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Evaluation of Dynamic On-Resistance and Trapping Effects in GaN on Si HEMTs Using Rectangular Gate Voltage Pulses
A Novel Harmonic Clocking Scheme for Concurrent N-Path Reception in Wireless and GNSS Applications
Cybersecurity Applications of Near-Term Large Language Models
Optimizing FPGA Resource Allocation in SDR Remote Laboratories via Partial Reconfiguration
Automated Test Generation and Marking Using LLMs

Journal Description

Electronics

Electronics is an international, peer-reviewed, open access journal on the science of electronics and its applications published semimonthly online by MDPI. The Polish Society of Applied Electromagnetics (PTZE) is affiliated with Electronics and their members receive a discount on article processing charges.

Open Access— free for readers, with article processing charges (APC) paid by authors or their institutions.
High Visibility: indexed within Scopus, SCIE (Web of Science), CAPlus / SciFinder, Inspec, Ei Compendex and other databases.
Journal Rank: JCR - Q2 (Engineering, Electrical and Electronic) / CiteScore - Q1 (Electrical and Electronic Engineering)
Rapid Publication: manuscripts are peer-reviewed and a first decision is provided to authors approximately 16.8 days after submission; acceptance to publication is undertaken in 2.4 days (median values for papers published in this journal in the first half of 2025).
Recognition of Reviewers: reviewers who provide timely, thorough peer-review reports receive vouchers entitling them to a discount on the APC of their next publication in any MDPI journal, in appreciation of the work done.
Companion journals for Electronics include: Magnetism, Microwave, Network, Signals, Software and Microelectronics.
Journal Cluster of Electronic Engineering and Hardware Systems: Chips, Electronics, Hardware, Journal of Low Power Electronics and Applications, Microelectronics and Microwave.

Impact Factor: 2.6 (2024); 5-Year Impact Factor: 2.6 (2024)

Imprint Information Journal Flyer Open Access ISSN: 2079-9292

Latest Articles

25 pages, 3867 KB

Open AccessArticle

Edge Computing Task Offloading Algorithm Based on Distributed Multi-Agent Deep Reinforcement Learning

by Hui Li, Zhilong Zhu, Yingying Li, Wanwei Huang and Zhiheng Wang

Electronics 2025, 14(20), 4063; https://doi.org/10.3390/electronics14204063 (registering DOI) - 15 Oct 2025

As an important supplement to ground computing, edge computing can effectively alleviate the computational burden on ground systems. In the context of integrating edge computing with low-Earth-orbit satellite networks, this paper proposes an edge computing task offloading algorithm based on distributed multi-agent deep [...] Read more.

As an important supplement to ground computing, edge computing can effectively alleviate the computational burden on ground systems. In the context of integrating edge computing with low-Earth-orbit satellite networks, this paper proposes an edge computing task offloading algorithm based on distributed multi-agent deep reinforcement learning (DMADRL) to address the challenges of task offloading, including low transmission rates, low task completion rates, and high latency. Firstly, a Ground–UAV–LEO (GUL) three-layer architecture is constructed to improve offloading transmission rate. Secondly, the task offloading problem is decomposed into two sub-problems: offloading decisions and resource allocation. The former is addressed using a distributed multi-agent deep Q-network, where the problem is formulated as a Markov decision process. The Q-value estimation is iteratively optimized through the online and target networks, enabling the agent to make autonomous decisions based on ground and satellite load conditions, utilize the experience replay buffer to store samples, and achieve global optimization via global reward feedback. The latter employs the gradient descent method to dynamically update the allocation strategy based on the accumulated task data volume and the remaining resources, while adjusting the allocation through iterative convergence error feedback. Simulation results demonstrate that the proposed algorithm increases the average transmission rate by 21.7%, enhances the average task completion rate by at least 22.63% compared with benchmark algorithms, and reduces the average task processing latency by at least 11.32%, thereby significantly improving overall system performance. Full article

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30 pages, 1971 KB

Open AccessArticle

Integrating VMD and Adversarial MLP for Robust Acoustic Detection of Bolt Loosening in Transmission Towers

by Yong Qin, Yu Zhou, Cen Cao, Jun Hu and Liang Yuan

Electronics 2025, 14(20), 4062; https://doi.org/10.3390/electronics14204062 (registering DOI) - 15 Oct 2025

The structural integrity of transmission towers, as the backbone of power grids, is critical to overall grid safety, relying heavily on the reliability of bolted connections. Dynamic loads such as wind-induced vibrations can cause bolt loosening, potentially leading to structural deformation, cascading failures, [...] Read more.

