Search Results (153)

To solve the drawbacks of conventional long-cycle wear tests for miniature standing- wave linear ultrasonic motors, an accelerated equivalent wear model and test system were proposed in this work. After primary screening of multiple pair materials, graphite and Al₂O₃ were adopted to modify epoxy films. The optimal friction pair is composed of 6061 hard anodic oxidation film and ECA105 composite film. The matched pair exhibits excellent driving stability and low wear loss, with fatigue wear as the main wear form. Graphite and Al₂O₃ exert synergistic anti-wear and load-bearing effects via forming a stable transfer film on the friction interface. Experimental results confirm that the accelerated test is equivalent to a full-life durability test. The presented method and optimized friction pair can effectively guide the development of high-performance ultrasonic motors. Full article

Global agriculture is seriously threatened by plant diseases, which result in large losses in both productivity and quality. Timely and accurate disease detection is essential for effective crop management and food security. This work presents an improved MobileNetV2 architecture with Modified Lite Attention (MLA) Model for detecting plant leaf disease. Our methodology incorporates pre-processing, feature extraction through attention model, convolution layers, and classifying into diseased or healthy categories. Further, multiclassification of diseases is performed on a dataset comprising 4432 samples including whitefly, leaf spot, leaf curl, yellowish and healthy leaves. The proposed attention model is compared with existing attention models like CBAM (Convolutional Block Attention Model), SE (Squeeze and Excitation), ECA (Efficient Channel Attention) and SDMnet (Spatially Dilated Multi-Scale Network) to validate our hybrid MLA feature extraction technique. Customizing the categorization with fully connected layers and utilisation of a pre-trained MobileNetV2 model allow the system to achieve excellent results. Findings show encouraging accuracy, surpassing 97% compared to existing techniques for multiclass dataset classification. The integration of MobileNetV2 with custom dense layers enables robust detection even with limited datasets, making it ideal for use in mobile or low-resource agricultural environments. Further, the proposed method is tested on the PlantVillage dataset consisting of 10,836 samples using K-Fold cross-validation for K = 5 and K = 4 to obtain an average accuracy of 98.4% and 98.69%, respectively. Full article

Aircraft skin defect recognition is a safety-critical visual inspection task in which lightweight models must maintain high diagnostic accuracy while suppressing false alarms caused by complex surface textures, illumination variations, and weak defect patterns. This study proposes HQCA-Net, a simulation-based hybrid quantum-classical channel attention network for reliable aircraft skin defect recognition. The core component, termed Residual Quantum Channel Attention (RQCA), embeds a 10-qubit variational quantum circuit into a classical ResNet-18 backbone to perform compact and structured nonlinear feature recalibration, introducing only 30 trainable quantum-gate parameters. The quantum circuit is evaluated using state-vector simulation, and this study focuses on model-level feature recalibration, reliability, and robustness within the evaluated dataset rather than implementation on physical quantum hardware. Experiments on a six-class aircraft skin defect dataset show that HQCA-Net achieves 97.93% classification accuracy and a global false positive rate of 0.49%, outperforming ResNet-18 and classical lightweight attention mechanisms including SE, ECA, and SimAM. Additional analyses using confidence calibration, Grad-CAM visualization, Gaussian noise perturbation, few-shot training, and circuit-depth ablation further indicate that the proposed RQCA module improves feature discrimination and false-alarm suppression under compact parameter constraints. These results suggest that the hybrid quantum-classical attention module can serve as a parameter-efficient nonlinear feature recalibration strategy for reliable visual defect inspection under the tested experimental conditions. Full article

