy, and innovative computational methodologies. The pharmaceutical industry traditionally faces challenges characterized by protracted timelines, often exceeding 10 years, and prohibitively high attrition rates in clinical trials. This comprehensive review examines the integration of AI technologies, including machine learning (ML), deep learning (DL), and generative models, demonstrating how these computational approaches are redefining target identification, de novo drug design, high-throughput virtual screening, and optimization of clinical development. Robust evidence and quantitative case studies are presented, affirming that AI integration has reduced the average development duration to an estimated 3?6 years and increased Phase I trial success rates for AI-designed drugs to 80?90%, compared to the traditional 40?65% range. The paper further details the specific architectures (e.g., Recurrent Geometric Networks (RGN), Reinforced Adversarial Neural Computers (RANC)) and critical datasets (MISATO, ChemDiv) driving these advances, while rigorously analyzing prevailing challenges concerning data quality, model interpretability, and regulatory harmonization. The findings strongly support the strategic, continued investment in AI-driven pharmaceutical research to enable more efficient, effective, and accessible therapeutic development.

This paper investigates how strategic Human Resource Management (HRM) practices can optimize educational outcomes in the Udham Singh Nagar district education system (Uttarakhand, India). Grounded in HRM and educational leadership literature, the study proposes an integrated HRM framework tailored to local needs: recruitment and selection, professional development, performance management, motivation and retention, and community engagement. Drawing on a mixed-methods design document analysis, stakeholder interviews, and a pilot survey the paper identifies key HR bottlenecks affecting learning outcomes and offers actionable policy recommendations for district education authorities, school leaders, and policymakers. Implementation of the proposed HRM interventions is expected to improve teacher effectiveness, reduce absenteeism, raise student achievement, and strengthen school community links.

Motherhood in urban Ghana is shaped by rapidly changing social, economic, and cultural landscapes that often create uncertainty for women navigating the demands of childcare, employment, financial pressures, and family expectations. This phenomenological study explores the lived experiences of mothers in Accra Metropolis who negotiate daily uncertainties while caring for their children. Using in-depth interviews with purposively selected mothers from diverse socioeconomic backgrounds, the study examines how uncertainty manifests in maternal decision-making, emotional well-being, support systems, and coping strategies. The findings are expected to reveal the complex interplay between urban stressors, cultural norms, and individual resilience in shaping motherhood experiences. Insights from this study aim to inform maternal support interventions, urban social policy, and future research on women?s lived realities in rapidly urbanizing contexts.

The National Education Policy (NEP) 2020 mandates a shift to experiential, competency-based learning to cultivate 21st-century skills. A key pedagogical challenge is teaching abstract concepts, such as Computational Thinking (CT), to middle school students, as conventional teaching methods often fail to engage them. This paper presents a structured framework using toy-based pedagogy, an innovative method aligned with NEP 2020, to demystify core CT principles. The framework utilises traditional Indian toys and games, embedding cultural heritage directly into the learning process. It operationalises CT tenets using manipulatives, such as traditional Channapatna wooden toys, to teach decomposition. Additionally, it employs strategic board games, such as Chaturanga, for algorithmic thinking and collaborative games, like Lagori (seven stones), to model problem-solving sequences. This approach transforms abstract concepts into tangible, culturally rooted activities. For example, students learn abstraction by using simple wooden figures in storytelling, a method aligned with traditional puppetry. This interdisciplinary approach integrates CT concepts into STEAM (Science, Technology, Engineering, Arts, and Mathematics) education, fostering a holistic and culturally proud educational experience. The application of this framework resulted in increased student engagement, enhanced collaborative skills, and improved self-efficacy in problem-solving. The paper details replicable classroom activities and corresponding assessment strategies, such as observational rubrics and project-based portfolios, that align with the policy?s emphasis on transformative, 360-degree evaluation. The study concludes that toy-based pedagogy is a practical, equitable, and scalable strategy for embedding CT into the curriculum. It offers practical insights for educators to implement the transformative vision of NEP 2020 and nurture a new generation of innovators for a Viksit Bharat.

Constructing an appropriate landing trajectory for a UAV plays a crucial role in ensuring both flight safety and operational efficiency. When the system must cope with challenges such as varying weather, dynamic environmental conditions, and strict precision requirements during descent, the Pontryagin principle serves as a powerful optimization approach. This principle offers a systematic way to determine the optimal landing path by establishing optimality conditions and maximizing the relevant performance functions. In this study, a Pontryagin-based optimization method is utilized to design the UAV?s landing trajectory. Simulation results obtained using Matlab?Simulink demonstrate that this approach significantly enhances landing accuracy and reduces potential risks.

South Asia is one of the world?s most disaster-prone regions. Although women are disproportionately affected by disasters, they also hold essential local knowledge, coping strategies, and leadership potential that can strengthen community resilience. This paper examines how women?s voices have been integrated into national and local Disaster Risk Reduction (DRR) frameworks across India, Bangladesh, Nepal, Sri Lanka.. Using a comparative policy analysis of government documents, multilateral frameworks, NGO reports, and country case studies, the study identifies recurring gaps between gender-inclusive policy language and on-the-ground participation of women in DRR decision-making. The paper concludes with actionable recommendations institutional reforms, capacity building, gender-sensitive budgeting, data disaggregation, and community-led participatory approaches to operationalize women?s meaningful participation and leadership in DRR across South Asia.

Elder abuse is a growing global concern, affecting millions of older adults in various forms,including physical,emotional,financial,and neglectful mistreatment. According to the world Health Organization(WHO,2022), elder abuse has increased significantly in recent years, particularly during the COVID-19 pandemic, which heightened social isolation and caregiver stress. Families, communities,and government play a crucial role in preventing and addressing elder abuse. This review explores the prevalence and addressing elder abuse. This review explores the prevalence,risk factors,and consequences of elder abuse, highlighting cultural perspectives, legal frameworks,and support systems aimed at safeguarding older adults.strategies for improving prevention, intervention,and policy enforcement are also discussed. The paper synthesizer research studies on elder abuse trends, the effectiveness of legal Protections, and best practices for ensuring the well- being of elderly individuals.

Nanomedicine is an advanced version of Paul Ehrlich?s ?magic bullet? concept. Targeted drug delivery is a system of specifying the drug moiety directly into its targeted body area (organ, cellular, and subcellular level of specific tissue) to overcome the specific toxic effect of conventional drug delivery, thereby reducing the amount of drug required for therapeutic efficacy. To achieve this objective, the magic bullet concept was developed and pushed scientists to investigate for more than a century, leading to the envisioning of different nanometre-sized devices ? today?s nanomedicine. Different carrier systems are being used and investigated, which include colloidal (vesicular and multiarticulate) carriers, polymers, and cellular/subcellular systems. This review addresses the need for and advantages of targeting, with its basic principles, strategies, and carrier systems. Recent advances, challenges, and future perspectives are also highlighted.

This research paper explores the Human Resource (HR) practices adopted by Shiv Parwati Grih Udyog, a snack food manufacturing unit located in Urkura, Raipur. The study aims to understand HR systems related to recruitment, training, performance management, payroll, welfare, and employee engagement within a labour-intensive manufacturing environment. Data was collected using a mixed-method approach through observation, structured questionnaires, and interviews with 30 employees from different departments. Findings indicate that while the organization maintains basic HR practices?such as timely wage payments, attendance management, and informal training?there is scope for strengthening formal HR policies, documentation, welfare provisions, and performance evaluation systems. The study highlights the essential role of HR in improving workforce productivity and ensuring smooth production operations. Recommendations are proposed to enhance HR efficiency and employee satisfaction.

This paper examines the systemic crisis undermining scientific integrity in Indian academia. Despite India&#39;s rising global rank in research output and innovation metrics, a critical analysis reveals a growing disparity between quantitative productivity and qualitative rigor. The study identifies a triad of institutional failures driving this crisis: a &#34;publish or perish&#34; culture that incentivizes quantity over quality, chronic underinvestment in R&D coupled with inequitable fund distribution, and a bureaucratic administrative system that stifles autonomy. These factors have led to a proliferation of ethical breaches, including fake peer review, data falsification, and plagiarism, resulting in a high retraction rate and a severe reproducibility crisis. The erosion of objectivity is further compounded by political interference and the encroachment of pseudoscience. The consequences are profound, damaging the global credibility of Indian research and posing tangible risks to evidence-based policymaking and public welfare. The paper concludes that the central challenge is not intellectual capacity but a profound governance failure. It proposes a multi-dimensional reform agenda centered on establishing independent oversight, promoting open science, incentivizing collaboration over competition, and embedding ethics training throughout the academic pipeline to rebuild a culture of integrity and restore trust in Indian science.

Mountain ecosystems are among the most ecologically significant and environmentally sensitive regions on Earth, playing a vital role in sustaining biodiversity, regulating global water cycles, and maintaining climate stability. Covering nearly one-quarter of the world?s land surface, mountains support the livelihoods of over a billion people and influence the environmental health of far wider populations through their provision of essential ecosystem services. These include freshwater regulation, air purification, carbon sequestration, disaster risk mitigation, and soil conservation. Additionally, mountain regions harbour a high degree of endemism and serve as biodiversity hotspots, especially in tropical and subtropical zones. Despite their ecological importance, mountain ecosystems face escalating threats due to climate change, deforestation, overexploitation, mining, pollution, and land degradation. Rapid glacial retreat, biodiversity loss, and the disruption of hydrological functions are already having measurable impacts on both local and global scales. Furthermore, mountain communities?many of which are indigenous?are disproportionately vulnerable due to their reliance on natural resources and exposure to environmental hazards. Global and local conservation strategies, including sustainable land-use practices, ecological restoration, community-led biodiversity management, and integrated policy frameworks, are essential for protecting mountain environments. These strategies not only safeguard ecological functions but also enhance public health, food security, and climate resilience. As global pressures mount, the urgency for coordinated policy action and sustainable development in mountain regions has never been greater.

The question of whether free will truly exists in the human mind is still a major debate in philosophy, neuroscience, and psychology. Free will is usually understood as the ability to make choices that are not fully controlled by outside forces. Philosophers often see this issue in three ways. Determinism says that everything is caused by prior events, so our choices are not really free. Libertarianism argues the opposite?that people can act with full independence. Compatibilism tries to find a middle ground, suggesting that we can still make meaningful choices even within a world shaped by cause and effect. Neuroscience adds more complexity. Experiments, like those of Benjamin Libet, show that brain activity begins before we become aware of making a decision. This raises doubts about whether conscious control comes first. Still, the brain?s ability to adapt and change (neuroplasticity) suggests that we do have some influence over our actions. Psychology also shows that our choices are shaped by hidden factors?like unconscious processes, genetics, environment, and cognitive biases. This challenges the idea of complete freedom. Yet, believing in free will still plays an important role. It supports responsibility, motivation, and social order. Some newer approaches, such as ?probabilistic free will? or compatibilist models, argue that our freedom lies within limits?we may not have absolute independence, but we do have room to choose. Future studies in neuroscience and artificial intelligence may help us understand this better. For now, it seems free will is not unlimited, but it still matters in how we live, make decisions, and take responsibility.

This study examined the effect of International Financial Reporting Standards adoption on earnings quality of industrial goods manufacturing firms in Nigeria using panel data from eight firms over the period 2012 to 2024. The study focused on three key indicators of IFRS adoption, which were the IFRS Compliance Index, the Extent of IFRS Coverage and the IFRS Adoption Indicator, while earnings quality was measured using earnings per share. Secondary data were extracted from the published annual reports of the selected firms, and the analysis was carried out using descriptive statistics, correlation analysis and panel least squares regression. The results showed that the IFRS Compliance Index had a positive and statistically significant effect on earnings per share, indicating that higher levels of compliance improved the credibility and transparency of reported earnings. The findings further revealed that the Extent of IFRS Coverage had a negative but significant effect on earnings per share, suggesting that broader disclosures required under IFRS revealed more detailed financial information that reduced reported earnings. The IFRS Adoption Indicator showed a negative and insignificant effect on earnings per share, implying that the mere adoption of IFRS did not automatically improve earnings quality without effective and consistent implementation. The study concluded that compliance with IFRS played an important role in enhancing earnings quality, while the depth of disclosure and the practical implementation of IFRS shaped the direction and strength of its effect. The study recommended stronger enforcement of IFRS compliance and improved capacity building for financial reporting personnel in the Nigerian manufacturing sector.

Amyotrophic Lateral Sclerosis (ALS), often referred to as Lou Gehrig's disease, is a devastating, adult-onset, neurodegenerative disorder characterized by the progressive death of both upper motor neurons (UMNs) in the motor cortex and lower motor neurons (LMNs) in the brainstem and spinal cord. This ubiquitous motor neuron loss culminates in muscle atrophy, weakness, fasciculations, spasticity, and, inevitably, respiratory failure. The etiology remains complex, with a small percentage being familial (fALS)?often linked to mutations in genes such as SOD1, C9orf72, and TARDBP?while the majority are sporadic (sALS). The underlying pathological hallmark involves the misfolding and aggregation of proteins, notably the trans-activating response (TAR) DNA-binding protein 43 (TDP-43), leading to cellular toxicity and death. This paper comprehensively reviews the pathobiology of ALS, focusing specifically on the mechanism of motor neuron demise. It details the clinical presentation, the differential and exclusionary diagnostic process, the current pharmacological (Riluzole, Edaravone) and non-pharmacological treatment modalities, and the universally poor prognosis. A detailed case study is presented to illustrate the typical progression of the disease and the multidisciplinary palliative treatment approach. Despite recent advancements in genetic and molecular understanding, the recovery rate for ALS is currently 0%, and treatments remain largely palliative, highlighting the urgent need for novel neuroprotective and disease-modifying therapies to halt or reverse the catastrophic progression of motor neuron death.

