Electronics

22 pages, 1655 KB

Open AccessArticle

Dynamic Security Assessment and Security Region Construction Based on the Maximum Lyapunov Exponent Criterion

by Qiuquan Deng, Xikai Liu, Cuiyun Luo, Yin Wu, Guangming Li, Xiejin Ling, Zhencheng Liang, Junzhi Ren, Yuan Zeng and Chao Qin

Electronics 2026, 15(10), 2191; https://doi.org/10.3390/electronics15102191 (registering DOI) - 19 May 2026

Abstract

With the advancement of wide-area measurement systems (WAMSs), response-driven methods for transient stability analysis have gained increasing attention in recent years. The maximum Lyapunov exponent (MLE)-based trajectory analysis technique enables online transient stability assessment by capturing the trend characteristics of system trajectories. Motivated [...] Read more.

With the advancement of wide-area measurement systems (WAMSs), response-driven methods for transient stability analysis have gained increasing attention in recent years. The maximum Lyapunov exponent (MLE)-based trajectory analysis technique enables online transient stability assessment by capturing the trend characteristics of system trajectories. Motivated by this capability, a rapid construction methodology for the practical dynamic security region (PDSR) is proposed based on the MLE criterion. Initially, through analyzing the dynamic characteristics of generator rotor angle trajectories after disturbances, the dynamic MLE characteristics of the generator’s angular velocity deviation trajectory are extracted to formulate the MLE-based stability criterion. Subsequently, a stability boundary function based on MLE trajectories is developed, and the linear relationship between the injection space parameters and the MLE stability boundary function is derived. Finally, leveraging the sensitivity of the stability boundary function to the variations in injection space parameters, the dynamic security region is constructed around the dominant instability critical point, thereby establishing a mapping function between transient stability and the injection space parameters. The effectiveness of the proposed method is demonstrated through simulations on the IEEE39 power system. Results show that the method exhibits promising performance in terms of speed and adaptability for transient stability analysis and boundary construction. Full article

27 pages, 2093 KB

Open AccessArticle

Wires, Patents and Growth: An Explainable Machine Learning Approach for What Drives Digital Competitiveness in the European Union

by Rareș Mihai Nițu, Raluca Iuliana Georgescu, Dumitru Alexandru Bodislav, Loredana Maria Popescu, Cristina Voicu and Andrei Josan

Electronics 2026, 15(10), 2190; https://doi.org/10.3390/electronics15102190 (registering DOI) - 19 May 2026

Abstract

This study investigates the predictive contribution of digital infrastructure to GDP per capita growth across 27 European Union Member States over the period 1995–2024, using a balanced panel of 810 country–year observations and an explainable machine learning framework. An XGBoost model trained on [...] Read more.

This study investigates the predictive contribution of digital infrastructure to GDP per capita growth across 27 European Union Member States over the period 1995–2024, using a balanced panel of 810 country–year observations and an explainable machine learning framework. An XGBoost model trained on six World Bank indicators—fixed broadband subscriptions, internet users, mobile subscriptions, patent applications, R&D expenditure, and secure internet servers—achieves a training R² of 0.804 and a test R² of 0.430 under temporal out-of-sample validation spanning the COVID-19 structural break. TreeSHAP decomposition identifies fixed broadband as the strongest predictor of model-estimated GDP per capita growth (mean |SHAP| = 0.948; bootstrap rank 1 in 78% of 50 resamples; Friedman Chi-square (5) = 168.16, p < 0.001), providing predictive support for Hypothesis H1. Innovation indicators, represented by patent applications and R&D expenditure, exceed the pre-specified materiality threshold, providing predictive support for H2, while SHAP dependence plots reveal pronounced non-linear threshold patterns consistent with S-curve diffusion theory, supporting H3. Temporal SHAP decomposition identifies three structural phases: broadband dominance (1995–2007), crisis-induced reconfiguration (2008–2013), and quality convergence (2014–2024). The framework reconciles contradictory findings from prior literature by visualizing the complete functional form of the broadband–growth relationship without imposing a parametric specification. Full article

(This article belongs to the Section Artificial Intelligence)

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28 pages, 996 KB

Open AccessArticle

Improving Heart-Failure Predictive Tasks with Patient Health Knowledge Graphs and Sequential Graph Neural Networks

by Shervin Mehryar and Michel Dumontier

Electronics 2026, 15(10), 2189; https://doi.org/10.3390/electronics15102189 (registering DOI) - 19 May 2026

Abstract

Patient health knowledge graphs provide a means for high-quality and interoperable clinical data representation, while graph neural networks are a key enabler in order to learn their underlying relationships for downstream prediction tasks. In this work, we propose a sequential graph neural network [...] Read more.

