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.

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).

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 project was pushed through to engage people towards proper waste collection, through the utilization of mobile devices of the communities in different municipalities. The study aims to develop and implement a sustainable and efficient waste management collection system by informing the residents of the garbage truck collection schedule available on their mobile devices. Additionally, the platform utilizes optimization and decision support algorithms, including queuing algorithms, to receive and review complaints efficiently. The researcher employed an incremental software development methodology, allowing the software to be developed and tested even when requirements are still evolving. The study is descriptive-correlational, as it involves evaluating the developed system based on feedback from expert respondents. The evaluation yielded an overall mean performance score of 4.75, interpreted as “Strongly Agree,” indicating that the system is well-prepared for deployment.

Children in street situations are prone to physical and emotional abuse, exploitation, medical and financial problems. This research aims to examine the root causes of children in street situations' deprivations, to contribute to the welfare of children in street situations, and to propose strategies for addressing the matter through a multimedia project, specifically by a hybrid 2D & 3D educational animated series and website. It will also explore the impact of educational animated series and websites that will provide a comprehensive understanding of the challenges faced by children in street situations. It serves to raise awareness, promote understanding, and potentially generate solutions to improve the lives of children in street situations. This multimedia project can be an educational tool to various NGOs and organizations that cater to children in street situations. This was found to be effective by providing an educational series and website with information on how to access basic needs and to provide a safe and supportive environment for children in street situations. The study utilizes a mixed qualitative and quantitative research design, it provides valuable insights into the challenges through interviews, pre-assessment, post assessment, and survey questions. The study has important implications for policymakers, educators, government, and practitioners working with children in street situations and highlights the importance of providing a safe and supportive environment for children in street situations to thrive.

This study enabled the researcher to create a graduate tracer monitoring platform. It aimed to provide a centralized channel to monitor institutions' graduates in terms of their job employment, to assess academic programs using modified instruments so necessary interventions may be provided, and to provide a matching algorithm that can be used both by industry partners and respective alumni. This study employed a Decision Support System and mapping recommendation analysis using a rule-based algorithm to evaluate the results of alumni program evaluation on five areas or dimensions, namely curriculum, faculty, facility, laboratory, and student services. It sets the threshold to determine if the results of the areas mentioned above are beyond the passing rate and implements the interventions for each area. The content management system was also used in this study to change the contents of the Alumni Program Evaluation, the interventions, the threshold, and many more. Based on the results, no intervention must be implemented in all areas/dimensions since the mean and the composite mean were more than the 4.0 threshold that was set in the proposed system. The overall rating of the respondents using the technology acceptance model numerical rating is 4.42 with an interpretation of “Agree.” As observed all criteria are rated either agree or strongly agree which indicates a high standard has been set in the development of the system. This means that the system is ready for deployment.

This study explores the beneficial uses of Microsoft Copilot as a support tool for Baliwag Polytechnic College instructors' lesson planning and activity design. Researchers evaluate the influence of Copilot on the creation of instructional content by examining the experiences and opinions of educators. The study demonstrates the advantages, difficulties, and opportunities for customization that come with incorporating Copilot into the curriculum. The results indicate that Copilot can significantly improve the effectiveness and caliber of lesson design, but also highlight certain implementation issues. This research offers insights into the future of technology-enhanced education and contributes to the expanding body of research on AI-assisted teaching strategies.

Stroke, a leading cause of brain-related illnesses and mortality, occurs due to the blockage of blood flow to the brain or the rupture of blood vessels, leading to brain damage and long-term disability. A common consequence is weakness or paralysis, such as left-sided hemiplegia, which significantly impacts independence and daily activities. As technology advances, there is a growing need for innovative solutions to enhance the rehabilitation journey and empower stroke survivors to regain arm function. Traditional rehabilitation methods, often reliant on physical therapy and Neuromuscular Electrical Stimulation (NMES), may not fully optimize upper limb recovery, and accessibility to these treatments poses challenges, especially for patients with mobility issues. The ARMeD (Assistive Rehabilitation Monitoring Device) addresses these challenges by integrating NMES with Passive Range of Motion (PROM) during rehabilitation. This groundbreaking device, accessible via an Android app, allows patients to undergo rehabilitation from home while enabling physical therapists to monitor their recovery progress effectively.

In opportunistic networks, no end-to-end path is available from source to destination due to frequent movement of nodes with high speed. In such type of networks, transmission takes places between nodes during a contact event. These types of networks, follow store-carry and forward mechanism to forward messages from source to destination, Intermediate node stores messages into buffer and carries these messages until it meets another node. Already existing flooding protocols like epidemic may congest the network due to excessive flooding of the messages over the network. Replication based routing protocols introduces in which messages are replicating according to quota value. The replication based protocols have some limitations like delay which degrades the performance of network. Our proposed technique overcome the limitations of replication based routing protocols. Proposed technique provides replication based forwarding with optimal buffer management to increase the delivery ratio and minimize the delay. Extensive simulation of proposed technique is done in ONE simulator with different scenarios and comparing result of proposed scheme with already existing schemes such as epidemic, Rep-nodes and Spray & Wait. Result shows that our scheme has outperform as compare to already existing schemes in terms of delivery ratio and delay.

As global and local efforts tackle energy consumption and environmental sustainability, it is crucial to conduct detailed studies on energy demand. This study investigated the effects of wind, relative humidity, temperature, precipitation, and the number of operating days on the monthly energy consumption of a specific building using statistical techniques such as Pearson correlation analysis and time series modeling. Seasonal-trend decomposition using LOESS (STL) was utilized to model the deterministic component in the data and seasonal autoregressive integrated moving average with exogenous variables (SARIMAX) models to further capture the seasonality of energy consumption while taking account of the external effects of weather and operational factors. The forecasting accuracy of the models was benchmarked to naive modeling in terms of normalized Root Mean Squared Error (nRMSE) and Mean Absolute Error (nMAE), Mean Absolute Percentage Error (MAPE), and Skill Score (SS). The results indicate that among the exogenous variables, only the number of operating days significantly correlates with the target variable. Ensemble technique and inclusion of operating days, wind speed, ambient temperature, and total precipitation in the models significantly enhanced the forecasting accuracy. Consequently, the STL-Ensemble 2 model provides optimal forecasting accuracy in predicting building energy consumption with 8.65% nRMSE, 6.84% nMAE, and 7.92% MAPE, which is far superior to the naive model with 27.45% nRMSE, 24.07% nMAE, and 27.75% MAPE, and STL-SARIMA with 10.03% nRMSE, 8.67% nMAE, and 10.21% MAPE. Future research can use more granular data resolution and further explore advanced forecasting methods such as machine learning techniques to achieve improved model performance and realized effects of operational and weather variables.