The present study focuses on the evaluation of micronutrients in soil samples collected from Subhash Nagar, Ambikapur, Chhattisgarh, to understand their environmental significance and impact on soil fertility. Soil samples were collected using standard sampling techniques and analyzed for key micronutrients such as zinc (Zn), iron (Fe), manganese (Mn), and copper (Cu) along with basic physico-chemical parameters including pH and electrical conductivity (EC). The results indicated that the concentration of micronutrients varied across sampling sites, reflecting the influence of land use patterns and anthropogenic activities. Iron and manganese were found in relatively higher concentrations, while zinc and copper showed moderate levels within permissible limits. The soil pH ranged from slightly acidic to neutral, favoring micronutrient availability. The study highlights that although the soil is generally suitable for agricultural use, continuous monitoring is essential to prevent nutrient imbalance and potential environmental degradation in the region.

This research analyzes the consumer decision-making processes regarding sustainable and eco-friendly products among shoppers in Barrio Militar Market, Nueva Ecija. Examining data from 82 respondents through descriptive and mixed-methods approaches, it was found that consumers possess high environmental consciousness and strong positive attitudes toward green products. Specifically, respondents rated Quality as the highest priority (Mean: 4.37), followed closely by Limited Availability (Mean: 4.32) and Higher Cost (Mean: 4.30). While consumers demonstrate strong willingness to support environmental causes and are highly knowledgeable in identifying authentic products, their purchasing power remains a primary constraint. The study identified significant barriers to adoption, with the inconvenience of product locations and lack of clear labeling being foremost concerns, alongside the perception that premium prices are not justified by benefits. Demographic analysis indicates that the market is dominated by middle-aged to older adults, predominantly female, and belonging to the low-to-middle income bracket, suggesting that purchasing decisions are heavily defined by budget constraints and practicality. The main challenge to promoting sustainable consumption is not the lack of awareness or interest, but the insufficient availability, accessibility, and affordability of eco-friendly goods. Strategic interventions are necessary to improve product distribution, ensure competitive pricing, and provide clear information to bridge the gap between consumer intention and actual purchase behavior.

This study analyzed the trends of digital consumption in Santa Rosa while focusing on the importance of computer shops in the locality regardless of the growing prevalence of mobile gadgets. This study sought to analyze how the principles of scarcity and digital choice architecture affect FOMO among Millennials and Generation Z. The descriptive-correlational method of research was used, and survey questionnaires were administered to 82 respondents composed of three (3) shop owners and seventy-nine (79) customers. Tests such as t-test, Pearson correlation (r), and ANOVA were used in analyzing the gathered data. The majority of the respondents were male respondents who belong to the Millennials and Generation Z. It was revealed from statistical analysis that there was no significant relationship (p > .05) between the socioeconomic profiles of the respondents and their level of FOMO, thus proving that there was no effect of the demographic factors of the study population to the said phenomenon. Moreover, computer shops remain relevant as "third places" because they provide good connectivity, technical support, and opportunities for socialization. Also, digital fatigue was associated with prolonged screen time and online activity.

Background: Despite Nigeria’s remarkable progress in under-five survival, adolescent mortality (ages 10–19 years) has declined far more slowly and remains poorly understood, with an estimated 75,000–95,000 deaths annually. Methods: We constructed a multi-decade (1990–2023), sex- and region-disaggregated panel from WHO Global Health Observatory mortality estimates. After rigorous cleaning, temporal imputation, and feature engineering (including lagged child mortality indicators and sex-specific interactions), we applied Elastic Net, Random Forest, and XGBoost models with strict temporal validation (training ≤2016, testing 2017–2023). The best-performing XGBoost model (test RMSE 4,601 deaths; R² 0.85) was interpreted using SHAP for global and subgroup explanations and LIME for local case studies. Robustness was assessed via uncertainty-bound retraining and 100-iteration bootstrapping. Results: Historical under-5 (CM_01) and 5–14 (CM_02) mortality probabilities together with their lagged and MDG counterparts emerged as the dominant drivers, explaining the majority of predicted adolescent deaths and demonstrating strong temporal inertia. Sex-stratified analyses revealed that early-childhood survival exerts the strongest influence on female outcomes, whereas road traffic and external-cause indicators rise prominently for males. These core child-survival pathways remained stable across WHO uncertainty bounds and in >85% of bootstrap resamples. Conclusion: Adolescent mortality in Nigeria is predominantly a downstream reflection of early-life survival conditions, modulated by sex-specific risks. Sustaining and accelerating child survival programmes represent the highest-leverage strategy for further adolescent mortality reduction, complemented by targeted gender-transformative interventions injury prevention for males and adolescent reproductive health integration for females. All analyses are fully reproducible from openly accessible data, providing a transparent, actionable template for adolescent health policy in Nigeria and similar high-burden settings

Handwriting and drawing patterns carry meaningful information about how the nervous system and motor system function together. When someone writes or draws, their strokes reflect underlying neurological activity—and disruptions in that activity often show up as inconsistencies in pressure, shakiness, or uneven patterns. Building on this clinical insight, we developed NeuroScript AI, a system that uses deep learning to automatically detect neuromotor irregularities from handwriting and drawing images. Our approach centers on a Convolutional Neural Network (CNN) that processes spiral and meander drawings from the HandPD dataset. A key contribution of this work is the Neural Pressure Index (NPI)—a metric we designed to approximate pen pressure from image-level features like stroke thickness, pixel intensity, and stroke density, eliminating the need for pressure-sensing hardware. The combined model achieved 94.2% classification accuracy across normal, mild, and severe neuromotor categories, suggesting that image-based handwriting analysis can serve as a practical and scalable screening tool in clinical contexts.

Work-life balance has become an important concern among teachers due to increasing professional demands, administrative responsibilities, and personal obligations that affect their well-being and job satisfaction. This study explored the lived experiences of elementary teachers in Sablan, Benguet regarding work-life balance and job satisfaction using a qualitative research design grounded in Edmund Husserl’s transcendental phenomenology. Specifically, the study aimed to uncover the meanings, challenges, coping mechanisms, and perceptions of teachers concerning the interplay between their professional and personal lives. Purposive sampling was utilized in selecting participants who had direct experiences related to the phenomenon under investigation. Data were gathered through in-depth semi-structured interviews and analyzed using phenomenological thematic analysis through the process of bracketing and phenomenological reduction. Findings revealed three major themes: struggles in balancing professional and personal responsibilities, coping mechanisms and sources of support in managing work-life balance, and work-life balance as a determinant of job satisfaction and teaching commitment. The study found that teachers experienced stress and exhaustion due to heavy workloads and overlapping responsibilities; however, they demonstrated resilience through time management, family support, collaboration, and personal coping strategies. Furthermore, work-life balance significantly influenced teachers’ motivation, emotional well-being, job satisfaction, and commitment to teaching. The study concludes that teacher wellness and institutional support are essential in sustaining teacher satisfaction and effective educational delivery. The findings may serve as a basis for developing teacher wellness programs and policies promoting sustainable and supportive work environments in rural schools.

This study examines the influence of cognitive heuristics on the purchasing efficiency of rice retailers among consumers in Palayan City, Nueva Ecija. Specifically, it focuses on three types of heuristics—availability, representativeness, and anchoring—and their relationship with key purchasing efficiency factors such as price, quality, accessibility, and transaction convenience. A descriptive mixed-method research design was employed, involving 83 respondents selected through purposive sampling. Data were collected using a structured questionnaire and analyzed using descriptive statistics, Pearson correlation, and thematic analysis. The findings reveal that consumers strongly rely on cognitive heuristics when making purchasing decisions. Representativeness (mean = 4.29) and anchoring (mean = 4.33) heuristics showed strong influence, particularly in shaping perceptions of rice quality and price evaluation, while availability heuristic showed moderate influence (mean = 3.98). Correlation analysis indicates that representativeness and anchoring heuristics have significant positive relationships with purchasing efficiency variables, especially quality perception and transaction speed. In contrast, availability heuristic showed no significant relationship with efficiency outcomes.The results suggest that while heuristics improve decision speed and convenience, they may also lead to simplified judgments based on price cues, familiarity, and visual indicators rather than objective evaluation. The study concludes that cognitive heuristics play a critical role in consumer behavior in rice retailing, particularly among low-income and routine buyers. It recommends that retailers enhance pricing transparency, product quality consistency, and customer service to improve purchasing efficiency and consumer satisfaction.

This study examined the relationship between customer experience, customer satisfaction, and repurchase intention among customers in the Palengke ng Santa Rosa, Nueva Ecija. Specifically, the study aimed to describe the demographic profile of the respondents in terms of age, sex, frequency of purchase, and average monthly spending; determine the level of customer experience in terms of product quality, service quality, and perceived price fairness; assess the level of customer satisfaction and repurchase intention; identify the significant relationship between customer experience and customer satisfaction and repurchase intention using correlation analysis; and propose development strategies to improve customer experience and encourage repeat purchasing behavior.
The study utilized a quantitative descriptive-correlational research design with open-ended questions as supporting qualitative responses. A total of 83 respondents were selected using convenience sampling. Data were gathered through a researcher-made survey questionnaire and analyzed using frequency, percentage, weighted mean, and Pearson correlation analysis through Jamovi statistical software.
The findings revealed that most respondents were aged 26–35 years old, female, frequent market buyers, and moderate spenders. Product quality, service quality, perceived price fairness, customer satisfaction, and repurchase intention were all evaluated at high to very high levels. Pearson correlation analysis revealed that service quality, perceived price fairness, and customer satisfaction have significant positive relationships with repurchase intention. Product quality showed a significant relationship with customer satisfaction but did not show a statistically significant relationship with repurchase intention. The findings suggest that customers are more likely to continue purchasing when they experience quality service, fair pricing, and overall satisfaction.
The open-ended responses revealed that customers value fresh products, fair pricing, respectful treatment, and positive vendor attitudes. Respondents also suggested improving cleanliness, organization, promotional activities, and vendor responsiveness to enhance customer experience and market competitiveness.
The study concluded that customer experience significantly influences customer satisfaction and repurchase intention among customers in the Palengke ng Santa Rosa, Nueva Ecija. Therefore, improving service quality, maintaining fair pricing, and strengthening customer relationships may help vendors improve customer loyalty and long-term business sustainability.

Electronic Medical Records (EMR) implementation plays a crucial role in improving work efficiency and the quality of healthcare services in hospitals. The success of EMR implementation can be evaluated using the DeLone and McLean model, which encompasses system quality, information quality, and service quality, and impacts user satisfaction. This study aims to determine the relationship between system quality, information quality, and service quality and user satisfaction with EMR at Bahteramas Regional Hospital, Southeast Sulawesi Province. This study used a quantitative approach with a cross-sectional design. The sample in this study consisted of 274 respondents who were users of the EMR system, using a proportionate stratified random sampling technique. Data were collected through a structured questionnaire and analyzed using the fisher exact test. The results of the study indicate that system quality is significantly related to user satisfaction (p-value = 0.007), information quality is significantly related to user satisfaction (p-value = 0.000), and service quality is also significantly related to user satisfaction (p-value = 0.009). The conclusion of this study is that system quality, information quality, and service quality are related to user satisfaction in the implementation of electronic medical records. Therefore, improving system quality, information quality, and service quality requires attention to support user satisfaction and optimize the use of EMR in hospitals.

Vidyarthi Unique Identity Tag (VIDUIT) is an intelligent IoT-based attendance automation system developed to improve accuracy, security,and transparency in educational institutions. The proposed framework integrates geofencing mechanisms with GPS tracking, RFID-based identification, RF wireless communication, and real-time SMSnotifications to verify student presence effectively. The system architecture includes a portable transmitter unit carried by students and a fixed receiver unit installed on campus. The transmitter acquires real-time GPS coordinates and transmits them to the receiver using RF communication. The receiver checks whether the received location lies within thepredefined institutional geofence. Upon successful validation, RFID authentication is performed to uniquely identify the student and attendance is automatically recorded on a cloud platform using an ESP8266-enabled internal connection with website. Faculty members can access live attendance dashboards remotely. Additionally, a GSM module sendsinstant SMS alerts to parents, ensuring student safety, eliminating manual roll calls and preventing proxy attendance.

Landmine contamination poses a serious threat to human safety, especially in regions affected by wars and conflicts. Traditional methods of detecting landmines are slow, dangerous, and costly, exposing human operators to high risk. To overcome these issues, this project focuses on the development of a landmine detection robotic vehicle integrated with GPS technology for safe and efficient detection and location tracking of buried mines. The robot uses a metal detector sensor to identify metallic objects hidden underground, while an STM32 microcontroller processes the detection signals in real time. A GPS module provides the exact coordinates of the detected mines, which are shown on an LCD display and stored for future reference. Ultrasonic sensors are used to detect obstacles and help the robot move smoothly on uneven surfaces. The system operates in a semi-autonomous mode, using sensor data and embedded control logic to improve accuracy and ensure safe operation. With its energy-efficient and cost-effective design, the proposed system offers a reliable and scalable solution for defense and humanitarian demining efforts.

Breast cancer is one of the most prevalent and life-threatening diseases affecting women worldwide. Early detection plays a crucial role in improving survival rates and reducing the burden on healthcare systems. In recent years, advancements in Artificial Intelligence (AI) have revolutionized the medical field, especially in medical imaging and diagnostics. This paper explores the application of AI techniques, particularly deep learn-ing models such as Convolutional Neural Networks (CNNs), for the detection and classification of cancer cells from microscopic biopsy images. The goal is to develop an efficient and accurate system that assists pathologists in diagnosing cancer at its early stages, minimizing human error and time consumption. The proposed AI model processes histopathological images to identify malignancies based on cellular morphology, nucleus size, and texture variations. Experimental results demonstrate that AI-based detection provides significant improvements in diagnostic accuracy, sensitivity, and specificity compared to traditional manual methods. This research highlights the potential of AI as a reliable diagnostic tool for early cancer detection, paving the way for more accessible and cost-effective healthcare solutions.

This qualitative study explores how digital access inequalities shape student experiences, engagement, psychological well-being, and resilience among learners at Zimbabwe's state universities in Bulawayo. Despite global recognition of digital technologies as enablers of equitable education, persistent infrastructural, socio-economic, and institutional barriers continue to limit meaningful access for many students in developing contexts. Through semi-structured interviews and focus group discussions with 24 undergraduate students across two state universities in Bulawayo, this research examines the lived experiences of accessing digital learning resources, the consequences of digital inequality on academic participation and mental health, and the adaptive strategies students employ to sustain their learning. Findings reveal that students face significant challenges including unreliable internet connectivity, limited device ownership, high data costs, and inadequate institutional support. These barriers negatively influence academic engagement, increase psychological distress, and deepen existing educational inequalities. However, students demonstrate considerable resilience through peer collaboration, mobile-first learning strategies, offline resource sharing, and flexible study routines. The study identifies critical institutional and contextual factors such as infrastructure deficits, inconsistent policy implementation, and socio-economic disparities that perpetuate digital divides. This research contributes empirical evidence to inform policy interventions and institutional practices aimed at promoting digital equity and supporting student well-being in resource-constrained educational settings.

Today's customers have increasingly complex demands, making it crucial for businesses to employ staff who are emotionally adept at handling client disagreements. Emotional intelligence has emerged as a key factor directly influencing customer happiness, the quality of service provided, employee performance, and a company's long-term viability. This article explores the significance of emotional intelligence in customer service as a vital approach for resolving customer-related issues. It will delve into the concept and various aspects of emotional intelligence, identify the primary causes of customer conflicts, and highlight how emotional intelligence contributes to effective communication, understanding others, managing one's own feelings, and dispute resolution. Drawing upon recent research, it is evident that employees with strong emotional intelligence are better equipped to calm heated situations, address customer unhappiness, and foster lasting positive relationships. Ultimately, emotional intelligence significantly enhances how effective customer service is and boosts a company's ability to compete in the market. Therefore, it is recommended that organizations integrate emotional intelligence training into their hiring practices, ongoing staff development programmes, and overall customer service strategies.

This study examined the effect of Green Human Resource Management (GHRM) practices on organisational efficiency of fast food service businesses in Akwa Ibom State, Nigeria. The study adopted a survey research design. The population of the study comprised 460 employees drawn from selected fast food service businesses operating in major commercial areas within Akwa Ibom State. A sample size of 210 respondents was determined using the Taro Yamane formula, while simple random sampling technique was used in selecting respondents for the study. Data were collected through the use of a structured questionnaire designed on a five-point Likert Scale. The instrument was validated by experts, while Cronbach Alpha reliability coefficients of 0.81, 0.84, and 0.86 confirmed the reliability of the instrument. Data collected were analysed using simple linear regression with the aid of Statistical Package for Social Sciences (SPSS Version 25) at 0.05 level of significance. Findings from the study revealed that green recruitment and selection significantly affect organisational efficiency of fast food service businesses in Akwa Ibom State. The study further revealed that green training and development significantly influence organisational efficiency. The findings imply that organizations that recruit environmentally conscious employees and regularly train workers on sustainable workplace practices are more likely to improve operational performance, reduce waste, enhance productivity, and achieve long-term sustainability. The study concluded that Green Human Resource Management practices play a significant role in improving organisational efficiency among fast food service businesses in Akwa Ibom State. The study therefore recommended that management of fast food service businesses should integrate environmental sustainability into recruitment policies, organize regular green training programmes for employees, and promote environmentally responsible workplace behaviour in order to improve efficiency and organizational sustainability.

Automated coin detection, counting, and sorting constitute a critical challenge in modern financial automation, spanning applications in commercial banking, vending machine systems, retail point-of-sale environments, and smart transit infrastructure. This review article provides a comprehensive and critical analysis of state-of-the-art methodologies employed for automatic coin recognition and sorting, with particular emphasis on deep learning-based object detection frameworks—most notably the You Only Look Once (YOLO) family of architectures. The evolution of coin analysis systems is traced chronologically, from classical image processing pipelines relying on Hough Circle Transforms, morphological operations, and template matching, through handcrafted feature-based approaches employing Support Vector Machines (SVM) and Artificial Neural Networks (ANN), to the contemporary era of Convolutional Neural Network (CNN)-based detectors and anchor-based detection frameworks including Faster R-CNN and Single Shot Multibox Detector (SSD).
A systematic literature review, conducted following PRISMA-style methodology across IEEE Xplore, Springer Link, Elsevier ScienceDirect, MDPI, Google Scholar, and arXiv, yielded 25 primary studies published between 2019 and 2024. Comparative analysis reveals that YOLO-based architectures—particularly YOLOv5, YOLOv7, and YOLOv8—consistently outperform classical and two-stage detection methods in terms of mean Average Precision (mAP), inference speed, and deployability on resource-constrained hardware. YOLOv8 nano-variant achieves mAP@0.5 values exceeding 94% at frame rates surpassing 200 frames per second on mid-range GPUs, demonstrating superior suitability for real-time embedded applications. Critical gaps identified include the absence of standardized multi-currency benchmark datasets, insufficient research addressing counterfeit coin detection, limited exploration of transformer-based vision models in this domain, and a paucity of lightweight, quantization-aware models validated on edge hardware such as Raspberry Pi and FPGA platforms. Future research trajectories are outlined, encompassing Vision Transformers (ViT), YOLO-NAS, TinyML-based deployment strategies, and federated learning for privacy-preserving multi-institution coin datasets. This review synthesizes the current landscape, benchmarks competing approaches, and establishes a rigorous foundation for future research in intelligent currency handling systems.

Reading comprehension remains a persistent challenge among elementary learners, particularly in rural and multigrade classrooms where access to literacy-rich learning environments is limited. This study determined the effectiveness of the Learning Park in improving the reading comprehension performance of elementary learners at Balili Elementary School in La Trinidad during School Year 2025–2026. The study employed a quantitative quasi-experimental one-group pretest–posttest research design. Fourteen elementary learners served as respondents and were selected based on established inclusion criteria. Data were gathered using an adapted Philippine Informal Reading Inventory (Phil-IRI) reading comprehension assessment tool that measured literal, inferential, and critical comprehension skills. Descriptive statistics such as frequency and mean were used to describe learners’ performance, while a dependent t-test was utilized to determine the significant difference between pretest and posttest scores. Findings revealed that the learners obtained a mean pretest score of 8.64, interpreted as Instructional level, indicating that learners initially required teacher assistance in comprehending texts. After the implementation of the Learning Park intervention, the mean posttest score increased to 14.00, corresponding to the Independent level. Furthermore, the computed t-value of 8.121 was greater than the critical value of 1.771 at the 0.05 level of significance, indicating a statistically significant improvement in learners’ reading comprehension performance. The study concluded that the Learning Park is an effective, learner-centered, and activity-based instructional intervention that enhances reading comprehension, promotes learner engagement, and supports literacy development among elementary learners.

This quantitative cross-sectional study investigates the relationship between retirement and relationship realignment among Ghanaian retirees, with particular focus on how social network composition shifts during the retirement transition and how these shifts affect loneliness, life satisfaction, and psychological well-being after retirement. Drawing upon the Social Convoy Model (Kahn & Antonucci, 1980) and Network Theory of Social Capital (Burt, 1992), the study surveyed 218 retired workers from public and private sector organizations in Accra, Kumasi, and Tema. Participants completed validated instruments measuring workplace relationship intensity, post-retirement network realignment, loneliness (UCLA Loneliness Scale), life satisfaction (SWLS), and psychological well-being (GHQ-12). Data were analyzed using descriptive statistics, Pearson correlation, multiple regression, and one-way ANOVA. Results revealed that retirees who reported greater realignment of their social networks from work-based to non-work-based relationships experienced significantly lower loneliness (r = -0.59, p < 0.01) and higher life satisfaction (r = 0.63, p < 0.01) compared to those who maintained work-dominated networks without successful realignment. Post-retirement network realignment uniquely predicted 38% of the variance in loneliness after controlling for age, gender, years of service, and retirement duration. Retirees who actively cultivated new non-work relationships reported significantly better psychological well-being than those who attempted to maintain pre-retirement work networks without modification (t = 5.64, p < 0.001). Retirees from public sector organisations reported greater difficulty in network realignment than those from private sector organisations (t = 3.87, p < 0.001). The findings suggest that successful retirement adjustment depends not merely on maintaining existing relationships but on actively realigning social networks to replace work-based connections with meaningful non-work relationships. Recommendations include pre-retirement network mapping interventions, social integration programmes for recent retirees, and organisational policies that facilitate gradual rather than abrupt workplace separation.

Adenomatous hyperplasia of the rete testis is an unusual benign epithelial proliferation that can closely simulate malignant lesions on histopathological examination. Owing to its intricate glandular architecture, the lesion may be mistaken for adenocarcinoma of the rete testis or metastatic adenocarcinoma. We report a rare incidental case identified in a 52-year-old male who underwent surgery for an irreducible left inguinal hernia. Histopathological examination of the orchiectomy specimen revealed marked testicular atrophy associated with complex proliferation of interconnected tubular and glandular structures within the rete testis lined by bland cuboidal epithelium without cytological atypia or stromal invasion. Based on the characteristic morphological findings, a diagnosis of adenomatous hyperplasia of the rete testis was rendered. Recognition of this uncommon benign entity is essential to avoid overdiagnosis and unnecessary aggressive treatment.

Background: The quality of healthcare services in the Emergency Department (ED) is a crucial indicator of patient safety. The surge in BPJS Kesehatan membership towards Universal Health Coverage (UHC) in North Luwu Regency has imposed a high workload on RSUD Andi Djemma Masamba, often triggering issues of "perception gaps" or discrimination between JKN (BPJS) and general (Non-BPJS) patients. This study aims to deeply analyze patients' perceptions and experiences regarding healthcare service quality under the BPJS and Non-BPJS schemes at the ED of RSUD Andi Djemma Masamba in 2026. Method: This study employed a descriptive qualitative approach with a case study design. Informants were selected using purposive sampling, comprising key informants (general patients, BPJS patients, and accompanying family members) and regular informants. Data were collected through in-depth interviews, observation, and documentation, and then analyzed using the Miles and Huberman method (data reduction, data display, and conclusion drawing). Result: Based on the integration of Perception Process Theory (Robbins & Judge) and Organizational Justice Theory (Colquitt), the input process (cost, waiting time, access) was perceived as efficient and fair through the triage system. Regarding the perception process and interpersonal interaction, no disparity or discriminatory treatment was found between BPJS and Non-BPJS patients; care was delivered purely based on medical urgency. Under the SERVQUAL dimensions, responsiveness and assurance were rated highly. However, a quality gap was identified in the tangible dimension due to limited visitor seating capacity, and in the reliability dimension regarding the ambiguity of estimated waiting times for ancillary services (X-Ray). Conclusion: Overall, both BPJS and Non-BPJS patients were highly satisfied with the clinical service quality at the ED of RSUD Andi Djemma Masamba, which upholds equity. The hospital management is recommended to optimize waiting room facilities, improve transparency regarding estimated times for ancillary services, and conduct regular therapeutic communication training to strengthen the affective/empathy aspect of staff amid high workloads.

The rapid evolution of human–computer interaction systems has led to increasing demand for display technologies that move beyond conventional screens. Screenless display systems aim to eliminate physical display surfaces while enabling direct visualization of digital information through alternative sensory and optical mechanisms. Among emerging approaches, holography and optical computing have gained significant attention due to their ability to reconstruct three-dimensional (3D) visual environments and process information using light rather than electronic signals. This research explores a next-generation screenless display system based on the integration of digital holography and optical computing principles. The proposed system leverages coherent light interference patterns and optical signal processing to generate immersive visual experiences without requiring traditional screens. The study analyzes architectural models, system design methodologies, computational frameworks, and real-time rendering techniques. A comparative evaluation with conventional display technologies such as LCD, LED, AR/VR headsets, and projection systems is presented. Furthermore, the research reviews existing literature to identify advancements, limitations, and research gaps in holographic display systems and optical computing architectures. The results indicate that holography-based screenless systems offer superior depth perception, reduced hardware dependency, and enhanced immersion, while optical computing enables ultra-fast parallel processing capabilities. However, challenges such as computational complexity, hardware cost, and real-time implementation constraints remain significant barriers. The study concludes that integrating holography with optical computing and artificial intelligence can pave the way for next-generation immersive computing environments suitable for education, healthcare, defense, and industrial applications.

Distributed computing has evolved significantly over the past three decades, transforming traditional centralized systems into highly scalable and adaptive network-based architectures. Among the pioneering technologies that contributed to this evolution, Jini technology emerged as an innovative service-oriented distributed computing framework developed by Sun Microsystems in 1998 and later continued under the Apache River project. Jini technology introduced the concept of dynamic service federation, enabling devices, applications, and network services to discover, register, and communicate with each other automatically in distributed environments. The primary objective of Jini technology was to simplify network management through plug-and-play service interaction and autonomous distributed coordination.
This research seminar presents a detailed analytical study of Jini technology as a dynamic service-oriented architecture for distributed computing. The study examines the historical development, architectural components, operational mechanisms, protocols, advantages, limitations, and modern relevance of Jini-based distributed systems. The research particularly focuses on core mechanisms such as discovery protocols, lookup services, leasing, distributed events, JavaSpaces, transactions, and downloadable proxies. The seminar also evaluates how Jini concepts influenced modern technologies including cloud computing, microservices, Internet of Things (IoT), edge computing, and container orchestration frameworks.
A comprehensive literature review has been conducted using academic journals, conference papers, technical specifications, Apache River documentation, middleware research articles, and distributed systems studies. Comparative analysis identifies the novelty of Jini technology relative to CORBA, Java RMI, SOAP-based web services, and modern microservice architectures. The research methodology adopts a conceptual and comparative analytical framework using secondary data sources and middleware evaluation parameters including scalability, interoperability, reliability, adaptability, and fault tolerance.
The results reveal that Jini technology introduced several groundbreaking innovations in distributed computing, particularly dynamic service discovery and autonomous service federation. The leasing mechanism significantly improved fault tolerance and resource management, while JavaSpaces enabled efficient distributed coordination. However, the study also identifies limitations including Java dependency, complex security management, limited commercial adoption, and competition from lightweight web-service architectures. Despite these challenges, the conceptual foundations established by Jini continue to influence contemporary distributed computing systems such as Kubernetes service discovery, IoT middleware platforms, and cloud-native orchestration environments.
The seminar concludes that Jini technology was considerably ahead of its time and remains academically significant in understanding the evolution of distributed middleware systems. Future research opportunities include integrating Jini-inspired dynamic service architectures with artificial intelligence, blockchain systems, decentralized cloud infrastructure, and autonomous IoT ecosystems.

Ang pag-aaral na ito ay may layuning alamin ang mga hamon sa paggamit ng Wikang Filipino ng mga mag-aaral .sa Baitang 7 ng Huasiong College of Iloilo, sa Taong Panuruan, 2024-2025. Deksriptibo ang disenyo ng pag-aaral na nangalap ng datos sa pamamagitan ng talatanungan na naglalahad ng iba’t ibang hamon sa paggamit ng Wikang Filipino. Lumabas sa resulta ng pananaliksik na ang karaniwang hamon sa paggamit ng Wikang Filipino ay patungkol sa pag-unawa sa malalim na bokabularyo sa Wikang Filipino gayundin sa kahirapan sa pagsasalin at pag-intindi ng malalalim na salita. Natuklasan din sa pag-aaral na walang makabuluhang pagkakaiba sa paggamit ng wikang Filipino gayundin sa kasanayang pagganap sa Filipino batay sa mga baryabol tulad ng kasarian, antas ng edukasyon ng ina, nationalidad at mother tongue na ginagamit sa bahay. Dagdag pa nito, walang makabuluhang ugnayan ang mga hamon sa paggamit ng Wikang Filipino sa Kasanayang Pagganap sa Filipino.

Nilalayon ng pag-aaral na ito na paunlarin ang kasanayasan sa Filipino ng mga mag-aaral sa Filipino sa Baitang 11 gamit ang video presentation at tradisyunal na pamamaraan. Gumamit ng quasi-experimental design na may dalawang pangkat: ang experimental group na ginamitan ng video presentation at ang controlled group na tinuruan sa pamamagitan ng tradisyunal na paraan. Isinagawa ang pre-test at post-test upang masukat ang antas ng kasanayan bago at pagkatapos ng anim na linggong interbensyon. Ipinakita sa resulta na may pagtaas sa antas ng kasanayan sa parehong pangkat, batay sa mean scores ng pre-test at post-test. Gayunman, ang t-test analysis ay nagpakita na walang makabuluhang pagkakaiba sa kasanayan ng dalawang pangkat matapos ang interbensyon. Sa kabila nito, makabuluhan ang pagtaas ng marka sa loob ng bawat pangkat, na nagpapahiwatig na epektibo ang parehong paraan ng pagtuturo. Samakatuwid, lumalabas na ang paggamit ng video presentation ay kasing-epektibo ng tradisyunal na pamamaraan sa pagpapaunlad ng kasanayan sa Filipino.

The Mobile Medication Dispenser based on IoT technology is an innovative healthcare solution designed to automate and monitor the process of medication administration. This system integrates Internet of Things (IoT) technology with a smart dispensing device that ensures patients receive the correct dosage at the right time. By connecting the dispenser to a mobile application, patients and caregivers can receive real-time notifica-tions, dosage reminders, and alerts in case of missed medication. This IoT-based system enhances medication management through remote monitoring and data analytics. Healthcare providers can access patient adherence data through a secure cloud platform (ThingSpeak), allowing for timely interventions and personalized treatment adjustments. The integration of mobile connectivity, cloud storage, and smart sensors makes the device not only user-friendly but also reliable and efficient for home and clinical use. Experimental results demonstrate high accuracy in dispensing with stable Wi-Fi connectivity and consistent performance. Ul-timately, this technology aims to improve patient compliance, reduce human error, and enhance overall healthcare outcomes through intelligent automation and connectivity.

Soursop leaves (Annona muricata L.) are a renewable botanical source containing saponins and other secondary metabolites that may function as natural surfactants. This study developed a laboratory-scale, eco-friendly liquid detergent prototype based on soursop leaf extract obtained via ultrasonic probe-assisted extraction and evaluated its physicochemical, interfacial, and performance on cotton fabric. Mature soursop leaves were freeze-dried, milled, and extracted with 70% ethanol at a 1:20 w/v solid-to-solvent ratio using 20 min sonication, 50% amplitude, and 5 s ON/5 s OFF pulse mode. The dry extract was incorporated into five formulas at 0%, 2.5%, 5%, 7.5%, and 10% w/v. From 200 g dried leaf powder, 34.02 g dry extract was obtained, giving 17.01 ± 0.57% yield and 185.26 mg saponin equivalent/g extract. Increasing extract concentration reduced surface tension from 56.24 ± 0.55 to 32.52 ± 0.49 mN/m, increased E24 from 17.37 ± 0.52% to 70.55 ± 0.90%, and improved cleaning efficiency from 28.35 ± 1.61% to 70.52 ± 1.52%. Although F4 showed the highest interfacial activity, F3 was selected as the optimum prototype because it balanced cleaning efficiency, foam stability, viscosity, and physical stability. The findings indicate that ultrasound-assisted soursop leaf extract is a promising natural surfactant ingredient for developing sustainable cotton-fabric detergents.

Coconut residue from household coconut milk production is an abundant by-product that still contains nutritionally valuable components, particularly dietary fiber. This study evaluated the effect of oven-drying temperature on the chemical and functional characteristics of coconut residue flour intended for use as a high-fiber food ingredient. Fresh residue from mature coconuts was sorted, washed, pressed, steamed for 10 min, dried at 50°C, 60°C, or 70°C to constant weight, milled, and sieved through an 80-mesh screen. A one-factor completely randomized design with three replications was applied. Moisture, ash, protein, fat, carbohydrate by difference, total dietary fiber, yield, water absorption capacity, and oil absorption capacity were analyzed using one-way analysis of variance at a 5% significance level. Drying temperature significantly affected all observed parameters. The 60°C treatment produced the highest total dietary fiber (49.15%), yield (22.35%), and water absorption capacity (3.28 g/g), whereas 70°C produced the lowest moisture content (5.42%) and the highest oil absorption capacity (2.32 g/g). Although pairwise treatment differences could not be statistically confirmed because no post hoc test was conducted, the mean values indicated that 60°C provided the most balanced drying condition. These findings support the valorization of household coconut residue into a functional, high-fiber flour for bakery, noodle, cookie, and snack formulations.

The integration of Artificial Intelligence (AI) in higher education has transformed traditional teaching-learning processes across the globe. In the North Eastern Region of India (NEI), particularly in Assam, educational institutions are increasingly adopting AI-assisted teaching tools to enhance instructional delivery, student engagement, and academic performance. The present study investigates the comparative effectiveness of human teaching and AI-assisted teaching in higher education institutions with special reference to Assam. The study aims to analyze the pedagogical strengths and limitations of both teaching approaches and to examine their impact on students’ learning experiences, classroom interaction, conceptual understanding, and academic achievement.

A descriptive survey method was employed for the investigation. Data were collected from teachers and students belonging to selected colleges and universities of Assam through structured questionnaires and interview schedules. The study highlights that human teaching continues to play a significant role in developing emotional connection, ethical values, critical thinking, and personalized guidance among learners. At the same time, AI-assisted teaching demonstrates considerable effectiveness in improving accessibility, instant feedback, digital learning support, individualized instruction, and technology-based learning engagement.

The findings reveal that students perceive AI-assisted teaching as flexible, innovative, and resource-rich, whereas human teaching is considered more empathetic, interactive, and morally supportive. The study further indicates that the integration of AI with traditional classroom teaching creates a blended instructional model that can significantly improve the quality of higher education in Assam and the wider NEI region. However, challenges such as digital divide, inadequate technological infrastructure, lack of teacher training, and ethical concerns regarding excessive dependence on AI remain significant barriers to effective implementation.

The paper concludes that AI cannot replace human teachers entirely; rather, it should function as a supportive educational tool that complements human intelligence and pedagogical expertise. The study recommends capacity-building programmes, digital literacy initiatives, infrastructural development, and policy-level support for the successful integration of AI-assisted teaching in higher education institutions. The research contributes to the emerging discourse on educational technology and provides practical implications for educators, policymakers, and researchers in the field of higher education.

The proliferation of digital technologies has led to a significant increase in cybercrime incidents globally, including online fraud, phishing, identity theft, and unauthorized transactions. Traditional rule-based detection systems struggle to identify evolving cyber threats in real-time. This paper presents a machine learning-based Cybercrime Prediction System that leverages the Random Forest algorithm for classifying and predicting fraudulent activities. The system integrates comprehensive data preprocessing, feature engineering, and visualization techniques including geographic hotspot mapping to analyze cybercrime patterns. Implemented using Python, Flask, Scikit-learn, and Firebase, the system provides real-time prediction capabilities through a web-based interface.
Experimental results demonstrate the effectiveness of the Random Forest classifier in detecting suspicious transactions with high accuracy. The system also incorporates geolocation-based hotspot visualization to identify cybercrime-prone regions, enabling proactive cybersecurity measures. This research contributes to the growing field of AI-driven cybersecurity by demonstrating practical implementation of machine learning techniques for fraud detection and pattern analysis.

Artificial Intelligence (AI) has emerged as a transformative force in the field of higher education, influencing teaching-learning processes, academic administration, assessment practices, and educational research. The increasing integration of AI-driven technologies in educational institutions has created an urgent need for teachers to develop adequate AI literacy for effective pedagogical and professional engagement. AI literacy refers to the awareness, knowledge, skills, ethical understanding, and critical competencies required to understand, use, and evaluate AI technologies in educational settings. In the Indian context, the implementation of the National Education Policy (NEP) 2020 and the rapid expansion of digital learning environments have further emphasized the importance of technological preparedness among higher education teachers.

The present study focuses on examining AI literacy among teachers in higher education institutions in India with special reference to Assam. The study seeks to explore the awareness levels, attitudes, competencies, opportunities, and challenges faced by teachers regarding the use of AI in academic practices. It also aims to investigate the extent of preparedness among educators for integrating AI-based technologies into teaching and learning processes. The study highlights the significance of AI literacy in promoting innovative pedagogy, digital competency, professional development, and quality education in higher education institutions.
The research adopts a descriptive and analytical approach and emphasizes the regional realities of Assam, where issues such as technological infrastructure, digital divide, accessibility, and professional training continue to influence technology adoption in educational institutions. The study is expected to provide valuable insights for policymakers, educational administrators, teacher educators, and higher education institutions regarding the need for systematic AI literacy programmes, teacher training initiatives, and digital capacity-building strategies. The findings of the study may contribute to strengthening educational technology practices and enhancing the preparedness of teachers in the era of artificial intelligence.

Self-navigating delivery robots use artificial intelli gence (AI) and the Internet of Things (IoT) to improve how deliveries are made. This project aims to build such a robot that can move and deliver items on its own using GPS for route tracking, ultrasonic and sensor to avoid obstacles, and Bluetooth for wireless control. AI helps the robot make smart decisions while moving. The system uses ESP32 and Arduino Mega microcontrollers to connect the hardware and software smoothly. Raspberry pi to host the website, Early tests show that the robot can navigate well, avoid obstacles, and deliver safely in both indoor and outdoor areas. These robots can lower delivery costs, improve access, and support the future of smart, contactless delivery. With better AI and sensors, they could become an important part of modern delivery systems.

The global food supply chain suffers significant economic losses due to the spoilage of perishable commodities, particularly vegetables and fruits. Early and accurate detection of spoilage is critical for reducing waste, ensuring food safety, and optimizing inventory management. This survey paper presents a systematic review of modern techniques for vegetable and fruit spoilage detection using multiple sensors and camera-based imaging systems. It specifically focuses on the development of portable devices capable of real-time spoilage assessment and shelf-life display. The paper compares various sensor modalities including gas, temperature, humidity, and spectral sensors, along with computer vision and machine learning algorithms. Finally, challenges related to sensor fusion, portability, power consumption, and environmental variability are discussed, followed by future research directions.

This study examined the Influences of Consumer Choices Among Wet Markets in Bago General Tinio (Papaya), Nueva Ecija to determine the factors affecting the purchasing decisions of consumers. Descriptive-correlational research design was utilized, and data were gathered from 83 respondents through survey questionnaires. The results revealed that the majority of the consumers are adults, predominantly female with 78.31%, and belong to average-sized households 56.63%. Price obtaining a pooled mean of 4.39, where the statement “The ability to bargain (tawad) with vendors makes me prefer buying in wet markets” got the highest mean of 4.47. Product Quality also obtained a pooled mean of 4.39, and the statement “Visual appeal and quality assurance of goods make wet markets my preferred choice” registered the highest mean of 4.49. Market Accessibility obtained a pooled mean of 4.26, with “Reduced travel time and cost to reach the market influences my shopping habits” at 4.33, while Convenience garnered a pooled mean of 4.38, and “Extended operating hours allow me to shop at my preferred time” received the highest mean of 4.43. Furthermore, the Vendor-Consumer Relationship obtained the highest pooled mean of 4.45, with “The suki system (loyal customer discounts) builds my trust and loyalty” having the highest mean score of 4.58. Correlation analysis showed that demographic variables such as age and household size were found to have significant relationships with consumer preferences, proving that consumer choices are primarily driven by economic factors, practicality, product quality, accessibility, convenience, and the strong personal relationship between buyers and sellers. Thus, it is recommended that market administrators and vendors maintain competitive pricing, improve sanitation, and preserve the traditional practice of negotiation to sustain patronage.

