Social interaction has long been a subject of theoretical inquiry in both Computer-Mediated Communication (CMC) and Human-Computer Interaction (HCI), but seldom has it been examined through the lens of digital embodiment. As the metaverse gains traction as a platform for learning and collaboration, understanding how its affordances construct behavioral engagement demands empirical scrutiny. Thus, this study examines the effects of avatar customization and communication modality on behavioral engagement within a metaverse-based simulation. Using a 2×2 factorial design, participants were randomly assigned to avatar (customized vs. generic) and modality (voice vs. text) conditions, with engagement tracked via a stealth assessment approach across multiple sessions. Findings indicate that avatar customization facilitated broader spatial exploration, while voice-based communication elicited higher interpersonal interaction. Critically, the convergence of both factors produced a compounded effect that yielded selective interaction effects on temporal and social dimensions of engagement. This study contributes a framework of affordance convergence that informs both the theoretical modeling of digital embodiment and the practical design of immersive learning platforms. As educational experiences increasingly unfold within socio-technical systems, the challenge for both HCI and CMC is to design environments where social interaction is both mediated and dynamically co-constructed through the alignment of interactional affordances.

The integration of artificial intelligence (AI) is critically transforming the renewable energy sector. This review synthesizes AI's role in optimizing solar and wind energy systems, focusing on power forecasting, system optimization, and predictive maintenance. The research goal was to systematically analyze how diverse AI techniques enhance these critical aspects. Key findings indicate AI's capacity to substantially improve short-term solar irradiance and wind power forecasts (e.g., via SARIMAX, long short-term memory (LSTM), and hybrid deep learning models), dynamically manage energy flow in smart grids and microgrids, optimize maximum power point tracking (MPPT) in photovoltaic (PV) systems, and enable proactive maintenance through anomaly detection in wind turbines using IoT-integrated AI. Key conclusions reveal that AI significantly enhances the efficiency, reliability, and economic viability of solar photovoltaic and wind power generation, offering superior adaptability and predictive capabilities over traditional methods. While AI is important for the global transition to cleaner energy, persistent challenges related to data quality and availability, model interpretability, and cybersecurity must be addressed to fully unlock its potential in practical renewable energy applications.

Green economy is an approach to economic development while protecting the environment, that focuses on clean energy, resource saving activities, waste reduction and pollution. Several developed countries have aligned their educational focus, integrating outcomes-based education, exposing students to sustainable development goals (SDG), particularly, Green Economy. To date, however, the Philippines, a developing country, is still on its emerging stage of exposing higher education students on green economy, through, outcomes-based education (OBE) implementation. This study aims to predict generation Z's participation in the green economy based on OBE exposure, through survey data and machine learning techniques. Results show that random forest-based model predicts at rate of 95% accuracy, support vector machine (94%), gamma ray boosting (94%), extreme gradient boosting (92%), k-nearest neighbors (89%), k-nearest neighbors (89%), and decision tree (87%). Thus, machine learning models demonstrate the ability to determine participation and non-participation in green economy based on OBE exposure. Research and educational implications are offered.

Mental health concerns are on the rise among college students in the Philippines, where academic stress and limited access to counseling services continue to pose serious challenges. With mobile technology becoming more integrated into daily life, it offers a practical opportunity to support student well-being through accessible, self-help tools. This study presents the design and evaluation of a mobile application that combines art therapy and sound therapy to help reduce stress and promote relaxation among students in higher education. Guided by the Technology Acceptance Model (TAM), the research explored how users perceived the app's usefulness, ease of use, and overall experience. The app was developed using a blended Agile approach and tested by 50 purposively selected college students experiencing academic stress. Results showed strong user acceptance, with high ratings in ease of use (x=4.56), satisfaction (x=4.36), and intention to use (x=3.96). Perceived usefulness was strongly correlated with both satisfaction (r=0.73) and continued use (r=0.78), indicating that the app effectively supported stress relief and user engagement. This study contributes practical insights for integrating mobile wellness solutions in Philippine education, particularly in settings where traditional mental health support remains limited. It encourages the adoption of simple, evidencebased digital tools that promote emotional well-being and help bridge gaps in student support systems.