Mechanical Wear particle recognition is an important approach for equipment health monitoring and fault early warning. However, flow-field disturbances and high-speed particle motion in high-speed fluid environments can lead to image degradation, non-stationary electrostatic signals, and insufficient reliability of single-source recognition methods. Therefore, this study proposes a wear particle recognition method based on multi-source information fusion for high-speed fluid environments. The method establishes a multi-scale electrostatic sensing model to characterize the coupling relationship among particle material properties, motion states, and electrostatic response characteristics. Empirical mode decomposition and independent component analysis are combined for adaptive electrostatic signal denoising, and a Transformer network is used to extract multi-domain features. Meanwhile, an ECA-CNN model with an efficient channel attention mechanism is introduced to enhance the feature representation of degraded particle images. On this basis, a meta-learning-based sample-adaptive decision fusion framework is developed to achieve dynamic and complementary fusion of electrostatic and visual information. The experimental results demonstrate that the proposed method exhibits excellent recognition accuracy and robustness in the tested high-speed fluid environment of 10 m/s, achieving a fusion recognition accuracy of 96.0%, which is significantly superior to single-source recognition methods. Ablation experiments further show that removing the global scaling factor, guidance loss, interpolation loss, and category-specific weight generator decreases the average recognition accuracy by 0.7%, 1.2%, 0.4%, and 1.8%, respectively, confirming the contribution of each key module to fusion recognition performance. These findings provide a new technical approach for the online intelligent recognition of wear particles under high-speed fluid conditions and offer theoretical support and methodological guidance for condition monitoring, health assessment, and intelligent operation and maintenance of large-scale equipment. Full article

Background: Carotid atherosclerosis is a recognised manifestation of systemic vascular disease, and its association with coronary artery disease (CAD) has been well described. However, previous studies have largely been conducted under conventional diagnostic conditions and have focused on carotid plaque, intima–media thickness, or simple present-versus-absent stenosis classifications, rather than duplex-derived haemodynamic markers across the spectrum of angiographic CAD burden. The COVID-19 pandemic and post-pandemic period changed referral patterns and created more variable cardiovascular presentations, including symptoms that could resemble or mask obstructive CAD. Therefore, we investigated whether the established association between carotid stenosis severity and CAD burden remains detectable in a diagnostically heterogeneous real-world cohort, and whether routinely available carotid duplex haemodynamic parameters provide a clinically relevant signal in this setting. Methods: This single-centre, cross-sectional study was performed as a carotid-focused secondary analysis of the BG Study cohort. We included 902 consecutive patients who underwent invasive coronary angiography between 2021 and 2023 and carotid duplex ultrasonography during the same hospitalisation. CAD burden was defined according to the number of major coronary vessels with ≥70% diameter stenosis and classified as no CAD, one-vessel, two-vessel, or three-vessel disease. Carotid duplex parameters included peak systolic velocities of the common, internal, and external carotid arteries, as well as ICA stenosis severity graded according to NASCET criteria. Associations with CAD burden were assessed using a staged statistical approach combining χ² tests, Kruskal–Wallis tests with post hoc pairwise comparisons, Spearman correlation, inverse probability weighting, and ordered logistic regression. Results: The prevalence of measured ICA stenosis of any grade and severe ICA stenosis increased with greater CAD burden (both p < 0.001). Median PSV values of the bilateral ICAs and ECAs differed significantly across CAD groups on global intergroup testing. Post hoc pairwise analyses showed that significant corrected differences were concentrated between patients without CAD and those with multivessel or three-vessel CAD, particularly for ICA stenosis measures and bilateral ECA PSV. Spearman analysis demonstrated weak but statistically significant correlations between carotid parameters and CAD burden (ρ = 0.085–0.134). After inverse probability weighting, covariate balance was achieved, with all post-IPW standardised mean differences being <0.01. In ordered logistic regression (OLR) analysis, patient-reported history of carotid stenosis (OR 2.25, 95% CI 1.38–3.67; p < 0.001), right external carotid artery PSV per 10 cm/s (OR 1.31, 95% CI 1.09–1.57; p = 0.004), left ICA PSV per 10 cm/s (OR 1.17, 95% CI 1.01–1.36; p = 0.034), and left ICA stenosis per 10% (OR 1.24, 95% CI 1.11–1.39; p < 0.001) were independently associated with higher CAD burden. Exploratory ratio-based analyses showed that the ECA/CCA PSV ratio was associated with CAD presence and higher CAD burden, whereas the ICA/CCA ratio showed weaker associations; neither ratio-based index outperformed absolute ECA PSV. Conclusions: In this carotid-focused secondary analysis of a pandemic-era angiography cohort, carotid stenosis severity and duplex-derived haemodynamic parameters were independently but modestly associated with increasing angiographic CAD burden. These findings support carotid duplex markers as adjunctive indicators of systemic atherosclerotic burden rather than standalone tools for CAD detection or treatment decision-making. Future validation in vascular surgery populations is warranted to determine whether routinely available carotid duplex parameters can contribute to targeted cardiovascular risk recognition before major vascular procedures. Full article

