This study focuses on the evaluation of the develop enterprise-level information system that focuses on research, instruction and extension integration for State Universities and Colleges (SUCs) in the Philippines. The researcher used the descriptive- developmental type of research. Using the system, the leaders of the school will have a real-time overview of the status of the performance and accomplishment. Policymakers can use the results to inform the development of policies and guidelines that promote the effective integration of digital technologies in education. The application was developed based on the Agile Development Model. The Agile software development life cycle is a set of steps that a product goes through as it progresses from initiation to completion. The characteristics need to evaluate the product/software defined in ISO/IEC 25010 are the following: functional suitability, performance efficiency, compatibility, usability, reliability, security, maintainability, and portability. The system obtained the overall weighted mean of 3.73 which interpret as Excellent in terms of Product Quality evaluated by the IT Experts. This means that the system is approved by the IT Experts and highly recommended to use.

According to the Philippines Commission on Higher Education (CHED) Circular Memorandum Order (CMO) 101 series of 2017, one of the allowed Elective Courses for the Electronics Engineering (ECE) Program is Instrumentation and Control which includes Advanced Instrumentation and Control Systems and Robotics. With these elective courses, there will be a need to have designated laboratory facilities and equipment which includes Mechatronics and Automation Equipment. This study aims to bridge the academe-industry gap by integrating a project based learning approach. The students of Jose Rizal University designed and developed a project-based four-story elevator trainer that became a platform for the real-world model of an actual elevator. Basic and Advanced Programmable Logic Controller (PLC) and Human Machine Interface (HMI) programming skills of the students were enhanced as they developed twenty comprehensive laboratory experiments on the elevator trainer. A 94.2% usability result indicates substantial effectiveness in learning mechatronics. T-tests for means and equal variance were used to analyze the significance of the elevator trainer in comparing the pre-test and post-test scores of ECE students. An alpha (p-value of 0.00000000013) for the accumulated test scores of each ECE student and (p
-value of 0.0000291) for the comparison of average mean score on the six areas: PLC, HMI, Variable Frequency Drive (VFD), pneumatics, electropneumatics, and motor control were found to be both smaller than the alpha of 0.05 for the target 95% confidence level. The study showed that the effectiveness of the elevator trainer was statistically significant in the learning of the ECE students in a project-based approach. The study highlights the need for a more hands-on project in each laboratory course to increase the quality of produced electronics engineering students in the Philippines by allowing them to be well-trained, globally competitive, and equipped with the necessary skills to become industry-ready.

In military and expeditionary (maritime) health care environments where isolation, safety, limited personnel, and resource constraints can threaten the delivery of frontline health care, access to timely and knowledgeable medical assistance can be extremely valuable. The purpose of this paper is to investigate the use of large language models (LLMs) in military and maritime health care environments by creating an AI-powered, Over-the-Counter (OTC) Medication Assistance Bot using the Mistral-7B model. The bot is intended to be deployed within tactical, or even shipboard systems, and it would empower autonomous, in-the-moment recommendations of medications for specific symptoms, while mitigating the risks associated with deploying personnel self-medicating through potential non-fundamental use errors. For this work, we employed Low-Rank Adaptation (LoRA) to fine-tune the system, and the bot was trained on a specific dataset derived from material on pharmacological sources, contextualized for medical practices in the Philippines. Based on evaluation the model achieved an average F1-score of 0.7296, which is above the 0.60-0.70 expected levels of performance for medical dialogue systems. The research shows promise for the model as it enhances combat and maritime healthcare readiness by providing consistent, low-bandwidth, and local medical assistance when connected medical supervision may not be immediately available.

Accurate resource yield prediction is critical for military logistics, planning, and operational readiness, yet remains challenging due to numerous influencing factors such as environmental conditions, resource quality, and logistical constraints. This study examines the effectiveness of quantile-based data binning on classical machine learning algorithms in predicting resource yields pertinent to military applications. Furthermore, the effectiveness of Backpropagation Artificial Neural Networks (BP-ANN) and Naive Bayes classifiers with regression models such as K-Nearest Neighbors (KNN), Linear Regression, and Multi-Layer Perceptron Regressors (MLPRegressor) are compared using a robust dataset representative of global resource metrics. The results indicate that binning continuous data into quartiles substantially enhances model accuracy, precision, recall, and computational efficiency. In particular, the binned data enables the BP-ANN to achieve an accuracy of approximately 90.4%, with regression models such as KNN and MLPRegressor outperforming this benchmark by attaining accuracies exceeding 93%. Additionally, binning drastically reduced hyperparameter tuning duration from around 149 minutes to less than 10 minutes, underscoring its computational efficiency advantage. Overall, this research demonstrates that quantile-based data binning is a valuable preprocessing technique that improves predictive accuracy, reduces computational cost, and enhances the reliability of classical machine learning models for military resource forecasting.

