Search Results (3,210)

To address the challenges faced by heterogeneous Unmanned Aerial Vehicle (UAV) systems in complex task allocation, including over-reliance on centralized scheduling, training deadlock, inadequate capture of temporal collaboration, and unstable training under sparse reward conditions, this paper proposes a distributed task allocation algorithm based on reinforcement learning. The algorithm adopts a decentralized decision-making architecture, which enables the autonomous formation of UAV collaborative groups without the need for a global scheduling center. A cascaded submission timeout mechanism is introduced to prevent training deadlock; the combination of Long Short-Term Memory (LSTM) and attention mechanism is employed to accurately model temporal correlations and collaborative dependencies; and the Proximal Policy Optimization (PPO) algorithm is leveraged to optimize the training stability under sparse reward conditions. Experimental results demonstrate that the proposed algorithm achieves a 100% task success rate in scenarios of different scales, and its key metrics, including makespan, time cost and waiting time, are significantly superior to those of mainstream baseline methods such as the Genetic Algorithm (GA) and the Hungarian Algorithm (HA). Moreover, the algorithm still maintains excellent robustness under the conditions of UAV failures, parameter variations, and dynamic task perturbations. This method supports zero-shot generalization for any number of UAVs and tasks and provides an efficient and reliable solution for the real-time collaborative scheduling of heterogeneous UAV systems. Full article

Decentralized biomethane is vital for the energy transition; however, small-scale plants face significant energy penalties. This study evaluates the mass and energy balance of a TRL 6 pilot biorefinery treating pig manure, integrating anaerobic digestion with a microalgae-based photobioreactor coupled to an absorption column for biogas upgrading (>93 vol% CH₄, dry basis). A Life Cycle Inventory (LCI) was used to compared a theoretical mesophilic design (Scenario I, 35 °C) against an experimental psychrophilic baseline (Scenario II, avg. 12 °C). The results indicate that while winter mesophilic heating consumes 58% of gross energy production, the passive psychrophilic strategy eliminates this demand, ensuring a positive Net Energy Balance year-round. Both scenarios achieved competitive Specific Energy Consumption (SEC) (1.20 vs. 4.17 kWh·m⁻³ CH₄), while upgrading reached peak efficiency at a 10 min Hydraulic Residence Time. Furthermore, solar-synchronized load-shifting allowed for 100% electrical self-sufficiency. We conclude that although passive operation offers a superior Energy Return on Investment during cold periods (average EROI of 2.35 vs. 1.44 under winter mesophilic conditions), active mesophilic heating yields a 3-fold revenue increase, making it the superior economic choice despite the thermal penalty. Full article

Urban areas in Europe are increasingly challenged by water scarcity, climate variability, and pressure on municipal water systems. Rainwater harvesting (RWH) offers a decentralized, sustainable solution to reduce dependence on potable water, mitigate stormwater runoff, and support urban water resilience. This study presents a case study from Cluj-Napoca, Romania, where an RWH, storage, and on-site retention system was implemented in an educational building. Rainwater was analyzed for key physico-chemical parameters to assess its quality for non-potable applications. The results show that the system significantly decreases municipal water demand for irrigation and cleaning, while seasonal precipitation variability strongly influences storage efficiency. Most water quality parameters fall within acceptable ranges for non-potable uses, although pH and mineral content indicate that additional treatment is required for potable applications. The findings demonstrate the potential of decentralized RWH systems to enhance sustainable urban water management, reduce hydraulic stress on sewer networks, and provide economic benefits through avoided discharge costs. Full article

The increasing integration of renewable energy sources, such as solar photovoltaics, requires low-cost, scalable energy storage solutions suitable for decentralized systems. This work experimentally evaluates an iron chloride concentration redox flow battery (FeCl-CFB) coupled to a photovoltaic system. The battery, which employs the Fe²⁺/Fe³⁺ redox couple to store energy through a chemical concentration gradient, was electrochemically characterized using different carbon-based electrode materials and operated under solar charging for 25 charge–discharge cycles. A maximum power density of 6.3 W·m⁻² was achieved at the cell level, with stable cycling behavior under variable solar irradiance. Coulombic and energy efficiencies remained within ranges of 63–72% and 20–28%, respectively, throughout the cycles. Despite these moderate efficiencies, the system demonstrated a consistent and functional usable capacity. The main limitation identified was a decrease in maximum power after prolonged cycling, attributable to resistance and polarization losses rather than electrolyte instability. These preliminary results characterize the initial performance of the FeCl-CFB under solar-driven conditions, highlighting significant efficiency and stability challenges that must be addressed through further optimization to determine the future potential for decentralized energy storage. Full article

