Search Results (1,382)

In renewable-powered distribution systems and microgrids, reliable cable condition monitoring is essential for operational security and early fault detection. Sheath circulating current signals provide valuable information for identifying grounding abnormalities and incipient faults, but their diagnosis is difficult because the signals exhibit strong inter-phase coupling, and fault samples are limited in practice. This study proposes a dual-view Mixup-ResNet framework for fault diagnosis of cable sheath circulating current signals. Specifically, physical-range normalization is employed to retain magnitude-related fault information and inter-phase proportional relationships, while sample-wise z-score normalization is used to emphasize waveform morphology. These two complementary views are concatenated to form a six-channel input for a lightweight one-dimensional residual network, which is trained with Mixup, label smoothing, dropout, and cosine annealing. On an ATP-EMTP-generated eight-class dataset, the proposed method achieves an average accuracy of 91.50%, a weighted F1-score of 91.69%, and a macro F1-score of 91.69% under a unified 5 × 5 repeated stratified cross-validation protocol. Additional tests under 12% relative Gaussian noise show that the method maintains competitive Gaussian-noise tolerance within the tested condition, although broader field disturbances remain to be further validated. These findings suggest that the proposed method has potential for small-sample fault diagnosis of cable sheath circulating current signals and provides a preliminary basis for intelligent cable condition monitoring. Full article

As a core global food crop, the stability of maize yield is directly related to food security. However, maize leaf diseases exhibit diverse morphologies, small initial lesions, and high similarity among different types of lesions, posing significant challenges for timely and accurate identification. Therefore, this paper proposes a maize leaf disease diagnosis model based on an improved YOLO11n. First, a C3k2_AFE module is designed, which adaptively captures rich features by operating a spatial context module and a feature refinement module in parallel. In addition, an adaptive downsampling module is adopted to enhance edge and fine-grained feature extraction. Finally, by integrating the C2PSA module with the CASAttention attention mechanism, symmetric collaborative modeling between the spatial and channel domains is achieved, enhancing the model’s perception of diseases. Experimental results show that the improved YOLO11n model achieves an accuracy of 89.8%, mAP@50% of 88.7%, and mAP@50-95% of 71.8%, which are 1.9%, 1.5%, and 1.4% higher than those of the baseline YOLO11n model, respectively. The number of model parameters is 2.36 MB, which is 8% lower than that of the baseline model. The corn disease diagnosis model proposed in this study effectively addresses the problem of disease detection in corn under complex environmental conditions, significantly improving the accuracy of detection and providing a reference for the application of corn leaf disease detection. Full article

Polymeric microneedles are introduced as a promising platform for minimally invasive drug delivery and molecular transport control. In the present study, hollow dissolving nanocomposite microneedles based on a mixture of high- and low-molecular-weight hyaluronic acid (HA) in a 40:60 ratio, including zinc oxide nanoparticles (ZnO NPs), have been created and evaluated as hydrated polymer transport matrices. Surface modification of ZnO nanoparticles using citric acid was proposed to improve dispersion by reducing agglomeration of nanoparticles in the polymer matrix. ZnO nanoparticles in concentrations ranging from 1 to 10% (w/w) were used to study the effects of the loading level of nanoparticles on the structure, mechanical response, and controlled diffusion behavior of hydrated polymer matrices. The created nanocomposites exhibited clear hollow structures with tip radius of 18–23 μm, height of 1500 μm, and aspect ratio of 5.7. Nanoscale surface organization and particle dispersion in the polymer matrix were studied by scanning electron microscope (SEM) and atomic force microscope (AFM). Low nanoparticle concentrations were favorable for maintaining high matrix homogeneity, while high concentrations resulted in increased surface roughness and nanoparticle agglomeration. Mechanical compression testing confirmed that hydrated HA/ZnO microneedles were characterized by elastic bending behavior until fracture. Diffusion experiments performed in Franz diffusion cells showed that nanoparticle concentration significantly impacted the cumulative transport and flux of molecules through the hydrated microneedle matrix. Formulations with 5% and 7% ZnO nanoparticles were characterized by a prolonged diffusion behavior attributed to ZnO-induced tortuous transport channels in the polymer matrix. In contrast, formulations with 10% ZnO nanoparticles exhibited accelerated heterogeneous transport due to ZnO-induced changes in structure and morphology. The experimental diffusion data correlated well with the Higuchi kinetic model, and anomalous transport was detected using the Korsmeyer–Peppas model, which indicated a synergistic effect of diffusion and polymer relaxation on molecular transport. As compared to coating and tip-loaded microneedle designs, the obtained HA/ZnO nanocomposite microneedles offered a simple approach for embedding Ciprofloxacin in the hydrated polymer matrix. This was achieved due to the direct creation of microneedles containing dissolved particles. Full article

