Search Results (5,380)

Battery technology plays an increasingly vital role in portable electronic devices, electric vehicles, and renewable energy storage. During operation, batteries undergo performance degradation, which not only reduces device efficiency, but may also pose safety risks. The State of Health (SOH) is a crucial indicator for assessing battery condition. Traditional SOH prediction methods face limitations in real-time adjustment and accuracy under complex operating conditions. By determining electrode capacity loss and identifying complex patterns that traditional methods struggle to detect, prediction accuracy can be improved. Based on electrode capacity matching and compensation relationships, this paper proposes an electrode capacity balance model to evaluate battery development trends and degradation during cycling. We use q_Li − q_p state assessment as a trend criterion, q_p to quantify aging, and Q_c to identify thermal runaway risk levels, developing more efficient SOH prediction indicators and methods to ensure battery safety and performance. Full article

Degradation data plays a crucial role in the reliability assessment and condition monitoring of engineering systems. The stage-wise changes in degradation rates often signal turning points in system performance or potential fault risks. To address the issue of structural changes during the degradation process, this paper constructs a degradation modeling framework based on a two-stage Inverse Gaussian (IG) process and proposes a change-point detection method based on an adjusted CUSUM (cumulative sum) statistic to identify potential stage changes in the degradation path. This method does not rely on complex prior information and constructs statistics by accumulating deviations, utilizing a binary search approach to achieve accurate change-point localization. In simulation experiments, the proposed method demonstrated superior detection performance compared to the classical likelihood ratio method and modified information criterion, verified through a combination of experiments with different change-point positions and degradation rates. Finally, the method was applied to real degradation data of a hydraulic piston pump, successfully identifying two structural change points during the degradation process. Based on these change points, the degradation stages were delineated, thereby enhancing the model’s ability to characterize the true degradation path of the equipment. Full article

Leaching mining of ion-adsorption rare earths degrades soil organic matter and hampers vegetation recovery. High-resolution UAV remote sensing enables large-scale monitoring of reclamation, yet vegetation detection accuracy is constrained by key challenges. Conventional three-channel detection struggles with terrain complexity, illumination variation, and shadow effects. Fixed UAV altitude and missing topographic data further cause resolution inconsistencies, posing major challenges for accurate vegetation detection in reclaimed land. To enhance multi-spectral vegetation detection, the model input is expanded from the traditional three channels to six channels, enabling full utilization of multi-spectral information. Furthermore, the Channel Attention and Global Pooling SPPF (CAGP-SPPF) module is introduced for multi-scale feature extraction, integrating global pooling and channel attention to capture multi-channel semantic information. In addition, the C2f_DynamicConv module replaces conventional convolutions in the neck network to strengthen high-dimensional feature transmission and reduce information loss, thereby improving detection accuracy. On the self-constructed reclaimed vegetation dataset, MRV-YOLO outperformed YOLOv8, with mAP@0.5 and mAP@0.5:0.95 increasing by 4.6% and 10.8%, respectively. Compared with RT-DETR, YOLOv3, YOLOv5, YOLOv6, YOLOv7, yolov7-tiny, YOLOv8-AS, YOLOv10, and YOLOv11, mAP@0.5 improved by 6.8%, 9.7%, 5.3%, 6.5%, 6.4%, 8.9%, 4.6%, 2.1%, and 5.4%, respectively. The results demonstrate that multichannel inputs incorporating near-infrared and dual red-edge bands significantly enhance detection accuracy for reclaimed vegetation in rare earth mining areas, providing technical support for ecological restoration monitoring. Full article

The main aim of this work is to define the limits of detection (LOD) and quantification (LOQ) for polyethylene (PE) particles using a pyrolysis and oxidation-based method, the thermal Rock-Eval^® device, combined with a mathematical extrapolation procedure. The influences of particle size and shape on the thermal degradation of PE polymers are also investigated in this study. Thermal Total HC and Tpeak parameters, recently used to characterize polymer samples, are evaluated as a function of both polymer grain size and shape. Results indicate a LOD for the investigated PE polymers of around 1.7–2 μg in 60 mg of composite sediment (28–33 ppm). A conservative LOQ for the PE samples ranges between 5 and 6 μg (83–100 ppm). The LOQ is on the same order of magnitude for any size or shape of the studied PE polymers. By contrast, the LOD for the PE samples is slightly affected by both the polymer grain size and shape. Results also demonstrate that it is possible to detect PE nanoparticles of 79 nm in size. Finally, this study provides specific Rock-Eval^® parameters, linear regressions, and a mathematical extrapolation procedure that can be used to better quantify very small PE mass contents, including nanoplastics in environmental samples. Full article

