Search Results (10,608)

In terms of their high efficiency and low environmental impact, proton exchange membrane fuel cells (PEMFC) are becoming increasingly essential in the development of hydrogen electric vehicles. Despite these advantages, optimizing hydrogen consumption remains difficult because of the highly nonlinear behavior of PEMFC systems and their sensitivity to variations in operating conditions. This article outlines an intelligent control approach based on extremum seeking control (ESC), based on an artificial neural network (ANN) model, to improve hydrogen utilization in hydrogen electric vehicles. Experimental data on current, voltage, and temperature are collected, preprocessed, and used to train the ANN model of the PEMFC. The ESC algorithm uses this predictive ANN model to adjust the fuel cell current in real time, ensuring voltage stability while reducing hydrogen consumption. The simulation results demonstrate that the ANN-based ESC system provides voltage stability under dynamic load variations and achieves approximately 2.7% hydrogen savings without affecting the experimental current profile, validating the efficacy of the suggested strategy for effective hydrogen management in fuel cell electric vehicles. Full article

Nowadays, dried Moringa leaves (M. oleifera) are increasingly in demand due to their health benefits. High-quality ones have shown remarkable positive effects as antioxidants, antidiabetics, and anti-inflammatory agents. However, in the industry, the quality classification process into six categories is performed manually by farmers, which is time-consuming and error-prone. Particularly, the two highest categories of Class A and Class B are hard to distinguish, since they are visually similar. In this paper, to automate the classification process, we introduce a new high-resolution dataset, extract color and texture features using the Gray-Level Co-occurrence Matrix (GLCM) method, and present a two-stage classification method using the Light Gradient Boosting Machine (LightGBM) algorithm with them. The experimental results show that the proposal improved classification accuracy from 82% by the baseline algorithm to 90% while maintaining high processing efficiency, demonstrating its potential for real-time and scalable industrial applications in dried Moringa leaves quality grading. Full article

In froth flotation, the features of froth images are important information to predict the concentrate grade. However, the froth structure is influenced by multiple factors, such as air flowrate, slurry level, ore properties, reagents, etc., which leads to highly complex and dynamic changes in the image features. Additionally, issues such as the immeasurability of ore properties and measurement errors pose significant uncertainties including aleatoric uncertainty (intrinsic variability from ore fluctuations and sensor noise) and epistemic uncertainty (incomplete feature representation and local data heterogeneity) and generalization challenges for prediction models. This paper proposes an uncertainty quantification regression framework based on cross-modal interaction fusion, which integrates the complementary advantages of Selective Kernel Networks (SKNet) and Vision Transformers (ViT). By designing a cross-modal interaction module, the method achieves deep fusion of local and global features, reducing epistemic uncertainty caused by incomplete feature expression in single-models. Meanwhile, by combining adaptive calibrated quantile regression—using exponential moving average (EMA) to track real-time coverage and adjust parameters dynamically—the prediction interval coverage is optimized, addressing the inability of static quantile regression to adapt to aleatoric uncertainty. And through the localized conformal prediction module, sensitivity to local data distributions is enhanced, avoiding the limitation of global conformal methods in ignoring local heterogeneity. Experimental results demonstrate that this method significantly improves the robustness of uncertainty estimation while maintaining high prediction accuracy, providing strong support for intelligent optimization and decision-making in industrial flotation processes. Full article

The rapid proliferation of Electric Vehicle (EV) infrastructures has led to the massive generation of high-frequency streaming data uploaded to cloud platforms for real-time analysis, while such data supports intelligent energy management and behavioral analytics, it also encapsulates sensitive user information, the disclosure or misuse of which can lead to significant privacy and security threats. This work addresses these challenges by developing a secure and scalable scheme for protecting and verifying streaming data during storage and collaborative analysis. The proposed scheme ensures end-to-end confidentiality, forward security, and integrity verification while supporting efficient encrypted aggregation and fine-grained, time-based authorization. It introduces a lightweight mechanism that hierarchically organizes cryptographic keys and ciphertexts over time, enabling privacy-preserving queries without decrypting individual data points. Building on this foundation, an electric vehicle key management and query system is further designed to integrate the proposed encryption and verification scheme into practical V2X environments. The system supports privacy-preserving data sharing, verifiable statistical analytics, and flexible access control across heterogeneous cloud and edge infrastructures. Analytical and experimental evidence show that the designed system attains rigorous security guarantees alongside excellent efficiency and scalability, rendering it ideal for large-scale electric vehicle data protection and analysis tasks. Full article