The structural integrity of transmission towers, as the backbone of power grids, is critical to overall grid safety, relying heavily on the reliability of bolted connections. Dynamic loads such as wind-induced vibrations can cause bolt loosening, potentially leading to structural deformation, cascading failures, and large-scale blackouts. Traditional manual inspection methods are inefficient, subjective, and hazardous. Existing automated approaches are often limited by environmental noise sensitivity, high computational complexity, sensor placement dependency, or the need for extensive labeled data. To address these challenges, this paper proposes a portable acoustic detection system based on Variational Mode Decomposition (VMD) and an Adversarial Multilayer Perceptual Network (AT-MLP). The VMD method effectively processes non-stationary and nonlinear acoustic signals to suppress noise and extract robust time–frequency features. The AT-MLP model then performs state identification, incorporating adversarial training to mitigate distribution discrepancies between training and testing data, thereby significantly improving generalization and noise robustness. Comparison results and analysis demonstrate that the proposed VMD and AT-MLP framework effectively mitigates structural variability and environmental interference, providing a reliable solution for bolt loosening detection. The proposed method bridges structural mechanics, acoustic signal processing, and lightweight intelligence, offering a scalable solution for condition assessment and risk-aware maintenance of transmission towers. Full article

(This article belongs to the Special Issue AI-Driven Innovations for Smart Energy Systems: Prediction, Detection, and Optimization)

15 pages, 2937 KB

Open AccessArticle

Denoising Degraded PCOS Ultrasound Images Using an Enhanced Denoising Diffusion Probabilistic Model

by Jincheng Peng, Zhenyu Guo, Xing Chen and Ming Zhou

Electronics 2025, 14(20), 4061; https://doi.org/10.3390/electronics14204061 (registering DOI) - 15 Oct 2025

Currently, for polycystic ovary syndrome (PCOS), diagnostic methods are mainly divided into hormonal indicators and ultrasound imaging. However, ultrasound images are often affected by noise and artifacts during the imaging process. This significantly degrades image quality and increases the difficulty of diagnosis. This [...] Read more.

Currently, for polycystic ovary syndrome (PCOS), diagnostic methods are mainly divided into hormonal indicators and ultrasound imaging. However, ultrasound images are often affected by noise and artifacts during the imaging process. This significantly degrades image quality and increases the difficulty of diagnosis. This paper proposes a PCOS ultrasound image denoising method based on an improved DDPM. During the forward diffusion process of the original model, Gaussian noise is progressively added using a cosine-based scheduling strategy. In the reverse diffusion process, a conditional noise predictor is introduced and combined with the original ultrasound image information to iteratively denoise and recover a clear image. Additionally, we fine-tuned and optimized the model to better suit the requirements of PCOS ultrasound image denoising. Experimental results show that our model outperforms state-of-the-art methods in both noise suppression and structural fidelity. It delivers a fully automated PCOS-ultrasound denoising pipeline whose diffusion-based restoration preserves clinically salient anatomy, improving the reliability of downstream assessments. Full article

(This article belongs to the Special Issue Edge AI for Biomedical Applications: Innovations in Sensing, Computing and Security)

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23 pages, 6752 KB

Open AccessArticle

Failure Mechanism Analysis and Electromagnetic Protection Design of Electronic Systems Under High-Power Electromagnetic Pulse

by Chenxi Huang, Jinhong Wei, Youjie Yan, Kaiyue Zhang, Shoulong Zhang, Yifei Sun and Tongyu Wang

Electronics 2025, 14(20), 4060; https://doi.org/10.3390/electronics14204060 - 15 Oct 2025

In this paper, the failure mechanisms and electromagnetic protection design of an electronic switching system under high-power electromagnetic pulse (HPEMP) effects are studied. By integrating experimental testing and modeling simulation methods, the coupling characteristics of HPEMP energy within the electronic switching system, the [...] Read more.

In this paper, the failure mechanisms and electromagnetic protection design of an electronic switching system under high-power electromagnetic pulse (HPEMP) effects are studied. By integrating experimental testing and modeling simulation methods, the coupling characteristics of HPEMP energy within the electronic switching system, the response characteristics of sensitive components, and their physical failure processes were analyzed. The research indicates that the root cause of system failure under HPEMP irradiation lies in the intrusion of electromagnetic energy coupled through specific anode or gate cable ports, leading to unintended turn-on of the key thyristor device and consequent system functional failure. Mechanism analysis reveals that when the coupled voltage on the thyristor pins exceeds the gate trigger threshold, a carrier regeneration mechanism is activated within the device, resulting in polarity reversal of the PN junction and the formation of a positive feedback path, ultimately causing false triggering. Experimental and simulation results show good consistency in terms of effect thresholds. Based on these findings, effective electromagnetic protection hardening designs targeting the identified electromagnetic vulnerability paths and sensitive components were proposed, and the effectiveness of the protective measures was validated through experiments. Full article

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20 pages, 1777 KB

Open AccessArticle

Circuit–Temperature Coupled Research and Teaching Platform for the Resistive-Type Superconducting Fault Current Limiters

by Qinghua Zhao, Shirong Gong, Xiaoyuan Chen, Lin Fu, Miangang Tang, Jun Bai and Boyang Shen

Electronics 2025, 14(20), 4059; https://doi.org/10.3390/electronics14204059 - 15 Oct 2025

In order to break the bottleneck in the teaching and research of superconducting current limiting technology, this paper proposed an integrated platform based on a resistive-type superconducting current limiter (RSFCL). Through a user-programmable software interface, the dynamic working process of the RSFCL was [...] Read more.