Patient health knowledge graphs provide a means for high-quality and interoperable clinical data representation, while graph neural networks are a key enabler in order to learn their underlying relationships for downstream prediction tasks. In this work, we propose a sequential graph neural network (SeqGNN) framework that models patient visit trajectories for multiple binary clinical tasks, namely diagnosis, readmission, and mortality prediction. The proposed architecture integrates temporal dynamics with graph-based representations that enhances patient-level embeddings. Focusing on patients at the risk of Heart Failure (HF), our methodology achieves comparatively high accuracy and precision-versus-recall tradeoffs on highly heterogeneous graphs and imbalanced labeled data. We additionally quantify the uncertainty concerning each clinical decision making task and, compared with the state-of-the-art, show that AUROC and AUPRC scores reliably improve for onset diagnosis in particular, as high as 93.1 and 79.1 respectively. Our experiments conducted on real-world data from an intensive care unit demonstrate the potential for sequential representation learning over patient health knowledge graphs that can be provided for high-precision decision-making in clinical settings. Full article

(This article belongs to the Special Issue Knowledge Representation and Reasoning in Artificial Intelligence)

23 pages, 6399 KB

Open AccessArticle

Design and Optimization of a Dynamic Test Platform for Automotive-Grade IGBT Module

by Zhensheng Wang, Xiaofeng Hu, Han Yan, Bin Guo, Jiajun Wu and Yi Lu

Electronics 2026, 15(10), 2188; https://doi.org/10.3390/electronics15102188 (registering DOI) - 19 May 2026

Abstract

In the field of dynamic characteristic testing of automotive grade IGBT modules, high-frequency noise interference and stray inductance are universal technical challenges faced by the industry. High frequency noise and stray inductance can interfere with signal integrity, seriously reducing the testing accuracy of [...] Read more.

In the field of dynamic characteristic testing of automotive grade IGBT modules, high-frequency noise interference and stray inductance are universal technical challenges faced by the industry. High frequency noise and stray inductance can interfere with signal integrity, seriously reducing the testing accuracy of the platform and causing deviations in the dynamic characteristic evaluation of IGBT modules. To address this issue, this paper proposes a software hardware collaborative optimization strategy and designs a high-precision dynamic characteristic testing platform for automotive grade IGBT modules. At the software level, Savitzky–Golay filters are introduced and designed to process the test data, filter out high-frequency noise interference. After filtering, the noise mean square error and peak to peak value of the platform are generally reduced by 48–68%, and the switch ringing or peak value is reduced by about 45–53%. In terms of hardware optimization, an improved PCB laminated busbar was designed to reduce the overall stray inductance of the platform through simulation analysis of the structural parameters and via factors of the laminated busbar using ANSYS 2023 R1. The test results verification shows that the stray inductance of the testing platform is only 23 nH. The absolute error between the parameters measured by the testing platform and the reference values of the tested module is within 5%. Full article

(This article belongs to the Section Industrial Electronics)

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27 pages, 425 KB

Open AccessArticle

SEMA: Self-Evolving Multi-Agent Auditing for Smart Contracts

by Yepeng Ding, Ahmed Twabi, Junwei Yu, Lingfeng Zhang, Tohru Kondo and Hiroyuki Sato

Electronics 2026, 15(10), 2187; https://doi.org/10.3390/electronics15102187 (registering DOI) - 19 May 2026

Abstract

Smart contract auditing remains challenging because vulnerabilities often emerge only under complex execution conditions, cross-transaction interactions, and environment-dependent assumptions. Existing analysis techniques, including static analysis, symbolic execution, fuzzing, and recent LLM-assisted approaches, each provide useful but incomplete coverage, and monolithic auditing pipelines often [...] Read more.

Smart contract auditing remains challenging because vulnerabilities often emerge only under complex execution conditions, cross-transaction interactions, and environment-dependent assumptions. Existing analysis techniques, including static analysis, symbolic execution, fuzzing, and recent LLM-assisted approaches, each provide useful but incomplete coverage, and monolithic auditing pipelines often struggle to balance search breadth, reproducibility, and reporting reliability. This paper presents SEMA, a self-evolving multi-agent auditing framework for smart contracts that formulates auditing as a resource-bounded discovery of concrete counterexamples under replay-certified reporting semantics. SEMA combines heterogeneous specialized agents, an orchestrator, a shared artifact-centric knowledge base, and a replay-based referee. During auditing, agents generate and consume reusable artifacts, such as candidate invariants, refuted hypotheses, transaction templates, and coverage cues, allowing the shared search state to evolve across rounds without modifying the analyzers themselves. To ensure reporting reliability, findings are accepted only when the referee can replay the candidate scenario under a pinned execution configuration and confirm violation of an executable security property. We further evaluate SEMA on an annotated smart contract benchmark under a fixed 300 s budget per contract. The full system achieves 0.9469 instance recall, 0.9441 success rate, and 0.9445 macro-average category recall on the retained executable subset, outperforming both symbolic-only and fuzzing-only baselines, as well as multi-agent ablations that disable dynamic knowledge evolution or cross-agent artifact reuse. Full article