The sustainability of educational facilities is fundamental to the effectiveness, safety, and global competitiveness of tertiary institutions. In Nigeria, persistent infrastructural deterioration continues to undermine teaching, learning, research, and institutional performance. This paper examines the key challenges confronting sustainable maintenance of educational facilities in Nigerian tertiary institutions and proposes innovative strategies for addressing these challenges. Drawing on relevant literature and contextual analysis, the study identifies chronic underfunding, rapid student enrolment without proportional infrastructure expansion, poor maintenance culture, weak governance and accountability structures, shortage of skilled facility management personnel, substandard construction practices, bureaucratic procurement delays, vandalism, unstable electricity and water supply, and limited adoption of digital maintenance technologies as major constraints. The paper argues that the reactive and emergency-driven maintenance approaches prevalent in most institutions are economically inefficient and structurally unsustainable. To reverse this trend, the study advances innovative strategies such as the professionalization of facility management units, adoption of preventive and predictive maintenance systems, integration of ICT-based facility management tools, diversification of funding sources through public?private partnerships and alumni support, continuous capacity building for maintenance personnel, transparent procurement practices, and the implementation of green and sustainable campus initiatives. The paper concludes that sustainable facility maintenance is not merely a technical obligation but a strategic imperative for higher education reform and national development in Nigeria.

The quality of educational facilities in Nigerian tertiary institutions continues to shape the effectiveness of teaching, learning, and research. Yet, many universities, polytechnics, and colleges of education grapple with deteriorating infrastructure, overcrowded classrooms, obsolete equipment, and weak maintenance systems. These challenges undermine academic productivity and limit institutional competitiveness. This article explores the concept of facility management within the context of Nigerian higher education, identifies major constraints, and proposes actionable strategies for improving infrastructure planning, maintenance, funding, staffing, and sustainability. Drawing on recent scholarly perspectives, the article emphasizes the need for professionalized facility management units, ICT-driven systems, preventive maintenance, diversified funding, and strong stakeholder engagement. Implementing these strategies will strengthen the learning environment, promote institutional excellence, and enhance Nigeria?s educational development. It was recommended among others that Nigerian tertiary institutions adopt a holistic approach to facility management by increasing government funding, establishing professionally staffed facility management departments, and institutionalizing preventive maintenance systems.

Microbial secondary metabolites are emerging as vital agents in sustainable agriculture, offering eco-compatible alternatives to conventional agrochemicals. Produced by diverse microorganisms?including bacteria, fungi, and actinomycetes?these bioactive compounds, though not essential for microbial survival, exert profound influences on plant physiology and development. Acting as signaling molecules, phytohormone modulators, and antimicrobial agents, they enhance nutrient assimilation, stimulate root system architecture, and activate defense responses against biotic and abiotic stresses. As biostimulants, microbial metabolites promote plant vigor by modulating hormonal pathways, improving stress resilience, and fostering beneficial plant?microbe interactions. As bioprotectants, they suppress pathogens through mechanisms such as induced systemic resistance (ISR), quorum sensing disruption, and direct antimicrobial activity. Their multifunctional nature positions them as integral components of integrated crop management systems that emphasize environmental stewardship, soil health, and long-term productivity. This review consolidates current insights into the classification, biosynthetic pathways, and functional roles of microbial secondary metabolites in agriculture. It further examines advances in formulation technologies, field-level applications, and evolving regulatory frameworks, while identifying key challenges and future research directions. Harnessing these natural compounds can accelerate the transition toward resilient, resource-efficient, and ecologically balanced farming systems.

The rapid global adoption of electric vehicles (EVs) has placed unprecedented demand on charging infrastructure, necessitating open, scalable, and interoperable communication standards. OCPP 2.0.1, released in 2020 by the Open Charge Alliance, represents the most advanced version of the Open Charge Point Protocol, introducing smart charging, enhanced security, and device model management. However, its complexity and the dominance of proprietary implementations have limited adoption among researchers and small charge point operators (CPOs). This paper presents a fully compliant, open-source OCPP 2.0.1 ecosystem comprising a modular Central Management System (CMS) and a modern web-based Charge Point Simulator. The simulator supports multiple virtual charge points with real-time transaction tracking, connector status monitoring, and detailed message logging. The system demonstrates full compliance with OCPP 2.0.1 JSON over WebSocket specification, including BootNotification, ,TransactionEvent, and smart charging profile handling. Performance evaluation shows support for over 500 concurrent virtual charge points on standard hardware. The complete system is released as open-source, significantly lowering barriers to OCPP 2.0.1 research and deployment.

The integration of Ghanaian employees into American multinational companies operating in Ghana presents an important intersection of cultural work values, organisational expectations, and adaptation processes. As American firms bring Western managerial practices, performance systems, and corporate cultures into the Ghanaian context, employees are required to navigate differences between indigenous work ethics and global corporate standards. This study examines how Ghanaian employees adapt to the work expectations of American multinational companies, the challenges they encounter, and the strategies they use to reconcile cultural values with foreign organisational norms. Using a mixed-methods approach comprising surveys and semi-structured interviews with employees and managers in selected U.S.-affiliated companies in Accra and Tema, the research explores variations in time orientation, communication styles, leadership expectations, and team collaboration. The findings are expected to highlight areas of work cultural alignment, sources of tension, and the implications for managerial effectiveness, employee performance, and human resource strategies. Insights from this study will guide multinational managers in designing culturally responsive systems that enhance productivity while promoting cross-cultural harmony.

Organizational culture significantly shapes employee behavior, motivation, and work ethic, yet the nature of this influence varies across different employment environments. In Ghana, employees increasingly transition between public-sector institutions, private Ghanaian-owned companies, and multinational corporations, each of which embodies distinct cultural expectations, management styles, and norms surrounding productivity. This study investigates how organizational culture influences the work ethic of Ghanaian employees across these three employment contexts. Using a mixed-methods design, the study integrates quantitative survey data from 300 employees with qualitative insights from 24 in-depth interviews to explore variations in attitudes toward time management, initiative-taking, communication norms, accountability, and commitment to duty. The study aims to reveal both the structural and cultural factors that shape employee behavior, including leadership style, hierarchical relations, incentive structures, and workplace socialization. Findings are expected to illuminate how employees adapt their work ethic to differing organizational cultures, where conflicts emerge, and what factors promote positive work attitudes across settings. The results will contribute to organizational behavior scholarship in Africa and inform managers seeking to improve productivity and engagement in diverse employment environments.

The non-homogeneous ternary fifth degree Diophantine equation given by is analyzed for its patterns of non-zero distinct integral solutions.

The Python programming language has undergone exponential adoption across diverse computing domains over the past decade, achieving a dominant position in fields such as artificial intelligence (AI), data science, and web development. This paper presents a comprehensive survey identifying and analyzing the core technical, social, and economic factors responsible for this accelerated growth. We find that Python's competitive advantage stems from a tripartite foundation: first, its intrinsic design, characterized by simple syntax, which significantly reduces programmer cognitive load and enhances productivity; second, the maturity and efficiency of its specialized scientific ecosystem (NumPy, Pandas, Scikit-learn), which establishes it as the de facto standard for numerical and data-intensive tasks; and third, its architectural extensibility, which, through Just-In-Time (JIT) compilers and foreign function interfaces, effectively mitigates inherent performance bottlenecks. Furthermore, widespread academic adoption and robust community support reinforce its long-term sustainability and corporate relevance. This survey synthesizes contemporary peer-reviewed literature to articulate Python's trajectory from a general-purpose language to a critical infrastructure element in modern applied Computing.

The paper aims to explore the ideological foundation and enduring relevance of Thanthai Periyar?s Self-Respect Movement as it marks its centenary (1925?2025). It examines how the movement emerged as a powerful revolt against caste hierarchy, patriarchy, and religious orthodoxy, redefining the principles of rationalism, social justice, and equality in Tamil society. Periyar?s radical thought challenged the dominance of Brahminism, questioned authority, and emphasised the liberation of individuals, especially women and the marginalised, from oppressive traditions. Through a critical analysis of Periyar?s writings, speeches, and reformist activities, the study highlights how his advocacy for self-respect, women?s education, and Tamil linguistic identity became instruments of social transformation. The paper also investigates how the core ideals of the movement, rational inquiry, human dignity, and equality, continue to influence contemporary debates on caste and social reform. By situating Periyar?s ideology within the broader framework of the Dravidian movement and modern Indian thought, the paper underscores his contribution to constructing a secular, democratic, and egalitarian social order. Eventually, this centenary reflection reaffirms that Periyar?s message of self-respect and rationalism remains not only a historical legacy but also a living force guiding present struggles for social and cultural emancipation.

ESG matters have gradually moved closer toward traditional financial reporting; hence, the landscape for corporate disclosures has shifted. The review discusses that standardization in sustainability accounting remains an enduring challenge and reflects on implementation strategies taken up until this time by various organizations around the world. Drawing from the latest academic literature and industry developments, this paper demonstrates how ESG integration becomes problematic in fragmented reporting frameworks, fluid stakeholder expectations, and inconsistent measurement methodologies. The available evidence has shown that despite the fact that organizations are increasingly recognizing the strategic value of sustainability reporting, large gaps persist between their intentions and actual execution. Their successful implementation would thus appear to be contingent upon high-level leadership commitment, robust data infrastructures, and adaptive organizational cultures, rather than compliance-driven approaches. Synthesizing findings from a number of these studies, it would thus appear that the quest for standardization requires a careful balance between global consistency and contextual flexibility, especially in instances where business operations are exposed to different regulatory environments and market conditions. Key terminologies include sustainability accounting, ESG reporting, standardization, corporate disclosure, strategies of implementation, stakeholder engagement, and regulatory frameworks.

India?s healthcare system is witnessing a significant transition toward affordability, safety, and rational drug use, driven by growing concerns over high out-of-pocket expenditure, irrational prescribing, and the widespread availability of unsafe fixed-dose combinations (FDCs). Generic prescribing has emerged as a key strategy to improve access to essential medicines, reduce treatment costs, and support universal health coverage initiatives such as Ayushman Bharat ? Pradhan Mantri Jan Arogya Yojana (PMJAY) and Pradhan Mantri Jan Aushadhi Pariyojana (PMJP). Alongside this, regulatory authorities have taken decisive actions to prohibit irrational FDCs that lacked scientific justification and posed risks of adverse drug reactions and antimicrobial resistance. This review critically examines the current landscape of generic prescribing and FDC regulation in India, emphasizing their implications for healthcare delivery, pharmacists, and rational drug use. Evidence from national and international guidelines, regulatory frameworks, and published studies highlights that generic medicines, when manufactured and regulated appropriately, offer therapeutic equivalence to branded products at substantially lower costs. However, challenges such as prescriber bias, patient misconceptions, variable brand substitution, and weak pharmacovigilance limit their optimal utilization. The review further underscores the central role of pharmacists in promoting generic substitution, patient counselling, pharmacovigilance, and antimicrobial stewardship. Integration of World Health Organization (WHO) recommendations, Indian Council of Medical Research (ICMR) guidelines, National Pharmaceutical Pricing Authority (NPPA) pricing controls, Central Drugs Standard Control Organization (CDSCO) regulatory actions, and national health schemes provides a strong policy foundation for rational medicine use. Strengthening regulatory enforcement, enhancing professional education, and improving public awareness are essential to fully realize the benefits of generics and ensure patient safety in India.

Artificial Intelligence (AI) has emerged as a transformative force in entrepreneurship, reshaping processes, strategies, and market dynamics. By integrating AI into business models,entrepreneurs can harness big data, machine learning, and cloud-based tools to disrupt traditionalindustries, create new markets, and address complex challenges with greater speed and precision. This study combines literature review, real-world case analyses, and expert insights to exploreboth the opportunities and constraints of AI adoption. While AI integration offers significantbenefits such as operational automation, enhanced decision-making, and innovative customersolutions, it also presents barriers including ethical dilemmas, regulatory uncertainty, and talentacquisition challenges. The findings emphasize the critical need for strategic adoption frameworks and policy support toensure AI-driven entrepreneurship remains inclusive, sustainable, and competitive in theevolving global startup ecosystem.