In order to break the bottleneck in the teaching and research of superconducting current limiting technology, this paper proposed an integrated platform based on a resistive-type superconducting current limiter (RSFCL). Through a user-programmable software interface, the dynamic working process of the RSFCL was simulated and analyzed, along with the self-triggered quench characteristics, internal current distribution, and instantaneous temperature evolution process under different fault conditions. This platform employed a superconductor–circuit–temperature coupling model to simulate the current limiting characteristics of the RSFCL under various AC/DC and transient conditions. This effectively helps the users understand the electrothermal coupling mechanisms of the RSFCL but also provides the researchers with an efficient simulation tool to analyze superconducting properties, optimize fault current limiter topologies, and validate system-level fault protection strategies. The platform’s simulation results align well with theoretical analyses, offering a reliable auxiliary tool for teaching and research in superconducting power technology. Full article

22 pages, 370 KB

Open AccessArticle

AIRPoC: An AI-Enhanced Blockchain Consensus Framework for Autonomous Regulatory Compliance

by Sejin Han

Electronics 2025, 14(20), 4058; https://doi.org/10.3390/electronics14204058 - 15 Oct 2025

Following the stablecoin legislation (GENIUS Act) enacted under the second Trump administration in 2025, blockchain has become core digital economy infrastructure. However, privacy risks from decentralization and transparency constrain adoption in regulated industries, requiring solutions that harmonize blockchain architecture with regulatory compliance. Existing [...] Read more.

Following the stablecoin legislation (GENIUS Act) enacted under the second Trump administration in 2025, blockchain has become core digital economy infrastructure. However, privacy risks from decentralization and transparency constrain adoption in regulated industries, requiring solutions that harmonize blockchain architecture with regulatory compliance. Existing research relies on reactive auditing or post-execution rule checking, which wastes computational resources or provides only basic encryption or access controls without comprehensive privacy compliance. The proposed Artificial Intelligence-enhanced Regulatory Proof-of-Compliance (AIRPoC) framework addresses this gap through a two-phase consensus mechanism that integrates AI legal agents with semantic web technologies for autonomous regulatory compliance enforcement. Unlike existing research, AIRPoC implements a dual-layer architecture where AI-powered regulatory validation precedes consensus execution, ensuring that only compliant transactions proceed to blockchain finalization. The system employs AI legal agents that automatically construct and update regulatory databases via multi-oracle networks, using SPARQL-based inference engines for real-time General Data Protection Regulation (GDPR) compliance validation. A simulation-based experimental evaluation conducted across 24 tests with 116,200 transactions in a controlled environment demonstrates 88.9% compliance accuracy, with 9502 transactions per second (TPS) versus 11,192 TPS for basic Proof-of-Stake (PoS) (4.5% overhead). This research represents a paradigm shift to dynamic, transaction-based regulatory models that preserve blockchain efficiency. Full article

(This article belongs to the Special Issue Security, Privacy, Confidentiality and Trust in Blockchain, 2nd Edition)

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24 pages, 3847 KB

Open AccessArticle

A Large-Scale Neuromodulation System-on-Chip Integrating 128-Channel Neural Recording and 32-Channel Programmable Stimulation for Neuroscientific Applications

by Gunwook Park, Joongyu Kim, Minjae Kim, Minsung Kim, Byeongwoo Yoo, Jeongho Choi, Daehong Kim and Sung-Yun Park

Electronics 2025, 14(20), 4057; https://doi.org/10.3390/electronics14204057 - 15 Oct 2025

We present a large-scale neuromodulation system-on-chip (SoC) that integrates a 128-channel neural recording and 32-channel stimulation ASIC designed for a wide range of neuroscientific applications. Each recording channel achieves low-noise performance (~4 μV_rms) with a configurable bandwidth of 0.05 Hz–7.5 kHz [...] Read more.

