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Unsupervised video anomaly detection (VAD) methods learn from normal data to identify anomalies by capturing pattern deviations. However, they often struggle to model multi-scale features and distinguish between normal and abnormal instances. To address these limitations, we propose a Multi-scale U-shaped Adaptive Clustering Learning (MS-UACL) framework. Built on the U-Net architecture, we redesign it as a 3D-encoder/2D-decoder autoencoder. In the encoder, we introduce a Dual-scale Feature Cascading Module (IDCN), which adopts a pseudo-branch fusion mechanism to systematically model multi-scale spatiotemporal features, thereby enhancing the model’s representational capability. To further enhance the distinction between normal and anomalous patterns, we propose an MLP-based Adaptive Clustering Algorithm (MLP-ACA). Specifically, MLP-ACA employs an initial mapping mechanism to align cluster centers with the underlying normal data distribution, facilitating more accurate feature reconstruction. Additionally, we introduce an adaptive clustering update strategy that optimizes cluster centers by tuning solely the parameters of the MLP. This enables the cluster centers to autonomously converge toward optimal feature representations, thereby accelerating clustering convergence and enhancing pattern separability. Extensive experiments on three benchmark datasets demonstrate that the proposed MS-UACL framework outperforms most existing methods on small- and medium-scale datasets. Full article

(This article belongs to the Section Artificial Intelligence)

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18 pages, 3641 KB

Open AccessArticle

A Wavelet-Enhanced Detector for Tiny Objects in Remote-Sensing Images

by Weifan Xu and Yong Hu

Remote Sens. 2026, 18(8), 1109; https://doi.org/10.3390/rs18081109 (registering DOI) - 8 Apr 2026

Abstract

Accurate and efficient detection is pivotal for tiny objects in remote sensing. However, achieving a favorable accuracy-efficiency trade-off remains challenging due to the few informative pixels of small targets, frequent occlusions, cluttered backgrounds, and detail degradation introduced by downsampling and multi-scale fusion. To address these challenges, we propose WEYOLO, a wavelet-enhanced detector that explicitly models frequency components and adaptively strengthens high-frequency cues to improve tiny-object robustness while maintaining competitive efficiency in inference speed and model size for remote-sensing deployment. To preserve edges and textures when spatial resolution is reduced, we design a Frequency-Aware Lifting Haar (FaLH) backbone that decomposes features into directional sub-bands and retains them during downsampling, preventing the loss of high-frequency information. Next, to address the blurring and detail loss caused by conventional pooling during multi-scale fusion, we introduce a Frequency-Domain Pyramid-Pooling (FDPP) module that performs wavelet-based multi-resolution analysis for frequency-aware feature-pyramid fusion. Additionally, we propose a stable size-aware quality focal regression loss that unifies Focaler-CIoU and size-aware DFL into a single objective, improving robustness and overall accuracy for small objects. Comprehensive experiments show that WEYOLO improves precision and recall over the baseline by 3.2%/4.2% on VisDrone and 2.6%/9.7% on TT100K; on AI-TOD, it achieves 47.5% mAP@0.5 and 21.3% mAP@0.5:0.95. Meanwhile, it reduces the parameter count by 60%, achieving a strong accuracy-efficiency balance for practical aerial sensing deployment. Full article

(This article belongs to the Section AI Remote Sensing)

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20 pages, 509 KB

Open AccessArticle

Study on the Prisoner’s Dilemma Game Between Humans and Large Language Models Based on Human–Machine Identity Characteristics

by Bo Wang, Yi Wu, Ruonan Li, Weiqi Zeng and Dongming Zhao

Appl. Sci. 2026, 16(8), 3633; https://doi.org/10.3390/app16083633 (registering DOI) - 8 Apr 2026

Abstract

Employing a 4 (opponent type) × 2 (communication condition) between-subjects design, the study recruited 194 valid human participants to complete three rounds of game tasks. Results revealed: (1) The type of game counterpart exerted a significant main effect on participants’ remaining funds (F(3, 185) = 3.179, p = 0.025). Human participants retained significantly more funds when the counterpart was a real large model compared to other groups. (2) A significant interaction existed between the type of game counterpart and communication conditions (F(3, 185) = 3.318, p = 0.021). Specifically, when the opponent was a fake AI model (presented as human but actually an AI), human participants’ remaining funds were significantly higher under the communication condition than without communication (p = 0.012). This indicates that communication can promote rational decision-making in identity mismatch scenarios by providing additional behavioral cues. In the fake-human group (informed as human but actually AI), a numerical trend toward increased funds was also observed under communication conditions, though it did not reach statistical significance (p = 0.159); (3) The moderating effect of social value orientation did not reach significance. These findings extend the application of the theory of mind in human–machine games, revealing the complex influence mechanism of identity perception and communication dynamics on rational decision-making. Full article