This study offers a descriptive analysis of complex thinking as a form of cognitive competency among a group of 52 public servants holding local leadership positions in the Philippines. By extending the empirical examination of complex thinking beyond educational contexts and into local public leadership, the study contributes to an emerging line of research on the cognitive competencies associated with decision making in decentralized governance environments. Drawing on complexity theory applied to public decision making, it assumes that local governance requires the capacity to integrate heterogeneous information, anticipate interdependencies, and act under conditions of uncertainty. The assessment employed the eComplexity instrument using an adapted 21-item version structured into four dimensions: systemic, scientific, critical, and innovative thinking. Scores were rescaled to a 0–100 metric and, after confirming non-normality (Shapiro–Wilk), non-parametric tests were applied (Mann–Whitney, Kruskal–Wallis, and Dunn’s post hoc test with Bonferroni correction), along with Spearman’s rho correlations to examine dimensional coherence. No significant differences were observed by gender or income. Age showed overall variation across several dimensions, but robust pairwise differences were concentrated between the 31–40 and 41–50 age groups in systemic thinking and in the global score. Employment status differentiated only scientific thinking, with higher medians among permanent staff than contractual/project personnel. Correlations among dimensions were positive and significant, with particularly strong associations between systemic, critical, and innovative thinking, supporting the interpretation of complex thinking as an integrated competency in local public leadership. The findings should be interpreted considering the study’s descriptive design, localized convenience sample, and reliance on self-reported measures, which limit statistical generalizability beyond the analyzed context. Beyond its descriptive findings, the study offers initial empirical evidence relevant to governance research on the cognitive competencies associated with decision making among grassroots public leaders operating in decentralized institutional contexts. Examining complex thinking at this level helps illuminate how public actors interpret interdependencies, evaluate information, and navigate uncertainty in everyday governance practice. Full article

Federated recommender systems (FRS) enable privacy-preserving collaborative training without sharing raw user data, while explainable recommender systems (XRS) aim to improve transparency, trust, and accountability. However, research that integrates federation and explainability remains limited and fragmented. This survey reviews recent work at the intersection of Federated Learning (FL), Explainable Artificial Intelligence (XAI), and recommender systems, referred to as Explainable Federated Recommender Systems (XFRS). We analyze architectures, learning paradigms, personalization strategies, and explainability mechanisms, and discuss their trade-offs in explainability, privacy, and trustworthiness. We propose a unified conceptual framework that links these components in decentralized recommendation settings. Combining bibliometric analysis with a systematic categorization of the literature, we identify key gaps and emerging trends, including the limited adoption of explainability in federated settings. Finally, we summarize open challenges and future directions toward trustworthy, privacy-aware personalized recommender systems. Full article

The global transition towards sustainable and resilient energy systems has emphasized the need for efficient utilization of renewable energy sources (RESs) and rapid electrification of transportation. However, smart grids must address the intermittency of solar and wind power while accommodating the growing demand from electric vehicles (EVs). Hence, in this paper, a data-driven energy management system (EMS) is proposed that combines multivariable forecasting, generation scheduling, and EV charging coordination in a dual-level decentralized framework to increase the efficiency, reliability, and scalability of modern power grids. First, short-term forecasts of solar irradiance, wind speed, and load demand are addressed via five machine learning models ranging from nonlinear to ensemble models. Accordingly, a unified CatBoost-based platform for forecasting these three variables is selected because of its better performance and accuracy. These forecasts are subsequently utilized in a mixed-integer linear programming (MILP) framework for optimal generation scheduling in the considered network, fulfilling load demand at reduced electricity and emission costs while maintaining grid stability. Finally, a priority-based scheme is proposed for charging/discharging coordination of the aggregated EVs, minimizing demand variability while fulfilling vehicles’ charging needs and maintaining their batteries’ lifetime. The superiority of the proposed method lies in integrating a multivariable forecasting pipeline, linear MILP generation scheduling, and battery-health-aware V2G coordination in a unified decoupled framework, unlike many recent frontier works that treat these capabilities independently. Simulation results, under different scenarios, confirm that the proposed intelligent EMS can significantly reduce operational fluctuations, satisfy load and EV demands, optimize RES utilization, and support system cost-effectiveness, sustainability, and resilience. Full article

