Teachers play a central role in achieving the mission, vision, and goals of educational institutions. However, the multitude of responsibilities and obligations they must fulfill demands a high level of productivity. Consequently, productivity software is increasingly becoming a necessity for teachers to lessen their day-to-day work pressure and instead focus on offering quality education. Despite their popularity, the key antecedents and precursors affecting the intention to use productivity software have yet to be investigated. Therefore, the goal of this study was to determine what factors contribute to the adoption of productivity software by applying the theoretical lens of the Technology Acceptance Model (TAM). A total of 947 responses from basic and higher education teachers were analyzed using a structural equation modeling approach. Results show that the usefulness and ease of use of productivity software are key in predicting behavioral intention. It is also indirectly affected by external variables such as subjective norms, professional reputation, job relevance, and output quality through perceived usefulness as well as facilitating conditions and self-efficacy through perceived ease of use. Overall, the findings of this study support the applicability of the specific TAM version as well as its employment in the context of productivity software.

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.

As technology rapidly advances, the complexity of societal challenges grows, necessitating intelligent solutions that can adapt and evolve. However, developing such solutions requires a deep understanding of computational intelligence (CI) and its application in addressing real-world problems. Moreover, ethical considerations surrounding AI, such as bias and accountability, are crucial to ensure responsible development and deployment of intelligent systems. Open AI and Computational Intelligence for Society 5.0 offers a comprehensive exploration of CI, providing insights into intelligent systems' theory, design, and application. This book is a practical guide for scientists, engineers, and researchers seeking to develop thoughtful solutions for complex societal issues. Integrating disruptive technologies and frameworks illuminates the path toward creating intelligent machines collaborating with humans to enhance problem-solving and improve quality of life.

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.

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.