(This article belongs to the Section Computing and Artificial Intelligence)

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27 pages, 5409 KB

Open AccessArticle

Frequency-Domain Physics-Informed Neural Networks for Modeling and Parameter Inversion of Wave-Induced Seabed Response

by Weiyun Chen, Hairong Tao, Lei Wang and Shaofen Fan

J. Mar. Sci. Eng. 2026, 14(8), 690; https://doi.org/10.3390/jmse14080690 (registering DOI) - 8 Apr 2026

Abstract

Modeling the dynamic response of saturated marine soils is crucial yet computationally challenging for traditional methods. Meanwhile, purely data-driven models suffer from sparse data and lack of physical interpretability. To overcome these limitations, this study proposes an intelligent engineering framework based on a frequency-domain physics-informed neural network (FD-PINN) for the forward simulation and inverse parameter identification of saturated seabed soils. Constrained directly by physical laws during the learning process, FD-PINN remains highly reliable even when training data is sparse. By formulating the governing equations in the frequency domain, it directly predicts complex-valued displacement and pore-pressure phasors. Multiscale Fourier feature mappings mitigate spectral bias and capture boundary layers and high-frequency effects. For inverse problems, a phase-sensitive lock-in extraction strategy transforms time-domain measurements into robust frequency-domain targets, enabling the accurate and noise-tolerant identification of poroelastic parameters with clear physical meaning (nondimensional storage parameter S and permeability parameter

Γ

). Numerical experiments show that FD-PINN substantially outperforms conventional time-domain PINN, achieving relative

L^{2}

errors of

10^{- 2} \sim 10^{- 3}

for single- and multi-frequency excitations typical of wave-induced loadings. In particular,

Γ

is consistently recovered with sub-percent relative error, while S can be reliably identified with multi-frequency data. The framework offers a data-efficient, noise-robust approach for high-fidelity modeling and robust parameter inversion, which is particularly valuable in offshore environments where high-quality data is scarce. Full article

(This article belongs to the Special Issue Advances in Marine Geomechanics and Geotechnics)

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24 pages, 2013 KB

Open AccessArticle

Capacity-Enhanced Li-Fi Transmission Using Autoencoder-Based Latent Representation: Performance Analysis Under Practical Optical Links

by Serin Kim, Yong-Yuk Won and Jiwon Park

Photonics 2026, 13(4), 356; https://doi.org/10.3390/photonics13040356 (registering DOI) - 8 Apr 2026

Abstract

Visible light communication (VLC)-based Li-Fi systems suffer from limitations in transmission capacity expansion due to the restricted modulation bandwidth of LEDs. In this study, a latent representation-based NRZ-OOK Li-Fi transmission framework that exploits the statistical feature distribution of the latent space is proposed to improve transmission efficiency without expanding the physical bandwidth. An autoencoder is employed to transform input images into low-dimensional latent vectors, which are then quantized and modulated for transmission. At the receiver, hard decision and inverse quantization are performed, and the image is reconstructed through a trained decoder by leveraging the distribution characteristics of the latent representation. The effective transmission capacity gain

G_{c a p}

is defined to quantify the amount of representable information relative to the original data under the same physical link resources according to the latent dimension, achieving up to a 49-fold data representation efficiency. The experimental results over practical optical links (0.5–1.5 m) showed that, in short-range conditions, larger latent dimensions maintained higher reconstruction PSNR, whereas under channel degradation conditions, smaller latent dimensions exhibited higher robustness, demonstrating a performance inversion phenomenon. Furthermore, it was confirmed that the dominant factor governing reconstruction performance shifts from the representational capability of the data to error accumulation characteristics depending on the channel condition. These results suggest that the latent representation-based transmission framework is an effective Li-Fi strategy that can simultaneously consider transmission efficiency and channel robustness through information representation optimization in bandwidth-limited environments. Full article

(This article belongs to the Special Issue Neural Networks in Optical Communications and Optical Computing: Implementation, Applications, and Prospects)

