Search Results (38,463)

Wireless power transfer (WPT) systems have garnered significant market attention owing to their broad applicability in portable electronic devices, electric vehicles, unmanned aerial vehicles, biomedical implants, and related fields. In these systems, operating frequency and efficiency are critical factors affecting both transmission efficiency and transmission distance, making high-frequency operation an important trend for improving overall WPT performance. However, elevating the switching frequency also introduces notable challenges, including increased switching losses in power devices, limited load adaptability, and poor anti-misalignment capability, which in practice often lead to degraded system efficiency and unsatisfactory waveform quality. Accordingly, this paper proposes a high-frequency inverter power supply system capable of operating at a maximum output voltage frequency of 25 KHz. Under conditions of a 10 KHz output frequency and a 20 KΩ load, the system achieves a peak efficiency of 94.01%. A prototype was implemented through the integration of a software algorithm based on ARM Cortex-M3 core control with a hardware architecture consisting of a driving circuit, a full-bridge inverter, and a switchable filtering module. This work offers practical design insights for the development of future high-frequency, high-voltage inverter systems, while also providing valuable experimental data to support further research in this area. Full article

To advance research in multi-agent reinforcement learning (MARL) for pursuit–evasion scenarios, this paper introduces a novel algorithm called Expert Knowledge and Opponent Modeling Multi-UAV Deep Deterministic Policy Gradient (EO-MADDPG). EO-MADDPG consists of two key components: the integration of expert knowledge and real-time sampled data and the prediction of evader UAV actions. The expert knowledge includes a multi-UAV formation control algorithm and an encirclement strategy, which incorporates consensus algorithms and Apollonius circle guidance. Additionally, the network-training framework is optimized by integrating information about opponent actions under a fixed policy for improved prediction accuracy. The experiments focus on three vs. one and three vs. two scenarios, where pursuer UAVs utilize EO-MADDPG and evader UAVs follow fixed policies with Gaussian perturbations. Experimental results show that EO-MADDPG achieves success rates of 99.9 ± 0.3% and 97.5 ± 1.4% (mean ± std over five seeds) in three vs. one and three vs. two pursuit–evasion simulations, respectively, outperforming the baseline MADDPG (72.7 ± 6.0% and 64.4 ± 34.4%). Ablation studies and cooperative landmark tasks further demonstrate improved training stability and interpretability. Full article

Large-scale integration of electric vehicle charging stations into the energy distribution network introduces highly variable power demands leading to additional voltage drops, increase in power losses, and quality degradation. Conventional mitigation strategies, including reactive power control only and multi-loop dq-axis-based controllers, often suffer from high computational complexity and limited flexibility for simultaneous active and reactive power control. This study presents a delay-modulated pulse width modulation control scheme for coordinated active and reactive power control in an energy distribution network with high penetration of electric vehicle charging load that are both time-varying and site-shifting in nature. The scheme uses a unified system comprising a solar photovoltaic array, battery storage system and a distribution STATCOM system. In this scheme, the control of active and reactive power is directly incorporated in the PWM pulse generation process by adding an adjustable delay parameter that controls the phase shift between the inverter current and the grid voltage. The proposed scheme is validated using a representative distribution feeder supplying the electric vehicle charging loads. The result illustrates that the feeder receiving end bus voltage drop is about 35% lower, the active power losses are about 40% lower, and the total harmonic distortion is at about 3%, which is within the IEEE 519 limit recommendations. Thus, the proposed control scheme is seen to be effective and computationally efficient, providing a scalable solution for real-time voltage regulation and power loss reduction. Full article