Background: Blockchain and Multi-Agent System (MAS) are increasingly combined to support decentralized, secure, and autonomous peer-to-peer energy trading in microgrid environments. Objectives: This systematic review investigates how blockchain and MAS are integrated to support microgrid energy trading, identifies architectural and operational models, examines real-world implementations, and highlights technical, regulatory, and security challenges. Unlike prior reviews that focus on blockchain or MAS in isolation, this study provides a unified and comparative analysis of their joint integration. Methods: Following PRISMA 2020 guidelines, a systematic search was conducted in IEEE Xplore, ACM Digital Library, and ScienceDirect, with the last search performed on 10 January 2025. Eligible studies focused on blockchain–MAS integration in microgrid energy trading; non-energy and non-microgrid applications were excluded. Study selection was performed independently by two reviewers, and methodological quality was assessed using an adapted Joanna Briggs Institute (JBI) checklist. A narrative synthesis categorized integration levels, blockchain platforms, MAS roles, and implementation contexts. Results: A total of 104 studies were included. Three dominant integration levels were identified—basic, intermediate, and advanced—distinguished by how decision-making responsibilities are distributed between MAS and smart contracts. Ethereum and Hyperledger Fabric were the most commonly used platforms. MAS agents perform concrete operational functions such as bid and offer generation, price negotiation, matching, and local energy optimization, fundamentally transforming control and monitoring processes. By enabling distributed, intelligent agents to perform real-time sensing, analysis, and response, an MAS enhances system resilience and adaptability. This architecture allows for proactive fault detection, dynamic resource allocation, and coherent, large-scale operations without centralized bottlenecks. Blockchain ensured transparency, trust, and secure transaction execution. Major challenges include scalability constraints, interoperability limitations with legacy grids, regulatory uncertainty, and real-time performance issues. Limitations: Most included studies were simulation-based, with limited real-world deployment and substantial heterogeneity in evaluation metrics. Conclusions: Blockchain–MAS integration shows strong potential for secure, transparent, and decentralized microgrid energy trading. Addressing scalability, regulatory frameworks, and interoperability is essential for large-scale adoption. Future research should emphasize real-world validation, standardized integration architectures, and AI-enabled MAS optimization. Funding: No external funding. Registration: This systematic review was not registered. Full article

The Internet of things (IoT) is rapidly pervading daily life and linking everything. Although higher connectivity offers many benefits, including higher productivity, robotic processes, and decision-making guided by data, it also poses a number of security dangers. Modern risks to data authenticity and confidence are getting harder to handle through typical central safety solutions. In this paper, we present a detailed investigation of the latest innovations and approaches for assessing reputation and confidence in the blockchain-empowered Internet of Things (BIoT) area. A comprehensive literature search was conducted across major electronic databases, including IEEE, Springer, Elsevier, Wiley, MDPI, and top indexed conference proceedings. The publication year was restricted to the period from 2018 to 2025. The methodological quality of a total of 122 studies met the inclusion criteria assessed using predefined quality measures. We figure out existing flaws at each layer of IoT architecture, illustrating how autonomous, transparent, and impenetrable blockchain ledgers address these flaws. Plus, we analytically compare public, private, consortium, and hybrid blockchain networking architectures to emphasize the underlying compromises among security, reliability, and decentralization. We also assess how reputation evaluation techniques evolved over time, moving from classical fuzzy logic and weighted average models to modern mature game theory and machine learning (ML) models, addressing their limitations in terms of computational overhead, scalability, adaptability, and deployment feasibility in IoT systems. Additionally, we outline future directions for BIoT system trust assessment and identify research limitations and potential solutions. Our research indicates that although ML-driven models offer more accurate predictions for identifying illicit node activities, they are still constrained by limited unbalanced data and high processing overhead. Full article