Aflatoxin (AF) contamination in pistachios remains a critical food safety and trade challenge, given the potent carcinogenicity of AF-B₁ and the nut’s high susceptibility to Aspergillus infection throughout production and storage. Traditional decontamination methods such as roasting, irradiation, ozonation, and acid/alkaline treatments can reduce AF levels but often degrade sensory and nutritional quality, implying the need for more sustainable approaches. In recent years, innovative nonthermal interventions, including pulsed light, cold plasma, nanomaterial-based adsorbents, and bioactive coatings, have demonstrated significant potential to decrease fungal growth and AF accumulation while preserving product quality. Biosensing technologies such as electrochemical immunosensors, aptamer-based systems, and optical or imaging tools are advancing rapid, portable, and sensitive detection capabilities. Combining these experimental strategies with artificial intelligence (AI) and machine learning (ML) models can increasingly be applied to integrate spectral, sensor, and imaging data for predicting fungal development and AF risk in real time. This review brings together progress in nonthermal reduction strategies, biosensing innovations, and data-driven approaches, presenting a comprehensive perspective on emerging tools that could transform pistachio safety management and strengthen compliance with global regulatory standards. Full article

This study presents the development and evaluation of an automated ultraviolet-C irradiation system for maize seed treatment, emphasizing disinfection performance, environmental control, and vision-based monitoring. The system features dual 8-watt ultraviolet-C lamps, sensors for temperature and humidity, and an air extraction unit to regulate the microclimate of the chamber. Without air extraction, radiation stabilized within one minute, with internal temperatures increasing by 5.1 °C and humidity decreasing by 13.26% over 10 min. When activated, the extractor reduced heat build-up by 1.4 °C, minimized humidity fluctuations (4.6%), and removed odors, although it also attenuated the intensity of ultraviolet-C by up to 19.59%. A 10 min ultraviolet-C treatment significantly reduced the fungal infestation in maize seeds by 23.5–26.25% under both extraction conditions. Thermal imaging confirmed localized heating on seed surfaces, which stressed the importance of temperature regulation during exposure. Notable color changes ($\Delta E>2.3$) in treated seeds suggested radiation-induced pigment degradation. Ultraviolet-C intensity mapping revealed spatial non-uniformity, with measurements limited to a central axis, indicating the need for comprehensive spatial analysis. The integrated computer vision system successfully detected seed contours and color changes under high-contrast conditions, but underperformed under low-light or uneven illumination. These limitations highlight the need for improved image processing and consistent lighting to ensure accurate monitoring. Overall, the chamber shows strong potential as a non-chemical seed disinfection tool. Future research will focus on improving radiation uniformity, assessing effects on germination and plant growth, and advancing system calibration, safety mechanisms, and remote control capabilities. Full article

GPS spoofing remains a critical threat in the use of autonomous vehicles. Machine-learning-based detection systems, particularly support vector machines (SVMs), demonstrate high accuracy in their defense against conventional spoofing attacks. However, their robustness against intelligent adversaries remains largely unexplored. In this work, we reveal a critical vulnerability in an SVM-based GPS spoofing detection model by analyzing its decision boundary. Exploiting this weakness, we introduce novel evasion strategies that craft adversarial GPS signals to evade the SVM detector: a data location shift attack and a similarity-based noise attack, along with their combination. Extensive simulations in the CARLA environment demonstrate that a modest positional shift reduces detection accuracy from $99.9\%$ to $20.4\%$, whereas similarity to genuine GPS noise-driven perturbations remain largely undetected, while gradually degrading performance. A critical threshold reveals a nonlinear cancellation effect between similarity and shift, underscoring a fundamental detectability–impact trade-off. To our knowledge, these findings represent the first demonstration of such an evasion attack against SVM-based GPS spoofing defenses, suggesting a need to improve the adversarial robustness of machine-learning-based spoofing detection in vehicular systems. Full article

In today’s digital era, cyberattacks are rapidly evolving, rendering traditional security mechanisms increasingly inadequate. The adoption of AI-based Network Intrusion Detection Systems (NIDS) has emerged as a promising solution, due to their ability to detect and respond to malicious activity using machine learning techniques. However, these systems remain vulnerable to adversarial threats, particularly data poisoning attacks, in which attackers manipulate training data to degrade model performance. In this work, we examine tree classifiers, Random Forest and Gradient Boosting, to model black box poisoning attacks. We introduce FortiNIDS, a robust framework that employs a surrogate neural network to generate adversarial perturbations that can transfer between models, leveraging the transferability of adversarial examples. In addition, we investigate defense strategies designed to improve the resilience of NIDS in smart city Internet of Things (IoT) settings. Specifically, we evaluate adversarial training and the Reject on Negative Impact (RONI) technique using the widely adopted CICDDoS2019 dataset. Our findings highlight the effectiveness of targeted defenses in improving detection accuracy and maintaining system reliability under adversarial conditions, thereby contributing to the security and privacy of smart city networks. Full article