19 pages, 692 KB

Open AccessArticle

A Hierarchical Key Management Scheme for Efficient Outsourced Computation in Cloud Storage Services

by Nan-I Wu, Tsuei-Hung Sun, Cheng-Ying Yang and Min-Shiang Hwang

Electronics 2026, 15(10), 2185; https://doi.org/10.3390/electronics15102185 (registering DOI) - 19 May 2026

Abstract

This study addresses the high computational overhead associated with updating encryption keys and authentication information by delegating these tasks to cloud storage services. This approach reduces the burden on data owners and facilitates the establishment of a robust cloud storage ecosystem. Our evaluation [...] Read more.

This study addresses the high computational overhead associated with updating encryption keys and authentication information by delegating these tasks to cloud storage services. This approach reduces the burden on data owners and facilitates the establishment of a robust cloud storage ecosystem. Our evaluation focuses on three critical performance metrics for cloud security mechanisms: 1. Computational Complexity: The processing time and frequency required for encryption, decryption, and verification. 2. Communication Complexity: The volume and length of message exchanges. 3. Storage Overhead: The space required to maintain a comprehensive cloud storage system. We propose a hierarchical key management scheme that enables data owners to update file encryption keys efficiently without impacting other users within the Cloud Storage Service (CSS) environment. By eliminating the need for owners to store all keys in a hierarchical tree, our scheme minimizes the computational cost of key updates. Ultimately, this approach enhances efficiency for both data owners and receivers, making it particularly suitable for resource-constrained mobile devices. Full article

(This article belongs to the Special Issue Digital Security and Privacy Protection: Trends and Applications, 3rd Edition)

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41 pages, 1702 KB

Open AccessReview

Impact of EU Laws and Regulations on the Adoption of Artificial Intelligence in Cyber–Physical Systems: A Review of Regulatory Barriers, Technological Challenges, and Cross-Sector Implications

by Bo Nørregaard Jørgensen and Zheng Grace Ma

Electronics 2026, 15(10), 2184; https://doi.org/10.3390/electronics15102184 (registering DOI) - 19 May 2026

Abstract

Artificial intelligence is increasingly embedded in cyber–physical systems that coordinate sensing, computation, communication, and control across critical and semi-critical physical environments. Within the European Union, however, its adoption is shaped not only by technological maturity and economic value, but also by an increasingly [...] Read more.

Artificial intelligence is increasingly embedded in cyber–physical systems that coordinate sensing, computation, communication, and control across critical and semi-critical physical environments. Within the European Union, however, its adoption is shaped not only by technological maturity and economic value, but also by an increasingly dense regulatory landscape governing data processing, cybersecurity, product security, accountability, traceability, interoperability, and safety-relevant deployment. A PRISMA ScR-informed scoping review is used to examine how European Union regulation influences artificial intelligence adoption across four representative domains: energy and smart grids, smart buildings, mobility and transport systems, and industrial and manufacturing environments. The analysis draws on primary legal sources, the peer-reviewed literature, and policy and standards-related materials, and is structured around three dimensions: regulatory barriers, technological and architectural challenges, and cross-sector implications for governance, innovation, and competitiveness. The results show that regulation functions simultaneously as a constraint and an enabling condition. It increases compliance burden, raises integration complexity, and slows deployment in higher risk settings, while promoting trustworthy artificial intelligence, stronger cybersecurity, lifecycle governance, clearer accountability, and more interoperable digital infrastructures. The central finding is that regulation is not external to artificial intelligence adoption in cyber–physical systems, but actively shapes the design space within which such systems can be developed, integrated, validated, and scaled. Future progress therefore depends on regulation-aware systems engineering, stronger implementation guidance, and cross-sector reference architectures capable of aligning legal compliance with technical architecture and operational value creation. Full article

(This article belongs to the Special Issue Cyber-Physical Systems: Recent Developments and Emerging Trends)

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13 pages, 11970 KB

Open AccessArticle

Switchable Reflective Excitation Method for Rapid Characterization of Monopulse Comparators

by Shu-Yu Lin, Ming-Hsiang Cho and Jwo-Shiun Sun

Electronics 2026, 15(10), 2183; https://doi.org/10.3390/electronics15102183 (registering DOI) - 19 May 2026

Abstract

In high-precision tracking radars, the angular resolution depends critically on the amplitude and phase balance of the monopulse comparator. However, accurately and efficiently characterizing this highly integrated microwave component remains challenging, as its complex structure and high port count increase the difficulty of [...] Read more.

