Blue carbon ecosystems (mangroves, saltmarshes, seagrasses) are globally significant carbon sinks, absorbing an estimated 50% of oceanic carbon despite covering only 2% of the ocean surface. However, these important habitats face rapid degradation, with 25% to 50% loss over the past 50–70 years, transforming them into carbon sources. This paper presents a climate-smart communication and sensing framework for real-time monitoring of these critical marine ecosystems. It integrates Artificial Intelligence (AI) with advanced sensing technologies, including satellite, Uncrewed Aerial Vehicles (UAVs), LiDAR, and in-situ sensors, for comprehensive data acquisition and analysis. The framework evaluates energy-efficient and secure underwater (acoustic, optical) and overwater (satellite, cellular, LoRaWAN) communication strategies to ensure continuous data flow. AI algorithms enhance data processing, pattern recognition, predictive modeling, and autonomous operations of platforms like Autonomous Underwater Vehicles (AUVs). This integrated approach not only supports accurate carbon accounting but also yields substantial co-benefits for climate change mitigation and adaptation, biodiversity conservation, and maritime security by deterring illegal activities and pollution. The framework provides a transformative pathway for sustainable blue economy and resilient coastal communities.

Climate change is intensifying maritime risks, including sea level rise, stronger storms, and disrupted ocean currents—posing threats to coastal infrastructure, navigation, and security. Traditional monitoring systems lack the predictive capabilities and coverage to address these evolving challenges. This paper explores the integration of Artificial Intelligence (AI) and satellite-based Earth observation as a next-generation early warning system (EWS) for maritime security. We assess observed and projected hazard trends, propose an AI-enhanced system architecture, and evaluate readiness in climate-vulnerable geographies. The findings highlight actionable strategies to improve forecasting, risk detection, and climate resilience in coastal and maritime domains.

Soil pollution in urban environments is a critical issue with significant implications for public health, environmental quality, and sustainable urban development. This review paper explores the various aspects of soil pollution in urban areas, including its sources, types of pollutants, consequences, mitigation strategies, and the importance of sustainable urban development. The sources of soil pollution in urban environments are diverse, ranging from industrial activities and waste disposal to emerging sources like microplastics and pharmaceuticals. The types of pollutants found in urban soils include heavy metals, polycyclic aromatic hydrocarbons (PAHs), and organochlorine pesticides, among others. The consequences of soil pollution in urban areas encompass public health risks, environmental impacts, and contamination of water resources. Mitigation strategies such as remediation techniques, pollution prevention measures, urban green infrastructure, and public awareness are crucial for addressing soil pollution in urban environments. Sustainable urban development plays a key role in maintaining soil health and integrating soil considerations into urban planning. By understanding the sources, types, consequences, and mitigation strategies of soil pollution in urban areas, cities can work towards healthier environments and sustainable development.

The transition from a centralized to a decentralized energy network has grown significantly. As a result, bidirectional energy platforms for consumers and producers have been established. In support, technological advancement in distribution networks is carried out, encouraging local consumers to participate in energy production, leading to the emergence of LECs, empowering them to captivate, generate and share renewable energy. A comprehensive review of LECs is presented in this paper, addressing their operational characteristics, control strategies, market interactions, regulatory frameworks, and real-world implementation challenges. Moreover, the paper provides the technical challenges related to grid integration, economic, and regulatory policies associated with peer-to-peer interaction are some of the hindrances in deploying a successful LEC strategy. The paper identifies a critical gap between the academic and real-world environment that can be bridged through realistic assumptions, adaptive control strategies, and measurements of cyber threats. Additionally, pilot projects are assessed to understand the feasibility and impact of LECs in real-world environments. This study proposes the requirements for establishing advanced network and monitoring technology, dynamic pricing strategy, and revised regulatory policies for the successful implementation of the LEC strategy.

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.

Insulation coordination and surge protection devices (SPDs) are fundamental to the safety and reliability of modern power systems, especially in renewable energyintegrated grids. These systems protect critical electrical infrastructure from transient overvoltages, ensuring stable and sustainable operation. However, traditional methods, such as simulation-based analyses and manual fault detection, face challenges in scalability, adaptability, and efficiency, particularly in dynamic energy environments. Advancements in artificial intelligence (AI) and Internet of Things (IoT) technologies have introduced transformative capabilities for insulation coordination and SPDs. AI techniques, such as machine learning and neural networks, enable precise fault prediction, real-time monitoring, and adaptive control, significantly enhancing grid reliability. IoT-enabled SPDs further improve operational efficiency through predictive maintenance and continuous performance monitoring, aligning with sustainable energy goals. These innovations also address the needs of resilient infrastructure development, smart grid implementation, and urban sustainability. This paper explores the evolution of these systems, emphasizing the shift from traditional to AI-driven and hybrid approaches. By integrating advanced technologies, power systems can achieve enhanced reliability, efficiency, and resilience.