Abstract
Wireless sensor networks have become essential tools for environmental monitoring, enabling distributed data acquisition in remote and dynamic environments. However, challenges related to communication reliability, energy efficiency, synchronization, and real-time data availability remain critical for long-term deployments. This work presents the design, implementation, and validation of a synchronous wireless sensor network tailored for environmental monitoring in the challenging conditions of an estuarine setting. The proposed architecture is based on Digi XBee SX 868 RF modules operating in DigiMesh mode with synchronized cyclic sleep, enabling coordinated measurements and low-power operation. The network comprises monitoring, repeater, and coordinator/gateway nodes, integrated with a web-based platform for real-time data visualization and management. A custom message exchange format was developed to support seamless transmission of monitoring information from sensors to the web server through the wireless mesh infrastructure. Field experiments were conducted to evaluate network coverage, synchronization performance, communication reliability, and energy consumption. The results demonstrated successful multi-hop communication over the estuarine area, stable synchronization among distributed nodes over extended periods, and energy savings through synchronized sleep operation. The developed web platform enabled reliable real-time data access and network management. The proposed system demonstrates the feasibility of deploying scalable, energy-efficient, and synchronized wireless sensor networks for long-term environmental monitoring in estuarine environments.

Abstract
This paper introduces Paraconsistent Reactive Graphs, as an extension of Reactive graphs that incorporates paraconsistency into the ground edges to address vagueness and inconsistency within dynamic systems. By assigning pairs of truth values to ground edges, this framework captures the uncertainty and contradictions stemming from incomplete or conflicting information. We explore the semantics of these graphs and provide a practical example to illustrate the proposed approach.

Abstract
Background: Multiple sclerosis (MS) is a chronic neurodegenerative disease of the central nervous system (CNS) that affects nearly 3 million people worldwide. It can lead to cognitive impairment, physical disability, and a reduced quality of life. Technological innovations have demonstrated significant potential in supporting individuals living with chronic conditions, including MS. Purpose: This study aims to synthesize existing evidence on technological solutions designed to support people with MS across various aspects of disease management and daily living. Methods: A literature search was conducted in PubMed, Scopus, Web of Science, and the Cumulative Index of Nursing and Allied Health Literature (CINAHL), following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Results: Forty-nine studies were included. These studies investigated a wide range of technologies, such as mobile applications, websites, video games, wearables, and virtual reality, used to support individuals with MS in several domains, including fatigue, cognition, mental health, motor function, physical activity, medication and treatment adherence, and communication and decision-making. Conclusion: This study highlights the growing role of technological solutions in supporting assessment, self-management, health literacy, and telerehabilitation for people with MS. Continued research is essential to enhance the development, adoption, and long-term effectiveness of these technologies in promoting sustainable self-management of the condition.

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Abstract
Dataset complexity poses a significant challenge in classification tasks, especially in real-world applications where a combination of factors such as class overlap, data imbalance, noise, and dimensionality can jeopardize a machine learning algorithm's performance. While measures to quantify complexity have been proposed and studied in depth for tabular datasets, there is a lack of studies and toolkits focused on measuring complexity in non-structured image data. This limitation hinders our understanding of visual complexity, despite the importance of image data in fields such as healthcare, remote sensing, and autonomous navigation. To address this challenge, we introduce pycol-vis, a novel Python package that helps researchers estimate image complexity. The package implements 17 image complexity measures, specifically designed to capture complexity in real-world scenarios. This toolkit is essential for researchers dealing with complex classification problems in the vision domain, providing tools to assess difficulty and overlap in real-world image data.