In high-precision tracking radars, the angular resolution depends critically on the amplitude and phase balance of the monopulse comparator. However, accurately and efficiently characterizing this highly integrated microwave component remains challenging, as its complex structure and high port count increase the difficulty of microwave testing. To achieve rapid measurement without compromising accuracy, this paper presents a novel characterization technique utilizing switchable reflective excitation signals. By employing the inherent reflected signals as internal excitation sources, this physical innovation allows for the simultaneous multiport input of a four-horn comparator. In addition, a switchable calibration window and a multiport matched load were designed to precisely control the boundary conditions directly at the reference plane of the device under test (DUT). Experimental validation on an X-band waveguide monopulse comparator demonstrates that the proposed methodology can promptly extract the intrinsic device characteristics. Good agreement between the theoretical predictions and measurement results confirms that the proposed technique provides a rapid and robust diagnostic solution for microwave measurement of monopulse comparators. Full article

(This article belongs to the Section Microwave and Wireless Communications)

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31 pages, 13476 KB

Open AccessArticle

DroneTFS-SemNet: A Semantic Learning Framework for Multi-Task Recognition of Drones

by Yantian Shen, Bohang Xu, Na Wang, Liang Ma, Yang Yang, Yunxia Liu, Hongjun Wang and Hua Li

Electronics 2026, 15(10), 2182; https://doi.org/10.3390/electronics15102182 (registering DOI) - 19 May 2026

Abstract

The widespread deployment of unmanned aerial vehicles has raised growing privacy and security concerns, creating an urgent need for effective drone detection and identification. To address these challenges, this paper proposes a semantic learning framework termed Drone Time–Frequency-Semantic Network (DroneTFS-SemNet) for drone recognition. [...] Read more.

The widespread deployment of unmanned aerial vehicles has raised growing privacy and security concerns, creating an urgent need for effective drone detection and identification. To address these challenges, this paper proposes a semantic learning framework termed Drone Time–Frequency-Semantic Network (DroneTFS-SemNet) for drone recognition. The main contributions are as follows. A Temporal Enhancement module is designed to improve model adaptability and robustness through radio frequency (RF) signal noise simulation, feature pre-extraction, learnable frequency-band partitioning, and an attention mechanism. A Cross-Channel Recalibration Module is introduced to promote cross-channel interaction of RF features via dilated convolutions and Channel Shuffle, thereby enhancing the model’s learning capability. Third, Feature Fusion Attention is employed to weight, align, and fuse RF features from different levels, thereby improving the model’s ability to represent differences among drone categories and operation modes. A semantic construction and decision module is further developed to extract high-level semantic features and accomplish drone detection, classification, and recognition. Experimental results on the DroneRF dataset show that DroneTFS-SemNet achieves a drone classification accuracy of 99.73% and an operation mode recognition accuracy of 99.65%, outperforming multiple comparison methods. These results demonstrate that the proposed model has strong recognition capability on this public dataset and under controlled experimental conditions, providing an effective method for RF-based drone recognition research. Full article

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23 pages, 8850 KB

Open AccessArticle

A Novel Enhanced Binary Classification Approach Based on Hybrid GWO-PSO Algorithms for Fault Detection in Smart Grids

by Mohammed Wadi, Ahlam AbuZahew, Muhammet Server Firat and Nour Husain

Electronics 2026, 15(10), 2181; https://doi.org/10.3390/electronics15102181 (registering DOI) - 19 May 2026

Abstract

Due to the complexity of recent power grids, any fault can dramatically affect the system’s quality, reliability, and stability. As a result, identifying faults becomes essential to maintaining the stability and reliability of power systems within acceptable thresholds. This article presents an innovative [...] Read more.

