Search Results (4,671)

This paper presents the development of handmade paper from fique residues, evaluating its technical and environmental viability through a scientific approach aimed at supporting low-income rural communities. The residues were characterized to assess their suitability for papermaking, with fiber crystallinity and chemical structure analyzed using X-ray diffraction (XRD) and ATR-FTIR spectroscopy. Pulps were produced from fique fibers and a 30:70 fique fiber–bagasse blend using a chemical-free mechanical pulping process, designed for easy implementation in rural settings. The effects of dyeing on pulp performance were also examined, and environmental impacts were assessed through a Life-Cycle Assessment (LCA). The average fiber length, diameter, and lumen of fique fibers were 1.83 mm, 26.5 μm, and 17.4 μm, respectively. Handsheets from fique pulp achieved a tensile index of 13.0 N·m/g and a burst index of 1.42 kPa·m²/g, while the fique fiber–bagasse blend reached 11.09 N·m/g and 1.05 kPa·m²/g. The corresponding sheet densities were 0.316 and 0.380 g/cm³. The dyeing process led to a reduction in the mechanical strength of the handmade paper. Environmental analysis indicated that fique tow fiber has a more favorable impact profile than other non-wood alternatives, such as aquatic weed fiber. Compared to results from similar studies, fique demonstrates strong potential as a high-quality, sustainable raw material for artisanal papermaking. These findings support its application in decentralized, eco-friendly production systems, contributing to rural development and circular economy strategies. Full article

As intelligent transportation systems (ITSs) evolve rapidly, the increasing computational demands of connected vehicles call for efficient task offloading. Centralized approaches face challenges in scalability, security, and adaptability to dynamic network conditions. To address these issues, we propose a blockchain-based decentralized task offloading framework with network-aware resource allocation and tokenized economic incentives. In our model, vehicles generate computational tasks that are dynamically mapped to available computing nodes—including vehicle-to-vehicle (V2V) resources, roadside edge servers (RSUs), and cloud data centers—based on a multi-factor score considering computational power, bandwidth, latency, and probabilistic packet loss. A blockchain transaction layer ensures auditable and secure task assignment, while a proof-of-stake (PoS) consensus and smart-contract-driven dynamic pricing jointly incentivize participation and balance workloads to minimize delay. In extensive simulations reflecting realistic ITS dynamics, our approach reduces total completion time by 12.5–24.3%, achieves a task success rate of 84.2–88.5%, improves average resource utilization to 88.9–92.7%, and sustains >480 transactions per second (TPS) with a 10 s block interval, outperforming centralized/cloud-based baselines. These results indicate that integrating blockchain incentives with network-aware offloading yields secure, scalable, and efficient management of computational resources for future ITSs. Full article

Background/Objectives: The concurrent circulation of SARS-CoV-2 with influenza A and B viruses and respiratory syncytial virus (RSV) represents a new diagnostic challenge in the post-COVID-19 area, especially considering that these infections have overlapping clinical presentations but different approaches to treatment and management. Multiplexed molecular testing on point-of-care platforms that focus on the simultaneous detection of multiple respiratory viruses in a single tube constitutes a useful approach for diagnosis of respiratory infections in decentralized clinical settings. This study evaluated the analytical performances of the VitaSIRO solo™ SARS-CoV-2/Flu/RSV Assay performed on the VitaSIRO solo™ Instrument (Credo Diagnostics Biomedical Pte. Ltd., Singapore, Republic of Singapore). Methods: With a view to accreditation, the criteria of the 2022-revised EN ISO 15189:2022 norma were applied for the retrospective on-site verification of method using anonymized respiratory specimens collected during the last 2024–2025 autumn–winter season in France. Results: Usability and satisfaction were comparable to current reference point-of-care platforms, such as the Cepheid GeneXpert^® Xpress System (Cepheid Diagnostics, Sunnyvale, CA, USA). Repeatability and reproducibility (2.34–4.49% and 2.78–5.71%, respectively) demonstrated a high level of precision. The platform exhibited a low invalid rate (2.9%), with most resolving on retesting. Analytical performance on 301 clinical samples showed high overall sensitivities: 94.8% for SARS-CoV-2 (Ct ≤ 33), 95.8% for influenza A and B viruses, 95.2% for RSV, and 95.4% for all viruses. Specificities were consistently high (99.2–100.0%). False negatives (2.6%) were predominantly associated with high Ct values. Agreement with the comparator reference NeuMoDx™ Flu A-B/RSV/SARS-CoV-2 Vantage Assay (Qiagen GmbH, Hilden, Germany) was almost perfect (Cohen’s κ 0.939–0.974), and a total of 91.1%, 94.8%, and 100.0% of Ct values were within the 95% limits of agreement for the detection of SARS-CoV-2, influenza A and B viruses, and RSV, respectively, by Bland–Altman analyses. Passing–Bablok regression analyses demonstrated good Ct values correlation between VitaSIRO solo™ and NeuMoDx™ assays, with a slight, non-significant, positive bias for the VitaSIRO solo™ assay (mean absolute bias +0.509 to +0.898). Conclusions: These findings support VitaSIRO solo™ Instrument as a user-friendly and reliable point-of-care platform for the rapid detection and differentiation of SARS-CoV-2, influenza A and B viruses, and RSV responding to the EN ISO 15189:2022 criteria for accreditation to be implemented in hospital or decentralized settings. Full article