To address the critical problem of 3D object detection in autonomous driving scenarios, we developed a novel digital twin architecture. This architecture combines AI models with geometric optics algorithms of camera systems for autonomous vehicles, characterized by low computational cost and high generalization capability. The architecture leverages monocular images to estimate the real-world heights and 3D positions of objects using vanishing lines and the pinhole camera model. The You Only Look Once (YOLOv11) object detection model is employed for accurate object category identification. These components are seamlessly integrated to construct a digital twin system capable of real-time reconstruction of the surrounding 3D environment. This enables the autonomous driving system to perform real-time monitoring and optimized decision-making. Compared with conventional deep-learning-based 3D object detection models, the architecture offers several notable advantages. Firstly, it mitigates the significant reliance on large-scale labeled datasets typically required by deep learning approaches. Secondly, its decision-making process inherently provides interpretability. Thirdly, it demonstrates robust generalization capabilities across diverse scenes and object types. Finally, its low computational complexity makes it particularly well-suited for resource-constrained in-vehicle edge devices. Preliminary experimental results validate the reliability of the proposed approach, showing a depth prediction error of less than 5% in driving scenarios. Furthermore, the proposed method achieves significantly faster runtime, corresponding to only 42, 27, and 22% of MonoAMNet, MonoSAID, and MonoDFNet, respectively. Full article

Silicon nanowire (SiNW) photodetectors exhibit high sensitivity for natural light detection but suffer from significant performance degradation due to thermal interference. To overcome this limitation, this paper presents a high-performance, CMOS-compatible SiNW array natural light photodetector with monolithic integration of an on-chip temperature sensor and an embedded intelligent compensation system. The device, fabricated via microfabrication techniques, features a dual-array architecture that enables simultaneous acquisition of optical and thermal signals, thereby simplifying peripheral circuitry. To achieve high-precision decoupling of the optical and thermal signals, we propose a hybrid temperature compensation algorithm that combines Particle Swarm Optimization (PSO) with a Back Propagation (BP) neural network. The PSO algorithm optimizes the initial weights and thresholds of the BP network, effectively preventing the network from getting trapped in local minima and accelerating the training process. Experimental results demonstrate that the proposed PSO-BP model achieves superior compensation accuracy and a significantly faster convergence rate compared to the traditional BP network. Furthermore, the optimized model was successfully implemented on an STM32 microcontroller. This embedded implementation validates the feasibility of real-time, high-accuracy temperature compensation, significantly enhancing the stability and reliability of the photodetector across a wide temperature range. This work provides a viable strategy for developing highly stable and integrated optical sensing systems. Full article

The automation of quality control in agricultural food processing, particularly the detection of incomplete peeling in taro, constitutes a critical frontier for ensuring food safety and optimizing production efficiency in the Industry 4.0 era. However, this domain is fraught with significant technical challenges, primarily stemming from the inherent visual characteristics of residual peel: extremely minute scales relative to the vegetable body, highly irregular morphological variations, and the dense occlusion of objects on industrial conveyor belts. To address these persistent impediments, this study introduces a comprehensive solution comprising a specialized dataset and a novel detection architecture. We established the Taro Peel Industrial Dataset (TPID), a rigorously annotated collection of 18,341 high-density instances reflecting real-world production conditions. Building upon this foundation, we propose MDB-YOLO, a lightweight, multi-dimensional bionic detection model evolved from the YOLOv8s architecture. The MDB-YOLO framework integrates a synergistic set of innovations designed to resolve specific detection bottlenecks. To mitigate the conflict between background texture interference and tiny target detection, we integrated the C2f_EMA module with a Wise-IoU (WIoU) loss function, a combination that significantly enhances feature response to low-contrast residues while reducing the penalty on low-quality anchor boxes through a dynamic non-monotonic focusing mechanism. To effectively manage irregular peel shapes, a dynamic feature processing chain was constructed utilizing DySample for morphology-aware upsampling, BiFPN_Concat2 for weighted multi-scale fusion, and ODConv2d for geometric preservation. Furthermore, to address the issue of missed detections caused by dense occlusion in industrial stacking scenarios, Soft-NMS was implemented to replace traditional greedy suppression mechanisms. Experimental validation demonstrates the superiority of the proposed framework. MDB-YOLO achieves a mean Average Precision (mAP50-95) of 69.7% and a Recall of 88.0%, significantly outperforming the baseline YOLOv8s and advanced transformer-based models like RT-DETR-L. Crucially, the model maintains high operational efficiency, achieving an inference speed of 1.1 ms on an NVIDIA A100 and reaching 27 FPS on an NVIDIA Jetson Xavier NX using INT8 quantization. These findings confirm that MDB-YOLO provides a robust, high-precision, and cost-effective solution for real-time quality control in agricultural food processing, marking a significant advancement in the application of computer vision to complex biological targets. Full article

