Search Results (1,224)

To address the insufficient consideration of system static voltage stability and PV–load coupling in distributed photovoltaic (PV) hosting capacity assessment, this study first investigates the impact of distributed PV integration on power system transient voltage stability based on a typical power supply system. Building on this analysis, we propose a Static Grid Stability Margin (SGSM) index. Subsequently, leveraging historical PV and load data, the copula function is introduced to establish a joint distribution function characterizing their correlation. Massive evaluation scenarios are generated through sampling, with robust clustering methods employed to form representative evaluation scenarios. Finally, a distributed PV bearing capacity assessment model is established with the objectives of maximizing PV bearing capacity, optimizing economic efficiency, and enhancing static voltage stability. Through simulation verification, the power system has a higher capacity for distributed PV when distributed PV is integrated into nodes with weak static voltage stability and a decentralized integration scheme is adopted. Full article

This paper introduces a novel similarity measure to link transactions which spend outputs of CoinJoin transactions, CoinJoin Spending Transactions (CSTs), by analyzing their on-chain properties, addressing the challenge of preserving user privacy in blockchain systems. Despite the adoption of privacy-enhancing techniques like CoinJoin, users remain vulnerable to transaction linkage through shared output patterns. The proposed method leverages timestamp analysis of mixed outputs and employs a one-sided Chamfer distance to quantify similarities between CSTs, enabling the identification of transactions associated with the same user. The approach is evaluated across three major CoinJoin implementations (Dash, Whirlpool, and Wasabi 2.0) demonstrating its effectiveness in detecting linked CSTs. Additionally, the work improves transaction classification rules for Wasabi 2.0 by introducing criteria for uncommon denomination outputs, reducing false positives. Results show that multiple CSTs spending shared CoinJoin outputs are prevalent, highlighting the practical significance of the similarity measure. The findings underscore the ongoing privacy risks posed by transaction linkage, even within privacy-focused protocols. This work contributes to the understanding of CoinJoin’s limitations and offers insights for developing more robust privacy mechanisms in decentralized systems. To the authors knowledge this is the first work analyzing the linkage between CSTs. Full article

Open-weight generative large language models (LLMs) can be freely downloaded and modified. Yet, little empirical evidence exists on how these models are systematically altered and redistributed. This study provides a large-scale empirical analysis of safety-modified open-weight LLMs, drawing on 8608 model repositories and evaluating 20 representative modified models on unsafe prompts designed to elicit, for example, election disinformation, criminal instruction, and regulatory evasion. This study demonstrates that modified models exhibit substantially higher compliance: while an average of unmodified models complied with only 19.2% of unsafe requests, modified variants complied at an average rate of 80.0%. Modification effectiveness was independent of model size, with smaller, 14-billion-parameter variants sometimes matching or exceeding the compliance levels of 70B parameter versions. The ecosystem is highly concentrated yet structurally decentralized; for example, the top 5% of providers account for over 60% of downloads and the top 20 for nearly 86%. Moreover, more than half of the identified models use GGUF packaging, optimized for consumer hardware, and 4-bit quantization methods proliferate widely, though full-precision and lossless 16-bit models remain the most downloaded. These findings demonstrate how locally deployable, modified LLMs represent a paradigm shift for Internet safety governance, calling for new regulatory approaches suited to decentralized AI. Full article

