Search Results (1,332)

Authentication is essential to hold users accountable across online services. Conventional authentication systems rely on centralized architectures or third-party identity providers, which, however, introduce single points of failure, privacy concerns, and limited user autonomy. Conversely, fully decentralized authentication frameworks often struggle to provide reliable identity attestation mechanisms. This makes them vulnerable to Sybil attacks and self-asserted claims, while limiting their interoperability with trust-based systems. This paper presents dAuth, a hybrid blockchain-based authentication architecture based on Ethereum smart contracts to provide cryptographic tokens that enable authentication to services. These tokens, anchored to the smart contract, are derived by users from institutionally certified base credentials issued by an accredited verifying authority and enable authentication to services without further involvement of the authority. Each token is cryptographically bound to a specific service, constrained in scope and duration, and verifiable off-chain through data and cryptographic commitments provided by the user. No plaintext personal information is published on-chain: identity attributes are committed as cryptographic digests, which anchor certified identity data on-chain while keeping the underlying personal information private and auditable. This design removes the verifying authority from the authentication process, as all authentication steps are assisted by the user-controlled smart contract. The verifying authority’s role is limited to initial identity certification and exceptional update procedures. The result is a privacy-preserving and verifiable hybrid authentication framework that leverages the cryptographic security properties of the underlying blockchain infrastructure and inherits its scalability characteristics. The proposed design has been implemented and experimentally evaluated on the Ethereum platform, addressing public blockchain-specific challenges such as scalability constraints and transaction costs to ensure practical deployment. Full article

The increasing adoption of renewable energy sources introduces significant variability in power generation, requiring effective strategies to ensure maintain grid stability. Incentive-based demand response programs provide a practical solution for balancing supply and demand, however disputes may arise over energy data integrity. The existing solutions frequently rely on centralized authorities, exposing a single point of failure, or high costs and privacy limitation of recording granular data on-chain. To address this challenge, we propose a decentralized framework that separates cloud storage from integrity certification. This system employs a community aggregator to collect high-frequency energy measurements, store the raw data in the cloud, while anchors unique cryptographic hashes for batch of raw data to a public blockchain. This process creates an auditable and tamper-evident record of data. By recording only hashes on chain, our approach achieves privacy and scalability. Evaluation using a real-world Australian dataset confirms that the system enables transparent dispute resolution, with blockchain transaction costs consistently representing less than 0.10% of the total incentives awarded to participants. Full article

As the Kingdom of Saudi Arabia (KSA) accelerates its transition toward smart mobility under Vision 2030, establishing a robust digital infrastructure is paramount for the safe deployment of autonomous vehicles (AVs). High-definition (HD) maps serve as a critical foundation for this infrastructure, yet their deployment is severely bottlenecked by extreme operational costs, massive data processing payloads, and rapid environmental variations across vast highway networks. To address these challenges, this paper proposes a comprehensive, localized national strategy structured around three key tasks. First, it establishes a unified national HD map standard to guarantee seamless interoperability and data sharing among competing AV manufacturers and government transport authorities. Second, it implements an AI-powered baseline workflow using Mobile Mapping Systems (MMS) for high-fidelity static map construction, anchored and validated within designated pilot zones, including the King Abdulaziz University campus and key sectors in the Kingdom. Third, it deploys a decentralized, vision-based crowdsourcing system that leverages active public and commercial vehicle fleets for real-time map maintenance. By integrating a sovereign edge-cloud AI infrastructure that respects local Personal Data Protection Law (PDPL), this framework bridges the gap between high-accuracy baseline mapping and long-term economic sustainability, offering an actionable technical roadmap for scaling a resilient digital transport layer across the Kingdom. Full article

Rabies remains a preventable yet fatal zoonotic disease and a major public health concern in Bangladesh, which aims to eliminate dog-mediated human rabies by 2030. Despite free availability of post-exposure prophylaxis (PEP), adherence to the WHO-recommended PEP regimen remains low. This study assessed PEP compliance and identified determinants of regimen completion among animal-exposed patients. We conducted a hospital-based observational study using secondary data from 457 patients who initiated PEP at the National Rabies Prevention and Control Centre (NRPCC) in Dhaka, from February 2023 to July 2023. Sociodemographic, clinical, and exposure-related factors were analyzed to identify predictors of compliance. Only 17.1% of patients completed the full PEP regimen, including rabies immunoglobulin (RIG) administration for WHO Category III exposures where indicated. Higher adherence was observed among females, individuals aged ≥15 years, lower-income groups, and those residing within 10 km of the treatment center. Exposure-related factors including dog bites, multiple exposures, unprovoked incidents, and appropriate exposure care were also associated with improved compliance. Despite free access, PEP completion remains critically low. Targeted strategies, including decentralized PEP delivery, improved public awareness, and strengthened follow-up systems, are essential to improve adherence and support progress toward rabies elimination by 2030. Full article