Background/Objectives: The aim of this study is to test different convolutional neural network (CNN) and Transformer-based models to detect and classify vertical misfit at the abutment-prosthesis interface on panoramic radiographs, and to develop a hybrid deep learning model enhanced with attention mechanisms. Methods: A dataset consisting of a total of 566 images, manually classified as 249 ‘fit’ and 317 ‘misfit’ cases by two experts, was created. Images were resized to 224 × 224 and divided into training, validation, and test groups. The deep learning model yielding the most successful results was determined as the backbone; a hybrid model was developed by integrating three different attention modules (SE, CBAM, and ECA) into this structure. Model performance was evaluated using accuracy, precision, sensitivity, and F1 score metrics. Results: CNN-based models (RegNetY-800MF, ConvNeXt-Tiny, EfficientNetV2-S, ResNet50) performed better than Transformer-based models (DeiT, Swin-Tiny) in all metrics. The proposed hybrid model exhibited the highest success among all tested models with a 99.12% accuracy rate. This model reached a 100% precision value in the misfit group and yielded no false positive results. The F1 scores of the hybrid model were recorded as 99.01% for the fit group and 99.21% for the misfit group. Conclusions: The findings of this study demonstrate that attention-enhancing deep learning frameworks have the potential to significantly improve the diagnostic utility of routine panoramic radiographs. It shows that panoramic imaging, when supported by advanced artificial intelligence, can provide valuable diagnostic support in detecting vertical misfit. The developed model has the potential to become a reliable clinical decision support system. Full article

Soil apparent electrical conductivity (EC_a), volumetric water content (VWC), and temperature are important parameters for evaluating soil condition and supporting irrigation and crop management practices. This study presents the design and experimental evaluation of a ultra-low-hardware-cost soil sensing system capable of estimating these three parameters through impedance-based measurements at different frequency ranges. The proposed system uses sinusoidal excitation in the kHz range for EC_α estimation and in the MHz range for VWC estimation, while temperature is also considered as a relevant factor affecting the electrical behavior of soil. The sensor was experimentally tested on three soil types under two moisture conditions, namely water addition with and without mixing, and the results were compared with those obtained from a commercial instrument (5TE Meter Group). The overall mean error of the developed system, without calibration, was 20.2%, with mean errors of 16.3% for EC_a and 24.2% for VWC. Although the accuracy achieved is lower than that of commercial instruments, the results demonstrate that the proposed system can provide a satisfactory preliminary assessment of soil conditions in applications where low cost, simplicity and ease of implementation are important. The results can be significantly improved if calibration is made initially for the soil type of the field to be measured. Electrode geometry, lack of calibration with a larger set of soil samples and PCB implementation issues are the main limitations affecting performance. Overall, the proposed approach shows potential as a supportive tool for low-cost agricultural monitoring and decision-making applications. The implementation of a system that measures soil conductivity and moisture in two frequency ranges measurement (kHz for EC_α/MHz for VWC), with synchronous soil temperature measurement, at a particularly low cost, is the innovation of the sensor system. Full article

