La Consolacion University Philippines is a private Roman Catholic co-educational basic and higher education institution administered by the Augustinian Sisters of Our Lady of Consolation (ASOLC) in Malolos City, Bulacan, Philippines. It was established by the Augustinian Sisters in 1937, originally named Colegio de Nuestra Señora del Carmen. It was later renamed Regina Carmeli College in 1967. In December 1997, the Philippines' Commission on Higher Education (CHED) granted university status to the school, under the name University of Regina Carmeli (URC.) In January 2011, the Augustinian Sisters once again renamed the university as La Consolacion University Philippines, in accordance a congregational decision that schools being run by the Augustinian Sisters of Our Lady of Consolation carry one name “La Consolacion School/College/University.
This mixed-methods study investigated Artificial Intelligence (AI) tool utilization among 81 student researchers in academic writing, assessing the frequency, integration extent, research performance, and adherence to ethical AI principles. The findings revealed that students predominantly use AI for language enhancement (grammar and paraphrasing) and literature review, with less engagement in complex analytical tasks. Critically, student researchers demonstrated strong self-reported ethical and human-centered AI practices, aligning with the United Nations Educational, Scientific and Cultural Organization (UNESCO) AI Competency Framework. Ethical AI adoption significantly predicted improved research performance, whereas the extent of tool integration did not. The Artificial Intelligence Utilization Scale (AIUS) Framework indicated primary engagement at Levels 3 (AI-Editing assistance) and 4 (AI-Task completion), reflecting responsible and human-supervised AI use. This study underscores the importance of fostering ethical AI literacy in effective integrity-driven academic research.
The rapid emergence and global dissemination of antimicrobial resistance pose a serious threat to public health, environmental sustainability, and economic development. Central to this crisis is the resistome, defined as the collection of all antimicrobial resistance genes (ARGs) present in pathogenic and non-pathogenic micro-organisms across clinical, agricultural, and natural ecosystems. The environmental resistome plays a crucial role in the evolution and transmission of resistance, serving as both a reservoir and a conduit for ARG exchange through horizontal gene transfer. This review provides a comprehensive overview of the structure, diversity, and dynamics of the resistome, with emphasis on the interconnected water-soil-air continuum. Key mechanisms driving resistome dissemination, including mobile genetic elements such as plasmids, integrons, transposons, and bacteriophages, are discussed alongside the major routes of gene transfer, conjugation, transformation, and transduction. The review highlights anthropogenic drivers that intensify resistome expansion, including antibiotic misuse, wastewater discharge, agricultural runoff, and exposure to heavy metals, pesticides, and disinfectants, which promote co-selection. Advances in resistome profiling approaches, such as quantitative PCR, metagenomics, long-read sequencing, and functional metagenomics, are critically evaluated for their capacity to resolve ARG diversity, mobility, and host associations.
Integrating large language model (LLM) APIs in an institutional web information system presents architectural challenges including service isolation, API fault tolerance, secured credential management, and cost-effective model routing. This paper presents service-oriented design patterns for embedding OpenAI GPT services within a Laravel-based system for community extension management, deployed at www.ceso.me. The architecture employs ten functional modules covering the full community extension lifecycle: user management with CSV bulk import and role-based access control (RBAC) across five user roles, activity management with unique entry code generation, content management system (CMS) and certification repository, and AI-powered feedback classification and automated reporting. The AI service layer uses a dual-model routing pattern: GPT-3.5-turbo (avg. latency: 0.8s, cost: P9.11/1K requests) handles dashboard insight synthesis, while GPT-4.1-mini (avg. latency: 1.4s, cost: P36.43/1K requests) performs granular sentiment classification with contextual reasoning, achieving 97% cost reduction compared to full GPT-4 deployment. Fault tolerance is implemented through exception-based fallback classification, achieving 98.7% API success rate with 1.3% graceful degradation during a 30-day observation period. Observed system performance metrics include average page load time of 1.2 seconds, database query time below 50ms, and successful handling of 500 concurrent users in load testing. Evaluation by 38 respondents using the ISO/IEC 25010 quality model yielded weighted mean scores of 4.82 (Functional Suitability), 4.55 (Performance Efficiency), 4.71 (Usability), 4.67 (Maintainability), and 4.75 (Decision Support Effectiveness). The design patterns and architecture presented are intended to be adaptable to institutional web systems seeking to integrate LLM services with production-grade reliability.
Academic integrity in higher education is a topic of choice, especially in the era of artificial intelligence (AI). Hence, institutions are making waves to establish a concrete, sustainable policy to regulate and monitor students and promote a credible academic experience. This study investigated the level of academic integrity and academic performance among tertiary students at a higher education institution in Olongapo City, Philippines. Using a descriptive-correlational research design, the study assessed the perceptions of 395 participants who were sampled via convenience sampling. The investigation employed a research tool adapted from a previous study comprising five latent variables: Honesty, Fairness, Respect, Trust, and Responsibility. This investigation was conducted during the first semester of the academic year 2025-2026. Employing both descriptive and inferential statistics using the Statistical Package for the Social Sciences (SPSS) version 23, the study revealed a high overall level of academic integrity, with honesty emerging as the highest-rated dimension. Academic performance was similarly robust, with 37.2% of the sample achieving a "Very Good" and 30.1% reaching "Excellent." Inferential analyses using t-tests and ANOVA indicated significant differences in integrity levels by sex at birth, college affiliation, and year level. At the same time, age specifically influenced perceptions of Fairness and Trust. Correlation analysis established significant positive relationships between all integrity dimensions and academic achievement. Furthermore, multiple regression analysis identified Trust as a significant unique predictor of academic performance, with honesty showing marginal significance. These findings suggest that a student's commitment to ethical standards is intrinsically linked to their scholastic success. The study concludes that educational institutions must implement tailored interventions that account for demographic diversity to cultivate a culture of Trust and Responsibility, ultimately fostering a sustainable learning environment that supports both ethical standards and academic outcomes.
The operational readiness of military reserve forces underpins national security and disaster response, yet persistent skill mismatches and manual task assignment hinder effective mobilization. This study developed a Web-Based Skill Management and Task Recommender System for Military Reserve Force Mobilization that integrates reservist profiling, skill tagging, task creation, automated recommendation, and notification delivery through a hybrid pipeline composed of (i) a rule-based filter that enforces minimum qualification constraints and (ii) a weighted multi-criteria scoring layer that ranks qualified personnel by skillset match, availability, proximity, and rank suitability. The system was built using the Agile methodology, and synthetic data modeled on the structure of real reservist profiles were used throughout development and evaluation to address confidentiality and operational security concerns. The system was evaluated by 26 respondents (11 IT experts and 15 military officers) selected through purposive sampling, using a structured questionnaire derived from the ISO/IEC 25010 Software Quality Model. The system was rated Very Acceptable across all eight quality characteristics, with weighted means ranging from 3.73 to 3.91. The hybrid approach was designed as a decision-support tool that surfaces transparent, auditable recommendations to reduce potential skill-to-task mismatches, while final deployment authority remains with qualified military personnel.