Accurate footprint image segmentation is challenging in forensic applications because fine anatomical structures, weak boundaries, and background interference can degrade segmentation performance. This study presents a task-oriented generative adversarial network (GAN)-based framework for forensic footprint image segmentation. Channel Prior Convolutional Attention (CPCA) modules are integrated into the decoder stages of the generator to recalibrate fused encoder–decoder features and preserve fine details in the toe, arch, and heel regions. In addition, a dual-branch discriminator processes image–mask pairs at the original and downsampled scales, providing complementary constraints on local boundary details and global footprint morphology. The framework is trained with a least-squares adversarial loss and a binary cross-entropy (BCE)–Dice segmentation loss. Experiments on the self-collected aFoot_2025 dataset show that the proposed framework achieves an IoU of 0.9448 and a Dice coefficient of 0.9713, outperforming the evaluated baseline and attention-based alternatives. Under the evaluated synthetic Gaussian-noise settings, the proposed method retained relatively stable segmentation performance. Furthermore, an exploratory footprint-based height-prediction analysis showed modestly lower prediction errors than the baseline GAN. These findings indicate that, under the controlled acquisition conditions of the aFoot_2025 dataset, CPCA-based feature calibration and dual-scale discrimination may improve segmentation-mask quality and provide a possible benefit for subsequent anthropometric analysis. Full article

Previous morphological studies have confirmed that Canton settlements maintain a stable cultural landscape sequence consisting of ponds, open spaces, ancestral halls, residences, and forests. Villages in the Dongjiang River Basin exhibit an inherent coordination between cultural landscape patterns and rainwater drainage and storage systems, contributing to strong resilience against frequent heavy rainfall events. This study selects Jiangbian Village in Dongguan as a case study and develops a quantitative analysis framework by integrating GIS and SWMM (Storm Water Management Model). Using DEM-derived terrain data and land use interpretation, a hydrological model incorporating ponds, drainage channels, paddy fields, and threshing floors was established. Five levels of functional failure severity and five design rainfall return periods were applied to systematically evaluate hydrological regulation performance. The results show that (1) ponds serve as the core water-storage component of the entire system, and a 25% reduction in their functionality leads to a substantial decline in flood mitigation capacity. Paddy field ridges and drainage channels jointly provide secondary buffering functions, while village boundary structures play a significant role in flood regulation. These landscape elements possess distinct hydrological functions and collectively shape the production, living, and ecological landscapes of the village. (2) Influenced by steep topographic gradients, the village adopts a spatial configuration characterized by horizontal alleys and terraced forms, which enhance transverse drainage and connect ponds through longitudinal channels. This comb-like settlement pattern demonstrates strong adaptation to local terrain conditions. This study reveals the terrain-adaptation characteristics of traditional Canton villages and provides valuable insights for the sustainable conservation of rural cultural landscapes. Full article

Fine-grained hyperspectral mineral classification remains challenging due to spectral homogeneity among minerals with different morphologies, severe spectral mixing from intergrowth, and high dimensionality. Existing methods rely on spectral separability assumptions, which become insufficient when spectral differences are subtle and spatial–structural ambiguity is high. To address these limitations, we propose S³AM-ECA-3DCNN, a spectral–structural collaborative feature learning framework. It uses a 3DCNN backbone to jointly model spectral–spatial features with progressive spectral downsampling. The spectral-similarity-based spatial attention module (S³AM) performs spatial purification by suppressing interference from spectrally mixed neighboring regions, and the efficient channel attention (ECA) module adaptively recalibrates discriminative spectral bands to enhance fine-grained representation. This establishes a spatial-first, channel-second collaborative optimization paradigm. To improve generalization under limited training samples, adaptive global pooling and a lightweight classification head are employed to reduce model complexity and mitigate overfitting. Experiments on a 146-class hyperspectral mineral dataset (covering silicates, carbonates, and sulfates) show that the framework achieves 93.424% overall accuracy, 91.099% average accuracy, and a Kappa coefficient (×100) of 93.368, outperforming mainstream methods. It significantly reduces misclassification among spectrally similar but morphologically distinct minerals, demonstrating strong robustness and discriminative capability for large-scale fine-grained tasks. Full article

