The Philippines is advancing its renewable energy goals to achieve a 35% share by 2030. This study evaluates solar photovoltaic (PV), and wind power output forecasting methods currently employed in the Philippines, aiming to assess their accuracy against electricity market standards and identify potential improvements. The study systematically reviews articles emphasizing forecasting methods, including physical, statistical, machine learning, and hybrid models. The methodologies encompass a range of forecasting horizons and utilize a diverse set of input variables that influence forecasting accuracy. A key finding from the literature is the variability in the accuracy of these forecasting models, with many not meeting the stringent Mean Absolute Percentage Error (MAPE) threshold of 18% set by the Philippines' Wholesale Electricity Spot Market (WESM). This emphasizes the need for enhanced forecasting models to mitigate economic losses and improve grid stability significantly. Furthermore, this study suggests integrating more sophisticated, data-driven forecasting models to improve accuracy. Such advancements are critical for managing the intermittent nature of solar and wind energy and making informed decisions on energy policy and investment in the Philippines. The study also identifies gaps in current forecasting practices and recommends avenues for future research, particularly in developing models that align better with the operational standards and real-time demands of the energy market.

Wave energy derived from oceanic kinetic forces is a highly promising renewable energy source. As global efforts to incorporate renewable energy into the grid increase, accurate wave energy forecasting becomes essential for optimizing energy harvesting and grid integration. This paper examines the latest developments in machine learning (ML) approaches, focusing on deep learning (DL), ensemble methods, and hybrid models used for forecasting ocean wave energy. It highlights the strengths and weaknesses of various approaches in capturing the complex nonlinear dynamics of ocean waves, including predicting energy flux, significant wave height (SWH), and wave period. Additionally, the paper explores how hybrid models, combining physical models with ML, have emerged as powerful tools for improving forecast accuracy over traditional methods. This review concludes with insights into future directions, emphasizing the potential of advanced techniques like transformers, generative adversarial networks (GANs), and real-time data assimilation for enhancing prediction reliability and computational efficiency.

Caring for a child with Autism Spectrum Disorder (ASD) presents numerous challenges for parents, often leading to psychological distress, depression, anxiety, and other health issues. Understanding and addressing the various behaviors exhibited by children with ASD can be particularly difficult. Despite advancements in the diagnosis and treatment of ASD, many families still struggle to access specialized care and support. To address these challenges, we developed ASDvisor, an innovative application designed to provide comprehensive support for parents of children with ASD. ASDvisor integrates valuable information, efficient documentation, decision support, educational resources, data tracking tools, and enhanced communication to improve the management of ASD care through a user-friendly web and mobile platform. The system's quality was evaluated using the FURPS model, which evaluates functionality, usability, reliability, performance, and supportability of the system. ASDvisor received excellent ratings, scoring 4.33 for the web application and 4.37 for the mobile application. Key findings highlighted the application's robust performance in tracking and managing ASD-related activities, offering valuable decision support through its Care Decision module, and fostering community engagement among users. ASDvisor effectively addresses the identified challenges, providing a reliable, efficient, and cost-effective tool for enhancing the quality of life for children with ASD and their families. This research demonstrates the potential for technology to significantly improve ASD care management.