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24 pages, 4042 KB

Open AccessArticle

Memory Cueing and Augmented Sensory Feedback in Virtual Reality as an Assistive Technology for Enhancing Hand Motor Performance

by Zachary Marvin, Sophie Dewil, Yu Shi, Noam Y. Harel and Raviraj Nataraj

Technologies 2026, 14(4), 217; https://doi.org/10.3390/technologies14040217 (registering DOI) - 8 Apr 2026

Abstract

Neurological injuries and disorders affecting hand motor control can severely impair the ability to perform activities of daily living and substantially reduce quality of life. Technologies such as virtual reality (VR) are increasingly used to address fundamental challenges in therapy, including motivation and engagement; further, programmable features of digital interfaces offer additional opportunities to personalize and optimize motor training. In this proof-of-concept study, we developed and evaluated a novel VR-based training framework to support improved dexterity and hand function using physiological (sensory-driven) and cognitive (memory) cues designed to promote greater task-relevant neural engagement. The proposed approach leverages the integration of augmented sensory feedback (ASF) with memory-anchored cues for motor learning of target hand gestures. Using a within-subjects design, thirteen neurotypical adults completed four training conditions: (1) control (baseline gesture-matching in VR), (2) visual ASF (enhanced visualization and feedback of gesture accuracy), (3) memory-anchored cues (associating gestures with semantically meaningful entities, loosely analogous to American Sign Language), and (4) hybrid multimodal (visual ASF + memory-anchored cues). Training with the hybrid condition produced the fastest skill acquisition (9.3 trials to reach an 80% accuracy threshold) and the steepest initial learning slope (1.86 ± 0.12%/trial), with all conditions differing significantly in initial slope (all p < 0.002). Post-training assessment showed that the hybrid condition achieved the highest gesture accuracy (95.2%), greatest normalized post-training accuracy gain (14.3% above baseline), fastest execution time to target gesture (1.14 s), and lowest variability in gestural kinematics (SD = 3.9%). Both ASF and memory-anchored cue conditions each also independently outperformed the control condition on gesture accuracy (both p ≤ 0.002), with omnibus ANOVAs indicating significant condition effects across metrics. Together, these findings suggest that pairing ASF cues with memory-based cognitive scaffolding can yield additive benefits for motor skill acquisition and stability. Pending validation in clinical populations, such approaches may inform the design of VR-based motor training frameworks for rehabilitation. Full article

(This article belongs to the Special Issue Assistive Technologies in Care and Rehabilitation: Research, Developments, and International Initiatives—Second Edition)

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15 pages, 906 KB

Open AccessReview

The Role of Brain-Derived Neurotrophic Factor (BDNF) in Neural Development and Cognitive Behavior in Pigeons: Advances and Future Perspectives

by Guanhui Liu, Luyao Li, Su Wang, Jiarong Sun, Yongyan Han, Yaxuan Gao and Dongmei Han

Curr. Issues Mol. Biol. 2026, 48(4), 384; https://doi.org/10.3390/cimb48040384 (registering DOI) - 8 Apr 2026

Abstract

Brain-Derived Neurotrophic Factor (BDNF), a key member of the neurotrophin family, is critically involved in neuronal survival, synaptic plasticity, learning, and memory. While its roles in mammals have been extensively documented, the molecular regulatory mechanisms governing BDNF expression and its causal contributions to complex cognitive behaviors remain poorly understood in non-mammalian vertebrates—particularly for the domestic pigeon (Columba livia domestica), a species distinguished by its remarkable spatial navigation and homing capabilities. This review synthesizes the current evidence on BDNF in the pigeon central nervous system across five thematic domains: molecular structure and isoform diversity, transcriptional and epigenetic regulatory networks, involvement in neural development, associations with cognitive and navigational behaviors, and potential translational applications. A particular emphasis is placed on the region-specific and activity-dependent expression patterns of BDNF in brain structures such as the hippocampal formation (HF), optic tectum, and striatum, and their functional relevance to visual processing, homing behavior, and stress adaptation. To date, most findings remain correlational; therefore, establishing a mechanistic understanding necessitates the integration of advanced methodologies—including single-cell omics, CRISPR-based gene editing, and high-resolution behavioral phenotyping—to causally link BDNF dynamics, neural circuit modulation, and spatial cognition. This synthesis aims to bridge gaps in comparative neurobiology, inform molecular approaches to avian cognitive enhancement, and support evidence-based strategies for racing pigeon breeding and welfare assessment. Full article

(This article belongs to the Special Issue Harnessing Genomic Data for Disease Understanding and Drug Discovery)

