Search Results (1,028)

Rapid expansion of electric vehicle adoption has led to increased dependence on a charging infrastructure that is tightly integrated with energy distribution systems and digital communication networks. As electric vehicle charging stations evolve into complex cyber–physical systems, cybersecurity risks pose a growing threat to grid reliability and user trust. This paper presents a hybrid decision-support framework for cybersecurity risk assessment in EV charging infrastructure that advances beyond prior multi-criteria decision-making approaches by combining interpretability with data-driven validation. Specifically, the framework integrates the Analytic Hierarchy Process (AHP) for expert-driven weighting of cybersecurity attributes with PROMETHEE for flexible threat prioritization, enabling transparent and auditable risk rankings. The framework categorizes cybersecurity criteria across four infrastructure layers—transmission, distribution, consumer, and electric vehicle charging stations—and assigns relative weights through expert-driven pairwise comparisons. PROMETHEE is then applied to rank potential cyber threats based on these weights, allowing for flexible prioritization of cybersecurity interventions. The methodology is validated using the real-world WUSTL-IIoT-2018 SCADA dataset, which includes simulated reconnaissance (network scanning), device identification, and exploitation attacks. While this dataset does not natively include OCPP 2.0 or ISO 15118 protocols, the experimental results demonstrate strong discrimination power (AUC = 0.99, recall = 95%) and provide a basis for extension to modern EVSE communication standards. The results identify critical metrics such as anomalous source packet behavior and encryption reliability as key vulnerability markers, aligning with documented EV charging attack scenarios. By bridging expert judgment with empirical traffic data, the proposed framework offers both technical robustness and explainability, supporting grid operators, SOC teams, and infrastructure planners in systematically assessing risks, allocating resources, and enhancing the resilience of EV charging ecosystems against evolving cyber threats. Full article

Despite abundant scholarship on religious architecture and urban history, a systematic city-wide analysis that treats the parish system as a territorially relevant infrastructure for planning remains uncommon. This article examines Barcelona’s network of 132 Catholic parish churches as a cartographic layer for interpreting distributed centralities and their relationships with public space. The study is grounded in an exhaustive inventory based on on-site visits and archival consultation, and on a standardised redrawing protocol (Sitte and Nolli conventions) developed from municipal cartography and architectural plans. Synthesis maps and fabric-specific drawings document spatial patterns that vary across phases of urban growth, as well as recurrent typologies of relationships between churches, squares, and urban axes. Across the corpus, at least 25 churches are associated with squares and can be grouped into four recurrent arrangements (12 with a single frontal square; 4 with concatenated lateral squares; 3 surrounded by open space; and 6 with squares severed by through-traffic infrastructure). District plates further reveal contrasting typological distributions between Ciutat Vella (n = 16), Eixample (n = 19), Gràcia (n = 11), and Nou Barris (n = 14). The findings show that Barcelona’s Catholic parish cartography constitutes a key interpretative layer for understanding the city’s complexity, including its social and urban transformations, neighbourhood-level mechanisms of resilience, and the interaction between religious networks, urban form, and civic culture. The resulting cartographic protocol is reproducible and transferable to studies of urbanisation and regional development, offering an operational framework for planning debates on the governance of public space, heritage conservation, and urban sustainability. Full article

Wi-Fi networks used in smart grids are essential for enabling communication between smart meters and data aggregation units. A key challenge, however, is the ability to hide the existence and traffic patterns of these communications, so that sensitive information exchanges cannot be easily detected or intercepted. Unfortunately, most existing solutions do not provide support for traffic prioritization and steganographic channel encryption. In this paper, we propose a novel covert channel with Quality of Service (QoS) and encryption support for smart grid environments based on the IEEE 802.11 standard. We introduce an original steganographic approach that leverages the backoff mechanism, the Enhanced Distributed Channel Access (EDCA) function, frame aggregation, and the StegoPaddingCipher algorithm. This design ensures QoS-aware traffic handling while enhancing security through encryption of the transmitted covert data. The proposed protocol was implemented and evaluated using the ns-3 simulator, where it achieved excellent performance results. The system maintained high efficiency even under heavily saturated network conditions with additional background traffic generated by other nodes. The proposed covert channel offers an innovative and secure method for transmitting substantial volumes of QoS-related data within smart grid environments. Full article