This paper proposes a local sliding mode control (SMC) strategy for frequency regulation and active power sharing in islanded microgrids (MGs). Unlike advanced strategies, either droop-based or droop-free, that rely on inter-inverter communication, the proposed method operates in a fully decentralized manner, using only measurements available at each inverter. In addition, it adopts a minimalist structure that avoids adaptive laws and consensus mechanisms, which simplifies implementation. A discontinuous control law is derived to enforce sliding dynamics on a frequency-based surface, ensuring robust behavior in the face of disturbances, such as clock drifts, sudden load variations, and topological reconfigurations. A formal Lyapunov-based analysis is conducted to establish the stability of the closed-loop system under the proposed control law. The method guarantees that steady-state frequency deviations remain bounded and predictable as a function of the controller parameters. Simulation results demonstrate that the proposed controller achieves rapid frequency convergence, equitable active power sharing, and sustained stability. Owing to its communication-free design, the proposed strategy is particularly well-suited for MGs operating in rural, isolated, or resource-constrained environments. A comparative evaluation against both conventional droop and communication-based droop-free SMC approaches further highlights the method’s strengths in terms of resilience, implementation simplicity, and practical deployability. Full article

Ultraviolet-light emitting diodes (UV-LEDs) offer several advantages over conventional mercury-based UV lamps, including wavelength selectivity, compact size, design flexibility, instant on/off, power output adjustment, and mercury-free operation. These features position UV-LEDs as ideal candidates for point-of-use (POU) water disinfection systems, particularly in decentralized or resource-limited environments. In this study, we evaluated the microbial inactivation performance and energy efficiency of a bench-scale flow-through UV-LED POU system using indigenous heterotrophic plate count (HPC) bacteria, E. coli, and MS2 bacteriophage. The system was tested under various flow rates (1–4 L/min) and wavelength configurations (265 nm, 278 nm, and dual-wavelength combinations). MS2 bacteriophage was further used in collimated beam testing to validate UV-fluence-response curves and to estimate delivered doses in the flow-through POU device. HPC inactivation was enhanced under dual-wavelength conditions, suggesting wavelength-specific synergy, while E. coli showed high susceptibility across all wavelength configurations, achieving >2-log inactivation at significantly reduced UV-LED power (1/6 of that required for HPC) even at 4 L/min. Specific energy consumption analysis showed energy demands as low as 0.032–0.053 kWh/m³ for achieving 4-log inactivation of E. coli, with an estimated annual operating cost for UV-LED irradiation below $1.70. These findings demonstrate the potential of UV-LED-based POU devices as safe, energy-efficient, and cost-effective technologies for decentralized water treatment. Full article

Background: Healthcare data interoperability faces significant barriers, including regulatory compliance complexities, institutional trust deficits, and technical integration challenges. Current centralized architectures demonstrate inadequate mechanisms for balancing data accessibility requirements with patient privacy protection, as mandated by HIPAA and GDPR frameworks. Traditional compliance approaches rely on manual policy implementation and periodic auditing, which are insufficient for dynamic, multi-organizational healthcare data-sharing scenarios. Objective: This study develops and proposes a blockchain-based healthcare data management framework that leverages Hyperledger Fabric, IPFS, and the HL7 FHIR standard and incorporates automated regulatory compliance mechanisms via smart contract implementation to meet HIPAA and GDPR requirements. It assesses the theoretical system architecture, security characteristics, and scalability considerations. Methods: We developed a permissioned blockchain architecture that employs smart contracts for privacy policy enforcement and for patient consent management. The proposed system incorporates multiple certification authorities for patients, hospitals, and research facilities. Architectural evaluation uses theoretical modeling and system design analysis to assess a system’s security, compliance, and scalability. Results: The proposed framework demonstrated enhanced security through decentralized control mechanisms and cryptographic protection protocols. Smart contract-based compliance verification can automate routine regulatory tasks while maintaining human oversight in complex scenarios. The architecture supports multi-organizational collaboration with attribute-based access control and comprehensive audit-trail capabilities. Conclusions: Blockchain-based healthcare data-sharing systems provide enhanced security and decentralized control compared with traditional architectures. The proposed framework offers a promising solution for automating regulatory compliance. However, implementation considerations—including organizational readiness, technical complexity, and scalability requirements—must be addressed for practical deployment in healthcare settings. Full article