Indirect treatment comparisons (ITCs) are essential in the context of joint clinical assessments (JCAs) under Regulation (European Union [EU]) 2021/2282, bridging evidence gaps where head-to-head data are lacking and enabling assessment across diverse national patient, intervention, comparator, and outcome (PICO) requirements. This paper critically reviews the EU Health Technology Assessment Coordination Group’s (HTACG) guidelines on direct and indirect comparisons, with particular focus on ITCs. While the guidelines promote transparency and rigorous evaluation of assumptions, they adopt a restrictive stance on assumption violations, the use of unanchored comparisons, and population-adjusted methods such as matching-adjusted indirect comparisons (MAIC) and simulated treatment comparisons (STC). The guidance shows limited support for Bayesian methods and undervalues meta-regression in favor of subgroup analyses. Operational implications for health technology developers (HTDs) are substantial, including new requirements for dual systematic reviews, multiple network structures, and shifted null hypothesis testing. Moreover, the guidelines effectively dissuade the use of non-randomized comparisons in rare or rapidly evolving indications and may inadvertently hinder access to effective treatments. Emerging practices such as external control arms (ECA) or target trial emulation are underdeveloped. Notably, there is no indication that the guidelines are grounded in systematic methodological validation studies. As JCAs evolve, greater methodological flexibility, empirical grounding, and clear operational guidance will be essential. Refining the guidelines along these principles would enhance their practical utility, mitigate intrinsic assessment variability, support consistent assessments across Member States (MS), and ultimately improve patient access to innovative therapies. Full article

In large-scale caged broiler houses, the combined effects of ventilation regulation, external meteorological disturbances, and changes in flock growth stages can easily cause the indoor thermal environment to deviate from the target settings, thereby increasing the difficulty of environmental control and production risks. Meanwhile, the broiler house environment exhibits pronounced spatial heterogeneity, while manual inspection is limited by low efficiency, high labor intensity, and delayed detection of anomalies. Therefore, this study proposes an inspection-robot-based method for predicting and providing early warnings for temperature deviation (TD) and temperature–humidity index (THI). In this method, TD is used to characterize the degree of deviation in environmental control, whereas THI is used to characterize the overall thermal environmental risk under the coupled effects of temperature and humidity. For the two prediction tasks, input variables were separately selected based on correlation analysis, and the same Wavelet–ECA–GRU model architecture was adopted for training to achieve short-term prediction over the next 30 min. The results show that the proposed model outperformed baseline models, including LSTM, TCN, and GRU. On the test set, the RMSE and R² for TD prediction were 0.2886 and 0.9238, respectively, while those for THI prediction were 0.4309 and 0.9287, respectively. Based on the TD classification results and THI risk thresholds, warning strategies for heat deviation, cold deviation, and humid-heat risk were established. The proposed method can identify potential thermal environmental anomalies in caged broiler houses in advance from the perspectives of environmental control deviation and comprehensive thermal environmental risk, thereby assisting farm managers in assessing whether timely adjustments to ventilation, heating, or cooling are needed. Full article

Rolling bearings represent one of the core functional components of rotating machinery, with their application scope continuously expanding into various sectors of modern social production and life, making the research on fault diagnosis of rolling bearings increasingly significant. Effective vibration feature extraction and improved classification models are crucial to achieving accurate and automated fault diagnosis of rolling bearings. We proposed a fault diagnosis approach based on a Swin Transformer–Improved ResNet module. In the data preprocessing stage, the frequency-domain features and time-domain multi-scale features of fault signals are extracted using FFT and VMD methods, respectively. And then, dual-channel feature extraction is employed using both the Swin Transformer and Improved ResNet module, followed by feature fusion through an ECA module, thereby enhancing diagnostic accuracy and model robustness. The architecture retains shallow-level feature details while incorporating global contextual information, improving feature representation and detection precision. Extensive experiments were carried out on data collected from an SEU bearing dataset, including model validation, ablation analysis, comparative evaluation and simulated noise testing. An average classification accuracy of 99.41% was achieved by the proposed model under uniform experimental conditions, as evidenced by the obtained experimental results, outperforming other models by at least 0.96%. Even under severe noise interference with a signal-to-noise ratio of −4, the model maintained an average accuracy of 91.92%, exceeding that of noise-resistant counterparts. Moreover, generalization experiments on the CWRU bearing dataset under varying load conditions revealed an average fault diagnosis accuracy exceeding 98%, confirming the model’s strong cross-domain adaptability. Full article