Algorithmic trading heavily relies on the optimization of rule-based strategies to maximize profitability and ensure robustness under volatile market conditions. Traditional optimization methods often face limitations when dealing with the nonlinear, high-dimensional, and dynamic nature of financial search spaces. This study introduces a Metaheuristic-based framework for financial strategy optimization that focuses on the modeling and resolution of the problem through population-based search algorithms. The framework evaluates four Metaheuristic optimization techniques within a unified design, enabling a consistent and fair comparison of their performance in optimizing trading rules. To ensure realistic and time-consistent evaluation, the experimental setup incorporates a Rolling Windows Validation approach, allowing the assessment of model performance across successive market periods. Beyond improving convergence behavior, Diversity is employed as a metric to assess the quality and exploration capability of the search process, providing deeper insight into algorithmic performance. Experimental results, obtained from real market data, demonstrate substantial improvements in profitability consistency and risk-adjusted performance compared to conventional optimization approaches. The findings confirm that Metaheuristic optimization offers a robust and flexible alternative for the design and refinement of algorithmic trading systems in complex and dynamic financial environments. Interestingly, Differential Evolution exhibited persistently high Diversity, suggesting the presence of multiple distant yet competitive optima in the financial search space, where functional convergence coexists with geometric dispersion. Full article

The article presents a PSpice software-based numerical model of a superconducting transformer with HTS 2G SCS and SF windings for the analysis of electrical circuits, developed using PSpice version 24.1 (Cadence, 2024),which allows for the determination of equivalent parameters and properties of such a transformer in the steady state and in emergency states. The model has user-defined ABM (Analogue Behavioural Modelling) computational blocks and avails itself of the level 2 Jiles-Atherton magnetic hysteresis model and Rhyner’s power law describing the E-J relationship of the HTS superconducting tape. This model was experimentally verified by measurements of a real 10 kVA HTS transformer. On this basis, an extensive numerical model of a superconducting transformer with a more complicated winding structure and a higher power of 21 MVA was developed. For such a transformer, power losses were analysed and the time courses of resistance, current and temperature of superconducting windings made of HTS 2G tapes of the SCS type with a copper stabiliser and SF without a stabiliser were examined during emergency states, such as connecting the transformer to the network and operational short circuit. A discussion was carried out on the effectiveness of using both types of HTS tapes to limit the current in emergency situations posing a risk of loss of superconductivity and destruction of superconducting windings. Full article

Dust accumulation on photovoltaic (PV) modules is a major factor contributing to reduced power output, lower efficiency, and accelerated material degradation, particularly in arid and industrialized regions. This study presents a comprehensive review and analysis of the influence of dust deposition on PV performance, covering its optical, thermal, and electrical impacts. Findings from global literature indicate that dust-induced efficiency losses typically range from 10% to 70%, depending on particle characteristics, environmental conditions, and surface orientation. Experimental and modeled I–V and P–V characteristics further reveal significant declines in current and power output as soiling levels increase. Through an extensive literature assessment, this paper identifies Machine Learning (ML)-based approaches as emerging and highly effective techniques for dust detection and mitigation. Recent studies demonstrate the integration of image processing, drone-assisted monitoring, and convolutional neural networks (CNNs) to enable automated, real-time soiling assessment. These intelligent methods outperform conventional manual and time-based cleaning strategies in accuracy, scalability, and cost efficiency. By synthesizing current research trends, this review highlights the growing role of ML and data-driven technologies in enhancing PV system reliability, informing predictive maintenance, and supporting sustainable solar energy generation. Full article

Wind power systems often introduce interfering DC components that distort power measurements and threaten grid stability. To address these issues, this paper proposes a novel delayed sampling-based grid parameter estimation method that explicitly accounts for DC disturbances. By transforming the estimation problem into a linear regression form via nonlinear algebraic transformation, an adaptive recursive identification algorithm is developed to estimate grid frequency, amplitude, phase, and DC component simultaneously. Rigorous stability analysis is provided to guarantee convergence and robustness of the estimator in the presence of DC components. Experimental results demonstrate fast transient response and zero steady-state error, validating the effectiveness of the proposed method for real-time grid parameter estimation. Full article

With the rapid development of information technology and the deepening of digitalization, operating systems are increasingly applied in critical information infrastructure, making data security issues particularly important. Traditional cloud storage auditing models based on third-party auditing authorities (TPA) face trust risks and potential data leakage during data integrity verification, which makes them inadequate to meet the dual requirements of high security and local controllability in the current information technology environment. To address this, this paper proposes a system-wide data security auditing method for the Chinese Linux operating system, constructing a lightweight and localized framework for sensitive information protection and auditing. By dynamically intercepting system calls and performing real-time content analysis, the method achieves accurate identification and visual masking of sensitive information, while generating corresponding audit logs. To overcome the efficiency bottleneck of traditional pattern matching in high-concurrency environments, this paper introduces a Chinese Aho-Corasick (AC) automaton-based character matching algorithm using a hash table to enhance the rapid retrieval capability of sensitive information. Experimental results demonstrate that the proposed method not only ensures controllable and auditable sensitive information but also maintains low system overhead and good adaptability, thereby providing a feasible technical path and implementation scheme for data security. Full article