This paper presents the design and implementation of a biometric and IoT-enabled smart medicine dispenser using the ESP32 microcontroller. The proposed system is aimed at ensuring accurate, secure, and timely medication dispensing for patients, particularly elderly and chronically ill individuals. The system utilizes a fingerprint authentication module (R307) to verify patient identity, a Real-Time Clock (RTC) for time-based scheduling, and Blynk IoT integration for cloud-based monitoring and remote configuration. The dispenser stores multiple patient profiles in EEPROM, allowing personalized medication schedules with multiple doses per day. A servo-controlled compartment system dispenses medication at the scheduled time only after successful biometric verification. The system sends real-time notifications via Blynk if a dose is missed or dispensed. By combining biometric security, real-time automation, and IoT connectivity, this project enhances patient compliance, reduces medication errors, and offers caregivers a reliable, low-cost healthcare automation solution.

The rapid proliferation of Internet of Things (IoT) devices has significantly increased the volume and complexity of network traffic, making in- trusion detection a critical component of modern cy- bersecurity systems. Traditional Intrusion Detection Systems (IDS) rely on centralized architectures, which introduce challenges such as data privacy risks, high communication overhead, and limited scalability. Fed- erated Learning (FL) has emerged as a promising decentralized machine learning paradigm that enables collaborative model training without sharing raw data, thereby preserving privacy and reducing communica- tion costs.
However, existing FL-based intrusion detection sys- tems assume that all participating clients are trust- worthy, which is not realistic in practical IoT environ- ments where devices may be compromised. Malicious clients can inject poisoned model updates, degrading the performance and reliability of the global model. This paper presents a comprehensive literature sur- vey of Federated Learning-based intrusion detection systems, highlighting their strengths, limitations, and vulnerability to adversarial attacks.
Based on the identified research gaps, a trust-aware federated intrusion detection framework is proposed, incorporating client reliability evaluation and robust aggregation mechanisms. The proposed approach aims to enhance detection accuracy, improve system robust- ness, and ensure resilience against malicious partici- pants while maintaining data privacy. This work pro- vides a foundation for developing secure and scalable intrusion detection systems in distributed IoT environ- ments.

The banking sector depends heavily on knowledge-based resources such as employee capability, organisational routines, customer relationships and technology-enabled processes. This paper examines intellectual capital efficiency as a potential driver of financial performance and growth in the Indian banking sector through a comparative assessment of the VAIC, MVAIC and Extended VAIC (EVAIC) frameworks. The paper first explains the conceptual logic of each framework and compares their formula structures, strengths and limitations. It then reviews 20 relevant studies in paragraph form and summarises their broad reported results in portrait-friendly tables. A research-ready methodology is proposed for a fuller panel study of Indian public and private sector banks. To demonstrate feasibility, the paper also provides an illustrative annual-report-based calculation trail for State Bank of India and HDFC Bank for FY2023-24 and FY2024-25. The evidence from prior literature broadly supports a positive association between intellectual capital efficiency and bank performance, although the effects of individual components are not always uniform. The paper concludes that VAIC remains the most practical baseline framework for Indian banking studies, whereas MVAIC and Extended VAIC offer stronger conceptual coverage when reliable relational and innovation-capital proxies are available.

Durability is a critical performance criterion for high strength concrete (HSC) exposed to aggressive environments. This paper presents an experimental investigation of the water absorption and sorptivity characteristics of M60 grade concrete incorporating Rice Husk Ash (RHA) as a partial cement replacement (5%, 10%, 15%, 20% by weight) and constant Polypropylene Fibres (PPF, 0.5% by volume). Water absorption was determined as per ASTM C642 after 28 days of curing. Sorptivity (capillary water absorption) was measured at 1, 3, 5, 7, 14, and 28 days following ASTM C1585. The results show that the combination of 10% RHA and 0.5% PPF (Mix M2) achieved the lowest water absorption (3.8% vs. control 5.2%, a 26.9% reduction) and the lowest sorptivity coefficient (0.12 mm/√min vs. control 0.21 mm/√min, a 42.9% reduction). The improved durability parameters are attributed to the pozzolanic reaction of RHA, which converts calcium hydroxide into additional C-S-H gel, densifying the microstructure and reducing capillary porosity. The fibres, while not directly reducing water absorption, help maintain matrix integrity by controlling micro-cracks. Beyond 10% RHA, water absorption and sorptivity increased due to incomplete pozzolanic reaction and the porous nature of excess RHA particles. The study concludes that 10% RHA with 0.5% PPF produces durable HSC suitable for aggressive environments.

Reactive oxygen species (ROS) and free radicals are continuously generated as byproducts of normal cellular metabolism, yet their unchecked accumulation drives oxidative stress — a central pathological mechanism in aging, cancer, cardiovascular disease, neurodegeneration, and diabetes. Antioxidants constitute a multi-tiered biochemical defense system that neutralizes ROS through diverse mechanisms including free radical scavenging, metal ion chelation, enzymatic detoxification, and transcriptional regulation of cytoprotective genes. This review comprehensively examines the molecular mechanisms by which both endogenous and exogenous antioxidants prevent oxidative damage to proteins, lipids, and DNA. We discuss the roles of enzymatic antioxidants (superoxide dismutase, catalase, glutathione peroxidase), small-molecule antioxidants (vitamins C and E, glutathione, coenzyme Q10), and dietary phytochemicals (polyphenols, carotenoids, flavonoids). Special attention is given to the Nrf2/Keap1 signaling axis as a master regulator of antioxidant gene expression, the interplay between redox signaling and cellular homeostasis, and the context-dependence of antioxidant activity. Clinical implications for supplementation strategies and the emerging concept of the &#39;antioxidant paradox&#39; are also addressed.

The rapid advancement of Artificial Intelligence (AI) has significantly transformed the fields of education, recruitment, and career development. Today, various AI-based platforms assist users in career guidance, resume building, ATS (Applicant Tracking System) evaluation, job recommendation, and government examination preparation. However, most existing systems operate independently and focus only on specific functionalities, forcing students and job seekers to rely on multiple platforms for complete career support. This fragmented approach often leads to inefficiency, lack of personalization, and difficulty in managing career planning effectively.
This survey paper analyzes the current landscape of AI-driven career support systems, including career recommendation platforms, ATS resume analyzers, intelligent resume builders, and government exam eligibility checkers. The study reviews more than 50 research papers and existing solutions published between 2018 and 2024 to identify the strengths, limitations, and research gaps in these technologies. The analysis reveals that while individual systems provide useful services, there is still a lack of an integrated platform capable of delivering end-to-end career assistance in a unified manner.
To overcome these limitations, the paper proposes an AI-powered integrated career intelligence platform that combines personalized career recommendations, resume generation, ATS compatibility scoring, skill-gap analysis, and government exam eligibility verification within a single system. The proposed framework leverages Machine Learning (ML), Natural Language Processing (NLP), and rule-based eligibility mechanisms to provide accurate, personalized, and real-time career support. By integrating multiple services into one ecosystem, the system aims to improve accessibility, reduce user effort, and enhance decision-making for students, graduates, and job seekers.
The study highlights the growing importance of unified AI-driven solutions in modern career planning and emphasizes the need for scalable, user-centric systems that can adapt to changing industry and recruitment requirements.

This study explored the experiences, challenges, and perceived contributions of home–school collaboration in supporting the behavioral development of elementary pupils in a rural public school in Kapangan, Benguet, Philippines. Using a qualitative case study design, the study involved fifteen participants composed of five parents, five teachers, and five school heads. Data were gathered through semi-structured interviews and analyzed using thematic analysis to identify recurring patterns and meanings related to home–school collaboration and pupil behavior. The findings revealed that home–school collaboration was experienced as meaningful and relationship-centered, characterized by open communication, mutual partnership, consistency, care, and active involvement. Despite the positive practices observed, participants identified several challenges affecting collaboration, including geographical distance, limited time, inadequate access to communication technologies, and external influences affecting pupils’ behavior. Participants further perceived that home–school collaboration contributed significantly to pupils’ moral development, discipline, and positive behavioral growth through shared guidance, coordinated discipline practices, positive role modeling, and continuous support from both home and school environments. Pupils were observed to demonstrate improved obedience, respectfulness, responsibility, self-discipline, and emotional maturity over time. The study concluded that effective home–school collaboration, even within the limitations of rural settings, plays a vital role in promoting positive behavioral development among elementary pupils when grounded in trust, shared values, and consistent support. The findings emphasize that the quality of collaboration and alignment between parents and schools are more important than the frequency of communication or access to technology. The study recommends the strengthening of flexible and context-sensitive partnership strategies and encourages further research on home–school collaboration across diverse educational settings to better understand its long-term impact on children’s behavioral development.

समावेशी दर्शन एक ऐसी विचारधारा है जो समाज के हर व्यक्ति को उसकी शारीरिक क्षमता, मानसिक स्थिति, सामाजिक पृष्ठभूमि या पहचान के बावजूद, समान सम्मान और भागीदारी का अधिकार देती है। इसका मूल मंत्र &#34;सबका साथ और सबका सम्मान&#34; है। मुख्य रूप से यह अवधारणा शिक्षा के क्षेत्र में &#39;समावेशी शिक्षा&#39; के रूप में प्रसिद्ध है, लेकिन इसका विस्तार जीवन के हर पहलू में है। शिक्षा मानव जीवन के सर्वांगीण विकास का आधार है। आधुनिक युग में शिक्षा का स्वरूप केवल साक्षरता तक सीमित नहीं है, बल्कि यह एक समावेशी समाज के निर्माण का साधन बन गया है। &#39;समावेशी शिक्षा&#39; एक ऐसी आधुनिक शिक्षण प्रणाली है, जो बिना किसी भेदभाव के समाज के प्रत्येक वर्ग के बच्चों को शिक्षा की मुख्यधारा से जोड़ने पर जोर देती है। इसका मूल मंत्र है— &#34;एक ही छत के नीचे, हर बच्चे की शिक्षा।&#34; समावेशी शिक्षा का अर्थ है ऐसी शिक्षा व्यवस्था जहाँ सामान्य बच्चे और विशिष्ट आवश्यकता वाले बच्चे (जैसे दिव्यांग, शारीरिक या मानसिक रूप से चुनौतीपूर्ण, या सामाजिक-आर्थिक रूप से पिछड़े बच्चे) एक साथ बैठकर शिक्षा ग्रहण करते हैं। यह अवधारणा इस विश्वास पर आधारित है कि विविधता एक बाधा नहीं, बल्कि सीखने का एक समृद्ध स्रोत है। इस दर्शन के अनुसार, विद्यालय को बच्चे की जरूरतों के अनुसार खुद को बदलना चाहिए। इसमें पाठ्यक्रम, शिक्षण विधियों और बुनियादी ढांचे को लचीला बनाया जाता है ताकि हर बच्चा अपनी क्षमता के अनुसार सीख सके।

The Automated Clinical Workflows Via Modular Monolithic Architecture is a multi-platform, enterprise-grade software solution designed to digitize and automate the comprehensive lifecycle of clinical operations. In an era where healthcare efficiency is critical, manual record-keeping in small-to-medium clinics leads to data fragmentation, administrative delays, and clinical errors. This project addresses these challenges by delivering a unified digital ecosystem consisting of a Django-based RESTful API, a React-powered Web Dashboard for staff, and a React Native (Expo) Mobile Application for patients. The core architecture of the system is built upon a Domain-Driven Modular Monolith design. This strategic decision ensures that the system maintains a high degree of internal organization by separating clinical domains—such as Patient Onboarding, Appointment Scheduling, Clinical Vitals, and Automated Billing—into self-contained modules. This architecture prevents technical debt and allows for the rapid scaling of the clinic’s digital services. Key technical implementations include a strictly validated Appointment State Machine to manage patient flows, stateless JWT Authentication for secure data access, and a multi-tenant database structure that ensures absolute data isolation between different clinic entities. Results from the implementation phase indicate a significant optimization in clinical workflows. The automation of pre-consultation vitals recording and the seamless transition to digital prescriptions have reduced patient waiting times and minimized human error in medical records. Furthermore, the integration of an automated billing engine has ensured financial transparency and eliminated revenue leakage. Ultimately, this project demonstrates the effectiveness of modern full-stack frameworks in transforming traditional healthcare practices into streamlined, data-driven, and patient-centric operations.

Digital asset management and secure transaction handling have become critical challenges in the growing field of blockchain and decentralized finance. Existing systems such as crypto wallets, transaction platforms, and monitoring tools operate independently, forcing users to rely on multiple solutions without unified security or intelligent decision-making support. This project proposes an AI Smart Vault system that integrates blockchain-based asset management, multi-signature authentication, and AI-driven risk analysis into a single platform. The system evaluates transaction patterns such as amount, frequency, and user behavior to dynamically adjust approval requirements and detect suspicious activities. By combining decentralized security with intelligent analysis, the proposed system enhances transaction reliability, improves user control, and provides a scalable and secure solution for modern digital asset management.

This quantitative cross-sectional study investigates the psychological impact of obligation-driven work relationships on retirees in Ghana, with particular focus on how superficial workplace connections affect mental health outcomes, loneliness, and life satisfaction after retirement. Drawing upon Self-Determination Theory (Deci & Ryan, 2000) and the Socioemotional Selectivity Theory (Carstensen, 1999), the study surveyed 210 retired workers from public and private sector organisations in Accra, Kumasi, and Tema. Participants completed validated instruments measuring obligation-driven relationship orientation, perceived superficiality of former work connections, loneliness (UCLA Loneliness Scale), and life satisfaction (SWLS). Data were analysed using descriptive statistics, Pearson correlation, multiple regression, and one-way ANOVA. Results revealed that retirees who reported higher levels of obligation-driven work relationships experienced significantly greater loneliness (r = 0.58, p < 0.01) and lower life satisfaction (r = -0.62, p < 0.01) compared to those who reported more authentic workplace connections. Perceived superficiality of work relationships predicted 34% of the variance in post-retirement loneliness after controlling for age, gender, and years of service. Retirees from public sector organisations reported significantly higher obligation-driven relationship orientation than those from private sector organisations (t = 4.32, p < 0.001). The findings suggest that workplace relationships maintained primarily out of professional obligation rather than genuine connection leave retirees psychologically vulnerable when the structural context of work is removed. Recommendations include workplace interventions to foster authentic relationships, pre-retirement counselling addressing relational transition, and organisational culture reforms that value genuine connection over performative collegiality.

This mixed-methods sequential explanatory study investigates the challenges faced by retired workers in Ghana who have school-age children, specifically examining the financial, emotional, and social impacts of navigating retirement while still meeting parental responsibilities. Drawing upon Role Strain Theory (Goode, 1960), Life Course Theory (Elder, 1998), and the Resource Drain Model (Moen & Wethington, 2021), the study first surveyed 312 retired workers in Accra, Kumasi, and Sekondi-Takoradi who had at least one child under the age of 18 at the time of retirement. Participants completed validated instruments measuring financial strain (Financial Difficulty Scale), emotional well-being (Depression Anxiety Stress Scales), perceived social support (Multidimensional Scale of Perceived Social Support), and parental stress (Parenting Stress Index). Quantitative data were analysed using descriptive statistics, Pearson correlation, multiple regression, and multivariate analysis of variance. Following the quantitative phase, 18 participants with varying levels of financial and emotional strain were purposively selected for in-depth semi-structured interviews exploring their lived experiences of managing retirement and active parenting simultaneously. Quantitative results revealed that 68.6 percent of retired workers with school-age children reported significant financial strain, with mean monthly expenditure on children exceeding pension income by an average of 42 percent. Financial strain was significantly correlated with depression (r = 0.62, p < 0.01), anxiety (r = 0.55, p < 0.01), and parental stress (r = 0.67, p < 0.01). Retirees with younger school-age children reported significantly higher emotional distress than those with older adolescents (F = 8.34, p < 0.001). Female retirees reported greater social isolation and lower perceived social support than male retirees (t = 3.87, p < 0.001). Qualitative findings yielded six major themes: (1) The Mathematics of Insufficiency: Pension Cannot Stretch to Cover School Fees; (2) The Shame of Financial Dependency on Adult Children; (3) Emotional Exhaustion from Dual Demands of Retirement Adjustment and Active Parenting; (4) Social Withdrawal as Self-Protection from Judgment; (5) The Physical Toll of Delayed Retirement on Health; and (6) Coping Through Faith, Informal Work, and Community Support. The integration of quantitative and qualitative findings reveals that retired workers with school-age children occupy a uniquely vulnerable position, experiencing the health and income declines associated with ageing while simultaneously bearing the financial and emotional demands of raising children. This group is largely invisible in both retirement policy and child welfare frameworks. Recommendations include pension reform to recognise dependent children of retirees, targeted financial assistance programmes, mental health support for this population, and further research on late parenting among ageing populations in sub-Saharan Africa.

Tungsten Inert Gas (TIG) welding, commonly referred to as Gas Tungsten Arc Welding (GTAW), is an advanced arc welding technique widely preferred for applications requiring superior weld quality and high precision. Despite its advantages, the TIG welding process is often limited by its relatively low welding speed and its difficulty in achieving full penetration in thicker materials during a single pass.
In the present study, autogenous TIG welding was carried out on 5 mm thick AISI 1020 mild steel plates without the addition of filler metal. Various combinations of welding current and travel speed were examined to achieve complete weld penetration. Additionally, activated flux was applied to enhance the penetration depth of the weld bead. Welding experiments were conducted by maintaining different root gaps between the plates, and the resulting weld bead geometry along with tensile strength were evaluated. The experimental results revealed that maintaining an optimum gap between the plates enables full penetration welding and produces joint strength nearly equivalent to that of the parent metal.

Biotechnology has rapidly transformed contemporary medicine, agriculture, and environmental science, offering powerful tools to address pressing global challenges such as disease, food insecurity, and climate change. However, the benefits of biotechnological advances are not distributed evenly across populations, raising critical concerns about social inequalities and equitable access to innovation. Emerging research highlights that while biotechnology can enhance human health and economic well being, disparities in access to biotechnological resources and their benefits may deepen existing social, economic, and geographic inequalities, especially between high income and low income communities (Mukherjee, 2021; Tait, 2022).

One prominent concern is the unequal global distribution of advanced healthcare technologies, including gene therapies, precision medicine, and diagnostics. High costs and infrastructure requirements for these innovations often limit their availability to affluent populations or countries with robust healthcare systems, leaving economically disadvantaged groups behind (Battisti & Heinz, 2023). For example, precision oncology and gene editing treatments can significantly improve outcomes for certain cancers, but their prohibitive expenses have made them inaccessible to many patients in low and middle income regions. This dynamic perpetuates a cycle in which those who are already socioeconomically advantaged benefit disproportionately from cutting edge biotechnologies, exacerbating health disparities both within and between nations.

Beyond healthcare, inequalities also arise in agricultural biotechnology. While genetically modified (GM) crops have the potential to increase yields and enhance food security, smallholder farmers often face barriers to adoption, including limited access to credit, seeds, regulatory support, and scientific knowledge. Research suggests that when the benefits of GM crops are unevenly distributed, wealthier agribusinesses and large scale farms are more likely to profit, while marginalized farmers struggle to compete, thus reinforcing rural economic disparities (Qaim, 2020; Smyth et al., 2021).

Social inequalities in biotechnology extend further into ethical and policy dimensions. Discussions around benefit sharing, intellectual property rights, and data ownership have raised concerns about biopiracy and the exploitation of indigenous knowledge without fair compensation. For instance, communities contributing traditional biological resources or genetic data may not receive equitable returns from subsequent commercial or scientific use of that information, undermining principles of justice and respect for local autonomy (Tazzyman & Bietz, 2020; de Vries et al., 2022).

Efforts to mitigate these inequalities must therefore integrate ethical reflection, inclusive policy-making, and targeted investment in capacity building. Policies that subsidize essential biotechnologies, ensure equitable pricing, and support local innovation ecosystems can help bridge gaps in access. In addition, programs that enhance scientific training and infrastructure in underserved regions are critical to empowering communities to participate in and benefit from biotechnological advances. International collaborations and benefit sharing frameworks, such as those promoted under the Convention on Biological Diversity and related agreements, aim to promote fairness in the distribution of biotechnological benefits, although challenges remain in implementation and enforcement (Secretariat of the Convention on Biological Diversity, 2022).

Robotic arms are used a lot in factories and other places where machines do work. The old way of controlling arms used set plans and rules that did not change which made it hard for them to work well in new or changing situations. These systems had trouble when things were not certain or when they had to make decisions
New ideas, in Artificial Intelligence like Reinforcement Learning and Deep Reinforcement Learning have helped robotic arms learn what to do by trying things and seeing what happens. People have tried ways to make robotic arms work better like having them work together using cameras to see and practicing in simulated worlds.
This paper looks at the ways to control robotic arms including using Reinforcement Learning to plan movements using deep learning to understand what they see and practicing in simulated worlds before doing real things. The paper compares these methods to see which ones work well are efficient and can be used in life. One important thing the paper finds is that most systems only look at one part of the problem and are usually tested in worlds.
The paper points out what is missing in research and suggests a new way that combines learning seeing and controlling for robotic arm systems that are smart.

Biodiversity, or biological diversity, refers to the variety of life on Earth, including diversity within species, between species, and across ecosystems. It includes all living organisms, from microorganisms to plants and animals, as well as the ecological systems they form. Biodiversity is essential for maintaining the balance of ecosystems and supporting life on the planet (Britannica, 2026; World Health Organization, 2025).

There are three main levels of biodiversity: genetic diversity, species diversity, and ecosystem diversity. Genetic diversity refers to the variation of genes within a species, which allows populations to adapt to environmental changes. Species diversity describes the variety of species within a region, while ecosystem diversity refers to the variety of habitats, ecosystems, and ecological processes. These levels are interconnected and contribute to the resilience and stability of natural systems (National Research Council, 1999).

Biodiversity plays a crucial role in supporting ecosystem services that are vital for human survival. These services include food production, pollination of crops, purification of water and air, and climate regulation. For instance, many medicines are derived from natural sources, and ecosystems such as forests help absorb carbon dioxide, reducing the effects of climate change. Additionally, biodiversity has cultural, aesthetic, and recreational importance, enriching human life in many ways (World Health Organization, 2025; BMC Biology, 2010).

Despite its importance, biodiversity is rapidly declining due to human activities. Major threats include habitat destruction caused by deforestation and urbanization, climate change, pollution, overexploitation of natural resources, and the introduction of invasive species. Scientists estimate that current extinction rates are significantly higher than natural levels, with many species facing the risk of extinction (Britannica, 2026; World Health Organization, 2025).

To address these challenges, various conservation strategies have been developed. These include the creation of protected areas such as national parks and reserves, the sustainable use of natural resources, and the restoration of damaged ecosystems. Education and environmental policies also play an important role in promoting biodiversity conservation. Protecting biodiversity is essential not only for preserving nature but also for ensuring human well-being and sustainable development (ScienceDirect, 2013).

In conclusion, biodiversity is the foundation of life on Earth. Its conservation is critical for maintaining ecological balance and supporting future generations. Addressing biodiversity loss requires global cooperation, responsible environmental management, and increased awareness of its importance.

Women’s safety remains a pressing concern worldwide, particularly in rapidly urbanizing regions where risks are dynamic and unpredictable. Existing solutions such as mobile safety applications, helplines, and wearable devices primarily function as reactive systems that depend on user intervention after a threat has occurred. This paper proposes a Graph Neural Network (GNN)-based real-time personal safety risk prediction system that shifts the paradigm from reactive response to proactive threat detection.The system integrates multiple data modalities, including GPS location tracking, environmental context (time, lighting, crowd density), historical crime data, real-time user behavior, and audio signals such as muffled noise and distress keywords. These heterogeneous inputs are modeled as a graph structure where nodes represent entities (users, locations, events), and edges represent relationships (proximity, interaction, temporal transitions).Using Graph Convolutional Networks (GCNs), the system learns complex dependencies and predicts risk levels dynamically. It also incorporates safety mechanisms such as emergency SOS alerts, community-based assistance, automatic dialing of emergency numbers, and keyword-triggered alerts. Experimental evaluation demonstrates improved accuracy and faster response times compared to traditional safety systems. The proposed approach highlights the potential of graph-based deep learning in enabling context-aware, real-time safety intelligence.

Mental health disorders such as depression, anxiety, and chronic stress are increasing rapidly across all age groups, yet early detection remains a major challenge due to social stigma, limited clinical resources, and delayed self-reporting. As traditional models struggle to meet rising demands, artificial intelligence (AI) has emerged as a promising tool for enhancing the detection and monitoring of psychological distress. Recent advances in Artificial Intelligence (AI) offer opportunities to support mental health monitoring through emotion recognition.
Digital health technologies have emerged as practical complements to conventional mental health services. These include mobile health (mHealth), telepsychiatry, wearable biosensors, and digital therapeutic platforms, which extend care beyond clinical settings. This paper presents a human-centered Emotion-Recognition AI framework that continuously analyses emotional patterns using multimodal inputs including text, speech, and facial expressions. Unlike traditional systems that rely on single data sources, the proposed approach integrates multiple emotional signals to improve reliability while preserving user privacy and maintaining human oversight.
The system is designed to assist mental health professionals by providing early warning indicators rather than automated diagnoses. This study discusses system architecture, ethical considerations, real-world applications, and future research directions, highlighting the role of AI as a supportive tool in mental healthcare.

Binary Neural Networks (BNNs) have gained significant attention for enabling efficient deep learning on resource-constrained devices by reducing memory usage and computational complexity through binary weights and activations. This survey paper presents a comparative study of three widely used BNN frameworks: Larq, Brevitas, and FINN. The study evaluates these frameworks based on important performance metrics such as accuracy, memory efficiency, and inference latency. It further analyzes their architecture, quantization techniques, hardware support, training flexibility, and deployment capabilities for edge AI applications. The comparison highlights the strengths and limitations of each framework, where Larq provides simplified TensorFlow integration, Brevitas offers flexible quantization support in PyTorch, and FINN delivers optimized FPGA-based acceleration with low latency. The survey aims to help researchers and developers understand the practical trade-offs among BNN frameworks and select suitable tools for efficient deep learning deployment.

This quantitative study examined the level of social media engagement among Grade 6 pupils in selected public elementary schools in Kidapawan City and its influence on their mental well-being. Using a descriptive-correlational design, data were collected from 258 pupils through stratified random sampling. The study assessed social media engagement across three dimensions—cognitive, behavioral, and emotional or affective engagement—and mental well-being across three dimensions: emotional health, social relationships, and academic-related coping strategies. Statistical analyses included mean and weighted mean, Spearman's Rank-Order Correlation, and Multiple Regression Analysis. Findings revealed that pupils were highly engaged in social media across all three dimensions (overall weighted mean = 4.49). Mental well-being scores were consistently high (overall weighted mean = 4.50, described as Strongly Agree). Correlation analysis showed that emotional or affective engagement was significantly associated with emotional health (r = .107, p = .008). Multiple regression analysis confirmed that social media engagement significantly influenced emotional health (F = 1.342, p = .001), social relationships (F = 1.023, p = .001), and academic-related coping strategies (F = 1.932, p = .000). These findings underscore the central role of social media in shaping pupils' psychological and academic well-being, and highlight the need for guided, balanced digital engagement in elementary education.

Acne vulgaris is a common dermatological disorder caused by excessive sebum production, microbial growth, and inflammation of the pilosebaceous unit. Ultraviolet (UV) radiation further aggravates acne conditions by inducing oxidative stress, erythema, and pigmentation. Conventional topical treatments are associated with adverse effects such as irritation, dryness, and antibiotic resistance. Hence, the present study aims to formulate and evaluate a herbal sunscreen-cum-anti-acne cream with moisturizing properties using natural ingredients. The formulation was developed using Azadirachta indica (Neem), Curcuma longa (Turmeric), and Aloe barbadensis (Aloe vera) extracts due to their antimicrobial, anti-inflammatory, antioxidant, and moisturizing properties. Carbopol 934 was used as a Creaming agent. The prepared cream was evaluated for physicochemical parameters including pH, viscosity, spreadability, extrudability, and homogeneity. Further, antimicrobial activity, Sun Protection Factor (SPF), moisturizing effect, and stability studies were conducted.
The results showed that the cream exhibited good homogeneity, acceptable pH (6–7), optimal viscosity, and excellent spreadability. The formulation demonstrated significant antimicrobial activity against acne-causing bacteria and moderate SPF value. Stability studies indicated no significant changes in physical parameters. The study concludes that the formulated herbal cream is safe, effective, and suitable for topical application with multifunctional benefits.

This study examined taxpayer perception of government accountability and tax compliance behaviour among urban residents in Nigeria, with empirical evidence drawn from Owerri, Umuahia, and Aba. The study was motivated by persistent concerns regarding low and inconsistent tax compliance despite growing dependence on internally generated revenue and continuous reforms in tax administration. Government accountability was operationalized through government transparency, government responsiveness, prudent utilization of public funds, and public service delivery, while tax compliance behaviour was measured using willingness to pay taxes voluntarily, timely tax payment, accurate declaration of taxable income, and voluntary compliance disposition. The study adopted a descriptive survey research design. A population of 3,000 taxpayers comprising civil servants and business owners was identified across the three selected urban centres, while a sample size of 353 respondents was determined using the Taro Yamane formula. Data were collected through a structured questionnaire and analyzed using descriptive statistics, Pearson Product Moment Correlation, and multiple regression analysis with the aid of SPSS at a 0.05 level of significance. The findings revealed that all dimensions of perceived government accountability exert significant positive influence on tax compliance behaviour. Public service delivery (β = 0.337) emerged as the strongest predictor of compliance behaviour, followed by prudent utilization of public funds (β = 0.312), government transparency (β = 0.276), and government responsiveness (β = 0.201). The regression model explained 70.9% of the variation in tax compliance behaviour (R² = 0.709). The study concluded that sustainable tax compliance among urban residents depends substantially on government accountability, visible public service delivery, and prudent management of public resources. The study therefore recommends improved transparency, stronger accountability systems, responsible utilization of public funds, and enhanced public service delivery as critical mechanisms for strengthening voluntary tax compliance in Nigeria.

Artificial Intelligence (AI) has emerged as one of the most transformative technologies of the 21st century, significantly influencing global political systems, governance, diplomacy, security, and international relations. AI technologies such as machine learning, predictive analytics, generative AI, and autonomous systems are increasingly integrated into political decision-making and strategic planning. This research paper examines the influence of AI on global political strategies, focusing on governance, electoral politics, cybersecurity, international diplomacy, military strategy, and information warfare. The paper also evaluates ethical challenges, geopolitical competition, and regulatory frameworks surrounding AI. The study employs qualitative and analytical methodologies using secondary data from scholarly journals, policy papers, and contemporary case studies.

The present research work focuses on the preparation and evaluation of a polyherbal lip balm using natural ingredients such as cocoa butter, coconut oil, castor oil, honey, aloe vera gel, beetroot extract, pomegranate extract, rose oil, and vitamin E oil. Lips are highly sensitive and are continuously exposed to environmental conditions such as sunlight, pollution, cold air, and dry weather, leading to dryness and cracking. Herbal lip balms provide moisturization, healing, nourishment, and protection without causing harmful side effects.
The prepared formulation was evaluated for different parameters including physical appearance, pH, spreadability, melting point, stability, irritation test, and fragrance stability. The formulation showed excellent smoothness, good spreadability, suitable pH, pleasant odor, and stability under different storage conditions. The natural ingredients used in the formulation possess antioxidant, anti-inflammatory, moisturizing, antimicrobial, and healing properties.

Deepfake technology uses Artificial Intelligence (AI) and Deep Learning techniques to create fake videos, images, and audio that appear highly realistic. While this technology has applications in entertainment and media production, it also creates serious risks including misinformation, fraud, cybercrime, political manipulation, and identity theft. Detecting deepfake videos has become a major research challenge because modern generative models produce highly convincing synthetic media. This research paper studies different AI-based deepfake detection techniques, datasets, challenges, and future directions. The paper focuses on Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), facial landmark analysis, and multimodal detection methods. It also discusses datasets such as FaceForensics++, Celeb-DF, and DFDC. The study concludes that AI-based detection systems can significantly improve fake media identification, but continuous advancements in generative AI require more robust and adaptive detection frameworks.

Agricultural intensification and vector control efforts have led to the widespread contamination of aquatic ecosystems with organophosphate pesticides, often ignoring the physiological toll on non-target organisms. This study investigated the oxidative stress profile and subsequent recovery dynamics in the African sharptooth catfish, Clarias gariepinus, following exposure to Dichlorvos. A total of 100 juveniles were acclimated for 14 days, followed by acute toxicity testing to establish a 96-hour LC₅₀. Subsequently, fish were subjected to chronic exposure at three sublethal concentrations (0.10, 0.20, and 0.40 mg/L) for 28 days. To evaluate recovery potential, a 72-hour depuration phase in pesticide-free water was conducted. Biochemical analysis revealed that Dichlorvos induced significant ($p < 0.05$) concentration- and duration- dependent oxidative disturbances. Exposure resulted in a marked elevation of Malondialdehyde (MDA), signaling extensive lipid peroxidation, alongside the significant suppression of the antioxidant enzymes Superoxide Dismutase (SOD), Catalase (CAT), and Glutathione Peroxidase (GPx). Following the depuration phase, partial restoration of enzyme activity was observed only in the lowest exposure group (0.10 mg/L). In contrast, biomarkers in the higher dosage groups remained significantly altered, indicating that 72 hours is insufficient for complete physiological remediation. These findings demonstrate that Dichlorvos-induced oxidative damage persists beyond the initial exposure period, highlighting the limited recovery dynamics of C. gariepinus. This research underscores the ecological risks of organophosphate pollution and advocates for extended depuration periods and stricter regulatory frameworks to protect aquatic biodiversity.

Machine learning regression models often face the challenge of overfitting when trained on real-world datasets. Regularization techniques are widely used to improve generalization performance. Among these, L1 regularization (Lasso) is particularly effective as it promotes sparsity by shrinking less important feature coefficients to zero, thereby performing implicit feature selection. However, the optimization of L1 regularization is challenging due to its non-differentiability at zero. This survey paper analyzes the use of gradient descent and its variants for training L1-regularized models on the California Housing dataset. It reviews existing research on optimization techniques such as sub-gradient descent, coordinate descent, and stochastic gradient descent. The study highlights the effectiveness of L1 regularization in reducing overfitting and improving model interpretability.

Time series forecasting is a critical task in business intelligence and decision support. This paper presents an engineering-level machine learning project that applies and compares multiple regression-based models for sales forecasting. Using a real-world monthly sales dataset, the study employs feature engineering techniques including lag features and rolling mean computation to capture temporal dependencies. Three models are evaluated: Linear Regression, Random Forest Regressor, and Support Vector Regression (SVR). Model performance is measured using Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and R² Score. The Random Forest model achieved the best performance, demonstrating superior accuracy and robustness. Automatic best model selection and visualization of actual versus predicted values are incorporated. The results confirm that ensemble methods with proper temporal feature engineering significantly outperform linear baselines for time series forecasting tasks.

This study investigates weak-form market efficiency on the Nairobi Securities Exchange (NSE) using a time-varying framework over the period January 2010 to December 2023. Departing from conventional static tests, we employ the Generalized Spectral (GS) test of Escanciano and Velasco (2006), the Automatic Variance Ratio (AVR) test, and a rolling-window sub-sample methodology to track efficiency dynamics across distinct market regimes. Using daily closing prices of the NSE All-Share Index (NASI) and a cross-section of 25 actively traded equities, we document pronounced time-variation in efficiency: markets exhibit predictability during periods of macroeconomic stress (2011–2012, 2015–2016, and 2020 COVID-19 shock) but approximate random-walk behavior during relative tranquility. The demutualization of the NSE in 2014 and the introduction of mobile-based trading in 2017 coincide with measurable improvements in informational efficiency. Panel-data regressions further reveal that trading volume, foreign investor participation, and market capitalization are significant positive determinants of efficiency, while inflation volatility exerts a negative effect. Our findings carry implications for portfolio managers, policymakers, and the Capital Markets Authority (CMA) of Kenya regarding market microstructure reforms and investor education programmers’.

The aim of this study was to explore teachers' instructional adaptability and the level of collaborative school culture in geographically isolated schools. It specifically examined teachers’ instructional adaptability in terms of differentiated instruction, flexible classroom management, learner-centered strategies, and technology integration, while assessing collaborative school culture through shared leadership, collegial support, professional learning communities, and collective decision-making. A combination of quantitative and qualitative methods was utilized, including surveys, Spearman's rho, multiple regression analysis, and thematic analysis of interview data. Findings revealed that teachers demonstrated high levels of instructional adaptability and a highly developed collaborative school culture, particularly in shared leadership and professional learning communities. However, technology integration was the only dimension with a significant relationship to shared leadership, and flexible classroom management individually influenced collegial support. The study concludes that fostering instructional adaptability and collaboration in isolated schools requires focused professional development, peer support systems, and enhanced leadership.

This qualitative study explored the challenges teachers face in influencing school policies, the opportunities available to them in shaping those policies, and how they effectively advocate for policy changes in elementary schools within the Kapalawan Cluster and Old Kaabakan Special Geographic Area (SGA) of BARMM, Maguindanao, Philippines. Using a phenomenological approach, data were gathered through in-depth interviews and focus group discussions with 20 teacher-participants selected from the same SGA BARMM context. Data were analyzed using thematic analysis (Moustakas, 1994; Braun & Clarke). Three global themes emerged: (1) Challenges of Teachers in Influencing School Policies, organized around two themes—parents' support as a limiting factor (economic constraints, low educational attainment, time-work conflicts, communication barriers) and administrative overload over support (paperwork, non-teaching tasks, excessive meetings); (2) Opportunities of Teachers in Shaping School Policies, organized around career growth and voice in decision-making; and (3) Teachers Effectively Advocate Policies, through PTA meetings and relying on instructions from school heads. These findings reveal that while systemic barriers constrain teachers' policy influence, professional growth pathways and participatory governance structures provide meaningful channels for teacher leadership, with the most effective advocacy emerging at the intersection of community engagement and institutional collaboration.

The rapid advancement of quantum computing introduces serious security challenges for existing blockchain and digital identity systems. Traditional cryptographic algorithms such as RSA and Elliptic Curve Cryptography (ECC), which are widely used in authentication and blockchain systems, are vulnerable to quantum attacks through Shor’s algorithm [2], [3]. At the same time, blockchain technology has enabled decentralized identity frameworks such as Self-Sovereign Identity (SSI), where users maintain ownership and control over their personal identity data without depending on centralized authorities [5], [11]. However, most blockchain identity systems still rely on classical cryptographic mechanisms and therefore remain vulnerable to future quantum threats. This survey paper presents a comprehensive study of Post-Quantum Blockchain-Based Identity Management Systems by integrating Post-Quantum Cryptography (PQC) with decentralized blockchain identity frameworks. The paper discusses the evolution from traditional centralized identity systems to blockchain-based decentralized identity and further toward quantum-resistant identity architectures. Important components such as decentralized identifiers (DIDs), verifiable credentials (VCs), smart contracts, zero-knowledge proofs (ZKPs), and quantum-resistant cryptographic algorithms including CRYSTALS-Kyber and CRYSTALS-Dilithium are examined. A comparative analysis of existing studies is presented to identify advantages, limitations, scalability issues, and security challenges. Finally, research gaps and future research directions are discussed for developing scalable, secure, privacy-preserving, and quantum-safe identity management systems

This qualitative study explored the lived experiences of preschool teachers in the West District of Kabacan, Cotabato, Philippines, regarding the issues and challenges they encounter in supporting the holistic development of preschool children, and the coping strategies they employ to address these. A phenomenological design was employed, with 15 preschool teachers as primary informants and 235 child development experts and coordinators as supplementary participants. Data were gathered through in-depth interviews and focus group discussions, and analyzed using Braun and Clarke's (2006) six-phase thematic analysis framework. Two global themes emerged: (1) Issues and Challenges Confronting Holistic Development — encompassing physical health and nutrition deficits, cognitive development barriers, socio-emotional challenges, and structural-environmental constraints; and (2) Coping Strategies and Interventions — including adaptive pedagogical practices, structured routines and emotional support, home-school collaboration, community health partnerships, and administrative program support. Findings reveal that holistic preschool development in this context is shaped by an ecology of intersecting challenges that substantially exceed classroom boundaries. Teachers respond with creative and multi-pronged strategies, yet systemic institutional support remains insufficient. The proposed KADALEM Intervention Program, validated by experts at M = 4.45 (Highly Valid), offers an ecologically grounded, multi-component response to the identified challenges.

This quantitative study examined the level of implementation of differentiated instruction (DI) strategies and their relationship to and influence on pupils' learning outcomes in Grade 5 classrooms in selected public elementary schools within the West District of Kabacan, Cotabato, Philippines, during School Year 2025–2026. A descriptive-correlational design was employed. Survey data were collected from 9 Grade 5 teachers and 241 Grade 5 pupils (total n = 250) across six public elementary schools using stratified random sampling. Instruments adapted from Tomlinson (2021), Santamaria (2020), and Tashakkori and Teddlie (2019) assessed DI strategy implementation across four dimensions — content, process, product, and learning environment — and learning outcomes across four dimensions — academic performance, critical thinking skills, classroom participation, and overall achievement — on five-point Likert scales. Data were analyzed using weighted means, Spearman's rho correlation, and multiple linear regression. Results revealed that DI strategies were consistently implemented at high levels (grand mean = 4.46, Always) and pupils demonstrated high learning outcomes (grand mean = 4.28, Always). Correlation analyses found no statistically significant relationship between most DI dimensions and learning outcomes, except for content differentiation's positive correlation with academic performance (r = 0.736, p = 0.024) and critical thinking skills (r = 0.709, p = 0.032). Regression analysis confirmed that content differentiation significantly predicted academic performance (R² = 0.683, F = 2.150, p = 0.038) and critical thinking skills (R² = 0.639, F = 1.774, p = 0.029), while process, product, and learning environment differentiation did not significantly predict any learning outcome. These findings confirm that content differentiation is the most impactful DI dimension for measurable pupil learning gains, while other dimensions appear to function as enabling supports rather than direct outcome predictors.