Geographical origin identification of fragrant pears is crucial for ensuring fruit quality, protecting regional brand value, and maintaining market order. However, pears from different origins often exhibit highly similar appearance and physicochemical properties, making rapid and nondestructive identification challenging for traditional methods. This study proposes a hyperspectral origin identification method based on an enhanced one-dimensional convolutional neural network (ECNN-1D) incorporating an Efficient Channel Attention (ECA) mechanism, using visible–near-infrared (Vis–NIR) and short-wave infrared (SWIR) spectral data. To address the technical challenges of highly similar spectra, redundant features, and complex information distribution, ECNN-1D enhances discriminative spectral feature representation, overcoming limitations of conventional machine learning and standard deep learning models in feature extraction and classification stability. Systematic comparisons with machine learning models (LDA, RF, KNN, SVM) and deep learning models (VGG-1D, ResNet-1D, CNN-1D) showed that while all models performed well on Vis–NIR spectra, ECNN-1D achieved the highest test accuracy of 98.94% and F1 score of 98.95% on the more challenging SWIR spectra, outperforming other approaches. These results indicate that ECNN-1D enables high-precision, nondestructive origin identification of fragrant pears, with potential cost advantages, providing a reliable technical solution for fruit traceability and quality supervision. Full article

Unauthorized unmanned aerial vehicles (UAVs) threaten Vehicle-to-Everything (V2X) spectrum security. Real-time edge detection faces strict hardware constraints, severe multipath fading, and Doppler distortions. This article proposes HA-EffNet, a physics-informed multi-task learning framework engineered for radio frequency (RF) sensing on roadside units (RSUs). The network restricts its temporal receptive field to align mathematically with the channel coherence time, thereby preventing deep noise overfitting. A hierarchical mechanism integrates Efficient Channel Attention (ECA) for shallow noise suppression and Receptive Field Attention (RFA) for deep signature extraction. Furthermore, the shared multi-task architecture simultaneously executes discrete classification and continuous spectral parameter regression, effectively halving computational overhead compared to redundant single-task deployments. Evaluations on the Microphase and DroneRFa datasets yield classification accuracies of 97.88% and 94.67%. Compound tests integrating Tapped Delay Line C (TDL-C) models and dynamic signal-to-noise ratio (SNR) variations validate algorithmic resilience against severe physical degradation. Utilizing a 0.12-million-parameter footprint, the network delivers a 0.84 ms inference latency and 1204.9 frames per second (FPS) throughput on the NVIDIA Jetson Orin Nano Super, providing a highly efficient edge-sensing solution. Full article

This study aims to elucidate the miRNA regulatory mechanisms during the developmental process of Kazakh horse testes at 1 and 3 years of age. Through miRNA sequencing and bioinformatics analysis of testicular tissues from 1-year-old and 3-year-old horses, a developmentally stage-specific miRNA expression profile was constructed. A total of 1640 miRNAs were identified, among which 437 (380 up-regulated and 57 down-regulated) exhibited significant differential expression between the two age groups, including eca-miR-16, eca-miR-17, eca-miR-103, and eca-miR-199a-5p. Functional enrichment analysis revealed that the target genes of these differentially expressed miRNAs were primarily involved in key processes such as oxidative stress response, hormone receptor signaling regulation, and cytoskeletal remodeling, suggesting that testicular maturation depends on a complex post-transcriptional regulatory network. Further KEGG analysis revealed significant enrichment of classic reproductive signaling pathways, including PI3K/AKT, Wnt/β-catenin, Hippo, and TGF-β, indicating their synergistic roles in spermatocyte proliferation/differentiation and testicular homeostasis establishment. Although limited by a small sample size, this study elucidates the molecular mechanisms underlying male reproductive maturation in Kazakh horses at the post-transcriptional regulatory network level, providing preliminary theoretical support and potential markers for evaluating stallion reproductive performance and molecular breeding. Full article