Cold-climate urban neighborhoods face mounting energy and thermal risks from extreme weather and power outages, creating trade-offs between different resilience capacities and objectives. This study develops a scalable, data-driven decision-making tool to support early-stage prioritization of resilience strategies at both the building component and neighborhood levels. A database of 48 active and passive strategies was systematically linked to 14 resilience objectives, reflecting energy- and thermally oriented capacities. Each strategy–objective pair was qualitatively assessed through a literature review and translated into probability distributions. Monte Carlo simulations (10,000 iterations) were performed to generate possible outcomes and several scores were calculated. Comparative scenario analysis—spanning holistic, short-term, long-term, energy-oriented, and thermally oriented perspectives—highlighted distinct adoption patterns. Active energy strategies, such as ESS, decentralized RES, microgrids, and CHP, consistently achieved the highest adoption (A) scores across levels and scenarios. Several passive measures, including green roofs, natural ventilation with passive heat recovery, and responsive glazing, also demonstrated strong multi-objective performance and outage resilience. A case study application integrated stakeholder-specific objective weightings, revealing convergent strategies suitable for immediate adoption and divergent ones requiring negotiation. This tool provides an adaptable probabilistic foundation for evaluating resilience strategies under uncertainty. Full article

Against the backdrop of a high proportion of distributed renewable energy sources being integrated into the power grid, distribution networks are confronted with issues of grid-wide and decentralized harmonic pollution and voltage deviation, rendering traditional point-to-point governance methods inadequate for meeting collaborative governance requirements. To address this problem, this paper proposes a source-tracing-oriented harmonic mitigation and optimization control strategy for distribution networks. Firstly, it identifies regional dominant harmonic source mitigation nodes based on harmonic and reactive power sensitivity indices as well as comprehensive voltage sensitivity indices. Subsequently, with the optimization objectives of reducing harmonic power loss and suppressing voltage fluctuation in the distribution network, it configures the quantity and capacity of voltage-detection-based active power filters (VDAPFs) and Static Var Generators (SVGs) and solves the model using an improved Spider Jump algorithm (SJA). Finally, the effectiveness and feasibility of the proposed method are validated through testing on an improved IEEE-33 standard node test system. Through analysis, the proposed method can reduce the voltage fluctuation rate and total harmonic distortion (THD) by 2.3% and 2.6%, respectively, achieving nearly 90% equipment utilization efficiency with the minimum investment cost. Full article

With the increasing penetration of photovoltaic systems in low-voltage distribution networks, new operational challenges arise for distribution system operators. This article focuses on a comprehensive analysis of the impact of single-phase and three-phase photovoltaic systems on voltage magnitude, voltage unbalance, and currents flowing through distribution lines. The steady-state operation was calculated using EA-PSM simulation software, and the assessment of the impact of photovoltaic systems on the network was carried out using the international standard EN 50160. Simulation results show that a high penetration of photovoltaic systems causes significant changes in the network’s voltage profile. The study also includes a proposal of measures aimed at mitigating the adverse effects of decentralized generation in photovoltaic systems on the distribution network. Among the most effective measures is the selection of an appropriate conductor cross-section for distribution lines. The results also indicate that, in terms of negative impact on the network, it is preferable to prioritize three-phase connection over single-phase connection, because for the same impact on the network, three-phase photovoltaic systems can inject several times more power into the network compared to single-phase systems. These and other findings may be beneficial, especially for distribution system operators in planning the operation and development of networks. Full article

