Search Results (707)

Power-to-X technologies play a crucial role in accelerating the energy and material transition. A key opportunity lies in integrating these systems with existing bio-based infrastructures such as anaerobic digesters, providing a reliable source of biogenic carbon. Developing effective Power-to-Methanol (PtM) pathways requires a comprehensive understanding of process behavior through detailed simulation, including technical performance, economic feasibility, and environmental consequences. Despite growing interest, substantial variation remains in published levelized methanol costs, and many assessments insufficiently account for the full environmental footprint of production routes. This study evaluates the potential of PtM deployment in the Netherlands by comparing two pathways that utilize biogenic carbon sources: (i) hydrogenation of captured CO₂ using green hydrogen and (ii) dry methane reforming (DMR) of biogas, followed by catalytic syngas conversion to methanol. Results indicate that operational expenses—mainly driven by renewable electricity consumption—far outweigh capital investment. Both routes yield an LCoMeOH of approximately €2630 per tonne, about five times the cost of fossil-based methanol. Life cycle analysis shows that DMR performs more favorably overall, although elevated freshwater ecotoxicity and eutrophication result from digestate application as fertilizer. Continued improvements in renewable energy integration and nutrient recovery technologies are essential for enhancing future economic and environmental performance. 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

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

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 energy transition represents a complex, multi-level system subject to profound uncertainty and recurrent shocks. Current policy design approaches predominantly rely on static optimization frameworks (centralized, calculative models that presume stable conditions and predictable technological trajectories). Yet evidence from the 2021–2023 energy crisis in Europe, coupled with structural challenges in market liberalization and renewable integration, demonstrates persistent challenges in policy implementation. Price interventions affect competitive dynamics; subsidies influence technology selection; capacity mechanisms create coordination tensions; and rigid tariff structures create misalignments with evolving grid needs. This paper argues that these recurrent policy tensions stem not from implementation gaps, but from an inadequate theoretical foundation: the treatment of energy systems as optimizable rather than as complex, adaptive systems operating under Knight–Mises uncertainty and Huerta de Soto dynamic efficiency. This work explores an alternative framework grounded in dynamic efficiency, complex–uncertain systems, decentralized incentives, and adaptive governance (international–domestic, public–private, etc.). This review uses the theoretical and methodological framework of the Heterodox Synthesis, an alternative to the Neoclassical Synthesis. There is a reinterpretation of some insights from Knight and Mises (uncertainty), Hayek (distributed knowledge), Huerta de Soto (dynamic efficiency) and contemporary complexity economics into operational criteria applicable to energy policy design: (1) robustness to deep uncertainty; (2) preservation of price signals and risk-bearing mechanisms; (3) alignment of incentives across distributed actors; (4) institutional adaptability; and (5) minimization of ex post policy corrections. Through illustrative application to four critical policy instruments (price caps, renewable subsidies, capacity mechanisms, and network tariff design), it is shown how this framework identifies systematic tensions and consequences that conventional analysis overlooks. The contribution is exploratory in a bootstrap way: theoretical, by integrating classical and contemporary economics into energy governance; methodological, by operationalizing dynamic efficiency into evaluable criteria distinct from existing adaptive governance frameworks; and sectorial, by providing policymakers and regulators with diagnostic tools for assessing design robustness in conditions of deep uncertainty and rapid transition. According to this review, improved energy policy design under uncertainty is not achieved through more sophisticated optimization (in a calculative way), but through institutional architectures that preserve creative and adaptive learning, maintain distributed decision-making capacity, and remain functional when assumptions prove incorrect or not well-known. Full article

The increasing demand for energy and the continued reliance on fossil fuels pose important environmental and social challenges, particularly for rural and isolated communities in developing countries that lack reliable access to the grid. Gravitational water vortex turbines (GWVT) are a run-of-river technology for low-head and moderate-flow sites that can provide decentralized electricity without the construction of large reservoirs. The expected environmental impacts are lower; nevertheless, to increase acceptance by the community, there is a necessity to identify and analyze the potential environmental impacts of GWVT in all its life-cycle phases (installation, operation, maintenance, and dismantling). The present study applies the Conesa cause–effect matrix to identify, classify, and analyze the potential environmental impacts associated with GWVT phases. Key identified impacts include removal of vegetation coverage and site disturbance (−32), sediment dynamics alterations (−39), formation of a depleted stretch (−45), accidental releases of hazardous maintenance products (−42), and remobilization of retained sediments (−46). These impacts can produce habitat alteration and fragmentation and loss of ecological connectivity. The relevant significance of energy generation that can have multiple benefits in the local communities was also identified. Primary mitigation measures include the incorporation of environmental flows in the design, sediment management, and strict protocols for hazardous materials. The findings underscore the necessity to conduct site-specific baseline surveys to preserve environmental, socio-economic, and cultural conditions in the local ecosystem and communities. Full article

