Southern Leyte State University

The increased prominence of ESG requires an understanding of how ESG practices relate to public policy systems. There are numerous reviews to comprehend the ESG-policy connection, but there are meaningful gaps. This paper addresses this by conducting a systematic review using the Scopus database, adhering to the PRISMA protocol. Of the 331 studies initially extracted, 96 studies remained for final analysis after applying the inclusion and exclusion criteria. This review explored trends, patterns, themes, and frameworks, while also identifying gaps and future research directions. The result demonstrates that ESG-public policy research is increasing rapidly but remains unevenly concentrated across various geographies, themes, theories, and methodologies. There is highly concentrated evidence for the USA and China, while other emerging economies remain significantly underrepresented, thus leaving unanswered the question of whether the results are generalizable. Thematically, there is a concentration of work on policy making, sustainability, governance, and corporate social responsibility. Theoretically, there is a narrow predominance of lenses, and behavioral and interdisciplinary approaches are not being utilized sufficiently. Methodologically, quantitative designs predominate, generating robust evidence but risking simplification. Furthermore, this research identifies research gaps and proposes future research questions to encourage further exploration in the field of ESG in public policy.

This review investigates the intellectual structure, key contributors, and thematic evolution of studies on work relationship and organizational performance based on Scopus-published articles from 1985 to 2025. This review also explores research gaps and future directions under this domain. The study used bibliometric tools such as VOSviewer and Bibliometrix to analyze publication trends, citation patterns, co-authorship networks, and keyword co-occurrences. This paper also used content analysis in determining the research gaps and future directions. Results show an outstanding surge in research production, especially from 2021 to 2024, where interest was most focused on employee engagement, effectiveness in leadership, and team dynamics. However, findings reveal methodological limitations and fragmented collaboration networks, particularly among institutions from underrepresented regions. The analysis identifies emerging themes such as corporate culture, diversity, and perceived organizational support as future research priorities. By mapping the field’s evolution and highlighting underexplored areas, this research offers strategic direction for advancing inclusive, evidence-based practice to improve work relations and organizational performance in various settings. The results inform scholars, practitioners, and policymakers who promote high-performing, resilient, and inclusive organizational cultures.

This study examined the usage and perceptions of AI-based learning tools among Grade 12 STEM learners at Hilongos National Vocational School (HNVS). Specifically, it aimed to identify the respondents’ demographic profile in terms of age and sex, determine the AI-based learning tools commonly used, assess the frequency of AI tool usage, evaluate students’ perceptions in terms of engagement, interaction, behavioral intentions, satisfaction, and perceived improvement in academic performance, and determine whether a significant relationship exists between AI tool usage and students’ perceptions. The study employed a descriptive-correlational quantitative research design. Using stratified random sampling, a total of 76 Grade 12 STEM learners were selected from the STEM population of HNVS during the School Year 2025–2026. Data were gathered through a 20-item standardized questionnaire adapted from Khairuddin, Kamaruddin, and Alwi (2024), with minor modifications to fit the context of the study. Descriptive statistics such as frequency, percentage, mean, and standard deviation were used to summarize the data, while Pearson correlation coefficient was used to determine the relationship between variables. Findings revealed that ChatGPT was the most commonly used AI-based learning tool, and the majority of respondents reported often using AI tools for academic purposes. Students’ overall perceptions of AI-based learning tools were interpreted as neutral, indicating moderate views regarding their influence on engagement, interaction, behavioral intentions, satisfaction, and academic performance. Furthermore, the results showed a moderate positive and statistically significant relationship between AI tool usage and students’ perceptions (r = 0.468, p < 0.001). The study concluded that AI-based learning tools can serve as supportive academic resources when used appropriately and responsibly, but their effectiveness depends on the extent and manner of their integration into students’ learning practices.

This cross‑sectional study assessed the prevalence, predictors, and substance use risk profile among 333 incarcerated males at the Freetown Male Correctional Centre. The WHO-ASSIST tool was used for data collection. The risk of substance-related issues was categorised as low, moderate, or high. Logistic regression was done to identify predictors of lifetime substance use (Model I) and recent use within the past three months within or outside prisons (Model II). The prevalence of lifetime substance use was 88.6% (95% CI: 0.85-0.92), recent use 73.9% (95% CI: 0.69-0.79), and polysubstance use 77.2% (95% CI: 0.73-0.82). Tobacco (80.5%), cannabis (70.0%), alcohol (68.2%), and opioids (64.0%) were the most used substances. In adjusted models, older age, unmarried status, and rural residence were significant predictors of both lifetime and recent use, while education status was specifically associated with recent use. Risk profiles revealed a high level of dependence among tobacco users and moderate risk among most cannabis and opioid users. Substance use was notably high, with tobacco, cannabis, and opioids emerging as the primary substances of concern. These findings underscore the burden of substance use and highlight the need for targeted interventions and the integration of cessation programs within the correctional centre.

Apple leaf diseases significantly affect crop yield and food security, underscoring the need for accessible and real-time diagnostic tools. This study proposes an integrated end-to-end prototype system that combines a custom Convolutional Neural Network (CNN) classifier trained on the PlantVillage apple leaf dataset with a cloud-based API and a FlutterFlow-based mobile application for automated disease classification. The methodology involves training a hybrid segmentation and region-based detection pipeline, deploying it via a FastAPI server hosted on Hugging Face Spaces, and integrating it into a mobile interface developed using FlutterFlow. The pipeline incorporates HSV-based image segmentation and contour-based region detection to isolate leaf areas of interest prior to classification, complemented by a 23-feature handcrafted descriptor extraction module spanning color statistics, Gray-Level Co-occurrence Matrix (GLCM) texture properties, and morphological shape attributes. End-to-end system testing was conducted within the FlutterFlow development environment to evaluate functionality, response time, and prediction consistency. The CNN model achieved 92.00% accuracy in isolation, with a weighted precision of 92.72%, recall of 92.00%, and F1-score of 91.65% on a four-class held-out test set comprising Apple_healthy, Apple_scab, Black_rot, and Cedar_apple_rust. However, end-to-end evaluation of the full hybrid pipeline — combining HSV segmentation, contour-based region detection, and CNN inference — yielded a lower accuracy of 71.43%, a gap attributable to a training–inference distributional mismatch that is analyzed in detail and identified as the primary limitation of this study. System testing confirmed that the mobile prototype successfully delivers annotated detection outputs with bounding box visualization and per-class confidence scores, with response behavior that was subjectively prompt during testing; quantitative latency, throughput, and localization (IoU/mAP) benchmarking were not performed and are identified as necessary future work. The findings demonstrate that the proposed framework provides a functional prototype with a clearly documented performance ceiling, indicating potential for future deployment as a decision-support tool for precision agriculture pending the improvements identified herein.