CRISPR-based diagnostics now detect viral, bacterial, and cancer-associated nucleic acids with sensitivities approaching quantitative PCR; however, their translation to decentralized care rests on computational design and interpretation that current datasets cannot sustain. Pandemic-era Cas12a assays reached 95% positive predictive agreement against reverse transcription quantitative PCR (RT-qPCR) at 10 copies/μL, and deep neural networks now design Cas13 detection assays spanning 1933 vertebrate-infecting viruses, ranking candidate guides at Spearman correlations of 0.69 to 0.84 across internal and external validation. Generative deep-learning systems improve single-nucleotide discrimination two- to three-fold, computer vision classifies lateral flow outputs at 96.5% accuracy, and multi-biomarker fusion reaches an area under the receiver operating characteristic curve (AUC) of 0.998 in lung cancer detection. These results mask a narrow data foundation. Cas13a guide prediction still draws from a single screening library of 19,209 guide–target pairs, Cas12a has one published diagnostic model, and signal classifiers almost uniformly validate on single-site cohorts. This review synthesizes mechanistic constraints, predictive and generative models, and point-of-care classifiers, and maps the path beyond this data ceiling. Evolutionary pretraining on RNA corpora and lab-in-the-loop agents that convert model failure into targeted data acquisition define the route forward. Full article

The widespread deployment of photovoltaic (PV) inverters with digital control and communication systems has increased the power grid’s attack surface, making it more vulnerable to cyberattacks. This creates a need for locally implementable attack-detection methods that do not disrupt inverter operation. This paper therefore proposes an irradiance-driven natural watermarking approach for decentralized detection of false data injection (FDI) attacks on inverter terminal measurements. The approach leverages irradiance-driven DC-link voltage variations to watermark the inverter outputs, generating a non-removable signature in the true measurements. The proposed method is evaluated using a real-time hardware-in-the-loop model of a three-phase grid-following PV inverter that captures PV-array and grid-connection dynamics. Implementation robustness is further assessed on a separate hardware grid-forming inverter testbed with non-idealized components. In the tested cases, the detection model identifies noise-injection and replay attacks within $15\mathrm{ms}$, while otherwise undetectable model-based attacks are revealed when DC-link voltage variations between 5% and 10% occur. These experimental results demonstrate that irradiance-driven natural watermarking can reveal FDI attacks without affecting normal inverter operation. Full article

As robotic systems evolve toward large-scale distributed architectures and cloud-edge collaboration, communication middleware has become a critical infrastructure that impacts system real-time performance and scalability. The traditional Robot Operating System 1 (ROS 1) communication architecture, which relies on a centralized master node, has limitations in dynamic network environments. Robot Operating System 2 (ROS 2) achieves decentralized communication through the introduction of DDS. However, the single Data Distribution Service (DDS) mechanism remains inadequate for cross-network communication and high-performance local data exchange. Addressing the current issue in ROS communication research: the coexistence of multiple mechanisms without a unified analytical framework or guidance for selection. This paper systematically traces the evolution of the ROS communication architecture from centralized to distributed systems. It constructs a unified analytical framework covering two dimensions: communication models and data transmission paths. Crucially, to overcome the unreliability of cross-protocol comparisons based on heterogeneous literature, this paper designs and executes a set of unified benchmark experiments on a controlled testbed. These experiments systematically evaluate the performance of two mainstream DDS implementations (CycloneDDS and FastDDS) across five key metrics: latency, throughput, jitter, scalability, and packet loss rate under load. Additionally, a comprehensive comparative analysis of the performance of three transmission modes is conducted. Based on this comprehensive evaluation, this paper summarizes the performance characteristics of different mechanisms and further proposes an optimization-based middleware selection method for quantitative communication mechanism selection under different workload and application requirements. This paper provides a systematic reference for the design and optimization of ROS communication systems and offers guidance for promoting the application of multi-middleware collaborative architectures in robotic systems. Full article