This article introduces Memetic/Metaphorical Digital Twins (MDTs) as a novel extension of Digital Twin typologies by twinning conceptual schemes, complementing Industrial, Human, and Cognitive Digital Twins. MDTs embed cultural, organizational, and semiotic knowledge into digital frameworks, enabling the recombination and evolution of knowledge structures across disciplines. Drawing on Schlaile’s economic perspectives and Mavromatidis’s architectural lens of entropy and constructal thermodynamics, this study demonstrates how MDTs can address systemic challenges in communication, knowledge transfer, and design. A Digital Community Platform, under development for supporting decentralized Personal Knowledge Management Systems (PKMS), provides the operational foundation, integrating iterative KM cycles to support knowledge co-creation. Its logic and logistics substitute the traditional document paradigm with a memetic approach by utilizing memes as replicable, adaptive knowledge units, thereby mimicking biological evolution and ecosystem resilience in digital platform environments. It aims to offer distributed, decentralized, bottom-up, affordable, knowledge-worker-centric applications prioritizing personalization, mobility, generativity, and entropy reduction; its mission is to serve a knowledge-co-creating community characterized by highly diverse individual Abilities, Contexts, Means, and Ends (ACME) facing increasingly volatile, uncertain, complex, and ambiguous futures (VUCA). A Boundary Object Taxonomy to Omnify Memetic Storytelling (BOTTOMS) is proposed to further structure atomic units of meaning—such as memes, mythemes, narratemes, and reputemes—into a unified framework for authorship and dissemination. The article situates MDTs within a design science research paradigm, outlines current implementation progress, and identifies future developments, including AI-supported curation, personalized metrics, and expanded boundary objects. Together, these contributions position MDTs as a universal framework for adaptive, transdisciplinary knowledge co-creation. Full article

The environmental pressure simulation facility is crucial to the development and testing of high-performance aeroengines. During environmental pressure simulation tests of aeroengines, a large amount of uncertain high-temperature and low-pressure gas is discharged into the pressure regulation system, resulting in significant disturbances and complex coupling among compressor unites, valves and the main pipe. To analyze the surge mechanism and support controller design, a control-oriented dynamic model of pressure regulation system is established. By considering the dominant pressure dynamics of the main pipe and the dynamic characteristics of compressors and regulating valves, the original complex system is simplified into a nonlinear model suitable for control analysis and safety-oriented design. Based on the developed model, the safe operation problem of compressor units is transformed into a constrained control problem. A cooperative sliding mode control (Co-SMC) method is then proposed to ensure that the compressor pressure ratio remains within a safe range while mitigating the impact of exhaust disturbances on the pressure regulation process. The proposed method enhances the robustness of pressure regulation system and the grid-connected efficiency of compressor units while guaranteeing the stability of closed-loop system. Comparative simulations under complex operating conditions demonstrate that the proposed method significantly improves both the safety level and control performance of pressure regulation system. Full article

Background/Objectives: The healthcare sector, despite its mission to protect health, is a major consumer of resources and emitter of greenhouse gases, giving rise to an ethical and governance paradox: how to reconcile the duty of care with the environmental impact of its activities. In the Spanish healthcare system, which is highly decentralized and regulated at multiple levels, this tension shapes the implementation of environmental policies. This study analyzes the governance and implementation of environmental regulations in Spanish healthcare institutions and the associated experiences. Methods: A qualitative, exploratory, descriptive study was conducted using effective meetings and semi-structured interviews with 20 participants, working in healthcare provision and environmental management within health institutions, across different regions of Spain, between September 2024 and November 2025. In addition, a documentary analysis of relevant regulations was undertaken. Results: The results indicate that Spanish healthcare institutions improve their environmental performance through the implementation of standards such as ISO or EMAS, although their adoption varies according to each institution’s level of development in environmental management. In addition, differences were observed in the environmental dynamics of healthcare institutions, linked to the decentralization of the Spanish healthcare system, as well as administrative barriers to accessing funding and gender disparities in environmental leadership. Conclusions: The standardization of environmental regulations and measures across the country, along with strengthening organizational capacity, could strengthen progress toward more sustainable healthcare. Full article