Due to the complexity of recent power grids, any fault can dramatically affect the system’s quality, reliability, and stability. As a result, identifying faults becomes essential to maintaining the stability and reliability of power systems within acceptable thresholds. This article presents an innovative binary classification fault detection method in recent power grids. The proposed methodology primarily consists of two preliminary stages before the training phase: data preparation and pre-training, aimed at improving the performance of the classifier. During the data preparation phase, the synthetic minority over-sampling approach balances the raw data, and the pre-training phase identifies the optimal features and hyperparameters. A novel hybrid approach combines the Grey Wolf Optimization (GWO) and Particle Swarm Optimization (PSO) methods to optimize feature selection and adjust hyperparameters. Furthermore, four machine learning models are trained and evaluated using an actual fault dataset. In addition, several evaluation criteria and receiver operating characteristic curves are used to validate the strength and robustness of the suggested method. All experimental evaluations were performed in an Azure Machine Learning Studio (AMLS) environment. The experimental results are compared to previous studies to verify the superiority of the suggested technique. Full article

(This article belongs to the Special Issue AI Applications for Smart Grid: 2nd Edition)

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22 pages, 8093 KB

Open AccessArticle

Robust Monocular Depth Estimation Under Crop-Resize-Induced Intrinsics Mismatch

by Huijun Kim and Deokwoo Lee

Electronics 2026, 15(10), 2180; https://doi.org/10.3390/electronics15102180 - 19 May 2026

Abstract

Monocular metric depth estimation models increasingly incorporate camera intrinsics or learn camera-aware representations to recover physically meaningful scales. However, existing camera-aware studies have paid limited attention to a practical deployment gap: common crop, resize, and padding operations alter the image coordinate system, but [...] Read more.

Monocular metric depth estimation models increasingly incorporate camera intrinsics or learn camera-aware representations to recover physically meaningful scales. However, existing camera-aware studies have paid limited attention to a practical deployment gap: common crop, resize, and padding operations alter the image coordinate system, but inference pipelines may still pass stale camera intrinsics to the model. This image–intrinsics inconsistency produces a crop-resize-induced intrinsics mismatch, which can lead to systematic depth bias and degraded geometric consistency. We show that once preprocessing parameters are fixed, the effective intrinsics are determined by a parametric affine mapping, enabling resize-induced focal-length scaling errors and crop-induced principal-point shifts to be analyzed separately. We further distinguish this parameter-conditioned mismatch from broader calibration uncertainty caused by noisy intrinsics, noisy preprocessing metadata, or missing metadata. Based on this formulation, we introduce a controlled evaluation protocol and a lightweight Mismatch-Aware Camera Module (MACM) that combines preprocessing metadata with image-derived camera cues to condition intermediate depth features. In our ablation study, MACM with the proposed consistency loss reduces the mismatched Abs.Rel from 0.141 to 0.114 and narrows the robustness gap from 0.038 to 0.017, while preserving accuracy under matched preprocessing. These results indicate that treating the image and intrinsics as a coupled representation is essential for robust monocular metric depth estimation in practical preprocessing pipelines. Full article

(This article belongs to the Special Issue Advances in Digital Signal and Image Processing, Techniques, and Computations with Multidisciplinary Applications, 2nd Edition)

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28 pages, 7499 KB

Open AccessArticle

HOSG-Nav: Hierarchical Open-Vocabulary Semantic Graph Navigation for Language-Guided Global Planning in 3D Gaussian Scenes

by Yuchen Li, Kai Qin, Weiyi Chen and Haitao Wu

Electronics 2026, 15(10), 2179; https://doi.org/10.3390/electronics15102179 - 19 May 2026

Abstract

Natural-language-driven robot navigation in complex indoor environments requires the joint capability of high-fidelity scene representation, structured semantic reasoning, and executable path planning. To address this challenge, this paper proposes HOSG-Nav, a unified framework for natural-language-driven global navigation that integrates open-vocabulary 3D Gaussian scene [...] Read more.

Natural-language-driven robot navigation in complex indoor environments requires the joint capability of high-fidelity scene representation, structured semantic reasoning, and executable path planning. To address this challenge, this paper proposes HOSG-Nav, a unified framework for natural-language-driven global navigation that integrates open-vocabulary 3D Gaussian scene representation, hierarchical semantic scene graph construction, and large-language-model-driven planning. First, an open-vocabulary 3D Gaussian field is constructed to jointly encode scene geometry, appearance, and semantic information, where compressed CLIP features are lifted into continuous 3D space and depth supervision is introduced to enhance geometric stability and metric-scale consistency. Second, the optimized Gaussian primitives are further abstracted into a semantic scene graph with a region–object hierarchical structure and traversable topological relations to support structured environment understanding. Finally, for natural language instructions, hierarchical semantic parsing is performed with the assistance of a large language model, and executable global navigation paths are generated through cross-modal target retrieval and graph-search-based planning. Experimental results on the Replica dataset demonstrate that HOSG-Nav achieves competitive performance in scene representation, semantic target retrieval, and global navigation, validating the effectiveness of jointly integrating multimodal 3D representation, hierarchical semantic abstraction, and language-guided planning. Full article