The compatibility of insecticides with biological control agents is a critical component of integrated pest management (IPM). In this study, the lethal and sublethal effects of acrinactrin, chlorantraniliprole, flupyradifurone, pyriproxyfen, spinosad, and spiromesifen on the egg stage of Orius niger (Wollf) (Hemiptera: Anthocoridae) were evaluated under laboratory conditions. Egg hatchability, immature survival, reproductive performance, and population parameters were analyzed using the age-stage, two-sex life table. Egg hatchability was lowest in the acrinactrin treatment (51%) and highest in the pyriproxyfen treatment (93%). Nymphal survival varied from 0% to 80%, with acrinactrin causing complete mortality and a significant reduction in spinosad, while the highest nymphal survival and population growth was recorded in spiromesifen treatment. The intrinsic rate of increase (r, day⁻¹) was 0.00, 0.05, 0.05, 0.08, 0.004, and 0.06 for acrinactrin, chlorantraniliprole, flupyradifurone, pyriproxyfen, spinosad, and spiromesifen, respectively, while fecundity (F, eggs female⁻¹) values were 0, 15.20, 15.83, 42.32, 10.37, and 21.85, respectively. According to the International Organization for Biological Control (IOBC) classification, acrinactrin was harmful, spinosad moderately harmful, and the remaining insecticides slightly harmful to O. niger eggs. Pyriproxyfen and spiromesifen were the most compatible with IPM programs. Caution is warranted for chlorantraniliprole due to its effects on reproductive parameters, whereas spinosad and acrinactrin should be avoided on O. niger eggs. Full article

The increasing digitization of manufacturing and the integration of CNC and industrial control systems into the industry 4.0 environment have introduced new cybersecurity risks that directly affect operational reliability. Traditional deterministic risk-assessment methods used for securing ICS—such as SCADA, PLC, and CNC systems—struggle to address uncertainty, dynamic operating conditions, and complex dependencies between technical and organizational factors. To overcome these limitations, this study develops a Bayesian Network (BN) model that captures probabilistic relationships between machine-level configuration parameters, network conditions, and potential security incidents. The model is applied to a CNC machining center (ZPS MCG1000i), where it supports scenario-based prediction of cybersecurity risks and provides interpretable outputs suitable for operator decision-making and human–machine interaction. The results demonstrate that BNs are effective in environments with limited data availability and high uncertainty, offering transparent and quantifiable insights into how specific misconfigurations—such as active remote access or irregular firmware updates—elevate overall system exposure. The proposed approach aligns with current regulatory and standardization requirements, including the NIS2 Directive (EU 2022/2555), ISO/IEC 27001:2022, ISO/IEC 27005:2022, and Regulation (EU) 2024/2847 (Cyber Resilience Act), which define cybersecurity obligations for products with digital elements. The study provides a reproducible and future-oriented methodology for integrating cybersecurity into machinery-safety evaluation in modern industrial environments. Full article

Rare-metal pegmatite–greisen systems are commonly small, structurally controlled, and difficult to delineate using conventional mapping alone. This study proposes a multiscale remote-sensing workflow for prospecting Li–Nb–Ta–Cs mineralisation in the Kalba–Narym rare-metal belt (East Kazakhstan) by integrating Sentinel-2 multispectral imagery, UAV-derived centimeter-scale orthomosaics, structural (lineament) analysis, and field-based mineralogical–geochemical validation. Sentinel-2 responses were first calibrated using known occurrences to derive alteration proxies related to greisenisation, silicification, Na-metasomatism, and oxidation. These proxies were combined into an Integrated Hydrothermal Alteration Index (IHAI) to highlight areas where multiple alteration processes overlap. Lineament mapping from Sentinel-2 and DEM products indicates dominant NW–SE and NE–SW structural trends, zones of elevated lineament density and intersection systematically coincide with high IHAI values. UAV orthomosaics refine satellite-scale anomalies by resolving quartz-vein networks, fracture corridors, and surface-alteration textures that are not detectable at 10–20 m resolution. Mineralogical and geochemical data confirm that high-IHAI targets correspond to albitised pegmatites and greisenised rocks enriched in Li, Nb, Ta, and Cs. The results demonstrate that combining freely available Sentinel-2 data with UAV observations and targeted ground validation provides a cost-effective and transferable framework for reducing false positives and prioritising exploration targets in structurally complex granitoid terranes. Full article