As a key tool for upholding the rule of law and enforcing basic rights, writ jurisdiction under Articles 226 and 32 of the Indian Constitution has a prominent place in the nation's constitutional system. Driven by judicial ingenuity, broad interpretation of rights, & innovations like Public Interest Litigation (PIL) and ongoing mandamus, this jurisdiction has transformed over time from a limited tool of administrative control to a potent and transformational weapon of social justice. Indian courts have expanded protection for dignity, livelihood, the environment, education, & privacy by adding substantive meaning to constitutional rights, especially under Article 21, via historic rulings. This essay critically assesses writ jurisdiction's institutional relevance, doctrinal underpinnings, and strengths while also looking at current issues that jeopardize its efficacy. It emphasizes how court action in areas of systemic injustice & government failure has been made possible by writ remedies, which have also reinforced administrative responsibility and improved access to justice. Serious issues including procedural overload, judicial delays, inconsistency across High Courts, abuse of PILs, and the escalating discussion over judicial overreach and separation of powers are also identified. The paper also examines new aspects of writ jurisdiction in the digital era, such as the necessity of digital due process, e-writs, virtual hearings, and algorithmic governance. In order to place India's distinctive and comprehensive writ framework within the context of international constitutional norms, comparative viewpoints from the United States, South Africa, Canada, and the United Kingdom are used. Additionally, judicial ethics, accountability, and openness are emphasized as crucial elements for maintaining public trust in constitutional adjudication.

This paper presents a comprehensive historical overview of sculpture, tracing its development from ancient civilizations to the contemporary era. Sculpture, as a dynamic art form, has continuously evolved, reflecting the technological, cultural, and philosophical changes of its time. By examining key periods including ancient Egypt, classical Greece and Rome, the Renaissance, and modern and contemporary art, this study highlights the shifting paradigms of form, function, and meaning in sculptural practice. It explores how religious devotion, political power, and mythological narratives shaped early sculptural traditions, from the monumental statuary of Mesopotamia and Egypt to the idealized human forms of Hellenistic Greece. The Roman adaptation and dissemination of Greek sculptural ideals laid the groundwork for the Renaissance revival, where artists like Michelangelo and Donatello redefined the human figure through anatomical precision and expressive realism.

The political landscape of India has undergone a significant transformation since 2014, marked by a shift in the nature, formation, and functioning of coalition politics across various states. While the period witnessed the rise of a strong central leadership and the consolidation of a dominant national party, the states continued to display diverse coalition patterns shaped by regional aspirations, identity politics, and local power configurations. This study offers an analytical examination of the evolving dynamics of coalition politics in Indian states after 2014, focusing on the interplay between national political trends and state-level electoral realignments. Using secondary data, election reports, party documents, and scholarly writings, the analysis explores how coalition strategies have adapted to changing voter behaviour, emerging regional parties, and competitive multiparty systems. The paper investigates the stability and instability of coalition governments, the role of ideological flexibility, leadership negotiations, and the increasing influence of issue-based alliances. It further highlights case studies from selected states such as Maharashtra, Bihar, Karnataka, Jammu & Kashmir, and Tamil Nadu, illustrating the complex negotiations and strategic recalibrations among political actors. The findings reveal that coalition politics remains a resilient feature of Indian federalism, even in an era characterised by attempts at centralised political dominance. The study concludes that coalition politics in Indian states after 2014 is not merely a response to electoral compulsions but represents a deeper democratisation of political processes, driven by socio-cultural pluralism, regional interests, and the evolving expectations of the electorate.

Advertising plays a critical role in influencing consumer preferences and purchasing decisions, especially in the fast-moving consumer goods (FMCG) sector. This study explores the impact of advertising on consumer behaviour towards FMCG products in Tirupur District. Using a structured questionnaire and statistical analysis, the study examines how various advertising channels, message appeals, and brand elements affect consumer buying behaviour. The results indicate that television and digital media are the most influential platforms, and emotional and informational appeals significantly shape consumer attitudes. The study concludes with practical recommendations for marketers to design more effective advertising strategies.

India's healthcare sector is poised for a revolutionary transformation with the integration of Artificial Intelligence (AI). As the government introduces new federal rules, pharmacists play a vital role in shaping this upgrade. This review explores the potential of AI in enhancing patient care, streamlining pharmaceutical services, and improving health outcomes. The convergence of AI technologies with pharmaceutical practice presents unprecedented opportunities to address longstanding challenges in medication management, patient safety, and healthcare accessibility. This comprehensive review examines current AI applications in pharmacy practice, analyzes the evolving regulatory landscape in India, and discusses the critical role pharmacists must embrace to successfully integrate these technologies. By synthesizing recent research and policy developments, this article provides insights into how AI can revolutionize pharmaceutical care delivery while highlighting the essential competencies pharmacists need to develop for effective collaboration with intelligent systems.

Financial literacy is described by different authors and different scholars as having their own meaning. I have narrated as financial literacy is individual specific skills, including savings. Earning, investing, analyzing the marketing, setting the financial estimated goal, and accumulating the financial information for future financial stability. It provides a stress-free life and financial security for old age. Each person follows their own method, but most people use five Cs ? content, capacity, community-led, communication, collaboration. Today?s women are more actively participating in all fields. Gender disparity is observed not only in India but also worldwide. Generally, women have a lower level of financial knowledge compared to men. Lack of financial literacy may be due to lack of education or shopaholics, lack of confidence, disparities in internet access especially in remote maintained villages where the internet is not available properly, lack of cultural awareness, and vulnerability to economic hardship are a few concrete reasons for financial literacy. In India, government regulatory bodies (RBI, SEBI, IRDAI, PFRDA, NCFE) have taken preventive action to address the gap.

Cobalt ferrite (CoFe?O?) may have its structural, magnetic, and electrical/dielectric characteristics tailored for advanced applications by substituting titanium (Ti?). The literature on Co???Ti?Fe??2?O? (or Co???Ti?Fe?O?, depending on stoichiometry) systems is examined in this review, along with different synthesis techniques (such as sol?gel auto combustion, solid-state reaction, thin-film deposition) and the effects of Ti content on lattice parameters, cation distribution, microstructure, magnetic parameters (saturation magnetisation, coercivity, anisotropy), and electrical/dielectric behaviour. Examined are mechanisms such cation redistribution, strain, and charge compensation. The study also identifies contradictory patterns in several investigations and suggests future lines of inquiry to maximise Ti-doped cobalt ferrite for use in spintronics, memory, and high-frequency devices.

This paper explores recent advancements in text classification and spam detection using Natural Language Processing (NLP). A comparative analysis is conducted between traditional machine learning algorithms and contemporary deep learning methods, emphasizing their performance, scalability, and practical applications. This document also incorporates notable references, flowcharts, and visualizations to elucidate the methodologies and outcomes.

The socio-political inclusion of minority communities has remained a crucial component in sustaining the democratic framework of India. This study examines the impact of minority rights policies on the socio-political participation and empowerment of minority groups in India, with a special focus on Assam. It evaluates constitutional and legal provisions, government schemes, institutional mechanisms, and challenges associated with their implementation. The paper analyses the lived experiences of minority communities in accessing social justice, political representation, and equitable development. The findings suggest that although India has formulated progressive minority rights frameworks, gaps in institutional performance, social discrimination, and identity-based politics continue to restrict full inclusion. Policy recommendations are provided to promote inclusive governance, cultural security, and participatory democracy.

The degree of consumer satisfaction is crucial for the widespread adoption of a new technology, such as solar energy in general and solar rooftop panels in particular. in order to address the unsatisfactory variables before devoting additional time and resources to it. Such prompt inspections guarantee a rapid and lively overall development. Only until end users are satisfied will the government's very ambitious goals be met. The degree of satisfaction with solar rooftop panels in the city of Gurugram is the subject of this article. The city is a center for multi-national corporations in northern India and is a part of the NCR-Delhi. The demand for housing is rising along with work prospects. Power demands obviously rise as a result. Installing solar rooftop panels can support and depend on this power source. Based on economic, technological, and environmental factors, a survey of the families where they are installed was conducted to determine the degree of satisfaction. In the relevant study area, the solar panels' performance has been deemed good.

Facial expression analysis plays a vital role in enhancing human?computer interaction and intelligent decision-making systems. This study presents a real-time facial emotion recognition framework powered by a Convolutional Neural Network (CNN) optimized for fast and accurate classification. The proposed model processes live video frames to detect and classify human emotions such as happiness, sadness, anger, fear, surprise, and neutrality. Preprocessing techniques?including face detection, normalization, and data augmentation?are applied to improve robustness against variations in lighting, pose, and background noise. A lightweight CNN architecture is designed to balance computational efficiency and recognition accuracy, enabling deployment on real-time applications such as surveillance systems, interactive learning environments, and assistive technologies. Experimental results demonstrate high accuracy and real-time performance on benchmark datasets, validating the effectiveness of the proposed approach. The system contributes to the development of responsive AI systems capable of understanding and interpreting human emotional states in dynamic environments.

This paper investigates the utility and effectiveness of the MERN (MongoDB, Express.js, React, Node.js) stack in the operationalization phase of Machine Learning (MLOps). While traditional Machine Learning (ML) model training workflows often rely on Python-centric stacks due to their superior numerical processing capabilities, the MERN stack offers a unified, high-performance platform strategically optimized for Model-as-a-Service (MaaS) deployment, inference serving, and real-time monitoring. The analysis details how Node.js's non-blocking Input/Output (I/O) architecture provides superior concurrency and low latency for I/O-bound API serving, offering a measurable advantage over many synchronous alternatives. Furthermore, MongoDB's flexible schema streamlines the management of complex ML data, features, and evolving metadata. Critical architectural patterns, specifically the mandated use of Node.js Worker Threads for CPU-bound inference calculations and the adoption of serverless and edge deployment models, are analyzed as necessary strategies to overcome the inherent limitations of the JavaScript runtime. This paper provides a detailed comparative architectural roadmap for deploying production ML systems using a JavaScript-native stack, identifying both its unique benefits?such as unified language development and rapid iteration velocity?and the essential optimization strategies required for maintaining stability and low latency at massive scale.

The National Assessment and Accreditation Council (NAAC) was instituted in 1994 to ensure quality assurance and accountability in India?s higher education sector. However, in recent years, allegations of corruption, grade manipulation, and bribery have marred its credibility. This paper explores the growing perception that NAAC accreditation has evolved into a commercial enterprise?a ?gaming metric? where universities purchase grades to enhance prestige, attract funds, and increase student enrolment. Drawing upon recent academic literature and verified cases, the paper investigates how accreditation processes are compromised through conflicts of interest, lack of transparency, and systemic inefficiency. The report concludes by recommending governance reforms and independent quality assurance mechanisms to restore integrity and fairness to India?s academic accreditation ecosystem.

This paper seeks to explore the legal standing of ancient women which was regulated and justified through the study of their sexuality and body from the male perspective. Through the rediscovered medical writings associated with Hippocrates as a source, we can find innumerable context on the medical systems and treatment of women in ancient world civilizations. What makes women ?women?? What makes her healthy? What is described as sickness in women? What in her body structure justifies her status as a ?second-class ?citizen? The study of the medical and legal systems used in Ancient Greek, Egypt and Rome; help draw several correlations and its profound impact that still affect modern women around the world. These classical medical texts provide evidence on the reality of women?s lives in ancient society as well as the extent to which particular images of female dominated male perspective. By the 16th century, Hippocratic medicine became established in the west as an important source of medical ethics and practice. As a powerful discourse, medicine can decide what is ?natural?, which behaviour is acceptable, what is ?sickness? and what requires treatment.

Dr. B.R. Ambedkar?s philosophical engagement with Buddhism represents one of the most significant intellectual and socio-religious interventions in modern India. For Ambedkar, Buddhism was not merely a religion but an ethical and rational framework capable of dismantling caste hierarchies, promoting human dignity, and establishing a just social order. This research article explores how Buddhism shaped Ambedkar?s ethical vision, his critique of Brahmanical orthodoxy, and his conception of equality and emancipation. Ambedkar?s reinterpretation of Buddhist doctrine?especially the concepts of Dhamma, Pragy?, Karu??, and ??la?reveals a dynamic socio-moral philosophy grounded in non-discrimination, compassion, and rational humanism. Through Navay?na Buddhism, Ambedkar transformed ancient Buddhist ideals into a modern emancipatory project, enabling marginalized communities to reclaim agency, moral worth, and collective identity. This article analyses the ethical foundations of Ambedkar?s Buddhism, its ideological distinctiveness, and its lasting impact on social justice movements in India.

This multidimensional review provides a study of depression and anxiety disorders. We aim to compare the effectiveness of different types of brief psychological treatments and psychological therapy for adult patients with anxiety, depression, or both common mental health problems treated in primary care, against standard primary care treatment. This paper examines the role of individuals suffering from anxiety and depression, emphasizing the importance of continuous professional development. Research highlights the importance of understanding these symptoms to develop more effective treatments and preventive strategies for reducing the effects of these disorders.

A compact, IoT-based solution ispresented for smart urban rooftop farming, integrating real-time environmental sensing and automation. This paper proposes an end to-end IoT-enabled platform designed to optimize resource use and enhance plant health management through artificial intelligence. The system utilizes a network of distributed sensors measuring soil moisture, pH, air temperature, humidity, and light intensity, with data streamed in real-time to both an edge gateway and a cloud backend. Machine learning algorithms, including Random Forest and LSTM, drive predictive irrigation, crop recommendations, and fertigation guidance. The platform?s automated actuation and intuitive dashboard minimize manual labor and facilitate efficient farming, even in complex urban microclimates. Evaluation results demonstrate reliable sensor performance, significant water savings, and a scalable architecture. Future expansions aim to incorporate nutrient sensing, automated fertilizer dosing, and collaborative community-driven model refinement, illustrating the transformative potential of combining IoT and intelligent analytics in urban agriculture.