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18 pages, 2034 KB

Open AccessArticle

Backbone-Level Enhancements in YOLOv9 for Traffic Accident Detection from Video Footage

by Sajid Ahmed, Tasnia Tabassum, Madhab Chandra Das, Uzair Hussain and Vung Pham

Electronics 2026, 15(10), 2178; https://doi.org/10.3390/electronics15102178 - 18 May 2026

Abstract

Traffic accidents remain a major challenge for intelligent transportation systems, requiring reliable and real-time detection under complex visual conditions. This study aims to investigate how backbone-level architectural modifications affect traffic accident detection performance in video-based scenarios. A dataset of 250 accident videos was [...] Read more.

Traffic accidents remain a major challenge for intelligent transportation systems, requiring reliable and real-time detection under complex visual conditions. This study aims to investigate how backbone-level architectural modifications affect traffic accident detection performance in video-based scenarios. A dataset of 250 accident videos was curated from a public traffic surveillance source. This resulted in approximately 3000 manually annotated frames covering diverse accident conditions such as motion blur, occlusion, and illumination variation. To improve detection performance, we introduce Cross Stage Partial (CSP)-based feature partitioning and extend Efficient Layer Aggregation Network (ELAN) structures within the YOLOv9 backbone. Experimental evaluation demonstrates that the CSP-enhanced YOLOv9-t model achieves the best performance among all tested variants, improving mAP50 from 0.35 to 0.50 (approximately 42.8% relative improvement) compared to the baseline YOLOv9-t model, while maintaining real-time inference speed. The results further reveal that CSP improves localization precision, whereas ELAN enhances recall, highlighting complementary behaviors of backbone-level modifications in traffic accident detection tasks. These findings provide insights into how targeted architectural refinements can improve detection robustness in challenging real-world traffic scenarios. Full article

(This article belongs to the Special Issue Advances in Data Analysis and Visualization)

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32 pages, 4016 KB

Open AccessArticle

Robustness Modeling and Optimization of Multi-Layer Storage and Supply Networks for Equipment Support

by Tao Li, Lin Zhang, Yuliang Xu, Yang Yang and Hui Wang

Electronics 2026, 15(10), 2177; https://doi.org/10.3390/electronics15102177 - 18 May 2026

Abstract

To investigate the vulnerability of equipment transportation systems under cascading failures, this study models the system as a two-layer coupled network consisting of a storage–supply layer and a transportation layer within a multilayer complex network framework. The model captures both intra-layer structural properties [...] Read more.

To investigate the vulnerability of equipment transportation systems under cascading failures, this study models the system as a two-layer coupled network consisting of a storage–supply layer and a transportation layer within a multilayer complex network framework. The model captures both intra-layer structural properties and inter-layer coupling dependencies. A unified multilayer node-importance metric is proposed, integrating layer-level structural contribution with intra-layer centrality to achieve cross-layer comparable and interpretable node ranking. Based on this, a load-capacity–state coupled cascading failure model is developed, incorporating node capacity constraints, dynamic state transitions, and importance-aware load redistribution. Benchmarking experiments against degree, betweenness, multiplex PageRank, and TOPSIS-based methods show that the proposed metric leads to the fastest robustness degradation and the smallest area under the robustness curve, indicating superior capability in identifying cascade-critical nodes. The results demonstrate that targeted attacks significantly degrade network performance, especially when coupled nodes fail. The findings suggest that prioritizing protection of critical nodes and optimizing load redistribution can effectively enhance system resilience. Full article

(This article belongs to the Section Computer Science & Engineering)

21 pages, 3479 KB

Open AccessArticle

A Hybrid Periodic and Event-Driven Rolling Horizon Optimization Approach for Airport Logistics Vehicle Scheduling

by Ran Feng, Zhihao Cai, Boyuan Li and Qian-Qian Zheng

Electronics 2026, 15(10), 2176; https://doi.org/10.3390/electronics15102176 - 18 May 2026

Abstract

The efficient scheduling of airport logistics vehicles is crucial for ensuring timely and cost-effective ground operations, particularly under dynamic disturbances such as flight delays, cancellations, and new task arrivals. With the increasing deployment of Internet of Things (IoT) technologies in airport environments, real-time [...] Read more.