Vulnerabilities in industrial control components (ICCs) can be exploited to launch attacks, potentially disrupting the operation of industrial control systems. Ensuring the security of such systems requires establishing explicit associations between vulnerabilities in ICCs and the attacks. The ICC vulnerability knowledge graph integrates multi-source data and facilitates these associations by reasoning models. However, the context of vulnerability entities in ICCs contains complex component semantics and structural features, which makes it challenging to capture accurate representations and limits the performance of existing reasoning models. To address these challenges, we propose a target attack entity reasoning method based on the ICC vulnerability knowledge graph, called ICC-VulKG-TAER. The core of this method is a link prediction algorithm that combines both local and global representations, leveraging features of entity texts, relational neighborhoods, and relation paths. Experimental results show that ICC-VulKG-TAER outperforms existing methods, achieving 80.87% Hits@1 and 87.13% MRR, with improvements of 4.75% and 6.83%, respectively. These results demonstrate the effectiveness of the proposed approach in enhancing the performance of vulnerability–attack association in ICCs. Full article

This paper addresses the three-dimensional trajectory tracking problem for underactuated autonomous underwater vehicles subject to external disturbances and model uncertainties in complex ocean environments. A robust control method integrating backstepping dynamic surface control and non-singular terminal sliding mode is proposed. Firstly, based on the kinematic and dynamic models of autonomous underwater vehicle, virtual velocity commands are constructed via backstepping approach to stabilize the position and attitude errors. To circumvent the “differential explosion” problem inherent in conventional backstepping control caused by repeated differentiations of virtual control variables, first-order low-pass filters are introduced to construct dynamic surface control, yielding smooth derivatives of virtual velocity commands. Secondly, to enhance convergence rate and robustness, a non-singular terminal sliding surface is designed at the dynamic level, and a terminal reaching law is formulated to achieve finite-time convergence of velocity tracking errors. Furthermore, to compensate for external disturbances and unmodeled dynamics, a disturbance observer based on the super-twisting algorithm is developed, enabling finite-time high-precision estimation of lumped disturbances, with the estimation results incorporated into the control law for feedforward compensation. Finally, comparative simulations are conducted under two typical disturbance scenarios. The results demonstrate that the proposed method achieves instantaneous disturbance estimation (reducing convergence time from 3 s to near zero), significantly smoother control inputs, and superior tracking accuracy with RMSE as low as 0.6788 m and MAE as low as 0.1468 m, reducing errors by up to 30.6% compared to baseline methods. Full article

Background/Objectives: Attention Deficit Hyperactivity Disorder (ADHD) is one of the most common neurodevelopmental disorders in childhood and it tends to remain during adulthood. It not only affects cognitive abilities and behavior but also often presents emotional disturbances and alterations in the perceived quality of life. These symptoms are primarily related to dysfunctions in the ventromedial and dorsolateral prefrontal network. The main objective was to evaluate the feasibility and explore the initial outcomes of an integrated protocol combining neuropsychological treatment and transcranial direct current stimulation (tDCS). Methods: This study presents a single-case experimental A-B design of a 21-year-old woman, diagnosed with predominantly inattentive ADHD, treated at the University Psychology Clinic of Loyola Andalucía University. The treatment was carried out twice a week for 5 weeks (10 sessions in total), with 20 min of anodal tDCS at F3 and cathodal tDCS at F4 (2 mA), while digital neurorehabilitation exercises and psychotherapeutic support were provided. Results: An overall significant improvement was observed in cognitive functions (p = 0.008), with clinically significant gains in cognitive flexibility, visual working memory, and planning. Mixed results were found in inhibition, with improvement in interference control but no change in response inhibition. No significant changes were observed in sustained attention, auditory working memory, or processing speed. In terms of emotional state, an overall improvement was noted (p = 0.046), particularly in depression symptoms and perceived quality of life related to physical and psychological health. However, no significant changes were observed in anxiety symptoms or in areas related to the environment and social relationships. These findings reflect pilot-level evidence of clinical change within a feasibility framework. Conclusions: The combined treatment was found to be safe and feasible, showing promising preliminary improvements in cognitive and emotional domains. As a single-case study, these results serve as hypothesis-generating evidence for future controlled trials. Full article