Formation control plays a vital role in coordinating multi-agent systems and swarm robotics, enabling collaboration in applications such as autonomous vehicles, robotic swarms, and distributed sensing. This paper introduces the formation-control problem, highlights its challenges, and compares centralized and decentralized schemes. We review recent advances and analyze popular algorithms, then propose a hybrid framework that combines leader–follower tracking with an artificial potential field (APF) safety layer. In three-UAV tests, the followers cross paths and one encounters a static obstacle. We run multiple simulations across scenarios with obstacles and varying formations. Results show the hybrid controller maintains the required formation while avoiding inter-agent collisions. Using quantitative metrics, we find the leader–follower baseline achieves the lowest formation error but has the most safety violations, whereas APF greatly improves safety at the cost of higher error. The hybrid combines these strengths—delivering APF-level safety with lower error and negligible runtime overhead—providing a practical balance between precise formation keeping and robust collision avoidance. Full article

Blockchain technology is now a useful tool that FinTech organizations use to increase transparency, optimize activities, and seize new possibilities. This research explores blockchain applications within the FinTech sector. This study systematically explores blockchain applications within the FinTech sector by 164 peer-reviewed articles, utilizing the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework. The review identifies eleven applications, such as smart contracts, financial inclusion, crowdfunding, digital identity, trade finance, regulatory compliance, insurance, asset management, investment, banking, and lending. A mixed-method strategy, combining quantitative and qualitative content analysis, was applied to examine the adoption and impact of blockchain across these subdomains. It further discusses current challenges such as regulatory ambiguity, interoperability limitations, and cybersecurity threats. This paper provides a consolidated framework of blockchain’s actual application in FinTech subdomains and identifies the main gaps in the existing literature. These results have practical implications for practitioners, researchers, and policymakers who seek to harness blockchain for achieving financial innovation and inclusive growth. Full article

This study first situates the discourse on Proudhon’s federalism and nationalism within the framework of his comprehensive economic, social, and philosophical system. Proudhon attempts to construct a federalism based on an associational and decentralized political structure that could accommodate plural groups and avoid the exclusive interpretation of sovereignty that prevailed in nationalism at the time. Such federalism is not only a design of political institutions but also a reflection of his economic mutualism and the idea of commutative justice. Then, this study proposes a relatively concise and intuitive dual critique framework to focus on how his federalism directly refutes nationalism. Proudhon’s federalism aims to protect the culture, language, and identity of minority groups from the oppression of the unitary nation-state internally, and advocates the establishment of an external confederation beyond national borders to eliminate national conflicts and achieve universal peace. Full article

Hospital design plays a pivotal role in improving patient outcomes, enhancing clinical efficiency, and strengthening infection control. Since the outbreak of COVID-19, research in this field has expanded significantly, showing a marked trend toward interdisciplinary integration. In this study, bibliometric analysis was conducted using CiteSpace (version 6.2.R3) as the primary tool, with Excel and Tableau (version 2024.3) as supplementary software. A total of 877 documents on hospital design published between 1932 and 2025 were retrieved from the Web of Science Core Collection and analyzed from multiple perspectives. The analysis examined publication trends, collaborative networks, co-citation structures, disciplinary evolution, and keyword dynamics. The results indicate that the field has entered a phase of rapid development since 2019. Global collaboration networks are becoming increasingly multipolar; yet, institutional and author-level connections remain decentralized, with relatively low overall density. Evidence-based design (EBD) continues to serve as the theoretical foundation of the field, while emerging themes such as healing environments, biophilic design, and patient-centered spatial strategies have become major research hotspots. Increasingly, the field reflects deeper integration across disciplines, including architecture, medicine, nursing, and environmental science. This study provides a clearer picture of the developmental trajectory, knowledge base, and future directions of hospital design research, offering systematic insights and theoretical guidance for both scholars and practitioners. Full article