The increasing complexity of digital crimes and the sheer volume of digital evidence have overwhelmed traditional forensic processes, leading to significant case backlogs and delayed investigations. Real-time and near-real-time digital evidence processing has emerged as a promising solution to these challenges, enabling faster case resolution and more efficient utilization of forensic resources. This paper explores the need for real-time evidence processing in modern forensic investigations, focusing on the impact of delays in traditional workflows, the advancements in technologies such as AI, machine learning, and cloud computing, and the specific techniques that make real-time analysis possible, including live data acquisition, streaming analytics, and edge computing.By examining real-world case studies and analyzing the benefits of these new approaches, this research highlights how real-time processing can significantly reduce backlogs, enhance case resolution speed, and improve resource allocation in forensic laboratories. Despite the evident benefits, the paper also addresses the technical, legal, and procedural challenges that must be overcome to fully integrate real-time digital forensics into everyday practice. Finally, this study provides a vision for the future, where real-time forensic processing, coupled with AI-enhanced analysis and scalable cloud solutions, could revolutionize the field, leading to more timely and effective investigative outcomes.

The growing need for enhanced security and real-time environmental monitoring in a retail store has led to the development of intelligent surveillance systems. On that basis, this study presents a comprehensive literature review of state-of-the-art techniques used for crime detection in indoor environments. The work examines various intelligent methodologies and tools for surveillance systems on cloud-integrated Internet of Things (IoT) platforms. Each approach is evaluated based on its strengths and weaknesses concerning accuracy and real-time performance. The findings of the study revealed that Artificial Intelligence (AI)-driven models most especially Convolutional Neural Networks (CNNs) offer high accuracy and robust behaviour recognition but require significant computational resources. Then on the other hand, IoT-based systems provide a cost-effective and energy-efficient solution for basic surveillance and environmental sensing but often lack the intelligence to detect complex human behaviour. The study concludes that a hybrid surveillance framework integrating the strengths of both AI and IoT technologies can offer a good solution for intelligent indoor monitoring system in resource-constrained retail environments.

Children's excessive dependence on screen media has created severe public health problems as it may impair their mental, language, and psychological development. This research investigates the impact of screen time on various aspects of development, as well as approaches for managing and limiting children's screen time. Screen technology has a wide spectrum of cognitive implications, with both positive and negative effects reported. Screens can help with education and learning but excessive time spent in front of a screen and multitasking with other media has been linked to poor mental health and academic achievement. Screen time, which affects both the quantity and quality of contacts between children and parents, can also have an influence on language development. Contextual factors such as co-viewing and topic relevance are critical for assessing how language development is affected. Furthermore, excessive screen time has a negative impact on social and emotional development, increasing the risk of obesity, sleep difficulties, and mental health disorders such as sadness and anxiety. It can disrupt emotional processing, motivate aggressive behavior, and undermine one's overall psychological health. Setting borders, using parental controls, and demonstrating appropriate screen behavior are all strategies that parents can use to regulate their children's screen time. We can limit the potential harmful effects of excessive screen time while still enhancing children's healthy development and well-being by raising information and supporting different activities that encourage growth and development.

The quality of instruction has a big effect on student learning outcomes, however, teacher education programs in India usually value theory over real life application. This study reviews India?s pre-service teacher education system with a new focus on removing the gap between ?theory and practice? through advanced reforms and globally accepted models. This discourse reviews the literature regarding highly effective teacher training, emphasising the importance of practicum and reflective practice in forming a teacher?s identity (Korthagen, Loughran, and Russell, 2006). In India, some of the biggest problems include outdated curricula, insufficient connections between schools and colleges, and inefficient entry standards for trainees (National Council for Teacher Education [NCTE], 2019). By looking over comparative case studies from Finland and Japan, we examine effective components such as rigid selection processes, extended internships, and ongoing professional development. In light of these lessons and future modifications to the law (Government of India, 2020; UNESCO, 2021), we propose the following reforms: four-year integrated teacher education programs that include school internships, enhanced mentorship and career pathways, and the use of reflective journaling practices. We review these recommendations and look at their repercussions for policy and practice, suggesting that India?s teacher education should be reformed to focus on practical application. The suggested solutions seek to ensure that future primary and secondary school teachers understand not just educational theories but also practical experience and reflective practice once stepping into the classroom.

The sound of the zither and drum can still resonate in the audience?s hearts because of the talented artist. Similarly, Buddhism has endured throughout history due to the propagation of the Sa?gha. From the very first days after his enlightenment, the Buddha established the Sa?gha, starting with the conversion of the five Venerable Konda??a brothers. This group of ascetic friends became the first members of the Buddhist Sa?gha. From there, the Sa?gha gradually developed into a large congregation. A few years later, the Buddha accepted the ordination of women and appointed Bhikkhu?? Mah?paj?pat? Gotam? to oversee the Bhikkhu?? Sa?gha. One of the Buddha?s most significant contributions was the establishment of the Sa?gha. The emergence of the Buddhist Sa?gha had a profound impact on society, transforming contemporary Indian philosophical thought and offering a new perspective on people and life. By including individuals from all social classes in the Buddhist Sa?gha, the Buddha initiated a social revolution, challenging the existing notions of class and gender discrimination that had long dominated society and contributed to human suffering without a clear path to resolution.

The integration of traditional Sanatan (Hindu) philosophical principles with modern medical science represents an emerging paradigm in contemporary healthcare research. This review examines the scientific validation of ancient wellness concepts including Ayurveda, yoga, meditation, and holistic health principles through the lens of evidence-based medicine. The convergence of these time-tested practices with current biomedical understanding offers promising avenues for addressing chronic diseases, mental health disorders, and lifestyle-related conditions. This article synthesizes current research demonstrating how Sanatan concepts of mind-body integration, preventive healthcare, and individualized treatment approaches align with modern medical frameworks including psychoneuroimmunology, epigenetics, and precision medicine. The review highlights mechanisms through which ancient practices influence physiological systems, discusses clinical applications, and identifies areas requiring further investigation. Understanding this intersection may facilitate more comprehensive, patient-centered healthcare models that honour traditional wisdom while maintaining scientific rigor.

The Cell-phone Based Water Pump Remote Controller Circuit is a cost-effective and efficient solution designed to remotely operate a water pump using simple mobile communication technology. In many rural and agricultural regions, farmers face challenges in manually controlling irrigation pumps due to long distances, irregular power supply, and labour shortages. This project eliminates the need for physical presence by enabling pump operation through a cell-phone interface. The system utilizes Dual Tone Multi-Frequency (DTMF) signals generated by pressing keys on a mobile phone, which are decoded by a DTMF decoder circuit. The decoded signals are processed through a monostable multivibrator to produce pulses, which in turn drive a relay mechanism connected to the water pump. This allows the user to switch the pump ON or OFF reliably and safely. The proposed system offers several advantages including low cost, ease of use, and wide accessibility, since it can function with any basic mobile phone without the need for internet connectivity. The design is particularly suitable for farmers, households, and industries where pumps are installed at remote or hard-to-reach locations. This project demonstrates the practical application of telecommunication and electronics in addressing real-life agricultural and water management challenges.

This paper deals with a new integer sequence generated from the recurrence relation subject to the initial conditions . Some observations among the members of the sequence are illustrated.
Keywords: Integer sequence , Relations connecting special numbers
Notations:
Mersenne number :
Kynea number :
Carol number :
Woodall number :
Cullen number :
Jacobsthal number :
Jacobsthal-Lucas number :
Thabit ibn Kurrah number :

Green concrete is produced using eco-friendly waste materials, significantly reducing CO2 emissions and minimizing environmental impact. It also reduces water consumption by up to 20%. Key factors driving the growth of the green concrete market include the reduction of carbon footprints by approximately 40-50% during production, the increasing construction activities in developing nations, and, most notably, the use of less water. In addition to these benefits, green concrete provides excellent thermal insulation and enhanced fire resistance, making structures built with it more durable and safer. There is considerable potential in using waste materials for green concrete production. By partially replacing traditional ingredients with waste materials and admixtures, green concrete can offer improved compressive and tensile strength, better sulfate resistance, reduced permeability, and enhanced workability.

The increasing prevalence of violence in public and private spaces necessitates the development of efficient, automated detection systems to ensure safety and timely intervention. This research explores the use of machine learning techniques for detecting violent behavior in video feeds, with applications in security and surveillance systems. A comprehensive dataset comprising diverse real-world scenarios is utilized to train and evaluate models. The proposed system utilizes a combination of deep learning architectures, which include CNNs for spatial feature extraction and RNNs for temporal behavior analysis. Data augmentation and transfer learning are used to mitigate the effects of data scarcity and variability. Experimental results demonstrate high accuracy in distinguishing violent from non-violent activities with promising real-time performance. The system is adaptable and scalable, so it is robust for smart cities, public safety, and private security systems' deployment. This research contribution advances the stateof-the-art in violence detection, pointing out the importance of applying machine learning in practical lifesaving applications.

The rise in global crime rates, particularly affecting women, has created an urgent need for innovative safety solutions. This study presents a Human Scream Detection and Analysis System using machine learning and deep learning techniques to identify human screams from background noise through acoustic analysis. The proposed system aims to enhance public safety and emergency response by enabling real-time scream recognition. It also addresses ethical concerns such as privacy and potential misuse, ensuring responsible implementation of the technology.

Antimicrobial resistance (AMR) has emerged as a global health emergency, diminishing the effectiveness of conventional antibiotics and posing major challenges to the management of infections such as Helicobacter pylori?associated peptic ulcer disease (PUD). Rising resistance to clarithromycin, metronidazole, levofloxacin, and other frontline agents has significantly reduced eradication success, prompting the need for safer, multimodal therapeutic approaches. Medicinal plants represent a rich source of structurally diverse and pharmacologically active compounds, alkaloids, flavonoids, terpenoids, tannins, coumarins, phenolics, and sulfur derivatives, that exert broad-spectrum antimicrobial, antioxidant, anti-inflammatory, and gastroprotective effects. Evidence from phytochemical, spectroscopic (UV/Vis, FTIR), chromatographic (TLC, HPTLC), and biological evaluations demonstrates strong antibacterial and antiulcer activities in plants such as Aegle marmelos, Terminalia chebula, Allium sativum, Phyllanthus niruri, Solanum nigrum, and Azadirachta indica. These herbs act through multiple mechanisms, including membrane disruption, urease inhibition, anti-adhesion effects, efflux pump suppression, free-radical scavenging, mucin enhancement, and mild acid-neutralizing properties. Such multi-target actions reduce the likelihood of resistance development and offer dual benefits against microbial infection and mucosal injury. Emerging therapies, including potassium-competitive acid blockers (P-CABs) like vonoprazan and probiotic strains such as Lactobacillus casei Shirota, provide additional advantages through deeper acid suppression and microbiota modulation. Taken together, current evidence supports an integrative, resistance-conscious approach that combines standardized herbal formulations with modern therapeutic advancements. These plant-derived strategies hold significant promise as sustainable, accessible, and safer alternatives for combating AMR and improving the management of PUD.

Women?s participation is crucial for achieving sustainable community development, yet in many rural settings they remain underrepresented in planning, decision-making, and leadership roles. This study examined women?s perceptions of their participation in community development projects, focusing on community water initiatives in Manyoni District Council, Tanzania. Guided by Empowerment Theory and Social Inclusion Theory, the study employed a mixed methods approach using a descriptive research design and a case study strategy. Data were collected from 110 respondents, including 100 women beneficiaries and 10 key informants, through questionnaires, interviews, focus group discussions, and documentary reviews. Quantitative data were analysed using descriptive statistics, while qualitative data were analysed thematically. The findings showed that 77% of respondents had positive perceptions of women?s participation in community water projects. They believed women?s involvement enhances project sustainability, fosters community ownership, strengthens relationships, boosts motivation, and promotes overall development. Despite these positive perceptions, actual participation remained low. Key barriers included socio-cultural norms, patriarchal structures, limited education, poverty, and restricted access to decision-making spaces. The study concludes that women in Manyoni District are willing and capable of contributing meaningfully to community development, but their involvement is hindered by structural and systemic challenges rather than lack of interest. It recommends strengthening gender sensitization efforts, improving women?s education and economic empowerment, enforcing gender-responsive policies, and adopting participatory development approaches to ensure women?s meaningful and sustainable engagement in community development projects.

Performance optimization remains a critical challenge in modern React applications, particularly as applications scale in complexity and user base. This comprehensive article analysis examines various optimization techniques across component-level, application-level, and data handling domains. The article presents a systematic evaluation of key optimization strategies including React.memo implementation, hook-based optimizations (useCallback, useMemo), code splitting with React.lazy and Suspense, and efficient large dataset management using React Virtualizer. Through detailed case studies of an e-commerce platform and a social media application, we demonstrate significant performance improvements: a 30% reduction in initial load times and enhanced user interaction responsiveness. Veeranjaneyulu Veeri https://iaeme.com/Home/journal/IJRCAIT 1166 The article identifies common implementation pitfalls and provides validated solutions for issues such as memoization overuse and inefficient component hierarchies. Performance metrics analysis reveals substantial improvements in load time, memory usage, and overall user experience. The findings provide a structured framework for implementing optimization strategies while balancing development complexity and maintenance overhead. This article contributes to the growing body of knowledge on React application optimization and offers practical guidelines for developers facing similar performance challenges.

