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
Purpose This study aims to develop an ecocentric perspective of service ecosystems by building on affordance theory to understand value co-creation among human and nonhuman actors and how their interactions can foster ecosystem well-being. Design/methodology/approach This study employed a qualitative approach based on the Gioia Methodology, involving 67 interviews with actors (forest and agricultural landowners, NGOs, industry, and academia representatives) related to a forest service ecosystem. Findings Study results enabled the uncovering of forest ecosystem-related affordances, revealing latent possibilities for action that enable value co-creation interactions between human and nonhuman actors, and their valence for each actor. By exploring the valence from interactions between actors, this research identifies whether these interactions are parasitic or mutualistic and systematizes four potential service ecosystem trajectories over time: thriving, recovery, decline and collapse. Originality/value Building upon study results, an Ecocentric Service Ecosystem Framework, where human and nonhuman actors are considered equal, is proposed. Using the Affordance Theory lens, this framework seeks to identify and promote mutualistic interactions between human and nonhuman actors.

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.

Abstract

Abstract
Federated Learning (FL) enables collaborative model training across decentralized organizations without direct data sharing, yet communicating raw gradient updates remains susceptible to reconstruction and membership inference attacks. Fully Homomorphic Encryption (FHE) provides cryptographic privacy during aggregation, but ciphertext expansion triggers Out-of-Memory (OOM, Exit Code 137) terminations across edge nodes and serverless execution environments. This article presents FHE-Cloud, an edge-to-cloud framework that reformulates monolithic homomorphic aggregation as an event-driven serverless MapReduce workflow. Model weights are partitioned into 4096-parameter vectors aligned with the Single Instruction, Multiple Data (SIMD) slot capacity of the Residue Number System (RNS) variant of the Cheon–Kim–Kim–Song (CKKS) scheme (N=8192), parallelizing homomorphic summations across stateless AWS Lambda instances orchestrated via Amazon S3 and EventBridge. Evaluated on decentralized medical image classification (PneumoniaMNIST) under a non-independent and identically distributed (non-IID) Dirichlet distribution (aDir=0.5), FHE-Cloud bounds peak Lambda memory consumption to 123.9±1.8 MB, achieves a mean warm-start aggregation latency of 330.4±12.4 ms, and prevents memory exhaustion at edge and cloud tiers. The framework attains 84.13% global accuracy, indicating that serverless chunking preserves aggregation fidelity within the evaluated workload’s precision envelope while substantially reducing idle infrastructure expenditure relative to always-on provisioning.