Forest devastation is a critical environmental issue with severe ecological consequences. The Philippines, in particular, faces significant challenges in conservation efforts, compounded by a lack of public awareness about the effects of deforestation. To address this gap, we developed “Leshy: A 3D Action-Adventure Game Using Character Switching Mechanics for Promoting Awareness on Combating Forest Devastation.” This game immerses players in a virtual environment that reflects the real-world impacts of deforestation, leveraging character-switching mechanics to enhance engagement and education. “Leshy” aligns with Sustainable Development Goal (SDG) No. 15 - Life on Land, offering a unique and interactive approach to inspire action against forest devastation. The game's effectiveness was evaluated based on Gameplay, Aesthetics, User Interface (UI), Sound Design/Audio, and Storyline, achieving an overall average score of 4.44, rated as “Excellent.” This high rating indicates the game's success in engaging players with its captivating gameplay, appealing visuals, intuitive interface, immersive sound, and compelling narrative. The accompanying website, assessed using the FURPS model (Functionality, Usability, Reliability, Performance, and Supportability), received an overall average score of 4.50, also rated as “Excellent.” This demonstrates the website's user-friendly interface, dependable reliability, and strong performance, ensuring a seamless user experience. In conclusion, “Leshy” effectively addresses the issue of forest devastation by combining educational content with an engaging gaming experience. This project highlights the potential of interactive media in raising environmental awareness and inspiring conservation efforts. Further development and promotion of similar educational games are recommended to enhance their impact on environmental conservation.

This study focuses on the development and implementation of an enterprise-level information system for State Universities and Colleges (SUCs) in the Philippines, specifically addressing the mandates of Instruction, Research, and Extension. The study adopts a sequential exploratory mixed-method approach, utilizing the Agile System Development Model for system development. The system's effectiveness and acceptability were evaluated using quantitative data from 20 IT experts and 100 end-users, and qualitative data from interviews and secondary data. The study also conducted a survey to assess the system's acceptability in terms of flexibility and configuration. The findings reveal that the system received an average weighted mean of 3.44 for flexibility and 3.39 for configuration, indicating a good level of acceptability among end-users. The study also identifies several strategic implementation strategies for the deployment of the system to interested SUCs, including policy integration and risk management. The study provides valuable insights into the development and implementation of enterprise-level information systems in educational institutions, highlighting the importance of aligning digital governance with institutional mandates and requirements.

This descriptive-developmental study enables the authors to create a graduate tracer monitoring platform. The paper aims to provide a centralized channel to monitor institutions' graduates in terms of their job employment, assessing academic programs using modified instruments which determine necessary interventions that may be provided, and to provide a matching algorithm that can be used both by industry partners and respective alumni. This study used a Decision Support System and mapping recommendation analysis using time series analysis to evaluate the results of alumni program evaluation on five areas or dimensions such as curriculum, faculty, facility, laboratory, and student services. The study may set the threshold to determine if the results of the areas mentioned above are beyond the passing rate and implement the interventions for each area. A content management system was also used in this paper to change the contents of the Alumni Program Evaluation, the interventions, the threshold, and many more. The developed web-based system includes an evaluation of the Alumni Program across key areas such as Curriculum, Faculty, Facility, Laboratory, and Student Services. This study employed a purposive sampling technique to identify the group of respondents. There are a total of 152 respondents who participated in this study from the Information Technology department and IALAP office. The study results indicate that no interventions are necessary in any of these areas, as both the mean and the composite mean surpasses the 3.50 threshold set in the system. Among the five areas, the faculty received the lowest passing mean, followed by student services and the laboratory. This underscores the potential for continuous improvement in these specific areas influencing the employability rate and skills of the alumni-participants. The time series analysis was conducted on a two-year dataset, covering 6 trimesters. The analysis revealed a positive improvement in evaluation scores as the trimesters progressed across five dimensions of alumni program evaluation. This suggests that alumni respondents consistently agreed in their evaluations of appreciation on the improvements made by the school administration which enhances their life experiences and technical skills during their stay in the campus.

This manifesto critically examines the unfolding integration of Generative AI (GenAI), chatbots, and algorithms into higher education, using a collective and thoughtful approach to navigate the future of teaching and learning. GenAI, while celebrated for its potential to personalize learning, enhance efficiency, and expand educational accessibility, is far from a neutral tool. Algorithms now shape human interaction, communication, and content creation, raising profound questions about human agency and biases and values embedded in their designs. As GenAI continues to evolve, we face critical challenges in maintaining human oversight, safeguarding equity, and facilitating meaningful, authentic learning experiences. This manifesto emphasizes that GenAI is not ideologically and culturally neutral. Instead, it reflects worldviews that can reinforce existing biases and marginalize diverse voices. Furthermore, as the use of GenAI reshapes education, it risks eroding essential human elements— creativity, critical thinking, and empathy—and could displace meaningful human interactions with algorithmic solutions. This manifesto calls for robust, evidence-based research and conscious decision-making to ensure that GenAI enhances, rather than diminishes, human agency and ethical responsibility in education.