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 quantitative study examined the effects of the ARAL (Academic Recovery and Accessible Learning) Program implementation on teaching efficiency and learner performance in Matalam South District, Matalam, Cotabato, Philippines for School Year 2025–2026. Using a descriptive-correlational design with complete enumeration, 209 elementary school teachers were surveyed using a validated, researcher-made questionnaire (Cronbach&#39;s Alpha = .835, .828, .738). ARAL implementation was assessed across five components: teacher preparedness, learning materials, learner identification process, monitoring, and scheduling. Teaching efficiency was measured through lesson planning and organization, instructional delivery, assessment and feedback, and classroom management. Learner performance was examined in terms of academic achievement, classroom engagement, learning outcomes, and motivation and attitude. Results revealed that all ARAL implementation components were highly implemented (WM range: 4.64–4.84). Teachers were highly efficient in all areas (WM range: 4.81–4.93), and learners demonstrated excellent performance across all dimensions (WM range: 4.75–4.94). Spearman rho analysis found significant relationships between learning materials, learner identification, monitoring, and scheduling with key teaching efficiency indicators. Regression analysis confirmed that scheduling, monitoring, and learner identification significantly predict instructional delivery, lesson planning, assessment, and classroom management. Learning materials significantly influenced classroom engagement (R² = 0.051), monitoring predicted learning outcomes (R² = 0.040), and learning materials and scheduling jointly influenced motivation and attitude (R² = 0.055). These findings affirm that structured ARAL implementation substantially enhances both teaching efficiency and learner performance in the Philippine public school context.

This quantitative study examined the level of digital literacy skills of junior high school teachers and their relationship to and influence on the effectiveness of classroom instruction in selected public schools in the Municipality of Kabacan, Cotabato, Philippines, during School Year 2025–2026. Using a descriptive-correlational design, survey data were collected from 250 junior high school teachers across three educational districts (Kabacan North, South, and West) through stratified convenience sampling. The questionnaire assessed four dimensions of digital literacy skills — information literacy, media literacy, ICT proficiency, and digital content creation — and four dimensions of classroom instruction effectiveness — instructional strategies, classroom management, assessment techniques, and integration of technology. Data were analyzed using descriptive statistics, Spearman&#39;s rank-order correlation, and multiple linear regression. Results revealed that teachers demonstrated highly skilled levels across all digital literacy dimensions (overall weighted mean = 4.73) and highly effective levels across all classroom instruction dimensions (overall weighted mean = 4.74). Correlation analyses confirmed significant relationships between specific digital literacy dimensions and instructional outcomes: information literacy and media literacy significantly correlated with instructional strategies and classroom management, while ICT proficiency and digital content creation strongly correlated with assessment techniques and technology integration. Regression analyses showed that digital literacy skills significantly predicted all four instructional dimensions, with technology integration achieving the highest explained variance (R² = 0.885). These findings affirm that digital literacy is a significant predictor of classroom instructional effectiveness and underscore the need for targeted professional development programs to sustain and enhance these competencies.

This study examined the barriers to educational access among internally displaced children (IDCs) and street-hawking girls in Katsina State, Nigeria, against the backdrop of rising educational exclusion linked to poverty, insecurity, gender inequality, and child labour. Guided by frameworks from UNICEF and UNESCO, the study adopted a comparative mixed-methods design as respondents equally drawn from both groups. Data were analyzed using descriptive statistics, chi-square, and independent samples t-tests at a 0.05 significance level. Findings reveal that poverty is the main barrier to education, followed by child labour/street hawking, displacement and insecurity, and cultural practices. Over 70% of respondents had irregular or no school attendance, with a significant link between vulnerability group and attendance (χ² = 18.62, p < 0.05). The study concludes that while both groups face similar challenges, the intensity and nature of barriers vary, requiring targeted, context-specific policies for inclusive education in Katsina State.

A strong, scalable, and secure network infrastructure is essential to the shift from traditional buildings to networked smart environments (homes and offices). Even while smart homes and businesses are becoming more and more popular, many IoT systems face difficulties like inconsistent connectivity, security risks, device incompatibilities, and limited scalability. These problems emphasize the necessity of a scalable, secure, and well-organized network architecture that guarantees smooth device connection while preserving effectiveness, privacy, and user ease. The study used both primary and secondary data collection methods. In primary data collection, network users were interviewed to find connectivity and security problems, and common device behaviors like bandwidth consumption, connectivity requirements, and cloud dependence were noted. Additionally reviewed were academic publications on IoT networking and smart environments, industry best practices, IoT security standards, and networking standards and protocols. VLAN division, integration of edge computing, and robust security measures. The suggested architecture supports high device density and offers dependable connectivity, according to performance evaluation.

Financial inclusion has emerged as an important component of sustainable economic development across the world. However, minority communities often remain excluded from formal banking systems due to economic inequality, religious concerns, lack of financial literacy, and institutional barriers. Islamic banking, based on principles of Shariah such as prohibition of interest (riba), risk-sharing, ethical investment, and social justice, has increasingly been viewed as an alternative financial system capable of enhancing inclusion among marginalized and minority populations.
This research paper examines the role of Islamic banking in promoting financial inclusion among minority communities from a global perspective. The study analyzes how Islamic financial institutions contribute to access to banking, poverty reduction, entrepreneurship, and socioeconomic empowerment in both Muslim-majority and non-Muslim countries. The paper further explores challenges such as regulatory issues, lack of awareness, technological barriers, and institutional limitations. Based on secondary data and existing literature, the study concludes that Islamic banking has significant potential to improve financial inclusion among minority communities worldwide.

In the era of globalization, cross-cultural business communication has become an essential component of international trade and economic cooperation. Among the world’s major languages, Arabic occupies a significant position due to the economic and geopolitical importance of Arab countries in global commerce, particularly in sectors such as oil, finance, tourism, logistics, and international trade. This research paper examines the role of the Arabic language in facilitating cross-cultural business communication and strengthening global trade relations. The study explores how linguistic competence, cultural understanding, and communication strategies influence business negotiations, trust-building, and long-term partnerships between Arab and non-Arab countries. The paper also discusses communication barriers, cultural differences, negotiation styles, and the growing importance of Arabic in international business environments. Through literature review and analytical discussion, the study highlights the necessity of intercultural competence for successful global trade relations.

This quantitative study examined the level of teachers' technological efficacy and its influence on class academic performance in selected public elementary schools of Antipas District, North Cotabato, Philippines, for School Year 2022–2023. Using a descriptive-correlational design, 100 teachers were purposively selected as respondents. Data were gathered through validated survey questionnaires and analyzed using mean, multiple regression analysis, and the Sobel-Z test. The study assessed teachers' technological efficacy across three dimensions: technical skills, digital literacy, and technological knowledge skills. Academic performance was measured through pupils' Mean Percentage Score (MPS) across the first, second, and third grading periods. Results revealed that teachers demonstrated a generally high level of technological efficacy, with technical skills rated as Highly Efficient (M = 4.24), while digital literacy (M = 4.16) and technological knowledge skills (M = 4.14) were both rated as Efficient. Pupils' weighted mean MPS was 89.44, classified as Satisfactory. Multiple regression analysis showed that teachers' technological efficacy did not significantly influence class academic performance (R² = 0.041, F = 1.35, p = 0.262). These findings suggest that while teachers are technologically capable, this alone is insufficient to drive measurable academic gains. The study underscores the need to complement technology proficiency with sound pedagogical practices.

This quantitative study examined the level of Project-Based Learning (PBL) implementation and its relationship to and influence on critical thinking skills and student engagement among 250 Grade 6 elementary learners in Kabacan North District, Province of Cotabato, Philippines, during School Year 2025–2026. A descriptive-correlational design was employed. Survey data were collected using validated questionnaires measuring PBL practices (real-world problems, collaboration, student-centered inquiry, reflective practice, and integration of subjects), critical thinking skills (analysis, evaluation, inference, problem-solving, and reasoning), and student engagement (behavioral, emotional, and cognitive). Spearman rank-order correlation and multiple linear regression analyses were performed. Results revealed that PBL was consistently Practiced across all dimensions (M = 4.00–4.11), critical thinking skills were uniformly Skilled (M = 4.00–4.14), and student engagement ranged from Engaged to Highly Engaged (M = 4.12–4.23). All PBL dimensions correlated significantly with all critical thinking and engagement indicators (p < 0.001). Regression analyses showed that PBL components significantly predicted critical thinking skills (R² = 0.266–0.451) and student engagement (R² = 0.338–0.371). Collaboration and reflective practice were the most consistent predictors across outcomes. These findings confirm that PBL is a statistically significant driver of critical thinking development and student engagement in elementary education, particularly when collaborative, reflective, and interdisciplinary dimensions are emphasized.

This quantitative study examined the levels of teachers' readiness and pedagogical practices and their predictive influence on teachers' competence in implementing the MATATAG Curriculum in the Municipality of Banisilan, Cotabato, Philippines, during School Year 2025–2026. Using a descriptive-correlational design, survey data were collected from 311 public elementary and secondary school teachers through complete enumeration. The questionnaire assessed readiness in terms of attitudes, teaching experience, and access to learning resources; pedagogical practices in terms of classroom management, instructional methods, technology integration, and assessment strategies; and competence in terms of self-direction, empathy, and cooperation — all measured on a five-point Likert scale. Data were analyzed using descriptive statistics (mean, standard deviation), Spearman correlation, and multiple linear regression. Results revealed that teachers demonstrated high levels of readiness (attitudes: M = 4.63; teaching experience: M = 4.55; learning resources: M = 4.56) and highly practiced pedagogical approaches (classroom management: M = 4.71; instructional methods: M = 4.65; technology integration: M = 4.59; assessment strategies: M = 4.65). Competence dimensions were similarly high (self-direction: M = 4.65; empathy: M = 4.73; cooperation: M = 4.70). Correlation analyses confirmed significant positive relationships between readiness, pedagogical practices, and all three competency dimensions. Regression analyses further showed that teaching experience and learning resources significantly predicted self-direction (R² = 0.499); attitude and teaching experience predicted empathy (R² = 0.402); while classroom management, technology integration, and assessment strategies were the strongest predictors of all competence dimensions. These findings affirm that readiness and pedagogical practices are meaningful predictors of teacher competence in MATATAG Curriculum implementation.

The present study focuses on the assessment of physico-chemical characteristics of river water from the Gej River at Premabag Ghat, Baikunthpur, located in the Korea district of Chhattisgarh, India. The primary objective of this research is to evaluate the suitability of river water for drinking and irrigation purposes by analyzing key water quality parameters. Water samples were collected systematically from selected points at Premabag Ghat and analyzed using standard laboratory methods.
The physico-chemical parameters examined in this study include temperature, turbidity, colour, pH, electrical conductivity (EC), total dissolved solids (TDS), total alkalinity, total hardness, calcium, magnesium, chloride, nitrate, sulfate, and dissolved oxygen (DO). The obtained results were compared with standard guidelines prescribed by the Bureau of Indian Standards (BIS) and the World Health Organization (WHO) to determine the water quality status.
The analysis revealed that certain parameters were within the permissible limits, indicating acceptable water quality, while a few parameters showed deviations, possibly due to anthropogenic activities such as domestic waste discharge, religious practices, and surface runoff in the surrounding area. The variation in water quality highlights the influence of local environmental conditions on the river ecosystem. Based on the findings, the water quality of the Gej River at Premabag Ghat is moderately suitable for irrigation purposes but requires appropriate treatment before being used for drinking. The study emphasizes the need for continuous monitoring and implementation of effective water management strategies to prevent further deterioration of water quality.

This quantitative study examined the level of implementation of school-based mental health programs (SBMHPs) and their relationship to and influence on the academic engagement of junior high school students in the Municipality of Pikit, Cotabato, Philippines for School Year 2025–2026. Using a descriptive-correlational, cross-sectional design, 250 Grade 9 and Grade 10 students and 14 teachers from public secondary schools across Pikit West, Pikit South, and Pikit North Districts were surveyed. SBMHPs were assessed across three components: counseling and guidance services, peer support and awareness campaigns, and stress management and resilience-building activities. Academic engagement was measured in behavioral, emotional, and cognitive dimensions. Results showed that SBMHPs were rated as Implemented (overall WM = 3.59), with stress management and resilience-building receiving the highest mean (WM = 3.63). Students demonstrated overall Engaged academic engagement (WM = 4.07), with emotional engagement rated Highly Engaged (WM = 4.57). Spearman rho correlation revealed no statistically significant relationships between SBMHP components and engagement dimensions. Regression analyses showed that SBMHPs explain only limited variance in engagement (R² = 0.002–0.080). Only peer support significantly predicted behavioral engagement, though negatively (β = −0.251, p = 0.020). These findings suggest that SBMHPs function as indirect enabling mechanisms for engagement rather than direct predictors, emphasizing the need for their integration with instructional and classroom practices.

This study examined the level of teachers' awareness of metacognitive processes and their pedagogical competence among 192 elementary school teachers in the Schools Division Office (SDO) of Cotabato during the school year 2025-2026. Employing a descriptive-correlational design, the study assessed teachers' metacognitive awareness across five dimensions: teaching strategies, designing and structuring lessons, using prompts, embedding higher-order thinking (HOT) tasks, and understanding learner diversity. Pedagogical competence was assessed in terms of content and pedagogical knowledge, lesson planning and delivery, classroom management, use of ICT to enhance teaching and learning, and assessment and evaluation. Descriptive statistics, Pearson r correlation, and multiple linear regression were utilized to analyze the data. Findings revealed that teachers were practically aware of metacognitive processes overall (WM = 3.92) and were reasonably competent pedagogically (WM = 4.14). A very low and generally non-significant correlation was found between metacognitive awareness and pedagogical competence. Regression analysis confirmed that metacognitive processes did not significantly predict pedagogical competence, except for understanding learner diversity, which emerged as a negatively significant predictor of classroom management. These findings highlight the importance of targeted professional development programs that bridge metacognitive awareness and practical pedagogical application.

This study assessed the use of gender-fair language in the English learning modules for Grade 10 students. Specifically, it examined how gender fairness was mainstreamed in the portrayal of women, representation, stereotyping, and language; identified the Filipino gender role ideologies reflected in and/or challenged by the modules; and proposed a training design on gender-fair language to improve the current learning modules. The study used a qualitative research design with Critical Discourse Analysis to examine 23 Grade 10 English learning modules at Bukidnon National High School. The analysis focused on the lexical choices, syntactic patterns, semantic constructs, and larger cultural and ideological connotations buried in the texts. Findings revealed that the modules contain both gender-fair and gender-biased elements. Some texts continue to reflect traditional portrayals of women, stereotypical role assignments, male-centered language, and gendered assumptions. However, several module excerpts also challenge unequal gender norms by portraying women as decision-makers, leaders, rights-holders, professionals, and active social agents. The study further found that Filipino gender role ideologies, such as the Maria Clara archetype, colonial patriarchy, toxic masculinity, hiya, pakikisama, utang na loob, bahala na mentality, adult superiority bias, nationalist feminism, the Babaylan or decolonial lens, and the Catholic moral gender framework were reflected and/or challenged in the modules. The study recommended the development of a Gender-Fair Language and Gender-Responsive Module Training for educators, instructional material writers, and curriculum evaluators. The researcher advocates for the methodical evaluation and modification of educational resources to guarantee gender-responsive, inclusive, and equitable language in basic education.

Pharmaceutical analysis plays a very prominent role in quality assurance as well as quality control of bulk drugs and pharmaceutical formulations. Rapid increase in pharmaceutical industries and production of drug in various parts of the world has brought a rise in demand for new analytical techniques in the pharmaceutical industries. As a consequence, analytical method development has become the basic activity of analysis. RP-HPLC is the most, sensitive, universal analytical procedure. Quantitative estimation of Paracetamol and Eperisone Hydrochloride was estimated by RP-HPLC using MeOH: 0.1% Ortho phosphoric acid (60:40 %v/v) as a mobile phase and Phenomenex column (150mm×4.6mm, 5µ) as a stationary phase and the peaks were observed at 270nm which was selected as a wavelength for quantitative estimation. After development of the method it was validated for specificity, system suitability, accuracy, linearity, precision, ruggedness and robustness. The value of theoretical plates, tailing factor, retention time and peak area was found to be within limits, hence it is concluded that the system is suitable to perform assay.

Critical thinking is a foundational life skill in 21st-century education, essential for navigating rapid global change. This study examined the types of questions elementary teachers use to promote critical thinking, how these strategies are planned and implemented, and the resulting effects on learner engagement. Employing a qualitative phenomenological research design, data were collected through semi-structured interviews with 25 elementary teachers across five municipalities in the 2nd Congressional District of Cotabato. Findings indicated that while teachers utilize foundational and scaffolded questioning to build a factual baseline, they also employ analytical and higher-order thinking prompts to deepen cognitive engagement. The research concludes that intentional, scaffolded dialogue—supported by a mistake-friendly classroom climate—successfully fosters independent reasoning and student agency even within systemic constraints such as large class sizes and time pressure.

Indigenous storytelling plays a vital role in strengthening culturally responsive education, particularly in Indigenous Peoples (IP)–dominated schools where preserving heritage is central to learning. This study examined the integration of indigenous storytelling among elementary teachers in IP-dominated schools in the 2nd congressional district of the Division of Cotabato, focusing on its pedagogical functions, transmitted values, and sustainability within formal education. A qualitative micro-ethnographic design was employed involving elementary teachers as participants. Data were gathered through in-depth interviews and analyzed using thematic analysis. Findings revealed that storytelling enhances learner engagements, serves as a cross-curricular pedagogical tool, supports cultural integration and values formation, and contributes to communication and critical thinking. This study concludes that indigenous storytelling strengthens cultural identity and academic learning through culturally responsive education.

This study was conducted to examine teachers’ instructional strategies and their influence on the numeracy skills of Grade VI pupils in the 2nd Congressional District of Cotabato. Utilizing a mixed-methods design, the research assessed twenty-five elementary teachers through quantitative surveys and qualitative inquiry. The findings revealed that while teachers demonstrate a very high level of instructional strategies—particularly in planning, scaffolding, and contextualization—pupils showed strong performance only in number sense and computational fluency, while higher-order skills like problem-solving and measurement remained moderate. Statistical results highlighted that contextualized and real-life problem-solving strategies were the most significant factors in enhancing numeracy. The study recommends targeted teacher training and improved resource provision to support effective numeracy instruction.

This study examined the role of Artificial Intelligence (AI) in school governance among public school heads in the Division of Cotabato, focusing on AI literacy, AI application, and governance outcomes. Using a multi-phase mixed-methods design, the research combined quantitative and qualitative approaches. Phase 1 involved 353 school heads and utilized descriptive-correlational analysis to determine the relationships among variables. Phase 2 explored the experiences and challenges of 15 purposively selected school heads through semi-structured interviews, while Phase 3 focused on the development and validation of an intervention program to strengthen AI integration in school governance. Findings revealed that school heads demonstrated a high level of AI literacy and a high extent of AI application, particularly in administrative efficiency, data-driven decision-making, and stakeholder engagement. Governance outcomes in terms of efficiency, effectiveness, and trust were rated as very good. Significant relationships were found between AI literacy and AI application, as well as between AI application and governance outcomes, with critical appraisal emerging as a key predictor of effective AI use. Despite these positive findings, challenges such as limited resources, time constraints, and the need for continuous professional development were identified. The study concludes that strengthening AI competencies and institutional support systems is essential for optimizing AI-driven school governance. The proposed intervention program offers a practical framework to enhance leadership capacity and promote effective, ethical, and sustainable AI integration in education.

This study examined the effectiveness of the School Learning Action Cell (SLAC) as a collaborative professional development mechanism and its influence on teachers’ professional competence and learner engagement. A three-phase mixed-method design was employed, consisting of a quantitative assessment of relationships among variables, a qualitative exploration of teachers’ support needs, and the development and validation of SLAC-based collaborative practices. Findings revealed that SLAC is generally effective across leadership, collaboration, and professional support dimensions, with peer learning emerging as the strongest component. Teachers demonstrated high levels of professional competence, which significantly influenced learner engagement. The study highlights the importance of structured, collaborative professional learning in enhancing teaching quality and improving student engagement.

This study examined culturally responsive conflict resolution and its relationship with conflict management competence among school leaders in SOCCSKSARGEN. Using a three-phase mixed-method design, the research quantitatively assessed the levels and relationships of the variables, qualitatively explored the lived experiences of school leaders, and developed a dissemination plan based on the findings. Results revealed that school leaders consistently demonstrated highly responsive culturally grounded conflict resolution practices across dimensions such as culture and identity, context and history, relationships and communication, social justice and equity, and reflective practice and accountability. Likewise, school leaders exhibited a high level of competence in managing conflicts, particularly in communication, decision-making, negotiation, emotional intelligence, and professional responsiveness. A significant positive relationship was found between culturally responsive conflict resolution and conflict management competence. The study underscores the importance of culturally responsive leadership in fostering inclusive, equitable, and harmonious school environments.

Magnetic materials play a crucial role in modern science and technology, ranging from data storage to biomedical applications. This paper presents a comprehensive overview of the fundamental principles governing magnetism and provides a systematic classification of magnetic materials based on their atomic structure and magnetic behaviour. The study discusses the origin of magnetism, magnetic domains, and key parameters such as magnetic susceptibility and permeability. Furthermore, materials are categorized into diamagnetic, paramagnetic, ferromagnetic, antiferromagnetic, and ferrimagnetic types, with emphasis on their properties, mechanisms, and applications. The paper aims to serve as a foundational reference for students and researchers in physics, materials science, and engineering.

This quantitative study examined teacher autonomy in relation to job commitment among 283 Grade 3 elementary school teachers from the municipalities of Arakan, Antipas, Magpet, and President Roxas, Province of Cotabato, for the school year 2025–2026. Employing a descriptive-correlational design, data were gathered using a self-developed and validated questionnaire (Cronbach's alpha: .929 and .935) and analyzed using weighted mean, Spearman rho correlation, and multiple linear regression. Findings revealed that teachers highly practiced autonomy in all four dimensions: freedom to choose teaching methods (WM=4.73), flexibility to adapt the curriculum (WM=4.69), autonomy in classroom management (WM=4.72), and control over professional development choices (WM=4.58). Job commitment was equally high across all dimensions: participation in school and community activities (WM=4.68), emotional attachment to school and students (WM=4.66), perseverance despite challenges (WM=4.65), and willingness to go beyond required duties (WM=4.43). Spearman rho confirmed a highly significant positive relationship between all autonomy dimensions and all job commitment dimensions (p<0.01). Multiple regression analyses revealed that autonomy in teaching methods, curriculum adaptation, and professional development choices predicted willingness to go beyond duties; classroom management autonomy and professional development control predicted participation and emotional attachment; while curriculum flexibility, classroom management autonomy, and professional development control jointly predicted perseverance despite challenges. These findings establish teacher autonomy as a critical correlate and predictor of job commitment in the Philippine public elementary school setting.

This quantitative study examined the level of Child Protection Policy (CPP) implementation and its relationship with the extent of teachers' disciplinary actions in Arakan public schools for School Year 2025–2026. Using a descriptive-correlational design, 278 public school teachers from three districts—Arakan East, Arakan West, and Arakan North—were surveyed using a validated researcher-made questionnaire (Cronbach's alpha: .930 and .948). Data were analyzed using weighted mean, Spearman's rho correlation, and multiple linear regression. Findings revealed that CPP implementation was highly implemented across all domains: awareness (WM=4.38), compliance (WM=4.49), reporting and referral (WM=4.40), and monitoring and training (WM=4.37), with an overall mean of 4.41. Teachers' disciplinary actions were consistently applied across all dimensions: preventive strategies (WM=4.49), corrective measures (WM=4.55), frequency (WM=4.37), and perceived severity (WM=4.49), overall WM=4.48. Spearman's rho revealed a significant negative relationship between monitoring/training and preventive strategies (rs=-.158, p=.006), and between compliance and perceived severity (rs=-.118, p=.041). Regression analysis confirmed that awareness, compliance, and reporting/referral significantly predicted frequency of disciplinary actions (R²=.548, F=89.315), while awareness, reporting/referral, and monitoring/training jointly influenced preventive strategies (R²=.075, F=5.972). These findings establish that CPP implementation significantly shapes disciplinary practices, with monitoring and compliance serving as primary drivers of reduced severity and increased documentation.

This quantitative study examined the level of teachers' grit and self-efficacy and their relationship to and influence on learners' absorptive capacity in learning within selected district schools in Arakan East, Arakan West, and Arakan North, Division of North Cotabato, for School Year 2025–2026. Using a descriptive-correlational design, 270 teachers and 270 learners were surveyed through validated researcher-made questionnaires (Cronbach's alpha: .961, .980, .977). Data were analyzed using weighted mean, Spearman's rank-order correlation, and multiple linear regression. Findings revealed very high levels of teachers' grit (WM=4.42), with passion and commitment highest (WM=4.45) and consistency of interest lowest (WM=4.36). Teachers' self-efficacy was also very high (WM=4.46), with efficacy growth highest (WM=4.53) and self-confidence lowest (WM=4.35). Learners demonstrated very high absorptive capacity (WM=4.53), with knowledge acquisition and comprehension highest (WM=4.57). Despite these high individual scores, Spearman's rho analysis revealed no statistically significant relationship between any dimension of teachers' grit or self-efficacy and any dimension of learners' absorptive capacity (all p>.05). Multiple regression analysis confirmed that teachers' self-efficacy did not significantly predict learners' absorptive capacity across all three dimensions (R² range: 0.002–0.011, all F values non-significant). These findings reject the assumption that internal teacher traits automatically translate into measurable student outcomes and underscore the mediating role of systemic factors and active instructional scaffolding.

This quantitative study examined the relationship and predictive influence of teachers' self-determination and goal orientation on pupils' self-regulated learning (SRL) in selected district schools in Arakan, North Cotabato, for School Year 2025–2026. Using a descriptive-correlational design, 278 public school teachers selected through proportionate stratified random sampling responded to a validated self-made questionnaire (Cronbach's alpha: .902, .945, .955). Data were analyzed using weighted mean, Spearman's rho correlation, and multiple linear regression. Findings revealed that teachers had very high self-determination (WM=4.42), with competence (WM=4.49) as the highest domain, followed by relatedness (WM=4.40) and autonomy (WM=4.38). Goal orientation was also very high overall (WM=4.41), with work avoidance orientation highest (WM=4.44), followed by performance (WM=4.42) and mastery (WM=4.37). Pupils demonstrated very high SRL proficiency (WM=4.50), with goal setting highest (WM=4.51). Spearman's rho confirmed highly significant positive relationships between all self-determination and goal orientation dimensions and all SRL dimensions (p<.001). Regression analyses showed that relatedness was the strongest predictor of pupils' goal setting (β=.395), self-monitoring (β=.387), and strategy use (β=.304). For goal orientation, performance orientation was the strongest predictor across all SRL dimensions, while mastery orientation did not significantly predict any dimension. These findings establish teachers' relational bonds and performance-driven orientations as the primary mechanisms of pupils' academic self-regulation.

This quantitative study examined teachers’ curriculum integration skills and cognitive demand management in multigrade classrooms across Arakan, Antipas, and President Roxas, North Cotabato during School Year 2025–2026. Using a descriptive-correlational design, 210 multigrade teachers were surveyed through a validated questionnaire (Cronbach’s alpha: .910 and .935). Data analysis employed weighted mean, Spearman’s rank-order correlation, and multiple regression. Findings revealed that teachers demonstrated high proficiency in curriculum integration (WM=4.51), with lesson planning rated highest and assessment practices lowest. Cognitive demand management was likewise highly effective (WM=4.50), with pacing as the strongest dimension. Correlation analysis showed significant relationships between instructional strategies and task differentiation (r=.597, p=.044), as well as scaffolding (r=.607, p=.004). Lesson planning was significantly related to pacing (r=.589, p=.009). Regression confirmed lesson planning as a predictor of pacing, and instructional strategies as predictors of task differentiation and scaffolding. Results reject the null hypotheses, affirming that curriculum integration skills significantly influence cognitive demand management in multigrade classrooms.

This quantitative study examined the organizational climate as a correlate of teachers' motivation among 108 elementary school teachers in District 4, Kidapawan City Division, for the school year 2025–2026. Employing a descriptive-correlational design, data were gathered through a self-developed and validated questionnaire (Cronbach's alpha: 0.854 and 0.813) and analyzed using mean, Spearman rho correlation, and multiple linear regression. The study found that organizational climate in terms of leadership support (WM=4.45), open communication (WM=4.57), mutual trust and respect (WM=4.70), and adequate working conditions (WM=4.57) were all rated as highly practiced. Teachers were likewise highly motivated across all dimensions: passion for teaching (WM=4.70), commitment to student learning (WM=4.78), initiative to improve teaching (WM=4.79), and positive attitude toward duties (WM=4.79). Spearman rho analysis revealed a highly significant relationship between organizational climate and teachers' motivation. Regression analysis confirmed that mutual trust and respect and adequate working conditions were the most consistent and significant predictors of teacher motivation. These findings underscore the critical role of a positive and supportive organizational climate in sustaining teachers' professional motivation and instructional effectiveness.

Autism Spectrum Disorder (ASD) is a neurodevelopmental condition that affects social communi-cation, behavioral flexibility, and sensory processing. Delays in screening often postpone interven-tion, especially in settings where specialist access is limited. This paper presents a publication-style summary of an intelligent web-based ASD screening system that combines a conversational chat-bot interface with a supervised machine learning classifier. The proposed system uses a Random Forest model trained on validated ASD screening datasets from the UCI Machine Learning Repos-itory, integrating data from adult, child, and adolescent cohorts to support broader applicability. A 40-question assessment structure, inspired by the Autism-Spectrum Quotient (AQ), is delivered through a chatbot workflow to improve usability and reduce the perceived clinical burden of ini-tial screening. According to the supplied project documentation, the optimized model achieved a classification accuracy of 97.27% on held-out test data using 1,100 samples and a 300-tree Ran-dom Forest configuration. The full system combines a Flask backend, React frontend, and SQLite persistence layer to provide secure authentication, real-time inference, confidence-based feedback, and post-screening guidance. The paper argues that machine learning-enhanced conversational screening can improve accessibility and user engagement while remaining clearly positioned as a preliminary decision-support tool rather than a replacement for professional diagnosis.

Social security schemes play a significant role in promoting financial inclusion and social protection among economically weaker and vulnerable sections of society. In this context, the Government of India introduced social security schemes such as Pradhan Mantri Jeevan Jyoti Bima Yojana (PMJJBY), Pradhan Mantri Suraksha Bima Yojana (PMSBY), and Atal Pension Yojana (APY) to provide affordable life insurance, accidental insurance, and pension benefits to the unorganized and low-income population. The present study aims to examine the progress and effectiveness of these social security schemes in Chhattisgarh, with special reference to enrolment trends, awareness levels, accessibility, and beneficiary perception.The study is based on both primary and secondary data. Primary data were collected through structured questionnaires and interviews with beneficiaries from selected rural and urban areas of different divisions of Chhattisgarh. Secondary data were obtained from government reports, banking statistics, journals, and official publications related to financial inclusion and social security schemes. Descriptive and analytical research methods were used to analyze the collected data.The findings indicate that PMJJBY, PMSBY, and APY have significantly contributed toward expanding financial inclusion and social security coverage in Chhattisgarh. The schemes have witnessed increasing enrolment due to low premiums, government support, and banking outreach initiatives. However, the study also reveals challenges such as low awareness in rural areas, inadequate financial literacy, procedural difficulties in claim settlement, and limited understanding of pension benefits among beneficiaries.The study concludes that although these schemes have strengthened the social security framework in Chhattisgarh, greater awareness campaigns, financial education, and simplified operational procedures are necessary to improve their effectiveness and ensure inclusive socio-economic development.

This quantitative study investigated the implementation of the Department of Education’s Special Program in the Arts (SPA) and its influence on quality basic education in Cotabato Division for School Year 2025–2026. A descriptive-correlational design was employed, surveying 250 SPA students from five public secondary schools using a validated Likert-scale questionnaire. Results showed that specially designed instruction (WM=4.38) and speech therapy (WM=4.22) were highly provided, while occupational therapy (WM=4.14), physical therapy (WM=4.07), assistive technology (WM=4.20), and counseling (WM=3.90) were moderately provided. Arts integration—self-expression (WM=4.01), cultural heritage (WM=3.94), and creativity (WM=3.74)—was rated Artistic. Formal education (WM=4.22) was highly provided, with informal and non-formal education moderately provided. Indicators of quality basic education, including facilities, feeding programs, and new personnel positions, were rated Improved. Correlation and regression analyses confirmed significant relationships between SPA components and quality education, with assistive technology showing the strongest influence. Persistent gaps in therapy, counseling, and creative arts delivery highlight areas for targeted intervention.

In the landscape of modern education, accurately detecting student engagement has become a priority for improv-ing academic success and reducing dropout rates. Traditional methods, such as self-reports and subjective teacher assessments, are often limited by human bias and a lack of real-time resolution. This survey explores the shift toward automated systems that use computer vision and machine learning to analyze engage-ment through facial expressions and behavioral data. Current research highlights the effectiveness of deep learning models, particularly Convolutional Neural Networks (CNNs), in distin-guishing varying levels of student participation. These models have demonstrated near-human accuracy in binary classification tasks, such as identifying highly engaged versus disengaged states. Furthermore, studies show that automated engagement judg-ments correlate strongly with actual task performance and pre-test scores. The survey concludes that the integration of advanced architectures like hybrid CNNs and ensemble learning offers a robust framework for real-time monitoring. These technologies provide a scalable solution for diverse learning environments, including massive open online courses (MOOCs) and Affect-sensitive Intelligent Tutoring Systems (ITS). The findings suggest that automated tools can revolutionize how educators respond to student needs in dynamic classroom settings.

The rapid expansion of urban environments has significantly increased both the complexity and volume of civic grievances, placing substantial strain on traditional administrative frameworks. Conventional grievance redressal mechanisms, which depend heavily on manual processing and fragmented communication channels, frequently exhibit limitations such as delayed response times, lack of transparency, duplicate reporting, and inaccurate complaint classification. To address these challenges, modern smart city initiatives are progressively integrating Artificial Intelligence (AI)-driven solutions to enhance and automate grievance management processes.
This paper presents a comprehensive survey of contemporary digital and AI-enabled grievance redressal systems, tracing their evolution from conventional database-driven platforms to advanced intelligent frameworks. It systematically examines key enabling technologies, including Natural Language Processing (NLP), Computer Vision, deep learning models such as BERT and YOLO, and emerging Retrieval-Augmented Generation (RAG)-based multi-agent systems. A comparative evaluation of these approaches is conducted to highlight their respective strengths, limitations, and applicability within real-world urban governance contexts.
Furthermore, the study identifies several critical research gaps, including challenges related to multilingual processing, data authenticity, infrastructure limitations, and the absence of automated resolution verification mechanisms. In response, a scalable and modular system architecture is proposed, incorporating multimodal AI, geo-tagging, and predictive analytics. The findings indicate that AI-driven grievance systems can substantially improve operational efficiency, enhance transparency, and foster greater citizen engagement, thereby supporting the development of responsive and sustainable smart cities.

This quantitative study examined the level of disaster risk reduction and management (DRRM) implementation practices and the extent of disaster safety practices among elementary schools in Magpet, President Roxas, and Arakan. Using a descriptive-correlational design, data were gathered from teachers and DRRM coordinators through a validated survey questionnaire analyzed using Spearman's rho correlation and multiple linear regression. Findings revealed that DRRM implementation practices were generally implemented to highly implemented: emergency preparedness and response plans (WM=4.19, Implemented), disaster risk education and training (WM=4.23, Highly Implemented), physical infrastructure and safety measures (WM=4.23, Highly Implemented), and community and local government collaboration (WM=4.19, Implemented). Disaster safety practices were rated as generally practiced: response to disasters (WM=4.17), resilience (WM=4.14), and sustainability of DRRM practices (WM=4.19). Spearman's rho analysis confirmed highly significant positive relationships between all DRRM implementation dimensions and disaster safety practices (p=0.000). Multiple regression revealed that community and local government collaboration was the sole significant predictor of response to disasters (β=.425, p=.000, R²=0.480); disaster risk education and training and emergency preparedness were the significant predictors of resilience (R²=0.544); and all four DRRM dimensions significantly predicted sustainability of DRRM practices (R²=0.984). The null hypotheses were rejected, confirming that DRRM implementation practices significantly influence disaster safety practices in elementary schools.

The healthcare system in India is undergoing rapid transformation as part of the broader Digital India 2.0 initiative, which aims to establish a modern, technology-enabled infrastructure across various sectors. The integration of Big Data Analytics in Healthcare ERP (Enterprise Resource Planning) systems plays a critical role in this transformation. This paper explores how Big Data and advanced analytics can enhance patient care, streamline hospital operations, and ensure cost-effective healthcare delivery in India’s future healthcare landscape—Viksit Bharat 2047. By analyzing large-scale health data, healthcare providers can make informed decisions that improve treatment outcomes, optimize resource utilization, and reduce operational costs. This study investigates the challenges of integrating Big Data in Healthcare ERP systems, proposes strategies for overcoming these challenges, and presents a framework for leveraging Big Data Analytics in the healthcare sector of Viksit Bharat 2047.
The convergence of Big Data Analytics and Enterprise Resource Planning (ERP) systems is transforming healthcare delivery worldwide. In the context of India’s vision of Viksit Bharat 2047, the integration of advanced analytics into healthcare ERP systems offers unprecedented opportunities to enhance patient care, optimize operational efficiency, and enable data-driven decision-making. This paper examines the role of Big Data Analytics in healthcare ERP ecosystems, highlighting its impact on clinical outcomes, resource utilization, and administrative efficiency. It further explores technological enablers, challenges, and strategic frameworks necessary to align healthcare transformation with national development goals. The study concludes that data-centric healthcare ERP systems are critical for achieving equitable, efficient, and sustainable healthcare in India by 2047.
The integration of Big Data Analytics (BDA) with Healthcare Enterprise Resource Planning (ERP) systems represents a transformative shift in modern healthcare management. This paper critically examines how BDA enhances patient care and operational efficiency while addressing implementation challenges, ethical concerns, and system limitations. By synthesizing current research, the paper proposes a novel integrated framework combining predictive analytics, interoperability, and real-time decision support within ERP ecosystems. The findings suggest that while BDA-enabled ERP systems significantly improve clinical outcomes and resource optimization, issues related to data governance, integration complexity, and scalability remain substantial barriers.

The collectivization of women into self-help groups (SHGs) for their socio-economic empowerment has received considerable attention and legislative support over the past three decades. This study examines the socio-economic effects of Self-Help Groups (SHGs) on women in Odisha’s Bargarh district. The study assesses changes in income, decision-making authority, and social standing using a multi-stage sample technique across significant blocks. 80 SHG women were selected for the study across all blocks of the Bargarh district of Odisha to examine the effects of SHG membership on additional employment, income, spending, and savings. Additionally, it evaluated how various factors affect SHG members’ decision-making capacity. The results show a strong positive impact of SHG participation on household financial stability, despite ongoing issues with digital literacy and market saturation. After joining the SHGs, all members (100%) reported having more money. After joining an SHG, the women’s Gini ratio, a measure of inequality, improved from 0.489 to 0.154. There has been a noticeable increase in spending, with 85% of women spending more than INR 2000 after joining SHG, compared to only 11.25% before joining. Before joining SHGs, only 25% of women saved INR 100 or more per month; however, 85.25% of members reported saving INR 100 or more per month after joining SHGs. Even after joining a Self-Help Group, women’s ability to make financial and domestic decisions remains heavily influenced by their age, number of working days, and level of education.

Background: Generic medicines, defined as pharmaceutical products bioequivalent to innovator branded drugs, represent a critical cornerstone of sustainable, equitable healthcare delivery. Despite regulatory mandates ensuring their quality, safety, and efficacy, clinician and patient hesitancy toward generic substitution persists, often without sufficient pharmacoepidemiological justification.
Objective: This observational, comparative study systematically evaluated the therapeutic efficacy, pharmacokinetic duration of action, adverse drug reaction (ADR) profile, and side effect incidence of generic medicines versus their branded counterparts across 210 real-time patient observations, stratified by age, gender, renal function, hepatic function, and body mass index (BMI).
Methods: A prospective observational methodology was employed at a tertiary care hospital setting over a twelve-month period. Two hundred and ten (n=210) ambulatory and inpatient participants receiving pharmacotherapy across seven major drug categories were enrolled and monitored. Data were collected using standardized case report forms, Naranjo ADR causality scales, WHO-UMC classification, and patient- reported outcome measures. Statistical comparisons employed chi-square tests, independent t-tests, and ANOVA, with significance set at p<0.05.
Results: The overall therapeutic efficacy of generic drugs (87.1±4.2%) was not significantly different from branded drugs (89.1±3.8%) across all drug categories (p=0.08). Mean duration of action differences were clinically negligible (<0.8 hours in all categories). Total ADR incidence was 21.9% (Generic) versus 19.5% (Branded) (p=0.43). Physiological stratification revealed greater efficacy reduction in patients with severe renal impairment (GFR<30) and elevated BMI (≥30 kg/m²) for both groups, without statistically significant inter-group differences. Generic drugs offered 68.4–75.0% cost reduction relative to branded counterparts.
Conclusion: Generic medicines demonstrate clinically equivalent therapeutic efficacy, duration of action, and safety profiles compared to their branded alternatives across diverse physiological parameters. Their widespread adoption is strongly advocated from pharmacoeconomic, public health, and pharmaceutical equity standpoints.