This study examines the impact of social media on consumer buying decisions among young adult users aged 18-35 years through systematic analysis of secondary data sources. Drawing from peer-reviewed journals, industry reports, and market research databases, this research investigates how social media platforms influence the consumer decision-making process. Results indicate that social media significantly affects young adults' buying decisions through influencer marketing, user-generated content, targeted advertising, and peer recommendations. Findings reveal that 78% of young consumers discover new products through social media, while 71% are more likely to purchase based on social media referrals. Platform-specific effects vary, with Instagram and TikTok showing stronger influence on impulse purchases compared to Facebook and LinkedIn. This research contributes to understanding digital consumer behaviour and provides insights for marketers targeting young adult demographics.

Document Object Model (DOM) manipulation and event handling are fundamental concepts in JavaScript that enable developers to create dynamic, interactive, and user-friendly web applications. The DOM represents the structure of a webpage as a hierarchical tree, where each element can be accessed, modified, added, or removed using JavaScript. developers can improve performance, usability, and maintainability of web applications while ensuring seamless interaction between users and systems.

The Internet of Things (IoT) has emerged as a transformative technology connecting billions of devices, enabling seamless data exchange and intelligent automation. However, the large-scale deployment of IoT devices has introduced new security challenges, particularly in anomaly detection. Traditional security systems are often insufficient due to the heterogeneity and scalability of IoT networks. Artificial Intelligence (AI) provides advanced methods for detecting anomalies in real-time, identifying malicious behaviors, and enhancing the resilience of IoT ecosystems. This paper explores AI-powered approaches for anomaly detection in IoT networks, including machine learning, deep learning, and hybrid techniques. It highlights key challenges, frameworks, and future research directions to strengthen IoT security.

Cloud computing is gradually becoming central to Ghana?s digitalisation agenda, underpinning initiatives in e-government, digital financial services and private-sector innovation. Decisions about where and how data are stored, processed and moved across borders are shaped by a complex legal and regulatory environment. This paper analyses Ghana?s cloud-relevant legal framework, focusing on the Electronic Transactions Act, 2008 (Act 772), the Data Protection Act, 2012 (Act 843), the Cybersecurity Act, 2020 (Act 1038), and related sectoral instruments, together with emerging policy initiatives on data centres and cloud services. It situates Ghana?s approach within wider African and global debates on data sovereignty, data localisation and cross-border data flows, drawing on continental frameworks such as the African Union?s Digital Transformation Strategy for Africa and AU Data Policy Framework, as well as regional initiatives led by Smart Africa. Using a doctrinal and policy-analytic approach based entirely on secondary sources, the paper maps institutional mandates, identifies areas of overlap and fragmentation across key regulators, and examines the implications for cloud adoption by government, financial institutions and other organisations. It concludes by proposing options for a more coherent, risk-based cloud and data governance framework that can reconcile legitimate sovereignty and security concerns with the practical need for scalable, resilient cloud services in Ghana.

Green web development focuses on reducing the environmental impact of websites and web services by applying sustainable design, development, hosting, and product strategies without compromising usability or accessibility. This study proposes and evaluates a Sustainable-by- Design development pipeline incorporating Web Sustainability Guidelines (WSG) 1.0, performance-first engineering, green hosting, and carbon-aware delivery to minimize energy use and data transfer throughout the web stack. Using benchmark workloads and modern optimization practices (media compression, lazy loading, code minification, caching, CDNs, efficient frameworks), the approach demonstrates measurable reductions in page weight, requests, and energy proxies aligned with W3C WSG success criteria, while maintaining user experience and business outcomes. Experimental results indicate significant potential for emissions reduction in typical sites, contextualized within the broader evidence that the digital sector contributes roughly 2?5% of global emissions and that faster, lighter websites directly lower energy consumed across devices, networks, and data centers.

Asynchronous programming is a crucial paradigm in modern web development, enabling developers to design responsive, efficient, and scalable applications. In JavaScript, asynchronous programming plays a significant role due to its single-threaded event loop architecture. Traditionally, asynchronous behavior was handled using callbacks, which often led to callback hell and decreased maintainability. The introduction of Promises and, later, the async/await syntax has transformed how developers manage asynchronous tasks by providing cleaner, more intuitive abstractions. This paper explores the evolution of asynchronous programming in JavaScript, examines the benefits of Promises and async/awaits, and evaluates their role in improving web application performance and developer productivity.

The rapid advancement of Artificial Intelligence (AI) has significantly transformed the global education landscape, enabling new forms of teaching, learning, and academic administration. AI- driven systems, such as intelligent tutoring platforms, adaptive learning environments, automated assessment tools, and data-driven academic decision-making models, have enhanced the efficiency, accessibility, and personalization of education. These technologies enable continuous learner evaluation, real-time feedback, and customized instructional pathways, addressing the diverse needs of students across various educational levels. Moreover, AI contributes to reducing teacher workload by automating repetitive tasks, improving classroom management, and offering predictive insights that support strategic planning. However, despite its promising capabilities, the integration of AI in education presents multiple challenges. Issues such as data privacy, algorithmic bias, digital inequality, and reduced human interaction raise ethical and pedagogical concerns that must be carefully addressed. Additionally, the high financial cost and technical complexity of AI implementation limit its scalability in developing regions. This paper examines the major applications of AI in modern education, analyzes their advantages and limitations, and highlights the implications for learners, educators, and policymakers. The findings emphasize the need for responsible AI adoption that balances technological innovation with ethical, social, and human-centered considerations.

Web development has seen a revolution in the past decade, from simple server-rendered projects to modern dynamic single page applications and microservices based ecosystems. Python and React are blossoming as the new technologies driving this transformation. Python, using frameworks such as Django, Flask and FastAPI, offers a flexible and expressive backend ecosystem featuring: RESTful/GraphQL APIs; async programming model; fast dev cycles. A UI framework that changed the landscape for front end development thanks to its declarative, component-centric approach and virtual DOM structure, React is a project from Meta (formerly known as Facebook).

This paper explores the multifaceted dimensions of India&#39;s agricultural export potential and trade competitiveness in the global market. It meticulously examines the existing landscape, unearthing the opportunities and challenges that confront Indian agrarian commodities in their quest for international recognition and market share (Gilmour et al., 2012; Sharma et al., 2023). By employing rigorous analytical techniques and drawing on a wealth of empirical data, the study aims to provide a comprehensive assessment of India&#39;s strengths and weaknesses in the agricultural export sector, thereby informing policy recommendations that enhance its global presence and ensure sustainable growth.

Personalized learning, powered by artificial intelligence (AI), is revolutionizing education by tailoring instruction to individual students' needs, abilities, and learning styles. This paper explores the current state and future potential of AI- driven personalized learning systems. It examines how AI techniques such as machine learning, natural language processing, and knowledge representation can be leveraged to create adaptive learning experiences that optimize educational outcomes. The paper reviews existing research on AI in education, discusses key technologies and architectures for personalized learning systems, and presents case studies of successful implementations. Challenges and ethical considerations around AI in education are also explored. The paper argues that AI-driven personalized learning, combined with human instruction, has immense potential to enhance educational effectiveness, engagement, and equity. However, careful design and responsible deployment of these systems will be essential. The paper concludes with recommendations for future research and development in this field.

Artificial Intelligence (AI) is transforming web development by introducing automation, intelligence, and efficiency into the coding process. AI-powered tools such as ChatGPT and GitHub Copilot enable developers to accelerate development, reduce errors, and enhance productivity. This paper explores the integration of AI in web development, focusing on its technologies, key features, scalability, applications, authentication, and performance optimization. The study concludes that AI-driven tools represent a paradigm shift in web development by combining human creativity with machine intelligence.

India's Act East Policy (AEP), evolving from the Look East Policy (LEP), represents a pivotal strategic reorientation. This paper analyses AEP's role in catalysing India's strategic transformation within the Indo-Pacific region during the decade 2014-2024, utilizing the theoretical frameworks of strategic autonomy and multilateralism. It argues that AEP is not merely a continuation of LEP but a qualitatively distinct, proactive, and multifaceted strategy driven by the imperatives of a rising China, the United States' strategic rebalance, and India's own economic and security ambitions. Through enhanced defense diplomacy, financial integration, infrastructure connectivity, and robust multilateral engagement, particularly within ASEAN-centric frameworks and multilateral groupings like the Quad and AEP, India has significantly elevated its regional profile and influence. The policy demonstrates a sophisticated interplay between strategic autonomy, guiding India's independent decision-making and aversion to formal alliances, and multilateralism, enabling coalition-building to shape a rules-based regional order. While achieving substantial successes in deepening regional integration and asserting India's Indo-Pacific centrality, AEP faces persistent challenges, including implementation gaps, resource constraints, and navigating the internal cohesion of ASEAN, as well as the great power competition. This paper concludes that AEP has been instrumental in transforming India from a peripheral player to a consequential strategic actor in the Indo-Pacific, fundamentally reshaping regional dynamics and setting the course for India's future regional engagement.

The modern web is an intricate tapestry of documents, scripts, and resources loaded from countless different servers. A single web page can simultaneously display images from a content delivery network, run analytics scripts from a marketing service, and fetch data from a backend API. This interconnectedness, while powerful, presents a significant security challenge. Without a foundational security model, a malicious script from one website could potentially access and exfiltrate sensitive user data from another, completely compromising user privacy and security. The mechanism that prevents this chaotic scenario is a browser-enforced security feature known as the Same-Origin Policy (SOP). Understanding the SOP is not merely a prelude to understanding Cross-Origin Resource Sharing (CORS); it is the fundamental context that necessitates its very existence. CORS was designed not to replace the SOP, but to provide a standardized, controlled mechanism for relaxing its strictures when legitimate cross-domain communication is required.

In today?s rapidly advancing healthcare system, technology plays a vital role in improving patient outcomes, enhancing efficiency, and reducing errors. Artificial Intelligence (AI), robotics, telehealth, and automated monitoring systems have become integral components of modern hospitals. However, despite these advancements, technology can never replace the compassion, empathy, critical thinking, and emotional intelligence that nurses bring to patient care. Nursing is not just about performing tasks ? it is about healing, understanding, and connecting with patients on a human level. Technology has transformed nursing practice in numerous ways: Electronic Health Records (EHRs): Simplify data documentation and access to patient information. Automated Machines: Assist in medication dispensing, vital signs monitoring, and infection control. Telehealth Platforms: Enable remote consultations and follow-up care. Artificial Intelligence: Supports decision-making and early diagnosis through predictive analysis. While these tools improve the efficiency and accuracy of healthcare services, they remain tools?dependent on the human judgment and ethical considerations of the nurse. Technology Cannot Replace Nurses because Human Touch and Compassion Machines cannot express empathy or provide emotional comfort to patients in pain, fear, or grief. A nurse?s presence, comforting words, and caring gestures promote healing beyond physical treatment. Critical Thinking and Decision-Making Nursing requires complex clinical judgment in unpredictable situations. No algorithm can fully replace a nurse?s ability to assess, prioritize, and make decisions based on holistic patient needs. Ethical and Cultural Sensitivity Nurses deal with diverse populations, respecting beliefs, values, and emotions ? an area where technology lacks understanding and adaptability. Patient Advocacy Nurses advocate for patients? rights and safety, ensuring ethical care. Technology cannot negotiate or question medical decisions in favour of a patient?s well-being. Interpersonal Communication Nurses act as a bridge between patients, doctors, and families. Machines can relay information but cannot build trust or maintain therapeutic relationships. Recent studies show that while AI and automation can enhance data management and reduce workload: 85% of patients report feeling more satisfied when care involves direct nurse interaction. 92% of healthcare leaders agree that emotional support provided by nurses improves recovery outcomes. Hospitals with higher nurse?patient communication scores report 30% fewer readmissions and 20% lower mortality rates compared to tech-dependent models. These findings emphasize that technology supports nursing practice but cannot replace the nurse?s holistic and human-centered approach. Technology continues to evolve, but the essence of nursing lies in care, compassion, and connection ? qualities that no machine can replicate. While automation can handle data and devices, the nurse listens, comforts, and advocates for the patient. Therefore, technology should be viewed as an aid to nursing, not a replacement. The future of healthcare depends on the collaboration between technology and human touch, where nurses remain at the heart of patient care.

Polyene chromophores are integral to various biological, chemical, and technological applications due to their distinctive light-absorbing properties. This review focuses on the structural and functional diversity of polyene chromophores, with a particular emphasis on the roles of aldehyde and ketone functionalities. The synthesis, optical properties, and potential applications of these compounds in fields such as photodynamic therapy, nonlinear optics, and material sciences are discussed.Recent advances in polyene chromophores with carbonyl groups (aldehyde and ketone functionalities) have significantly expanded their potential in various scientific and technological fields. The incorporation of carbonyl groups into polyene chromophores has been shown to enhance electronic conjugation, leading to superior optical properties, including increased absorption and emission wavelengths. These functionalities allowing for precise control over optical spectra and quantum yields. Advances in synthetic methodologies have enabled the efficient and versatile creation of complex chromophore structures, facilitating their application in areas such as biological and chemical stability and the development of innovative materials. Overall, the integration of aldehyde and ketone functionalities into polyene chromophores marks a significant advancement, opening new avenues for research and application in material science and technology.