Unsupervised domain adaptation (UDA) has been extensively studied for bearing fault diagnosis under multiple operating conditions by mitigating distribution discrepancies across domains. However, in cross-domain imbalanced scenarios, bearing vibration signals are affected by both feature shift and class imbalance. Although a robust decision boundary learned from the source domain is critical for reliable transfer, classifier discriminability and robustness can be degraded by hard samples located near the boundary. As a result, the decision boundary may become ambiguous during adaptation, leading to degraded diagnostic performance in the target domain. To address these issues, a Maximum Margin Local Domain Adaptation (MMLDA) framework is proposed in which a multi-scale convolutional neural network is adopted as the backbone. Three core components are integrated into our framework: first, category-level reweighting to alleviate source-domain class imbalance; second, cross-domain local category alignment to reduce fine-grained feature discrepancies and feature shift; and finally, maximum-margin loss regularization to impose adaptive margin constraints on hard samples for improved decision boundary robustness. To evaluate the proposed method, cross-domain imbalanced transfer tasks under multiple operating conditions were constructed on two public bearing fault datasets, and comparative experiments were conducted. The results under different imbalance protocols demonstrate improved robustness and generalization of MMLDA. Full article

Symmetry and structural balance play a fundamental role in the collective behavior of networked agent systems (NASs). In particular, cluster formation tracking, representing the emergence and maintenance of symmetric group structures, has attracted significant attention due to its wide applications in robotics and autonomous systems. However, most existing approaches assume autonomous leaders, which may not be applicable in scenarios where human intervention is required. With this in mind, this paper addresses the cluster formation tracking problem for NASs with collision avoidance, where the leader receives control inputs from a human-in-the-loop (HiTL), making the leader a non-autonomous system. A distributed control protocol is developed so that followers can track the trajectories of their designated leaders using only relative information from neighboring agents. Sufficient conditions are established to guarantee collision-free cluster formation tracking, and Lyapunov-based analysis is employed to prove the asymptotic convergence of the subgroup tracking errors. In the proposed framework, human intervention is incorporated through external commands applied to the leaders, which makes the leader dynamics non-autonomous while preserving the distributed nature of the follower controllers. Simulation studies on a 13-agent network with three subgroups show that all followers achieve the desired time-varying cluster formations under HiTL-driven leader motions, with convergence times ranging from 4.21 s to 5.12 s. Moreover, the final tracking errors of all followers are reduced below $9.07\times {10}^{-5}$, while the minimum pairwise distances within each subgroup remain strictly above the prescribed safety threshold. These quantitative results verify both the effectiveness of the proposed protocol and the practical feasibility of integrating HiTL commands into collision-free cluster formation tracking. Full article

The rapid development of quantum computing poses severe threats to traditional blockchain security mechanisms, while existing full-quantum blockchains face challenges regarding high hardware costs and limited scalability. To address these issues, this paper proposes a secure and practical semi-quantum blockchain system. Specifically, a Semi-Quantum Delegated Proof of Stake consensus mechanism is constructed by integrating an adapted semi-quantum voting protocol with the Borda count method and a malicious behavior penalty model. Furthermore, a lightweight transaction verification framework is designed based on semi-quantum key distribution, enabling classical users with limited quantum capabilities to participate securely. Theoretical analysis demonstrates that the system achieves unconditional security against quantum attacks while maintaining high throughput. These results indicate that the proposed asymmetric resource design significantly lowers hardware barriers compared to full-quantum schemes, effectively balancing security, practicality, and cost-effectiveness for post-quantum blockchain networks. Full article

This scoping review summarizes current computational image analysis and artificial intelligence (AI) approaches for the assessment of hair and scalp disorders, with emphasis on quantitative trichoscopy and operator-independent evaluation. A deep Medline search was performed using a citation network-based approach using MeSH terms and complementary keywords covering diagnostic imaging, trichoscopy/videodermoscopy, image processing, algorithms, AI, and mobile/smartphone-based workflows. Overall, relatively few studies assess algorithms in real-world clinical pathways, and much of the retrieved literature is predominantly pre-clinical or methodology-driven. In parallel, commercially available AI-assisted trichoscopy platforms have little or no traceable peer-reviewed evidence; their validation methods and underlying datasets are often proprietary, undisclosed, and not directly comparable, limiting independent verification and cross-platform benchmarking. The most mature academic applications focus on follicular unit quantification (hair density, shaft diameter distribution, vellus-to-terminal ratio, and severity mapping), mainly using convolutional neural networks with object detection and instance segmentation. In conclusion, AI-assisted trichoscopy may support a shift toward standardized quantitative outputs, but clinical translation remains early and constrained by small or proprietary datasets, heterogeneous acquisition/annotation protocols, limited external validation, and scarce prospective studies. Full article