The COVID-19 pandemic has underscored a pressing demand for fast, precise, and deployable diagnostic solutions capable of supporting clinicians in identifying infected patients. Chest X-ray (CXR) imaging stands out as economical and broadly accessible for pneumonia screening. This study introduces an automated framework for COVID- 19 identification from CXR images, leveraging five CNN architectures—VGG-16, ResNet-50, InceptionV3, DenseNet- 121, and MobileNet-V2—evaluated under transfer learning and scratch training regimes using ImageNet pre-trained weights. Spatial feature enhancement is achieved through histogram equalization and CLAHE preprocessing. Experiments are performed on the COVID-19 Radiography Database and COVIDx dataset, targeting three-class classification: COVID-19, Normal, and Viral Pneumonia. DenseNet-121 with transfer learning recorded the best test accuracy of 97.4% and sensitivity of 96.8%, confirming the value of hierarchical feature reuse in medical imaging contexts.

Brain–Computer Interface (BCI) systems have gained significant attention in recent years due to their ability to establish a direct communication pathway between humans and machines without relying on traditional physical input methods. These systems primarily utilize physiological signals such as Electroencephalography (EEG) and Electrooculography (EOG) to interpret user intentions and convert them in toactionable commands. EEG signals capture the electrical activity of the brain and are widely used for identifying cognitive states such as attention, relaxation, stress, and drowsiness. On the other hand, EOG signals detect eye movement patterns and are commonly applied in assistive communication systems.
With the advancement of smart living technologies, BCI systems are increasingly being integrated into applications such as smart homes, healthcare monitoring, and assistive devices for individuals with disabilities. For instance, EEGbased systems can automatically adjust environmental conditions based on the user’s mental state, while EOG-based systems can enable users to control devices through simple eye movements. Despite these advantages, several challenges persist in the development and implementation of BCI systems. These include signal noise, variability among users, lack of realtime adaptability, and high computational requirements.
This paper presents a comprehensive literature survey analyzing twenty research works related to EEG and EOG-based BCI systems. The survey focuses on system architecture, signal processing techniques, machine learning models, and application domains. The findings reveal significant research gaps such as lack of multimodel integration,insufficient personalization, and limited real-world deployment.

Traditional seed quality assessment methods are labor-intensive, subjective, and time-consuming. This paper presents an automated Seed Quality Detection System (SQDS) utilizing the ESP32-CAM module integrated with machine learning algorithms for real-time seed quality classification. The system captures high-resolution images of seeds, preprocesses them using computer vision techniques, and employs a Convolutional Neural Network (CNN) model trained on a custom dataset of 5,000 seed images across five quality categories (Excellent, Good, Fair, Poor, Defective). Deployed on edge hardware, the system achieves 94.2% accuracy, 92% precision, and processes images in under 500ms, enabling field-deployable quality control. Experimental results demonstrate superior performance compared to manual inspection (85% accuracy) and existing mobile-based systems. The lightweight TensorFlow Lite model ensures efficient operation on resource-constrained ESP32 hardware, making it suitable for agricultural applications in resource-limited settings.

Depression is a severe and debilitating mental disorder affecting millions worldwide, with limitations in conventional pharmacotherapy such as delayed onset of action and poor brain targeting. Esketamine, the S-enantiomer of ketamine, has emerged as a rapid-acting antidepressant; however, its therapeutic efficacy is constrained by poor bioavailability and systemic side effects. The present study aims to formulate and characterize esketamine-loaded Nanoglobules to enhance brain delivery and improve therapeutic outcomes.Nanoglobules were prepared using high-speed homogenization followed by ultrasonication technique. Various formulations (NG1–NG4) were developed using different concentrations of lipids and surfactants. The prepared Nanoglobules were evaluated for particle size, polydispersity index (PDI), zeta potential, drug entrapment efficiency, drug content, and in vitro drug release. Fourier Transform Infrared Spectroscopy (FTIR) and Scanning Electron Microscopy (SEM) were employed for compatibility and morphological analysis.The optimized formulation exhibited a particle size of approximately 160 nm with a low PDI, indicating uniform distribution. Zeta potential values confirmed good stability of the formulation. High entrapment efficiency and controlled drug release profile were observed over 12 hours, demonstrating sustained drug delivery. FTIR studies confirmed the absence of drug-excipient interactions, while SEM analysis revealed spherical Nanoglobules with smooth surfaces.The developed esketamine-loaded Nanoglobules significantly enhance drug delivery efficiency, offering sustained release and improved targeting potential for depression treatment.

This study examined the effectiveness of the inquiry-based learning (IBL) approach in teaching science, focusing on its role in developing students’ skills, improving conceptual understanding, and identifying challenges encountered by teachers. Using a qualitative narrative research design, data were gathered from Junior High School science teachers and students in selected municipalities in Cotabato. Findings revealed that IBL promotes higher-order thinking skills, problem-solving abilities, and scientific interest through active engagement, collaboration, and real-world applications. However, challenges such as limited resources, time constraints, student readiness, and teacher preparedness were identified. Teachers addressed these challenges through strategies such as formative assessment, scaffolding, classroom management, and resource optimization. The study concludes that IBL is an effective approach in enhancing both academic and lifelong learning skills, emphasizing the need for adequate support systems to ensure its successful implementation.

Peptic ulcer disease remains a serious gastrointestinal illness linked to increased gastric acid output, NSAID use, and Helicobacter pylori infection. Berberine and glycyrrhizin are two phytoconstituents that have shown promise as gastroprotective and anti-inflammatory agents. However, their pharmacokinetic appropriateness for oral medication development must be thoroughly evaluated. The current study used the Swiss ADME web tool to evaluate berberine and glycyrrhizin's absorption, distribution, metabolism, and excretion (ADME) properties.
The physicochemical features, lipophilicity, water solubility, pharmacokinetic parameters, medicinal chemistry filters, and BOILED-Egg model predictions were investigated. Berberine had good gastrointestinal absorption, blood-brain barrier permeability, and complied with Lipinski, Veber, Ghose, Egan, and Muegge criteria without exception. The bioavailability value of 0.55 indicates modest oral bioavailability. However, berberine inhibited the CYP1A2, CYP2D6, and CYP3A4 enzymes, indicating the possibility of drug-drug interactions. In contrast, glycyrrhizin exhibited low gastrointestinal absorption and a lack of BBB permeability. It did not meet drug-likeness criteria due to its high molecular weight (822.93 g/mol), increased hydrogen bond donors and acceptors, and large topological polar surface area (267.04 Ų). The bioavailability score was low (0.11), indicating poor oral compatibility.
Overall, the in-silico research indicates that berberine has more favorable pharmacokinetic and drug-like properties than glycyrrhizin. While glycyrrhizin has pharmacological promise, formulation improvement or different delivery techniques may be required to increase its therapeutic efficacy.

The COVID-19 pandemic significantly disrupted global trade, affecting export performance, supply chains, and business operations worldwide. This study examines the risk perception and export resilience of Indian exporters in the post-COVID era. The research focuses on how exporters perceived risks, adapted strategies, and strengthened resilience to sustain their international trade operations. Primary data is collected through structured questionnaires, and analytical tools are used to evaluate relationships between risk perception, digitalization, and export performance. The findings reveal that digital adoption, market diversification, and government support have played a crucial role in enhancing export resilience. However, challenges such as supply chain disruptions, regulatory complexities, and financial constraints persist. The study concludes that strategic investments in technology, risk management, and policy support are essential for sustainable export growth.

The rapid growth of internet-based services, cloud computing, and digital communication has significantly increased the risk of cyber threats, particularly Distributed Denial of Service (DDoS) attacks. These attacks aim to disrupt services by overwhelming systems with excessive traffic, leading to service unavailability, financial losses, and reduced reliability of online platforms. Despite the availability of various security mechanisms, most existing systems either focus only on detection or rely on traditional rule-based techniques that are ineffective against dynamic and evolving attack patterns.
This project proposes a unified system that integrates machine learning-based DDoS detection, simulated prevention mechanisms, and real-time monitoring to address these limitations. The system analyzes network traffic data and extracts important features such as packet rate, protocol type, connection duration, and traffic flow behavior. Machine learning models are trained to classify traffic as normal or malicious, enabling accurate
detection of anomalies compared to traditional approaches.
Upon detecting abnormal traffic, the system initiates a prevention module that simulates actions such as IP blocking and rate limiting. Although actual network-level blocking is not implemented, the system demonstrates how real-world security mechanisms respond to threats. In addition, a real-time monitoring dashboard visualizes network activity, traffic trends, and alerts, providing users with better situational awareness and understanding of system behavior.

The pharmaceutical industry plays a significant role in global healthcare and international trade. This study examines the export performance of the pharmaceutical industry with a focus on India, which is recognized as a major supplier of generic medicines worldwide. The research analyzes export trends over the period 2015–16 to 2025–26 using secondary data. Key analytical tools such as year-wise export analysis, Compound Annual Growth Rate (CAGR), product-wise export performance, market-wise distribution, Revealed Comparative Advantage (RCA), SWOT analysis, and PESTLE framework have been applied. The findings reveal consistent growth in pharmaceutical exports, strong comparative advantage in vaccines and generic medicines, and increasing global demand. However, challenges such as regulatory compliance, dependency on imported APIs, and global competition persist. The study concludes that strategic investment in innovation, diversification, and supply chain efficiency is essential for sustaining export growth.

The toy manufacturing industry plays a significant role in both economic development and child development by combining creativity, education, and industrial production. This study examines the structure, growth, and export performance of the global and Indian toy industries with a focus on India’s emerging potential in international markets. Despite having strong advantages such as a large domestic market, skilled labor, and rich traditional craftsmanship, India’s toy export performance remains relatively low compared to global leaders. The research is based on secondary data collected from government reports and international trade databases over the period 2014–2025. Analytical tools such as trend analysis, CAGR, export intensity ratio, and Revealed Comparative Advantage (RCA) are used to evaluate industry performance. The findings reveal that although India has shown gradual improvement, challenges such as low innovation, weak branding, and limited technological adoption hinder its global competitiveness. The study suggests that focusing on innovation, quality improvement, and export-oriented strategies can significantly enhance India’s position in the global toy market. The research concludes that with the right policy support and strategic direction, India has strong potential to become a major global toy manufacturing hub.

This quantitative study investigated how professional development and instructional resource practices predict teacher competence among 159 public elementary teachers in the Municipality of Colombio, Sultan Kudarat, for the school year 2025–2026. Employing a descriptive-correlational design with stratified sampling using the Slovin formula, data were gathered through a validated self-developed questionnaire (Cronbach's alpha: .860, .752, and .814) and analyzed using weighted mean, Spearman rho correlation, and multiple linear regression. Findings revealed that teachers were highly practiced in all professional development dimensions: participating in in-service training (WM=4.40), engaging in continuing education (WM=4.58), and receiving coaching or mentoring (WM=4.67). Instructional resource practices were also highly practiced: using adequate teaching materials (WM=4.78), accessing digital tools for instruction (WM=4.76), and using visual aids and learning equipment (WM=4.81). Teachers demonstrated high competence in mastery of subject matter (WM=4.81), using varied teaching strategies (WM=4.83), and actively engaging students (WM=4.86). Spearman rho revealed significant positive relationships between all professional development dimensions and teacher competence dimensions, with coaching and mentoring showing the strongest associations. All instructional resource practices were significantly and positively correlated with all competence dimensions, with visual aids having the strongest correlations. Multiple regression confirmed that in-service training and coaching or mentoring were the most consistent predictors of competence from the professional development side, while all three instructional resource dimensions significantly influenced subject matter mastery. These findings confirm that both professional development and instructional resources are essential and complementary predictors of teacher competence in the Philippine elementary school context.

This quantitative study examined the extent of teachers' pedagogical practices and their relationship to and influence on Grade VI students' composition writing proficiency in selected public elementary schools within the 2nd Congressional District of Cotabato Province, Philippines. Using a descriptive-correlational and multiple regression design, data were gathered from 192 Grade VI English teachers through validated survey questionnaires. Five pedagogical practices were assessed: differentiated instruction, guided writing, collaborative writing, feedback engagement, and technology integration. Students' writing proficiency was evaluated across five dimensions: content and idea development, organization and structure, language use/vocabulary, grammar and punctuation, and mechanics. Spearman correlation analysis revealed significant relationships between all pedagogical practices and most writing proficiency dimensions. Multiple regression analysis identified feedback engagement as the strongest and most consistent positive predictor across all five dimensions (β = 0.695 to 1.188, p < .001), while technology integration significantly predicted mechanics (β = 0.230, p = .007). Differentiated instruction showed a significant negative influence on content (β = -0.262, p = .049), organization (β = -0.372, p = .015), and grammar (β = -0.275, p = .050), suggesting implementation challenges. The overall R² values ranged from .340 to .666, confirming that pedagogical practices account for a substantial proportion of variance in students' writing outcomes. Findings underscore the centrality of feedback and technology integration in effective composition writing instruction.

The unknow and unclarified capabilities of structural elements are always a black mark even you have the knowledge of understanding and analyzing the structures and its performances. To understand the aircraft structures, loads acting on them and the deformation/failures of these structures, one should know the physics behind these structures. To simply the various loads acting on the aircraft structures, lets specifically consider the main structural element of aircraft wing which takes majority of the loads, which is wing spar of an aircraft. As we already know wing of an aircraft represents the cantilever beam which allows us to learn the loads acting and calculate them. In this case study, we will study, compare and analyze the cantilever beam, simply supported beam and overhanging beam which are subjected to point loads and uniformly distributed loads.
We use the beam theory, analytical and numerical solutions to compare the beams resulting in finding the efficiency in taking the load and the beams behavior under different loads. We have hand-calculated and ANSYS software generated solutions, which were compared and analyzed making the experiments ease for engineers who would like to know the actual engineering behind the structures. Bending moment, shear force diagram and bending moment diagram are compared with the analytical answers using the theory and formulae, and the numerical answers using ANSYS static structural under point loading and UDL. The animated results of deformation of the beams produced using ANSYS are helpful to visualize and understand the physics more than just reading the data. The results more precisely talk about their own beam pros, field of use and applications in engineering and real-life scenarios.

This quantitative study investigated the levels of digital self-efficacy, traditional barriers to parental involvement, parental involvement, and perceived student outcomes among parents in public secondary schools of Matalam North District, North Cotabato, Philippines. Using a descriptive-correlational, cross-sectional design, 286 parents were selected through stratified random sampling across four schools. A validated five-point Likert-scale questionnaire was administered. Results showed high digital self-efficacy (M = 4.02), with parents most confident in basic mobile communication (M = 4.32) but less so in app troubleshooting (M = 3.76). Traditional barriers were at a moderate level (M = 3.01), dominated by work schedule conflicts. Parental involvement was high (M = 3.58), strongest in teacher collaboration (M = 3.92) but weakest in physical attendance (M = 3.03). Perceived student outcomes were also high (M = 4.03), with greatest gains in children&#39;s motivation to study (M = 4.17). Significant positive correlations were found among parental involvement, digital efficacy, and student outcomes. Traditional barriers significantly correlated with parental involvement but not with student outcomes. These findings underscore the primacy of parental engagement as a driver of student success in rural agricultural communities and point to the need for targeted digital literacy training and flexible school engagement models.

This study examined the levels of parental involvement and community support and their relationship and influence on teachers' role satisfaction in the Arakan North District, Arakan, Cotabato, Philippines for school year 2025–2026. Using a descriptive-correlational design, the study surveyed 132 elementary school teachers through complete enumeration. Data were collected using a validated researcher-made questionnaire with five-point Likert scales and analyzed using weighted means, Spearman rho correlation, and multiple regression analysis. Results revealed that parental involvement and community support were both rated as Highly Involved and Highly Supported, respectively. Teachers' role satisfaction was rated Highly Satisfied across all dimensions. Spearman rho analysis showed that parental involvement—particularly communication with teachers and participation in school activities—was significantly related to professional growth opportunities. Regression analysis confirmed that parental participation in school activities significantly influenced teachers' sense of accomplishment (t = 3.061, p = 0.003) and workload balance (t = 2.215, p = 0.028), while communication with teachers strongly predicted professional growth opportunities (t = 16.704, p = 0.000). Community resource support and participation in school governance also significantly influenced teachers' sense of accomplishment. These findings affirm that strengthening parental and community partnerships significantly enhances teachers' professional satisfaction and performance.

This quantitative study examined the levels of professional identity and thriving at work among 189 elementary school teachers in District III of the Schools Division Office (SDO) of Kidapawan City, Philippines, and investigated their relationship and influence. Using a descriptive-correlational and multiple linear regression design, data were collected through validated survey questionnaires assessing four dimensions of professional identity (teaching beliefs, professional socialization, career progression, and professional competence) and seven dimensions of thriving at work (psychological well-being, emotional well-being, social well-being, work-life integration, basic needs, job design and experience, and health and physical/mental well-being). Complete enumeration was employed. Findings revealed that teachers demonstrated a strong professional identity overall (M = 4.52, Strongly Agree), led by teaching beliefs (M = 4.58) and career progression (M = 4.52). Thriving at work was generally high across most dimensions, with psychological well-being (M = 4.40) and social well-being (M = 4.31) rated highest, and health/physical-mental well-being (M = 3.85) lowest. Spearman rho correlation analysis confirmed significant positive relationships between all professional identity dimensions and most thriving dimensions. Multiple linear regression identified professional competence as the strongest predictor of psychological (β = 0.521, p

An IoT-based solar-powered soil monitoring system designed for real-time measurement of soil temperature and NPK (Nitrogen, Phosphorus, Potassium) levels critical for optimal seed germination and plant growth. The system utilizes ESP32 microcontroller integrated with DS18B20 temperature sensor and NPK sensor, powered entirely by solar energy. A responsive web dashboard displays real-time data alongside optimal temperature ranges for various crops including Beans, Beets, Cucumber, Lettuce, Peas, Spinach, and Squash. The system enables precision agriculture by providing actionable insights for farmers to maintain ideal soil conditions, potentially increasing germination rates by 25-40%.

Biologics have transformed modern medicine by offering targeted therapies for complex diseases such as cancer, autoimmune disorders, and genetic conditions. However, their high cost has limited accessibility. Biosimilars, which are highly similar versions of approved biologics, provide a cost-effective alternative while maintaining comparable safety and efficacy. This article explores the regulatory framework governing biologics and biosimilars, along with their therapeutic significance and challenges.
Regulatory frameworks for biosimilars emphasize a stepwise, evidence-based comparability approach prioritizing robust analytical characterization, supplemented by non-clinical and clinical data as warranted. In the United States, the Biologics Price Competition and Innovation Act (BPCIA) of 2009 established the 351(k) abbreviated licensure pathway, requiring demonstration of "highly similar" attributes to a reference product. The FDA has approved over 80 biosimilars to date, with recent draft guidance (2025) streamlining development by reducing the routine need for comparative efficacy studies when supported by strong analytical and pharmacokinetic/pharmacodynamic evidence, alongside efforts to refine interchangeability designations to facilitate substitution. Europe, through the EMA, has led globally since 2006 with numerous approvals and high market uptake, recently reflecting similar trends toward waiving certain Phase III trials under robust similarity data.
Therapeutically, approved biosimilars exhibit equivalent efficacy, safety profiles (including immunogenicity), and switching outcomes compared to originators, as evidenced by extensive real-world data, particularly from Europe and emerging US experience. They deliver substantial cost savings—often 20–40% lower pricing—while increasing treatment access for chronic conditions.
This review synthesizes current regulatory perspectives, highlighting evolving global harmonization, recent streamlining initiatives, and therapeutic equivalence supported by scientific and clinical evidence. Biosimilars represent a mature paradigm shift balancing biologic innovation with affordability, poised to further reshape healthcare delivery in oncology, rheumatology, gastroenterology, and beyond as patent expirations accelerate competition.

The Charaka Samhita, one of the foundational triads of classical Ayurveda, stands as a monumental treatise on ancient Indian medicine, attributed to the sage-physician Charaka (circa 300 BCE-200 CE). The Charaka Samhita is often celebrated as a cornerstone of ancient medicine, but its primary significance lies in its rigorous epistemological and methodological framework, which established an early template for scientific investigation. This paper examines how the text transitioned Indian medicine from a magico-religious tradition to an evidence-based clinical science. By bridging ancient medical wisdom with contemporary paradigms of personalized and evidence-based medicine, this research underscores the Charaka Samhita&#39;s role as a precursor to modern scientific temperament and its enduring relevance in the history of global healthcare. Special attention is given to Charaka’s emphasis on ethical practice, the physician-patient relationship, medical education, and the integration of mind-body-spirit in healing. In modern times, it serves as a primary reference for evidence-based validation of traditional practices, integration with biomedicine, standardization of herbal drugs, and development of integrative healthcare models.

Reading comprehension is a fundamental skill in education that enables learners to construct meaning from written texts and develop academic knowledge across disciplines. This paper explores theoretical insights into reading comprehension and its role in knowledge construction within educational contexts. It examines how cognitive, constructivist, sociocultural, and metacognitive perspectives explain the processes involved in understanding written texts. The cognitive perspective emphasizes mental processes such as memory, attention, and information integration, while constructivist theory highlights the active role of learners in building knowledge through interaction with texts and prior experiences. Sociocultural theory underscores the importance of social interaction and cultural context in shaping comprehension, and metacognitive theory focuses on learners’ awareness and regulation of their reading processes. The study also discusses how reading comprehension contributes to knowledge construction by enabling learners to connect new information with existing cognitive structures, generate inferences, and develop deeper conceptual understanding. Furthermore, the paper highlights instructional implications, including strategy-based instruction, scaffolding, activation of prior knowledge, metacognitive training, and collaborative learning, all of which support effective comprehension development. The analysis demonstrates that reading comprehension is a multidimensional and dynamic process that integrates cognitive, social, and regulatory factors. It concludes that strengthening reading comprehension is essential for enhancing learners’ ability to construct, apply, and transfer knowledge in educational settings.

This study examined the level of teachers' pragmatic awareness and classroom interactional skills (CIS) and the nature of their relationships and predictive influence among public school teachers in Arakan, North Cotabato, Philippines. Employing a descriptive-correlational design, the study surveyed 278 teachers selected through complete enumeration sampling. Pragmatic awareness was operationalized across three dimensions: sociopragmatic sensitivity, pragmalinguistic repertoire, and adaptive stance. Classroom interactional skills were assessed across four domains: questioning and elicitation, feedback, scaffolding, and follow-up. Descriptive statistics revealed that teachers demonstrated high pragmatic awareness (overall M = 4.40) and satisfactory-to-high classroom interactional skills (overall M = 4.37). Spearman correlation analyses indicated significant positive relationships between overall pragmatic awareness and all four CIS dimensions (r = .278 to .501, p < .001), with follow-up exhibiting the strongest association. Multiple regression analyses identified adaptive stance as the sole significant predictor across all four CIS domains (β ranging from t = 3.949 to t = 8.629, p < .001), underscoring its role as the master lever for effective classroom interaction. These findings suggest that targeted professional development focusing on adaptive stance — real-time contextual adjustment — holds the greatest promise for improving classroom interactional quality. Implications for teacher education, instructional coaching, and school-based professional development are discussed.

E-commerce platforms generate large volumes of data, making accurate sales forecasting essential for effective decision-making. Traditional models such as ARIMA can capture trends and seasonality but struggle with complex patterns in real-world data. Recent advancements in machine learning and deep learning, including models like LSTM and XGBoost, have significantly improved forecasting accuracy. This paper presents a survey of various forecasting approaches, including statistical methods, machine learning techniques, and hybrid models that combine their strengths. The study highlights the advantages and limitations of these methods and emphasizes the growing importance of hybrid and AI-driven approaches. It also identifies research gaps and discusses future directions for developing more efficient and scalable forecasting systems in e-commerce.

This quantitative study examined the extent to which culture serves as a foundation and ethnic identity is explored among Indigenous Peoples Education (IPED) secondary school students in Magpet East District, Cotabato, and the relationship of these variables with the development of academic resilience. Employing a descriptive-correlational research design, data were collected from 280 respondents drawn from ten IPED-implementing secondary schools through simple random sampling. Three adapted instruments were used: a Culture as Foundation Questionnaire, an adapted Multigroup Ethnic Identity Measure–Revised (MEIM-R), and an adapted Academic Resilience Scale (ARS-30). Statistical analyses included weighted mean for descriptive purposes, Spearman Rho for relational hypotheses, and multiple regression for predictive modeling. Findings revealed that culture as a foundation was rated Highly Cultured across all six dimensions (overall grand mean = 4.56), ethnic identity exploration was rated Highly Explored across all six dimensions (overall weighted mean = 4.60), and academic resilience was rated Highly Developed across all three dimensions (overall weighted mean = 4.66). Spearman Rho correlations confirmed highly significant relationships between all dimensions of culture as foundation and academic resilience (r = .600** to .715**, all p < .001), and between all dimensions of ethnic identity exploration and academic resilience (r = .626** to .766**, all p < .001). Multiple regression analysis revealed that culture as a foundation explained 54.2% to 62.5% of the variance in academic resilience outcomes, while ethnic identity exploration explained 72.2% to 76.2% of the variance—with mental practice, language, and culture emerging as the most powerful predictors. These findings affirm that for indigenous learners, cultural identity is not peripheral to academic success but constitutes its very foundation.

The prediction ofelection outcomes in India is a complex and challenging task due to the country's vast demographic diversity, multi-party system, and dynamic socio-political
environment. This research presents a comprehensive framework for predicting Indian election outcomes using machine learning techniques. The study integrates historical election data, candidate attributes, constituency-level demographics, and socio-economic indicators to
develop predictive models.
A structured data science pipeline is implemented, including data collection, preprocessing, feature engineering, model selection, and evaluation. Among various algorithms tested, the Random Forest Classifier demonstrates superior performance due to its robustness,
scalability, and ability to capture non-linear relationships. The model achieves an accuracy of approximately 96.21%, validating its effectiveness.
Additionally, the study explores system design, real-time prediction capabilities, and ethical considerations such as bias and fairness. The results highlight the potential of machine learning in enhancing electoralanalysis and decision-making for policymakers, analysts, and researchers.

With the quick progress in space exploration by humans, there is a need for efficient and autonomous systems to monitor the health of astronauts. Healthcare methods used in current space missions depend on the delayed data analysis and support from earth, which cannot be effective for deep space missions. This paper provides a detailed discussion about health monitoring technologies used in astronaut missions, including wearable biosensors, IoT technology, AI analytics, PHM, and multimodal sensors. Latest advancements made between 2024 and 2025 are also highlighted in this study. A comparison among the discussed methods is provided with respect to their capabilities, challenges, and future scope.

This study explores how elementary science teachers implement culturally responsive teaching (CRT) in diverse classrooms within the 2nd Congressional District of Cotabato. Using a qualitative phenomenological approach, the research investigates teachers’ strategies, challenges, and perspectives in integrating learners’ cultural and linguistic backgrounds into science instruction. Findings reveal that teachers enhance engagement by connecting lessons to students’ lived experiences, local environments, and indigenous knowledge systems. However, challenges such as limited localized resources, rigid curricula, and insufficient professional development hinder full implementation. The study also identifies effective dissemination strategies, policy directions, and intervention plans to strengthen CRT practices. Overall, the research highlights the importance of contextualized science education in promoting inclusivity, equity, and meaningful learning experiences.

This study explored the utilization of pedagogical approaches in teaching MAPEH and students' participation in secondary schools across the Municipalities of Antipas, Arakan, Magpet, Makilala, and Pres. Roxas for the 2023-2024 school year. It examined the level of pedagogical approach usage in MAPEH, the level of student participation, and the relationship between these variables. A mixed-methods research design was utilized, combining quantitative data from 70 MAPEH and 63 non-MAPEH major teachers and qualitative data from 10 department heads, using purposive sampling. Reliability tests (Cronbach's Alpha: .950 and .909) confirmed the instruments' high reliability. Data were gathered through communication with Schools Division Superintendents, and statistical analysis was performed using Spearman’s rho and multiple linear regression. The findings indicated that reflective, constructivist, integrative, and collaborative approaches were the most commonly used pedagogical strategies in MAPEH. Students were highly engaged in activities, peer collaboration, and classroom contributions, but less so in completing assignments and projects. The study revealed a strong correlation between pedagogical approaches and student participation, with integrative and constructivist approaches predicting engagement, and reflective approaches predicting classroom contribution and task completion. Challenges identified included resource shortages, gaps in teacher preparedness, and curriculum constraints. Coping mechanisms involved strategic planning, innovative teaching, and fostering professional development. The study also proposed an intervention plan, including communication materials and policy suggestions.

This study investigated the relationship between teachers’ positive psychology, professionalism, and happiness at work among public elementary school teachers in Kidapawan City and Cotabato during the school year 2025–2026. A concurrent triangulation mixed-methods design was utilized, combining quantitative and qualitative data collection. A total of 260 teachers participated in the quantitative phase using stratified random sampling, while 25 teachers were purposively selected for in-depth interviews. Data were gathered through adapted survey questionnaires measuring positive psychology based on the PERMA model, teacher professionalism, and workplace happiness. Statistical tools such as weighted mean, Pearson correlation, and multiple regression analysis were employed, while thematic analysis was used for qualitative data. Findings revealed that teachers demonstrated very high levels of positive psychology, professionalism, and happiness at work. Teachers exhibited strong character, commitment to continuous improvement, high pedagogical competence, and active collaboration beyond the classroom. Likewise, teachers reported high engagement, satisfaction, and emotional attachment to their work. However, results indicated that most dimensions of positive psychology and professionalism showed weak and non-significant relationships with happiness at work. Qualitative findings highlighted teachers’ lived experiences emphasizing fulfillment, resilience, collaboration, and challenges such as workload and resource limitations. The study concludes that while teachers exhibit strong psychological and professional attributes, these do not always directly translate into measurable increases in workplace happiness, suggesting the influence of external organizational and contextual factors.

This study examined the relationship between teachers’ intervention and communication strategies and students’ learning behavior in selected schools in Kidapawan City for the school year 2025–2026. A mixed-method approach was employed, combining a descriptive-correlational quantitative design and a qualitative phenomenological inquiry. A total of 300 respondents participated in the quantitative phase, while 25 informants were selected for the qualitative phase. Data were gathered using a self-developed and reliable survey instrument and analyzed using weighted mean, Spearman rho, and multiple regression analysis. The findings revealed that teachers consistently implemented individualized feedback, differentiated instruction, and tailored academic support at a very high level. Similarly, communication strategies such as culturally responsive communication, positive interpersonal communication, and empathetic communication were strongly practiced. Students’ learning behavior, including emotional regulation, interpersonal skills, learning motivation, and cognitive abilities, was also rated very high. The results further showed that both teachers’ interventions and communication strategies had significant relationships and strong influences on students’ learning behavior. However, teachers encountered challenges such as financial constraints, workload demands, curriculum changes, and technological limitations, which they addressed through coping mechanisms like self-care, collaboration, and professional development. The study concluded that effective teacher practices significantly enhance students’ learning behavior and overall classroom engagement.

This study examined the influence of educational subsidy on the attendance of Grade 10 students in the Schools Division of Kidapawan City during the school year 2025–2026. Utilizing an explanatory sequential mixed-method design, data were collected from 302 students across four districts through survey questionnaires and follow-up interviews. The findings revealed that students reported high levels of satisfaction with the educational subsidy and a very high level of attendance. Statistical analysis showed a significant relationship between educational subsidy and student attendance. However, regression analysis indicated that among the three dimensions of subsidy, only the scope of educational expenses consistently influenced attendance, while the amount of assistance and regularity of disbursement demonstrated mixed effects. Qualitative findings further revealed that the subsidy functioned as a critical support system that helped students overcome socioeconomic barriers to schooling. The study concluded that educational subsidy significantly contributes to improved student attendance, although its effectiveness varies depending on how the assistance is structured and delivered.

This study examined the factors affecting the competency status of Senior High School Technical-Vocational-Livelihood (TVL) Track graduates in Bread and Pastry Production (BPP) and Computer Systems Servicing (CSS) National Certificate II (NC II). A mixed-method approach was employed, combining quantitative assessment of competency levels and qualitative analysis of graduates’ lived experiences. Findings revealed that teacher competency, curriculum effectiveness, on-the-job training (OJT) experiences, and graduates’ perceptions were rated very high, while industry expectations and socio-economic factors were rated high. Graduates in BPP were found to be very competent in bakery, pastry, cakes, petits fours, and desserts, while CSS graduates were very competent in maintaining systems and competent in installation and configuration. A highly significant relationship was identified between influencing factors and competency status across all areas. Qualitative findings further showed that graduates encounter technical, financial, academic, and psychological challenges but demonstrate resilience through strategies such as independent learning, collaboration, and self-regulation. The study concludes that strengthening curriculum alignment, teacher competence, and practical training opportunities is essential to improve competency outcomes and employability.

This study explored the pedagogical challenges encountered by mathematics teachers and the coping strategies they employ, including the role of professional development in improving instructional practices. Using a qualitative phenomenological approach, the research examined the lived experiences of elementary mathematics teachers in the 2nd Congressional District of the Province of Cotabato. Findings revealed that teachers face multiple challenges such as diverse learning needs, students’ math anxiety, conceptual difficulties, and limited resources. To address these, teachers implement differentiated instruction, integrate technology, foster growth mindset, and engage in collaborative practices. Professional development emerged as a key factor in enhancing teachers’ confidence, instructional strategies, and classroom effectiveness. The study concludes that strengthening teacher capacity through continuous training, adequate support systems, and innovative teaching strategies is essential for improving mathematics instruction and student outcomes.

This study investigated the digital literacy of early childhood teachers, focusing on their attitudes, knowledge, and skills in using Information and Communication Technology (ICT) for language teaching. Using a qualitative phenomenological design, the research explored teachers’ lived experiences in integrating ICT in early childhood education within the 2nd Congressional District of the Province of Cotabato. Findings revealed that teachers generally have positive attitudes toward ICT, recognizing its ability to enhance creativity, engagement, and language development. However, their willingness to integrate ICT is influenced by factors such as access to technology, level of training, and personal confidence. While ICT improves teaching effectiveness and learner engagement, challenges such as limited resources, inadequate training, and concerns about screen time hinder its full utilization. Teachers respond by adapting their practices, engaging in professional development, and collaborating with peers. The study concludes that strengthening digital competence through continuous training, adequate resources, and institutional support is essential for effective ICT integration in early childhood language education.

This study examined the integration of Information Technology (IT) in teaching English among elementary learners in the 2nd Congressional District of the Province of Cotabato. Using a qualitative phenomenological approach, the research explored how teachers utilize IT tools, the improvements brought to the teaching-learning process, the challenges encountered, and the strategies used to address these issues. Findings revealed that teachers integrate IT through multimedia tools, interactive applications, and collaborative digital platforms to enhance language learning. These approaches improve lesson delivery, learner engagement, motivation, and student-centered learning. However, challenges such as poor internet connectivity, limited resources, lack of training, and time constraints hinder effective implementation. Teachers respond by adapting instruction through offline materials, flexible strategies, and continuous self-improvement. The study concludes that while IT integration significantly enhances English language teaching, sustained support, infrastructure development, and professional training are essential for maximizing its effectiveness.

This study explored the creativity of elementary teachers in implementing differentiated instruction in English as a Second Language (ESL) classrooms within the MATATAG Curriculum. Using a qualitative phenomenological research design, data were gathered from teachers in the 2nd Congressional District of the Province of Cotabato. Findings revealed that teachers employ a wide range of creative strategies, including differentiated and inclusive instruction, technology-enhanced learning, visual and experiential approaches, and interactive collaborative activities to address diverse language needs. Teachers also integrate multisensory tools, flexible grouping, and task-based strategies to enhance engagement and comprehension. However, several challenges were identified, such as varying language proficiency, limited resources, time constraints, and insufficient training. To address these, teachers adapt the curriculum through scaffolding, language simplification, multilingual strategies, and the use of visual and digital tools. The study concludes that creativity in differentiated instruction plays a crucial role in improving ESL learning outcomes, although continuous support, training, and resource provision are necessary for effective implementation.

This study explored the strategies employed by kindergarten teachers in preparing learners’ school readiness in terms of academic, social, and emotional domains. Using a qualitative phenomenological research design, data were gathered from kindergarten teachers in the 2nd Congressional District of the Province of Cotabato. Findings revealed that teachers utilize play-based and hands-on learning, foundational literacy and numeracy instruction, differentiated teaching, and structured classroom routines to enhance academic readiness. For social and emotional readiness, strategies such as emotion recognition activities, role-playing, social-emotional learning (SEL), and teacher modeling were applied. Teachers also used various assessment methods, including observational, informal, and standardized tools such as ECCD checklists, to address diverse learner needs. The study found that a safe, structured, and supportive classroom environment plays a crucial role in promoting holistic development. Overall, the research concludes that effective, adaptive, and child-centered strategies significantly contribute to learners’ readiness for formal schooling.

This study examined the effectiveness of the inquiry-based learning (IBL) approach in teaching science, focusing on its role in developing students’ skills, improving conceptual understanding, and identifying challenges encountered by teachers. Using a qualitative narrative research design, data were gathered from Junior High School science teachers and students in selected municipalities in Cotabato. Findings revealed that IBL promotes higher-order thinking skills, problem-solving abilities, and scientific interest through active engagement, collaboration, and real-world applications. However, challenges such as limited resources, time constraints, student readiness, and teacher preparedness were identified. Teachers addressed these challenges through strategies such as formative assessment, scaffolding, classroom management, and resource optimization. The study concludes that IBL is an effective approach in enhancing both academic and lifelong learning skills, emphasizing the need for adequate support systems to ensure its successful implementation.

This study explored the role of laboratory experiments in strengthening practical learning in science education beyond the COVID-19 pandemic. It focused on the lived experiences of Junior High School students, the challenges they encountered, and the coping mechanisms they employed, as well as how teachers facilitated laboratory skill development. Using a qualitative phenomenological approach, data were collected through interviews with selected students from public secondary schools in Cotabato. Findings revealed that laboratory experiments enhanced students’ understanding, curiosity, and engagement through hands-on and collaborative learning. However, students faced significant challenges during and after the pandemic, including limited resources, communication barriers, and gaps in prior knowledge. Despite these difficulties, students coped through collaboration, self-directed learning, resilience, and critical thinking. The study highlights the importance of experiential learning and the continued integration of laboratory activities in science education to improve student outcomes.

This study investigates the interactional feedback practices of language teachers using a qualitative micro-ethnographic approach. Conducted among secondary language teachers in the 2nd Congressional District of the Schools Division of Cotabato, the research explores how feedback is delivered in real-time classroom interactions, the interactional dynamics that emerge during these exchanges, and the role of linguistic diversity in shaping these practices. Data were gathered through classroom observations, audio recordings, and semi-structured interviews. Findings reveal that teachers employ adaptive and multimodal feedback strategies, including real-time scaffolding, metacognitive prompting, structured critique, and the use of visual and digital tools. Interactional dynamics highlight a shift toward collaborative, student-centered engagement, while linguistic diversity significantly influences feedback choices, requiring culturally responsive and linguistically sensitive approaches. Based on these findings, intervention and dissemination plans were proposed to enhance teacher practices. The study contributes to improving language instruction by emphasizing responsive, inclusive, and context-sensitive feedback practices in diverse classrooms

This study explores the lived experiences of early career teachers experiencing imposter syndrome, a psychological condition marked by persistent self-doubt despite demonstrated competence. Although widely studied in other professions, limited qualitative research exists in education, particularly among novice teachers navigating identity formation and workplace pressures. The study aims to understand how early career teachers construct and negotiate their sense of competence, manage imposter feelings, and sustain their professional identity. Using a qualitative Interpretative Phenomenological Analysis design, data were collected from 25 early career elementary teachers in the 2nd Congressional District of Cotabato through semi structured interviews. Purposive sampling ensured participants had relevant lived experiences, while thematic analysis enabled identification of recurring patterns across cases. Trustworthiness was ensured through established qualitative research criteria. Findings reveal that imposter syndrome is shaped by classroom challenges, social expectations, and institutional pressures that distort self-perception and reduce confidence. Teachers cope through mentorship, reflective practices, and professional development, though many experience emotional strain, reduced wellbeing, and career uncertainty. The study provides context specific insights into teacher identity and wellbeing and informs interventions focused on mentoring, psychological support, and professional growth for early career educators.

This study examined the extent of technology integration in teaching and its effect on teachers' classroom performance in selected public elementary schools under the Schools Division Office (SDO) of Cotabato for School Year 2025–2026. Using a quantitative descriptive-correlational and regression design, data were collected from public elementary school teachers through a researcher-developed survey questionnaire. The instrument assessed technology integration across four dimensions—lesson planning, instructional delivery, assessment and feedback, and learning activities—while classroom performance was evaluated in terms of establishing rules and routines, managing students' behavior, and sustaining learner engagement. Results revealed that teachers highly practiced technology integration across all domains (WM = 4.39 to 4.60) and exhibited high classroom performance overall (WM = 4.63). Spearman rho correlation analyses revealed significant positive relationships between technology integration and classroom performance, with instructional delivery strongly correlated to establishing rules and routines (r = 0.543, p < 0.001), and learning activities demonstrating the strongest link to sustaining learner engagement (r = 0.771, p < 0.001). Multiple regression analysis further confirmed that technology integration significantly predicted classroom performance, explaining 37.2% to 62.9% of the variance. These findings underscore the critical role of digital tools in enhancing instructional effectiveness and highlight the need for sustained ICT professional development and infrastructure support.