To address the conflict between real-time performance and optimal resource allocation in large-scale digital array radars, this paper proposes a novel resource scheduling framework that integrates graph-theoretic modeling with an adaptive heuristic strategy. Unlike traditional methods, we formulate the multi-beam scheduling problem as a constrained connected subgraph optimization task. To solve this NP-hard problem, an Improved Ant Colony Optimization (I-ACO) algorithm is designed, incorporating pheromone boundary constraints and elite update strategies to effectively balance exploration and exploitation within complex solution spaces. Furthermore, a load-aware Adaptive Algorithm Switching (AAS) strategy is introduced. This mechanism dynamically transitions between the globally optimized I-ACO and a rapid, utility-guided greedy approach based on real-time system load, effectively resolving the trade-off between solution quality and response speed. Experimental results demonstrate that the proposed method reduces solution costs by up to 23.5% compared to greedy algorithms and increases the scheduling success rate to 99.2% under high-load conditions, while significantly improving long-term system load balancing by 41.5%. Full article

COVID-19 lockdown announcements triggered global waves of panic buying, leading to widespread panic buying of essential goods and supply chain disruptions. Although the acute phase of the pandemic has passed, panic buying continues to emerge during natural disasters, extreme weather events, and other crisis-related disruptions, highlighting the ongoing need for evidence-based strategies to address its psychological drivers. Social cognition constructs, including willingness, intentions, attitudes, subjective norms, and risk perceptions, have been identified as modifiable psychological predictors of panic buying. However, few studies have experimentally tested theory-driven interventions aimed at modifying these mechanisms. This study evaluated the effectiveness of a brief, online intervention based on integrated social cognition models in reducing panic-buying-related cognitions during a hypothetical lockdown scenario. A pre-registered randomized controlled trial was conducted with Australian grocery shoppers (N = 140), who were randomly allocated to an intervention or control condition. Participants completed self-report measures assessing their willingness, intentions, attitudes, subjective norms, and risk perceptions at both pre- and post-intervention times. The hypotheses were partially supported. Compared with the control condition, the intervention group reported greater reductions across targeted psychological constructs. For hygiene products, significant decreases were observed across all five constructs, and for non-perishable foods, willingness, intention, and attitudes significantly decreased. For cleaning products, reductions were evident for attitudes, subjective norms, and intentions. These findings suggest that theory-informed, scalable interventions can effectively modify the social cognition processes underlying panic buying. This study extends existing research and demonstrates the potential for brief, theory-based communication strategies to reduce panic-buying-related cognitions. Future research should evaluate these interventions in real-world settings and explore mechanisms to target automatic cognitive processes. Full article

Urban towns and smart city governments face increasing challenges in maintaining ecological balance as urbanization, industrial activity, and climate dynamics evolve. The degradation of ecological gardens, biodiversity parks, and waterways adversely affects ecosystem stability, air and water quality, and community well-being. Conventional urban ecological systems rely on reactive assessment methods that detect damage only after it occurs, leading to delayed interventions, higher maintenance costs, and irreversible environmental harm. This study introduces a Graph–Transformer Neural Network (GTNet) as a data-driven and predictive framework for sustainable urban ecological management. GTNet provides real-time estimation of smart city garden health, addressing the gap in proactive environmental monitoring. The model captures spatial relationships and contextual dependencies among multimodal environmental features using Dynamic Graph Convolutional Neural Network (DGCNN) and Vision Transformer (ViT) layers. The preprocessing pipeline integrates Principal Component Aggregation with Orthogonal Constraints (PCAOC) for dimensionality reduction, Weighted Cross-Variance Selection (WCVS) for feature relevance, and Selective Equilibrium Resampling (SER) for class balancing, ensuring robustness and interpretability across complex ecological datasets. Two new metrics, Contextual Consistency Score (CCS) and Complexity-Weighted Accuracy (CWA), are introduced to evaluate model reliability and performance under diverse environmental conditions. Experimental results on Melbourne’s multi-year urban garden datasets demonstrate that GTNet outperforms baseline models such as Predictive Clustering Trees, LSTM networks, and Random Forests, achieving an AUC of 98.9%, CCS of 0.94, and CWA of 0.96. GTNet’s scalability, predictive accuracy, and computational efficiency establish it as a powerful framework for AI-driven ecological governance. This research supports the transition of future smart cities from reactive to proactive, transparent, and sustainable environmental management. Full article