The convergence of Industrial Internet of Things (IIoT) and digital twin technology offers new paradigms for process automation and control. This paper presents an integrated IIoT and digital twin framework for intelligent control of a gas–liquid separation unit with interacting flow, pressure, and differential pressure loops. A comprehensive dynamic model of the three-loop separator process is developed, linearized, and validated. Classical stability analyses using the Routh–Hurwitz criterion and Nyquist plots are employed to ensure stability of the control system. Decentralized multi-loop proportional–integral–derivative (PID) controllers are designed and optimized using the Integral Absolute Error (IAE) performance index. A digital twin of the separator is implemented to run in parallel with the physical process, synchronized via a Kalman filter to real-time sensor data for state estimation and anomaly detection. The digital twin also incorporates structured singular value ($\mu$) analysis to assess robust stability under model uncertainties. The system architecture is realized with low-cost hardware (Arduino Mega 2560, MicroMotion Coriolis flowmeter, pneumatic control valves, DAC104S085 digital-to-analog converter, and ENC28J60 Ethernet module) and software tools (Proteus VSM 8.4 for simulation, VB.Net 2022 version based human–machine interface, and ML.Net 2022 version for predictive analytics). Experimental results demonstrate improved control performance with reduced overshoot and faster settling times, confirming the effectiveness of the IIoT–digital twin integration in handling loop interactions and disturbances. The discussion includes a comparative analysis with conventional control and outlines how advanced strategies such as model predictive control (MPC) can further augment the proposed approach. This work provides a practical pathway for applying IIoT and digital twins to industrial process control, with implications for enhanced autonomy, reliability, and efficiency in oil and gas operations. Full article

Multifractality in time series analysis characterizes the presence of multiple scaling exponents, indicating heterogeneous temporal structures and complex dynamical behaviors beyond simple monofractal models. In the context of digital currency markets, multifractal properties arise due to the interplay of long-range temporal correlations and heavy-tailed distributions of returns, reflecting intricate market microstructure and trader interactions. Incorporating multifractal analysis into the modeling of cryptocurrency price dynamics enhances the understanding of market inefficiencies. It may also improve volatility forecasting and facilitate the detection of critical transitions or regime shifts. Based on the multifractal cross-correlation analysis (MFCCA) whose spacial case is the multifractal detrended fluctuation analysis (MFDFA), as the most commonly used practical tools for quantifying multifractality, we applied a recently proposed method of disentangling sources of multifractality in time series to the most representative instruments from the digital market. They include Bitcoin (BTC), Ethereum (ETH), decentralized exchanges (DEX) and non-fungible tokens (NFT). The results indicate the significant role of heavy tails in generating a broad multifractal spectrum. However, they also clearly demonstrate that the primary source of multifractality encompasses the temporal correlations in the series, and without them, multifractality fades out. It appears characteristic that these temporal correlations, to a large extent, do not depend on the thickness of the tails of the fluctuation distribution. These observations, made here in the context of the digital currency market, provide a further strong argument for the validity of the proposed methodology of disentangling sources of multifractality in time series. Full article

Greater Casablanca, one of Africa’s largest metropolitan regions, is undergoing significant spatial and demographic transformation. Yet, the underlying patterns of these dynamics remain poorly understood. This study investigates population dynamics and spatial inequalities in Greater Casablanca between 2014 and 2024. The analysis combines geospatial data, regression modeling, and clustering techniques to explore the interplay between demographic change, housing affordability, public-transport accessibility, and economic activity, providing a data-driven perspective on how these factors shape spatial inequalities and the region’s urban development trajectory. The results reveal a clear core–periphery divide. The central prefecture has lost population despite continued land consumption, while peripheral communes have experienced rapid demographic and economic expansion. This growth is strongly associated with affordable housing and high rates of new-firm formation, but it occurs where transport access remains weakest. Cluster analysis identifies four socio-spatial types, ranging from a shrinking but well-served core to fast-growing, poorly connected peripheries. The study underscores the need for integrated policy interventions to improve transport connectivity, implement inclusive housing strategies, and manage economic decentralization in ways that foster balanced and sustainable metropolitan development. By situating Greater Casablanca’s trajectory within global urbanization debates, this research extends core–periphery and shrinking-city frameworks to a North African context and provides evidence-based insights to support progress towards Sustainable Development Goal 11. Full article