This study assesses the potential of advanced materials, specifically graphene, perovskites, and nanostructured ceramics to enhance the energy efficiency, durability, and environmental resilience of 5G and 6G communication systems deployed in harsh environments. A comparative evaluation was conducted based on electrical conductivity, thermal stability, mechanical strength, optical performance, and corrosion resistance, drawing on recent experimental data and life-cycle analyses. Graphene demonstrates electrical conductivity near 10^8 S/m and thermal conductivity up to 5000 W/m-K, enabling transistors with 200 times higher speeds and coatings reducing corrosion by over 90%. Perovskite-based devices achieve solar cell efficiencies up to 34% and optical modulators operating at 170 Gbps. Nanostructured ceramics offer low dielectric loss and stability above 1000°C, supporting high-frequency operation in challenging conditions. Integrating these materials is projected to extend device lifespans by up to 40% and reduce energy and cooling demands by 30%. These findings indicate that adopting advanced materials can significantly improve the performance and sustainability of next-generation communication infrastructure.

This study proposes a Blockchain-Integrated Circular Economy Framework to improve lifecycle tracking and emissions reporting for maritime ICT and energy materials. The system combines Digital Product Passports, IoT telemetry, and smart contracts on a permissioned blockchain to record operational data and end-of-life events for batteries, photovoltaic modules, and navigation equipment. A parametric algorithm calculates emissions by combining production impacts, usage profiles, and recycling credits, while automated incentives promote material recovery. Synthetic data simulations illustrate the framework's ability to monitor degradation, quantify emissions, and enforce circularity incentives. Results indicate that production emissions dominate lifecycle impacts, highlighting the value of integrated tracking and verified recovery to support low-carbon maritime operations.

Blue carbon ecosystems (mangroves, saltmarshes, seagrasses) are globally significant carbon sinks, absorbing an estimated 50% of oceanic carbon despite covering only 2% of the ocean surface. However, these important habitats face rapid degradation, with 25% to 50% loss over the past 50–70 years, transforming them into carbon sources. This paper presents a climate-smart communication and sensing framework for real-time monitoring of these critical marine ecosystems. It integrates Artificial Intelligence (AI) with advanced sensing technologies, including satellite, Uncrewed Aerial Vehicles (UAVs), LiDAR, and in-situ sensors, for comprehensive data acquisition and analysis. The framework evaluates energy-efficient and secure underwater (acoustic, optical) and overwater (satellite, cellular, LoRaWAN) communication strategies to ensure continuous data flow. AI algorithms enhance data processing, pattern recognition, predictive modeling, and autonomous operations of platforms like Autonomous Underwater Vehicles (AUVs). This integrated approach not only supports accurate carbon accounting but also yields substantial co-benefits for climate change mitigation and adaptation, biodiversity conservation, and maritime security by deterring illegal activities and pollution. The framework provides a transformative pathway for sustainable blue economy and resilient coastal communities.

Climate change is intensifying maritime risks, including sea level rise, stronger storms, and disrupted ocean currents—posing threats to coastal infrastructure, navigation, and security. Traditional monitoring systems lack the predictive capabilities and coverage to address these evolving challenges. This paper explores the integration of Artificial Intelligence (AI) and satellite-based Earth observation as a next-generation early warning system (EWS) for maritime security. We assess observed and projected hazard trends, propose an AI-enhanced system architecture, and evaluate readiness in climate-vulnerable geographies. The findings highlight actionable strategies to improve forecasting, risk detection, and climate resilience in coastal and maritime domains.

This volume, Mental Health Challenges in Academia: Stressors Faced by Students and Faculty, bravely confronts the issues that many in higher education endure but few openly discuss. From the strains of balancing teaching, research, and administrative duties to the financial pressures, cultural challenges, and emotional burdens faced by students, it brings together a diverse range of perspectives to paint a holistic picture of academic life. It highlights both the systemic issues and the deep personal stories that reveal how intertwined our professional achievements are with our personal well- being. In doing so, it not only informs but also reassures its readers: you are not alone, and there are ways forward.

Since teaching is the foundation of education, program accreditation and institutional performance are directly correlated with its effectiveness. By creating a competitive and supportive learning environment, faculty performance has a direct impact on an academic institution’s ability to fulfill its vision and goal. To provide a thorough and impartial assessment of teaching performance, this study uses data mining algorithms to extract insightful information about the elements that go into good instruction, including both structured and unstructured data. This is done in response to the urgent need for faculty performance evaluation. To help institutions identify their strengths, rectify their flaws, and encourage ongoing growth in their teaching and learning processes, the system was created. Looking for trends in teacher data. Furthermore, sentiment analysis methods are employed to assess qualitative input, and Laravel 8.0 provides the framework for putting these algorithms into practice. A grand mean score of 4.38, which is considered “Very Acceptable,” was obtained from expert evaluations of the system, demonstrating its dependability and efficacy in assisting with faculty performance reviews.