In 2025, the Democratic Republic of the Congo (DRC) experienced its 16th Ebola Virus Disease (EVD) outbreak, centered in the Bulape Health Zone of Kasai Province, amid multiple concurrent epidemics and limited health infrastructure. Genomic sequencing revealed a novel zoonotic spillover genetically related to the 1976 Yambuku strain. A Root Cause Analysis (RCA) using the “5 Whys” framework, integrating epidemiological data, genomic analysis, and surveillance reports, identified key contributors to delayed detection and response, with comparative insights drawn from the 2018–2020 North Kivu outbreak. The Mweka outbreak resulted in 28 confirmed, probable, or suspected cases and 15 deaths, including four healthcare workers. Root causes included inadequate ecological surveillance, weak community alert systems, diagnostic delays due to reliance on centralized laboratories, health system overload from concurrent outbreaks, and structural underfunding of preparedness and coordination. Unlike North Kivu, where security issues drove response delays, systemic and ecological vulnerabilities predominated in Mweka. These findings highlight how ecological and structural weaknesses facilitate novel Ebola spillovers and their escalation, emphasizing the need for sustained investment in One Health surveillance, decentralized diagnostics, and resilient public health governance to strengthen outbreak response capacity. Full article

The exponential growth of Internet of Things (IoT) ecosystems is driving a paradigm shift from centralized cloud computing towards decentralized architectures to mitigate latency and bandwidth constraints. While edge computing addresses some of these challenges, data transmission to local gateways still raises critical security and privacy concerns. This study explores the Compute Continuum by pushing intelligence to the extreme edge using TinyML. We propose a secure, privacy-preserving multimodal biometric authentication system designed for resource-constrained embedded devices. Our solution implements a hierarchical processing chain: an ultra-lightweight person-detection filter acts as an intelligent wake-up mechanism, followed by robust facial and voice authentication modules. Operating as a strict hierarchical pipeline, the system achieves a combined False Acceptance Rate (FAR) of just 0.12%. Experimental results on an ESP32 microcontroller demonstrate exceptional energy efficiency, requiring only 0.15 J per inference cycle. This allows the system to operate autonomously for over 39 h of continuous inference on a standard 600 mAh battery, proving the viability of standalone, privacy-by-design biometric sensors in intelligent IoT environments. Full article

A predictive gate-to-gate life cycle assessment (LCA) of plasma-assisted ammonia synthesis at TRL 4 is presented according to ISO 14040/44 standards. General plasma-assisted synthesis was evaluated through a mini-review‚ sensitivity analysis‚ and predictive LCA. The specific DBD needle-to-plate configuration LCA is performed using previously published experimental data. Two distinct scenarios were investigated. In the literature-based baseline scenario derived from sensitivity analysis, electricity consumption was 533 kWh/kg NH₃, giving a carbon footprint of 26.65–639.60 kg CO₂-eq/kg NH₃; electricity contributed 98.5% of total emissions, and impacts remained about 2.05 times higher than conventional Haber–Bosch. In contrast, the experimental DBD case study required 63,450 kWh/kg NH₃, showing reactor efficiency as the dominant driver of environmental performance. The BCS (≈1.39 kWh/kg NH₃) suggests that optimized plasma systems could potentially surpass conventional ammonia synthesis in energy efficiency. The environmental performance of plasma-assisted ammonia synthesis is affected by NH₃, NO_x, N₂O, and hydrogen emissions due to impacts on climate, air quality, water systems, and biodiversity. Future improvements may come from reactor and electrode optimization, catalyst integration, alternative plasma sources, and better process and heat integration, although deployment will likely depend on major efficiency gains and may be limited to niche decentralized applications. Full article

Decentralized Autonomous Organizations (DAOs) enable blockchain-based collective governance, yet existing studies often evaluate DAO governance through isolated mechanisms, particularly voting systems. This narrow view does not sufficiently explain recurring problems such as governance capture, weak accountability, inadequate safeguards, and inefficient resource allocation. This paper proposes a Layered Governance Coverage Model that conceptualizes DAO governance as a system of seven interdependent institutional functions spanning participation, agenda formation, collective choice, safeguards, execution, incentives, and meta-governance. The model uses a four-level strength scale to assess not only whether governance functions are present, but also how strongly they are institutionalized. It is empirically applied to thirty-seven active DAOs through evidence-based coding of publicly available governance artifacts. The results show that governance breadth does not necessarily imply governance maturity: collective choice and execution mechanisms are more developed than accountability, safeguards, and meta-governance. Beyond DAO-native settings, the paper positions governance maturity as a trust and resilience regime for blockchain-based IoT and AI infrastructures, where governance affects security, reliability, data integrity, and risk oversight. The paper discusses AI-enabled governance analytics as a support mechanism for monitoring governance activity, detecting anomalies, and improving governance observability. The proposed framework contributes a structured approach for evaluating and designing resilient governance architectures in DAOs and blockchain-based IoT/AI systems. Full article