Carbon nanotube (CNT)-based hydrogels continue to present a persistent challenge of material comparability, as systems that appear equivalent frequently generate different mechanical, electrical, and biological responses. Although experimental variability is frequently cited as the primary explanation, many discrepancies arise from comparing systems whose nanotubes differ structurally in ways that are rarely documented. Diameter distribution, defect density, residual catalyst content, and surface chemistry directly influence CNT dispersion, network integration, and interactions in hydrated polymer matrices. When these parameters are insufficiently reported, formulations that appear comparable may represent materially distinct systems. In this review, the CNT–hydrogel literature is reconsidered from the perspective of material comparability. Rather than focusing only on whether reported results agree across studies, this review evaluates whether sufficient structural and processing information is available to determine if the systems being compared are materially equivalent. Selected publications were analyzed using a reporting-based descriptor framework encompassing nanotube origin, structural characterization, dispersion, microstructure, transport behavior, and biological relationships. A consistent pattern emerges: reproducibility becomes more interpretable when nanotube identity and processing history are documented with sufficient resolution. This enables meaningful cross-study comparison without requiring strict protocol standardization. Full article

(1) Background: The scarcity and imbalance of real fault data significantly limit the development of data-driven fault diagnosis methods for liquid rocket engines (LREs), especially under few-shot conditions. (2) Methods: To address this issue, this study proposes a GAN-based fault data augmentation framework for multivariate LRE time-series signals and a hybrid diagnostic classifier combining convolutional neural networks (CNNs), bidirectional long short-term memory (BiLSTM), and multi-head attention (MHA). The GAN component is introduced to alleviate fault-data scarcity and class imbalance by generating additional fault-like samples, while the classifier is designed to capture local features, long-range temporal dependencies, and diagnostically informative temporal regions. (3) Results: A multidimensional evaluation based on temporal similarity, statistical consistency, and global distribution discrepancy indicates that the generated samples preserve important characteristics of the original signals under the current evaluation protocol. On the augmented LRE dataset, the proposed classifier achieved strong diagnostic performance. In addition, supplementary experiments on the public HIT aero-engine dataset further support the effectiveness of the classifier architecture, its component-wise contribution, and its behavior under imbalanced few-shot settings, while also demonstrating the value of uncertainty-aware prediction. (4) Conclusions: The results provide encouraging evidence that the proposed framework can improve LRE fault diagnosis under data-scarce conditions. However, the present findings should be interpreted within the scope of the available data and evaluation setting. More comprehensive generator-side ablation, broader external validation, and physics-oriented assessment of the generated signals are still needed before stronger conclusions can be made. Full article

The rapid integration of renewable generation, distributed energy resources, and advanced monitoring infrastructures has increased the demand for data-driven methods in modern power systems. Artificial neural networks (ANNs) have become widely adopted for load forecasting, fault diagnosis, state estimation, stability assessment, and energy management. Despite substantial publication growth, large-scale operational deployment of ANN-based solutions remains limited. This study presents a bibliometric and engineering assessment of ANN applications in power systems between 2020 and 2024, based on 1511 SCI-Expanded journal articles retrieved from the Web of Science. Beyond conventional science mapping, the study integrates an engineering-oriented deployment-readiness evaluation that systematically links ANN architectures with core operational problem classes. The results reveal a significant imbalance between reported algorithmic performance and operational validation rigor. Forecasting and energy management applications demonstrate relatively higher readiness due to real-world dataset usage, whereas fault diagnosis and state estimation remain predominantly simulation-driven and lack explainability and robustness validation. A deployment-readiness matrix is applied to quantitatively evaluate dataset realism, interpretability integration, and reliability considerations across domains. The findings indicate that the primary barriers to ANN integration in power systems stem from insufficient validation protocols and resilience-oriented design rather than algorithmic limitations, highlighting key engineering priorities for reliable real-world implementation. Full article