To address the low efficiency of manual quality grading for Pleurotus ostreatus in factory-scale production and the difficulty existing computer vision models face in balancing high localization accuracy with real-time edge deployment for food processing, a lightweight non-destructive detection model named POC-DETR-Prune is proposed. Based on an improved RTDETR framework, FasterNet is introduced to optimize feature extraction, reducing memory access latency while ensuring deep feature representation for complex food morphologies. A Small Object Enhancement Pyramid (SOEP) module is designed to mitigate the loss of subtle features caused by dense mushroom clustering. Furthermore, the Inner-MPDIoU loss function is proposed to significantly improve bounding box localization accuracy in highly overlapped food sorting scenarios. To adapt to industrial hardware constraints, a Random channel pruning strategy compresses computational overhead. Experimental results demonstrate that POC-DETR-Prune achieves a mAP@0.5:0.95 of 83.7% with a computation load of only 38.2 GFLOPs. Deployment testing on the NVIDIA Jetson Orin Nano Super edge computing platform achieves a real-time detection rate of 30.2 FPS. This emerging technology provides a certain level of visual algorithm support for automated quality grading equipment in the edible fungi industry. Full article

The present study investigates how bed roughness affects the velocity field in a laboratory water channel designed for testing hydrokinetic turbines. The main aim is to evaluate the impact of bed morphology on flow hydrodynamics and, consequently, on the turbines’ operating conditions. Experimental studies were carried out in two hydraulic regimes—smooth channel bed and bed with artificially created irregularities—at flow velocities of 0.3 and 0.4 m/s, with a depth of 180 mm. The results indicate that bed roughness significantly affects the velocity field, leading to increased turbulent fluctuations, the formation of vortex structures, and momentum redistribution. There is also localized velocity acceleration within the measurement region caused by local acceleration between the bed irregularities, which is influenced by the geometry of the water channel. A clear vertical velocity distribution is established, with larger fluctuations being registered in the surface layer, while near the channel bed, the flow is more stable. The results obtained emphasize the importance of bed roughness as a key factor in laboratory modeling and analysis of hydrokinetic turbine performance, with a direct impact on their efficiency and load. Full article

Fine-grained monitoring of aquatic vegetation in crab ponds is essential for regulating water quality, sustaining ecological balance, and optimizing Chinese mitten crab (Eriocheir sinensis) aquaculture. However, owing to the complex water environment, fragmented vegetation morphology, and the absence of dedicated annotated datasets, traditional remote sensing techniques struggle to achieve highly accurate semantic segmentation and classification. In this study, we construct the first unmanned aerial vehicle (UAV) multispectral dataset for crab pond aquatic vegetation, encompassing four species, Alternanthera philoxeroides, Vallisneria natans, Hydrilla verticillata, and Elodea nuttallii, with pixel-level annotations verified by field surveys across typical aquaculture sites in Jiangsu Province, China. Furthermore, we introduce the Multi-scale Frequency–Spatial Collaborative Network (MFSCNet), built upon a MedNeXt backbone and augmented with distributed modules, including Channel Reduction Attention, Spatial Frequency Selection, a spatial–frequency fusion module, and Mobile Graph Convolution that operate cooperatively across the encoder, skip connections, decoder, and output head. This design suppresses complex water-background interference, enhances vegetation texture representation, and preserves the spatial continuity of vegetation patches. Experimental results demonstrate that, with a lightweight parameter size of merely 19.38 M, MFSCNet achieves a remarkable mean Intersection over Union (mIoU) of 0.9044, outperforming various mainstream convolutional neural network (CNN) and Transformer-based architectures. This study not only provides a high-precision remote sensing technical framework for the accurate multi-class identification and quantitative assessment of aquatic vegetation in crab ponds but also establishes reliable data support for refined aquaculture management and aquatic ecological conservation. Full article

Medical image segmentation is critical for clinical diagnosis, yet existing methods face a persistent trade-off: CNN-based approaches are constrained by local receptive fields, while Transformer-based methods suffer from semantic dilution when modeling global context. To address these limitations, we propose SCAGC-UNet, a region-aware graph convolutional network that bridges local detail extraction and global dependency modeling through structured region-level reasoning. The architecture features a dual-layer residual encoder for hierarchical feature extraction and a Spatial-Channel Graph Convolution (SC-GCN) module at the bottleneck, which simultaneously captures inter-region spatial topology and intra-region channel semantics via dual-branch graph inference. Feature refinement in the decoder is further enhanced by Context-Corrected Modules and Backward-Aided Modules to reduce the semantic gap across skip connections. We validate SCAGC-UNet on three public benchmarks covering distinct imaging challenges. On Kvasir-SEG, the model achieves a Dice score of 92.28% and MIOU of 92.41%, surpassing the strongest CNN-based baseline CCBANet by 0.73% in DSC and outperforming TransUNet by 11.76% in DSC. On BUSI, it attains an IOU of 78.10% and MIOU of 87.68%, outperforming UNet by 2.82% in IOU and TransUNet by 6.91% in DSC. On COVID-19 CT, it achieves a DSC of 82.51%, surpassing UNet by 4.99% and TransUNet by 7.47%, demonstrating robust performance on irregular lesion morphologies. These results confirm that SCAGC-UNet achieves consistent and robust segmentation performance across three public benchmark datasets spanning distinct imaging modalities, suggesting its potential clinical relevance. Full article