We present a large-scale neuromodulation system-on-chip (SoC) that integrates a 128-channel neural recording and 32-channel stimulation ASIC designed for a wide range of neuroscientific applications. Each recording channel achieves low-noise performance (~4 μV_rms) with a configurable bandwidth of 0.05 Hz–7.5 kHz and supports 16-bit digitization with scalable sampling rates up to 30 kS/s. To enhance signal quality, the ASIC includes an adjustable digital high-pass filter and a fast-settling function for rapid recovery from stimulation artifacts. SoC also incorporates on-chip electrode-impedance measurements as a built-in safety feature by reusing the recording channels. The stimulation subsystem generates current-controlled monopolar biphasic pulses with a high compliance voltage of ±6 V using standard low-voltage (1.8 V/3.3 V) CMOS devices. Each of the 32 stimulation channels provides arbitrary 9-bit programmable waveforms and dual current modes (4 μA/bit and 8 μA/bit), supporting both fine-resolution microstimulation and high-current applications such as spinal-cord and deep-brain stimulation. On-chip charge-balancing switches in each channel further ensure safe and reliable stimulation delivery. The SoC supports digital communication via a standard SPI with both 3.3 V CMOS and low-voltage differential signaling options and integrates all required analog references and low-dropout regulators. The prototype was fabricated in a standard 180 nm CMOS process, occupying 31.92 mm² (equivalently, 0.2 mm² per recording-and-stimulation channel), and was fully validated through benchtop measurements and in vitro experiments. Full article

(This article belongs to the Section Bioelectronics)

14 pages, 3251 KB

Open AccessArticle

An Objective Effect Evaluation Framework for Vectorization Models on Patent Semantic Similarity Measurement

by Jiazheng Li, Jian Zhou and Mengyun Cao

Electronics 2025, 14(20), 4056; https://doi.org/10.3390/electronics14204056 - 15 Oct 2025

How to objectively evaluate the effect of different vectorization models in measuring similarity between patents is a fundamental issue, which can help to select high-performance vectorization models to support advanced patent services. Based on the rank consistency index and hypothesis testing approach, a [...] Read more.

How to objectively evaluate the effect of different vectorization models in measuring similarity between patents is a fundamental issue, which can help to select high-performance vectorization models to support advanced patent services. Based on the rank consistency index and hypothesis testing approach, a framework for evaluating the effect of different vectorization models on patents’ similarity is proposed based on whether the model can accurately predict the similarity ranking of patents. Integrating the factors of time and technical field, an empirical study is conducted under the proposed framework to objectively evaluate the effect of six mainstream text vectorization models for assessing the semantic similarity of patents, which is evaluated based on Chinese patents (English Translation) from 2010 to 2024. The results show that the performance of Llama 2 is the best among six compared models in all years and in all technical fields. The proposed framework can objectively evaluate the similarity measurement effect of different vectorization models and provides a basis for the selection of the vectorization model for patent semantic similarity measurement for advanced patent services. Full article

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27 pages, 3555 KB

Open AccessArticle

Research on Abnormal Radio Detection Method Combining Local Outlier Factor and One-Class Support Vector Machine

by Yue Zhao, Xueguang Zhou, Lu Chen, Yihuan Mao and Meishuang Yan

Electronics 2025, 14(20), 4055; https://doi.org/10.3390/electronics14204055 - 15 Oct 2025

The widespread application of wireless communication has led to increasingly complex electromagnetic environments, where spectrum abuse and malicious interference frequently cause abnormal signals. Radio anomaly detection technology has emerged to address these challenges. This paper focuses on the performance limitations of existing radio [...] Read more.

The widespread application of wireless communication has led to increasingly complex electromagnetic environments, where spectrum abuse and malicious interference frequently cause abnormal signals. Radio anomaly detection technology has emerged to address these challenges. This paper focuses on the performance limitations of existing radio anomaly detection methods under low interference-to-signal ratio (ISR)conditions. We propose a fusion detection algorithm, LOF-OCSVM, integrating local outlier factor (LOF) and one-class support vector machine (OCSVM). Innovatively, we introduce three novel features: fluctuation entropy (FE), fluctuation mutual information (FR-MI), and lognormal distribution fitting parameters derived from signal fluctuation sequences. These features quantify the disorderliness, adjacent correlation, and statistical distribution characteristics of signal fluctuations, significantly enhancing the detection sensitivity for weak interference signals. Simulation experiments demonstrate that: feature effectiveness: the new features improve recall by >30% and F1-score by >23% at −20 dB ISR. Model superiority: the LOF-OCSVM fusion model achieves an F1-score of 0.8634 at −20 dB ISR through a hierarchical decision mechanism, outperforming single model and simple hybrid approaches. Robustness: compared to Deep SVDD and E-GAN, our method improves AUC by 3% under low ISR conditions while maintaining stronger robustness. Full article

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24 pages, 3264 KB

Open AccessArticle

Development of a New Solid State Fault Current Limiter for Effective Fault Current Limitation in Wind-Integrated Grids

by Mohamed S. A. Zayed, Hossam E. M. Attia, Manal M. Emara, Diaa-Eldin A. Mansour and Hany Abdelfattah

Electronics 2025, 14(20), 4054; https://doi.org/10.3390/electronics14204054 - 15 Oct 2025

The increasing penetration of wind energy into modern power grids introduces new challenges, particularly regarding fault current levels and voltage stability during disturbances. This study proposes and evaluates a new Solid State Fault Current Limiter (SSFCL) topology for mitigating the adverse effects of [...] Read more.