With the increasing importance of securing quantum communication networks, practical and robust entity authentication is a critical requirement. Accordingly, we propose and experimentally validate a quantum entity authentication (QEA) protocol specifically designed for integration with BB84-type quantum key distribution (QKD) workflows and existing terminal architectures. We analyze the protocol’s security against intercept–resend man-in-the-middle (MitM) impersonation, showing that an unauthenticated adversary induces a characteristic 25% correlation error and that the rejection probability approaches unity as the number of detected authentication events increases. For practical realization, the protocol is deployed using weak coherent pulses (WCPs) with decoy-state estimation to bound single-photon contributions and mitigate photon-number-splitting (PNS)-enabled leakage. The system is demonstrated over a field-deployed fiber link of approximately 20 km with ~8 dB optical loss using signal/decoy intensities of ~0.5/~0.15 and sending probabilities 0.88/0.10/0.02 (signal/decoy/vacuum). Across both verification directions, stable operation is observed with quantum bit error rate (QBER) typically fluctuating between 1% and 4% while the sifted key rate remains constant over time. These results provide an experimental basis for integrating physical-layer entity authentication into deployed quantum communication networks. Full article

Cross-institutional collaborative fraud detection is essential for combating increasingly sophisticated financial fraud, yet privacy regulations and data silos severely constrain knowledge sharing among institutions. This study aims to develop a privacy-preserving framework that enables effective collaborative fraud detection while protecting raw data, with particular emphasis on exploiting symmetry properties in federated architectures and graph topology analysis. We propose an Adaptive Federated Graph Attention Network (FedGAT), which employs spatio-temporal graph attention mechanisms to capture topological structures and dynamic fraud patterns within institutional transaction networks. The framework introduces a symmetric similarity matrix derived from graph topological features, where the symmetry property ($s_{ij}=s_{ji}$) ensures consistent and unbiased measurement of structural relationships between any pair of institutions. Based on this symmetric similarity metric, an adaptive weighted aggregation mechanism is designed for cross-institutional parameter fusion, enabling balanced knowledge transfer that respects the symmetric collaborative relationship among participating institutions. The symmetric information exchange protocol between local institutions and the central server further guarantees equitable contribution and benefit distribution throughout the federated learning process. The framework is evaluated on the Elliptic Bitcoin transaction dataset and the IEEE-CIS fraud detection dataset, with recall rate and false positive rate as primary performance metrics. Results show that FedGAT achieves a recall of 0.85 and a false-positive rate of 0.038 in single-institution detection, representing approximately 40% and 70% improvements over existing methods, respectively. In collaborative detection across five virtual institutions, the symmetry-aware adaptive aggregation mechanism enables all participants to achieve performance gains exceeding 15% while completely eliminating negative transfer effects observed in simple averaging approaches. This work contributes a novel symmetry-based federated learning framework that balances privacy protection with detection performance, advancing the literature on cross-institutional financial risk management. Full article