Facial expressions provide a reliable and non-intrusive means of understanding human emotions, enabling enhanced personalization in digital systems. This paper presents an emotion-aware music recommendation framework based on Facial Expression Recognition (FER) using Convolutional Neural Networks (CNNs). The proposed system captures real-time facial inputs, analyzes user emotions, and generates music recommendations that align with the user’s current affective state.

Unlike traditional recommendation systems that rely heavily on historical user interactions and collaborative filtering, the proposed approach incorporates real-time emotional context as implicit feedback. This helps address inherent limitations such as the cold start problem and lack of contextual adaptability. A CNN-based deep learning model, trained on the FER-2013 dataset, is employed to classify facial expressions into key emotional categories, including happiness, sadness, anger, and neutrality.

The recognized emotional states are mapped to corresponding music genres and curated playlists, facilitating context-aware and personalized recommendations. Experimental results indicate that the model achieves high accuracy in emotion classification and enhances the relevance of recommendations compared to conventional methods. Furthermore, the integration of emotion recognition reduces dependency on prior user data, improving system adaptability for new users.

The findings demonstrate the effectiveness of combining computer vision techniques with recommender systems to develop intelligent, adaptive, and emotionally responsive music recommendation platforms.

This quantitative study examined the levels of self-efficacy, technological competence, expectancy belief, and attitudes of senior high school teachers, and determined the relationships and influences among these variables. Using a descriptive-correlational design, data were gathered from 325 public senior high school teachers across five municipalities in the 2nd Congressional District of North Cotabato, Philippines, through total population sampling. Findings revealed that teachers possess generally high levels of self-efficacy (M=4.17), technological competence (M=4.17), and expectancy belief (M=4.16), all rated as Agree on a five-point Likert scale. Teachers&#39; attitudes in terms of curiosity, compassion, and openness to new ideas were similarly rated as Agree (M=4.13). Spearman Rho correlation analysis revealed significant positive relationships between self-efficacy (r=0.75, p=0.000), technological competence (r=0.77, p=0.000), and expectancy belief (r=0.71, p=0.000) with teachers&#39; attitudes. Multiple regression analysis further confirmed that imaginal experience and physical-emotional state significantly influenced curiosity (R²=0.556) and compassion (R²=0.808), while social persuasion and physical-emotional state predicted openness to new ideas (R²=0.481). These findings underscore the multidimensional nature of teacher attitude formation and highlight the critical roles of internal cognitive processes, emotional well-being, and professional confidence in shaping positive teaching dispositions.

Single points of failure, large-scale data breaches, and excessive data exposure are some of the critical vulnerabilities that have been introduced by the proliferation of centralized digital identity systems. Over 422 million personal records were stolen across the globe in 2022, and it is necessary to discover stronger options [1]. The current paper discusses Decentralized Digital Identity using Decentralized Identifiers (DIDS) and Verifiable Credentials (VCs) founded on Semantic Web standards as one of the solutions to such problems. The paper is founded on the W3C specifications of both the DID Core and the Verifiable Credentials Data Model v2.0, and discusses both the overall credential lifecycle and the trust architecture of issuers, holders, and verifiers, and the use of RDF, JSON-LD, and SPARQL to achieve semantic interoperability of heterogeneous systems. A good example of how educational credentials can be used is provided, where educational institutions in India can employ tamper-evident cryptographically signed credentials which can be authenticated by employers without the need of contacting the issuer. The comparative analysis is centralized, federated, and decentralized identity and SSI has advantages of user control, privacy of selective disclosure and regulatory consistency with the laws such as the DPDP Act in India and the GDPR in the EU. The most recent issues such as blockchain scalability, metadata privacy and lack of usability are addressed, as well as future research directions such as quantum-resistant cryptography and AI-assisted verification. There is a claim that this architecture is a viable and standards-compliant way to user-centric, trust-minimized digital identity management.

Urban traffic congestion has become one of the defining infrastructure challenges of modern cities, driven by rising vehicle ownership and road networks that were never designed to accommodate today’s demand. Traditional traffic signal systems rely on fixed-cycle timers or basic threshold rules that are incapable of adapting to the moment-to-moment fluctuations that characterise real-world traffic. This paper investigates the use of Reinforcement Learning (RL) as a principled, data-driven alternative for intelligent traffic signal control at a four-lane intersection. The system is formulated as a Markov Decision Process (MDP) in which an agent observes per-lane vehicle density, queue length, and waiting time, then selects signal phases to maximise a delay-reduction reward. Two RL formulations are evaluated—tabular Q-Learning and Deep Q-Networks (DQN)—and compared against fixed-time, rule-based, and random baselines across multiple performance metrics. The RL agent achieves an average vehicle waiting time of 18.5 seconds, representing a 32 % reduction relative to the fixed-time baseline of 27.2 seconds, and a 22 % improvement over the rule-based system. A dedicated learning-rate sensitivity study using the CartPole benchmark confirms that a moderate learning rate of α = 0.25 offers the best balance between convergence speed and training stability. Taken together, the findings demonstrate that RL-based signal control is a scalable, adaptive, and practically viable framework for smart-city traffic management.

This quantitative study examined the extent of pedagogy through technology and its effects on classroom performance among teachers in selected public elementary schools within the Schools Division Office (SDO) of Cotabato, Philippines. Employing a descriptive-correlational research design, data were gathered from 250 teacher-respondents across the municipalities of Matalam, Kabacan, and M'lang through stratified sampling. Pedagogy through technology was assessed across four dimensions: technology-integrated teaching strategies, digital instructional design, use of interactive digital tools, and technology-supported assessment practices. Classroom performance was evaluated in terms of learner engagement, teaching effectiveness, and classroom management efficiency. Descriptive findings revealed that teachers demonstrated a highly practiced level of pedagogy through technology (overall M = 4.73) and very effective classroom performance (overall M = 4.67). Spearman's rho correlation analyses showed significant positive relationships between technology-integrated teaching strategies and learner engagement (r = .437, p < .001) and teaching effectiveness (r = .525, p < .001), and between digital instructional design and learner engagement (r = .321, p < .001) and teaching effectiveness (r = .681, p < .001). Multiple regression analyses confirmed that technology-integrated teaching strategies significantly predicted learner engagement (β = 0.326, t = 4.809, p < .001, R² = 0.207), and digital instructional design significantly predicted teaching effectiveness (β = 0.615, t = 9.232, p < .001, R² = 0.468). Classroom management efficiency was not significantly predicted by any pedagogy-through-technology dimension (R² = 0.012, p = .578), suggesting it is influenced primarily by non-technological factors. These findings highlight the need for structured professional development focused on digital instructional design and sustained technology integration to enhance instructional outcomes.

Recent research on engineered nanoparticles in drug delivery systems (DDSs) has led to the development of a variety of innovative nanocarriers. This review examines both traditional and modern drug carriage systems. Because of the limitations of conventional DDSs, nanocarriers have attracted significant attention. These include polymeric nanoparticles, mesoporous nanoparticles, nanomaterials, carbon nanotubes, dendrimers, liposomes, metallic nanoparticles, nanomedicine, and engineered nanomaterials, which are designed to deliver drugs specifically to targeted sites within the body.
Nanomedicine has advanced rapidly and is being applied to treat conditions such as brain cancer, lung cancer, breast cancer, cardiovascular diseases, and others. These systems offer several advantages, including improved drug bioavailability, faster absorption, controlled release, prevention of drug aggregation, and enhanced solubility in the bloodstream. Nanomedicine represents a transformative advancement in drug delivery, optimizing the therapeutic potential of active pharmaceutical ingredients encapsulated in nanoparticles.
This review compiles key insights on engineered nanoparticles and their applications in targeted drug delivery for various diseases. Most of these nanocarriers have undergone both in vitro and in vivo testing. Looking ahead, further integration of advanced techniques into nanomedicine is expected to enhance the effectiveness of drug delivery systems, thereby improving human health significantly.

Rice is one of the most significant staple food crops globally and plays a crucial role in ensuring food security and economic development. India, being one of the largest producers and exporters of rice, holds a dominant position in global rice trade. However, its presence in the European Union (EU) market remains relatively limited despite the EU being a high-value and quality-conscious market. The present study aims to analyse the export performance and future trade potential of Indian rice in the European Union market using secondary data sources.
It also evaluates export trends, growth patterns, and market share of Indian rice in major EU countries such as the Netherlands, Germany, France, Italy, and Spain further identifies key challenges including strict sanitary and phytosanitary regulations, pesticide residue limits, and high compliance costs faced by Indian exporters. The findings reveal that although Indian rice, especially basmati, has strong demand due to its quality and aroma, regulatory barriers restrict its full potential in the EU market.
The study concludes that by improving quality standards, strengthening supply chain efficiency, adopting sustainable farming practices, and enhancing branding strategies, India can significantly increase its share in the European Union rice market.

Overfitting is one of the most persistent challenges in supervised machine learning, where a model learns the noise and random fluctuations in the training data rather than the underlying pattern, resulting in poor generalization to unseen data. This paper presents a comprehensive study on controlling overfitting using two powerful regularization techniques: Ridge Regression (L2) and Lasso Regression (L1). The study implements a structured experimental pipeline using a high-dimensional synthetic dataset with 1000 samples and 100 features, of which only 10 are truly informative, designed to simulate real-world overfitting conditions.

The proposed system integrates feature scaling, hyperparameter tuning via cross-validated Grid Search over a logarithmic alpha range, and comparative model evaluation. Performance is assessed using Root Mean Squared Error (RMSE) and R² Score metrics across standard Linear Regression, Tuned Ridge, and Tuned Lasso models. Results demonstrate that regularization significantly improves generalization accuracy, with Lasso achieving additional benefit through automatic feature selection by shrinking irrelevant coefficients to zero.

Tuberculosis (TB) remains a major public health concern, particularly in developing countries, despite being a preventable and curable disease. The Directly Observed Treatment, Short-course (DOTS) program has been widely implemented to improve treatment outcomes and ensure patient compliance. However, patient adherence to anti-tubercular therapy and the occurrence of adverse drug reactions (ADRs) continue to influence the overall success of TB control efforts.

This study aims to assess the level of patient adherence to treatment under the DOTS program and to evaluate the pattern and frequency of adverse drug reactions experienced during therapy. Poor adherence can result in treatment failure, relapse, and the development of drug-resistant TB, while ADRs may further discourage pa- tients from continuing treatment.

The study involves the collection of data from TB patients enrolled in the DOTS program, focusing on their treatment-taking behavior, factors affecting adherence, and any reported side effects. The findings are expected to highlight the relationship between adherence and ADRs and identify key barriers to successful treatment completion.

It is necessary to monitor the animals and livestock for health, welfare and productivity in the contemporary pre-cision farming domain. Traditional visual inspection techniques for monitoring are labor-intensive and inaccurate, thus raising the requirement for intelligent and automated monitoring meth-ods. With the breakthrough of artificial intelligence techniques, sensor-based, vision-based and multi-modal behavior recognition methods have been designed and implemented. Vision-based behavior recognition methods based on deep learning networks, such as convolutional neural networks (CNN) and transformer-based networks (e.g., ViT) have shown excellent performance for recognizing actions and postures of livestock. IoT based sensor systems achieve a real-time monitoring and anomalous detection on the animals with the aid of unsupervised learning methods. Additionally, multi-modal methods combining the audio and visual and sensor data are robust and effective to address some of the drawbacks of single modal based techniques, providing a higher recognition accuracy and a better generalization abil-ity. Meanwhile, emerging technology including vision-language model (VLM), edge computing framework, and lightweight de-tection architectures facilitate intelligent monitoring on resource constrained system for real-time deployment.However, challenges of small data set, varied environmental interference and com-putational intensity exist and further exploration is necessary to achieve robust and accurate intelligent livestock monitoring. In this paper, we will provide an in-depth survey on current animal behavior recognition techniques, focusing on methods, performance and research issues and discuss future opportunities to develop accurate and real-time intelligent monitoring systems for the smart livestock.

Traditional knowledge encompasses cumulative knowledge, innovations, and practices that remain relevant for human welfare. Among these, Ayurveda represents a vital system of Indian traditional wisdom, contributing to holistic health and the attainment of higher knowledge. Rooted in the Vedic tradition, its goal extends beyond disease management to achieving wisdom (jnana).

The classical texts—Charaka Samhita, Sushruta Samhita, and Ashtanga Hridaya—outline a multidisciplinary learning approach. Acharya Charaka emphasizes Adhyayan (study), Adhyapan (teaching), and Sambhasha (discussion) as key methods, while Acharya Sushruta highlights experiential learning from diverse sources.

Ayurveda advocates an interdisciplinary approach, stating that knowledge of a single discipline is insufficient for proper understanding. Principles like Pragnyparadha, Achara Rasayana, and Sadvṛitta further guide ethical and intellectual development. Thus, Ayurvedic learning ultimately aims at cultivating wisdom through integrated, experiential, and value-based education.

This study aimed to develop an intelligence-based artificial intelligence (AI) framework for elementary teachers in the Division of Cotabato by examining the relationship between AI competencies and decision-making, as well as exploring teachers’ perceptions and experiences in integrating AI into classroom instruction. A mixed-methods research design was employed, combining a descriptive-correlational approach for quantitative data and a phenomenological approach for qualitative insights. The findings revealed that ICT coordinators demonstrated high levels of competence across all AI domains, particularly in data literacy and transdisciplinary knowledge, which significantly influenced their decision-making processes. Furthermore, teachers perceived AI as a valuable tool for enhancing instruction, although challenges such as limited training and ethical concerns were identified. Based on these results, a dissemination plan was proposed to strengthen AI integration in elementary education. The study contributes to the advancement of AI literacy among educators and supports the achievement of quality education

This study aimed to develop a model of moral governance and examine its relationship with school heads’ leadership performance and teachers’ performance in the Special Geographic Area (SGA) of the Bangsamoro Autonomous Region in Muslim Mindanao (BARMM). Anchored in the Moral Governance Praxis Theory, the study employed a mixed-methods research design integrating qualitative and quantitative approaches. Data were collected from 300 teachers and school heads through survey questionnaires and from 24 informants through in-depth interviews.

Findings revealed that moral governance consists of five key dimensions: faith, freedom, moral authority, common good, and social ethics. Results further indicated that teachers demonstrated very high levels of moral governance across all dimensions. Statistical analyses confirmed significant relationships between moral governance, school heads’ leadership performance, and teachers’ performance. Mediation analysis showed that moral governance significantly mediates the relationship between leadership performance and teachers’ performance.

Qualitative findings supported these results by highlighting teachers’ faith-driven decision-making, integrity in teaching, and leadership through moral example. The study concludes that moral governance is a critical factor influencing leadership effectiveness and teacher performance in BARMM and recommends strengthening policy frameworks and professional development programs.

This study examined the relationship between work role motivation and career advancement among school administrators in the Division of Cotabato. Anchored on Self-Determination Theory, the study employed a mixed-methods research design that integrated both quantitative and qualitative approaches. Data were gathered from school administrators using structured questionnaires and supported by qualitative insights from focus group discussions. The findings revealed that administrators demonstrated very high levels of work role motivation across intrinsic motivation, role clarity, goal orientation, introjected regulation, and external regulation. Similarly, career advancement was rated very high in terms of professional competence, leadership and management effectiveness, interpersonal capacity, and innovation adaptability. Statistical analysis showed significant relationships between work role motivation and career advancement, with goal orientation emerging as the strongest predictor. Qualitative results further supported these findings by highlighting administrators’ strong sense of responsibility, commitment to goals, and adaptability in leadership practices. The study concludes that work role motivation plays a crucial role in shaping career advancement and recommends strengthening leadership development programs and institutional support systems.

This study examined the relationship between epistemological beliefs and the application of higher-order thinking skills (HOTS) among elementary master teachers in the Schools Division of Cotabato. Anchored on Schommer’s Epistemological Belief Theory and Bloom’s Revised Taxonomy, the study employed a mixed-methods research design integrating quantitative and qualitative approaches. Data were collected from 300 elementary master teachers using survey questionnaires, while selected participants were engaged through in-depth interviews.

Findings revealed that teachers exhibited moderate to high levels of epistemological beliefs in terms of certainty of knowledge, source of knowledge, and control of learning. Similarly, teachers demonstrated very high levels in the application of HOTS across instructional design, authentic assessment, art of questioning, formative feedback, and metacognitive support. Correlation analysis indicated weak but significant relationships between certain dimensions of epistemological beliefs and HOTS practices, particularly in formative feedback and metacognitive support. Regression results further showed that source knowledge and control of learning significantly influenced instructional design and authentic assessment.

Qualitative findings supported these results by highlighting teachers’ beliefs in collaborative knowledge construction, effort-based learning, and reflective teaching practices. The study concludes that epistemological beliefs play a meaningful role in shaping teachers’ application of HOTS and recommends strengthening professional development programs to enhance both belief systems and instructional practices.

Student engagement is a cornerstone of educational success, particularly in active learning environments like Physical Education (PE). This study investigated the relationship between multidimensional PE engagement and sports skills development among Senior High School students in the Schools Division of Cotabato. Using a mixed-methods approach, the study combined a descriptive-correlational design with a qualitative phenomenological approach to explore students' lived experiences. Quantitative results revealed that students are generally "Engaged" across four dimensions: agentic, cognitive, behavioral, and emotional, with behavioral engagement scoring the highest. Sports skills development was similarly rated as "Developed," with the highest scores observed in mental toughness and game intelligence. A very strong positive correlation was found between overall engagement and skill development, leading to the rejection of the null hypothesis. Multiple regression analysis indicated that while all dimensions significantly influence skill acquisition, emotional engagement emerged as the strongest predictor of overall sports skills development. Qualitative themes highlighted that fun, interactive class designs, teacher support, and peer collaboration significantly foster engagement. Conversely, consistent practice and mastery experiences were primary drivers of confidence. The study concludes that a holistic, student-centered PE curriculum that empowers student agency and fosters emotional connection is vital for optimizing athletic and personal growth.

The integration of Artificial Intelligence (AI) into education has introduced new methods for enhancing English as a Second Language (ESL) learning, especially through conversational agents like chatbots. This paper investigates the role of AI-powered chatbots in improving ESL instruction and learning outcomes. It provides a comparative analysis between traditional ESL teaching methods and a proposed hybrid model that combines classroom teaching with AI chatbot support. Experimental results demonstrate notable improvements in vocabulary gain, grammar accuracy, speaking fluency, and student engagement when using the hybrid system. The findings suggest that AI chatbots can significantly enrich the ESL learning experience by providing interactive, adaptive, and learner-friendly practice opportunities.

This paper presents the design, modelling, and performance analysis of a solar photovoltaic (PV) system integrated with a Cascaded H-Bridge (CHB) multilevel inverter and Maximum Power Point Tracking (MPPT) control using MATLAB/Simulink. A Perturb and Observe (P&O) MPPT algorithm is implemented to continuously extract maximum power from the PV array under varying irradiance and temperature conditions. A DC-DC boost converter regulated by the MPPT-generated duty cycle stabilizes the DC-link voltage. The conditioned DC power is then converted to high-quality AC power by a five-level CHB multilevel inverter controlled via a pulse-based switching strategy. Results demonstrate that the proposed system reduces Total Harmonic Distortion (THD) from 21.58% (full-bridge) to approximately 11% (five-level CHB), confirming the effectiveness of multilevel inverter technology in renewable energy applications.

Social network friend recommendation is a core problem in online platforms, requiring accurate modeling of user relationships and latent preferences. This paper presents an engineering-level machine learning system that combines Node2Vec graph embeddings with a Graph Attention Network (GAT) to predict missing links in a social graph. A synthetic Erdős-Rényi graph of 500 nodes and approximately 1,898 edges is used as the social network. Node2Vec generates 32-dimensional node embeddings by simulating random walks, capturing structural proximity. A two-layer GAT refines these embeddings using multi-head attention, weighting neighbors by relevance. Link prediction is framed as a binary classification task; edge scores are computed as dot products of node embeddings. The model is trained for 60 epochs with the Adam optimizer and evaluated using the ROC-AUC metric. A Flask web application allows users to input a node ID and receive the top-5 friend recommendations in real time. Experimental results demonstrate a test ROC-AUC of 0.9134, confirming the effectiveness of attention-based graph learning for social recommendation tasks.

This study evaluated the effects of different levels of Biossa Agri Enzyme and Swamp Cabbage Extract on the ratoon performance of sugarcane (Saccharum officinarum L.) variety PSR 2000-171. The experiment was conducted in La Suerte, M'lang, North Cotabato from January to March 2025, using a factorial split-plot design arranged in a Randomized Complete Block Design (RCBD) replicated three times. The main plot consisted of four levels of Biossa Agri Enzyme (B0 – control; B1 – 3.25 mL/L; B2 – 6.25 mL/L; B3 – 9.25 mL/L) and the sub-plot consisted of four levels of Swamp Cabbage Extract (S0 – control; S1 – 10 mL/L; S2 – 20 mL/L; S3 – 30 mL/L). Parameters measured were plant height, number of leaf storeys, number of tillers/shoots, stalk diameter, and estimated yield. Results showed that B3 produced the tallest plants (200.69 cm), highest leaf storey count (11.50), most tillers (12.11), widest stalk diameter (2.82 cm), and greatest estimated yield (353 mt/ha). S3 similarly produced the highest values across all parameters. Both Biossa Agri Enzyme and Swamp Cabbage Extract showed highly significant individual effects on all agronomic characteristics; however, their interaction was not significant. The highest treatment combination (B3+S3) consistently outperformed all others, demonstrating the potential of these organic biostimulants in improving sugarcane ratoon productivity and sustainability.

This study evaluated the growth and yield performance of black rice (Oryza sativa) as influenced by different levels of Biossa-Agri Enzyme and Fermented Plant Juice (FPJ) applied as foliar biostimulants. A factorial experiment arranged in a Randomized Complete Block Design (RCBD) with three replications was conducted in Barangay Jose Rizal, Makilala, North Cotabato from March 26 to July 23, 2025. Factor A consisted of Biossa-Agri Enzyme at 3 mL/L (A1), 6 mL/L (A2), and 9 mL/L (A3) of water, while Factor B consisted of Fermented Plant Juice at 0.5 mL/L (B1), 1.0 mL/L (B2), and 1.5 mL/L (B3) of water. Growth parameters — plant height and number of tillers measured at 21, 30, and 60 days after transplanting (DAT) — and yield parameters — panicle length, number of grains per panicle, grain weight, and grain yield per hectare — were recorded. Cost and return on investment (ROI) were also computed. Results showed that treatment A3B3 (9 mL/L Biossa-Agri Enzyme + 1.5 mL/L FPJ) consistently produced the tallest plants (120.03 cm at 60 DAT), the most tillers (14.80 at 60 DAT), the heaviest grain weight (549 g), the highest yield per hectare (5.49 mt/ha), and the highest ROI (43.16%). Significant interaction effects between Factors A and B were observed in plant height at 21 DAT, tiller count at all observation periods, and grain weight. The combined application of Biossa-Agri Enzyme at 9 mL/L and FPJ at 1.5 mL/L is recommended for optimizing the growth, yield, and profitability of black rice production under organic biostimulant management.

This study investigates how risk‑management practices can bolster global supply‑chain resilience within the Federal Capital Development Authority (FCDA) of Nigeria. As the agency responsible for planning and delivering infrastructure in the Federal Capital Territory, FCDA faces a complex risk environment characterized by material‑procurement delays, vendor reliability issues, transportation disruptions, and regulatory compliance challenges. Guided by three objectives—(i) to identify and categorize the primary risk factors affecting FCDA’s supply‑chain operations, (ii) to assess the current state of supply‑chain resilience, and (iii) to develop evidence‑based recommendations for improvement—this research employs a mixed‑methods design. A survey of 108 stakeholders (90 % response rate) and 12 key‑informant interviews yield quantitative and qualitative data.
The analysis revealed that supplier‑related risks, especially late cement deliveries, and logistics disruptions are the most frequent and highest‑impact threats. FCDA’s composite Resilience Index stands at 2.93 (on a 5‑point scale), indicating moderate resilience, with visibility (2.6) and redundancy (2.9) as the weakest dimensions. Regression results show that risk‑management practices explain 38 % of the variance in resilience (β = 0.68, p < 0.001) and are positively linked to cost efficiency and delivery reliability. Qualitative insights highlight a reactive, siloed culture and limited use of real‑time technology.
Based on these findings, the study proposes a six‑point action plan: (1) create a centralized risk register with a heat‑map for prioritization; (2) institutionalize quarterly risk‑identification workshops; (3) conduct a resilience audit and develop a tracking index; (4) design an integrated risk‑management framework with clear trigger points for contingency actions; (5) deploy a cloud‑based supply‑chain control tower and pilot dual‑sourcing for critical materials; and (6) establish a cross‑functional Supply‑Chain Resilience Committee, embed risk‑management into procurement policies, provide regular training, and publish an annual resilience report.
Implementing these recommendations will shift FCDA from a reactive to a proactive risk‑management posture, enhancing cost efficiency, delivery reliability, and stakeholder satisfaction. The study contributes to the limited literature on public‑sector supply‑chain resilience in developing economies and offers a replicable framework for other infrastructure agencies.

KEYWORDS: Risk Management, Supply‑Chain Resilience, FCDA, Infrastructure Development, Public Procurement, Nigeria.

1. INTRODUCTION
The Federal Capital Development Authority (FCDA) serves as the principal government agency tasked with the comprehensive planning, development, and coordination of infrastructure within Nigeria's Federal Capital Territory, Abuja. Established under Decree No. 6 of 1976, the FCDA operates at the intersection of urban planning, infrastructure development, and public administration, managing complex supply chains that support the capital city's continuous expansion and modernization (Abubakar & Doan, 2020). The agency's mandate encompasses the procurement and distribution of construction materials, coordination of multiple contractor networks, management of equipment and machinery supplies, and oversight of service delivery systems that sustain one of Africa's fastest-growing capital cities.
In recent years, global supply chain disruptions have intensified, exposing vulnerabilities in procurement and logistics systems worldwide. The COVID-19 pandemic, geopolitical tensions, climate-related disasters, and economic uncertainties have collectively highlighted the critical importance of supply chain resilience for organizations operating in both private and public sectors (Christopher & Peck, 2020; Ivanov, 2020). For public infrastructure agencies like FCDA, these disruptions translate directly into project delays, budget overruns, compromised service delivery, and diminished public confidence in governmental capacity.
The FCDA's supply chain ecosystem comprises diverse stakeholders including international and domestic suppliers, construction contractors, logistics service providers, regulatory bodies, and community stakeholders. This complexity creates multiple points of vulnerability where disruptions can cascade throughout the system. Historical analysis of FCDA operations reveals recurring challenges: procurement delays averaging 6-8 months beyond scheduled timelines, material cost fluctuations exceeding budget provisions by 15-30%, contractor performance variability, and coordination difficulties among multiple project sites (Oyewobi et al., 2021).
Nigeria's position as a developing economy introduces additional supply chain considerations. The country's heavy reliance on imported construction materials exposes FCDA to foreign exchange volatility, international shipping disruptions, and customs clearance uncertainties. Domestic supply limitations for specialized materials necessitate global sourcing strategies, while inadequate local manufacturing capacity constrains options for supply chain localization. Furthermore, infrastructure deficits in transportation networks, power supply, and digital connectivity compound logistical challenges, increasing operational costs and delivery uncertainties (Oke et al., 2020).
Despite FCDA's critical role in national development, the organization faces persistent supply chain vulnerabilities that compromise project delivery and resource utilization. Current risk management approaches remain largely reactive, focusing on addressing disruptions after occurrence rather than implementing proactive mitigation strategies. The absence of a comprehensive risk assessment framework prevents systematic identification and prioritization of supply chain threats. Limited integration of digital technologies constrains visibility across the supply chain, hindering real-time monitoring and rapid response capabilities (Adejare et al., 2022).
Existing procurement procedures, while designed to ensure transparency and accountability, often lack the flexibility required to respond to dynamic market conditions. Rigid adherence to lowest-price selection criteria may inadvertently prioritize cost over reliability, sustainability, and long-term value creation. Insufficient supplier relationship management results in adversarial rather than collaborative partnerships, limiting opportunities for joint risk mitigation and innovation. Moreover, organizational silos within FCDA impede information sharing and coordinated decision-making across departments involved in supply chain activities.

Irritable Bowel Syndrome (IBS) is a common and chronic functional gastrointestinal (GI) disorder with a poorly understood pathogenesis that presents with visceral hypersensitivity, gut microbiota alterations, and dysfunction of the brain-gut-microbiome axis. Early adverse life events causing alterations in the brain-gut-microbiome axis has been proposed as a possible etiological factor of IBS. The aim of this literature review is to investigate if early adverse life events (EALS) specifically cause the alterations in serotonin levels and the correlation to IBS type that is seen in the possible pathogenesis of IBS. In order to investigate this proposal, the case files of patients who were diagnosed with IBS when

The current investigation into the accomplishment motivation of high school pupils withThe Achievement Motivation Inventory (AMI) by Jansari makes reference to gender and place of residence.It made use of (2012). 120 students in all made up the sample, with 60 coming from the boys.60 were from girls (30 urban and 30 rural) and 60 from the 30 urban and 30 rural urban areas. The informationwas gathered from the Moradabad District. The data was assessed and analyzed in accordance with the handbook.The 'F' test was being computed. According to the findings, 1. The boys higher secondary schoolThe student group is more motivated to achieve than the girls in higher secondary school.2. The student group's average achievement score shows no discernible distinction.3. motivation among the higher secondary school students of urban and rural areas.The interactive effect of the average accomplishment scores is not substantially different.regarding gender and location of residence, motivation.

Effective financial planning acts as a base for attaining long-term financial security and stability, wealth accumulation. It enables individuals to systematically organize their income, expenditures, savings, along with investment activities in alignment with their life goals. In a progressively evolving context, uncertain economic environment, structured financial planning helps mitigate risks, improve financial decision-making, and ensure future security.
This research explores how financial planning contributes to wealth generation and highlights the factors that help in its development. behavioral and economic factors influencing individuals’ financial decisions. Primary information was gathered via a structured questionnaire administered to respondents.
The results indicate that individuals who actively engage in financial planning demonstrate stronger saving habits, better financial discipline, and improved investment outcomes. However, limited financial literacy, risk aversion, and lack of long-term perspective continue to act as significant constraints.

Background: Vitiligo is a chronic autoimmune pigmentary disorder characterized by the loss of melanocytes, often leading to significant psychological distress and social stigma. In homoeopathy, it is viewed as a "one-sided disease" originating from a deep-seated internal derangement or miasmatic dyscrasia rather than a mere local skin condition.
Objective: This study aims to explore the miasmatic evolution of vitiligo and evaluate the efficacy of individualized homoeopathic management based on anti-miasmatic principles.
Methods: Prospective / Observational study. Patients were selected based on clinical diagnosis (ICD-10 Code L-80) and prescribed individualized remedies after thorough case-taking and miasmatic analysis. Assessment was conducted using the Vitiligo Area Scoring Index (VASI) both before and after treatment.
Results: Analysis revealed that vitiligo often manifests through a complex interplay of miasms, with the Syphilitic and Psoro-syphilitic miasms being the most frequently identified underlying states. Post-treatment evaluations showed a significant reduction in symptom scores (p < 0.0001), indicating marked re-pigmentation and cessation of spread in [82 %] of cases.

The performance of the neural network is heavily reliant on the architecture of the neural network itself, which is usually constructed manually by means of experimenting with different designs. This research proposes a system capable of automatically creating architectures of neural networks using NAS technology based on the application of evolutionary algorithms. It is shown how a number of CNN architectures can be generated in the form of genomes, evaluated based on their performance on the CIFAR-10 dataset, and improved using the methods of selection and mutation of the best individuals. This process is repeated until certain criteria are fulfilled. A population-based algorithm is implemented whereby elite individuals serve as a basis for generating new generations.

Women’s safety is a major concern today, and how quickly we can detect a threat directly affects how well the situation is handled. Right now, most safety checks are done manually, which takes a lot of time and effort. This can be a problem because human judgment isn't always consistent, especially during high-stress or panic situations. To address these issues, this project introduces an automated machine vision system designed to classify safety levels.Rapid threat detection is critical for effective emergency response in women's security. While existing solutions rely on manual triggers or expensive hardware, this paper proposes a cost-effective machine vision system that automates danger classification. Using image preprocessing and rule-based logic via TensorFlow, the system distinguishes between "Safe" and "Danger" environments by analyzing RGB images for specific SOS gestures or weapons. Experimental results across a dataset of threat and normal images demonstrate the system's ability to reduce response times by eliminating human intervention.

Performance is the result achieved by an individual in carrying out tasks in accordance with assigned responsibilities, both in terms of quality and quantity. One of the factors influencing performance is leadership in directing and motivating health workers to achieve organizational goals. The performance achievement at Poasia Primary Health Care Center is still considered suboptimal, as indicated by several health programs that have not yet reached their targets (<100%). This study aimed to determine the relationship between transformational, transactional, and servant leadership styles and the performance of health workers at Poasia Primary Health Care Center.
This research employed a quantitative design. The sample consisted of 83 respondents selected using purposive sampling. Data were collected using a questionnaire and analyzed using the Chi-square test.
The results showed that there were statistically significant relationships between transformational leadership (p-value = 0.002), transactional leadership (p-value = 0.020), and servant leadership (p-value = 0.015) and the performance of health workers.
Therefore, primary health care leaders are expected to implement effective leadership styles to improve motivation, discipline, and the quality of health services provided to the community.

Artificial intelligence (AI) has significantly contributed to advancements in biomedical sciences; however, its impact on regulatory science remains limited. With continuous progress in drug development, in silico and in vitro approaches are increasingly explored as alternatives to animal studies to identify and mitigate safety concerns at earlier stages. Despite the availability of numerous AI-based tools, their acceptance in regulatory decision-making for drug efficacy and safety evaluation continues to be challenging.
It is commonly believed that AI models improve with the availability of larger datasets, but this assumption may not always hold true in drug safety assessments. For regulatory applications, AI models must consider multiple characteristics, including adaptability, which refers to a model’s ability to adjust its performance when retrained on new and unseen data. Adaptability is a critical factor that should be evaluated before implementing AI models in regulatory frameworks.
In this study, we conducted a comprehensive assessment of model adaptability by simulating real-world conditions, including the annual introduction of new drugs into the market. We utilized Deep DILI, a previously developed deep learning model for predicting drug-induced liver injury (DILI). Our results demonstrated that the selection of the target test dataset plays a crucial role in evaluating adaptive behaviour. Furthermore, the inclusion of additional drugs in the training dataset did not result in a significant improvement in predictive performance.
Overall, this study highlights the importance of adaptability assessment and suggests that the proposed framework can effectively evaluate the long-term performance and reliability of AI models for regulatory drug safety applications.

Solar photovoltaic (PV) systems are increasingly adopted for sustainable electricity generation, but their output characteristics are nonlinear and vary significantly with solar irradiance and temperature. To maximize energy extraction, efficient Maximum Power Point Tracking (MPPT) techniques are required. Conventional MPPT methods such as Perturb and Observe (P&O) and Incremental Conductance (INC) exhibit slow convergence and oscillations around the maximum power point under rapidly changing environmental conditions. This paper proposes a hybrid Artificial Neural Network (ANN) and Fuzzy Logic (FL) based MPPT controller for solar PV systems implemented in MATLAB/Simulink. In the proposed method, ANN predicts the optimal reference voltage corresponding to the maximum power point, while the fuzzy logic controller adjusts the converter duty cycle for accurate tracking. The hybrid controller is integrated with a PV module, DC–DC boost converter, and resistive load. Simulation results under varying irradiance conditions demonstrate that the proposed hybrid ANN–Fuzzy MPPT method achieves faster response, lower steady-state oscillations, and higher efficiency compared to standalone ANN and FL techniques. The proposed controller achieves an average tracking efficiency of 99.86%, making it suitable for high-performance photovoltaic energy systems.

The English language has emerged as a dominant global lingua franca, playing a crucial role in communication, education, business, and technological advancement. This research paper explores the historical evolution, global significance, and pedagogical methodologies of the English language, particularly in the context of English as a Second Language (ESL) and English as a Foreign Language (EFL). The study analyzes traditional and modern teaching approaches, including Grammar-Translation Method, Communicative Language Teaching (CLT), and Task-Based Learning (TBL), highlighting their effectiveness in improving language proficiency. It also examines the role of digital technology and artificial intelligence in enhancing language learning. Furthermore, the paper discusses challenges such as linguistic inequality, cultural impact, and accessibility issues in non-native contexts. The research concludes by emphasizing the need for inclusive, adaptive, and technology-integrated teaching strategies to ensure effective English language education in the 21st century. The rapid advancement of technology has significantly transformed the field of English Language Teaching (ELT) in the 21st century. This research paper explores the role of digital tools, artificial intelligence, and online learning platforms in enhancing English language learning and teaching practices. It examines how technology has shifted traditional teacher-centered classrooms to learner-centered environments, improving accessibility, engagement, and personalized learning experiences. The study also analyzes the challenges associated with technological integration, such as digital divide, lack of teacher training, and over-dependence on digital tools. Using qualitative analysis based on existing literature and research studies, this paper highlights the benefits and limitations of technology in ELT and suggests strategies for effective implementation. The findings indicate that while technology has revolutionized English education, its success depends on balanced and pedagogically sound usage. This research contributes to the ongoing discourse on modern ELT practices and emphasizes the need for continuous innovation and teacher adaptation.

The practice of earnings management has called for increased attention; yet, factors affecting earnings management in Nigeria’s public sector remain ambiguous. This study examined earnings management on financial performance of manufacturing firms in Nigeria. The sample consisted of five (5) manufacturing companies listed on the Nigeria Exchange (NGX). Period covered spanned from 2019 to 2023. Data was collected from the annual reports of sample companies and the Nigeria Exchange (NGX) data portal. Test for Equity of Variables, Correlation Test, Hausman Specification Test and Panel Unit Root Result diagnostic were implemented along with Random Effect Panel Data Regression for data analysis. The results of the estimations exposed that biased accounting judgement has significant positive effect on manufacturing firms in Nigeria. The study recommended that manufacturing firms should review their cost allocation practices to ensure that costs are allocated appropriately and reflect the actual consumption of resources by products. This might involve reassessing the allocation of fixed manufacturing overhead costs, as they can significantly affect profitability under absorption costing.

Social networks play a critical role in shaping opinions, behaviors, and information dissemination in modern society. Identifying influential users within such networks is essential for applications including marketing, misinformation control, and recommendation systems. Traditional machine learning approaches often fail to capture the complex relational dependencies inherent in social graphs. This study proposes a Graph Neural Network (GNN)- based approach to predict influential nodes within a social network. Using a real-world Twitter network dataset, the social structure is modeled as a graph where users represent nodes and interactions represent edges. Node-level features such as degree centrality are utilized, and a Graph Convolutional Network (GCN) is trained to classify users as influential or non- influential. The model leverages neighborhood aggregation to learn latent representations of influence. Experimental results demonstrate that the proposed GNN model effectively captures social influence patterns and outperforms traditional feature-based approaches. The findings highlight the suitability of graph-based deep learning methods for influence prediction in large- scale social networks.

In rapidly growing African cities, inadequate management of faecal sludge represents one of the major sources of degradation of urban aquatic environments. In Bamako, direct or insufficiently treated discharges of sludge from septic tanks and high-rise buildings significantly contribute to the pollution of the Niger River. This paper analyzes the role of faecal sludge treatment as a key lever for the environmental valorization of the Niger River, based on a detailed comparison of physicochemical and microbiological characteristics of effluents before and after treatment. The results reveal a substantial reduction in organic loads, suspended solids, and pathogenic microorganisms, indicating a significant improvement in the environmental compatibility of treated discharges. The study highlights the strategic importance of Faecal Sludge Treatment Plants (FSTPs) within an integrated water resources management and urban sustainability framework.

Dandruff is a common scalp condition that causes itching, flaking, and discomfort. The present study involves the preparation and evaluation of a polyherbal anti-dandruff shampoo using natural ingredients such as Aloe vera gel, Neem powder, Fenugreek powder, Amla powder, and Tea tree oil. These ingredients are known for their antimicrobial, antifungal, and soothing properties.
The shampoo was prepared using distilled water as a base and evaluated for parameters like pH, skin test, foaming ability, and cleansing action. The formulation showed good stability, satisfactory foaming, and effective cleansing properties. It also exhibited antimicrobial activity against dandruff-causing microorganisms.
The results suggest that the prepared polyherbal shampoo is safe, effective, and helpful in reducing dandruff and scalp itching. It can be used as a natural alternative to chemical-based shampoos.

Women empowerment in modern history represents a gradual yet profound transformation from systemic marginalization to active participation and leadership across social, political, and economic domains. This paper examines the evolution of women&#39;s status from the 18th century to the present, with particular focus on social reform movements, feminist waves, and institutional developments in India and globally. It highlights the contributions of reformers, the expansion of education, the effects of industrialization, and the impact of globalization in reshaping gender relations. The study argues that contemporary empowerment is deeply rooted in sustained struggles against patriarchy, inequality, and exclusion. Despite notable progress, persistent structural, cultural, and economic challenges continue to hinder the realization of full gender equality.

