Effective thermal management is essential for ensuring the safety, performance, and longevity of lithium-ion batteries across diverse applications, from electric vehicles to energy storage systems. This paper presents a thorough review of thermal management strategies, emphasizing recent advancements and future prospects. The analysis begins with an evaluation of industry-standard practices and their limitations, followed by a detailed examination of single-phase and multi-phase cooling approaches. Successful implementations and challenges are discussed through relevant examples. The exploration extends to innovative materials and structures that augment thermal efficiency, along with advanced sensors and thermal control systems for real-time monitoring. The paper addresses strategies for mitigating the risks of overheating and propagation. Furthermore, it highlights the significance of advanced models and numerical simulations in comprehending long-term thermal degradation. The integration of machine learning algorithms is explored to enhance precision in detecting and predicting thermal issues. The review concludes with an analysis of challenges and solutions in thermal management under extreme conditions, including ultra-fast charging and low temperatures. In summary, this comprehensive review offers insights into current and future strategies for lithium-ion battery thermal management, with a dedicated focus on improving the safety, performance, and durability of these vital energy sources.

Microgrids play a crucial role in modern energy systems by integrating diverse energy sources and enhancing grid resilience. This study addresses the optimization of microgrids through the deployment of high-efficiency converters, aiming to improve energy management and operational efficiency. This study explores the pivotal role of AC-DC and DC-DC bidirectional converters in facilitating energy conversion and management across various sources and storage systems within microgrids. Advanced control methodologies, including model-based predictive control and artificial intelligence, are analyzed for their ability to dynamically adapt to fluctuations in power generation and demand, thereby enhancing microgrid performance. The findings highlight that implementing high-efficiency converters not only enhances power stability and quality but also reduces operational costs and carbon emissions, thereby reinforcing microgrids as a sustainable and effective solution for contemporary energy management challenges. This research contributes to advancing the understanding and implementation of efficient energy systems in microgrids, promoting their widespread adoption in diverse applications.

Biomass-based energy systems represent a strategic and dispatchable renewable option for sustainable energy transitions in Latin America, where agricultural and agro-industrial residues provide significant potential for circular economy integration. This study presents a PRISMA-compliant systematic literature review synthesizing dominant biomass conversion pathways in the region, with emphasis on biofuels and bioelectricity applications and their reported technical, techno-economic, and environmental indicators. A comprehensive search of Scopus, IEEE Xplore, and ScienceDirect yielded 64 peer-reviewed studies published between 2010 and 2025. Results show a marked growth in scientific output after 2016, although evidence remains concentrated in Brazil, Colombia, and Mexico. Anaerobic digestion emerges as the most frequently assessed route, particularly for agro-industrial effluents, municipal organic waste, livestock residues, and wastewater streams, followed by combustion-based cogeneration linked to sugarcane industries. Electricity generation and biomethane dominate evaluated outputs. Overall, the review highlights technological maturity alongside persistent barriers, including fragmented supply chains, investment constraints, and limited harmonized reporting, underscoring the need for standardized frameworks and system-scale deployment across the region.

This study aims to characterize and analyze water quality in urban environments using an IoT embedded system, focusing on San Camilo’s community of Quevedo, Ecuador, where population growth has increased the demand for drinking water. An embedded system was designed to make use of a microcontroller and sensors to monitor critical water quality parameters in real time. The collected data is transmitted to a cloud platform for storage, analysis, and visualization. Spearman's correlation coefficient was preferred for statistical analysis of the data. This allowed for the identification of patterns and relationships between the variables examined. The data revealed significant associations in non-linear data, facilitating a decision regarding the development of predictive models for water management and treatment. The results demonstrate the feasibility and efficiency of using IoT technologies in the continuous monitoring of water quality, offering a scientific basis for the collection and processing of relevant data on the quality of drinking water in residential areas, providing a robust tool to monitor public health and the comfort of the inhabitants.

This study proposes a reproducible exploratory framework to link long-term territorial development with electricity demand in data-scarce contexts, and applies it to Ecuador’s Costa region. The pipeline combines three commonly available input streams: periodic census microdata, an official demand series, and macroeconomic aggregates. Socioeconomic heterogeneity across five non-uniform census rounds (1974, 1982, 1990, 2001, 2010) is summarized through Principal Component Analysis (PCA), and territorial indicators are projected to the demand horizon using low-order polynomial functions. Eleven regression specifications are compared on a log-transformed demand variable, and a rollingorigin backtesting scheme plus a 2020–2024 holdout are used for validation. The selected Trend OLS log model attains R2 = 0.551 and MAPE = 6.08%, and projects a regional demand of approximately 6,940 MW by 2050, equivalent to a compound annual growth rate of 3.45%. Beyond the Ecuadorian case, the results show that transparent, low-data pipelines based on harmonized census information, macroeconomic drivers and simple regression models can provide defensible medium- and long-term demand signals for planners in other emerging economies with limited high-frequency data.