The increasing penetration of wind energy into modern power grids introduces new challenges, particularly regarding fault current levels and voltage stability during disturbances. This study proposes and evaluates a new Solid State Fault Current Limiter (SSFCL) topology for mitigating the adverse effects of faults in wind-integrated power systems. The proposed SSFCL consists of a bridge section and a shunt branch, designed to limit fault current while maintaining power quality. Unlike conventional SSFCLs, the proposed topology incorporates both DC and AC reactors with an Integrated Gate-Commutated Thyristor (IGCT) switch, to provide current limiting and voltage stabilization, effectively mitigating the negative impacts of faults. A comprehensive MATLAB/Simulink-based simulation is conducted on a realistic grid model. First, appropriate AC and DC reactor impedances are selected to balance fault current suppression, cost, and dynamic response. Then, three fault scenarios, transmission line, distribution grid, and domestic network, are analyzed to assess the fault current limiting performance and voltage sag mitigation of the SSFCL. In the simulation analysis, the DC reactor current and the voltage across the SSFCL device are continuously monitored to evaluate its dynamic response and effectiveness during fault and normal operating conditions. In addition, the fault current contribution from the wind farm is assessed with and without the integration of the SSFCL, along with the voltage profile at the Point of Common Coupling (PCC), to determine the limiter’s impact on system stability and power quality. Finally, the performance of the proposed SSFCL is compared to that of the resistive-type superconducting fault current limiter (R-SFCL) under identical fault scenarios to assess the technical and economic standpoints of the proposed SSFCL. Simulation results show that the SSFCL reduces the peak fault current by up to 29% and improves the voltage profile at the PCC by up to 42%, providing comparable performance to the R-SFCL while avoiding the need for cryogenic systems. Full article

(This article belongs to the Special Issue Advanced Applications of Smart Power Technologies and Wide-Bandgap Semiconductors)

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38 pages, 564 KB

Open AccessReview

AI Methods in Network Slice Life-Cycle Phases: A Survey

by Evangelos Thomatos, Aggeliki Sgora, Athanasios Tsipis and Periklis Chatzimisios

Electronics 2025, 14(20), 4053; https://doi.org/10.3390/electronics14204053 - 15 Oct 2025

Network slicing (NS) plays a vital role in enabling flexible and efficient resource allocation, tailored to diverse use cases and network domains. This survey paper explores the synergy between NS and Artificial Intelligence (AI), emphasizing how Machine Learning (ML) techniques can address challenges [...] Read more.

Network slicing (NS) plays a vital role in enabling flexible and efficient resource allocation, tailored to diverse use cases and network domains. This survey paper explores the synergy between NS and Artificial Intelligence (AI), emphasizing how Machine Learning (ML) techniques can address challenges across the slice life-cycle. A key contribution of this work is an in-depth analysis of AI and primarily ML applications in each phase of the slice life-cycle, delving into their specific tasks and discussing the techniques applied to these tasks. Furthermore, we present a taxonomy based on different slicing criteria, offering a structured perspective to enhance understanding and implementation. Full article

(This article belongs to the Special Issue Design, Control, Optimization, and Security of Next-Generation Communications Networks)

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32 pages, 678 KB

Open AccessFeature PaperArticle

The IDRE Dataset in Practice: Training and Evaluation of Small-to-Medium-Sized LLMs for Empathetic Rephrasing

by Simone Manai, Laura Gemme, Roberto Zanoli and Alberto Lavelli

Electronics 2025, 14(20), 4052; https://doi.org/10.3390/electronics14204052 - 15 Oct 2025

Integrating emotional intelligence into AI systems is essential for developing empathetic chatbots, yet deploying fully empathetic models is often constrained by business, ethical, and computational factors. We propose an innovative solution: a dedicated empathy rephrasing layer that operates downstream of a chatbot’s initial [...] Read more.

Integrating emotional intelligence into AI systems is essential for developing empathetic chatbots, yet deploying fully empathetic models is often constrained by business, ethical, and computational factors. We propose an innovative solution: a dedicated empathy rephrasing layer that operates downstream of a chatbot’s initial response. This layer leverages large language models (LLMs) to infuse empathy into the chatbot’s output without altering its core meaning, thereby enhancing emotional intelligence and user engagement. To implement this layer, we extend and validate the IDRE (Italian Dialogue for Empathetic Responses) dataset. We evaluated small- and medium-scale LLMs across three configurations: baseline models, models augmented via few-shot learning with IDRE exemplars, and models fine-tuned on IDRE. Performance was quantitatively assessed using the LLM-as-a-judge paradigm, leveraging custom metrics. These results were further validated through an independent human evaluation and supported by established NLP similarity metrics, ensuring a robust triangulation of findings. Results confirm that both few-shot prompting and fine-tuning with IDRE significantly enhance the models’ capacity for empathetic language generation. Applications include empathetic AI in healthcare, such as virtual assistants for patient support, and demonstrate promising generalization to other domains. All datasets, prompts, fine-tuned models, and scripts are publicly available to ensure transparency and reproducibility. Full article

