Background Artificial intelligence (AI) has evolved into a more and more practical and influential partner in the field of ophthalmology and optometry. Because these specialties rely heavily on standardized imaging and measurable clinical data, they are particularly well suited to computational analysis. Today, AI applications extend far beyond simple diagnostics and now contribute to prognosis assessment, workflow optimization, and clinical decision support. Methods The review included English-language, peer-reviewed literature published over the period from 2015 to 2025. The Literature searches were conducted using PubMed, Scopus, and Google Scholar. Studies focusing on diagnostic accuracy, clinical implementation, and AI-based models in eye care were considered eligible for inclusion. Results Deep learning systems, especially convolutional neural networks (CNNs), continue to dominate image-based AI applications in eye care, often demonstrating diagnostic performance approaching that of experienced clinicians (AUC > 0.90). AI technologies are now being applied across retinal, glaucoma, corneal, pediatric, and neuro-ophthalmic care, where they contribute to improved screening efficiency and greater diagnostic consistency. More recently, transformer-based and multimodal models have shown promise in longitudinal prediction and in combining imaging with broader clinical data. Conclusion AI has transitioned from experimental systems to clinically relevant tools. However, successful implementation requires external validation, ethical oversight, bias mitigation, and clinician supervision. AI should augment not replace clinical judgment in modern eye care.

School heads influence teacher performance through the implementation of effective human resource management (HRM) practices. However, the way teachers emotionally experience their work may affect how these management practices influence their performance. This study examined the role of teacher emotional states in the relationship between school heads’ HRM practices and teacher performance in Caluya Zone II elementary schools during School Year 2025–2026. The study employed a quantitative descriptive–correlational research design. With purposive selection of 100 elementary teachers from seven public elementary schools as respondents. Data were collected through structured survey questionnaires measuring school heads’ HRM practices, teacher emotional states, and teacher performance. Data were analyzed using descriptive statistics, correlation, and regression analysis. Results indicated that school heads implemented HRM practices at high levels, while teachers demonstrated satisfactory to very satisfactory performance. Teachers generally reported positive emotional states. Findings revealed a significant relationship between HRM practices and teacher performance, with teacher emotions playing an important role in influencing how these practices affect performance. The study highlights the importance of supportive leadership and initiatives that promote teachers’ emotional well-being to enhance professional performance and improve educational outcomes.

The Maram Naga tribe, indigenous to the Senapati district of Manipur, possesses a rich socio-cultural heritage heavily defined by agrarian traditions and deep-rooted customary laws. Despite women serving as the indispensable backbone of the community’s economic and domestic sustenance, they face systemic political disenfranchisement, most notably through their explicit exclusion from traditional village councils and the critical half-yearly and annual community discussions. This research paper explores the dichotomy between the high economic participation and low political agency of Maram women. Utilizing a qualitative, exploratory approach that synthesizes primary focus group discussions with secondary literature, such as The Place of Women in Naga Society. This study examines the traditional justifications for this exclusion, which are deeply rooted in patriarchal hegemony, patrilineal land ownership, and customary taboos. The findings reveal that the exclusion of women from formal decision-making severely impacts community welfare, manifesting in unaddressed socio-economic crises such as the rampant rise in alcoholism, gender-based violence, and stark disparities in health and educational investments. Furthermore, the psychological toll of this political exclusion stifles female agency and creates bottlenecks in sustainable community development. To rectify this imbalance, the paper proposes actionable pathways to inclusive governance. By leveraging the rising influence of women’s collectives like the Maram Women Union, celebrating female-centric cultural institutions such as the Mangkang festival, and capitalizing on the economic independence fostered by Self-Help Groups (SHGs), women are increasingly challenging the status quo. The study concludes by recommending a hybrid governance model that respects indigenous customary laws while integrating modern, democratic mandates for female representation, ensuring that the "silent half" becomes an active, institutionalized voice in the sustainable development of the tribe.

The nexus between employability, lifelong learning (LL), and leadership development has emerged as a defining priority for organizations and individuals navigating rapid technological disruption and shifting labor market demands. This study examines the career readiness outcomes of 1,800 professionals across five primary learning modalities — formal education, online courses, workplace training, mentorship and coaching, and self-directed learning — using a structured dataset. Three machine learning models were applied to predict employability readiness: Logistic Regression, Gradient Boosting, and Random Forest. Logistic Regression achieved the highest performance, with an accuracy of 81.50% and a Macro-AUC of 0.924, while Gradient Boosting and Random Forest achieved accuracies of 76.25% and 75.83%, respectively — all surpassing the 70% benchmark. Feature importance analysis identified the Lifelong Learning Index, Leadership Program Hours, and Mentor Support Score as the most influential predictors of employability readiness. Inferential statistical analysis confirmed significant differences in readiness levels across learning mode groups (p < 0.001). Participants engaged in online courses and mentorship-based learning reported the highest average employability readiness scores (74.2 and 71.8, respectively) and the lowest burnout rates (4.82% and 3.94%). These findings suggest that passive or isolated learning modes are insufficient for building career resilience. Rather, organizations and policymakers must adopt integrated, active learning ecosystems that combine digital upskilling, mentorship, and structured leadership development to produce measurable improvements in workforce employability.

Agriculture plays a significant role in Odisha’s rural economic development. In 2018 Odisha government introduced an innovative program known as the KALIA Scheme. Providing direct income support, the scheme aims to enhance farmers’ income in agricultural sector. To assess agricultural performance the study examines trends in paddy production and yield over the periods 2013-14 to 2022-23. The study based on secondary data, data collected from various sources such as the Odisha Economic Survey 2019-20, government reports, and existing literature. The discussion highlights that paddy production and yield exhibit an overall increasing trend; fluctuations persist year to year. While the scheme has contributed to increasing agricultural production, external factors such as climatic variability and structural factors affect outcomes. After implementation of the scheme, it reflects more stability in agricultural performance. The study concludes that while the KALIA Scheme has contributed to inclusive agricultural development by strengthening agricultural productivity, sustainable growth requires more development in irrigation, technological adoption, and risk mitigation strategies.