Distributed energy systems have gained increasing popularity due to their plethora of benefits. However, their evaluation in terms of reliability mostly concerns the time frequency domain, and, thus, merits associated with the spatial scale are often overlooked. A recent study highlighted the benefits of distributed production over centralized one by establishing a spatial reliability framework and stress-testing it for decentralized solar photovoltaic (PV) generation. This work extends and verifies this approach to wind energy systems while also highlighting additional challenges for implementation. These are due to the complexities of the non-linear nature of wind-to-power conversion, as well as to wind turbine siting, and turbine model and hub height selection issues, with the last ones strongly depending on local conditions. Leveraging probabilistic modeling techniques, such as Monte Carlo, this study quantifies the aggregated reliability of distributed wind power systems, facilitated through the capacity factor, using Greece as an example. The results underscore the influence of spatial complementarity and technical configuration on generation adequacy, offering a more robust basis for planning and optimizing future wind energy deployments, which is especially relevant in the context of increasing global deployment. Full article

With the increasing deployment of unmanned aerial vehicle (UAV) swarms in scenarios such as disaster response, environmental monitoring, and military reconnaissance, the need for secure and scalable formation control has become critical. Traditional centralized architectures face challenges such as limited scalability, communication bottlenecks, and single points of failure in large-scale swarm coordination. To address these issues, this paper proposes a blockchain-based decentralized formation control framework that integrates smart contracts to manage UAV registration, identity authentication, formation assignment, and positional coordination. The system follows a leader–follower structure, where the leader broadcasts formation tasks via on-chain events, while followers respond in real-time through event-driven mechanisms. A parameterized control model based on dynamic angle and distance adjustments is employed to support various formations, including V-shape, line, and circular configurations. The transformation from relative to geographic positions is achieved using Haversine and Euclidean methods. Experimental validation in a simulated environment demonstrates that the proposed method achieves lower communication latency and better responsiveness compared to polling-based schemes, while offering enhanced scalability and robustness. This work provides a feasible and secure decentralized control solution for future UAV swarm systems. Full article

The current research aims to discover applications of QML approaches in realizing liabilities within smart contracts. These contracts are essential commodities of the blockchain interface and are also decisive in developing decentralized products. But liabilities in smart contracts could result in unfamiliar system failures. Presently, static detection tools are utilized to discover accountabilities. However, they could result in instances of false narratives due to their dependency on predefined rules. In addition, these policies can often be superseded, failing to generalize on new contracts. The detection of liabilities with ML approaches, correspondingly, has certain limitations with contract size due to storage and performance issues. Nevertheless, employing QML approaches could be beneficial as they do not necessitate any preconceived rules. They often learn from data attributes during the training process and are employed as alternatives to ML approaches in terms of storage and performance. The present study employs four QML approaches, namely, QNN, QSVM, VQC, and QRF, for discovering susceptibilities. Experimentation revealed that the QNN model surpasses other approaches in detecting liabilities, with a performance accuracy of 82.43%. To further validate its feasibility and performance, the model was assessed on a several-partition test dataset, i.e., SolidiFI data, and the outcomes remained consistent. Additionally, the performance of the model was statistically validated using McNemar’s test. Full article

Indoor environmental quality is essential for pupils‘ health, comfort, and academic performance. However, recent studies indicate that indoor air quality (IAQ) in classrooms is often inadequate. This observational study examines the impact of three ventilation concepts on IAQ and thermal comfort under real-life school conditions: manual window airing combined with CO₂ traffic lights, decentralized mechanical ventilation, and centralized mechanical ventilation. Eight classrooms in three elementary schools were monitored from October 2023 to April 2024. Continuous long-term measurements covered CO₂, PM_2.5, VOCs, indoor air temperature, relative humidity and window opening states in the classrooms, and ambient data including PM_2.5 at each school. Significant differences were found in all five indoor parameters across the three ventilation concepts. The decentralized ventilation group achieved the lowest CO₂ concentrations (18–22% lower), while the window airing group showed the highest PM_2.5 levels (mean of 6 µg/m³) and the lowest temperatures (21% of the time below 20 °C). Relative humidity tended to be too low for all concepts, particularly with mechanical ventilation (medians below 40%). Windows in the window airing group were opened approximately twice as long. The findings highlight the benefits of well-operated mechanical ventilation systems and underscore the importance of user awareness and system management. Full article

