Visual impairment continues to be a global health concern. People with visual impairment experience difficulty moving around indoors, especially in unfamiliar spaces. While existing assistive technologies like smart canes offer point-to-point navigation or rely on infrastructure like RFID tags or beacons, they lack the ability to provide comprehensive indoor navigation with obstacle detection and avoidance. This paper presents a novel indoor navigation system for visually impaired individuals using deep learning and audio guidance. The system utilizes 3D-printed glasses equipped with a Raspberry Pi v2 camera, audio user interface and a processing unit comprising a Raspberry Pi 4B and Google Coral Edge tensor processing unit (TPU). As validated in a controlled indoor environment, the deep learning models for localization, navigation, obstacle detection, and obstacle avoidance achieve high results in terms of accuracy, precision recall, and F1-score. Based on user tests using the System Usability Scale, this wearable assistive device appears to offer a promising solution for promoting independent navigation and spatial awareness among visually impaired individuals.

Amidst the clear-cut changes constantly happening in the educational landscape, Higher Education Institutions (HEIs) are continuously pursuing graduates that meet global standards. The rise of remote jobs from previous years opened a gateway of opportunities for Filipino graduates to ensure employment from various multinational employers. To maintain this, HEIs in the Philippines must be able to offer quality education and programs that meet exceptional standards. This study aims to address the inability of tertiary institutions to track the competencies that the students have gained by integrating the outcome-based education (OBE) framework through an online platform. This paper also enumerates the benefits of having an OBE Management system such as achieving a holistic view of evaluating students' competencies, the system integrates educational data from various sources such as grading system, Learning Management System (LMS), and surveys. The system development research process is conducted in this study. One of the objectives of this study is the integration of drill-down analysis into the OBE Management system. This allows users to create reports easily and faster, furthermore, it aids the country in achieving Sustainable Development Goal (SGD) 4 for Quality Education. The premise of the study also contributes to the impact of system development on attaining quality education for HEIs.

Social relationships are important to human well-being and interaction. Recently, there has been growing interest in how they are formed in various digital media, including social media and video games. Amid the rise of digital engagement, the metaverse has emerged as an essential virtual environment for social interaction. Unfortunately, there is limited understanding of how social relationships are developed and maintained within the metaverse. In this study, we explored the dynamics of social relationship development in a metaverse world. Using a one-shot case study, we assessed the roles of immersion and embodiment, as well as the moderating effect of copresence, in building social relationships through multiple regression analysis. Our findings show that with higher levels of immersion and embodiment, the formation of social relationships in the metaverse is significantly improved. Copresence further intensifies these effects, which is indicative of its crucial role in virtual social interactions. These results indicate that enhancing the immersion, embodiment, and copresence elements in metaverse environments can encourage stronger social bonds among users. Overall, this study advances our understanding of online social relationship formation in the metaverse environments and its design and development.

Part of speech tagging (POS) is crucial in natural language processing, identifying the grammatical categories of words in sentences. This research highlights the lack of focus on POS tagging for Asian languages, particularly Cebuano. Limited research on Cebuano has hindered linguistic documentation and understanding of its grammar and vocabulary. This study introduces a Cebuano POS tagger using the Hidden Markov Model (HMM) to improve Cebuano text processing. The researchers also propose a method for handling unfamiliar words. Results show the algorithm performs well on a news text corpus of 25,000 datasets, with an accuracy of 84 %, precision of 80%, recall of 81.52%, and F1-score of 82%. These outcomes demonstrate the algorithm's effectiveness in addressing language challenges in specific genres. Additionally, the research contributes to the Sustainable Development Goals (SDGs) by promoting linguistic diversity and fostering inclusive language technologies. The study provides insights into Cebuano's linguistic traits and grammatical structures, offering a foundation for further research in natural language processing.

Energy consumption significantly increased in recent decades, notably at the household level, due to economic development, rising population, and technological advancements. To address this sustainability concern, peer-to-peer energy sharing service (P2PESS) is introduced as a solution to household level energy needs. However, P2PESS has yet to be fully explored in terms of development and adoption. As such, this study attempts to provide an understanding of the adoption intention on artificial intelligence (AI) in P2PESS a developing country. This study is realized by developing an extended adoption intention model analyzed through partial-least squares - structural equation modeling (PLS-SEM). Results show that attitude is the most significant predictor of AI in P2PESS adoption intention. This study also reveals that the trust dimension has the strongest effect on attitude, while attitude toward use has the strongest effect on behavioral intention. Also, this study confirms ease of use and usefulness as critical factors in adoption intention. Meanwhile, AI-anxiety is the least significant predictor in the model. Finally, this study is the first evidence of AI in P2PESS adoption intention from the perspective of household level users.

Sustainability is a present concern in many developing countries, where the role of the household is pivotal in realizing its benefits. This study aims to explore artificial intelligence in green energy technologies (AIGET) adoption intention among household-level respondents selected in the National Capital Region (NCR), Philippines. The study has 446 respondents and analyzed using partial least squares and structural equation modeling approaches (PLS-SEM). Among the factors tested, results revealed that perceived usefulness is the strongest predictor of AIGET adoption intention. Factors such as usefulness, ease of use, subjective norms, and perceived risk have a positive effect on attitude. This confirms that attitude has a positive impact on behavioral intention on AIGET. Finally, this study shows that household-level participants have a positive interest in adopting AIGET, considering its usefulness and ease of use. This study presents useful theoretical and practical contributions to further its uptake in the Philippines and other developing countries.

Sustainability in transportation technologies is growing in all parts of the world through electric and micro-mobility sharing services. As such, there is a need to explore the factors that influence its adoption and use behavior. However, this is relatively underexamined in many developing countries. This study attempts to understand the intention and use behavior of micro-mobility as a service (MaaS) in the Philippines, a developing country. This study used survey data, and analysis was performed using partial least squares and structural equation modeling (PLS-SEM). Results show that performance expectancy is the strongest predictor of intention, while satisfaction is the least significant predictor. Factors such as social influence, price value, and habit have a positive effect on intention. Overall, the predictive model is explained by the coefficient of determination, revealing that behavior intention, satisfaction, and use behavior have large predictive relevance. This study provides theoretical and practical implications for further micro-mobility research in the future.

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.

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.

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.