This study investigated technology proficiency and health awareness among community health workers in Akwa Ibom North-West Senatorial District, Nigeria. The increasing integration of digital communication technologies into healthcare delivery has made it necessary to examine how such tools contribute to improving the knowledge and effectiveness of frontline health workers, particularly in resource-constrained settings. An ex-post facto research design was adopted for the study. The population comprised 1,431 community health workers across 174 primary healthcare facilities in the study area. A sample size of 316 respondents was selected using a multi-stage sampling technique. Data were collected using a structured instrument titled Technology Proficiency and Health Awareness Questionnaire (TPHAQ), which was validated by experts and yielded a reliability coefficient of 0.86 using the Cronbach Alpha method. Descriptive statistics (mean and standard deviation) were used to answer the research questions, while dependent t-test was employed to test the hypotheses at the 0.05 level of significance. The findings revealed that both SMS and mass media significantly influence health awareness among community health workers. Specifically, SMS was found to enhance access to timely and relevant health information, while mass media demonstrated a stronger influence due to its wide reach and ability to provide frequent health-related updates. The hypotheses tested confirmed that these influences were statistically significant. Based on these findings, the study concluded that digital communication tools, particularly SMS and mass media, are effective in improving health awareness among community health workers. It was recommended that health authorities and government agencies should strengthen the use of these platforms through training, improved infrastructure, and strategic health communication programmes. The study contributes to existing knowledge by providing empirical evidence on the role of communication technologies in enhancing health awareness among community health workers in a Nigerian context. It also highlights the need for continuous investment in digital health communication to support effective healthcare delivery.

Artificial Intelligence (AI) has become one of the most talked-about technologies today, playing a crucial role in improving business productivity and driving innovation. For Micro, Small, and Medium Enterprises (MSMEs), however, the adoption of AI is still at a relatively early stage compared to larger organizations and even other digital tools. In India, the use of AI among MSMEs is gradually increasing and shows strong potential to generate significant economic value—running into billions. Around 45% of MSMEs have started using AI in some capacity, but its full-scale implementation is still limited. This is mainly due to challenges such as high investment costs, shortage of skilled professionals, and lack of awareness about AI capabilities.

Traditional credential systems are often expensive, inefficient, and susceptible to forgery and security breaches, which undermine public trust. This survey paper explores how Decentralized Ledger Technology (DLT), commonly known as blockchain, can address these challenges through its core features—immutability, cryptographic hashing, and smart contracts—which enhance data integrity and reduce administrative costs. A key focus is the Blockchain-Enabled Two-Factor Honeytoken Authentication (B2FHA) system, designed to actively detect credential misuse and prevent phishing attacks. The study concludes that this hybrid, tamperresistant approach offers significantly greater security and efficiency than traditional non-blockchain and standard multi-factor authentication systems, highlighting its potential for adoption in critical sectors such as finance, healthcare, and e-commerce.

Cyber-attacks are becoming more sophisticated with the rise of advanced technologies, posing serious threats to data security and privacy. Traditional cryptographic methods and security systems are increasingly vulnerable, especially with the advent of quantum computing. This project proposes an intelligent cyber threat detection system that combines Post Quantum Cryptography (PQC) with Machine Learning (ML) techniques to ensure both proactive detection and quantum resistant data protection. Machine learning algorithms such as SVM, Random Forest, and Decision Tree are used to identify multiple types of attacks including DDoS, Botnet, Data Theft, and Backdoor. PQC algorithms like lattice-based and hash-based cryptography secure communication against quantum-level threats. The integration of ML and PQC provides a robust, adaptive, and future-ready cyber security framework capable of detecting, classifying, and preventing complex cyber threats in real time.

Every day, billions of digital images are transmitted across global communication networks. However, uncompressed high-resolution imagery requires massive storage capacity and network bandwidth, creating a significant engineering bottleneck. This paper investigates the software engineering principles behind digital image compression, focusing specifically on the Discrete Cosine Transform (DCT) algorithm used in standard lossy compression systems. The study examines how software strips away imperceptible visual data by translating spatial pixel layouts into frequency coefficients. By analyzing the trade-off between file size reduction and structural picture degradation, this research provides a clear, mathematical overview of how compression metrics operate. The findings demonstrate how optimizing mathematical quantization matrices allows media professionals to balance transmission speed with visual fidelity, providing a foundational understanding of data efficiency in modern digital communication.

This study presents a systematic approach to the design optimization of a propulsion system for a small-scale unmanned ground vehicle (UGV) intended for payload delivery over uneven terrain. Drawing on empirical performance data from an existing prototype, mathematical models were derived to characterize the relationships between payload mass, motor speed, and battery energy consumption. A speed–load regression model (Average Speed = −0.0806 × Load + 1.0341) and a battery drain model (Battery Drain = 2.5517 × Load + 5.7241) were used to quantify the degradation in system performance under increasing load conditions. At the maximum test payload of 6 kg, the vehicle velocity dropped to 0.5 m/s and battery drain reached approximately 23% per operational cycle. To overcome these limitations, design optimization parameters were computed analytically. With an assumed rolling resistance coefficient of μ = 0.2 for uneven terrain and a total system mass of 21 kg, the required wheel torque was determined to be 6.28 N·m, with a corresponding electrical input power of 27.47 W. A 24 V operating voltage was selected to minimize resistive losses, requiring a continuous current draw of 1.14 A at steady-state maximum load. The optimized propulsion system specifies a Brushless DC (BLDC) motor with gear reduction, rated at 35–50 W mechanical output, paired with a 24 V Lithium-ion battery pack of 4–10 Ah capacity and a 5C–10C discharge rating. The proposed design is projected to achieve a target speed of ≥0.8 m/s at full payload with an operational endurance of 1.4–3.5 hours, depending on battery capacity selection. The outcomes of this study provide a replicable analytical framework for engineers designing energy-efficient mobile robotic platforms under payload and terrain constraints.

Breast cancer remains a major healthcare challenge that requires early and accurate diagnosis to improve patient survival and reduce treatment complexity. Manual interpretation of mammography, ultrasound, MRI and histopathological images is clinically important but may suffer from diagnostic variability, workload pressure and dependence on radiologist expertise. This review presents a compact analysis of AI-enhanced breast tumor classification using medical imaging and deep learning. It examines conventional machine learning, Convolutional Neural Networks, Dense Neural Networks, transfer learning, preprocessing methods, evaluation metrics and explainable AI approaches. The review shows that deep learning models generally outperform handcrafted-feature methods because they learn complex imaging patterns automatically. The Anjalee framework emphasizes DNN-based classification supported by preprocessing and regularization, with reported diagnostic performance of 95.61%. Major challenges include limited datasets, class imbalance, overfitting, interpretability, privacy and clinical validation. Future directions include explainable AI, federated learning, multi-modal imaging and lightweight deployment for practical healthcare systems.

The rapid growth of cyber attacks, network vulnerabilities, cloud services, IoT devices, and interconnected digital infrastructures has created a strong demand for intelligent and adaptive intrusion detection systems. Traditional rule-based and signature-based mechanisms remain useful for known threats, but they often fail to detect zero-day attacks, polymorphic malware, insider threats, and evolving intrusion patterns because they depend on predefined rules and manually updated signatures. This review paper presents a concise analysis of autoencoder-based anomaly detection for cyber security applications using unsupervised deep learning techniques. The paper examines the evolution of intrusion detection from statistical and machine learning methods to deep autoencoder models, reconstruction-based anomaly scoring, hybrid Autoencoder-LSTM frameworks, and explainable AI approaches. It also discusses cyber security datasets, preprocessing strategies, threshold selection, comparative model performance, practical applications, major research gaps, and future directions. The review shows that autoencoders are highly suitable for detecting unknown anomalies because they learn normal behaviour and flag deviations through reconstruction error without requiring large labelled attack datasets. However, challenges such as class imbalance, false-positive alarms, computational complexity, threshold sensitivity, encrypted traffic, and limited interpretability continue to affect real-world deployment. Future research should focus on adaptive thresholds, explainable anomaly detection, federated learning, edge AI, Transformer-based architectures, and multi-modal cyber analytics. Overall, autoencoder-based frameworks provide a scalable and promising direction for intelligent cyber security anomaly detection in modern digital environments.

Frequency-Modulated Continuous-Wave (FMCW) radar systems are widely used in autonomous vehicles, aerospace navigation, industrial automation, robotics, surveillance, and defense applications because they provide accurate range estimation, velocity measurement, and real-time object detection. However, their reliability can be affected by hardware degradation, environmental interference, signal distortion, electromagnetic disturbances, calibration errors, and operational instability. Traditional anomaly detection methods based on threshold monitoring and statistical signal analysis often fail to detect subtle or evolving abnormalities under dynamic conditions. This review presents a compact analysis of artificial intelligence-based anomaly detection techniques for FMCW radar systems, with emphasis on Autoencoder, Long Short-Term Memory (LSTM), and Hybrid Autoencoder-LSTM frameworks. Autoencoders support reconstruction-based spatial feature learning, whereas LSTM networks model temporal dependencies in sequential radar signals. The hybrid approach combines these capabilities and provides improved robustness, adaptability, and predictive maintenance support. The review discusses anomaly sources, motivation for AI-based detection, deep learning frameworks, performance evaluation practices, application areas, research gaps, and future directions. Comparative evidence from recent studies indicates that Hybrid Autoencoder-LSTM models generally outperform conventional machine learning methods such as Support Vector Machine and Random Forest in terms of detection accuracy, false-alarm control, and real-time applicability. The paper further highlights challenges related to dataset availability, computational complexity, model interpretability, cybersecurity threats, and deployment on edge devices. Overall, AI-enabled hybrid deep learning frameworks provide a scalable and intelligent direction for reliable anomaly detection and predictive maintenance in next-generation FMCW radar systems.

This review paper presents a comprehensive and critical examination of the dissertation titled “Development of a Chatbot System for Student Support Using Artificial Intelligence.” The study addresses the increasing demand for intelligent, scalable, and efficient student support systems within higher education institutions, where traditional support mechanisms often struggle to handle large volumes of repetitive queries. The proposed system leverages Natural Language Processing (NLP) techniques and a deep learning architecture based on Bidirectional Long Short-Term Memory (BiLSTM) integrated with an attention mechanism to perform multi-class intent classification. The chatbot is designed to categorize student queries into six key domains, namely admission, examination, faculty, fees, hostel, and library, representing common areas of academic and administrative support. A significant strength of the study lies in its evaluation under realistic, data-constrained conditions rather than idealized environments, thereby providing a practical perspective on system performance. The model is assessed using multiple evaluation metrics, including accuracy, precision, recall, and F1-score, along with confusion matrix analysis and training-validation behaviour. The experimental findings reveal an overall classification accuracy of approximately 20%, highlighting the substantial impact of limited dataset size and class imbalance on model generalization and predictive reliability. Despite the modest accuracy, the study demonstrates stable learning behaviour and the capability of deep learning models to capture dominant linguistic patterns. This review synthesizes the methodological framework, critically evaluates performance outcomes, identifies key limitations, and outlines future research directions, thereby contributing valuable insights into the development and deployment of AI-driven chatbot systems in educational environments.

This paper develops a conceptual framework to examine how artificial intelligence (AI) integration in private equity (PE) may influence national innovation performance. As AI increasingly transforms financial decision-making and investment strategies, private equity firms are adopting AI-driven tools to improve deal sourcing, due diligence, portfolio management, and resource allocation. At the same time, private equity continues to play a significant role in supporting innovative firms and technological development. Despite growing interest in AI and financial innovation, limited research has explored how AI-enabled private equity may contribute to broader national innovation systems. Drawing upon interdisciplinary literature in artificial intelligence, private equity, financial innovation, and innovation systems theory, this paper proposes that AI-powered PE may strengthen national innovation indirectly through improvements in firm-level innovation. More specifically, AI integration may enhance the ability of PE firms to identify innovative companies, reduce information asymmetry, and support long-term technological development. The framework further suggests that institutional quality, including regulatory effectiveness, digital infrastructure, and financial market development, may shape the effectiveness of AI-enabled investment activities across countries. Rather than providing empirical testing, this study offers a theoretical synthesis that connects firm-level innovation processes with broader national innovation outcomes. The paper contributes to emerging discussions on AI-driven financial transformation and highlights several directions for future empirical research on AI, private equity, and innovation ecosystems.

Modern English literature reflects a profound shift from external realities to the intricate landscapes of human consciousness. This research paper examines how silence, introspection, and inner dialogue are portrayed as powerful narrative tools in twentieth and twenty-first century English literature. It explores the philosophical dimensions of silence, its connection to identity, and its linguistic representation through stream of consciousness, fragmented narration, and symbolic imagery. By analyzing selected literary works and theoretical perspectives, the paper argues that silence is not merely the absence of speech but a meaningful presence that shapes character, narrative structure, and thematic depth. The study also highlights how modern writers use silence to challenge traditional forms of communication and to depict psychological complexity. Ultimately, this paper demonstrates that silence functions as a central medium through which literature engages with existential questions of self, meaning, and human experience.

The Mahatma Gandhi National Rural Employment Guarantee Scheme (MGNREGS) is a flagship rural development programme aimed at providing guaranteed wage employment and improving the livelihood security of rural households in India. This study focuses on analyzing the effectiveness of MGNREGS in Tamil Nadu, with special reference to Erode District. The study is based on both primary and secondary data. Primary data were collected from 120 beneficiaries of MGNREGS in selected rural areas of Erode District using a structured questionnaire and personal interviews. Secondary data were gathered from government publications, official MGNREGS reports, journals, and related research articles. The population of the study consists of all registered MGNREGS beneficiaries in Erode District, from which a representative sample was selected using convenient sampling methods. To analyze the data, appropriate statistical tools such as percentage analysis, mean score analysis, and chi-square tests were employed. These tools helped in examining the relationship between demographic factors and beneficiary satisfaction, as well as evaluating the overall effectiveness of the scheme. The study concludes that MGNREGS has been effective in improving the socio-economic conditions of rural households in Erode District. However, strengthening implementation, ensuring timely wage payments, and improving awareness among beneficiaries are essential to enhance its overall effectiveness.

This paper is a conceptual and theoretical paper where author tried to develop and propose a model explaining the likely impact of economic crisis on an economy. Discussion on the Model is suitably supported through the relevant literature to justify this model’s path progression of the economic crisis. This Proposed Model shows the probable path of progression of economics crisis and explains how the Economic crisis affects an economy and further the authors identified the strategies and policy options available to mitigate the effects and impact of the crisis.
JEL Classification: E320, E370, E62

The rapid advancement of artificial intelligence (AI) and unmanned aerial vehicle (UAV) technologies has significantly transformed modern agricultural practices, enabling efficient and intelligent farm monitoring systems. AI-enabled drone operations provide real-time, high-resolution data acquisition and analysis, facilitating precision agriculture and improving crop productivity. This review paper presents a comprehensive analysis of the integration of AI and drone technologies for farm monitoring applications. It explores various AI algorithms such as convolutional neural networks, support vector machines, and deep learning models used for crop health assessment, disease detection, and yield prediction.

Additionally, the study reviews commonly used datasets and highlights their role in training and validating intelligent agricultural models. The applications of AI-enabled drones in irrigation management, pest detection, and soil analysis are also discussed. Furthermore, the working mechanism of AI-driven smart farming systems is explained through a systematic architecture. The review identifies key challenges, including data processing complexity, high implementation costs, and scalability issues. Finally, future research directions are suggested to enhance the effectiveness and adoption of AI-based drone systems in agriculture.

Melanoma skin cancer is one of the most aggressive dermatological malignancies and is associated with high mortality when diagnosis occurs at advanced stages. Early detection is therefore essential for improving patient survival and reducing treatment complexity. Recent advancements in artificial intelligence, particularly deep learning, have enabled the development of automated medical image analysis systems capable of assisting clinicians in diagnostic decision-making. This review paper examines a deep learning-based framework designed for the early detection and classification of melanoma using dermoscopic images. The framework employs convolutional neural networks (CNNs) to automatically learn hierarchical visual representations without relying on handcrafted feature extraction techniques. Preprocessing strategies such as image resizing, normalization, and data augmentation are integrated to enhance dataset diversity and improve model generalization. Experimental evaluation demonstrates exceptional classification performance, achieving an overall accuracy of 0.9978, precision of 1.000, recall of 0.9956, and F1-score of 0.9978. Confusion matrix analysis indicates 225 correctly identified melanoma cases, 224 correctly classified benign lesions, one false negative, and zero false positives. Furthermore, Receiver Operating Characteristic analysis reveals an Area Under Curve approaching unity, confirming strong discriminative capability. These findings highlight the significant potential of CNN-based systems in supporting automated melanoma screening and improving early diagnostic outcomes.

The analysis in this study looks at the factors that influence FDI inflows to India, the movement of FDI within the sectors, and the absorption of FDI in different regions of the country during a 25-year period. The research aims to answer three related hypotheses related to the macroeconomic determinants of FDI inflows, relative contribution of the services and manufacturing sectors, and the moderating effect of Absorptive Capacity on FDI-led growth at the state-level. The findings validate the previous studies and research which hold that the main national drivers of FDI are trade openness and macroeconomic stability and confirm that the services sector accounts for the largest proportion of the total FDI inflows, but that the manufacturing sector has seen an increased relative indexed growth rate. In addition, the results suggest that state level of infrastructure and human capital are independent drivers of economic performance.

Margaret Newman’s Theory of Health as Expanding Consciousness (HEC) offers a transformative framework for understanding health within the discipline of nursing. Newman reconceptualises health as a dynamic process of expanding awareness rather than merely the absence of disease. In this perspective, illness is not viewed as an opposing state to health but as a meaningful expression of an individual’s evolving life pattern. The theory is grounded in the principles of unitary human beings and emphasises the inseparability of person and environment. Central to Newman’s theory is the concept of pattern recognition, whereby nurses engage in authentic, mutual relationships with patients to facilitate insight into recurring life themes and experiences. Through caring presence and reflective dialogue, the nurse supports individuals in recognising meaning within their health situations, thereby fostering personal growth and transformation. The theory has significant implications for nursing practice, education, and research. It promotes holistic, relationship-centred care and encourages qualitative methodologies to explore lived experiences. By shifting the focus from disease management to consciousness expansion, Newman’s framework strengthens nursing’s humanistic foundation and underscores the profession’s role in facilitating transformative health experiences.

This comprehensive survey systematically reviews energy-aware resource allocation strategies for big data process- ing in distributed data centers from 2022-2025. We analyze 14 state-of-the-art works categorized into AI/ML-driven predictive allocation (40%), sustainable frameworks with renewable inte- gration (30%), Hadoop/Spark-specific optimization techniques (20%), and advanced metaheuristic algorithms (10%). These ap- proaches demonstrate 25-80% energy reductions while maintain- ing Service Level Agreement (SLA) compliance rates above 85%. Through detailed quantitative comparison across multiple perfor- mance metrics and in-depth methodological analysis, we identify critical research gaps in exascale scalability, GPU/TPU workload optimization, and real-time renewable energy forecasting. Future research directions emphasize carbon-aware global orchestration, quantum-inspired optimization, neuromorphic edge computing, and zero-carbon cluster architectures for sustainable big data ecosystems.

Autism Spectrum Disorder (ASD) is a neurodevelopmental disorder which impacts communication, social interaction, learning ability, and behavioral patterns in children and adults. Early detection of ASD is critical, as early intervention can have a significant impact on cognitive, behavioral and social outcomes. The traditional diagnosis methods are mainly based on clinical observation, behavioral assessment, questionnaires, psychological evaluation, etc., and these methods are time-consuming, subjective, and costly. In order to address these challenges, researchers have increasingly turned to Machine Learning (ML) and Deep Learning (DL) techniques for automated ASD detection and prediction. Intelligent ASD detection systems have been developed using recent developments in artificial intelligence, medical imaging, natural language processing, eye-tracking analysis, EEG analysis and facial image recognition, which have improved the accuracy and efficiency of ASD detection. This review paper provides a detailed survey of the different machine learning and deep learning techniques for autism detection. Various datasets, feature extraction techniques, classification approaches, evaluation techniques and recent research work are discussed. In addition, the paper identifies existing problems, gaps in research, and future research avenues for ASD detection systems. The review finds that AI tools can be a valuable aid to clinicians in early and accurate autism diagnosis.

Teacher education plays a crucial role in shaping the quality of education and overall development of a nation. However, disparities between rural and urban teacher education systems often create unequal learning opportunities and teaching effectiveness. This study aims to comparatively analyze rural and urban teacher education in terms of infrastructure, curriculum implementation, teaching methodologies, access to resources, and professional development opportunities. The paper adopts a descriptive and analytical approach based on secondary data, research studies, and policy reports. The findings reveal significant gaps in infrastructure, digital access, exposure, and pedagogical practices between rural and urban teacher education institutions. The study concludes with recommendations to bridge these gaps through policy reforms, technological integration, and improved training strategies.

This study examined the relationships of gender-responsive teaching and emotional intelligence with the performance of public elementary teachers in Kibawe West District for the school year 2025–2026. Specifically, it assessed teachers’ gender-responsive teaching in terms of instructional practices, classroom management, and assessment strategies, alongside their emotional intelligence across self-awareness, self-regulation, motivation, empathy, and social skills. Teacher productivity was evaluated across six Key Result Areas (KRAs): content knowledge and pedagogy, learning environment, diversity of learners, curriculum and planning, assessment and reporting, and personal growth and professional development.
Findings revealed that teachers consistently practiced gender-responsive teaching, employing inclusive instructional methods, equitable classroom management, and gender-fair assessment strategies. Emotional intelligence was high across all domains, with notable strengths in self-awareness, empathy, and social skills. Teacher productivity was consistently high across all KRAs. Correlation analyses showed significant positive relationships between all dimensions of gender-responsive teaching and teacher productivity, as well as between emotional intelligence and teacher productivity, indicating that teachers who apply inclusive practices and possess strong emotional competencies are more effective and productive.
The study recommends professional development programs, integrated training in inclusive teaching and emotional intelligence, institutional support mechanisms, and further research on the long-term impact on student outcomes and school performance.

Ang pag-aaral na ito ay naglalayong suriin ang ugnayan ng paggamit ng mga Artificial Intelligence (AI) tools at ang antas ng kritikal na pag-iisip ng mga mag-aaral sa Junior High School ng Distrito ng Concepcion, Schools Division of Iloilo, para sa Taong Panuruan 2025–2026. Ang mga tagatugon ng pag-aaral ay binubuo ng apat na raan walumpu’t anim (486) na piling mag-aaral mula sa mataas na Paaralan ng Deogracias G. Arlos National High School, Polopiйa National High School, at Lo-ong National High School, na aktibong gumagamit ng AI tools gaya ng ChatGPT, Google Gemini, AI Image Generator, at AI CICI sa kanilang akademikong gawain. Ang malayang baryabol ng pag-aaral ay ang lawak ng paggamit ng AI tools, samantalang ang di-malayang baryabol ay ang antas ng kritikal na pag-iisip ng mga mag-aaral. Gumamit ang mananaliksik ng researcher-made structured questionnaire bilang pangunahing instrumento sa pangangalap ng datos. Ang datos ay sumaklaw sa propayl ng mga tagatugon, lawak ng paggamit ng AI tools, at pagtataya sa antas ng kritikal na pag-iisip. Bumuo ng dalawampu’t apat na mga tanong ang mananaliksik mula sa modyul ng mag-aaral sa Junior High School. Ang mga nabuong tanong ay nakabatay sa Most Essential Learning Competencies (MELCS) ng Department of Education (DepEd) at ginamitan ng Table of Specifications (TOS). Ang mga nasabing datos ay inanalisa gamit ang descriptive at inferential statistical analyses, kabilang ang frequency, percentage, weighted mean, Mann–Whitney U Test, Kruskal–Wallis H Test, at Spearman’s Rho Correlation. Ipinakita ng resulta na ang karamihan sa mga mag-aaral ay nasa mataas na antas ng kritikal na pag-iisip (M = 16.83) at may mataas na lawak ng paggamit ng AI tools (M = 3.49), partikular na sa paggamit ng ChatGPT at AI CICI. May makabuluhang pagkakaiba sa lawak ng paggamit ng AI tools batay sa edad at lokasyon ng paaralan, gayundin sa uri ng AI tools na ginagamit. Subalit, walang makabuluhang pagkakaiba sa antas ng kritikal na pag-iisip batay sa kasarian, uri ng AI tools, at lokasyon ng paaralan. Wala rin makitang makabuluhang impluwensiya ang paggamit ng AI tools sa antas ng kritikal na pag-iisip ng mga mag-aaral. Batay sa mga natuklasan, iminungkahi ang pagpapalawak ng kaalaman at kasanayan sa paggamit ng AI tools sa mga mag-aaral, ang pagbuo ng mga aktibidad na nagpo-promote ng kritikal na pag-iisip, at ang patuloy na pananaliksik hinggil sa epekto ng AI sa iba pang aspeto ng pagkatuto. Ang pag-aaral ay nagbibigay ng mahalagang pananaw sa kung paano maaaring gamitin ang teknolohiya upang mapabuti ang kakayahan ng mga mag-aaral sa lohikal na pag-aanalisa at pagsusuri ng impormasyon sa akademikong konteksto.

Ang pag-aaral na ito ay naglayong matukoy ang lawak ng paggamit ng social media at ang impluwensiya nito sa performans ng mga mag-aaral sa ika 9 na baitang sa Distrito ng Guimbal, Dibisyon ng Iloilo, Pilipinas sa Taong Panuruan 2025–2026. Ginamit ang descriptive-correlational na disenyo upang suriin ang relasyon ng paggamit ng social media sa akademikong performans ng mga mag-aaral. Ang mga kalahok ay purposively na piniling mga mag-aaral mula sa iba't ibang paaralan sa distrito. Ang datos ay nakalap gamit ang researcher-modified questionnaire na sumailalim sa pagsusuri ng validity at reliability. Ang mga nakalap na impormasyon ay sinuri gamit ang descriptive at inferential statistics tulad ng mean, frequency, percentage, Mann-Whitney U test, Kruskal-Wallis H test, at Spearman’s rho sa tulong ng SPSS. Ipinakita ng resulta na ang karamihan sa mga mag-aaral ay kabataan, mas maraming babae, at nag-aaral sa urban na lokasyon, na may inang may mataas na antas ng edukasyon. Ang lawak ng paggamit ng social media ay Malaking Lawak at ginagamit sa iba't ibang layunin kabilang ang edukasyon, libangan, at pakikipag-ugnayan. Walang makabuluhang pagkakaiba sa paggamit ng social media batay sa edad, kasarian, antas ng edukasyon ng ina, lokasyon ng paaralan, o distansya ng tirahan mula sa paaralan. Ang performans ng mga mag-aaral ay Mahusay, na may makabuluhang pagkakaiba batay sa edad, kasarian, at lokasyon ng paaralan. Gayunpaman, walang makabuluhang impluwensiya ang paggamit ng social media sa akademikong performans. Ipinapakita ng pag-aaral na bagamat mahalaga ang social media sa araw-araw na pamumuhay ng kabataan, ang akademikong tagumpay ay higit na naaapektuhan ng maturity at learning environment ng mga mag-aaral. Ang mga natuklasan ay nagbibigay ng batayan para sa mga guro at paaralan na higit pang pagtuunan ang mga estratehiya sa pagtuturo at pagbuo ng positibong learning environment.

The mental stress has turned into a serious problem in the present day because of the rising academic, professional, social and emotional stress in the society. Chronic stress can have detrimental impacts on physical and mental well-being, causing anxiety and depression, heart disease, sleep problems, and decreased productivity. The traditional stress assessment methods are largely based on questionnaires, clinical interviews and self-reporting which are time-consuming and not appropriate for continuous real-time monitoring. The development of intelligent stress detection systems has been driven by recent developments in Artificial Intelligence (AI), wearable devices, and Machine Learning (ML) that have made it possible to automatically detect stress levels based on physiological, behavioral, and multimodal data. Stress classification and prediction have been achieved using various ML and deep learning techniques including Support Vector Machine (SVM), Random Forest (RF), Decision Tree (DT), K-Nearest Neighbor (KNN), Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), XGBoost and hybrid learning models. Research in this area has also been enhanced by publicly available datasets like WESAD, SWELL-KW, DEAP and SWEET. This review paper provides a detailed summary of the machine learning approaches for stress detection systems such as physiological signal analysis, wearable-based monitoring, multimodal learning, explainable AI, and real-time prediction of stress. The paper also covers the major challenges, limitations, future research directions and emerging trends of intelligent stress detection systems.

The exponential growth of global energy demand, coupled with the urgency of climate change mitigation, necessitates intelligent and adaptive energy management systems. This paper presents a comprehensive Machine Learning-Based Smart Energy Monitoring System (ML-SEMS) that leverages the Internet of Things (IoT) infrastructure to deliver real-time energy consumption analytics, predictive load forecasting, and automated demand-response mechanisms. The proposed system integrates edge-deployed IoT sensor nodes with a cloud-based analytical engine comprising ensemble machine learning models—specifically, Long Short-Term Memory (LSTM) networks for time-series forecasting, Random Forest classifiers for appliance identification, and an Isolation Forest algorithm for anomaly detection. Data collected from smart meters, current transformers, voltage sensors, and environmental sensors are transmitted over MQTT and HTTP REST protocols to a centralized data lake. A total of 18 months of real-world energy consumption data from 120 residential and 30 commercial premises were used for model training and validation. Experimental results demonstrate that the LSTM forecasting model achieves a Mean Absolute Percentage Error (MAPE) of 3.47%, compared to 8.92% for a traditional ARIMA baseline, while the Random Forest appliance classifier attains 96.3% accuracy. The anomaly detection module successfully identifies 94.7% of energy theft and equipment fault events. The system further incorporates a user-friendly dashboard and mobile application, enabling consumers to monitor consumption patterns, receive actionable energy-saving recommendations, and participate in utility demand-response programs. The proposed ML-SEMS achieves an average energy saving of 18.6% across test premises, demonstrating significant potential for large-scale smart grid deployment.

Air pollution is a huge issue these days, especially with all the factories popping up and cities getting bigger, plus more cars on the road all the time. It leads to stuff like breathing problems, cancer in the lungs, even heart issues from just breathing bad air day after day. And the environment takes a hit too, like everything gets messed up around us. I think thats why we need better ways to track it.

Traditional setups for monitoring pollution, you know those stations, they cost a ton and stay in one spot. They dont really give you up to date info on whats happening right where you are. So that makes it hard to respond quickly or get detailed local data.

This project I worked on is about a smart city system for watching air pollution. Its all simulated using IoT ideas, built with MQTT for communication, Firebase to store stuff in the cloud, and Python to run things. Oh, and it draws from LoRaWAN for the wireless part, but without any real hardware involved. The whole thing is just a model that acts like its collecting data in real time, no actual sensors lets you test ideas without spending money on equipment.

Data comes in from these virtual points around a fake city, things like AQI levels, PM2.5 particles, and PM10 too. It gets sent through the MQTT setup to a central spot in the cloud. From there, its saved in Firebase realtime database. Then for seeing it all, theres a dashboard made with Streamlit. It has graphs that update live, alerts when pollution spikes, ways to compare different city areas, and you can access it from anywhere online.


The system feels scalable I guess, and way cheaper than real ones. Its good for real time checks in smart cities, or just analyzing environmental data, even for research on IoT. Some parts might need tweaking for actual use, but overall it works for what its meant to do. Pollution data keeps flowing in those virtual nodes, repeating the process over and over to simulate ongoing monitoring.

Cancer remains one of the leading causes of mortality worldwide, necessitating the development of novel therapeutic approaches beyond conventional chemotherapy and radiotherapy. Recent advances in targeted therapy, phytochemical research, and nanocarrier-based drug delivery systems have revolutionized cancer pharmacology. Targeted therapies specifically inhibit molecular pathways critical for tumor growth and survival, thereby minimizing systemic toxicity. Phytochemicals derived from medicinal plants offer multi-targeted anticancer mechanisms with fewer adverse effects. Meanwhile, nanocarrier systems enhance drug bioavailability, target specificity, and reduce off-target toxicity. This review summarizes recent advances, mechanisms of action, and pharmacological insights into these three key areas shaping the future of cancer treatment.

Water leakage in distribution networks causes significant economic losses, environmental damage, and resource wastage. Traditional leak detection methods rely on manual inspection, acoustic sensors, or simple pressure monitoring, which are slow, inaccurate, and labor- intensive. This paper presents a comprehensive survey of modern water leakage detection systems, including acoustic, pressure-based, flow-based, fiber optic, satellite, and AI-driven methods. We review state-of- the-art literature from 2015–2025, categorize technologies by working principle, compare their performance metrics, and discuss real-world applications in municipal, industrial, and residential settings. Key challenges such as false alarms, sensor cost, real-time processing, and network complexity are analyzed. Finally, we identify future research directions including edge AI, digital twin integration, and selfcalibrating sensor networks. This survey serves as a reference for researchers and practitioners designing next-generation intelligent leak detection systems. Keywords—Water leakage detection, acoustic sensors, pressure monitoring, IoT, machine learning, digital twin, smart water networks.

Blue carbon ecosystems, particularly mangroves, salt marshes, and seagrass meadows, are among the most efficient natural carbon sinks, capable of capturing and storing significant amounts of atmospheric carbon over long periods. Accurate estimation of carbon sequestration within these ecosystems is essential for environmental monitoring, restoration planning, and carbon credit generation. However, conventional carbon assessment techniques rely heavily on field-based measurements, multispectral remote sensing data, and complex geospatial pro- cessing methods that require specialized expertise and substantial resources. Recent advancements in artificial intelligence and computer vision have introduced new possibilities for vegetation analysis using standard RGB satellite imagery, reducing depen- dence on specialized spectral indices and extensive field surveys. Deep learning techniques such as semantic segmentation models enable automated extraction of vegetation regions by learning visual characteristics including color patterns, textures, and spatial structures directly from image data. This paper presents a comprehensive survey of AI-based carbon estimation systems using RGB satellite imagery and computer vision techniques, examining existing methodologies, technological architectures, practical applications, and research challenges. Furthermore, the paper highlights future directions including advanced deep learn- ing models, multisource data integration, and scalable automated systems for environmental monitoring and carbon assessment.

Women’s safety is a major concern in modern society, especially in isolated and unsafe environments. Existing solutions like mobile apps and panic buttons rely on manual activation and internet connectivity, which may fail during emergencies. This paper proposes a Smart Women Safety Jacket using AI and IoT for automatic distress detection. The system uses voice and emotion recognition to identify danger situations and sends alerts even in offline mode via SMS or RF. It also provides smart safe route suggestions using GPS. Additional features like automatic camera capture and a non-lethal shock mechanism enhance safety, making the system a reliable real-world solution.

The fast expansion of Internet of Things (IoT) tech has really boosted the need for smart, and automated home systems. Usual ways to manage home appliance stuff often ask for manual input, which can feel annoying sometimes, also causes energy wastage, and it reduces efficiency. In this paper, we suggest an IoT Based Home Automation System, built to automate and manage household devices using Arduino UNO together with Bluetooth communication The design mainly emphasizes wireless appliance control. In other words , the home devices are connected using the HC-05 Bluetooth module, and an Android mobile application is used to drive the commands. LEDs are included to act as smart lighting in the house, and a servo motor is added so the door can be controlled automatically. Overall, the system is meant to be simple, budget-friendly , and fairly easy for users to handle. Besides that, it improves comfort and energy efficiency because it supports appliance control without wires. Unlike classic manual systems, this proposed approach lets users manage appliances remotely while staying inside the Bluetooth communication coverage. That leads to better operational efficiency, and less manual effort. The proposed IoT Based Home Automation System is intended to deal with these issues by developing a straightforward, low-cost, and user- focused smart home automation setup using Arduino UNO and HC-05 Bluetooth technology.

This study provides a comprehensive and critical evaluation of the relationship between economic growth and unemployment dynamics in Kenya, grounded in the theoretical framework of Okun's Law. Traditionally, Okun’s Law posits an inverse relationship between output growth and unemployment, suggesting that higher economic growth leads to a proportional reduction in unemployment. However, the applicability and stability of this relationship in developing economies such as Kenya remain empirically contested.
The paper interrogates the extent to which Kenya’s economic growth trajectory—characterized by periodic expansions driven by sectors such as services, agriculture, and infrastructure—has translated into meaningful employment creation. Despite sustained GDP growth rates averaging above regional benchmarks in Sub-Saharan Africa, unemployment and underemployment levels remain persistently high, particularly among youth. This paradox raises fundamental questions about the structural composition of growth, labor market rigidities, and the prevalence of informal employment.
Using time-series data and econometric modeling techniques, including cointegration and error correction mechanisms, the study examines the short-run and long-run dynamics between GDP growth and unemployment rates in Kenya. The findings are expected to reveal a weakened or unstable Okun coefficient, reflecting structural breaks, sectoral imbalances, and the dominance of informal labor markets that dilute the responsiveness of employment to growth.
Furthermore, the analysis situates Kenya’s experience within broader macroeconomic and institutional contexts, including demographic pressures, education–skill mismatches, policy inconsistencies, and external shocks such as global financial crises and the COVID-19 pandemic. These factors are critically assessed to explain deviations from the theoretical expectations of Okun’s Law.
The study concludes that while Okun’s Law offers a useful benchmark for understanding growth-employment linkages, its predictive power in Kenya is limited without accounting for structural and institutional realities. It recommends policy interventions focused on inclusive growth strategies, labor market reforms, and sectoral transformation to enhance employment elasticity of growth. This research contributes to advanced macroeconomic discourse by contextualizing a classical economic relationship within a developing economy framework and highlighting the need for nuanced, country-specific policy approaches.

The advancement of digital education has led to the emergence of AI-based tutoring systems, e-learning platforms, and virtual assistants. However, most existing solutions lack integration of interactive, multimodal, and personalized learning approaches. This survey reviews recent developments in AI-powered audio and video tutoring systems that enable interactive explanations and intelligent content delivery.
The paper analyzes techniques based on Natural Language Processing (NLP), speech recognition, and machine learning, highlighting their role in real-time interaction and adaptive learning. It also identifies key challenges such as limited personalization, lack of contextual understanding, and insufficient integration of audio-visual learning methods. Finally, the survey emphasizes the need for unified systems that combine voice interaction, video explanation, and AI-driven personalization to improve learning effectiveness.

Microplastics have become ubiquitous environmental pollutants with growing evidence of human exposure and possible danger. These particles, which are usually smaller than 5 mm, come from direct industrial manufacture or the breakdown of bigger plastics. They are found in food, water, air, and even human biological samples, according to recent research, which raises grave worries about their potential toxicological effects. This review critically examines exposure pathways, toxicokinetic, cellular and molecular mechanisms, and associated health risks. Emphasis is placed on oxidative stress, inflammation, endocrine disruption, and organ-specific toxicity. Significant gaps in our knowledge of long-term effects persist despite mounting evidence, calling for additional study and regulatory attention.

This paper presents the design and development of an AI-powered multi-agent system for intelligent document analysis and automated report generation. The proposed architecture integrates Retrieval-Augmented Generation (RAG) with LangGraph-based agent orchestration to enable dynamic, intent-driven workflows. Specialized agents handle document retrieval, contextual analysis, chart generation, and structured report synthesis. An intent classification module routes user queries to the appropriate agent pipeline, while a validation agent mitigates hallucination through RAG grounding and fact-verification. Experimental evaluation demonstrates that the proposed system achieves 93.7% retrieval accuracy, reduces hallucination rate to 4.2%, and generates comprehensive reports in under 8 seconds—outperforming baseline single-agent and standalone RAG approaches. The modular architecture ensures extensibility across diverse document domains.

Neurodegenerative diseases such as Alzheimer’s, Parkinson’s, and amyotrophic lateral sclerosis (ALS) are challenging to diagnose early because of their complex and progressive nature. Artificial Intelligence (AI) has become an important tool in healthcare, offering advanced methods for the early detection and management of these disorders. AI-based diagnostic systems use machine learning, deep learning, and multimodal data analysis to study neuroimaging scans, genetic information, speech patterns, and behavioural data. By identifying subtle disease-related changes, these systems help doctors provide earlier treatment, personalized care, and improved patient outcomes. AI also improves diagnostic accuracy by reducing human error and analysing large medical datasets to generate better clinical insights. Despite its advantages, challenges such as data privacy, algorithm bias, and the need for clinical validation continue to limit its widespread use. This paper examines the techniques used in AI-driven diagnosis systems, reviews recent developments, and discusses future opportunities for integrating AI into clinical practice. The study highlights the significant role of AI in improving the diagnosis of neurodegenerative diseases and emphasizes the importance of interdisciplinary collaboration for its effective implementation.

Academic stress has emerged as a significant psychological concern among students, particularly in higher education contexts. While contemporary psychological models explain stress through cognitive appraisal and coping mechanisms, they often lack culturally embedded perspectives. Drawing upon the traditional Indian system of Ayurveda, the present conceptual paper proposes a framework linking Tridosha (Vata, Pitta, Kapha) with academic stress responses and student well-being. Existing literature suggests that Tridosha represents psychophysiological regulatory principles governing behavior, cognition, and emotional functioning. This paper integrates Ayurvedic theory with modern stress and coping frameworks to conceptualize how doshic dominance influences stress perception, coping styles, and academic outcomes. Prior empirical and theoretical studies on Tridosha and psychological attributes, well-being, and stress regulation are reviewed to support the framework. The proposed model offers a culturally relevant approach to understanding individual differences in stress responses and highlights implications for personalized interventions in educational settings.