Cyberattacks on pipeline operational technology systems pose growing risks to energy infrastructure. This study develops a physics-informed simulation and optimization framework for analyzing cyber–physical threats in petroleum pipeline networks. The model integrates networked hydraulic dynamics, SCADA-based state estimation, model predictive control (MPC), and a bilevel formulation for stealthy false-data injection (FDI) attacks. Pipeline flow and pressure dynamics are modeled on a directed graph using nodal pressure evolution and edge-based Weymouth-type relations, including control-aware equipment such as valves and compressors. An extended Kalman filter estimates the full network state from partial SCADA telemetry. The controller computes pressure-safe control inputs via MPC under actuator constraints and forecasted demands. Adversarial manipulation is formalized as a bilevel optimization problem where an attacker perturbs sensor data to degrade throughput while remaining undetected by bad-data detectors. This attack–control interaction is solved via Karush–Kuhn–Tucker (KKT) reformulation, which results in a tractable mixed-integer quadratic program. Test gas pipeline case studies demonstrate the covert reduction in service delivery under attack. Results show that undetectable attacks can cause sustained throughput loss with minimal instantaneous deviation. This reveals the need for integrated detection and control strategies in cyber–physical infrastructure. Full article

Expectation Propagation (EP) has emerged as a promising detection algorithm for large-scale multiple-input multiple-output (MIMO) systems owing to its excellent performance and practical complexity. However, transmit antenna correlation significantly degrades the performance of EP detection, especially when the number of transmit and receive antennas is equal and high-order modulation is adopted. Based on the fact that the eigenvector matrix of the channel transmit correlation matrix approaches asymptotically to a discrete Fourier transform (DFT) matrix, a DFT precoder is proposed to effectively eliminate transmit antenna correlation. Simulation results demonstrate that for high-order, high-dimensional massive MIMO systems with strong transmit antenna correlation, employing the proposed DFT precoding can significantly accelerate the convergence of the EP algorithm and reduce the detection error rate. Full article

This paper presents a comparative study on the impact of robust hashing in enhancing image-based malware classification. While Convolutional Neural Networks (CNNs) have shown promise when working with image-based malware samples, their performance degrades significantly when obfuscation techniques are taken into consideration to hamper the malware classification or detection. To address this, we apply a robust hashing technique that generates invariant visual representations of malware samples, enabling improved generalization under obfuscation implemented as image salting. Using a custom obfuscation method to simulate polymorphic variants, we evaluate MobileNet, ResNet, and DenseNet architectures across five salting conditions (0% to 40%). The results demonstrate that robust hashing substantially boosts classification accuracy, with DenseNet achieving 89.50% on unsalted data, compared to only 68.00% without hashing. Across all salting levels, models consistently performed better when robust hashing was applied, confirming its effectiveness in preserving structural features and mitigating adversarial noise. These findings position robust hashing as a powerful preprocessing strategy for resilient malware detection. Full article

This paper presents a novel adversarial Convolutional Neural Network (ConvNet) method for generating adversarial perturbations in 3D point clouds, enabling gradient-free robustness evaluation of object detection systems at inference time. Unlike existing iterative gradient methods, our approach embeds the ConvNet directly into the detection pipeline at the voxel feature level. The ConvNet is trained to maximize detection loss while maintaining perturbations within sensor error bounds through multi-component loss constraints (intensity, bias, and imbalance terms). Evaluation on a Sparsely Embedded Convolutional Detection (SECOND) detector with the KITTI dataset shows 8% overall mean Average Precision (mAP) degradation, while CenterPoint on NuScenes exhibits 24% weighted mAP reduction across 10 object classes. Analysis reveals an inverse relationship between object size and adversarial vulnerability: smaller objects (pedestrians: 13%, cyclists: 14%) show higher vulnerability compared to larger vehicles (cars: 0.2%) on KITTI, with similar patterns on NuScenes, where barriers (68%) and pedestrians (32%) are most affected. Despite perturbations remaining within typical sensor error margins (mean L2 norm of 0.09% for KITTI, 0.05% for NuScenes, corresponding to 0.9–2.6 cm at typical urban distances), substantial detection failures occur. The key novelty is training a ConvNet to learn effective adversarial perturbations during a one-time training phase and then using the trained network for gradient-free robustness evaluation during inference, requiring only a forward pass through the ConvNet (1.2–2.0 ms overhead) instead of iterative gradient computation, making continuous vulnerability monitoring practical for autonomous driving safety assessment. Full article

