In the modern era of creating and consuming digital content, efficient, and scalable video processing and archiving systems are essential. This paper explores and leverages the broad and extensive functionalities of the Amazon Web Services (AWS), that aim to streamline video processing workflows, enhance content delivery, and ensure cost-effective long-term storage. The paper utilizes the Amazon Simple Storage Service (S3) as the primary storage, AWS Lambda to automate workflow and efficiently sends transcoding jobs to the Amazon Elastic Transcoder where it processes the video files into its optimal formats, ensuring high quality transcoded videos. Additionally, the Amazon Glacier is incorporated for archiving the infrequently accessed videos, where the lifecycle policy feature automates the transition after 30 days, providing durable and secure storage solution. The adoption of Amazon CloudFront significantly improves the end-user experience by reducing latency and secure access to the processed videos. The integration of AWS managed services in this paper results in a scalable, secure and cost-effective solution for video processing and archiving in the cloud.

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.

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.

3D printed shelters offer a promising approach to accelerating shelter provision and enhancing resilience in disaster-affected regions. This study investigates the potential of 3D printing technology in revolutionizing shelter construction, particularly for disaster relief and humanitarian aid. By examining the technical feasibility, social implications, and environmental impacts of 3D printed shelters, this research aims to identify key challenges and opportunities for their widespread implementation. The study explores the integration of essential utilities into 3D printed shelters, their adaptability to various environmental conditions, and the importance of community engagement in the construction process. Additionally, the economic viability and environmental sustainability of this technology are assessed. Through a comprehensive analysis of existing research and case studies, this paper provides insights into the potential of 3D printing to address the critical need for rapid, affordable, and resilient shelter solutions in disaster-affected areas.

The adoption of innovative technologies is critical for improving teaching practices and student learning outcomes. Among these, artificial intelligence (AI) is emerging as a transformative tool capable of reshaping traditional educational paradigms. ChatGPT, a sophisticated language model developed by OpenAI, offers numerous opportunities for educators to enhance pedagogical effectiveness and streamline lesson preparation processes. This study explores the efficacy of ChatGPT in lesson preparation by surveying and interviewing teachers at Dr. Josefa Jara Martinez High School in the Philippines. It aims to understand their attitudes towards and experiences with integrating ChatGPT into their teaching practices. Despite the promising potential of AI in education, the adoption of such technologies in the Philippines faces significant barriers, including limited awareness, access issues, and concerns about technology integration. The findings reveal that while teachers recognize the benefits of using ChatGPT, such as improved efficiency and personalized instruction, challenges like lack of training and ethical concerns remain prevalent. The study underscores the need for comprehensive professional development programs and robust ethical guidelines to support the effective and responsible use of AI tools in education. The results show that teachers have a wide range of opinions, but many of them agree that ChatGPT has the potential to make lesson planning easier, offer individualized learning resources, and keep students interested in class. On the other hand, issues with consistency with curriculum requirements, dependability, and general efficacy were also apparent. The study sheds light on the challenges associated with integrating AI into education and makes recommendations for professional development, focused assistance, and ethical considerations to help high schools adopt AI technologies responsibly. Teachers can optimize learning experiences, improve teaching effectiveness, and give students the tools they need to succeed in the digital age by tackling these issues and utilizing AI's transformative potential.

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.

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.

This research study uses the Multinomial Nave Bayes (MNB) algorithm to categorize and analyze the user experience (UX) of users of mobile commerce applications. The goal of the study is to give business owners insightful information on how well their mobile applications are performing. The study's goals are to establish evaluation standards for categorizing user experiences, use MNB to classify user experience reviews to their appropriate UX elements, analyze the results of the classification, and suggest areas for improvement to enhance the usability of m-commerce. The research plan consists of a number of sprints, including data extraction, data cleaning, classification system creation using the Multinomial Naive Bayes algorithm, and model accuracy rate evaluation. The proposed system integrates the algorithm and uses data from m-commerce applications. The results of the analysis provide insights into the different UX elements such as Value, Adoptability, Desirability, and Usability. The analysis's findings shed light on many UX components like Value, Adoptability, Desirability, and Usability. The classification model was evaluated for accuracy, achieving a result of 89.243%. This means that the model correctly classified 89.243% of the user experience reviews in the evaluation dataset, indicating a satisfactory level of accuracy. However, there were some misclassifications in the remaining 10.757% of the reviews. Therefore, the research successfully developed a system that analyzed and classifies user experiences from customer reviews using MNB. The classification model demonstrated a satisfactory level of accuracy. The findings provide valuable insights and recommendations for improving the mobile application browsing experience based on user feedback and experiences.

Immersive technologies have generated significant interest across various academic disciplines. The necessity for more authentic, interactive, and immersive artificial environments led to the growing popularity of the metaverse. Unfortunately, not all metaverse types have been broadly covered in educational research. This inadequacy highlights a gap in understanding the potential benefits and drawbacks of metaverse technologies for education. To address this research gap, we created a metaverse called “MILES Virtual World” that embodies the concepts of lifelogging and mirror worlds. Following the principles of the Embodied Social Presence Theory, the application allows students to socialize through customizable avatars and engage in a variety of activities that closely resemble those in the physical world. We adopted a mixed-method approach using a convergent parallel design to evaluate the application. Our quantitative analysis reveals that students feel highly present and engaged in the virtual environment, with a sense of agency and immersion. It also underscores the importance of enhancing embodiment and copresence to create more effective virtual world experiences and opportunities for social interactions. Meanwhile, our qualitative analysis uncovers several underlying subthemes, including avatar customization, identity exploration, virtual items, communication, entertainment, autonomy, freedom of expression, realism, challenges, shared experiences, and a sense of belonging within the metaverse. Overall, our study provides valuable insights into the potential of metaverse technology in the educational context, and how it can be harnessed to create more effective and engaging academic experiences for students.

The emergence of the metaverse has expanded the vital role of virtual worlds in education. It extends the traditional configuration of virtual spaces by offering a more immersive, interactive, and social experience. Unfortunately, there is insufficient literature on how to develop a metaverse application aligned with academic standards and best practices. In this study, we adopted a co-design approach in remodeling our mobile educational metaverse to draw a blueprint for existing and future metaverse technology adopters. We sent a follow-up survey to stakeholders inviting free-text comments. This survey focused on the challenges, issues, and expected features from the perspectives of developers, teachers, and students, respectively. Our results revealed several challenges that developers face when building a metaverse, such as technical infrastructure, student engagement, avatar customization, and performance optimization. Meanwhile, teachers emphasized potential issues that may arise from the use of metaverse technology in educational settings, such as learning outcomes, health and safety, digital citizenship, and ethics and morality. Finally, students expected several key features of an educational metaverse application, such as socialization and collaboration, virtual world interactions, metaverse optimization, realistic graphics, and minigames and activities. The implications of these findings are significant and can help other educational institutions seeking to integrate metaverse technology into their academic services. Overall, our study presents an initial foundation for further exploration and advancement within the rapidly evolving field of metaverse technology.