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23 pages, 10573 KB

Open AccessArticle

Reddit Depression Communities as Spaces of Emotion Regulation: A Data-Informed Analysis of Coping and Engagement

by Virginia Morini, Salvatore Citraro, Elena Sajno, Maria Sansoni, Giuseppe Riva, Massimo Stella and Giulio Rossetti

Future Internet 2026, 18(4), 198; https://doi.org/10.3390/fi18040198 (registering DOI) - 8 Apr 2026

Abstract

Online social platforms increasingly function as informal self-help environments for individuals experiencing depression, offering spaces for emotional expression and peer support outside traditional clinical settings. However, how coping strategies and psychological engagement states—individuals’ emotional and cognitive involvement in managing their condition—are reflected through online self-disclosure remains poorly understood. We analyzed a large-scale dataset from Reddit depression-related communities to investigate how different psycho-linguistic profiles and coping orientations emerge from users’ language. We collected posts and comments from over 300,000 users across six depression-focused subreddits over two years. User-generated text was characterized through multiple psychological and linguistic dimensions capturing emotions, sentiment, subjectivity, and related features, then aggregated at the user-month level and analyzed using unsupervised clustering techniques. Our analysis identifies four distinct groups characterized by different emotional profiles and dominant coping orientations. These states exhibit meaningful correspondences with established theoretical frameworks, including the Coping Orientations to Problems Experienced model and the Patient Health Engagement model. Our findings demonstrate that large-scale textual data from online communities can provide interpretable insights into coping behaviors and engagement patterns, offering a complementary perspective to traditional approaches for studying mental health. Full article

(This article belongs to the Special Issue Information Networks with Human-Centric LLMs)

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30 pages, 649 KB

Open AccessArticle

Generative AI Adoption in B2B Firms: Ethical Governance, Innovation Capabilities, and Long-Term Competitive Performance

by Michele Alves, Domingos Martinho, Ricardo Marcão and Pedro Sobreiro

Systems 2026, 14(4), 410; https://doi.org/10.3390/systems14040410 (registering DOI) - 8 Apr 2026

Abstract

The rapid diffusion of generative artificial intelligence (GenAI) is reshaping organisational systems and digital transformation strategies, yet it remains unclear which organisational conditions are associated with long-term competitive performance in business-to-business (B2B) contexts. This study adopts a systems-informed perspective and examines how ethical governance, environmental dynamism, exploratory and exploitative innovation, and GenAI adoption are associated with long-term competitive performance in B2B firms. Using survey data from 104 Portuguese B2B managers and Partial Least Squares Structural Equation Modelling (PLS-SEM), the findings show that ethical governance is the strongest organisational correlate of long-term competitive performance, underscoring the central role of governance structures in responsible GenAI use. GenAI adoption is positively associated with performance, but its role is complementary rather than dominant. Exploratory innovation does not show a significant direct association with performance; instead, its association with performance operates through GenAI adoption in the estimated model, suggesting that experimentation becomes more performance-relevant when translated into digitally enabled routines. In contrast, exploitative innovation is directly associated with performance through incremental efficiency mechanisms. These findings challenge technology-deterministic assumptions and suggest that long-term competitive performance in B2B firms is more closely associated with the organisational alignment of governance structures, innovation capabilities, and GenAI adoption than with technology adoption alone. Full article

(This article belongs to the Section Systems Practice in Social Science)

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21 pages, 4667 KB

Open AccessArticle

Vibration Suppression and Dynamic Optimization of Multi-Layer Motors for Direct-Drive VICTS Antennas

by Xinlu Yu, Aojun Li, Pingfa Feng and Jianghong Yu

Aerospace 2026, 13(4), 346; https://doi.org/10.3390/aerospace13040346 (registering DOI) - 8 Apr 2026

Abstract

Weight reduction and dynamic performance optimization are critical for airborne direct-drive VICTS satellite communication antennas, which require lightweight, high-speed, and high-precision rotation. Traditional vibration suppression methods, such as uniform support layout and added damping, rely heavily on empirical trial and error, lack targeted modal control, and cannot balance lightweight design with dynamic stiffness. To address these issues, this paper proposes a wave-theory-based dynamic modeling and rapid optimization method for multi-layer rotating components in direct-drive VICTS antennas. The kinematic model of the rotating ring and ball revolution excitation are derived using the annular wave equation and bearing kinematics. A Modal Blocking Mechanism is established: placing support balls at positions satisfying the half-wavelength constraint suppresses target mode shapes via wave interference, achieving vibration attenuation at the source. A homogenization equivalent method based on RVE is developed for irregular cross-section rings, yielding analytical expressions for in-plane equivalent elastic modulus and out-of-plane equivalent shear modulus. These parameters are integrated into the wave equation to analytically solve vibration modes, avoiding iterative finite element computations. A rapid multi-objective optimization framework is then constructed, minimizing the structural weight and maximizing the modal separation interval under dynamic stiffness and excitation frequency constraints. Numerical simulations, FE analysis, and prototype tests validate the method: the maximum analytical error is only 3.1%. Compared with uniform support designs, the optimized structure achieves a 40% weight reduction, a 40% increase in minimum modal separation, and a 65% reduction in the RMS tracking error. This work provides an efficient, deterministic dynamic design method for large-diameter ring structures, transforming vibration control from empirical adjustment into a precise, physics-informed optimization. Full article