The efficient scheduling of airport logistics vehicles is crucial for ensuring timely and cost-effective ground operations, particularly under dynamic disturbances such as flight delays, cancellations, and new task arrivals. With the increasing deployment of Internet of Things (IoT) technologies in airport environments, real-time data from sensors and connected devices enables efficient and adaptive scheduling. This paper considers a dynamic Airport Logistics Vehicle Scheduling (ALVS) problem that aims to minimize both vehicle usage and total task waiting time while satisfying task precedence and time window constraints. To address this problem, we propose a hybrid optimization framework, termed Periodic and Event-Driven Rolling Horizon Optimization (PERHO), which integrates periodic updates with event-driven rescheduling to adapt to real-time task variations in airport ground operations. Within PERHO, an Order-aware Adaptive Strategy Selection (OASS) algorithm is developed to dynamically select the most appropriate task sequencing heuristic from a candidate set based on recent performance and order relationships. Extensive experiments across various instance scales and dynamic scenarios demonstrate the effectiveness of the proposed PERHO-OASS approach. In experiments considering dynamic events, PERHO-OASS reduces vehicle usage and task waiting time by an average of 23.55% and 61.95%, respectively, over fixed heuristic algorithms, and by an average of 3.77% and 17.30% over adaptive selection methods, demonstrating strong robustness under uncertainty. The proposed approach can support airport operators in improving the efficiency and reliability of ground logistics operations. Full article

(This article belongs to the Special Issue Empowering IoT with AI: AIoT for Smart and Autonomous Systems)

18 pages, 6619 KB

Open AccessArticle

Modeling of SiC MOSFETs and Analysis of Turn-Off Overvoltage Mechanism in Low-Voltage DC Solid-State Circuit Breaker Applications

by Qingguang Xia, Jin Wu, Xueyan Zhang, Nan Wu, Zheng Fu and Qiyong Zhou

Electronics 2026, 15(10), 2175; https://doi.org/10.3390/electronics15102175 - 18 May 2026

Abstract

To address the turn-off overvoltage challenge arising from the rapid interruption of Low Voltage DC Solid-State Circuit Breakers (SSCBs), this paper proposes a high-precision behavioral modeling method for domestic SiC MOSFETs. The model is constructed based on the physical structure of the device, [...] Read more.

To address the turn-off overvoltage challenge arising from the rapid interruption of Low Voltage DC Solid-State Circuit Breakers (SSCBs), this paper proposes a high-precision behavioral modeling method for domestic SiC MOSFETs. The model is constructed based on the physical structure of the device, integrating a modified EKV-based static current model and a voltage-dependent nonlinear parasitic capacitance model described by piecewise functions. Model parameters are efficiently extracted from datasheets and measurement data using a composite optimization strategy combining the Genetic Algorithm and the Levenberg–Marquardt algorithm. The model is implemented in LTspice, and its accuracy in both static and dynamic characteristics is validated by comparing the simulation waveforms with experimental results. Based on the validated model, the turn-off process is subdivided into four distinct stages, with an equivalent circuit established for each. A systematic analysis reveals the intrinsic physical mechanism of the voltage spike and oscillation, which results from interaction among the drive circuit parameters, system parameters, and the nonlinear capacitances of the device. The research outcomes provide effective theoretical guidance and a design tool for simulation modeling, turn-off stress assessment, and snubber circuit optimization for SSCBs utilizing SiC MOSFETs. Full article

(This article belongs to the Topic Power Transmission, Distribution and Transformation Equipment)

33 pages, 3121 KB

Open AccessArticle

Damping Enhancement Control Strategy for Heterogeneous Hybrid Low Short Circuit Ratio System with GFL-PV and GFM-ESS

by Haoli Chen, Yuansheng Liang, Haifeng Li and Gang Wang

Electronics 2026, 15(10), 2174; https://doi.org/10.3390/electronics15102174 - 18 May 2026

Abstract

Small-disturbance damping characteristics have become a critical concern in renewable-dominated power systems under low short circuit ratio (SCR) conditions. In heterogeneous systems composed of grid-following photovoltaic (GFL-PV) and grid-forming energy storage system (GFM-ESS) units, strong dynamic coupling may weaken the damping of critical [...] Read more.

Small-disturbance damping characteristics have become a critical concern in renewable-dominated power systems under low short circuit ratio (SCR) conditions. In heterogeneous systems composed of grid-following photovoltaic (GFL-PV) and grid-forming energy storage system (GFM-ESS) units, strong dynamic coupling may weaken the damping of critical oscillation modes, thereby complicating stability analysis and coordinated parameter tuning. This paper proposes a damping enhancement strategy for a low-SCR GFL-PV/GFM-ESS system. The main innovation is an integrated damping-oriented framework that links detailed small-disturbance modeling, dominant-mode identification, participation-factor analysis, parameter-sensitivity evaluation, and coordinated optimization. First, a dynamic model including GFL-PV, GFM-ESS, and their coupling is established, and the corresponding linearized model is verified. Then, eigenvalue, modal, participation-factor, and sensitivity analyses are performed to identify weakly damped modes, key state variables, and sensitive parameters. Furthermore, a Joint Opposite Selection-enhanced particle swarm optimization (JOS-PSO) strategy is proposed to tune multiple coupled parameters. Simulation results under different operating conditions show that the proposed method improves damping characteristics, small-disturbance stability, and dynamic performance. Full article