Stock-ranking prediction is an important task in quantitative finance because it directly influences portfolio construction and alpha generation. Recent Graph Neural Network (GNN) models provide a promising way to describe inter-stock dependencies, but many existing methods still have difficulty balancing rapidly changing market interactions with relatively stable structural relationships. They are also easily affected by financial micro-structure noise. To address these issues, this paper proposes USTGCN, a Unified Spatio-Temporal Graph Convolutional Network for stock-ranking prediction. USTGCN adopts a dual-stream temporal encoder based on ALSTM and GRU to capture short-term dynamic patterns and longer-horizon structural information, respectively. We further introduce a rolling-window correlation smoothing strategy to build a more stable dynamic graph, and then integrate the dynamic and structural graph views through a shared fusion layer. Skip connections are used to preserve original temporal information during spatial aggregation. Experiments on the CSI100 and CSI300 benchmark datasets show that USTGCN achieves IC values of 0.141 and 0.154, respectively, and exhibits improved drawdown control during stressed market periods, indicating its practical value for quantitative trading. Full article

Effective integrated pest management (IPM) relies on precise knowledge of pest developmental biology, particularly the identification of larval instars, which is fundamental for predicting population dynamics and timing control interventions. This study established a morphometric framework for the larval staging of a sap beetle pest infesting pear orchards. Specimens were collected and reared under laboratory conditions, with their identity confirmed as Urophorus humeralis through integrated morphological and molecular (COI barcoding) analysis. To determine the number of larval instars, head capsule width (HCW), inter-antennal distance (IAD), and inter-caudal distance (ICD) were measured. Frequency distribution analysis and validation using Dyar’s rule via linear regression revealed three distinct larval instars. Head capsule width was identified as the most reliable and consistent morphological character for instar discrimination. This study reports for the first time the infestation of pear fruits by U. humeralis and provides detailed morphometric criteria for larval staging, delivering essential baseline data for the biology of Nitidulidae and a scientific basis for developing stage-specific pest management strategies. Full article

Deep marine shale reservoirs are controlled by multi-factor coupling effects, and the genetic mechanism of “sweet spots” exhibits strong complexity, leading to prominent difficulties in quantitative prediction and precise evaluation of sweet spots. Aiming at the problems of an unclear lithofacies-controlled sweet spot evolution law and insufficient accuracy of multi-parameter quantitative evaluation in traditional evaluation methods, this paper takes the Wufeng Formation and Long1 member of the Longmaxi Formation in the LZ block, Southern Sichuan, as the research object. Innovatively integrating machine learning (ML), grey correlation analysis (GRA), and three-dimensiona (3D) geological modeling technologies, a refined prediction model for reservoir sweet spot evaluation indicators under lithofacies constraint conditions is established, and a multi-parameter fusion quantitative evaluation method for deep marine shale gas sweet spots with high prediction accuracy is proposed. The results demonstrate that the LightGBM-based prediction model for sweet spot evaluation indicators achieved excellent performance. Based on a total of 380 preprocessed samples divided into training and test sets in a 7:3 ratio, the coefficient of determination (R²) of the model exceeded 0.9 in both the test and validation datasets. The “sweetness index”, a comprehensive evaluation index of reservoir sweet spots constructed via GRA-based multi-factor fusion, shows a correlation coefficient of 0.91 with respect to actual gas well production, presenting a high fitting degree. The 3D sweet spot geological model reveals that Class I sweet spots are mainly developed in the 1st to 3rd sub-layers of the Long1 member, while Class II sweet spots are distributed in the 5th and 6th sub-layers, which is highly consistent with the actual development law of the gas field. This study breaks through the limitations of single evaluation methods and weak lithofacies control consideration in traditional sweet spot evaluation and forms a set of innovative technical process integrating “precision prediction—multi-factor fusion—3D characterization”. It provides a new technical approach for efficient and accurate evaluation of deep marine shale reservoir sweet spots and has important guiding significance for the efficient development of shale gas. Full article