Ammoniacal nitrogen (NH₃-N) is a major pollutant in municipal, industrial, and agricultural wastewaters and is a key driver of eutrophication and aquatic ecosystem degradation. This review paper assessed the potential of water hyacinth (Eichhornia crassipes) as a sustainable phytoremediation option for removing ammoniacal nitrogen from wastewater. This paper focused on the plant’s biological characteristics, nutrient uptake pathways, and adaptability to varying environmental conditions. Specific mechanisms examined include direct root uptake of ammonium, internal translocation, and microbial-assisted nitrification and denitrification within the rhizosphere. The influence of pH, temperature, salinity, retention time, and plant density on removal efficiency was also assessed in this study. Across laboratory, pilot, and field-scale studies, water hyacinth achieved ammoniacal nitrogen removal efficiencies ranging from 74% to 97% under favorable conditions, alongside significant reductions in biochemical oxygen demand (BOD), chemical oxygen demand (COD), and total dissolved solids (TDS). Integration with constructed wetlands, microbial systems, and hybrid treatment approaches further enhanced nitrogen removal and process stability. This paper also highlighted opportunities for biomass valorization through biogas, bioethanol, and compost production while identifying challenges related to salinity sensitivity and biomass management. Overall, water hyacinth emerges as a cost-effective, nature-based solution for decentralized wastewater treatment, with strong potential to support sustainable water management and circular bioeconomy initiatives. Full article

The rapid convergence of the Internet of Things (IoT) and decentralized finance (DeFi) is reshaping the digital economy by enabling autonomous, trustless, and value-driven interactions among connected devices. This paper provides a comprehensive survey of the emerging paradigm that combines IoT’s pervasive sensing and communication capabilities with DeFi’s programmable financial infrastructure. We first discuss the motivation behind this convergence and explore key opportunities, including autonomous machine-to-machine (M2M) payments, decentralized data marketplaces, and trustless IoT service provisioning. Despite its potential, IoT–DeFi integration introduces significant security and privacy challenges related to smart contract vulnerabilities, consensus protocol risks, oracle manipulation, and constrained device capabilities. We review existing mitigation approaches such as lightweight cryptography, secure contract design, and decentralized identity management, and critically assess their limitations in heterogeneous, resource-limited environments. Building on this analysis, identify research gaps and propose future directions emphasizing formal verification of IoT-integrated smart contracts, robust oracle design, interoperability frameworks, and privacy-preserving trust models. This survey systematically maps opportunities, threats, and open issues. In doing so, it guides researchers and practitioners toward building secure, scalable, and energy-efficient IoT–DeFi ecosystems for next-generation decentralized applications. Full article

By incorporating opportunistic coding, network throughput is enhanced, resulting in improved overall performance. However, applying this paradigm to Flying Ad-hoc Networks (FANETS) faces significant challenges due to the highly dynamic topology caused by the high-velocity mobility of UAVs, alongside the NP-hard complexity of identifying optimal coding opportunities in rapidly evolving aerial network architectures. To address these challenges, this paper proposes a novel coding-aware routing protocol based on Multi-Agent Deep Deterministic Policy Gradient (MADDPG). We formulate the routing problem as a multi-agent continuous decision-making process, employing the MADDPG algorithm to optimize routing policies in real-time through decentralized execution and centralized training. To maximize network utility, we design a comprehensive reward function that integrates coding benefits, throughput, energy distribution, and end-to-end delay, ensuring a balance between throughput maximization and the energy sustainability of individual UAV nodes. Simulation results demonstrate that the proposed protocol significantly outperforms state-of-the-art coding-aware routing protocols in terms of throughput, Packet Delivery Ratio (PDR), and transmission delay, exhibiting superior robustness in highly dynamic FANET scenarios. Notably, at a network density of 20 UAVs, MARL-CAR outperforms COPE, DCAR, TSCAR, and RLCAR in terms of coding ratio by 32.23%, 18.93%, 20.35%, and 5.5%, respectively. This research provides a scalable and intelligent networking solution for the next generation of autonomous UAV swarms and collaborative aerial missions. Full article