Glucose-6-phosphate dehydrogenase (G6PD) deficiency is a common enzymopathy with significant clinical implications, particularly in malaria-endemic regions and in the management of neonatal hyperbilirubinemia. Timely and accurate detection of G6PD deficiency is critical to prevent life-threatening hemolytic events following oxidative drug administration. This study evaluated the MyG6PD device, a quantitative point-of-care (PoC) tool, for the assessment of hemoglobin concentration and G6PD enzyme activity. Analytical performance was benchmarked against laboratory spectrophotometry and the STANDARD G6PD Analyzer™ (SD Biosensor; Suwon-si, Republic of Korea). MyG6PD demonstrated excellent linearity (R² ≥ 0.99), accuracy (bias < ±15%), and precision (CV < 15%) across normal, intermediate, and deficient activity ranges, including heterozygous females with intermediate phenotypes. The device’s compact, battery-operated design, rapid turnaround, and minimal training requirements support its use in decentralized and resource-limited settings. Furthermore, cost-effective consumables and robust detection of intermediate activity highlight its potential for large-scale deployment. Overall, MyG6PD provides a reliable, accessible, and clinically actionable solution for urgent G6PD deficiency screening, enabling safer administration of oxidative therapies and improving patient outcomes in high-risk populations. Full article

Decentralized electricity markets are increasingly shaped by the proliferation of distributed energy resources, the rise of prosumers, and growing demands for privacy-aware analytics. In this context, federated learning (FL) emerges as a promising paradigm that enables collaborative model training without centralized data aggregation. This review systematically explores the application of FL in energy systems, with particular attention to architectures, heterogeneity management, optimization tasks, and real-world use cases such as load forecasting, market bidding, congestion control, and predictive maintenance. The article critically examines evaluation practices, reproducibility issues, regulatory ambiguities, ethical implications, and interoperability barriers. It highlights the limitations of current benchmarking approaches and calls for domain-specific FL simulation environments. By mapping the intersection of technical design, market dynamics, and institutional constraints, the article formulates a pluralistic research agenda for scalable, fair, and secure FL deployments in modern electricity systems. This work positions FL not merely as a technical innovation but as a socio-technical intervention, requiring co-design across engineering, policy, and human factors. Full article

The convergence of decentralized artificial intelligence (DAI), blockchain technology, and smart contracts is reshaping the design and governance of intelligent systems. As these technologies rapidly evolve, addressing privacy within their architecture, usage models, and associated risks has become increasingly critical. This systematic literature review examines architectural patterns, governance frameworks, real-world applications, and persistent challenges in DAI systems. It identifies prevailing designs such as federated learning integrated with consensus protocols, smart contract-based incentive mechanisms, and decentralized verification methods. Drawing from a diverse body of recent literature, the review highlights implementations across sectors, including healthcare, finance, IoT, autonomous systems, and intelligent infrastructure, each demonstrating significant contributions to privacy, security, and collaborative innovation. Despite these advancements, DAI systems face ongoing obstacles such as scalability limitations, privacy trade-offs, and difficulties with regulatory compliance. The review emphasizes the need for integrative governance approaches that balance transparency, accountability, incentive alignment, and ethical oversight. These elements are proposed as co-evolving pillars essential to establishing trustworthiness in decentralized AI ecosystems. This work offers a comprehensive review for understanding the current landscape and guiding the development of responsible and effective DAI systems in the Web3 era. Full article

The Bitcoin network is a sophisticated accounting system that facilitates consensus and verification of transactions through cryptographic proof, eliminating the need for a central authority. Given its success, the underlying technology, generally referred to as blockchain, has been proposed as a means to improve legacy accounting and reporting systems. However, integrating real-world data into a blockchain requires the use of oracles: third-party systems that, if poorly selected, may be less decentralized and transparent, potentially undermining the expected benefits. Through a systematic review of the existing literature, this study investigates whether research articles on the integration of blockchain technology in accounting and reporting have addressed the limitations posed by oracles, under the rationale that the omission of oracles constitutes a theoretical bias. Furthermore, this study examines oracle-based solutions proposed for reporting applications and classifies them based on their intended purpose. While the overall consideration of oracles remains limited, the findings indicate a steadily increasing interest in their role and implications within accounting, auditing, and ESG-related blockchain implementations. This growing attention is particularly evident in ESG reporting, where permissioned blockchains and attestation mechanisms are increasingly being examined as practical responses to data verification challenges. Full article