(This article belongs to the Section Astronautics & Space Science)

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16 pages, 1100 KB

Open AccessReview

Tumor Microenvironment Acidosis and Alkalization-Oriented Interventions in Advanced Solid Tumors: A Narrative Review and Science-Based Medicine Perspective on Long-Tail Survival

by Kazuyuki Suzuki, Shion Kachi and Hiromi Wada

Cancers 2026, 18(8), 1193; https://doi.org/10.3390/cancers18081193 (registering DOI) - 8 Apr 2026

Abstract

Median overall survival remains a central endpoint in oncology, but it can obscure a clinically meaningful long tail of patients with advanced solid tumors who survive well beyond the median. One biological context in which this pattern may be relevant is tumor microenvironment (TME) acidosis. Driven by aerobic glycolysis, hypoxia, impaired perfusion, and proton-export programs, acidic TME is increasingly implicated in invasion, therapeutic resistance, and immune suppression. This narrative review examines TME acidosis as the primary biological framework and considers long-tail survival as a clinical lens through which its implications may be interpreted. We summarize the biological basis and heterogeneity of acidic TME, review current approaches to clinical and translational assessment of tumor acidity, including acidoCEST magnetic resonance imaging (MRI) and positron emission tomography (PET)-based approaches, and discuss the potential and limitations of alkalization-oriented interventions such as buffering and diet-based strategies. Particular attention is given to the distinction between direct measurements of tumor acidity and clinically feasible but indirect markers such as urinary pH, which should not be interpreted as a direct surrogate for local tumor extracellular pH. From a science-based medicine perspective, long-tail survival is treated here as a hypothesis-generating clinical signal rather than proof of causality. Overall, alkalization-oriented interventions appear biologically plausible and clinically testable, but current clinical evidence remains limited and context-dependent. Future progress will require mechanistically informed biomarkers, careful safety evaluation, and trial designs capable of detecting delayed separation of survival curves and tail-oriented patterns of benefit. Full article

(This article belongs to the Special Issue Modulating the Acidic Tumor Microenvironment to Enhance the Efficacy and Reduce the Toxicity of Cancer Treatment)

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33 pages, 875 KB

Open AccessReview

Artificial Intelligence for High-Availability Systems: A Comprehensive Review

by Lidia Fotia, Rosario Gaeta, Fabrizio Messina, Domenico Rosaci and Giuseppe M. L. Sarné

Computers 2026, 15(4), 231; https://doi.org/10.3390/computers15040231 (registering DOI) - 8 Apr 2026

Abstract

High-availability (HA) systems—essential in many contemporary contexts—are designed to guarantee the availability of processes and data for more than 99% of their operational time. These systems are typically implemented as Cloud/Edge infrastructures that are properly maintained by human operators and intelligent agents in order to guarantee the required level of availability. Moreover, we are witnessing the widespread adoption of AI-based automation across many industries. AI-based software agents are increasingly being adopted to introduce more automation in highly available systems, particularly for monitoring and fault detection, fault prediction, recovery, and optimization processes. In this review paper, we discuss the state of the art of AI-based solutions for HA systems. In particular, we focus on the use of AI for the core operational mechanisms of monitoring, failure detection, and recovery. Our discussion begins by reviewing a few key background concepts of HA architectures, then we review recent work on AI-based solutions for monitoring, fault detection and recovery in HA systems. Full article

(This article belongs to the Special Issue Recent Trends in Dependable and High Availability Systems)

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7 pages, 526 KB

Open AccessCase Report

Progressive Multifocal Leukoencephalopathy in AIDS: The Diagnostic Role of PET Imaging

by Virginia Donini, Riccardo Paggi, Alberto Farese, Costanza Malcontenti, Enrico Tagliaferri, Claudio Caroselli, Spartaco Sani, Maria Matteini, Alessandro Bartoloni and Lorenzo Zammarchi