The global shift towards renewable energy sources necessitates efficient methods for assessing solar potential in urban areas. Rooftop photovoltaic (PV) systems present a sustainable solution for decentralized energy production; however, their effectiveness is influenced by structural and environmental factors, including roof slope, azimuth, and shading. This study aims to develop and validate a UAV-based methodology for assessing rooftop solar potential in urban areas. The authors propose a low-cost, innovative tool that utilizes a commercial unmanned aerial vehicle (UAV), specifically the DJI Air 3, combined with advanced photogrammetry and 3D modeling techniques to analyze rooftop characteristics relevant to PV installations. The methodology includes UAV-based data collection, image processing to generate high-resolution 3D models, calibration and validation against reference objects, and the estimation of solar potential based on rooftop characteristics and solar irradiance data using the proposed Model Analysis Tool (MAT). MAT is a novel solution introduced and described for the first time in this study, representing an original computational framework for the geometric and energetic analysis of rooftops. The innovative aspect of this study lies in combining consumer-grade UAVs with automated photogrammetry and the MAT, creating a low-cost yet accurate framework for rooftop solar assessment that reduces reliance on high-end surveying methods. By being presented in this study for the first time, MAT expands the methodological toolkit for solar potential evaluation, offering new opportunities for urban energy research and practice. The comparison of PVGIS and MAT shows that MAT consistently predicts higher daily energy yields, ranging from 9 to 12.5% across three datasets. The outcomes of this study contribute to facilitating the broader adoption of solar energy, thereby supporting sustainable energy transitions and climate neutrality goals in the face of increasing urban energy demands. Full article

Small-scale wind turbines (SWTs) represent a promising solution for the energy transition and the decentralization of electricity generation in non-interconnected areas. Conventional strategies to improve SWT performance often rely on active pitch control, which, while effective at rated conditions, is too costly and complex for small systems. An alternative is passive pitch control through bend–twist coupling in the blade structure, which enables self-regulation and improved power generation. This work proposes a novel blade design methodology for a 5 kW SWT that integrates passive bend–twist coupling with conventional pitch adjustment, thereby creating a hybrid passive–active control strategy. The methodology encompasses the definition of aerodynamic blade geometry, laminate optimization via genetic algorithms combined with finite element analysis, and experimental characterization of composite materials. Aerodynamic–structural interactions are studied using one-way fluid–structure simulations, with responses analyzed through the blade element momentum method to assess turbine performance. The results indicate that the proposed design enhances power generation by about 4%. The study’s originality lies in integrating optimization, structural tailoring, and material testing, offering one of the first demonstrations of combined passive–active pitch control in SWTs, and providing a cost-effective route to improve efficiency and reliability in decentralized renewable energy systems. Full article

This research investigates the strategic interplay between pricing, delivery promises, and handling strategies for service-level returns—products returned by consumers due to operational issues like late delivery rather than product defects. In a vertical decentralized supply chain with a manufacturer and an e-tailer, a shorter promised delivery lead time (PDL) attracts more customers but also increases the risk of late delivery, making products more return-prone. Modeling the return rate as an endogenous variable dependent on the e-tailer’s PDL decision, we develop a Manufacturer-Stackelberg (MS) game-theoretic model to examine whether service-level returns should be handled by the manufacturer (Buy-Back strategy) or the e-tailer (No-Returns strategy). The results suggest that the optimal handling strategy depends on the e-tailer’s reselling ratio—a measure of its efficiency in extracting value from returns. A win-win situation is achieved when the reselling ratio is smaller than a threshold, as the manufacturer’s decision to buy back these returns also benefits the e-tailer. Surprisingly, when the manufacturer leaves the e-tailer to handle FFRs, a higher reselling ratio is not necessarily profitable for the e-tailer. Extending the analysis to a retailer-Stackelberg (RS) scenario reveals that the supply chain’s power structure is a fundamental determinant of the optimal returns handling strategy, shifting the equilibrium from a counterintuitive, power-distorted outcome in a MS system to an intuitive, profit-driven one in a RS system. Full article