As digitalization is widely used, Industrial Control Systems (ICSs) face severe cybersecurity challenges, where traditional defenses often lack real-time detection and immutable audit trails. Therefore, we propose a security alert platform that integrates blockchain, smart contracts, and homomorphic encryption. By leveraging the decentralized architecture of blockchain, the platform ensures the integrity and non-repudiation of operational logs. Concurrently, anomaly detection logic is embedded within smart contracts to enable an automated, real-time alerting mechanism. Furthermore, to preserve industrial data privacy, homomorphic encryption is employed, allowing the system to perform anomaly detection directly on encrypted data, thereby maintaining confidentiality throughout the data lifecycle. Preliminary analysis indicates that the proposed platform effectively enhances the resilience of ICS, strengthening both defense against unauthorized operations and post-incident forensic capabilities. Full article

The increasing volume of digital health data generated through Electronic Health Records (EHRs), emergency care systems, and real-time monitoring technologies has intensified the need for secure cross-institutional healthcare analytics. However, privacy concerns, regulatory restrictions, institutional mistrust, and risks associated with centralized data aggregation continue to limit large-scale healthcare data sharing. This paper presents OPAL4Health, a governance-oriented and privacy-preserving distributed healthcare analytics framework grounded in the MIT Open Algorithms (OPAL) paradigm. The framework integrates federated analytics, blockchain-based auditability, explainable artificial intelligence (XAI), and institutional governance mechanisms within a unified computation-to-data healthcare ecosystem. Unlike conventional federated healthcare systems that primarily focus on decentralized computation alone, OPAL4Health emphasizes governance, transparency, auditability, and policy-aligned distributed analytics while preserving institutional data sovereignty. The privacy protections supported by OPAL4Health are primarily architecture-based and governance-oriented, relying on local institutional data retention, controlled query execution, and blockchain-auditable analytical workflows rather than formally provable cryptographic privacy guarantees. The framework was evaluated through a real-world urgent care pilot across seven hospitals in Riyadh, Saudi Arabia, using 184 anonymized patient cases collected between May 2015 and September 2016. Analytical findings identified a median onset-to-arrival delay of 285 min (95% Confidence Interval (CI): 270–302), low ambulance utilization (18.5%), and hospital bypass behavior in 42% of cases. Peak Emergency Department (ED) congestion periods were also identified. Scenario-based modeling projected potential long-term healthcare savings of approximately $602 million over 15 years through improved Emergency Medical Services (EMS) allocation and reduced disability-adjusted life years (DALYs). The findings demonstrate the feasibility of governance-oriented, privacy-preserving distributed healthcare analytics within OPAL4Health while generating actionable operational and policy-relevant insights without centralizing sensitive patient-level records. The proposed framework provides a transferable model for secure, transparent, and accountable digital health collaboration across healthcare ecosystems. Full article

This paper proposes a decentralized data trading approach based on the Automated Market Maker (AMM) mechanism, aiming to address the bottlenecks in data trading efficiency and fairness via the collaborative innovation of market-oriented pricing mechanisms and automated trading processes. We focus on constructing an automatic pricing and matching mechanism based on liquidity pools. Subsequently, mathematical modeling and simulations reveal the slippage generation mechanism in data liquidity pools under trading shocks and imbalances. To address these issues, a novel dual slippage optimization mechanism integrating dynamic trade splitting and alternating order sorting is proposed, which decomposes orders into sub-orders and reorganizes their timing, establishing a dynamic equilibrium model. Experiments show that the method reduces the average slippage amplitude from 2.1% to 0.5%, representing a 76.2% reduction, and significantly enhances price stability and market efficiency. This research explores the mechanism of applying AMM to data asset trading and mitigates the limitations of AMM in this scenario, providing a theoretical and empirical foundation for building low-cost, high-fairness data trading systems through mechanism innovation and technical optimization. Full article

Microgrids are increasingly recognized as transformative and crucial constituents within advanced smart grid systems. This study introduces a decentralized energy management approach for interconnected microgrids that leverage renewable energy sources such as wind and solar, alongside distributed energy generators and storage mechanisms. An energy coalition manager (ECM) plays a key role in facilitating each microgrid’s integration to optimize power exchanges, enhance data communication, and reduce costs. The alternate-direction multiplier method is adapted to address optimization challenges, incorporating modifications to develop a censored version that enhances communication efficacy. This refined approach involves the exchange of information among neighboring entities, evaluated against a preset threshold. Through this precise comparison, ECMs strategically reveal their local variables to ensure convergence towards an optimal solution. A detailed case study was conducted to assess the performance, efficiency, and scalability of both methodologies comprehensively. Full article