The study investigated the effect of cost benefit analysis on small scale and medium enterprise businesses in Kisumu Town. The objectives of the study were, to determine what extent small businesses in Kisumu adopt cost benefit analysis, to determine the roles of cost benefit analysis in survival of small-scale firms and to determine the factors responsible for failure to carry out cost – benefit analysis among small businesses and to what extent has market imperfection influenced the survival of small-scale firms. The researcher adopted simple random sampling designs to select a sample. The study employed questionnaires in collecting data which was later analyzed. Data was presented in frequency tables and percentages. The findings established that the major forms through which small businesses owners use cost benefit analysis are through project teams, through joint consultation, through partnership schemes and through collective representation. Factors influencing the use of cost benefit analysis are decision making on the investment to be undertaken, nature and urgencies of the issues at hand, the education level of the small entrepreneurs and their experience. The effectiveness of small entrepreneurs’ participation in the use of cost benefit analysis to correct business management decisions leads to varied options; it enhances improved quality services to customers, it gives broader perspective and more alternative solutions, and it leads to total customers satisfaction hence improved profits. The researcher recommended that the small-scale business owners should and must carry out cost benefit analysis in relation to decision making for proper business management. The researcher also recommended that further research be carried out to determine other effects of cost benefit analysis which the research may not have covered in its scope, this paper contributes to a deeper understanding of how Cost Benefit Analysis Shapes Econometric behavior and business efficiency.

A thorough data collection underpins the analysis of Chhattisgarh's policies, innovative wood products, markets for wood energy, value-added wood products, and housing. The review emphasizes the importance of sustainable forest products in global markets, discusses policies about forests and forest products, and identifies key drivers and trends. It also examines the overall state of the economy and the uncertainty surrounding forest product markets in the challenging economic climate. A thorough data collection forms the basis of the analysis. The Review emphasizes the importance of sustainable forest products in global markets, discusses policies about forests and forest products, and identifies key trends and drivers. In the challenging economic climate, it also examines the overall state of the economy and the uncertainties surrounding forest product markets. The markets for forest products are impacted by a wide range of policies, some of which directly impact how wood is perceived as a component of an emerging green economy. These policies include trade-related agreements and regulations on illegal logging, as well as policies on renewable energy, greenhouse gas reduction targets, carbon accounting, and green building. Several policies directly impact the perception of wood as a component of a developing green economy. These include trade-related agreements and laws about illicit logging. Carbon accounting, green construction policies, renewable energy legislation, and greenhouse gas reduction targets also impact wood markets. The potential for Chhattisgarh's production centers to be used more widely could increase those impacts. Forest certification policies frequently intersect with forest product policies, which could be seen as opportunities or threats, putting the green credentials of wood products under scrutiny. This allows the forest sector to modify its practices to reduce impacts and improve its monitoring and reporting of responsible production practices in Chhattisgarh's forest products.

Chatbots are AI-driven conversational agents that interact with users through natural language. They are widely used in customer support, education, healthcare, and business applications to automate tasks, reduce operational costs, and enhance user experience. This research focuses on the development of an intelligent chatbot using Natural Language Processing (NLP) and AI techniques. The system is designed to understand user input, detect intent, retrieve relevant information, and generate accurate responses.
The research employs the Agile Software Development Life Cycle (SDLC), supporting iterative development, continuous testing, and integration. System design incorporates Data Flow Diagrams (DFD), Entity-Relationship (ER) models, Use Case diagrams, Class diagrams, and Activity diagrams to ensure a structured approach. Testing demonstrates high accuracy, reliability, and usability, highlighting the potential of chatbots for practical applications. Future enhancements include multi-language support, voice interaction, and integration with external services.

The garment export sector in Coimbatore and Tiruppur is an important contributor to India’s economy, but exporters often face risks such as damage to goods, delayed or non-payment, and regulatory issues. This study explores the insurance solutions used by garment exporters, with a focus on the types of policies they prefer and how these help in managing risks. The research is based on primary data collected from 35 exporters through a structured Google Form questionnaire, and the data is analyzed using percentage analysis, ranking method, Chi-square test, and ANOVA. The results show that marine cargo insurance and export credit insurance are the most commonly chosen options, as they help reduce financial losses and improve confidence in export operations. The study concludes that while insurance plays a key role in risk management, there is still a need to improve awareness, simplify procedures, and design more suitable insurance products to meet the specific needs of garment exporters.

Skin diseases are among the most common health issues worldwide, affecting individuals irrespective of age, gender, or geographical location. Early diagnosis plays a crucial role in preventing severe complications and ensuring timely treatment. This paper presents an intelligent skin disease detection system using advanced deep learning techniques for accurate classification of various dermatological conditions.
The proposed system is inspired by the base study, where multiple deep learning models such as Simple CNN, CNN with Dropout Layers, and CNN+LSTM were implemented. Among them, the CNN with Dropout Layers achieved the highest test accuracy of 88.6%.
To further enhance performance, this work proposes the implementation of advanced architectures such as DenseNet, ResNet, and Xception, aiming to achieve more than 90% classification accuracy. The system utilizes labeled image datasets, preprocessing techniques, and performance evaluation metrics including accuracy, precision, recall, and F1-score.
The results demonstrate improved classification capability, better generalization, and suitability for real-time applications in dermatological diagnosis.

This study assessed the use of gender-fair language in the English learning modules for Grade 10 students. Specifically, it examined how gender fairness was mainstreamed in the portrayal of women, representation, stereotyping, and language; identified the Filipino gender role ideologies reflected in and/or challenged by the modules; and proposed a training design on gender-fair language to improve the current learning modules. The study used a qualitative research design with Critical Discourse Analysis to examine 23 Grade 10 English learning modules at Bukidnon National High School. The analysis focused on the lexical choices, syntactic patterns, semantic constructs, and larger cultural and ideological connotations buried in the texts. Findings revealed that the modules contain both gender-fair and gender-biased elements. Some texts continue to reflect traditional portrayals of women, stereotypical role assignments, male-centered language, and gendered assumptions. However, several module excerpts also challenge unequal gender norms by portraying women as decision-makers, leaders, rights-holders, professionals, and active social agents. The study further found that Filipino gender role ideologies, such as the Maria Clara archetype, colonial patriarchy, toxic masculinity, hiya, pakikisama, utang na loob, bahala na mentality, adult superiority bias, nationalist feminism, the Babaylan or decolonial lens, and the Catholic moral gender framework were reflected and/or challenged in the modules. The study recommended the development of a Gender-Fair Language and Gender-Responsive Module Training for educators, instructional material writers, and curriculum evaluators. The researcher advocates for the methodical evaluation and modification of educational resources to guarantee gender-responsive, inclusive, and equitable language in basic education.

Chatbots are AI-driven conversational agents that interact with users through natural language. They are widely used in customer support, education, healthcare, and business applications to automate tasks, reduce operational costs, and enhance user experience. This research focuses on the development of an intelligent chatbot using Natural Language Processing (NLP) and AI techniques. The system is designed to understand user input, detect intent, retrieve relevant information, and generate accurate responses.
The research employs the Agile Software Development Life Cycle (SDLC), supporting iterative development, continuous testing, and integration. System design incorporates Data Flow Diagrams (DFD), Entity-Relationship (ER) models, Use Case diagrams, Class diagrams, and Activity diagrams to ensure a structured approach. Testing demonstrates high accuracy, reliability, and usability, highlighting the potential of chatbots for practical applications. Future enhancements include multi-language support, voice interaction, and integration with external services.

This study utilized an explanatory sequential mixed-methods design to investigate the influence of Work-Life Interface (WLI) toward Turnover Intention among 124 elementary school teachers in Arakan. Quantitative data revealed high levels of turnover intention driven by health fatigue, family dissatisfaction, and promotional stagnation, despite high vocational satisfaction. Multiple regression analysis established that Work Interference with Personal Life was the sole unique, statistically significant driver of intent to leave across all examined organizational withdrew catalysts. Qualitative analysis contextualized this statistical strain, identifying catalysts of conflict such as digital telepressure and administrative voluminosity forced into domestic hours. These demands led to socio-emotional cognitive saturation and secondary traumatic stress. Teachers perceived the organizational culture as transactional, characterized by career stagnation, rigid scheduling, and a lack of participatory shared leadership. To mitigate this vocational withdrawal, the study proposed a formal Teacher Retention and Wellness Initiative.

As technology companies grow from early-stage startups to unicorns, minority shareholder rights undergo a transformation that may include modification and, in some cases, may even include strengthening. Traditional interpretations of minority shareholder protection do not adequately explain this phenomenon in the context of technology companies. In this scenario, minority shareholder protection is the result of a complex layer of contractual and governance mechanisms that include shareholder agreements, board rights, information rights, veto rights, anti-dilution rights, transfer rights, secondary sale provisions, and selective applications of statutory minority shareholder remedies. This research employs a combined doctrinal analysis and market assessment of contemporary business law and venture financing and unicorn data for the years 2025 and 2026 from WIPO, BVK, Carta, Cooley, Morrison Foerster, Blume Ventures, and other officially sourced legal data. The data demonstrates that the present market is more restrained than the 2021 valuation boom. Down rounds, pay-to-play provisions, and secondary sales have come to dominate the post-valuation landscape. The secondary market, unlike the venture market, provides liquidity to minority shareholders, employees, and angel investors. Finally, the mandatory governance of unicorns has begun to influence the design and governance of some startups. The modern regulatory framework emphasizes the need to include greater ex ante contracting and ex post remedies, to diminish information asymmetries, to clarify disclosure, to provide fairer exit mechanisms, and to provide greater governing rights.

Artificial Intelligence (AI) is one of the most transformative technologies of the modern era. It refers to the development of computer systems that can perform tasks requiring human intelligence such as learning, reasoning, decision-making, and problem-solving. However, its rapid deployment raises critical ethical concerns including bias, privacy violations, lack of transparency, and accountability gaps. An ethical AI ecosystem provides a holistic framework integrating stakeholders, governance mechanisms, technical tools, and societal values. It moves beyond high-level principles to practical, actionable tools that embed accountability into the entire AI lifecycle. This paper explores the architecture, principles, components, challenges, and future directions of ethical AI ecosystems, emphasizing the transition from abstract principles to actionable systems. Existing research highlights a persistent gap between ethical guidelines and implementation, necessitating ecosystem-level thinking.

Fingerprint-based biometric systems are widely used for personal identification due to the uniqueness and reliability of fingerprint patterns. However, these systems face challenges from altered fingerprints, where individuals intentionally modify ridge structures to evade recognition. Over the years, various techniques have been developed, ranging from traditional image processing methods based on ridge orientation and minutiae features to advanced machine learning and deep learning approaches.To address these challenges, modern systems utilize Convolutional Neural Networks (CNNs) trained on datasets such as SOCOFing to automatically extract features like ridge patterns, texture, and fine details, enabling accurate detection of altered fingerprints. In addition, fingerprint analysis is also used for gender prediction through ridge density analysis, though this task is often handled separately.By integrating both approaches, the proposed HybridPrint framework combines ridge density-based gender prediction with CNN-based alteration detection. This hybrid model enhances the accuracy, robustness, and overall performance of fingerprint-based biometric systems.

This study examines the growth and market potential of Indian spices exports in the post- financial crisis period from 2015 to 2025. It analyzes export performance using indicators such as growth trends, export composition, market concentration, and comparative advantage. The study also evaluates the impact of global demand, competition, and evolving consumer preferences on spice exports. The findings reveal that Indian spice exports have experienced steady growth with a Compound Annual Growth Rate (CAGR) of approximately 6.8%, supported by rising demand for natural and value-added products. The study highlights that although India maintains a strong global position, there is scope for improvement through quality enhancement, technological adoption, and market diversification. Overall, the study emphasizes the need for strategic measures to strengthen India’s competitiveness in the global spice market.

Because hydrogel-based systems offer special structural, physicochemical, and biological characteristics, they have become cutting-edge platforms for the treatment of dermatological illnesses. These three-dimensional, crosslinked polymeric networks allow for the controlled and prolonged administration of therapeutic drugs across the epidermal barrier because of their high water content, customisable mesh architecture, and tunable mechanical strength. The design of hydrogels has changed recently from traditional sustained-release matrices to disease-driven, microenvironment-responsive systems that may adjust to pathological triggers such microbial dysbiosis, oxidative stress, enzyme overexpression, and pH imbalance. Contemporary hydrogels can offer targeted, localised, and stimuli-sensitive drug release that is suited to inflammatory dermatoses, acne, chronic wounds, and diabetic ulcers by combining natural and synthetic polymers, dynamic crosslinking chemistries, nanocarriers, and multifunctional bioactive components. New approaches that broaden their therapeutic scope toward precision dermatology include follicular targeting, living bioactive hydrogels, hybrid microneedle–hydrogel systems, multi-therapeutic co-delivery platforms, and digitally integrated smart dressings. Translational hurdles, such as sterility assurance, regulatory classification, microbiome safety, and scalable manufacturing, persist despite encouraging preclinical results. Clinical progress depends on overcoming these obstacles by integrating biomaterials science, dermatology, and regulatory frameworks in an interdisciplinary manner. All things considered, precision-responsive hydrogel therapies offer flexible, multipurpose, and patient-centered treatments for intricate skin conditions, marking a revolutionary advancement in dermatological drug delivery.

A bad reaction to medicine happens when someone gets hurt by a drug meant to help. Worldwide, experts agree it’s harm that wasn’t planned during treatment. Sometimes these effects come out even when doses are correct. Not every negative effect means danger, but some need quick care. Problems tied to drugs add stress on hospitals and clinics. Yet many of them could be avoided with better systems in place. How doctors and nurses handle reactions differs quite a bit across regions. One reason might be gaps in how training is delivered. Another factor could involve unclear rules or mixed messages at work sites. Fixing this may depend less on new tools, more on smarter learning plans. Teaching teams well might lift both speed and accuracy in reporting issues. Better records often follow when staff truly understand what to watch for. Improvements here tend to spread once trust builds around sharing mistakes. Still, progress moves slow if support stays uneven between departments. Clearer routines plus steady guidance often lead to fewer repeated errors. When people feel safe speaking up, problems get caught earlier. Learning together changes habits more than warnings ever do.
ADR draws broad backing for being straightforward, adaptable, quick, less expensive when settling disputes, also easing pressure on courts. Yet here, the view tilts skeptical - focus lands on risks and drawbacks. Peeling apart standard civil procedures then lining them beside ADR's traits shapes what comes next. Balance tips between gains and losses get laid bare. Outcome? ADR might weaken traditional court paths instead of standing alongside as choice. Still, Sweden’s latest push for two court-linked ADR models could bring real value - if handled right.
Owing to the consummate significance of monitoring, managing, and precluding ADRs in assuring health benefits, perfecting trust in medicine non supervisory authorities, gaining acceptance of new medicine blessings, promoting Out of nowhere, detailed checking in patient files ties into how info gets shared through team-based learning setups. Suddenly, hospital and neighborhood pharmacists step in, linking doctors with real-world examples inside drug reaction tracking efforts.

Tradition-al medicine refers to health practices rooted in ancient cultures and used before modern science was applied to medicine. These practices have been around for centuries and often involve the use of medicinal plants.
Medicinal plants are commonly used to treat cardiovascular diseases, which include conditions that affect the heart and blood vessels, such as heart attacks, stroke, high blood pressure, and heart failure. High blood pressure can make the heart work harder and increases the risk of atherosclerosis, which in turn raises the chances of heart attack and stroke.
While many drugs are available for these conditions, typical antihypertensive medications often come with significant side effects. Medicinal herbs, on the other hand, contain multiple active compounds that have both healing and preventive properties and can be useful in treating high blood pressure. This review gives an overview of some medicinal plants that have been found to help lower blood pressure.

This study investigated the environmental consequences of implementing dry construction systems in specific peri-urban residential projects in South-West Nigeria, concentrating on assessing their sustainability performance through the lens of Environmental Impact Assessment (EIA) and life-cycle analysis. The project aimed to develop ecologically sustainable building options that allow quick housing supply while reducing ecological disruption in rapidly expanding urban peripheries. The research used a mixed-method methodology, including literature review, case study analysis, and environmental performance assessment. Peri-urban settings were analyzed to evaluate construction processes, material use, waste production trends, energy implications, and regulatory compliance related to dry construction systems in contrast to traditional building techniques. Key environmental factors were emphasized, including water use, construction waste production, embodied carbon, site disruption, noise pollution, and operating energy requirements. Research indicates that dry construction technologies provide substantial environmental benefits throughout the building period, such as less on-site waste, limited water use, expedited project completion, and reduced dust and noise pollution. These attributes are especially advantageous in peri-urban areas where environmental management infrastructure is deficient. The research also revealed trade-offs, particularly the elevated embodied energy linked to prefabricated components and possible rises in operational cooling requirements if climate-responsive design strategies are insufficiently included. The study emphasized legal and institutional deficiencies in Nigeria's EIA system, notably the inadequate integration of life-cycle assessment approaches in project appraisal procedures. The research indicates that dry building systems are a feasible and sustainable option for peri-urban residential growth in South-West Nigeria, contingent upon suitable legislative changes, climate-responsive design techniques, and enhanced environmental governance. This study enhances previous knowledge by situating dry building within the dynamics of Nigerian peri-urban areas and offering evidence-based suggestions for incorporating life-cycle environmental assessment into sustainable construction methods.

Visual impairment significantly affects an individual’s ability to navigate and interact with their surroundings, creating challenges in performing everyday activities independently. Recent advancements in Artificial Intelligence (AI) and Computer Vision have enabled the development of intelligent assistive systems that enhance environmental awareness for visually impaired individuals. This survey paper presents a comprehensive review of AI-powered assistive systems that utilize real-time object detection and speech synthesis to provide auditory feedback. The paper explores various techniques and technologies, including deep learning models such as YOLO (You Only Look Once) for object detection, along with tools like OpenCV, PyTorch, and Text-to-Speech (TTS) systems. It analyzes different system architectures, implementation approaches, and hardware platforms such as Raspberry Pi used for building portable solutions. Additionally, the survey highlights key challenges, including accuracy in complex environments, real-time processing constraints, power efficiency, and user adaptability. Furthermore, this paper identifies existing research gaps and discusses future directions, such as improving model efficiency, integrating context- aware intelligence, and enhancing user experience through adaptive feedback systems. The objective of this survey is to provide a clear understanding of current advancements and to guide the development of efficient, affordable, and user-friendly assistive technologies for visually impaired individuals.

Dry eye disease (DED) is a complex condition affecting the ocular surface, marked by tear film instability, inflammation, and irritation, which can considerably reduce a patient’s quality of life. Traditional treatment options, including lubricating eye drops and anti-inflammatory medications, often provide only short-term relief due to rapid clearance from the ocular surface, low drug absorption, and limited retention time. To address these challenges, novel drug delivery systems (NDDS) have been developed to enhance the effectiveness of dry eye management. These innovative delivery approaches include nanoparticles, liposomes, niosomes, micellar systems, hydrogels, in situ forming gels, ocular inserts, and drug-eluting contact lenses. Such systems are designed to improve drug stability, increase residence time on the eye surface, and provide sustained and controlled drug release, thereby enhancing drug penetration across ocular tissues. Furthermore, they allow for targeted delivery, reduce dosing frequency, and improve patient adherence while minimizing adverse effects. Recent advancements also focus on stimuli-responsive and mucoadhesive delivery systems, along with nanotechnology-based formulations, which show great promise in targeting the underlying inflammatory and degenerative pathways of dry eye disease. Although these systems demonstrate encouraging results in research and clinical settings, issues related to stability, scalability, regulatory considerations, and long-term safety still need to be addressed. Overall, novel drug delivery systems offer a promising advancement in the treatment of dry eye disease, with the potential to provide more effective and sustained therapeutic outcomes than conventional therapies. Ongoing research and clinical validation are necessary to support their widespread application in ophthalmology.

सतत विकास (Sustainable Development) वर्तमान वैश्विक आवश्यकता है, जिसका उद्देश्य पर्यावरण संरक्षण, सामाजिक समानता और आर्थिक विकास के बीच संतुलन स्थापित करना है। शिक्षा इस लक्ष्य को प्राप्त करने का एक महत्वपूर्ण माध्यम है, विशेष रूप से शिक्षक-प्रशिक्षण कार्यक्रमों के माध्यम से। प्रस्तुत अध्ययन का उद्देश्य बी.एड. प्रशिक्षुओं में सतत विकास के प्रति जागरूकता एवं दृष्टिकोण का विश्लेषण करना है। यह अध्ययन वर्णनात्मक सर्वेक्षण विधि पर आधारित है, जिसमें बी.एड. प्रशिक्षुओं से प्रश्नावली के माध्यम से डेटा संकलित किया गया। अध्ययन में पाया गया कि अधिकांश प्रशिक्षुओं में सतत विकास के प्रति सामान्य जागरूकता तो है, परंतु गहन समझ और व्यवहारिक अनुप्रयोग में कमी देखी गई। दृष्टिकोण के स्तर पर अधिकांश प्रशिक्षुओं का रवैया सकारात्मक पाया गया, लेकिन इसे प्रभावी शिक्षण व्यवहार में परिवर्तित करने की आवश्यकता है। अध्ययन यह भी इंगित करता है कि शिक्षक शिक्षा कार्यक्रमों में सतत विकास शिक्षा को अधिक व्यवस्थित और व्यावहारिक रूप से शामिल करने की आवश्यकता है।
अतः बी.एड. प्रशिक्षुओं को सतत विकास के सिद्धांतों से जोड़ना भविष्य के शिक्षकों के रूप में अत्यंत आवश्यक है।

There is consensus that education is a basic human right and a driver for the growth and development of individuals. For women, education is a shared tool of human agency and achievement for social justice, gender parity and sustainable development on a national scale, rather than just personal fulfilment. Despite great progress in the past century, there remain vast disparities in women's access to education across geographies, especially in South Asia and Sub-Saharan Africa, and some regions of the Middle East. Cultures, especially with patriarchal expectations and social norms, limit women's educational aspirations well beyond their economic circumstances. This article reflects on the interconnectedness of the family and principles of society in supporting women's education, utilising a gendered lens to illustrate how personal choice and social norms intersect to act as either constraints or enablers of women's autonomy and empowerment. Gendered values are primarily embedded in family structures, as they are the primary unit of socialisation. Parents' choices about their daughter’s education, their encouragement and their assigned household duties are significant precursors to women's educational pathways. Families who value girls’ education tend to have aspirations beyond basic needs, have vast resource allocation, and practice equality. Conversely, families that do not support girls’ education perpetuate cycles of dependency and illiteracy. Society also plays a role by providing the structural context that either maintains or disrupts normative patriarchal traditions or aspirations to create gender equality.
This study examines the collaborative efforts of society and the family in supporting women's education, drawing on historical experiences, international case studies, and policy frameworks. It talks about the role of reformers, the historical restrictions on women's education, and current issues like early marriage, gender-based violence, and socioeconomic inequality. Examples from Bangladesh, Rwanda, India, and other places where family and social support have been essential in increasing girls' access to education are highlighted. In the end, the paper makes the case that although governments and international organisations can create programs, families' readiness to give their daughters' education top priority and societies' willingness to break down restrictive norms are crucial to the success of women's education. Promoting women's education necessitates a cooperative change in both macro-level social structures and micro-level family practices.

Corporate fraud in India has evolved from a problem of episodic accounting manipulation into a broader governance, disclosure, audit, cyber, and market-integrity challenge. The modern Indian regulatory response no longer treats fraud as a matter to be addressed only after collapse. It increasingly relies on ex ante prevention through board responsibility, internal financial controls, audit committee oversight, statutory auditor reporting, specialized investigation by the Serious Fraud Investigation Office (SFIO), disciplinary action by the National Financial Reporting Authority (NFRA), disclosure-driven surveillance under the Securities and Exchange Board of India (SEBI), and risk-based compliance requirements issued by the Reserve Bank of India (RBI). This paper analyses the preventive architecture of Indian law and evaluates whether the existing framework is sufficiently integrated, credible, and future-ready. The paper combines doctrinal analysis with recent public data from SFIO, NFRA, RBI, and SEBI-related regulatory materials. The evidence shows that India has moved toward a multi-agency model of prevention, but implementation remains uneven. Stronger statutory duties exist on paper, yet their effect depends on data quality, auditor independence, continuous monitoring, whistleblower trust, cyber resilience, and inter-regulator coordination. Recent RBI fraud-risk rules and SEBI’s 2024 cybersecurity framework demonstrate a decisive preventive turn, while SFIO and NFRA trends indicate that enforcement visibility has improved. Even so, recurrent spikes in reported fraud value, audit failures in high-profile entities, and the continuing dependence on post-facto investigations show that deterrence is not yet fully internalised within Indian corporate practice. The paper argues that the next stage of reform should focus on governance quality, real-time red-flag analytics, stronger whistleblower assurance, consistent treatment of related-party and beneficial ownership risks, and deeper convergence between company law, securities regulation, audit regulation, and banking supervision.

The rapid growth of machine learning has resulted in its use in crucial areas like healthcare, cloud services, and the Internet of Things. As machine learning models continue to grow in value and play an increasingly important role in decision-making, the need to protect against threats like theft, malicious use, and unauthorized copying is becoming more significant. This literature review examines recent efforts to secure machine learning models via different protection methods, such as watermarking, anomaly detection, resistance to distillation, and federated security mechanisms. Each of these methods helps to protect machine learning models from various types of attacks, including model extraction, reverse engineering, and unauthorized knowledge transfer. The various research papers selected cover a wide variety of solutions. Examples include backdoor-based watermarking, which embeds hidden ownership data into machine learning models without degrading their performance; and trace rewriting techniques, which prevent unauthorized copying of machine learning models by encoding knowledge into a form that makes it impossible to distill via knowledge distillation. Anomaly detection techniques are commonly used in both the cloud and the Internet of Things to monitor and identify suspicious activities in real time. Finally, distributed and federated verification systems provide a decentralized method for ensuring machine learning model integrity and ownership across multiple nodes.

The export of handicraft and handloom products has gained increasing importance in recent years, especially with the growing global demand for traditional and culturally rich products. This study is undertaken to analyse the country-wise and state-wise export performance of handicraft and handloom products from India. It focuses on identifying major importing countries and examining the contribution of different Indian states in export activities. The research is based on secondary data collected from various government reports and published sources. Various analytical tools such as percentage analysis, descriptive statistics, and comparative analysis were used to interpret the data. The findings indicate that certain countries account for a major share of India’s handicraft and handloom exports, while a few states contribute significantly to the overall export performance. Overall, the study provides useful insights into the country-wise and state- wise export trends of handicraft and handloom products from India.

Gabriel García Márquez’s A Very Old Man with Enormous Wings explores the dynamics of otherness through the portrayal of a mysterious winged figure who disrupts the mundane life of a small coastal community. This paper examines how the narrative constructs and critiques the politics of marginalisation by depicting the treatment of the “alien” figure. Using postcolonial and sociocultural frameworks, the study argues that the old man becomes a site upon which societal fears, curiosity, exploitation, and indifference are projected. Márquez exposes the mechanisms through which societies dehumanise what they cannot understand, thereby reflecting broader issues of exclusion, power, and ethical failure.

Heterocyclic compounds form the backbone of most medicinal drugs in clinical use today. Their synthesis, however, often relies on long reaction times, high temperatures, and large amounts of toxic solvents under conventional heating. Microwave-assisted synthesis (MAS) has emerged as a powerful and green alternative, dramatically reducing reaction times from hours to minutes while improving yields and purity. This review covers the principles behind microwave chemistry, compares it with conventional methods, and surveys recent advances in the microwave-assisted construction of key bioactive heterocycles such as benzimidazoles, pyrimidines, quinolines, thiazolidines, imidazoles, oxazoles, and indoles. Strategies including solvent-free conditions, catalyst use, ionic liquid media, and continuous flow microwave reactors are discussed. The review concludes with current challenges and future directions for scaling up microwave chemistry in pharmaceutical development.

Blockchain is a distributed ledger and one of the most promising, disruptive and revolutionary technology of today. decentralized systems are the backbone of cryptocurrencies and the technology behind these currencies are block chain. Crypto, a digital currency, which is an alternative form of payment created using encryption algorithms. A cryptocurrency is a digital currency designed to work through a computer network that is not reliant on any central authority, such as a government or bank, to uphold or maintain it, Bitcoin is block-chain but it actually consists of chain of consecutive blocks of transactions. Block chain is a decentralized, peer-to-peer (P2P) network and distributed in nature. Each and every participant of the network can control the network. Blochchain technology network consists of many computers connected together and the block cannot be altered without consensus of the whole network. Block can be considered as a container for the data. Blockchain is continuously growing chain of blocks which are interconnected and protected with the help of cryptographic functions also. Validating of new blocks is followed by a set of protocols and consensus from every participant of the network. The revenue records are kept basically in linear chain. Pointers and linked list data structures are used in blockchain for the block representation. Blocks are arranged in sequence. Block is a collection of data that stores the transaction details such as timestamp, link to the previous block which is generated by secure hash algorithm. Every block has contained two parts: block header and block body. This Research on Block chain tax payment system basically features organized, distributed and immutable authenticated system that ensure security, transparent, and reliable distributed ledger by having multiple network citizens and government views on transactions, data storage and many other nodes distribution in internally generated revenue of Ebonyi state government, preventing alterations to past records immutability, and verify the identity of the payer. blockchain is widely considered a budding technology, meaning it is a rapidly developing, promising, and emerging technology with significant potential to disrupt industries by providing secure, transparent, and decentralized methods for recording and verifying transactions.

Osilodrostat is a steroidogenesis inhibitor widely used in the management of Cushing’s syndrome, necessitating the development of reliable analytical methods for routine quality control and stability assessment. The present study focused on the development and validation of a simple, rapid, accurate, and stability-indicating RP-HPLC method for the quantitative estimation of Osilodrostat in bulk drug and pharmaceutical dosage form following ICH Q2(R2) guidelines. Chromatographic separation was achieved using a YMC Accura Triart C18 column (250 × 4.6 mm, 5 µm) with a mobile phase comprising 10 mM ammonium bicarbonate in water and acetonitrile (85:15 v/v) at a flow rate of 0.7 mL/min. Detection was performed at 245 nm with a runtime of 10 minutes. Under optimized conditions, Osilodrostat exhibited a sharp and symmetrical peak at a retention time of 3.936 minutes. System suitability studies showed an average peak area of 1268445, theoretical plate count of 5041, and tailing factor of 1.345 with %RSD for peak area of 0.058%, confirming excellent precision and chromatographic reproducibility. The method demonstrated linearity over the concentration range of 1.44–5.4 µg/mL with proportional detector response. Sensitivity studies produced signal-to-noise ratios of 5.65 for LOD and 11.30 for LOQ. Accuracy studies at 50%, 100%, and 150% levels showed mean recoveries of 100.83%, 100.46%, and 101.16%, respectively. Robustness studies under varied flow rates and temperatures showed minimal chromatographic variation. Forced degradation studies revealed stability under acidic, alkaline, and thermal conditions, while oxidative stress caused noticeable degradation. The assay of Isturisa tablets showed 101.08% drug content. Overall, the developed RP-HPLC method was found to be precise, robust, sensitive, and suitable for routine analysis and stability studies of Osilodrostat.

Pepper is one of the most important spice commodities traded in the global market and plays a vital role in international agricultural trade. The present study examines the production trends and export performance of pepper among major producing countries such as India, Vietnam, Brazil, Indonesia, and Sri Lanka over the period 2019–2025. The study is based on secondary data collected from reliable sources such as international trade databases, government publications, and research reports. Analytical tools such as Compound Annual Growth Rate (CAGR) and trend projection analysis have been employed to evaluate growth patterns and forecast future performance.
The findings reveal that Vietnam dominates global pepper exports despite experiencing a decline in production, indicating strong export-oriented strategies and efficient supply chain management. Brazil demonstrates steady and balanced growth in both production and exports, while Sri Lanka shows the highest growth rate despite its smaller production base. India exhibits moderate production growth but significant export expansion, reflecting increasing integration into global markets. Indonesia shows relatively slow growth, indicating structural limitations in productivity and export performance.
The study highlights that export performance is influenced not only by production levels but also by factors such as quality standards, value addition, logistics efficiency, and global demand trends.

The research concludes that the future growth of the pepper industry depends on technological advancement, sustainable farming practices, and effective export strategies.

This article examines New Kingdom ancient Egyptian love poetry as a culturally embedded form of emotional expression, advancing the concept of an “archaeology of emotion.” While existing scholarship has emphasized symbolism and genre, the cultural structuring of affect in these texts remains underexplored. Drawing on performance theory, cultural memory studies, and close textual analysis, the study situates these poems within their historical, social, and symbolic contexts. It argues that love is represented not as a static state but as a dynamic, embodied process structured through recurring patterns of longing, anticipation, and union. By analyzing natural imagery, gendered voice, and performative context, the article demonstrates how emotional experience is articulated through shared cultural frameworks. Rather than opposing dominant ideological forms, love poetry functions as a complementary mode of representation, introducing subjectivity and variability into a system oriented toward order and permanence. The study contributes to interdisciplinary debates on emotion, literature, and culture in antiquity.

This qualitative study explored the challenges and issues encountered in implementing transformational leadership practices and identified intervention programs that school heads employ to strengthen teachers' pedagogical practices in public elementary schools under the Schools Division Office of Cotabato. Using phenomenological inquiry through semi-structured in-depth interviews (IDI) and focus group discussions (FGD) with 15 purposively selected school heads, principals, and teachers-in-charge, the study employed thematic analysis to derive meaningful themes from participant narratives. Two overarching global themes emerged from the data: (1) Workload and Time Management—encompassing sub-themes of high administrative workload and insufficient access to instructional resources; and (2) Professional Development Programs—comprising structured training and in-service sessions, and instructional coaching and mentoring. Participants revealed that heavy administrative burdens significantly limited opportunities for pedagogical leadership and instructional supervision. Resource scarcity further constrained teachers' ability to implement innovative, learner-centered strategies. In response, school heads employed structured professional development and individualized mentoring as primary interventions. The study concludes with an intervention plan recommending structured training, instructional coaching, collaborative professional learning communities, and technology integration support to enhance teacher effectiveness and learner outcomes.

वर्तमान समय में शिक्षा का स्वरूप तीव्र गति से परिवर्तित हो रहा है, जिसमें पारंपरिक शैक्षणिक पद्धतियों के स्थान पर व्यावसायिक (Vocational) तथा कौशल-आधारित शिक्षा की महत्ता निरंतर बढ़ती जा रही है। विशेष रूप से ब्यूटीशियन, मेकअप आर्टिस्ट, हेयर स्टाइलिंग तथा स्किन केयर जैसे क्षेत्रों में युवाओं, खासकर युवतियों, की भागीदारी उल्लेखनीय रूप से बढ़ी है। यह अध्ययन युवाओं के इस बदलते रुझान का विश्लेषणात्मक मूल्यांकन प्रस्तुत करता है।
अध्ययन में पाया गया कि रोजगार के बढ़ते अवसर, आत्मनिर्भर बनने की इच्छा, कम समय में व्यावहारिक कौशल अर्जित करने की सुविधा तथा सोशल मीडिया प्लेटफॉर्म के प्रभाव जैसे कारक इस प्रवृत्ति को प्रमुख रूप से प्रभावित कर रहे हैं। इंस्टाग्राम और यूट्यूब जैसे माध्यमों ने न केवल इस क्षेत्र को लोकप्रिय बनाया है, बल्कि इसे एक आकर्षक करियर विकल्प के रूप में भी स्थापित किया है। इसके अतिरिक्त, पारंपरिक शिक्षा प्रणाली में बढ़ती प्रतिस्पर्धा, सैद्धांतिक ज्ञान की अधिकता तथा रोजगार के सीमित अवसरों ने भी युवाओं को व्यावसायिक शिक्षा की ओर प्रेरित किया है।
यह शोध मुख्यतः द्वितीयक आंकड़ों पर आधारित है, जिसमें विभिन्न शोध पत्रों, पुस्तकों तथा रिपोर्टों का विश्लेषण किया गया है। अध्ययन के निष्कर्षों से यह स्पष्ट होता है कि व्यावसायिक शिक्षा न केवल युवाओं को शीघ्र रोजगार उपलब्ध कराने में सहायक है, बल्कि उन्हें आत्मनिर्भर, आत्मविश्वासी तथा उद्यमशील बनाने में भी महत्वपूर्ण भूमिका निभाती है। इसके साथ ही, यह प्रवृत्ति देश की अर्थव्यवस्था को सुदृढ़ करने तथा बेरोजगारी की समस्या को कम करने में भी प्रभावी सिद्ध हो रही है।
इस प्रकार, व्यावसायिक शिक्षा वर्तमान समय की आवश्यकता के अनुरूप एक सशक्त एवं प्रासंगिक शैक्षिक विकल्प के रूप में उभरकर सामने आई है।

This study examines the role of biometric technology in enhancing modern security systems, focusing on its effectiveness, benefits, and inherent challenges. Adopting a qualitative research approach with a descriptive design, the research analyzes existing scholarly literature, reports, and documented case studies to provide a comprehensive overview of the field. The findings indicate that biometric technologies—including fingerprint, facial, iris, and voice recognition—significantly improve identity verification accuracy and reliability compared to traditional knowledge-based methods like passwords or PINs. The study highlights the widespread application of these technologies across diverse sectors such as banking, border control, law enforcement, and mobile security. Furthermore, it identifies a growing trend in integrating artificial intelligence (AI) and machine learning to enhance system performance and the adoption of multimodal biometrics to reduce authentication errors. Despite these advancements, the study reveals critical concerns regarding privacy protection, data security, and ethical implications, noting that compromised biometric data poses long-term risks. The paper concludes that while biometrics are essential for modern security infrastructures, their successful implementation requires robust legal frameworks, advanced technological safeguards, and continuous system improvements to protect individual rights and sensitive data.

This qualitative phenomenological study investigates the lived experiences, resilience factors, and coping strategies of young adults in Ghana who lost both parents before age 21. Drawing upon Resilience Theory (Masten, 2014) and the Dual Process Model of Coping (Stroebe & Schut, 2010), the study recruited 16 participants (10 female, 6 male) aged 22–35 years through purposive and snowball sampling from Accra, Kumasi, and Cape Coast. Participants completed in-depth semi-structured interviews exploring their experiences of loss, subsequent living arrangements, emotional and psychological responses, support systems, coping mechanisms, and pathways to resilience. Data were analysed using Interpretative Phenomenological Analysis (IPA), yielding six superordinate themes: (1) The Cascading Loss of Parents, Home, and Identity; (2) Sibling Separation as Secondary Trauma; (3) Extended Family as Both Refuge and Source of Strain; (4) Faith as Central Coping Resource; (5) Academic Achievement as Survival Strategy; and (6) Forged Independence and Premature Adulthood. Findings reveal that double orphanhood extends beyond the loss of parents to encompass loss of home, siblings (through separation), financial security, and sense of identity. Participants described profound loneliness, the burden of being passed between reluctant relatives, and pressure to achieve academically as proof that they were worthy of investment. Faith in God was the most frequently cited coping resource, followed by academic focus and self-reliance. Protective factors included at least one consistent adult supporter, sibling reunification in adulthood, and meaning-making through helping other orphans. These findings inform psychosocial interventions, extended family support programmes, and policy for orphaned young adults in Ghana.

This paper is aimed at contributing to the topic of guerrilla marketing and complimenting on the efforts of other researchers on what is guerilla marketing, and how can new businesses with low budgetary income utilize it to advertise their goods? The foundation of this review is based on the work of Hutter and Hoffmann (2011). Guerrilla marketing is an alternative, cost-effective advertising strategy that only makes use of unpaid media. It does this by designing an atypical campaign that elicits an unexpected response, inspiring others to spread the word. The exact definition should serve as a direction for where attention should be focused rather than as a rigid set of standards that must be fulfilled. The startup is forced by the low budget to think of alternate, non-traditional advertising techniques. This is critical to eliciting a response in the recipients, which could spur them on to spread the word further. Encouragement of influencers and the media to spread the word is another option. The campaign's distribution expenses are eliminated by using these tactics. Additionally, an approach for developing a guerilla marketing campaign to promote a startup's product is suggested as follows: Campaign objectives should be developed, the target audience should be determined, the message and campaign should be designed, the distribution should be prepared, the budget should be established, and the outcomes should be measured. Like the concept put out, the approach is not a series of steps that must be taken but rather a set of advertising principles.

The effectiveness of administrative operations in higher institutions largely depends on the efficiency and commitment of non-academic staff. Attendance monitoring is a fundamental aspect of workforce management that influences productivity, accountability, and institutional service delivery. However, many universities still rely on manual attendance registers, which are susceptible to manipulation, inaccuracies, and inefficiency. This seminar examines the adoption of fingerprint biometric machines as an innovative solution for monitoring the attendance of non-academic staff in Ignatius Ajuru University of Education. The study discusses conceptual issues related to biometric technology, attendance management, and service delivery. It also explain the relevance of biometric attendance systems in institutional administration. The paper highlights the operational mechanism, benefits, and implementation challenges associated with fingerprint biometric machines. It concludes that the adoption of fingerprint biometric attendance systems can significantly enhance accountability, improve punctuality, reduce absenteeism, and ultimately promote effective service delivery in Ignatius Ajuru University of Education.