Infect. Dis. Rep. 2026, 18(2), 33; https://doi.org/10.3390/idr18020033 (registering DOI) - 8 Apr 2026

Abstract

Introduction: The majority of progressive multifocal leukoencephalopathy (PML) cases is still represented by patients affected by acquired immunodeficiency syndrome (AIDS). Diagnosis of PML relies on histopathological findings or by the combination of clinical signs, radiological evidence, and molecular positivity of the JC virus in cerebrospinal fluid. However, AIDS status predisposes to various diseases involving the brain, testing the diagnostic ability of the clinician. Case description: We describe a PML case in a patient with AIDS, in whom lumbar puncture was initially impossible for severe thrombocytopenia and magnetic resonance showed an hyperintense lesion and was unable to distinguish between PML and lymphoma. In this case, [¹⁸F]-fluorodeoxyglucose (FDG)-PET imaging showing a hypometabolism of the lesion helped to initially orient toward PML, as diagnosis was later confirmed by lumbar puncture. We collected 21 cases in the literature in which [¹⁸F]-FDG-PET was helpful in cases of PML. Discussion and Conclusions: PET imaging is not considered a standard diagnostic tool for PML. However, in selected cases, it may provide valuable information to direct the diagnosis towards PML. Full article

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15 pages, 6086 KB

Open AccessArticle

Horizon Calibration in Highly Deviated Wells and Implications for Velocity-Model Building

by Hailong Ma, Liping Zhang, Ting Lou, Yao Zhao, Lei Zhong, Xiaoxuan Chen and Xuan Chen

Appl. Sci. 2026, 16(8), 3628; https://doi.org/10.3390/app16083628 (registering DOI) - 8 Apr 2026

Abstract

Highly deviated wells commonly exhibit large errors in horizon calibration because the logging path follows an inclined borehole trajectory, whereas post-stack seismic processing effectively treats wave propagation as vertical. This mismatch has received limited attention. Here, we performed horizon calibration and velocity-model building for highly deviated wells drilled in the Mahu Sag, Junggar Basin, and obtained three key findings. First, the assumed vertical travel path in post-stack data is the primary cause of the initial mis-tie for highly deviated wells. Second, calibration in the deviated interval requires a strategy distinct from that of vertical wells and may involve substantial stretching or squeezing of the original logs to achieve a consistent time-depth relationship. Third, the map-view projection of a highly deviated well is essentially linear; relative to vertical wells, it provides denser in situ velocity constraints and, with pseudo-well control, supplies 2D velocity information along the well-trajectory plane, thereby improving velocity-field modeling. Validation against drilling data showed that this workflow improved well ties and refined the velocity model, providing practical guidance for geological well planning and reducing drilling risk. Full article

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21 pages, 5738 KB

Open AccessArticle

How Space Charge Reveals the Electric Field Self-Adaptive Regulation of ZnO-Filled Nonlinear Composites

by Shuojie Gao, Zhikang Yuan, Lijun Jin and Yewen Zhang

Appl. Sci. 2026, 16(8), 3624; https://doi.org/10.3390/app16083624 (registering DOI) - 8 Apr 2026

Abstract

Electric field distortion remains a fundamental challenge to the operational reliability of HVDC cable accessories, where localized stress intensifies space charge injection and accelerates insulation degradation. While nonlinear conductive composites incorporating functional fillers such as ZnO have shown potential for adaptive field grading, their dynamic interaction with space charge under non-uniform fields has yet to be fully resolved. This study experimentally examines the spatiotemporal evolution of space charge in double-layer dielectric structures comprising linear low-density polyethylene (LLDPE) and ZnO-based nonlinear composites, using the laser-induced pressure pulse (LIPP) technique. Localized field enhancement is introduced via metallic pin defects embedded on the cathode side. Comparative analysis reveals that composites with 40 vol% ZnO microvaristors markedly suppress charge injection compared to conventional semiconductive ethylene-vinyl acetate (EVA) layers. Specifically, interfacial charge accumulation during polarization is reduced by 71%, and residual charge density after depolarization decreases by 88%, leading to a more uniform internal field distribution. These findings provide direct experimental evidence of the field-regulating mechanism of nonlinear composites from the perspective of charge dynamics, supporting their application in intelligent HVDC insulation systems. Full article

(This article belongs to the Special Issue Advances in Electrical Insulation Systems)

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