Artificial Intelligence (AI) has emerged as a transformative force in educational research and technology, reshaping teaching-learning processes, assessment systems, and data-driven decision-making. The integration of AI in education has enabled personalized learning, predictive analytics, and enhanced administrative efficiency. However, despite its promising potential, AI presents several limitations, including ethical concerns, data privacy risks, algorithmic bias, and the lack of human sensitivity in pedagogical contexts. This study explores both the opportunities and constraints of AI in educational research and technological implementation. Using a qualitative analytical approach based on secondary sources, the paper critically examines existing literature and identifies key challenges in applying AI effectively in education. The findings suggest that while AI can significantly augment educational practices, its limitations necessitate cautious, ethical, and context-sensitive integration. The study concludes that AI should complement rather than replace human intelligence in educational environments.

The production of conventional plastics, which are essential to modern economies, primarily relies on fossil-based resources and is associated with significant environmental impacts. Global plastic production has increased markedly from 204 million tons in 2002 to 335 million tons in 2016 and is projected to rise further, leading to critical issues such as waste accumulation and increased greenhouse gas emissions. In response to these challenges, considerable research efforts have been directed toward the development of environmentally sustainable alternatives, particularly bioplastics. Among these, microbial derived polymers such as Polyhydroxyalkanoates and Polyhydroxybutyrate have attracted significant attention due to their biodegradable nature and the presence of hydrolysable ester bonds. These biopolymers are synthesized by microorganisms under specific stress conditions that are influenced by environmental and nutritional parameters, including pH, temperature, incubation period, and the availability of carbon and nitrogen sources, as well as the carbon to nitrogen ratio. In the present study, soil samples were collected from agricultural and coastal regions of South Gujarat and Goa for the isolation of potential Polyhydroxyalkanoate and Polyhydroxybutyrate producing bacteria. A total of isolates were screened and characterized based on their morphological and biochemical properties. Biopolymer production was further enhanced through the optimization of growth conditions under controlled laboratory settings. The developed bioplastic will be evaluated for safety prior to its potential application. This study demonstrates the potential of microbial bioplastics as sustainable alternatives to conventional plastics and underscores their relevance in promoting environmental protection and sustainable development.

Osteoarthritis (OA) remains one of the leading causes of chronic pain and disability worldwide, with conventional pharmacological options often limited by gastrointestinal, cardiovascular, or renal side effects. Curcumin, the principal bioactive polyphenol in *Curcuma longa* (turmeric), has attracted considerable attention for its pleiotropic anti-inflammatory, antioxidant, and chondro protective properties.

The increasing reliance on digital platforms has significantly transformed the hospitality industry, particularly in how customer feedback is generated, disseminated, and utilized for service improvement. Online customer reviews, commonly referred to as electronic word-of-mouth (eWOM), serve as a critical source of information that influences both consumer behavior and managerial decision-making. This study examined the influence of online customer reviews on service improvement strategies among hotels in Eastern Samar, Philippines. Anchored on the Stimulus–Organism–Response (S–O–R) Theory and Expectation–Confirmation Theory (ECT), the study conceptualized online reviews as external stimuli that shape managerial cognition and operational responses. A quantitative descriptive-correlational research design with multiple regression analysis was employed. The study involved 150 respondents, consisting of hotel guests and hotel managers. Data were collected using a validated questionnaire with a reliability coefficient of Cronbach’s α = 0.87. Statistical analyses included descriptive statistics, Shapiro–Wilk test for normality, Pearson correlation, and multiple linear regression. Results revealed that online customer reviews significantly predict service improvement strategies (R² = 0.64, p < 0.001), explaining 64% of the variance in service enhancement practices. Among the predictors, review credibility emerged as the strongest determinant, followed by review sentiment and review frequency. These findings highlight the importance of credible and meaningful customer feedback in guiding service-related decisions. The study concludes that online customer reviews function as a data-driven mechanism for continuous service improvement. The findings provide practical implications for hotel managers in emerging tourism destinations and contribute to the growing literature on digital feedback systems.

Agriculture is currently facing rising global food demand, decreasing arable land availability, and intensifying climate-related stresses. Traditional farming practices rely heavily on chemical fertilizers and pesticides, leading to soil degradation, water contamination, and increased greenhouse gas emissions. To ensure sustainable food production, adopting eco-friendly, resource-efficient green technologies has become crucial. This study examines the combined impact of precision agriculture, advanced water management systems, and bio-based inputs on plant growth and productivity while minimizing environmental harm. A multi-factorial experiment was conducted using a Controlled Environment Agriculture (CEA) system equipped with IoT-enabled sensors, renewable-energy-powered smart irrigation, and innovative biofertilizers. Real-time monitoring of soil moisture, nutrient status, temperature, and plant health was performed using drones and AI-driven analytics. This integrated system enabled continuous evaluation of crop conditions and timely decision-making. Results showed a significant improvement in the integrated green technology treatment over the control plots. Water usage dropped by 55% and fertilizer consumption by 20%, while overall plant biomass and yield increased noticeably. Additionally, early pest and disease detection through sensor-based and AI-supported monitoring reduced reliance on chemical pesticides. These outcomes confirm that combining precision farming tools with biological inputs and smart monitoring solutions improves crop productivity, resource efficiency, and environmental sustainability. This study presents a scalable, climate-resilient agricultural model. The integration of digital technologies with biological solutions offers a promising pathway for sustainable food production and ecological conservation in the era of climate change.