This review aims to summarise the development of milking systems, which influenced the quality of the milk and milk produced substantially, in past few decades. The milking environment is considered as one of the important factors for clean milk production. The milking systems were developed so as to keep the animal calm and comfortable while milking, which is attributed to have an advantageous effect on the quantity of the milk produced. Mechanisation of milking units has decreased the human intervention tremendously, yielding low labour requirement and reduced contamination due to human attributes. Many modernized techniques have been developed to facilitate proper and consistent milking practices. On the other hand, it has also improved the efficiency, safety and comfort of the labours involved in dairying. Quality milk production can only be achieved by adopting modern milking systems and good hygienic practices in and around the farm.

India?s approach to conflict in West Asia reflects a carefully measured equilibrium between moral principles and strategic pragmatism. As a nation historically committed to non-alignment and peaceful coexistence, India continues to uphold its normative stance of non-interference and dialogue-based conflict resolution. However, evolving geopolitical realities, including energy security imperatives, the welfare of 9.8 MN Indian diaspora, and deepening partnerships with competing regional powers such as Iran, Israel, and Saudi Arabia, have compelled New Delhi to adopt a more nuanced, interest-driven diplomacy. This study analyzes India?s strategic neutrality through the framework of analytical eclecticism, blending elements of realism, liberal institutionalism, and constructivism to explain its multidimensional engagement in West Asia. By examining India?s responses to key regional crises, such as the Iran?US tensions, the Yemen conflict, and the Israel?Palestine issue, the paper highlights how India maintains a delicate balance between ethical commitments and pragmatic national interests. It argues that India?s foreign policy in West Asia embodies a ?strategic equilibrium? that seeks stability, energy access, and global credibility while avoiding entanglement in sectarian or great-power rivalries. The analysis contributes to a broader understanding of how emerging powers like India navigate complex regional conflicts through diplomacy rooted in both principle and pragmatism.

Artificial Intelligence (AI) has revolutionized multiple industries, significantly impacting engineering and finance. In engineering, AI enhances efficiency through predictive maintenance, automated design, robotics, and structural analysis, reducing human error and optimizing performance. Meanwhile, in finance, AI-driven algorithms power fraud detection, risk assessment, algorithmic trading, and financial forecasting, leading to faster and more accurate decision-making. Despite its vast potential, challenges such as ethical concerns, data security, and algorithmic bias remain critical. This paper explores the transformative role of AI in engineering and finance, comparing their applications, challenges, and future directions. The findings suggest that AI will continue to shape these fields, driving innovation and improving decision-making processes.

Asynchronous programming is a crucial paradigm in modern web development, enabling developers to design responsive, efficient, and scalable applications. In JavaScript, asynchronous programming plays a significant role due to its single-threaded event loop architecture. Traditionally, asynchronous behavior was handled using callbacks, which often led to callback hell and decreased maintainability. The introduction of Promises and, later, the async/await syntax has transformed how developers manage asynchronous tasks by providing cleaner, more intuitive abstractions. This paper explores the evolution of asynchronous programming in JavaScript, examines the benefits of Promises and async/awaits, and evaluates their role in improving web application performance and developer productivity.

In today's fast-paced world, childhood is increasingly becoming a fleeting memory, overshadowed by the pressures of academic achievement, digital distractions, and societal expectations. As children are hurried through their early years, the essence of childhood ? its joy, curiosity, and sense of wonder ? is at risk of being lost. Children are increasingly treated as miniature adults, burdened with expectations to excel in every domain from academics to extracurricular, often at the expense of their natural joy, exploration, and unhurried development. This study encapsulates a call to action for parents, educators, and society to reclaim the innocence and wonder of youth. By embracing principles such as active parental involvement, maintaining a positive and balanced attitude, avoiding comparison, creating a pressure-free environment, reducing screen dependency, promoting hands-on learning, encouraging creativity and curiosity, recognizing curiosity as a driving force, appreciating efforts instead of criticizing, fostering unhurried growth, and understanding the long-term impact, people can nurture children who grow into resilient, innovative, and fulfilled adults. This research draws on psychological insights, real-world examples, and practical advice to illustrate how prioritizing play, exploration, and emotional well-being can preserve the magic of childhood.

In the 21st century, social media platforms have become major places where people talk to each other. This paper looks at how social media is actively changing modern English words through the quick creation, spread, and acceptance of new words, meaning changes in old words, and even mixing text with pictures and symbols. Using recent studies, word analysis, and language theory, this paper explores how new words are made, spread, and accepted, as well as social and language effects. The findings show that social media works not just as a way to share information but as a strong force of language change that speeds up new ideas, challenges traditional language authorities, and makes what counts as "words" bigger.

Campus hiring constitutes a defining milestone in a student?s transformation from academic life to the professional world, often moulding their career trajectory and bespeaking the effectiveness of an institution?s training and guidance. Nevertheless, the ground reality in India reveals a gap?according to Unstop (2024), only 7% of institutions achieved full placements. This statistic highlights a compelling need to modernize how placement data is accumulated, analysed, and used for Training Need Analysis. Relying on traditional methods like scattered spreadsheets or paper records not only exhausts time but also compounds the risk of errors, making it knackered to procure meaningful insights that can drive strategy. In response, this study introduces a centralized, interactive, and scalable placement analytics dashboard that amalgamates the strengths of Python (Pandas) for Extract, Transform, and Load (ETL) operations with Microsoft Power BI for visualization. This Power BI dashboard can serve as the backbone for placement teams to monitor trends, track performance, and gain real-time, strategic insights. The dashboard proposes placement metrics in a visually intuitive manner to explore the data from multiple angles, making the insights more accessible and meaningful. The integration of Python and Power BI simplifies placement reporting, improves transparency, and strengthens the ability to make data-driven decisions. With future enhancements like ERP connectivity, automation, and broader user training, the approach can be scaled to other departments and institutions.

The increasing global shift toward natural and holistic wellness solutions has significantly boosted the demand for Ayurvedic products. Governmental interventions play a crucial role in influencing consumer behavior and enhancing purchase intentions through strategic promotional efforts. This research paper explores the impact of government-driven promotional initiatives on consumers' purchase intention toward Ayurvedic products. The Ayurvedic sector, governed under the AYUSH (Ayurveda, Yoga & Naturopathy, Unani, Siddha, and Homeopathy) framework, has seen a surge in popularity, especially in the wake of growing consumer awareness towards natural and holistic healthcare. This study explores the promotional efforts employed by Ayurvedic brands and their impact on consumers' purchase intentions. The paper evaluates digital marketing strategies, traditional advertising, influencer endorsements, and government initiatives to analyze their effectiveness.

The global shift towards digital transactions has created a parallel, alarming rise in credit card fraud, posing a significant threat to consumers, merchants, and financial institutions. Addressing the sophisticated nature of this fraud requires automated, real-time detection systems. This paper presents a comparative analysis of four prominent machine learning algorithms?Logistic Regression, Support Vector Machines, Random Forest, and XGBoost?to determine their effectiveness in this task. The primary objective is to benchmark their performance on a highly unbalanced, real-world Kaggle dataset from ULB's Machine Learning Group. A key methodological step involves applying the Synthetic Minority Over-sampling Technique (SMOTE) to the training set. This technique rectifies the severe class imbalance, enabling the models to learn the nuanced characteristics of the rare fraud class. We evaluated the models using a comprehensive suite of metrics: accuracy, precision, recall, F1-score, and AUC-ROC. Our empirical findings reveal that the ensemble methods, Random Forest and XGBoost, significantly outperform the other models. They achieve an optimal balance between precision and recall, which is crucial for simultaneously minimizing financial losses from missed fraud and reducing customer friction from false positives. The study affirms that these advanced models offer a robust and effective framework for enhancing fraud detection in the financial industry.

The study examines the transformational impact of social media on the 2024 Lok Sabha elections, with a focus on how digital platforms have altered political communication, voter mobilisation, and campaign strategies for the Bharatiya Janata Party (BJP) and the Indian National Congress. Drawing on secondary data, official reports, and current analysis, the study illustrates the transition from traditional mass-media campaigning to a digitally driven paradigm characterised by targeted advertising, influencer outreach, WhatsApp-based micro-mobilisation, and data-driven communication. The comparison study demonstrates that, while the BJP maintained a significant lead in digital reach and advertising expenditure, the INC achieved excellent engagement growth through personalised content and grassroots mobilisation. The paper also identifies significant hazards associated with digital campaigns, including disinformation, algorithmic manipulation, unequal access, and regulatory gaps. The paper concludes that digital politics is already crucial to Indian elections and will continue to impact political competitiveness and voter behaviour in the future.

The proliferation of digital interfaces has created a paradox: while technology offers unprecedented connectivity and information, it often comes at the cost of mental and emotional health. This paper addresses the growing crisis of digital ill-being, identifying the web design practices of the "attention economy" as a primary contributor to user anxiety, distraction, and addiction. We synthesize research from Human-Computer Interaction, psychology, and technology ethics to analyze the mechanisms through which design patterns manipulate user behavior and erode wellbeing. This study proposes a novel, multi-layered framework?The Ethical Design Pyramid?for integrating digital wellbeing principles directly into the web design process. The framework prioritizes user autonomy, cognitive respect, and the promotion of human flourishing over simple engagement metrics. Through a comparative analysis of existing web platforms, we demonstrate the framework's utility in evaluating and guiding the creation of healthier digital environments. The findings indicate that a paradigm shift towards wellbeing-centric design is not only a moral imperative but also a strategic necessity for building sustainable, trust-based relationships with users in an increasingly saturated digital world.

In today?s digital era, social media has emerged as one of the most influential platforms for communication, information sharing, and public discourse. Platforms such as Twitter (X), Facebook, Instagram, and YouTube collectively generate millions of posts, comments, and reactions each day, offering an invaluable source of insights into user opinions and emotions. Social Media Sentiment Analysis (SMSA), a subfield of Natural Language Processing (NLP), focuses on computationally identifying and categorizing sentiments expressed in this unstructured textual data to determine the polarity (positive, negative, or neutral) and, in advanced models, specific emotions such as joy, anger, sadness, or fear.

Rapid urbanization and increasing land scarcity have led to the widespread development of high-rise residential buildings. This shift toward vertical living has introduced new social dynamics that may influence residents? psychological well-being. The present study examines the relationship between social issues and psychological well-being among occupants of high-rise buildings, with a focus on the predictive role of key psychosocial factors in a Tier-II city, Bhopal. A correlational research design was employed, using stratified random sampling to select 400 residents of which 214 were from high-rise and 186 were from low-rise buildings in Bhopal. Regression analyses were conducted to determine the extent to which four components of social issues predict five dimensions of psychological well-being. The findings revealed that four social issues components significantly predicted psychological well-being. However in most cases limited community engagement was found to be the most potent predictor of psychological well being followed by the other three components of social issues. The models explained between 19% and 21% of the variance in psychological well-being, providing substantial support for rejecting the null hypothesis. The results highlight the crucial role of social dynamics in issues pertaining to psychological well being in vertical housing. The study emphasizes on the need for resident-centered planning, improved community-building mechanisms, and enhanced safety measures to promote healthier and more cohesive high-rise living environments.

Inflammation is a protective biological response that maintains tissue integrity following injury, infection or exposure to harmful stimuli. However, dysregulated or persistent inflammation contributes to the development of chronic diseases such as arthritis, diabetes, cardiovascular disorders and neurodegenerative conditions. Synthetic drugs including NSAIDs, COX-2 inhibitors, corticosteroids, DMARDs and biologics remain the primary therapeutic options, but their long-term use is often limited by gastrointestinal injury, renal toxicity, immunosuppression and cardiovascular complications. This has encouraged a growing interest in medicinal plants as safer, multi-targeted alternatives for managing inflammatory disorders. This review systematically highlights a wide range of medicinal plants traditionally used in inflammation, along with their extraction types, phytochemical profiles and mechanisms of action. Many of these plants contain flavonoids, alkaloids, terpenoids, saponins, phenols and essential oils that exert anti-inflammatory effects by inhibiting COX/LOX enzymes, suppressing NF-?B activation, modulating cytokines such as TNF-?, IL-1? and IL-6, and reducing oxidative stress. Experimental evidence from various in-vivo models?including carrageenan-induced paw edema, FCA-induced arthritis, LPS-stimulated macrophages and granuloma assays?supports their pharmacological potential. Overall, medicinal plants represent promising therapeutic resources with significant anti-inflammatory activities. Further research on standardization, toxicity profiling and clinical validation is required to facilitate their safe integration into modern medicine.

In this paper, we use a Hexagonal fuzzy number and its membership function. We set out a way of dealing with to solve fuzzy sequencing problem where processing time taken as Hexagonal fuzzy numbers. Fuzzy sequencing problem are transformed into a crisp valued sequence problem which is illustrated with a numerical example.