The detection of allergens is essential for ensuring food safety, protecting public health, and providing accurate information to consumers. Wheat (Triticum aestivum L.) and maize (Zea mays L.) are recognized as important food allergens. In this study, novel PCR methods were developed for the reliable detection of wheat and maize allergens, including wheat high-molecular-weight glutenin subunit (HMW-GS) and low-molecular-weight glutenin subunit (LMW-GS), as well as three maize allergens, namely, Zea m 14, Zea m 8, and zein. Wheat and maize genomic DNA, as well as allergen genes, were examined during 60 min of baking at 180 °C and 220 °C. Agarose gel electrophoresis revealed degradation of genomic DNA and amplified PCR fragments in correlation with increasing baking temperature and time. For each target gene, the best primers were identified that could detect HMW-GS and LMW-GS genes in wheat samples and Zea m 14, Zea m 8, and zein genes in maize samples after baking at 220 °C for 60 min and 40 min, respectively. The results indicate that these PCR methods can be used for the reliable and sensitive detection of wheat and maize allergens in processed foods. Full article

Microplastics pose a growing environmental concern, necessitating accurate and scalable methods for their detection and classification. This study presents a novel deep learning framework that integrates a transformer-based architecture with domain adaptation techniques to classify microplastics using reflectance micro-FTIR spectroscopy. A key challenge addressed in this work is the domain shift between laboratory-prepared reference spectra and environmentally sourced spectra, which can significantly degrade model performance. To overcome this, three domain-adaptation strategies—Domain Adversarial Neural Networks (DANN), Deep Subdomain-Adaptation Networks (DSAN), and Deep CORAL—were evaluated for their ability to enhance cross-domain generalization. Experimental results show that while DANN was unstable, DSAN and Deep CORAL improved target domain accuracy. Deep CORAL achieved 99% accuracy on the source and 94% on the target, offering balanced performance. DSAN reached 95% on the target but reduced source accuracy. Overall, statistical alignment methods outperformed adversarial approaches in transformer-based spectral adaptation. The proposed model was integrated into a reflectance micro-FTIR workflow, accurately identifying PE and PP microplastics from unlabelled spectra. Predictions closely matched expert-validated results, demonstrating practical applicability. This first use of a domain-adaptive transformer in microplastics spectroscopy sets a benchmark for high-throughput, cross-domain analysis. Future work will extend to more polymers and enhance model efficiency for field use. Full article

To maintain product reliability and stabilize performance, it is essential to prioritize the identification and resolution of latent defects. Advanced products such as high-precision electronic devices and semiconductors are susceptible to performance degradation over time due to environmental factors and electrical stress. However, conventional performance testing methods typically evaluate products based solely on predefined acceptable ranges, making it difficult to predict long-term degradation, even for products that pass initial testing. In particular, products exhibiting borderline values close to the threshold during initial inspections are at a higher risk of exceeding permissible limits as time progresses. Therefore, to ensure long-term product stability and quality, a novel approach is required that enables the early prediction of potential defects based on test data. In this context, the present study proposes a machine learning-based framework for predicting latent defects in products that are initially classified as normal. Specifically, we introduce the Sigma Deviation Count Labeling (SDCL) method, which utilizes a Gaussian distribution-based approach. This method involves preprocessing the dataset consisting of initially passed test samples by removing redundant features and handling missing values, thereby constructing a more robust input for defect prediction models. Subsequently, outlier counting and labeling are performed based on statistical thresholds defined by 2σ and 3σ, which represent potential anomalies outside the critical boundaries. This process enables the identification of statistically significant outliers, which are then used for training machine learning models. The experiments were conducted using two distinct datasets. Although both datasets share fundamental information such as time, user data, and temperature, they differ in the specific characteristics of the test parameters. By utilizing these two distinct test datasets, the proposed method aims to validate its general applicability as a Predictive Anomaly Testing (PAT) approach. Experimental results demonstrate that most models achieved high accuracy and geometric mean (GM) at the 3σ level, with maximum values of 1.0 for both metrics. Among the tested models, the Support Vector Machine (SVM) exhibited the most stable classification performance. Moreover, the consistency of results across different models further supports the robustness of the proposed method. These findings suggest that the SDCL-based PAT approach is not only stable but also highly adaptable across various datasets and testing environments. Ultimately, the proposed framework offers a promising solution for enhancing product quality and reliability by enabling the early detection and prevention of latent defects. Full article