Maintaining physical fitness is a significant challenge for students due to irregular schedules, limited budgets, and diverse cultural food habits. Most existing fitness applications provide generic workout and diet plans that fail to address these individual constraints, resulting in low adherence and ineffective outcomes. This paper presents an AI-based Personalized Fitness Planner designed specifically for students, aiming to deliver practical and customized health recommendations.
The proposed system collects user-specific inputs such as age, fitness goals, lifestyle, and dietary preferences to generate tailored workout routines and budget-conscious meal plans. It integrates a Python-based backend for data processing and validation, a Gradio interface for user interaction, and a large language model via the OpenAI GPT API for generating adaptive recommendations. The application is deployed on a cloud platform, ensuring accessibility and scalability.
The developed solution emphasizes personalization, affordability, and cultural relevance, making it more suitable for student use. The results demonstrate that the system can provide realistic and sustainable fitness guidance. This work highlights the potential of artificial intelligence in enhancing health and wellness applications by addressing user-specific needs effectively.
Index Terms: Artificial Intelligence, Personalized Recommendation Systems, Fitness and Nutrition Planning, Machine Learning, Large Language Models, Human-Centered Computing, Health Informatics, Cloud-Based Applications.

Urinalysis is a fundamental diagnostic test widely used for screening and monitoring renal and systemic diseases. Manual urine analysis has traditionally been considered the gold standard, but it is labor-intensive and subject to observer variability. Automated urine analyzers have emerged to improve efficiency, standardization, and reproducibility. This review compares both methods in terms of accuracy, performance, advantages, and limitations, highlighting the importance of a combined diagnostic approach.

The adoption of digital technology has become a significant driver of SMEs performance, particularly among business owners seeking innovative ways to scale their ventures. In Benue State, the extent to which digital technology influences the performance of Small and Medium Enterprises remains an area of interest. This study investigates the effect of digital technology (cloud computing, e-commerce, social media, and digital payment platforms) on the performance of Small and Medium Enterprises in Benue State. The study had a population of 3,369 SMEs. A survey research design was employed, targeting 375 registered Small and Medium Enterprises drawn using Krejcie and Morgan's (1970) sample size table. Primary data was collected through Five-point Likert Scale structured questionnaires and analyzed using Partial Least Squares Structural Equation Modelling (PLS-SEM). The findings revealed that cloud computing does not have a statistically significant effect on performance of Small and Medium Enterprises in Benue State. However, e-commerce, social media, and digital payment platforms exhibited a significant positive effect on the performance of Small and Medium Enterprises, enhancing business accessibility, funding opportunities, and transaction efficiency. Based on these findings, it was recommended that policymakers and business support organizations should provide targeted training programmes to enhance awareness and practical applications of cloud services for business operations, government agencies and private sector partners should facilitate better internet access, affordable digital tools, and digital literacy programs to enable more Small and Medium Enterprises to take advantage of online business opportunities, establish regulatory bodies that ensure and maintain the use of social media to reduce cybercrime. And financial institutions should work towards expanding secure and accessible digital payment systems. Additionally, entrepreneurs should be trained on the benefits and best practices of using digital transactions to improve business efficiency and customer trust. The study concludes that digital technology plays a crucial role in fostering the performance of Small and Medium Enterprises (SMEs) in Benue State

Oral health is a foundational component of overall human well-being, influencing daily comfort, self- confidence, and quality of life. Yet dental care delivery remains predominantly reactive—patients seek help only after problems escalate rather than acting on early warning signs. Widely available applications offer little beyond static information, placing the burden of symptom interpretation entirely on the individual and offering no intelligent analysis or adaptive support. The resulting delays contribute to worsening conditions, increased discomfort, and avoidable treatment costs. PearlNest confronts these shortcomings with an AI-driven platform that transforms oral care through automation and data-informed insights. Its core capabilities span symptom-based condition analysis, camera-assisted visual screening, personalized care recommendations, and location-aware dentist discovery. Upon receiving user inputs and dental imagery, the system produces preliminary diagnoses, urgency assessments, and clear next-step guidance. A habit- monitoring module with an integrated health-score tracker enables continuous oral health oversight, helping users recognize behavioural patterns and shift toward preventive routines. Automated nudges and evidence-based hygiene recommendations reinforce timely professional consultations. The platform is engineered for broad accessibility through a multilingual, intuitive interface suited to diverse populations. Taken together, PearlNest represents a modern, proactive paradigm for dental care— one that fuses artificial intelligence, personalization, and accessibility to accelerate early detection, shorten treatment delays, and sustain long-term oral health.

This paper is aimed at contributing to the topic of scientific management theory and productivity on tertiary education. topic the study aimed to determine the impact of the best way/ method (science of work) on academic performance, determine the impact of division of labour on academic performance and to determine the impact of Scientific selection of workmen on academic performance. The study concluded base on the evaluation of other empirical works that Tertiary institution should adopt some of the scientific management theory in order to achieve the much-needed improvement/ productivity. In the quality of graduate produce yearly. When Tertiary institution adopts the scientific management principle of scientifically selecting the best methods, best lecturers and students. The study therefore suggest that tertiary institutions should follow the tradition of scientific management approach which will enable high product outcome of perfect efficiency through application of Science in management if school. There should be an established procedure or lay down principle to be followed by workers in executing task, The best qualified staff should be selected and place accordingly, there should be cordial and mutual understanding between the management, staff, and students.

Artificial Intelligence (AI) and Machine Learning (ML) have emerged as transformative technologies in the pharmaceutical industry, particularly in the field of drug formulation. Traditional formulation approaches rely heavily on trial-and-error methods, which are time-consuming, expensive, and often inefficient. AI and ML provide data-driven methodologies that significantly enhance the efficiency, accuracy, and speed of formulation development. These technologies enable prediction of drug
properties, optimization of excipient combinations, and simulation of drug release profiles.
Moreover, AI facilitates Quality by Design (QbD) and real-time process monitoring, ensuring better product quality and regulatory compliance. This review article discusses the role, applications,
advantages, and challenges of AI and ML in pharmaceutical formulations. It also highlights recent advancements and future perspectives in this rapidly evolving domain.

Medication non-adherence remains a major healthcare problem because patients frequently miss doses, misunderstand prescription instructions, or fail to manage refills and schedules correctly, which affects treatment outcomes and increases healthcare burden. [5] Recent research shows that artificial intelligence can improve medication management through reminder systems, prescription interpretation, patient-friendly instruction generation, conversational assistance, and multimodal healthcare interaction. [5][10] This survey paper reviews recent studies relevant to the proposed project, MediAssist: AI-Powered Smart Medication Management System, by analyzing mobile reminder applications, AI-based prescription decoding, prescription-direction simplification, and multimodal healthcare assistant platforms. [6][8][10] The reviewed literature indicates that existing systems are often fragmented, with separate focus on reminders, inventory tracking, OCR-based prescription understanding, or general medical consultation, while few works integrate these capabilities into a single patient-centered platform. [5][6][10] The survey identifies key research gaps in personalization, accessibility for elderly users, multilingual support, caregiver collaboration, privacy, and real-world validation. [5][6] Based on these findings, the paper argues that a smart medication management system should combine reminders, medication tracking, prescription interpretation, simplified instructions, adherence monitoring, and AI-assisted interaction in a unified architecture aligned with current journal-based research trends. [5][8][10].

Intercontinental/international trade plays an important role in influencing economic growth and development across countries. By facilitating the trade of goods, services, capital, and technology, trade enhances creation, efficiency, and transformation within national economies. This manuscript highlight the connection between intercontinental trade and economic expansion through theoretical perspectives and experimental studies that have been carried out by different scholars their view or argument and their suggestions.
This manuscript reviews studies from other scholars who came up with their own theories and perspectives like the absolute advantage, classical and modern trade theories including , comparative advantage, and factor endowment theory, as well as modern trade openness models. A broad literature identified empirical facts from industrial and rising economies, indicating of how exports, imports, trade policies and economic growth relate to each other.
This manuscript furthermore identifies clarification gaps in the existing literature, mainly concerning the responsibility of trade in least industrial countries and the relations between trade sincerity and institutional quality. The result point out that although global trade generally promotes trade and industrial growth through improved effectiveness, knowledge transfer, and market growth, the degree of its force differ across countries depending on economic structure, institution guidelines, and principle guidings on the environments.
This manuscript concludes that countries should adopt strategic trade regulations that support export diversification develop trade communications, and support institutional potential to make best use of the growth benefits of intercontinental trade.

Background: One of the most common and controllable risk factors for cardiovascular morbidity and death globally is hypertension, which is defined by the 2017 ACC/AHA recommendations as a persistent rise of systemic arterial blood pressure at or above 130/80 mmHg. Over 1.28 billion adults worldwide are affected, yet less than half receive appropriate therapy [1,2].
Objective: The physiology of blood pressure regulation, the complex pathophysiology of hypertension, its clinical consequences, and the receptor-level processes by which the main antihypertensive medication classes work are all collected in this overview.
Methods: PubMed literature, reputable pharmacology and physiology textbooks, and recommendations from the WHO, JNC 8, and ACC/AHA were used in a narrative synthesis.
Results & Conclusion: Pressure inside blood vessels depends on how hard the heart pumps, how narrow the arteries are, how kidneys handle salt, a hormone chain called RAAS, along with nerve signals that adjust bodily functions moment to moment. When tiny blood vessel linings fail to work properly, when body's alert system runs too high, when cells face chemical imbalance, or kidneys respond poorly, higher pressure often follows. Treatment choices like water pills, heart-slowing drugs, artery relaxers, enzyme blockers, or receptor shields rely heavily on what else is wrong in the person and what’s driving their condition beneath symptoms. Figuring out which drug pairs work best over time, helping those whose levels stay high despite treatment, plus making sure people everywhere get proper care regardless of country income - these remain open questions worth exploring next. Despite progress, uneven reach across regions leaves some without needed solutions even now.

Mimosa pudica plant additionally referred to as sensitive plant is a creeping annual and perennial herb. In Latin it is called as Mimosa pudica Linn. Mimosa belongs to the taxonomic group Magnoliopsida and circle of relatives Mimosaseae. This plant which folds itself when touched and spreads its leaves all over again after some time. This review offers a quick compilation of its phytochemical and pharmacological activities. Ayurveda has declared that its root is sour, acrid, cooling, vulnerary, alexipharmic. It is used for the treatment of leprosy, dysentery, vaginal and uterine infections, also for inflammations, burning sensation, bronchial asthma, leucoderma, fatigue and blood illnesses. Decoction of root is used as gargle to lessen toothache. Mimosa pudica also used for treatment of several disorders like cancer, diabetes, hepatitis, weight problems and urinary infections. It shows some pharmacological properties like antioxidant, antifungal, antibacterial, antidepressant and and so forth.

Nitric oxide (NO) is the smallest endogenous signaling molecule produced in the human body. It was identified in the 1980s as an important biological mediator involved in several physiological processes.
The concept of a relaxing factor released from the endothelium was first proposed by Robert Furchgott and John Zawadzki, who suggested that endothelial cells produce a soluble substance responsible for vascular relaxation, which they termed endothelium-derived relaxing factor (EDRF). [1] Later, in 1987, Louis Ignarro confirmed that EDRF is chemically identical to nitric oxide. This discovery significantly advanced the understanding of vascular physiology and cell signaling.[2] In recognition of their contributions, Robert Furchgott, Ferid Murad, and Louis Ignarro were awarded the Nobel Prize in Physiology or Medicine 1998.[3]

In the rapidly evolving landscape of modern travel, travelers often struggle to consolidate destination research, real-time weather conditions, local attractions, transportation options, and budget planning into a coherent and actionable itinerary. Existing travel platforms p
rimarily offer fragmented, static information without intelligent orchestration, personalized recommendations, or dynamic cost awareness, resulting in time-consuming manual planning and suboptimal travel experiences. To address these challenges, the proposed Agentic Travel Planner introduces an intelligent Agentic AI-based travel planning framework that integrates Large Language Models (LLMs), a suite of real-world data tools. Unlike traditional travel applications, the system operates as an autonomous planning agent capable of interpreting natural-language travel requests, dynamically invoking specialized tools for weather lookup, attraction and restaurant discovery, transportation guidance, currency conversion, and expense estimation, and synthesizing the gathered information into structured, markdown-formatted travel itineraries. receive comprehensive trip plans, and observe the agent's step-by-step reasoning trace. By continuously reasoning over retrieved data and adapting responses to user-specified parameters such as duration, group size, and budget, the agentic system reduces planning overhead, improves itinerary quality, and enables scalable, personalized travel assistance—transforming conventional travel search into an intelligent, self-guided trip planning experience.

The rapid increase in digital legal documents has made it harder to understand and analyze contracts, agreements, and policies. These documents are often long, unstructured, and filled with specialized legal terms, which makes manual review both time- consuming and prone to mistakes. This paper proposes a Smart Legal Assistance System that automates the analysis of legal documents using advanced Natural Language Processing(NLP) and machine learning techniques.

The system mainly works at the clause level, where legal documents are divided into meaningful sections and grouped into categories like liability, payment, termination, and confidentiality. Along with classification, it also evaluates risks by detecting unclear or potentially harmful clauses and assigning severity levels based on their legal impact. This helps users quickly grasp the important parts of a document without needing strong legal knowledge. The system also incorporates contextual analysis techniques to understand relationships between different clauses within a document, enabling more accurate interpretation of legal meaning. Unlike traditional approaches that rely on isolated clause evaluation, the proposed system considers the overall document structure, thereby improving reliability.

In addition, the inclusion of a recommendation module turns the system from a simple analysis tool into an active decision-support system. This approach highlights how AI-based solutions can improve legal services by making them more efficient, easier to access, and capable of handling larger-scale applications.

Nutritional deficiencies such as iron- deficiency anemia, vitamin B12 deficiency, calcium deficiency, and zinc insufficiency remains major public health burdens worldwide. Traditional diagnostic methods rely on laboratory testing, requiring infrastructure often inaccessible in low-resource settings. Fingernails serve as a non-invasive biomarker that reflects systemic nutritional and metabolic health. With recent advances in computer vision, deep learning, and mobile imaging, AI- driven analysis of fingernail photographs presents an emerging opportunity for scalable nutritional screening. This review provides a comprehensive survey of (1) clinical evidence linking nail biomarkers to nutrient status, (2) the state of AI and computer vision systems for nail and skin analysis, (3) illumination correction and color normalization for smartphone imaging, (4) multi-task learning for simultaneous medical condition prediction, and (5) explainable AI for clinical interpretability. Existing literature demonstrates strong clinical validity and technological feasibility, yet major gaps remain, particularly the absence of multi-deficiency diagnostic systems. This review outlines future research challenges and proposes directions for developing an integrated, explainable, smartphone-based nutritional screening framework.

Polycystic Ovary Syndrome (PCOS) is a common endocrine and metabolic disorder affecting women of reproductive age, characterized by hyperandrogenism, menstrual irregularities, ovulatory dysfunction, and polycystic ovarian morphology, and is associated with significant reproductive and metabolic complications. Women with PCOS have a higher risk of insulin resistance, impaired glucose tolerance, type 2 diabetes mellitus, cardiovascular disease, infertility, and endometrial abnormalities, making timely diagnosis and effective management essential. Current treatment strategies are mainly symptom-oriented, with lifestyle modification considered the first-line approach, as weight loss through dietary changes and regular physical activity improves hormonal balance, insulin sensitivity, menstrual regularity, ovulation, and pregnancy outcomes, even with modest weight reduction of around 5% of initial body weight. Pharmacological treatment plays an important role, particularly insulin-sensitizing agents such as metformin, which has been extensively studied and shown to improve metabolic parameters, reduce androgen levels, and enhance ovulatory function in selected PCOS populations. For the management of infertility, ovulation induction agents including clomiphene citrate, aromatase inhibitors, and gonadotropins are commonly used, while surgical options and assisted reproductive techniques are reserved for cases unresponsive to medical therapy. In recent years, emerging and adjunctive therapies such as statins, inositols, melatonin, and resveratrol have gained attention due to their potential benefits in improving insulin resistance, lipid profile, androgen excess, and inflammatory markers; however, current evidence remains limited and further clinical studies are required to confirm their safety and effectiveness. Overall, the complex and multifactorial nature of PCOS highlights the need for an integrated, individualized, and evidence-based treatment approach to optimize both short- and long-term health outcomes.

The pharmaceutical industry has transformed global healthcare, yet the way drugs are traditionally synthesized often comes at a high environmental cost. Conventional methods rely on toxic solvents, hazardous reagents, and energy-intensive processes, producing large amounts of waste. Green chemistry offers a way forward by encouraging safer, cleaner, and more efficient approaches to drug development. This review highlights recent progress in applying green chemistry to pharmaceutical synthesis, including the use of sustainable solvents such as water, ionic liquids, and deep eutectic solvents, as well as advances in catalysis through biocatalysts, organocatalysts, and photocatalytic systems. Energy-efficient tools like microwave, ultrasound, and continuous-flow chemistry are also reshaping manufacturing practices. In addition, the integration of renewable raw materials and circular economy strategies is helping to reduce reliance on fossil resources. While challenges such as scalability, costs, and regulatory hurdles remain, future opportunities lie in digital tools, automation, and green analytical techniques. Together, these developments show that green chemistry is not just an environmental ideal but a practical necessity for a more sustainable and responsible pharmaceutical industry.

Artificial Intelligence (AI) and Machine Learning (ML) are rapidly transforming the pharmaceutical industry, particularly in the field of pharmaceutical formulations. Traditional formulation development is often expensive, time-consuming, and dependent on repeated trial-and-error experimentation. Scientists must optimize numerous variables such as excipient selection, drug release profile, stability, manufacturing parameters, and patient acceptability before achieving a successful product. AI and ML provide a powerful data-driven alternative by analysing large datasets, identifying hidden relationships, and predicting outcomes with high precision. These technologies can significantly reduce development timelines, lower costs, improve product quality, and accelerate innovation.
In pharmaceutical formulations, AI models are increasingly used in preformulation studies to predict solubility, stability, polymorphism, hygroscopicity, and compatibility between drug substances and excipients. During product development, ML algorithms can optimize concentrations of binders, disintegrants, lubricants, polymers, and coating materials while minimizing the number of laboratory experiments required. Neural networks, support vector machines, random forest models, and deep learning approaches have demonstrated strong predictive performance for tablet hardness, friability, dissolution, bioavailability, and shelf-life.
AI also plays a vital role in manufacturing and quality assurance. By integrating with Process Analytical Technology (PAT), sensors, and real-time monitoring systems, AI enables continuous process verification, predictive maintenance, deviation detection, and autonomous process control. In quality control laboratories, computer vision systems can detect visual defects in tablets, capsules, and packaging more efficiently than manual inspection.
Furthermore, AI supports personalized medicine by enabling patient-specific dosage forms, especially in conjunction with 3D printing technologies. Despite these advantages, challenges remain, including poor data quality, limited datasets, model interpretability, cybersecurity risks, lack of regulatory harmonization, and the need for trained professionals. Nevertheless, the future of pharmaceutical formulations will increasingly depend on intelligent systems integrated with robotics, digital twins, generative AI, and smart manufacturing platforms. This review highlights the major applications, benefits, limitations, and future opportunities of AI and ML in pharmaceutical formulations.

Moringa oleifera, commonly known as drumstick tree, has gained significant attention due to its wide range of pharmacological properties, including antioxidant, anti-inflammatory, and adaptogenic effects. The present systematic review aims to evaluate the stress-relieving and osteogenic potential of Moringa oleifera based on in vivo studies. A comprehensive literature search was conducted using scientific databases, focusing on animal studies that assessed behavioral, biochemical, and bone-related outcomes following Moringa oleifera administration. The findings indicate that Moringa oleifera exhibits significant stress-reducing effects by modulating cortisol levels, oxidative stress markers, and behavioral responses. Additionally, it demonstrates osteogenic potential by improving bone mineral density, enhancing bone formation markers, and reducing bone resorption. These effects are primarily attributed to its rich phytochemical profile, including flavonoids, phenolics, and essential minerals. However, limitations such as variability in study design and lack of clinical trials highlight the need for further research. Overall, Moringa oleifera shows promising potential as a natural therapeutic agent for stress management and bone health.

Deepfake technology, powered by artificial intelligence and machine learning, has emerged as one of the most disruptive innovations in digital media. While it offers creative and technological advancements in entertainment, education, and communication, its misuse poses a significant threat to media credibility and public trust. This research paper examines the impact of deepfake technology on media trust, focusing on its implications for journalism, public perception, misinformation, and democratic processes. The study adopts a qualitative and analytical approach, drawing on recent research, case studies, and theoretical frameworks. Findings suggest that deepfake technology undermines the reliability of digital content by blurring the distinction between reality and fabrication. Repeated exposure to manipulated media has been found to erode trust in news sources and create a generalized scepticism toward all forms of digital information. Moreover, the “liar’s dividend” phenomenon allows individuals to dismiss authentic content as fake, further complicating truth verification. The paper also highlights the role of media literacy, technological detection tools, and regulatory frameworks in mitigating the adverse effects of deepfakes. The research concludes that while deepfake technology presents serious challenges to media trust, a multi-dimensional response involving education, policy intervention, and technological innovation can help restore confidence in digital media ecosystems. The study emphasizes the need for global collaboration to address the ethical and societal consequences of synthetic media. The paper also explores countermeasures such as AI detection tools, media literacy initiatives, and regulatory frameworks aimed at restoring trust. Ultimately, the study concludes that while deepfake technology is inevitable, its negative impact on media trust can be mitigated through collaborative efforts involving governments, technology companies, and society. The findings highlight the urgent need for ethical governance and public awareness to preserve trust in the digital information ecosystem.

Extracellular vesicles (EVs) are nano-sized, lipid bilayer–enclosed structures secreted by plant, microbial, and mammalian cells. Recent scientific advances have revealed that EVs function as nutritional messengers, mediating dynamic communication between dietary components, gut microbiota, and host physiological processes. Unlike traditional nutrients, EVs carry highly stable bioactive cargos—including microRNAs, lipids, peptides, metabolites, and functional proteins—which can withstand gastrointestinal digestion and be absorbed into systemic circulation. Dietary EVs derived from milk, fruits, and vegetables regulate intestinal immunity, epithelial barrier integrity, and gene expression pathways involved in metabolism. Concurrently, gut microbiome-derived EVs act as potent biological signals modulating host inflammation, cellular signaling, and metabolic homeostasis. A growing body of evidence suggests that diet alters the microbiome’s EV secretion profile, while microbial EVs shape host metabolic and immune responses, thus forming a bidirectional communication axis. This review synthesizes current knowledge on EV biogenesis, dietary uptake mechanisms, microbiome-derived EVs, and diet–microbiome–host interactions, with emphasis on technological advances in EV isolation and characterization. We also highlight the therapeutic potential of EVs in functional foods, nutraceuticals, and targeted drug delivery. Understanding EV-mediated crosstalk provides a new paradigm in nutrition science and opens avenues for personalized diet-based interventions.

The translation of natural language into executable front-end code represents a transformative step toward democratizing web development. This research explores AI models designed to convert plain English descriptions—such as "create a navigation bar with three links and a centered logo"—into functional HTML and CSS code. We evaluate current approaches leveraging large language models (LLMs), code-specific transformers, and prompt-engineering techniques to interpret and generate structured markup and styling. Our study introduces a benchmark dataset of English-to-HTML/CSS pairs and assesses model performance based on code correctness, layout fidelity, and responsiveness. Experimental results show that combining semantic parsing with layout-aware generation significantly improves code quality over traditional sequence-to-sequence models. We also discuss challenges including ambiguity in natural language, context understanding, and generating responsive design patterns. This work highlights the potential of natural language interfaces to streamline front-end development and make web design more accessible to non-programmers.

Econometrics, is a discipline,that uses theory from economics, mathematics, and statistical technique to examine economic phenomena quantitatively. on the other hand, its expansion and relevance widen beyond this discipline, it has demonstration from other disciplines such as computer science, sociology, psychology, finance, and data science. This article examines the interdisciplinary nature of econometrics, emphasizing how these fields enhance model formulation, evaluation techniques, data treatment, and explanation of results. The study reviews literature that had existed on contributions of the interdisciplinary identifies gaps in current research, and illustrare how application of econometric models using a sample calculation. Findings imply that the integration of different disciplinary perspectives improves the strength, projecting power, and policy significance of econometric models. The article concludes that econometrics cannot function in isolation and must continue to develop through relationship with other disciplines to tackle complex economic problems in a data-driven world.

This study provides a comprehensive review of literature on corruption in higher education in Sub-Saharan Africa (SSA) from 2015 to 2025, focusing on its nature, causes, effects, and mitigation strategies. Drawing on empirical and theoretical studies across multiple SSA countries, the paper conceptualizes corruption as a systemic and multidimensional phenomenon manifesting in academic, administrative, financial, and institutional forms. The review reveals that corruption is deeply embedded in weak governance structures, inadequate funding, socio-cultural norms, and political interference. Theoretical frameworks such as Principal-Agent Theory, Institutional Theory, and Cultural Theory are used to explain the persistence of corrupt practices within tertiary institutions. Empirical evidence indicates that corruption is widespread across SSA, with common manifestations including examination malpractice, bribery for grades, admission racketeering, financial mismanagement, and sexual exploitation. These practices significantly undermine academic integrity, reduce the quality of graduates, erode institutional credibility, and exacerbate inequality in access to education. Furthermore, corruption contributes to broader socio-economic challenges such as unemployment, brain drain, and weakened national development. The study also identifies key anti-corruption strategies, including institutional reforms, digital governance systems, ethical reorientation, and strengthened legal frameworks. However, significant gaps remain in the literature, particularly the lack of longitudinal studies, limited methodological rigor, and insufficient evaluation of intervention strategies. The paper concludes that addressing corruption in SSA higher education requires a holistic, multi-stakeholder approach that integrates governance reforms, technological innovation, and cultural transformation to promote transparency, accountability, and institutional integrity.

Cantor’s Intersection Theorem is a key result in the field of metric spaces and mathematical analysis. This theorem highlights a crucial relationship between completeness and the nested intersection property of closed sets. This paper offers a succinct examination of Cantor’s Intersection Theorem, encompassing preliminary concepts, a formal statement, proof, examples, and its applications in analysis and topology. The theorem is pivotal in fixed point theory, arguments concerning compactness, and the analysis of convergence. Additionally, the paper addresses the importance of completeness in metric spaces and includes illustrative examples to enhance comprehension.

This research paper outlines and examines the concept of bail as an essential component in the administration of justice and criminal jurisprudence. The paper discusses in detail the philosophy of bail in order to understand its nature and significance by interpreting various definitions of bail laid down by the Apex Court and different high courts through their landmark judgements. It further examines the objectives of bail, particularly the need to ensure the attendance of the accused before the court during investigation and trial, while safeguarding presumption of innocence and his fundamental right to personal liberty. This paper also discusses various kinds of bail and their relevance in the practical application. Through this discussion, the paper outlines the significance of bail in maintaining a balance between individual liberty and interests of society. The study highlights that the concept of bail serves as a safeguard against arbitrary and unlawful detention of an individual and incorporates broader constitutional values of justice, equity and fairness.

Digitalization has transformed the operational structure of small businesses by improving communication, marketing, financial management, and customer engagement. The adoption of digital technologies such as digital payments, online marketing, e-commerce platforms, and internet-based communication has significantly influenced the growth and efficiency of small business enterprises. The present study analyses the impact of digitalization on small businesses in Calicut district. The study is based on primary data collected from 100 respondents through a structured questionnaire. Percentage analysis and interpretative analysis were employed to examine digital adoption patterns, internet accessibility, digital marketing practices, business efficiency, revenue generation, and challenges faced by small businesses. The findings reveal that digitalization positively influences operational efficiency, customer communication, market expansion, and revenue growth. However, financial constraints, inadequate technical skills, and poor internet connectivity remain major barriers to digital adoption. The study concludes that digital transformation has become essential for the growth and sustainability of small businesses in the modern competitive market.

This article explores the Harlem Renaissance and the Negritude Movement as two significant literary and cultural movements that emerged in response to racial subjugation and colonial domination. While the Harlem Renaissance developed in the United States during the 1920s, the Negritude Movement arose in the 1930s among French-speaking African and Caribbean intellectuals. Through a comparative analysis of major poets such as Langston Hughes, one of the leaders of this movement, Claude McKay, Aimé Césaire, and Léopold Sédar Senghor, this study examines themes of racial identity, cultural assertion, resistance, and the politics of language. The paper argues that although both movements share a common goal of reclaiming Black identity, they differ in their stylistic approaches, ideological frameworks, and cultural contexts.

This research evaluates the application of lean construction methods and their effect on project outcomes in the Khudi Substation project placed in Nepal. A mixed-methods approach was used, incorporating questionnaire surveys, interviews, and site remarks. Data were gathered from 25 professionals in building and evaluated using the Relative Importance Index (RII) to prioritize major factors influencing performance. Findings indicate that ineffective planning (RII = 0.75), interruptions in material supply (RII = 0.82), and geographical challenges (RII = 0.88) are the most critical elements leading to delays and inefficiencies. The research points out a lack of awareness and implementation of lean practices within Nepal&#39;s infrastructure sector. It suggests the need for better planning systems, improved supply chain management, and focused training initiatives to align lean theory with practical application.

The rapid emergence of the gig economy and flexible work models has significantly reshaped traditional employment patterns, especially among students. With the increasing accessibility of digital platforms and freelancing opportunities, students are actively engaging in part-time, remote, and project-based work alongside their academic commitments. This study aims to examine the extent of student participation in gig work, its influence on academic balance, and its contribution to income generation and financial independence.
The research adopts a quantitative method, collecting primary data through structured questionnaires administered to college students involved in various forms of flexible work. The study analyzes key variables such as motivations for participation, types of gig work undertaken, time allocation, stress levels, academic performance, and financial outcomes. Findings suggest that the primary drivers for student involvement in the gig economy include the need for financial support, desire for independence, and opportunities for skill development and real-world exposure.
While gig work offers several advantages, including flexible schedules, income generation, and enhanced employability skills, it also presents notable challenges. Increased work hours and poor time management may lead to academic stress, reduced focus on studies, and potential declines in academic performance. Additionally, the lack of structured work environments can further complicate students’ ability to maintain a healthy balance between education and employment.

In the ultramodern banking area, digital banking has come an essential part of fiscal services. With the growth of technology, banks are shifting from traditional styles to digital platforms similar as mobile banking, internet banking, and online deals. This exploration paper aims to study the impact of digital banking on cost reduction and fiscal performance of marketable banks in Pune area. The study is grounded on both primary and secondary data. Primary data was collected through structured questionnaires from bank workers and guests, while secondary data was collected from journals, reports, and online sources. The findings of the study reveal that digital banking significantly reduces functional costs by minimizing paperwork, reducing homemade work, and lowering structure changes. It also improves financial performance by adding effectiveness, client base and profitability. Still some challenges similar as specialized issues and cybersecurity risk still live. Regardless these challenges, digital banking plays an important part in appropriate banking operations. The main conclusion of the study is that digital banking has a clear and significant impact on both cost reduction and financial performance of commercial banks.

Speculation is an vital budgetary movement through which speculators point to gain returns whereas overseeing related dangers. In the stock advertise, chance and return are closely related, making portfolio administration an fundamental viewpoint of venture decision-making. The show ponder titled “A Study of Portfolio Diversification and Its Impact on Investment Risk” looks at the part of enhancement in lessening venture chance and making strides portfolio performance.

The primary objective of the think about is to analyze the affect of portfolio broadening on venture chance and to get it its significance in viable portfolio administration. The consider is based on a graphic and survey-based inquire about plan. Essential information was collected from 100 financial specialists through a organized survey, whereas auxiliary information was accumulated from books, diaries, and inquire about papers.

The think about applies Chi-square examination to test the relationship between portfolio enhancement and speculation chance. The calculated Chi-square esteem was found to be more prominent than the table esteem, driving to dismissal of the invalid theory and acknowledgment of the elective theory. The discoveries show that portfolio expansion has a critical affect on lessening venture risk.

The pattern of export destinations plays a critical role in shaping the performance, stability, and resilience of a country’s textile sector. This study provides a comparative analysis of the top export destinations of India, China, and Bangladesh over the period 2013-2014 to 2024-2025, with a focus on understanding market concentration, diversification, and evolving trade dynamics. Using secondary data, the study evaluates how each country positions itself in the global textile market through its choice of export regions.
The analysis reveals that China maintains a highly diversified export structure, enabling it to reduce dependency on specific markets and sustain long-term growth. Bangladesh, while achieving rapid expansion in textile exports, shows a high level of concentration in a few developed markets, which increases its exposure to external risks. India demonstrates a relatively balanced distribution of export destinations, but its diversification remains moderate compared to China. The study highlights that strategic market diversification, along with strong trade relationships, is essential for ensuring stability and competitiveness in the global textile industry.

The Government of India’s social security initiative, Pradhan Mantri Jeevan Jyoti Bima Yojana (PMJJBY) for life insurance, is crucial in Chhattisgarh to provide financial safety nets, especially for its large rural, tribal, and under-privileged population. Launched on 9th May , 2015, these low-premium scheme enable citizens aged 18-50 to secure coverage against death due to any reason through bank accounts, supporting the state's welfare efforts.The objective of this study is to evaluates the efficiency of claim settlements and the overall social security impact of these scheme within the Chhattisgarh state.The study is descriptive in nature and is based on secondary data collected from government publications and websites, bank annual reports,Jan Dhan-Yojana reports, Loksabhaquestions_annexture, government publications and websites, bank and insurance companies annual reports, Ministry of Finance statistics, Department of financial services and peer- reviewed journals, published journals, past research papers, articles, etc. the paper highlights that while enrollment has risen significantly, claim settlement efficiency faces bottlenecks. The study finds that high awareness gaps and documentation issues hinder the full impact of these scheme, necessitating improved digital integration and financial literacy.

This papers analyses the complex relationship between human beings, machines and cinematic expression in 'Ajantrik' (1958), directed by Ritwik Ghatak. In this film the director Ritwik Ghatak shows an unusual emotional bond between a man and his car, stimulating traditional ideas about technology and humanity. Through innovative camera techniques Ghatak highlights themes of loneliness, attachment and social alienation. Through Bimal's character the director shows that a person turns to machine when obsession turns into profession. At the same time a machine can possess human emotions, as represented by Jagaddal.Overall, this article highlights the film's deeper philosophical meaning.

The rapid expansion of e-commerce has significantly transformed the structure and functioning of the retail sector across developing regions. The present study examines the economic impact of e-commerce on the retail sector in Bijnor District. The study is based on primary data collected from 320 respondents through a structured questionnaire. Statistical tools such as frequency, percentage, reliability analysis, and one-sample t-test were used for data analysis. The findings reveal that e-commerce has positively influenced the retail sector by increasing sales, improving retailer income, altering pricing patterns, enhancing market reach, reducing operational costs, and strengthening competitiveness. The study also finds that consumer attitudes toward e-commerce are largely favorable, particularly in terms of convenience, trust, product variety, and price comparison. Reliability analysis confirms that all constructs used in the study are statistically acceptable, with Cronbach’s Alpha values above 0.70. Hypothesis testing shows that the impact of e-commerce on the retail sector, retail efficiency, and consumer attitude is statistically significant. The study concludes that e-commerce has emerged as a powerful driver of retail transformation in Bijnor District and has made a substantial contribution to the local retail economy.

Managing supplier relationships has never been a simple task. Businesses today are navigating an increasingly complex web of global suppliers, fluctuating market demands, and mounting pressure to cut costs without compromising quality. In this environment, the way organisations select and manage their suppliers can make or break their competitive edge. This study explores how Artificial Intelligence (AI) and Machine Learning (ML) are stepping in to reshape that process — making it smarter, faster, and far more reliable.
For years, procurement teams have relied heavily on spreadsheets, gut instinct, and periodic reviews to evaluate suppliers. While these methods have served their purpose, they struggle to keep pace with the speed and scale of modern supply chains. AI changes that equation entirely. By processing enormous volumes of data — from delivery performance records and pricing trends to geopolitical developments and financial health indicators — AI-powered systems can surface insights that would take human team weeks to uncover, in a matter of seconds.
This research takes a closer look at how these technologies are being applied in real-world procurement environments, particularly within manufacturing and retail sectors. Using a mixed-methods approach that combines hard procurement data with on-the-ground case studies, the study assesses just how much of a difference AI adoption is actually making in terms of lead time reduction and cost savings. It also honestly examines the bumps in the road — the data privacy issues, the risk of algorithmic bias, and the very real challenge of getting AI tools to play nicely with older legacy systems that many organisations still depend on.

Health is essential for personal happiness, productivity, and fulfilment. It affects physical well-being, mental health, longevity, academic and professional success, and relationships. Good health lowers healthcare costs, supports economic growth, and enhances community well-being. This paper looks at the state of the healthcare system in rural and urban India. It reviews existing research and gathers data from rural and urban hospitals. It also examines efforts by the Indian government and other organizations, such as Ayushman Bharat, which aims to strengthen primary healthcare and protect vulnerable populations. Addressing these complex issues requires ongoing efforts, adequate funding, and proactive steps in healthcare infrastructure, workforce development, and policy changes. The data presented highlights the ongoing challenges and also the ratio of the benefiters in rural and urban areas and shows the urgent need for proper treatment and care. Like the ratio of Minor and Adults in rural area benefitted from National Health Mission is 2:3 and in Urban areas, the ratio is 1:1.7. By focusing on these aspects, the healthcare landscape in rural and urban India can improve and lead to better health outcomes for its residents. This paper discusses the problems and solutions observed during visits to hospitals and families in rural-urban north central India. Research findings highlight the importance of using supply chain strategies to improve healthcare delivery. It concludes requirements for improvement, along with several data findings, can save many lives in rural areas by ensuring timely access to healthcare solutions.

Image caption generation is a fundamental problem in artificial intelligence that combines computer vision and natural language processing to generate textual descriptions for images. The task requires a model to identify objects, understand their relationships, and express this information in coherent natural language. This paper presents a detailed experimental study of image caption generation using CNN–LSTM architectures and Transformer-based models, including Vision Transformer with GPT-2 and BLIP-based captioning systems. The models are evaluated on real-world images using quantitative metrics such as BLEU score and extensive qualitative analysis. Experimental results demonstrate that Transformer-based models, particularly BLIP-Large, produce more descriptive and context-aware captions compared to traditional CNN–LSTM approaches. The study highlights the strengths, limitations, and practical trade-offs of each model.

Early parental loss is among the most profound disruptions a child can experience, yet the impact of such loss on children raised in affluent Ghanaian families remains largely unexamined. This qualitative phenomenological study investigates the lived experiences of Ghanaian adults who experienced parental death before age 12 while growing up in socioeconomically privileged households. Using a phenomenological design grounded in Attachment Theory and Resilience Theory, the study recruited 18 participants (12 female, 6 male) aged 25–40 years through purposive and snowball sampling from Accra and Kumasi. Participants completed in-depth semi-structured interviews exploring their childhood experiences of loss, family responses, emotional and behavioural outcomes, and coping resources. Data were analysed using Interpretative Phenomenological Analysis (IPA), yielding six superordinate themes: (1) The Paradox of Material Privilege and Emotional Neglect; (2) Silenced Grief and Unprocessed Loss; (3) Caregiver Instability Despite Household Stability; (4) Academic Achievement as Coping and Identity; (5) Long-Term Relational Consequences in Adulthood; and (6) Resilience Factors in Affluent Contexts. Findings reveal that affluence does not buffer against the psychological impact of parental loss and may paradoxically exacerbate emotional neglect when surviving parents delegate caregiving to hired staff. Participants described profound loneliness, unexpressed grief, pressure to maintain academic performance, and enduring difficulties with trust and intimacy in adult relationships. Protective factors included supportive extended family members, therapeutic relationships, and meaning-making through career achievement. These findings inform psychosocial interventions, school-based support programmes, and clinical practice for bereaved children in affluent Ghanaian families.

Early parental death represents one of the most severe adversities a child can experience, yet the ways in which urban and rural contexts shape this experience remain largely unexamined in Ghana. This comparative qualitative study investigates the challenges and coping mechanisms of adults who experienced parental death before age 12, comparing experiences across urban (Accra) and rural (Eastern Region) settings. Using a comparative qualitative design grounded in Attachment Theory and Ecological Systems Theory, the study recruited 24 participants (12 urban, 12 rural) aged 22–45 years through purposive and snowball sampling. Participants completed in-depth semi-structured interviews exploring their childhood experiences of loss, family responses, educational trajectories, social support, and coping strategies. Data were analysed using thematic analysis, yielding eight superordinate themes: (1) The Immediate Aftermath: Urban Isolation Versus Rural Communal Absorption; (2) Educational Disruption: Urban Continuity Versus Rural Termination; (3) Economic Shock: Urban Asset Protection Versus Rural Destitution; (4) Caregiving Arrangements: Urban Instability Versus Rural Absorption; (5) Emotional Expression: Urban Silencing Versus Rural Ritualised Grief; (6) Stigma and Social Exclusion: Urban Invisibility Versus Rural Visibility; (7) Coping Mechanisms: Urban Individual Achievement Versus Rural Communal Reciprocity; and (8) Long-Term Outcomes: Urban Professional Success Versus Rural Relational Resilience. Findings reveal that urban children retained educational continuity and economic assets but experienced emotional isolation and caregiving instability. Rural children experienced severe educational and economic disruption but were absorbed into extended family networks that provided consistent emotional and practical support. Coping mechanisms reflected context: urban participants relied on individual achievement and professional success, while rural participants relied on communal reciprocity and extended family networks. These findings inform context-sensitive psychosocial interventions, educational support policies, and family-based programmes for bereaved children in Ghana.