Metformin, a biguanide, is still the standard of oral treatment of type 2 diabetes mellitus (T2DM). Its long-standing position owes to its strong efficacy, good safety profile, and new pleiotropic advantages. This review proposes to make a thorough synthesis of the existing knowledge on metformin's mechanisms, highlighting its pharmacokinetic (PK) and pharmacodynamic (PD) features. Pharmacokinetically, metformin is distinguished by incomplete intestinal absorption through specific transporters (OCT1, PMAT), absence of hepatic metabolism, and sole renal excretion through OCT2 and MATE transporters. Clinically, this profile directly affects its dosing and contraindications. Pharmacodynamically, the key action of metformin is inhibiting hepatic gluconeogenesis. This is done through its intracellular accumulation in hepatocytes and consequent inhibition of mitochondrial respiratory chain complex I. The subsequent rise in the cellular AMP/ATP ratio activates AMP-activated protein kinase (AMPK), a key regulator of cellular energy. AMPK activation and AMPK-independent mechanisms control hepatic glucose and lipid metabolism, enhance peripheral insulin sensitivity, and enhance glucose uptake. Clinically, this is reflected by lowered fasting and postprandial hyperglycemia with negligible risk of hypoglycemia. This review summarizes these molecular and systemic effects, vindicating the justification for metformin's ongoing dominance as a first-line drug in the treatment of T2DM.

Background: Knee osteoarthritis (OA) is a leading cause of chronic disability in the elderly population, often impairing mobility and limiting participation in recreational sports such as golf. Robotic-assisted unicompartmental knee arthroplasty (UKA) has emerged as a minimally invasive surgical option with improved component alignment, accuracy, and faster recovery. However, evidence on return to sport?particularly golf?following robotic-assisted UKA, remains limited. Purpose: This manuscript presents a detailed case analysis of a 71-year-old recreational female golfer who successfully returned to golf following robotic-assisted UKA supported by structured physiotherapy. Methods: A case-based observational design was used. Preoperative assessments included pain severity, functional scores, mobility testing, and golf-specific movement analysis. Postoperative physiotherapy incorporated early mobilization, strengthening, balance training, and sport-specific golf rehabilitation. Data were documented using functional assessment tools and outcome measures. Tables and figures summarize the rehabilitation timeline, exercises, and clinical progression. Results: The patient demonstrated progressive improvement in pain reduction, quadriceps strength, gait mechanics, and knee mobility. By 10 weeks, she resumed putting and chipping; by 16 weeks, she returned to full 18-hole recreational golfing without pain. Functional scores improved significantly, and she reported enhanced confidence and quality of life. Conclusion: Robotic-assisted UKA combined with structured physiotherapy facilitated an efficient and safe return to golf in an elderly patient. The case underscores the effectiveness of integrating robotic precision surgery with physiotherapy-guided sports rehabilitation for older adults seeking to maintain active lifestyles.

Confined-space inspection presents persistent challenges in industrial and municipal infrastructure maintenance. This paper presents an autonomous control framework for miniaturized mobile robots capable of navigating and mapping unknown, GPS-denied pipe networks. The proposed system integrates lightweight onboard hardware, AI-based sensor fusion, and reinforcement learning path planning to achieve high mapping accuracy and energy efficiency. A modular test bed replicating real-world pipe networks, including obstacles and varying lighting conditions, was developed to evaluate the system. Experimental trials demonstrated a mean mapping accuracy of 94.3%, exploration coverage of 98%, and a 23% reduction in mission completion time compared to baseline control methods. The AI-optimized motion planning reduced energy consumption by 21%, extending operational endurance. Results confirm the system?s robustness, adaptability, and suitability for inspection in complex confined environments, offering a scalable solution for industrial asset monitoring and maintenance.

This paper conducts a comprehensive examination of the practical applications of Cauchy?s Integral Formula in evaluating complex contour integrals, a fundamental concept in complex analysis. It adopts a dual approach, integrating theoretical foundations with illustrative examples to demonstrate the formula?s efficacy and sophistication. Furthermore, the study investigates the impact of singularities within a closed contour on the evaluation of complex functions and elucidates how Cauchy?s formula facilitates the solution process. Notably, the paper highlights applications in both pure and applied mathematics, with particular emphasis on integral evaluations pertinent to mathematical physics and potential theory.

Convolutional Neural Networks (CNNs) are a fundamental class of neural networks widely employed in tasks such as image recognition and classification. They have diverse applications, including object detection, image processing, computer vision, and facial recognition. CNNs take images as input and automatically learn a hierarchy of features for classification, eliminating the need for manually engineered features. This is achieved by constructing multiple layers of feature maps, where each layer is generated by convolving the input with learned filters. Through this hierarchical approach, deeper layers are capable of capturing increasingly complex features that are robust to variations in position and distortion. The primary aim of this study is to provide a comprehensive understanding of CNNs, highlighting existing research gaps while examining their core components, functions, and other essential considerations.

The establishment of administrative tribunals in India marked a significant development in the evolution of administrative justice, aimed at providing specialized, speedy, an less formal adjudication of disputes involving public administration. However, the proliferation of tribunals has led to ongoing debates about their jurisdictional overlap with regular courts, particularly the high courts and the Supreme Court. This research paper critically examines the functional and constitutional boundaries between administrative tribunals and courts, assessing the extent to which tribunals have achieved their purpose of reducing judicial burden while maintaining fairness and accountability. It exposes the implications landmark judgement like L.Chandra kumar v. Union of India (1997), which reaffirmed the supremacy of judicial review under Article 32, 226 of the constitution. This study also analyses on the challenges faced by the dual system of adjudication, including issues of accessibility, independence, and the equality of justice delivered by tribunals. Through a doctrinal and a comparative approach. This paper evaluated whether the current framework successfully balances administrative efficiency with constitutional principles of separation of power and rule of law. Ultimately, it argues that while administrative tribunals play an important role enhancing the efficiency and specialization in the dispute related to governance, clearer demarcation of jurisdictional boundaries and a stronger institutional safeguards are essential to prevent conflict, ensure uniformity in justice delivery and preserve the integrity of India?s judicial system

Artificial intelligence (AI) systems offer effective support for online learning and teaching, including personalizing learning for students, automating instructors? routine tasks, and powering adaptive assessments. However, while the opportunities for AI are promising, the impact of AI systems on the culture of, norms in, and expectations about interactions between students and instructors are still elusive. In online learning, learner?instructor interaction (inter alia, communication, support, and presence) has a profound impact on students? satisfaction and learning outcomes. Thus, identifying how students and instructors perceive the impact of AI systems on their interaction is important to identify any gaps, challenges, or barriers preventing AI systems from achieving their intended potential and risking the safety of these interactions.

The present study investigated the impact of a High-Intensity Circuit Training (HICT) program on speed and coordination among male Kabaddi players of Andhra University. The purpose was to determine whether a structured, short-duration, high-intensity training regimen could significantly enhance game-specific motor ability components essential for Kabaddi performance. A total of 30 male Kabaddi players, aged 18?25 years, were selected using purposive sampling and randomly assigned into experimental (n=15) and control groups (n=15). The experimental group underwent an 8-week HICT program, three sessions per week, incorporating sport-specific drills, plyometrics, sprint intervals, agility stations, and body-resistance exercises. The control group continued their regular practice without additional conditioning. Speed was assessed using the 50-meter dash, and coordination was measured through the Eye?Hand Coordination Test and Alternate Hand Wall Toss Test. Pre- and post-test data were analyzed using paired and independent t-tests. Results revealed a significant improvement (p < 0.05) in both speed and coordination among the experimental group compared to the control group. The findings indicate that HICT is an effective and time-efficient training method to enhance essential motor fitness parameters for competitive Kabaddi performance. The study recommends integrating structured circuit-based conditioning into regular training programs for university-level Kabaddi players to optimize performance outcomes.

The present study aimed to examine the effect of selected yoga practices on the stress levels of U-19 volleyball players from Andhra Pradesh state. A total of 30 adolescent volleyball players (aged 16?19 years) were randomly assigned to an experimental group (yoga intervention) and a control group (no intervention). The experimental group participated in a structured yoga program, including asanas, pranayama, and meditation, conducted 45 minutes per day, five days a week, for eight weeks. Stress levels were measured before and after the intervention using the Perceived Stress Scale (PSS-10). Results indicated a significant reduction in stress levels in the experimental group, whereas the control group showed no significant changes. The study concludes that selected yoga practices are effective in reducing stress among U-19 volleyball players and can be recommended as a regular component of training programs to enhance psychological well-being and performance.

Astrophysical systems such as neutron stars represent some of the most extreme environments in the universe, yet their vast distances typically hundreds of light-years away make direct exploration impossible. Our understanding of these dense stellar remnants is therefore limited to indirect observations, including the radiation they emit and the gravitational effects they imprint on nearby celestial bodies. Developing methods to simulate neutron-star physics in controlled laboratory settings would revolutionize our ability to probe their internal structure and dynamics. A similar challenge arises in condensed-matter physics, where strongly correlated electron phenomena such as the integer and fractional quantum Hall effects give rise to entirely new states of matter under extreme magnetic fields and ultralow temperatures. Although these conditions can be engineered on Earth using two-dimensional heterostructures and quantum wells, current experimental tools cannot track the microscopic motion of individual electrons within these complex systems. Advancing our ability to emulate and resolve such strongly correlated behavior would therefore mark a major breakthrough in uncovering the fundamental mechanisms that govern both astrophysical and quantum-material phenomena.

Parkinson?s disease (PD) is a progressive neurodegenerative disorder characterized by the loss of dopaminergic neurons in the substantia nigra, leading to both motor and non-motor symptoms. While current therapies such as levodopa and dopamine agonists offer symptomatic relief, they do not halt disease progression, emphasizing the urgent need for disease-modifying therapies (DMTs). Emerging evidence suggests that incretin-based agents, especially GLP-1 and GIP receptor agonists, possess significant neuroprotective properties, including anti-inflammatory, antioxidant, and mitochondrial-supporting effects. This review focuses on tirzepatide, a novel dual GLP-1/GIP receptor agonist, and evaluates its therapeutic potential in PD. Comprehensive insights into the molecular mechanisms of PD pathogenesis?including ?-synuclein aggregation, mitochondrial dysfunction, oxidative stress, and neuroinflammation?are presented. Additionally, we explore the ability of tirzepatide to modulate these pathological pathways based on in silico target prediction, protein-protein interaction network analysis, GO/KEGG pathway enrichment, and molecular docking studies. Tirzepatide demonstrated significant binding affinity to key targets involved in neuroinflammation and dopaminergic signaling, supporting its potential as a multitarget therapeutic agent. Overall, the evidence suggests that tirzepatide may serve as a promising DMT candidate for PD by targeting key neurodegenerative mechanisms. Further preclinical and clinical studies are warranted to validate its efficacy and safety profile in human PD populations.

The integration of Quality by Design (QbD) into pharmaceutical research has provided a structured and scientifically driven pathway for developing liposomal drug delivery systems with enhanced safety, efficacy, and reproducibility. Liposomes, as versatile nanocarriers, can significantly improve the solubility, stability, and bioavailability of both synthetic and herbal drugs. This review provides a comprehensive overview of QbD principles and their application in the systematic design, optimization, and control of liposomal formulations. Key elements such as Quality Target Product Profile (QTPP), Critical Quality Attributes (CQAs), Critical Material Attributes (CMAs), and Critical Process Parameters (CPPs) are discussed in detail, along with the role of Design of Experiments (DoE) in establishing a robust design space. Representative case studies including curcumin, doxorubicin, and silymarin-loaded liposomes demonstrate the effectiveness of QbD in achieving optimized formulations with superior therapeutic outcomes. The discussion also encompasses challenges, regulatory expectations, and the future integration of Artificial Intelligence (AI), machine learning, and Process Analytical Technology (PAT) for real-time process monitoring. Overall, this review underscores the significance of QbD as a transformative tool in nano pharmaceutical development, promoting reproducibility, scalability, and regulatory compliance in the formulation of next-generation liposomal and herbal nanomedicines.

The establishment of administrative tribunals in India marked a significant development in the evolution of administrative justice, aimed at providing specialized, speedy, an less formal adjudication of disputes involving public administration. However, the proliferation of tribunals has led to ongoing debates about their jurisdictional overlap with regular courts, particularly the high courts and the Supreme Court. This research paper critically examines the functional and constitutional boundaries between administrative tribunals and courts, assessing the extent to which tribunals have achieved their purpose of reducing judicial burden while maintaining fairness and accountability. It exposes the implications landmark judgement like L.Chandra kumar v. Union of India (1997), which reaffirmed the supremacy of judicial review under Article 32, 226 of the constitution. This study also analyses on the challenges faced by the dual system of adjudication, including issues of accessibility, independence, and the equality of justice delivered by tribunals. Through a doctrinal and a comparative approach. This paper evaluated whether the current framework successfully balances administrative efficiency with constitutional principles of separation of power and rule of law. Ultimately, it argues that while administrative tribunals play an important role enhancing the efficiency and specialization in the dispute related to governance, clearer demarcation of jurisdictional boundaries and a stronger institutional safeguards are essential to prevent conflict, ensure uniformity in justice delivery and preserve the integrity of India?s judicial system

Parents of special children having Disabilities like physical, Intellectual, Developmental Etc. face multiple challenges that can adversely affect their mental health. This paper critically examines existing literature to explore the extent and nature of mental health issues among these parents, the factors contributing to these problems, the impacts ? on both parents and children what the various coping strategies and interventions, gaps in current research; and implications for policy, practice and future research. The review suggests that parental mental health is a key component for well?being of both parent and child, but often neglected. Thoughtful, culturally sensitive, and supportive policies and programs are required to address this.

