The growing adoption of renewable energy, particularly photovoltaic (PV) solar systems, has led to the development of numerous simulation software tools to simplify system design, analysis, and optimization. This study evaluates five widely used PV simulation software packages—SAM, PVsyst, HOMER, PV∗SOL, and RETScreen—by analyzing their features and performance across ten critical criteria, including cost, solar database accessibility, modeling capabilities, and ease of use. Using the Analytic Hierarchy Process (AHP), the criteria are ranked by importance, with the working platform identified as the most influential factor in software selection, followed by economic modeling capabilities and software cost. Additionally, the flexibility of simulation data requirements, reporting and analysis options, and user friendliness and ease of use are identified as important criteria, albeit ranking lower in importance. Performance analysis using simulation and real data further validates the evaluation process, providing insights into the accuracy and reliability of the simulation results generated by each software. Our findings indicate that SAM delivers superior accuracy when compared to real-world data, making it the most reliable tool for PV system analysis. PV∗SOL also ranks highly for its robust reporting and modeling capabilities. These results provide valuable insights for professionals and researchers in selecting the most suitable software for PV system design and optimization, emphasizing the balance between functionality, cost-effectiveness, and user-friendliness.

Cavite is a highly urbanized province situated near Metro Manila and has the highest population growth rate in the country. Water resource management and water-related risk mitigation is one of the major challenges the province faces. Cañas River Watershed is one of the major river systems in the province which covers major cities and municipalities. Effective streamflow monitoring in this watershed has not been achieved due to the inadequacy of monitoring stations around the province. This study aimed to develop an LSTM model to predict the streamflow in Cañas River Watershed at the Panaysanayan river gauge using the available weather parameters in two weather stations in the province, namely: Sangley Point Synoptic Station and Cavite State University (CvSU) Agrometeorological Station. Using the short-term data dated from 2014 to 2019 obtained from the stations and the river gage, the Long Short-Term Memory (LSTM) model successfully predicted the streamflow. Based on the model performance evaluation the values of Nash-Sutcliffe Efficiency (NSE) for the training and test were 0.90-0.91 and 0.87-0.89, respectively which indicates a high predictive accuracy. On the other hand, the Percent Bias (PBIAS) results in training and testing ranges 0.60% -8.04% and 1.92% -8.32%, respectively, which indicates a low bias prediction. The model tends to underestimate values, especially high magnitude flows. The RMSE-to-Standard Deviation Ration (RSR) results in training and testing ranges from 0.30-0.31 and 0.34-0.35, respectively, which indicates a good predictive power. The model results also show a good performance in developing a flow duration curve in the river to determine its dependable flow. The R2-value between the observed and predicted flow at different probability of exceedance is 0.9938. The dependable flow of Cañas River Watershed at Panaysanayan river gauge was 60 liters per second based on the observed flows and 61.12 liters per second based on the predicted flows.

Power system protection is essential for maintaining the reliability and stability of electrical grids, ensuring continuous service, and preventing catastrophic failures. As power systems evolve to incorporate renewable energy and increasingly complex configurations, the role of Artificial Intelligence (AI) in enhancing protection mechanisms has become indispensable. This paper reviews the integration of AI in power system protection, highlighting its potential to improve fault detection, adaptive protection strategies, predictive maintenance, and real-time monitoring. AI techniques, including machine learning, deep learning, and expert systems, offer significant advancements in overcoming the limitations of traditional protection schemes. Furthermore, the integration of AI contributes to the development of resilient and sustainable infrastructure, supports innovation in intelligent urban systems, and enhances the reliability of modern power grids. Despite its promising potential, challenges such as data scarcity, model scalability, and real-time processing need to be addressed for effective implementation. This review synthesizes the current literature on AI applications in power system protection, comparing them with conventional methods, and provides information on future research directions and practical applications to improve energy reliability, sustainable urban development, and industrial innovation.

The integration of 3D printing technology into water infrastructure offers innovative solutions to the pressing challenges faced during disaster relief and response. This review paper explores the advancements in 3D printing technology and its applications in enhancing water infrastructure, especially during disaster relief operations. It compiles existing literature to highlight the current state of knowledge, focusing on the potential benefits, challenges, and future potential of 3 D printing for creating essential water infrastructure components. The study emphasizes the technology's capability to produce customized, on-demand solutions that are both cost-effective and efficient. By addressing the critical aspects of 3D printing applications in disaster scenarios, this paper aims to provide a comprehensive understanding of how this technology can revolutionize clean water accessibility and improve quality during emergencies.

3D printing, or additive manufacturing, is transforming electrical engineering by driving advancements in sustainable energy systems, urban infrastructure, and industrial innovation. This paper explores its applications in fabricating energy-efficient components, such as photovoltaics, wind turbine parts, and energy storage systems, as well as its role in advanced prototyping and smart grid technologies. The adoption of advanced materials, including conductive polymers and biodegradable composites, supports the development of renewable energy systems and customized solutions for urban and industrial applications. By reducing material waste, lowering production costs, and accelerating innovation cycles, 3D printing fosters sustainable manufacturing practices and resilient infrastructure development. Challenges such as material compatibility, scalability, and costs are discussed, alongside emerging technologies like artificial intelligence (AI) and the Internet of Things (IoT), which enhance optimization and broaden applications. This study highlights the critical role of 3D printing in advancing sustainable energy, urban development, and industrial modernization.