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.

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.