The drug delivery systems of hydrogels presented themselves with a very versatile platform by virtue of their capability for encapsulating therapeutic agents and controlled release. Recent efforts limiting hydrogel-based drug delivery aim at developing systems more responsive toward a change in external stimuli like pH, temperature, or light for targeted and ondemand drug release. Recent advances in polymer chemistry have fabricated hydrogels with improved biocompatibility, mechanical strength, and degradation profiles, thereby yielding a wide range of biomedical applications. Moreover, the combination of nanotechnology with hydrogels has rendered new opportunities not only for drugs but also for the delivery of complex drugs such as proteins, peptides, and nucleic acids, which are difficult to administer by traditional drug delivery methods. Hydrogels, with their distinctive three-dimensional networks of hydrophilic polymers, drive innovations across various biomedical applications. The ability of hydrogels to absorb and retain significant volumes of water, coupled with their structural integrity and responsiveness to environmental stimuli, renders them ideal for drug delivery, tissue engineering, and wound healing. This review delves into the classification of hydrogels based on cross-linking methods, providing insights into their synthesis, properties, and applications.

Today's AI chatbots, powered by "transformer" models, can generate surprisingly human-like text. But they still struggle with a core element of real conversation: context. They often forget what was said moments earlier, miss subtle meanings, lack common sense, and can't reliably use outside knowledge. In this article, author argues that the key to solving this isn't just building bigger AI models, but creating hybrid systems that cleverly combine them with other tools, like a search engine for facts, a digital memory for past conversations, and a web of common-sense knowledge. Author explores the promise and trade-offs of these hybrid approaches and outline a path toward creating truly context-aware AI that can navigate the complexities of the real world.

The two major problems in the development of new drugs are low aqueous solubility and low oral bioavailability. Although, drug delivery via oral route is most preferred for years but it also has some drawbacks. Various techniques for improving the solubility have been developed, however the success of these techniques depends on the physical and chemical properties of the drug under development. In recent years, mucoadhesive drug delivery gained high popularity in comparison to other routes of drug delivery as it can circumvent the drawbacks of conventional delivery system such as first pass metabolism, enzymatic degradation, GI toxicity of some drugs, instability in acidic or alkaline environment and poor bioavailability. Various mucoadhesive dosage forms have been developed recently including tablets, patches, films, ointments, gels etc. The objective of current review is to provide a comprehensive overview of mucoadhesive drug delivery including the mechanism and theories behind mucoadhesion, factors affecting mucoadhesion, different dosage forms, polymers used in mucoadhesive formulations, characterization techniques, marketed products and current scenario & future challenges.There are many advantages of mucoadhesive buccal drug delivery system that made this a novel drug delivery system for the local as well as systemic delivery of various drugs. The main advantage of this drug delivery system is that it prolongs the residence time of the dosage form at the site of application. Due to the high blood supply and relatively high permeability of the buccal mucosa, the buccal cavity is the best option for both local as well as systemic delivery of various drugs. The term bioadhesion can be defined as a phenomenon of interfacial molecular attractive forces in the midst layer of surface of a biological membrane and the natural or synthetic polymers, which allows the polymer to adhere the surface of that membrane for an extended as well as prolonged period of time. In this review we have discussed the various types of mucoadhesive dosage forms along with a brief knowledge about the various types of mucoadhesive polymers.

The concept of Alternative Dispute Resolution (ADR) has evolved as a vital mechanism for ensuring timely, cost-effective, and amicable settlement of disputes outside the traditional court system. In India, the evolution of ADR reflects the nation?s continuous efforts to enhance access to justice and reduce the burden on the judiciary. Rooted in ancient traditions such as panchayats and village councils, India has long practiced informal dispute resolution. The modern legal framework for ADR began to take shape with the enactment of the Arbitration Act, 1940, which was later replaced by the Arbitration and Conciliation Act, 1996, aligning domestic law with the UNCITRAL Model Law. Over time, mechanisms such as mediation, conciliation, negotiation, arbitration, and lok adalats have gained prominence in both civil and commercial disputes. Judicial pronouncements and legislative reforms?like the Arbitration and Conciliation (Amendment) Acts and the establishment of Commercial Courts?have further strengthened the institutional framework of ADR in India. Despite its growth, challenges remain in terms of awareness, uniform implementation, and quality of arbitral institutions. This paper traces the historical development, statutory evolution, and judicial approach toward ADR in India while highlighting its role in promoting speedy and participatory justice. The study concludes that a strengthened and accessible ADR system is indispensable for achieving the goals of justice, efficiency, and harmony in India?s legal landscape.

The rapid advancement of Information and Communication Technologies (ICT) has significantly influenced modern sports, and fencing, as a highly technical Olympic discipline, is no exception. Officiating in fencing requires accuracy, fairness, and quick decision-making, often beyond the capacity of the human eye alone. This study aimed to examine the role of ICT in the development of fencing officiating, with a particular focus on its impact on accuracy, training, communication, and global standardization. A sample of 80 national fencing officials was surveyed using questionnaires, interviews, and competition observations. Descriptive statistics (Mean and Standard Deviation) and inferential tests (t-test, ANOVA) were employed to analyze the data. The results indicated that ICT tools, such as electronic scoring systems and video replay technologies, significantly enhance fairness and transparency in decision-making. Officials also acknowledged the importance of ICT-based training platforms, online certification programs, and communication networks in strengthening professional development and ensuring uniform interpretation of international rules. However, limitations such as technical malfunctions, high equipment costs, and unequal access to advanced systems were also reported. The study concludes that ICT plays a transformative role in fencing officiating, contributing to the credibility, consistency, and modernization of the sport. Continued investment in technological infrastructure, referee training, and cost-effective solutions is essential to maximize the benefits of ICT and align fencing officiating with global standards of excellence.

The paper examines the primary themes of John Keats? ?Ode to a Nightingale.? Through extensive study, it manifests his philosophical notions about life, melancholy, imagination, and the way art transcends humankind. Keats equates the brevity of human life to the nightingale?s seemingly endless song. Further, he demonstrates the way life is bound and the way art and imagination can provide us with something enduring. The paper foregrounds the susceptibility and risks in human lives. It incorporates thoughts from critics such as Brooks, Vendler, Leavis, and Bate to appreciate the way Keats makes his emotions into something that all can relate to. Ultimately, the poem serves as a reminder that no matter how short life might be, human imagination and creativity are incredibly able to impact in a lasting way that endures beyond themselves.

This research paper examines an in-depth analysis of the provisions governing international taxation in India, focusing on the legal framework, compliance mechanisms, and their implications for cross-border transactions. Anchored in the Income Tax Act, 1961, the paper examines key provisions such as Double Taxation Avoidance Agreements (DTAAs), General Anti-Avoidance Rules (GAAR), and the Base Erosion and Profit Shifting (BEPS) framework adopted by India. Through a mixed-methods approach, combining legal analysis with case studies of multinational enterprises and quantitative data from the Central Board of Direct Taxes (CBDT), the study evaluates the effectiveness of these provisions in curbing tax evasion while promoting foreign investment. Findings reveal that DTAAs under Section 90 have significantly reduced tax conflicts for non-residents, yet complexities in transfer pricing regulations and GAAR implementation pose challenges for compliance. The study also explores the impact of recent amendments, such as the Equalization Levy and Significant Economic Presence (SEP) rules, on digital economy taxation. Limitations include the evolving nature of global tax standards and India?s ongoing alignment with OECD guidelines.

This paper presents the design, development, and evaluation of an intelligent mock interview system that leverages Natural Language Processing (NLP) to simulate realistic interview experiences and provide automated, objective feedback. The system addresses critical challenges in interview preparation, including limited access to human interviewers, subjective feedback, and inadequate practice opportunities. The proposed framework utilizes advanced NLP techniques, including transformer-based models for speech recognition and sentiment analysis, combined with neural networks for response evaluation. The system is designed to conduct multi-turn conversations, analyze verbal content, assess non-verbal cues, and generate comprehensive feedback reports. Performance evaluation demonstrates a functional, accurate system that significantly improves interview preparedness, with user performance improvement exceeding 40% after multiple sessions. Extensive testing with 500 users showed a system accuracy of 89% in evaluating response quality and a reduction in interview anxiety by 65%, validating the system's potential for real-world deployment in educational and corporate environments.

Consumer insight in the emerging digital economy has surpassed traditional market research. While quantitative measures like views and likes give a shallow account, they fail to convey the nuance of consumer opinion. A more nuanced, better source of consumer insight lies hidden in the unstructured, vast amounts of data from YouTube comment streams. Such online communities are a global agora where consumers openly share views on products, services, and brands. This report outlines a model for officially mining the data, transforming raw public opinion into actionable customer segments. With advanced sentiment analysis, businesses can transition from tallying what people watch to knowing what they feel, tapping into a powerful driver of strategic growth.

In an era of rapid digital transformation and moral decline, the future of education depends upon re-educating educators themselves. This research integrates three original conceptual frameworks developed by Dr. Aditya PeriSubramanya?M.O.N.E.Y. (Motivation, Organisation, Nurture, Education, Yield), FOCUS (Future Ambition, Organised Objectives, Concentration, Understanding Talents, Success by Achievement), and the Global Educator Development Framework (GEDF)?to propose a holistic model of teacher development that unites ethical, personal, and professional dimensions of growth. Drawing on qualitative synthesis of global policy documents (UNESCO SDG 4, OECD TALIS, NEP 2020) and lived classroom practice, the study establishes that genuine educational reform begins within the educator?s moral and motivational core. Through the M.O.N.E.Y. model, teachers cultivate inner purpose and ethical wealth; through the FOCUS model, they refine self-discipline and goal clarity; through the GEDF, they achieve digital, cultural, and reflective competence. The paper advances a Unified Educator Vision Model (UEVM)?a triadic framework aligning self-realisation, systematic practice, and societal contribution. Findings highlight that integrating moral education with digital pedagogy produces educators who are compassionate innovators and lifelong learners. The study concludes that ?educating educators is educating humanity,? calling for global collaboration to prepare teachers who combine technology with humanity, intellect with integrity, and leadership with love.

The regulation of intimate partnerships in India reveals tensions between constitutional morality, political interests, and entrenched social norms. Live-in relationships challenge the centrality of marriage and have invited judicial intervention in the absence of legislative clarity. Unregistered marriages, though socially sanctioned, remain legally ambiguous and expose contradictions in India?s pluralistic legal system. This paper examines how electoral politics, identity-based mobilisation, judicial activism, and gendered socio-legal dynamics interact to shape the evolving framework for intimate partnerships. Through an analysis of constitutional protections, statutory developments, and landmark judicial decisions, the article argues that India?s governance of intimate life is shaped by ideological struggles over modernity, cultural nationalism, and state authority. A comparative discussion of live-in relationships and unregistered customary marriages demonstrates inconsistencies in regulatory priorities. The paper concludes that a coherent, rights-based legal framework addressing both live-in and unregistered marital unions is essential for gender justice and democratic legitimacy.

This article describes the early and betrothed life of Hanumanthagowuda. After purchasing Honnatti Vatan, he acquired titles such as Diwan, Mulki, and Faujdar. With the help of Harapanahalli Palegar, he eliminated the harassment of merchants in Bankapur Paragan.Further, with the assistance of Sarjakhana, he received seven mahals - Gutthal, Hadagali, Ranebennur, Hirekerur, Chikkerur, and Thilavalli - as jagir from Adil Shah.During his 14-year rule, Hanumanthagowda:Built forts and templesEstablished a strong foundation for the principalityEnsured stability and security. Hanumanthagowda, a renowned warrior, earned the title "Sangrama Dhurina" (Brave Warrior) and adopted the Hanuman Flag as his emblem. Further this article helps to give information about his tragedy ending which was assassinated in 1595 CE due to a trivial reason.

Vijay Tendulkar stands as one of the most powerful and controversial dramatists in modern Indian theatre. His plays revolutionized Marathi and Indian drama by questioning social, moral, and political structures while redefining theatrical realism. While many of his plays appear grounded in the conventions of social realism, Tendulkar uses the very tools of realism?authentic dialogue, psychological depth, and social settings?to undermine its limits and expose the violence beneath social respectability. This paper examines how Tendulkar both employs and subverts realism in Indian drama through his themes, characters, and dramatic techniques. Focusing on major plays such as Silence! The Court Is in Session, Sakharam Binder, and Kanyadaan, it explores how realism in his works becomes a means of radical critique rather than mere representation.

This case series explores the effectiveness of Cognitive Behavioural Therapy (CBT) in treating Panic Disorder (PD) of varying severity?mild, moderate, and severe?among three Indian clients. Interventions included psychoeducation, breathing retraining, cognitive restructuring, graded exposure, and mindfulness. All participants showed significant improvement, with reductions in Panic Disorder Severity Scale (PDSS) and Beck Anxiety Inventory (BAI) scores, and enhanced functioning. The results highlight CBT?s adaptability across different symptom levels and its cultural relevance in the Indian context. Incorporating CBT into educational and workplace settings can promote early intervention, reduce stigma, and improve overall mental health outcomes.