Using transaction cost economics (TCE) and agency theory, this paper examines how blockchain, smart contracts, and decentralized autonomous organizations (DAOs) reconfigure financial services across payments, wealth management, real estate, and corporate governance. Three research questions are addressed: (1) What are the quantifiable efficiency gains from blockchain-based real-time settlement compared with legacy systems? (2) How do blockchain technologies reduce intermediation and agency costs in wealth management and real estate? (3) Finally, to what extent do DAOs resolve or transform traditional corporate governance problems? By combining a present-value model calibrated to U.S. Automated Clearing House (ACH) data ($86.2 trillion in annual volume), comparative institutional analysis, and synthesis of empirical evidence from pilot implementations and on-chain governance metrics, this paper makes three principal contributions. First, real-time settlement yields approximately $12 billion in annual opportunity cost savings at the baseline 7.5% discount rate, with sensitivity analysis producing a range of $8–15 billion. The majority of gains accrue from moving to same-day or within-hour settlement. Second, tokenization and smart contract escrow substantially reduce real estate intermediation costs, blockchain-based digital identity streamlines wealth management onboarding, and a stablecoin taxonomy classifies fiat-collateralized, crypto-collateralized, and algorithmic designs by risk profile. Third, on-chain data reveal persistent governance token concentration (Gini > 0.98) and low voter participation (typically below 10%), exposing a gap between DAO theory and practice. Blockchain-specific risks are mapped to National Institute of Standards and Technology (NIST) Cybersecurity Framework 2.0, and mechanism design solutions, such as quadratic voting and AI-assisted proposal evaluation, are proposed to address whale dominance. Effective adoption requires hybrid architecture combining on-chain automation with off-chain structures for accountability and regulatory compliance. Full article

Renewable Energy Communities (RECs) represent a key instrument for enabling decentralized energy systems and enhancing local renewable energy utilization. Preliminary assessment of REC performance relies on simulation tools that differ in computational complexity, assumptions, and input data. Despite the growing literature, a systematic comparison of tools applied to identical community configurations is still missing. This study provides a systematic cross-comparison of four tools representing different modelling paradigms: a VBA-based prefeasibility model (MERCm), a MATLAB-based detailed framework (UNIPGm), a national open-access simulator (RECON), and a commercial platform (COMMm). The tools were applied to six residential configurations in three Italian provinces representing different solar irradiation levels. Scenarios are defined to ensure high energy sharing potential, considering a ratio of shared energy to energy fed into the grid above 60%. Key performance indicators, including physical self-consumption and shared energy, are analyzed. Results show broadly consistent trends across tools, although these findings refer to PV-only, residential RECs and may differ in more complex community configurations, with coefficients of variation below 15% for most relevant indicators, particularly shared energy, while confirming that differences in input data and modelling assumptions can still influence outcomes. These findings support the reliability of simplified simulation tools for preliminary REC feasibility assessments and provide guidance for policymakers and technical operators. Full article

The generation from decentralized energy resources strongly depends on weather conditions, which causes fluctuations and degrades power grid quality. One of the most effective solutions in modern power systems to mitigate this issue is the use of energy storage systems (ESSs). These systems enhance the network performance by reducing power fluctuations. In this scope, and for frequency analysis, a model consisting of two interconnected microgrids was considered in this work. The frequency of these microgrids varies due to sudden changes in load or generation (or both). The frequency regulation was performed by an efficient load frequency controller (LFC). This regulation was essential and was employed to improve control performance, reduce the impact of load disturbances on frequency, and minimize power deviations in the power flow tie-lines. A fuzzy logic-based optimizer was installed in each microgrid to optimize the proposed proportional–integral–derivative (PID) controllers by generating their optimal parameters. The main objective of the LFC was to ensure zero steady-state error for system frequency and power deviations in the tie-lines. However, with the increasing integration of renewable energies and the intermittent nature of their production due to climate change, frequency fluctuations arise. To mitigate this issue, a coordinated AGC–PMS (automatic generation control–power management system) regulation with hybrid energy storage systems and interconnected microgrids was designed to enhance the quality and stability of the power network. This paper focuses on the load frequency control (LFC) technique applied to interconnected microgrids integrating renewable energy sources (RESs). It presents an optimization study based on artificial intelligence (AI) combined with the use of energy storage systems (ESSs) and high-voltage direct current (HVDC) transmission link for power management and control. The renewable energy sources used in this work are photovoltaic generators, wind turbines, and a solar thermal power plant. A hybrid energy storage system has been installed to ensure energy management and control. It consists of redox flow batteries (RFBs), a superconducting magnetic energy storage (SMES) system, electric vehicles (EVs), and fuel cells (FCs).The system behavior was analyzed through several case studies to improve frequency regulation and power management under renewable energy integration and load variation conditions. Full article