Properties of composite materials with polymer matrix and inorganic filler are affected by preparation methods and starting components’ properties. For example, filler powder particle size distribution, phase composition and presence/absence of dopants can greatly affect properties of resulting composites. The present research attempts to clarify the influence of synthetic CaCO₃ powder properties on alginate/CaCO₃ composite material preparation process. Composite materials in the form of granules, networks and films were created from suspensions of synthetic powders of calcium carbonates CaCO₃ in aqueous solutions of sodium alginate. Powders of calcium carbonates CaCO₃ were synthesized from 0.5 M aqueous solutions of calcium chloride CaCl₂ and aqueous solutions of potassium K₂CO₃ (at molar ratio Ca/CO₃ = 1), sodium Na₂CO₃ (at molar ratio Ca/CO₃ = 1), and ammonium (NH₄)₂CO₃ (at molar ratios Ca/CO₃ = 1 and Ca/CO₃ = 0.5) carbonates. Phase composition of powder synthesized from CaCl₂ and K₂CO₃ was presented by calcite. Phase composition of powders synthesized from other soluble carbonates included calcite and vaterite. The powder preparation protocol excluded the stage of synthesized powder washing for by-product removal. This preparation protocol provided preservation of reaction by-product in the synthesized powder at a very low level. The presence of NH₄Cl as a reaction by-product even in small quantities can be taken as a reason for visually observed subsequences of cross-linking reaction at the stage of suspensions preparation. Aqueous solution of sodium alginate and suspensions containing powders synthesized from potassium K₂CO₃ and sodium Na₂CO₃ carbonates demonstrated similar dependence of viscosities from shear rate. The presence of (NH₄)₂CO₃ in the powder synthesized at molar ratio Ca/CO₃ = 0.5 was the reason for the lower viscosity of the suspension in comparison with suspensions loaded with powders containing KCl, NaCl and (NH₄)₂Cl as reaction by-products due to decomposition of unstable (NH₄)₂CO₃ and gas phase formation. The presence of (NH₄)₂Cl in the powder synthesized at molar ratio Ca/CO₃ = 1 in contrast was a reason for the highest viscosity suspension in comparison with those under investigation. Additionally, (NH₄)₂Cl presence in synthetic powders shows the ability to facilitate partial dissolution of CaCO₃ providing a higher concentration of Ca²⁺ cations at the stage of suspension preparation, thus aiding the cross-linking process of alginate hydrogel. Granules, meshes and films were created via interaction of suspensions of calcium carbonates CaCO₃ in aqueous solutions of sodium alginate with 0.25 M aqueous solutions of calcium chloride CaCl₂ to provide the formation of matrix of composites via Ca-crosslinking of sodium alginate followed by washing and freeze drying under deep vacuum. The created composite materials in the form of granules, meshes and films based on Ca-cross-linked alginate and powders of synthetic calcium carbonate can be recommended for skin wound and bone defect treatment and drug delivery carriers. Full article

Industrial Control Systems (ICSs) are the backbone of modern critical infrastructure, such as electric power, water treatment, oil and gas distribution, and manufacturing operations. While the convergence of IT and OT has greatly increased efficiency and observability, it has also greatly expanded the attack surface of these once-isolated systems. High-profile cyber-physical attacks, including Stuxnet (2010), TRITON (2017), and the Colonial Pipeline ransomware attack (2021), have shown that ICS-targeted cyberattacks can cause physical damage, disrupt economic stability, and put public safety at risk. Despite the growing prevalence and intensity of such threats, ICS-based cybersecurity education remains largely under-resourced and underfunded. Traditional ICS training laboratories require highly specialized hardware, vendor-specific tools, and expensive licensing that significantly raise barriers to entry. Traditional labs typically require on-site participation and pose physical safety concerns when cyber-physical attack scenarios are performed. These barriers leave students unable to get necessary security training for ICSs. Therefore, this paper introduces ${\mathrm{AE}}^3$GIS: Agile Emulated Educational Environment for Guided Industrial Security—a fully virtual, lightweight, open-source platform designed to democratize ICS cybersecurity education. Based on the GNS3 network simulation tool, ${\mathrm{AE}}^3$GIS enables rapid deployment of comprehensive ICS environments containing IT and OT systems, industrial communication protocols, control logic, and diverse security tools. ${\mathrm{AE}}^3$GIS is designed to provide practical training for students using realistic ICS cybersecurity scenarios through a local or remote training platform without the cost, safety, or accessibility limitations of hardware-based labs. Full article

This paper proposes a Q-Learning-Based Distributed Energy-Efficient Routing (QDER) protocol for underwater acoustic sensor networks (UASNs). The routing problem is formulated as a Markov Decision Process (MDP) and a distributed Q-learning approach is proposed. Each sensor node is treated as an agent that independently selects its next-hop node based on a Q-table. The rewards function is designed that jointly considers node residual energy and depth information, enabling each node to learn an effective routing policy through distributed decision-making. Unlike centralized routing approaches that rely on extensive global information exchange, the proposed scheme allows nodes to make local decisions, thereby reducing communication overhead and energy consumption while maintaining efficient routing paths. In addition, link quality is designed in the reward to account for channel conditions, which improves the robustness of the routing strategy under noisy underwater acoustic environments. Simulation results demonstrate that the QDER achieves better system performance compared with Depth-Based Routing (DBR) and Deep Q-Network-Based Intelligent Routing (DQIR). Considering channel attenuation and noise, the proposed method with the link quality metric achieves improved network lifetime and energy efficiency. It also shows good robustness and adaptability under different signal-to-noise ratio (SNR) conditions. Full article