(This article belongs to the Special Issue Natural Language Processing Based on Neural Networks and Large Language Models)

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28 pages, 2797 KB

Open AccessArticle

TTBCA⁺: An Enhanced Besiege and Conquer Algorithm with Three-Three Tactics and Collision Theory for Complex Optimization Problems

by Jianhua Jiang, Xiangyu Xin, Jun Tian and Hao Li

Electronics 2025, 14(20), 4051; https://doi.org/10.3390/electronics14204051 - 15 Oct 2025

Besiege and Conquer Algorithm (BCA) is a swarm intelligence algorithm proposed in 2025 based on tactical concepts. However, its besiege and conquer strategies have some problems, such as insufficient diversity and local stagnation. To solve the above problems, this paper proposes an Enhanced [...] Read more.

Besiege and Conquer Algorithm (BCA) is a swarm intelligence algorithm proposed in 2025 based on tactical concepts. However, its besiege and conquer strategies have some problems, such as insufficient diversity and local stagnation. To solve the above problems, this paper proposes an Enhanced Besiege and Conquer Algorithm with Three-Three Tactics and Collision Theory, referred to as TTBCA⁺. TTBCA⁺ has four innovations. Firstly, a three-three allocation mechanism of battlefield and roles is proposed to enhance its besiege capability; secondly, a three-three deployment mechanism of soldiers is proposed to enhance its conquer capability; thirdly, the balance factor between exploration and exploitation is modified for three-three tactics implementation; finally, the collision mechanism from collision theory is introduced to deal with soldiers beyond the search space. The performance of the proposed TTBCA⁺ is verified at the IEEE CEC 2017 and IEEE CEC 2022 benchmark functions, compared with 13 swarm intelligence algorithms, including classical algorithms, well-known algorithms such as JADE, lshade_rsp, AGWO, and recent 5 years algorithms, such as BCA, HOA, PLO, CFOA, HLOA, DBO, BOA. Meanwhile, the proposed algorithms are applied to two practical complex optimization problems. The results show that TTBCA⁺ effectively solved the limitations in BCA, and it is superior to other compared algorithms. Full article

(This article belongs to the Special Issue AI Applications of Multi-Agent Systems)

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24 pages, 3583 KB

Open AccessArticle

Explainable Model of Hybrid Ensemble Learning for Prostate Cancer RNA-Seq Classification via Targeted Feature Selection

by Ahmet Demiröz and Nesrin Aydın Atasoy

Electronics 2025, 14(20), 4050; https://doi.org/10.3390/electronics14204050 - 15 Oct 2025

High dimensional, small sample ribonucleic acid sequencing (RNA-seq) data pose a major challenge for reliable classification due to the curse of dimensionality and the risk of overfitting. This study addresses that challenge for prostate cancer by coupling machine learning (ML) based feature selection [...] Read more.

High dimensional, small sample ribonucleic acid sequencing (RNA-seq) data pose a major challenge for reliable classification due to the curse of dimensionality and the risk of overfitting. This study addresses that challenge for prostate cancer by coupling machine learning (ML) based feature selection with a hybrid ensemble classifier. RNA-seq datasets retrieved from the University of California Santa Cruz (UCSC) Xena platform have been pre-processed, and the number of features has been reduced to 30 through systematic feature selection. Three complementary learners have been combined into a majority voting ensemble to improve robustness and generalization. Performance has been assessed using evaluation metric criteria and the area under the curve. The results have been corroborated on liver, lung, and thyroid cancer datasets from the literature. The proposed hybrid ensemble method has been achieved 97.82% accuracy with Lasso feature selection. Compared with conventional single model approaches, the proposed hybrid model has yielded accurate and reliable predictions. Additionally, biologically meaningful information has been presented using explainable artificial intelligence techniques regarding the importance of genes. These findings suggest that the joint use of feature selection and hybrid ensembles provides a practical and interpretable framework for classifying high dimensional genomic profiles under limited sample sizes. Full article

(This article belongs to the Special Issue Explainable Artificial Intelligence: Concepts, Techniques, Analytics and Applications)

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26 pages, 6031 KB

Open AccessArticle

Model-Based Design and Sensitivity Optimization of Frequency-Output Pressure Sensors for Real-Time Monitoring in Intelligent Rowing Systems

by Iaroslav Osadchuk, Oleksandr Osadchuk, Serhii Baraban, Andrii Semenov and Mariia Baraban

Electronics 2025, 14(20), 4049; https://doi.org/10.3390/electronics14204049 - 15 Oct 2025

This study presents a model-driven approach to the design, calibration, and application of frequency-output pressure sensors integrated within an intelligent system for real-time monitoring of rowing performance. The proposed system captures biomechanical parameters of the “boat–rower” complex across 50 parallel channels with a [...] Read more.