Seepage is a critical factor influencing the stability of earth dams, as uncontrolled seepage can result in internal erosion, piping, and structural failure. This paper proposes assessing how effective a filter drain is in reducing the exit gradient and managing seepage near the downstream slope of a homogenous earth dam. The study utilizes SEEP/W software for modeling and analyzing seepage dynamics in a homogenous and isotropic earth dam. The results indicate that without a filter drain, seepage flow is directed toward the toe of the dam, a particularly vulnerable point where structural collapse or damage is most likely to occur. However, with the installation of a filter drain, the seepage flow direction and the phreatic line are shifted away from the toe, thereby reducing the risk of instability. The findings also reveal that variations in the length of the filter drain influence the exit gradient, while the assumed permeability values have a minimal impact on the exit gradient. These results provide valuable insights into optimizing filter drain design for improving the stability and safety of earth dams.

An HHO generator is a device that electrolyzes water to produce a mixture of hydrogen and oxygen gases, which can be used as a fuel source for various applications such as welding, cutting, and combustion engines. The efficiency and production rate of HHO generators are influenced by various parameters, including electrode materials, voltage, current, and electrolyte solutions. This study aimed to evaluate the oxyhydrogen production of an HHO generator by conducting parametric analysis. A complete setup consisting of a modular HHO generator, a bubbler, and a device for measuring the volume flow rate was constructed and used in a series of experiments to determine the effects of electrode material, electrolyte solution, applied current, and plate geometry on oxyhydrogen production. The results were evaluated and analyzed using the Pareto principle, which indicated that plate geometry and input current were the most significant factors, while the other two were considered less critical. The surface area of the plates was the most significant factor affecting oxyhydrogen production, while the type of material used as an electrode was the least significant. The highest oxyhydrogen production rate, averaging 0.504 L/min over three trials, was achieved using grooved stainless-steel 316L plates in a Potassium Hydroxide (KOH) solution with a 280-ampere current. Corrosion tests indicated that stainless steel 316L in KOH solution had the lowest corrosion rate (5.043 × 10-4 MPY), while stainless steel 304 had the highest (1.009 × 10-3 MPY).

The study investigates the role of Augmented Reality (AR) for business operations enhancement, overcoming technological challenges, and advancing industrial sustainability. A systematic literature review was conducted, analyzing AR applications across sectors such as healthcare, education, e-commerce, manufacturing, maintenance, libraries, and museums. Academic databases like Google Scholar, IEEE Xplore, and Scopus were used. AR's effectiveness was assessed based on its ability to improve workflows, reduce errors, and enhance user engagement. Findings indicate that AR facilitates immersive training, reduces product return rates through virtual previews, and personalizes user interactions, leading to increased operational agility and innovation. Also, AR supports sustainability by promoting eco-friendly behaviors, optimizing resource usage, and enabling lean production practices. However, widespread adoption remains limited due to high implementation costs, technical complexity, and a shortage of skilled professionals. We offer a set of targeted recommendations to mitigate growth barriers: investing in scalable, cost-effective AR solutions, improving technical infrastructure, developing industry-specific AR applications, and offering specialized training to build AR expertise within the workforce. These strategies are essential for full AR's potential realization to drive sustainable and transformative business practices across industries. By addressing these current limitations, organizations can leverage AR not only as a tool for operational improvement but also as a strategic asset for advancing sustainability and long-term innovation.