This study developed the Digital Banks PH Notebook, a mobile application designed to support financial literacy among Filipinos by optimizing savings through high-yield digital banking platforms. The application featured a savings portfolio tracker, interest forecasting calculator, and savings goal management to address gaps in financial planning and savings behavior. Development followed a blended Agile methodology integrating Scrum, Extreme Programming, and Feature-Driven Development, ensuring iterative improvements aligned with user needs. Software quality was assessed using the ISO/IEC 25010 model, while qualitative feedback was analyzed through word cloud visualization to capture user sentiment and key focus areas. Findings indicated that the application effectively enhanced users' understanding of savings strategies and promoted responsible saving practices. The tool successfully connected the opportunities presented by digital banking with the practical requirements of financial education, providing users with actionable insights to manage their savings more strategically. By leveraging agile development practices and rigorous evaluation frameworks, the project demonstrated that technology-driven solutions can play a significant role in advancing financial literacy and supporting sustainable financial behaviors in an evolving digital economy.

This study investigates the integration of generative artificial intelligence (AI) into video production and editing workflows using the Technology Acceptance Model (TAM) as the theoretical framework. Given the creative industry's substantial economic contribution globally and in the Philippines, the research focuses on how video professionals perceive the usefulness and ease of use of AI tools such as Adobe Premiere Pro, After Effects and OpenAI's Sora. A purposive sample of 60 professionals, including editors, motion graphics artists and content creators, was surveyed to assess behavioral intention, user attitude and the influence of external factors on AI adoption. Findings indicate a generally positive attitude toward AI, with many respondents highlighting increased efficiency and automation of repetitive tasks. However, the study also identifies key barriers such as skill gaps, tool complexity and limited training opportunities, emphasizing the need for competency-based training and institutional support. Correlation and word cloud analysis reinforce the importance of enhancing education quality and multimedia learning to support effective adoption. The results confirm TAM's applicability in this context and suggest that with improved accessibility, policy alignment and targeted capacity building, generative AI can significantly enhance creative workflows while supporting the development of employability skills in the digital content sector.

Color is a foundational element in digital design, yet individuals with color vision deficiency (CVD) often encounter significant barriers when engaging with mainstream graphic design platforms. This study explores the ethical and practical implications of designing for users with CVD, focusing on the experiences of Filipino creatives in both professional and academic settings. It aims to uncover common usability challenges, evaluate the effectiveness of existing accessibility features, and propose improvements that reflect user needs and promote inclusive design practices. Employing a qualitative, exploratory methodology, the study integrates word cloud analysis and the Diffusion of Innovation theory to interpret user feedback and assess attitudes toward accessible tools such as colorblind-friendly palettes and vision simulators. Results show strong support for built-in, customizable accessibility features, with many users expressing openness to adoption if these tools are integrated seamlessly into their workflows. The study contributes to the discourse on digital inclusion by presenting an ethical design framework that promotes accessibility as a standard, not optional, element in graphic design platforms.