This study presents a model-driven approach to the design, calibration, and application of frequency-output pressure sensors integrated within an intelligent system for real-time monitoring of rowing performance. The proposed system captures biomechanical parameters of the “boat–rower” complex across 50 parallel channels with a temporal resolution of 8–12 ms. At the core of the sensing architecture are parametric pressure transducers incorporating strain-gauge primary elements and microelectronic auto-generator circuits featuring negative differential resistance (NDR). These oscillating circuits convert mechanical stress into high-frequency output signals in the 1749.9–1751.9 MHz range, with pressure sensitivities from 0.365 kHz/kPa to 1.370 kHz/kPa. The sensor models are derived using physical energy conversion principles, enabling the formulation of analytical expressions for transformation and sensitivity functions. These models simplify sensitivity tuning and allow clear interpretation of how structural and electronic parameters influence output frequency. The system architecture eliminates the need for analog-to-digital converters and signal amplifiers, reducing cost and power consumption, while enabling wireless ultra high frequency (UHF) transmission of sensor data. Integrated algorithms analyze the influence of biomechanical variables on athlete performance, enabling real-time diagnostics. The proposed model-based methodology offers a scalable and accurate solution for intelligent sports instrumentation and beyond. Full article

(This article belongs to the Special Issue Wearable Sensors for Human Position, Attitude and Motion Tracking)

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24 pages, 2273 KB

Open AccessReview

Low-Frequency Measurements in Electrochemical Impedance Spectroscopy: A Brief Review

by Armando Piccardi, Sara Pettinato, Maria Cristina Rossi, Marco Girolami and Stefano Salvatori

Electronics 2025, 14(20), 4048; https://doi.org/10.3390/electronics14204048 - 15 Oct 2025

Some properties of electrochemical systems can be properly characterized by low- and extremely low-frequency impedance measurements. The frequency range down to 1 mHz requires peculiar attention in terms of accuracy, measurement speed, portability and costs, and commercial systems are often unable to guarantee [...] Read more.

Some properties of electrochemical systems can be properly characterized by low- and extremely low-frequency impedance measurements. The frequency range down to 1 mHz requires peculiar attention in terms of accuracy, measurement speed, portability and costs, and commercial systems are often unable to guarantee good performances without expensive or bulky solutions. Here, we first review some of the applications of electrochemical impedance spectroscopy and their requirements and then report on recent works proposing circuits and systems focused on optimizing performances, reducing costs, and increasing versatility. Circuits for generating controllable current sources are described, optimizing output impedance and measurable impedance range. Then, integrated systems are taken into account, aiming at realizing portable and versatile instruments. Full article

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25 pages, 2373 KB

Open AccessArticle

AI-Driven Models for Forecasting Public Expenditures in the Digital Era

by Bogdan Drăgulin, Veronica Ștefan, Alina-Iuliana Tăbîrcă, Mircea-Constantin Șcheau, Valentin Radu and Victor Munteanu

Electronics 2025, 14(20), 4047; https://doi.org/10.3390/electronics14204047 - 15 Oct 2025

This paper proposes an innovative methodological framework that combines machine-learning and deep learning algorithms with established econometric methods for the critical problem of expenditure forecasting in the budget process. The paper aims to develop, test, and validate an artificial intelligence model capable of [...] Read more.

This paper proposes an innovative methodological framework that combines machine-learning and deep learning algorithms with established econometric methods for the critical problem of expenditure forecasting in the budget process. The paper aims to develop, test, and validate an artificial intelligence model capable of improving the accuracy of expenditure forecasting in the budget process and supporting financial accounting decisions in public institutions. Using historical and statistical data from a group of public institutions, the research applies both univariate and multivariate forecasting strategies, evaluated with performance metrics. The research focuses on the development of an innovative forecasting model based on AI, using historical and statistical data from public sources and case studies of local public institutions to transform them into smart cities. The selected AI algorithms include artificial neural networks, support vector machines, and deep learning models, implemented and evaluated using Python v3.14. The research results show that AI can significantly improve the accuracy of budget forecasts compared to traditional methods, such as linear regression and econometric models. The use of AI contributes to increasing transparency and accountability in the management of public funds, providing more detailed and well-founded forecasts. Full article

(This article belongs to the Special Issue Emerging Trends in Artificial Intelligence and Intelligent Systems: From Cybersecurity to Smart Societies)

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18 pages, 3873 KB

Open AccessArticle

An Adaptive JPEG Steganography Algorithm Based on the UT-GAN Model

by Lina Tan, Yi Li, Yan Zeng and Peng Chen

Electronics 2025, 14(20), 4046; https://doi.org/10.3390/electronics14204046 - 15 Oct 2025

Adversarial examples pose severe challenges to information security, as their impacts directly extend to steganography and steganalysis technologies. This scenario, in turn, has further spurred the research and application of adversarial steganography. In response, we propose a novel adversarial embedding scheme rooted in [...] Read more.