Electric power station confined spaces are high-risk and complex environments characterized by significant illumination variations. Whether safety helmets are properly worn directly affects the operational safety of workers in confined spaces. However, helmet detection in such environments faces several challenges, including drastic lighting changes and difficulties in small-object detection. Moreover, existing object detection models typically contain a large number of parameters, making real-time helmet detection difficult to deploy on field devices with limited computational resources. To address these issues, this paper proposes a lightweight safety helmet wearing detection algorithm named GSA-YOLO. To mitigate the effects of severe illumination variation and detail loss in confined spaces, a GCA-C2f module integrating GhostConv and the CBAM attention mechanism is embedded into the backbone network. This design reduces the number of parameters and computational cost while enhancing the model’s feature extraction capability under challenging lighting conditions. To improve detection performance for occluded targets, an improved efficient channel attention (I-ECA) mechanism is introduced into the neck structure, which suppresses irrelevant channel features and enhances occluded object detection accuracy. Furthermore, to alleviate missed detections of small objects and inaccurate localization under low-light conditions, a P2 detection branch is added to the head, and the WIoU loss function is adopted to dynamically adjust the weights of hard and easy samples, thereby improving small-object detection accuracy and localization robustness. A confined space helmet detection dataset containing 5000 images was constructed through on-site data collection for model training and validation. Experimental results demonstrate that the proposed GSA-YOLO achieves an mAP@0.5 of 91.2% on the self-built dataset with only 2.3 M parameters, outperforming the baseline model by 2.9% while reducing the parameter count by 23.6%. The experimental results verify that the proposed algorithm is suitable for environments with significant illumination variation and small-object detection challenges. It provides a lightweight and efficient solution for on-site helmet detection in confined space scenarios, thereby contributing to the reduction in industrial safety accidents. Full article

In the intelligent tomato-picking scenario, challenges such as insufficient accuracy in recognizing the growth pose of target tomatoes and inaccurate positioning of picking and grasping points have led to low efficiency in automated picking. To address these issues, this paper introduces an object detection optimization model based on Yolov8s, termed YOLOv8S-ECC. The model focuses on “Judging tomato pose by the spatial vector of the relative position between the calyx and the center point of the fruit,” aiming to enhance high-precision positioning of both the tomato calyx and fruit, thereby laying the groundwork for subsequent pose judgment and picking point positioning. We have integrated the ECA (Efficient Channel Attention) and Coordinate attention mechanisms into the Backbone network and introduced the CBAM (Convolutional Block Attention Module) attention mechanism into the Neck network. The combined effect of these attention mechanisms effectively overcomes the recognition challenges posed by the calyx’s color texture, which closely resembles the environment. This integration has also enhanced the model’s robustness in complex field environments. Test results indicate significant improvements: the accuracy rate, recall rate, and mAP@50 for detecting tomato fruits and calyces are 81.7% and 87.5%, 92.7% and 85.9%, and 89.7% and 91.3%, respectively, compared to the original model. By encapsulating the algorithm and integrating it with the picking robot, tests in a simulated environment (different lighting conditions and foliage occlusion situations) show picking success rates of 93.02%, with an average picking operation time of 14.2 ± 0.855 s, including an image recognition and processing time of 0.035 s. This research offers an effective technical solution for high-precision visual perception and pose judgment in fruit and vegetable picking robots, contributing to improved quality in tomato industry picking operations. Full article