To address the inherent limitations in the robustness of fully autonomous unmanned aerial vehicle (UAV) visual perception and the high cognitive workload associated with manual control, this paper proposes a human-in-the-loop brain–computer interface (BCI) control framework. The system integrates steady-state visual evoked potential (SSVEP) with deep learning techniques to create a spatio-temporally dynamic interaction paradigm, enabling real-time alignment between visual targets and frequency stimuli. At the perception level, an enhanced YOLOv11 network incorporating partial convolution (PConv) and shape intersection over union (Shape-IoU) loss is developed and coupled with the DeepSort multi-object tracking algorithm. This configuration ensures high-speed execution on edge computing platforms while maintaining stable stimulus coverage over dynamic targets, thus providing a robust visual induction environment for EEG decoding. At the neural decoding level, an enhanced task-discriminant component analysis (TDCA-V) algorithm is introduced to improve signal detection stability within non-stationary flight conditions. Experimental results demonstrate that within the predefined fixation task window, the system achieves 100% success in maintaining target identity (ID). The BCI system achieved an average command recognition accuracy of 91.48% within a 1.0 s time window, with the TDCA-V algorithm significantly outperforming traditional spatial filtering methods in dynamic scenarios. These findings demonstrate the system’s effectiveness in decoupling human cognitive intent from machine execution, providing a robust solution for human–machine collaborative control. Full article

Natural grasslands are among the most endangered habitats in Northern, Central and Eastern Europe due to the agricultural intensification, land abandonment and afforestation, urban expansion, and the loss of traditional low-intensity management, on which their biodiversity depends. One way to increase the number of natural grasslands is by integrating them into urban green infrastructure as a nature-based solution to enhance ecological resilience and urban livability: diverse grassland systems support pollinators, improve soil structure and stormwater infiltration, mitigate urban heat and provide restorative, experience-rich public spaces. The aim of the study is to explore opportunities and barriers to integrating different types of grasslands into the green infrastructure of Latvian cities, with a primary focus on public perceptions and institutional aspects of urban grassland implementation and management. A mixed-methods approach was applied, combining resident surveys, interviews with municipal experts—territorial development specialists, planners and maintenance managers—and comparative policy analysis. Results show that although residents acknowledge the ecological benefits of urban grasslands, they prefer them in peripheral or underused areas rather than in city centres and residential zones, as these areas are often aesthetically perceived as “untidy” or neglected, conflicting with cultural norms that favour short, intensively mown lawns and raising concerns about insects. Acceptance increases through communication and participatory practices. Municipal approaches range from structured maintenance guidelines, including delayed mowing, biomass removal, and invasive species control, to flexible experimentation. The study contributes scientifically grounded insights into governance, perception, and management interfaces critical for mainstreaming socially accepted urban grasslands. Full article

Cell-based immuno-biosensors are novel platforms for studying immune responses of biological cells, with real-time insights more similar to physiological and pathological conditions. These systems utilize living immune cells as their main components, enabling them to detect disease-related biomarkers and cellular traits in a way that is often highly sensitive and label-free. Integration with microfluidics and organ-on-chip technologies has facilitated precise manipulational control over the cellular microenvironment. Not only has this resulted in high-throughput screening, but it also enabled smaller, more portable systems which can be used at the point of care. In this work, we review the recent advance in microfluidic cell-based immuno-biosensing associated with immune cells such as neutrophils, macrophages, T cell and dendrite cells. Some of the exciting developments include fusion with methods such as advanced imaging, electrical impedance sensing and application of machine learning to phenotyping. We will also elaborate on the issues related to the standardization of these systems, cell heterogeneity, and the challenges for translating these technologies for clinical application. Taken together, such integrated platforms have potential to fill the gap left in-between cellular immunology with biosensor engineering. Full article