Adversarial examples pose severe challenges to information security, as their impacts directly extend to steganography and steganalysis technologies. This scenario, in turn, has further spurred the research and application of adversarial steganography. In response, we propose a novel adversarial embedding scheme rooted in a hybrid, partially data-driven approach. The proposed scheme first leverages an adversarial neural network (UT-GAN, Universal Transform Generative Adversarial Network) to generate stego images as a preprocessing step. Subsequently, it dynamically adjusts the cost function with the aid of a DCTR (Discrete Cosine Transform Residual)-based gradient calculator to optimize the images, ensuring that the final adversarial images can resist detection by steganalysis tools. The encoder in this scheme adopts a unique architecture, where its internal parameters are determined by a partially data-driven mechanism. This design not only enhances the capability of traditional steganography schemes to counter advanced steganalysis technologies but also effectively reduces the computational overhead during stego image generation. Full article

(This article belongs to the Special Issue Digital Intelligence Technology and Applications, 2nd Edition)

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43 pages, 6017 KB

Open AccessArticle

An Efficient Framework for Automated Cyber Threat Intelligence Sharing

by Muhammad Dikko Gambo, Ayaz H. Khan, Ahmad Almulhem and Basem Almadani

Electronics 2025, 14(20), 4045; https://doi.org/10.3390/electronics14204045 - 15 Oct 2025

As cyberattacks grow increasingly sophisticated, the timely exchange of Cyber Threat Intelligence (CTI) has become essential to enhancing situational awareness and enabling proactive defense. Several challenges exist in CTI sharing, including the timely dissemination of threat information, the need for privacy and confidentiality, [...] Read more.

As cyberattacks grow increasingly sophisticated, the timely exchange of Cyber Threat Intelligence (CTI) has become essential to enhancing situational awareness and enabling proactive defense. Several challenges exist in CTI sharing, including the timely dissemination of threat information, the need for privacy and confidentiality, and the accessibility of data even in unstable network conditions. In addition to security and privacy, latency and throughput are critical performance metrics when selecting a suitable platform for CTI sharing. Substantial efforts have been devoted to developing effective solutions for CTI sharing. Several existing CTI sharing systems adopt either centralized or blockchain-based architectures. However, centralized models suffer from scalability bottlenecks and single points of failure, while the slow and limited transactions of blockchain make it unsuitable for real-time and reliable CTI sharing. To address these challenges, we propose a DDS-based framework that automates data sanitization, STIX-compliant structuring, and real-time dissemination of CTI. Our prototype evaluation demonstrates low latency, linear throughput scaling at configured send rates up to 125 messages per second, with 100% delivery success across all scenarios, while sustaining low CPU and memory overheads. The findings of this study highlight the unique ability of DDS to overcome the timeliness, security, automation, and reliability challenges of CTI sharing. Full article

(This article belongs to the Special Issue New Trends in Cryptography, Authentication and Information Security)

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10 pages, 2551 KB

Open AccessArticle

Low-Cost Transmitarray Design with High Gain Bandwidth and Suppressed SLL

by Muhammed Malkoç, Sibel Ünaldı and Sibel Çimen

Electronics 2025, 14(20), 4044; https://doi.org/10.3390/electronics14204044 - 15 Oct 2025

This study presents a transmitarray antenna design operating at a center frequency of 9 GHz, addressing the need for high-gain and broad bandwidth antennas in modern data communication. The proposed design is structured as a quad-layer configuration using FR-4 dielectric substrates. The transmitarray’s [...] Read more.

This study presents a transmitarray antenna design operating at a center frequency of 9 GHz, addressing the need for high-gain and broad bandwidth antennas in modern data communication. The proposed design is structured as a quad-layer configuration using FR-4 dielectric substrates. The transmitarray’s phase profile is tailored to deliver a wideband flat response with low Side Lobe Level (SLL) and stable aperture efficiency across the operating band. It achieves a 1-dB gain bandwidth of 12.12% (from 8.56 GHz to 9.67 GHz) and a 3-dB gain bandwidth of 49.43% (from 8.45 GHz to 13.46 GHz) with an aperture efficiency of 21.3%. A prototype of the proposed design with 11 × 11 elements was fabricated and measured, and its measurement results closely aligned with simulation results, validating its performance. The proposed simple design realizes reduced complexity and fabrication costs while expanding operational gain bandwidth, thereby demonstrating substantial promise for next-generation X-band communication systems. Full article

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

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