The transition from conventional synchronous generators to inverter-based power systems has introduced significant challenges in stability, reliability, and protection coordination. Grid-forming inverters (GFMs) have emerged as a promising solution by emulating inertia and voltage regulation functions while enabling grid-supportive operation in weak or islanded networks. This study presents a structured qualitative review of the recent literature on GFM technologies. The selection process focused on control strategies, advanced semiconductor materials, protection frameworks, and cyber–physical security considerations. A thematic synthesis and comparative analysis were conducted to identify emerging trends and technical gaps. Among established approaches, virtual synchronous machine (VSM) and droop control remain widely adopted. More advanced strategies, including virtual oscillator control (VOC) and model predictive control (MPC), demonstrate improved dynamic performance in weak-grid conditions. Advances in semiconductor technologies, particularly Silicon Carbide (SiC) and Gallium Nitride (GaN), enable faster switching, higher efficiency, and enhanced thermal performance. The findings indicate a growing shift toward decentralized control architectures, fault-resilient converter topologies, and integrated protection–control co-design. Emerging solutions include grid-forming synchronization techniques that replace conventional phase-locked loop (PLL) structures, intrusion-tolerant inverter firmware with embedded anomaly detection, and predictive fault-clearing schemes tailored for low-inertia networks. Despite these advancements, several research gaps remain. These include limited large-scale validation of VOC and MPC strategies under high renewable penetration, insufficient interoperability metrics for legacy system integration, and a lack of standardized cybersecurity benchmarks across platforms. Future research should prioritize real-time experimental validation, robust protection co-design methodologies, and the development of regulatory and dynamic performance standards tailored to inverter-dominated grids. Strengthening protection coordination and interoperability frameworks will be essential to ensure the secure and stable deployment of GFMs in modern power systems. Full article

Accurately characterizing the relationship between nighttime human activity intensity and population distribution is essential for understanding urban development. This study proposes an integrated analytical framework that combines multilevel coupling quantification, regional trend detection, and interpretable machine learning to examine the Nighttime Lights and Population Coupling Coordination Degree (NPCCD) across China from 2012 to 2022. Based on this framework, NPCCD is evaluated from grid to regional level, and the characteristics of effective, persistent, and newly added coupled regions are identified. Twelve socioeconomic indicators are further constructed as explanatory variables to model NPCCD using machine learning algorithms, and Shapley Additive Explanations (SHAP) is applied to interpret the outputs. The results show that 49.07% of China’s overall NPCCD experienced steady improvement during the study period. Significant regional disparities were observed: in the eastern and central regions, more than 60% of grids fell into the improving category, whereas nearly half of the grids in the western and northeastern regions remained unchanged. Newly emerging coupling areas exhibited an average NPCCD of 0.03, markedly lower than the 0.07 observed in persistent effective areas, reflecting a mismatch between infrastructure development and population growth. Population density, human capital, industrial upgrading, and fiscal decentralization jointly explained 58.4% of the model’s variance and were identified as the major driving forces, each showing pronounced nonlinear and interaction effects. This study provides a quantitative framework for evaluating the coordination between nighttime lights and population distribution and offers insights for sustainable and balanced regional development. Full article

Blockchain technology is redefining the foundations of cybersecurity by introducing decentralized, tamper-resistant mechanisms for data integrity, trust management, and malware intelligence sharing. Traditional detection systems, which are dependent on centralized control and opaque validation, remain vulnerable to data manipulation and systemic compromise. The integration of blockchain transforms these paradigms because it provides verifiable provenance, distributed consensus, and autonomous enforcement through smart contracts. This review synthesizes fifteen years of progress (2010–2025) at the intersection of blockchain and malware detection and discusses core architectures, consensus protocols, and cryptographic properties that underpin decentralized defenses. The review follows a structured literature review methodology, which focuses on blockchain architectures, consensus protocols, and malware-detection pipelines reported in the cybersecurity literature. It also analyzes blockchain detection pipelines, performance tradeoffs, and data protection mechanisms in distributed learning systems and artificial intelligence models. Special attention is given to scalability constraints, regulatory compliance, and interoperability challenges that shape adoption. The review identifies three dominant design patterns: (i) decentralized threat-intelligence sharing with provenance guarantees, (ii) consensus-driven validation of malware artifacts, and (iii) on-chain trust and reputation mechanisms for detector accountability. Through the union of blockchain, artificial intelligence, edge computation, and federated learning, cybersecurity attains an auditable and adaptive architecture resilient to adversarial threats. The study concludes that blockchain provides a verifiable trust infrastructure for malware detection, but its practical deployment requires faster transaction validation and stronger protection of sensitive data; future research should address performance optimization and regulatory compliance. Full article

Sextortion has rapidly expanded into a global cyber-enabled crime that leverages anonymous digital communication and decentralized payment systems. This study examines the financial infrastructures underlying contemporary sextortion by conducting a two-phase analysis of 87 confirmed cases involving cryptocurrency payments. Using blockchain forensic tools and open-source intelligence, the research traces fund movements across perpetrator-controlled wallets, identifies laundering techniques such as mixers, peel-chain transfers, and exchange-based cash-outs, and links these behaviors to narrative patterns within victim reports. The results reveal a dual-tier ecosystem in which mass-produced, multilingual extortion scripts coexist with divergent laundering typologies that differentiate lower-value, high-volume scams from more organized and higher-yield operations. By integrating qualitative and quantitative evidence, this study provides a forensic framework for detecting illicit cryptocurrency activity, improving threat classification, and strengthening investigative and regulatory responses to sextortion and related crypto-enabled interpersonal crimes. Full article