(This article belongs to the Special Issue Advanced Technologies for Future Electric Power Transmission Systems)

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17 pages, 902 KB

Open AccessArticle

Contrastive Learning with Class Collision Awareness for Periodic Forecasting in 6G Urban Digital Twins

by Tong Lv, Yunhang Mao and Zhengnan Ma

Electronics 2026, 15(10), 2173; https://doi.org/10.3390/electronics15102173 - 18 May 2026

Abstract

Periodic time-series forecasting is central to 6G-enabled urban digital twins, where both cellular traffic management and environmental sensing demand accurate predictions over recurring diurnal and weekly regimes. Contrastive self-supervised learning has emerged as a promising approach for learning temporal representations, yet when applied [...] Read more.

Periodic time-series forecasting is central to 6G-enabled urban digital twins, where both cellular traffic management and environmental sensing demand accurate predictions over recurring diurnal and weekly regimes. Contrastive self-supervised learning has emerged as a promising approach for learning temporal representations, yet when applied to such periodic data, it suffers from class collision: temporally distant but semantically similar recurrent patterns are pushed apart as false negatives. We propose Clustering-Enhanced Contrastive Learning (CECL), which couples temporal contrastive learning with a clustering regularizer that maintains a Gaussian mixture structure over the latent space, preserving global periodic structure while retaining local discriminability. We evaluate CECL on five datasets across three tracks spanning two domains: cellular traffic forecasting (Milan CDR, 20 cells), multi-site hourly air-quality forecasting (Beijing Multi-Site, 12 stations; QUANT, 3 cities), and daily air-quality forecasting (Haikou and Taizhou). Across all tracks, CECL consistently outperforms supervised and contrastive baselines, reducing RMSE by 3–

10 %

relative to the strongest contrastive competitor (CoST). These results demonstrate that clustering-guided contrastive regularization yields robust gains for periodic forecasting in both 6G network management and environmental sensing scenarios. Full article

(This article belongs to the Special Issue Next-Generation Wireless Networks: Advances in 5G, 6G, and Broadband Communication)

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Open AccessArticle

DPCI-GPSR: A Directional Propagation Capacity Index for Enhanced GPSR Routing in VANETs

by Yue Liu, Duaa Zuhair Al-Hamid and Xue Jun Li

Electronics 2026, 15(10), 2172; https://doi.org/10.3390/electronics15102172 - 18 May 2026

Abstract

Vehicular ad hoc networks (VANETs) enable direct wireless communication between moving vehicles for safety and cooperative driving. Routing in VANETs is challenging due to high mobility, frequent topology changes, and variable node density. The Greedy Perimeter Stateless Routing (GPSR) protocol maintains only a [...] Read more.

Vehicular ad hoc networks (VANETs) enable direct wireless communication between moving vehicles for safety and cooperative driving. Routing in VANETs is challenging due to high mobility, frequent topology changes, and variable node density. The Greedy Perimeter Stateless Routing (GPSR) protocol maintains only a one-hop neighbor position table through periodic beacon exchanges, making it highly scalable. Each node forwards packets to the neighbor geographically closest to the destination. However, this distance-only criterion leads to a low packet delivery ratio (PDR). Existing improvements, such as Weight-Based Path-Aware GPSR (W-PAGPSR) combining distance progress, velocity direction, neighbor density, and link duration, incorporate multiple factors but complicate parameter tuning and lack a unified neighbor quality metric. This paper proposes Directional Propagation Capacity Index–GPSR (DPCI-GPSR), integrating neighbor information into a single directional metric capturing propagation capacity. Two enhancements are introduced: (1) an eight-direction DPCI computing a composite propagation capacity index per sector, exchanged via Hello packets, and (2) a trapezoidal link quality function treating 30–200 m as optimal while penalizing edge-zone neighbors. Implemented in NS-3 with SUMO-generated mobility, results across four node densities (30–120 vehicles), five concurrent sender–receiver pairs, and 15 random seeds show DPCI-GPSR achieves 63.08–98.39% PDR, outperforming both W-PAGPSR (52.38–80.14%) and standard GPSR (50.23–66.31%). Full article

(This article belongs to the Special Issue Advanced Technologies for Intelligent Vehicular Networks)

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