Part-of-speech tagging (POS) is a vital process in natural language processing, enabling the identification of grammatical categories within sentences. This research emphasizes the lack of attention given to POS tagging for Asian languages, particularly Waray-waray. Limited studies on Waraywaray religious texts have hindered linguistic documentation and the deeper understanding of its grammar and vocabulary. To address this gap, the study introduces a POS tagging system for Waray-waray utilizing a Modified Viterbi Algorithm, which also incorporates a strategy for handling unfamiliar words. Evaluated on a corpus of 50,000 religious text datasets, the algorithm demonstrates outstanding performance-achieving an accuracy of 93%, precision of 90%, recall of 90.52%, and an F 1 score of 92%. These results underscore the algorithm's effectiveness in navigating linguistic challenges across specialized genres. Beyond technical contributions, the study promotes linguistic diversity and fosters inclusive language technologies, advancing the goals of the Sustainable Development Goals (SDGs). Specifically, it enhances language learning and literacy among Waray-waray speakers, supports inclusive education through computational tools for minority languages, and aligns with SDG 4 by providing foundational resources for mother-tongue instruction and educational content development. Additionally, it offers new insights into Waray-waray's grammatical structures, laying a robust groundwork for future linguistic and computational research. Beyond technical contributions, the study promotes linguistic diversity and fosters inclusive language technologies, advancing the goals of the Sustainable Development Goals (SDGs). Specifically, it enhances language learning and literacy among Waray-waray speakers, supports inclusive education through computational tools for minority languages, and aligns with SDG 4 by providing foundational resources for mother-tongue instruction and educational content development. Additionally, it offers new insights into Waray-waray's grammatical structures, laying a robust groundwork for future linguistic and computational research.

Chloride-induced corrosion (CIC) is a primary reason of deterioration in reinforced concrete (RC), particularly in marine structures which causes cracking, degradation, and decreased service life. Advances in the 4th Industrial Revolution have enabled utilization of machine learning techniques in different fields of civil engineering. This study develops an Artificial Neural Network (ANN) enhanced by Particle Swarm Optimization (PSO) to predict rebar corrosion in polypropylene fiber reinforced concrete (PFRC). Accelerated corrosion tests were performed using the impressed current method on samples with varying polypropylene fiber content, concrete cover (CC), and bar diameter (BD). Experimental results showed that the 3-7-1 network structure (NS) (3 input neurons (IN), 7 hidden neurons (HN), 1 output neuron (ON)) achieved the highest accuracy with correlation coefficient (R) of 0.98969, mean squared error (MSE) of 0.18846, and mean absolute percentage error (MAPE) of 7.832 %. Employing the generated connection weights (CW) from the governing model (GM), through Olden's connection weights approach, observed that the concrete cover had the most significant influence on corrosion (-43.231%), followed by bar diameter (33.717%) and fiber content (-23.052%). It highlights that increasing concrete cover and fiber content significantly reduces corrosion in PFRC, which may be used by civil engineering professionals as it offers insights for enhancing the durability of reinforced concrete structures. This approach supports SDG 9 (Sustainable Development Goal 9: Industry, Innovation, and Infrastructure) by promoting resilient, innovative construction methods and contributes to SDG 11 (Sustainable Development Goal 11: Sustainable Cities and Communities) by enhancing the longevity and sustainability of urban infrastructure.

Filipino Sign Language (FSL) is an invaluable tool for communication within the deaf and mute communities, yet there is a shortage of proficient special education teachers and accessible learning materials. Current research on FSL recognition is limited to basic detection, often invasive, and lacks comprehensive systems that provide feedback to users. Additionally, FSL incorporates distinctive static and dynamic gestures, including contractions, which set it apart from other sign languages. This study presents the development of a machine vision-based FSL tutor that leverages the MediaPipe framework-specifically, MediaPipe Hands for static gesture recognition and MediaPipe Holistic for full-body dynamic gesture tracking. LSTM networks were used to classify dynamic gestures based on sequential landmark data to capture temporal dependencies in sign execution. The system supports a desktop application platform enabling learners to engage in interactive modules with real-time feedback through visual prompts and audio cues. It utilizes 42 static hand feature landmarks and over 1,662 key points derived from hand, pose, and facial data to ensure accurate recognition and feedback. A total of 50 essential FSL gestures-aligned with the kindergarten curriculum-were modeled, covering alphabet knowledge, vocabulary development, self-introduction, and polite expressions. Performance evaluation using computer vision metrics demonstrated high recognition accuracy for both gesture types. In addition, the System Usability Scale (SUS) and statistical comparisons with traditional instruction methods confirmed the platform's effectiveness and user acceptability. The results validate the system as a comprehensive and accessible solution for FSL education, particularly suited for early learners and self-guided instruction.