The protective effect of Compound terminalia chebula extract (HL) against colonic injury induced by Porcine epidemic diarrhea virus (PEDV) infection in neonatal piglets remains unclear. This study aimed to evaluate the mitigating effects of HL on PEDV-induced colonic injury and elucidate the underlying mechanisms. Eighteen 7-day-old Duroc × Landrace × Large White piglets (2.58 ± 0.05 kg) were randomly assigned to three groups (n = 6/group): CON (blank control), PEDV (infected), and HL + PEDV (HL-supplemented + infected). The 11-day trial included 3 days of acclimatization (days 0–3) and an 8-day experimental period (days 4–11). HL (10 mg/kg BW) was orally administered daily to the HL + PEDV group. On day 8, PEDV and HL + PEDV groups were challenged with 3 mL PEDV (3 × 10⁶ TCID₅₀/mL), while CON received Dulbecco’s Modified Eagle Medium (DMEM). All piglets were euthanized on day 11 for colonic tissue collection. Results indicated that PEDV infection induced colonic injury, manifested by a significant increase in crypt depth and disruption of intestinal homeostasis. This was evidenced by impaired barrier integrity (upregulation of matrix metalloproteinase-7 gene [MMP7] and matrix metalloproteinase 13 gene [MMP13], mucus disorganization (elevation of mucin 5AC gene [MUC5AC]), oxidative stress (reduced catalase [CAT] activity and increased malondialdehyde [MDA] levels in serum and colon), and inflammation (upregulation of regenerative islet-derived protein 3γ gene [REG3G], S100 calcium-binding protein A8/A9 gene [S100A8/A9], and interleukin-1β gene [IL-1β]). Additionally, PEDV impaired colonic ion transport by downregulating calcium channel genes (Transient Receptor Potential Cation Channel Subfamily V Member 6 gene [TRPV6], Transient Receptor Potential Cation Channel Subfamily M Member 6 gene [TRPM6]). Notably, HL supplementation effectively reversed these adverse effects. HL restored colonic morphology, increased CAT activity, reduced MDA accumulation, and suppressed inflammatory gene expression. Furthermore, HL modulated the expression of genes involved in water and ion transport upregulating Aquaporin 7 gene (AQP7), Chloride Channel Accessory 4 gene (CLCA4), Sodium-Hydrogen Exchanger 3 gene (NHE3), Transient Receptor Potential Vanilloid 6 (TRPV6), and Transient Receptor Potential Melastatin 6 gene (TRPM6) and significantly inhibited PEDV replication, as indicated by the downregulation of the transcription levels of PEDV membranegene (M), nucleocapsid gene (N), and spike gene (S). Taken together, HL alleviates PEDV-triggered colonic tissue damage in suckling piglets via improving colonic antioxidant capacity, mitigating inflammatory response, partially regulating intestinal barrier and ion/water transport-related genes, and downregulating the transcription of PEDV structural genes at molecular and histological levels. Full article

Varroa destructor is the most devastating ectoparasite of Apis mellifera, and early detection is critical for colony survival. This study systematically investigated how image preprocessing, model architecture, and feature map resolution jointly affect classification accuracy and Grad-CAM++ explainability in deep-learning-based Varroa detection. From comb-surface images of 20 A. mellifera colonies, 3400 region-of-interest images were processed through 12 preprocessing pipelines combining deblurring, histogram normalization, morphology-preserving resizing, and non-morphological resizing. Nineteen CNN architectures, including VarroaNet — a custom lightweight model with configurable channel attention — were screened across all pipelines, and the top six further evaluated at four feature-map resolutions (7 × 7 to 56 × 56); the two stages together comprised 1,548 classification training runs across 516 configurations. Resizing consistently improved classification accuracy, whereas histogram normalization degraded it. VarroaNet (r = 8) achieved the highest mean accuracy across configurations (97.28%) with the lowest cross-configuration variability (CV = 1.47%). The 28 × 28 resolution was jointly optimal for classification and localization at minimal computational overhead, whereas 56 × 56 degraded performance. Notably, classification accuracy and localization quality did not always coincide—the highest-accuracy configuration (ShuffleNet-V2-x1.0 at 14 × 14, 97.34%) achieved an IoU@30 of only 0.160, underscoring the need for explicit localization evaluation. Morphology-preserving resizing achieved higher localization efficiency with zero morphological distortion. The recommended configuration—VarroaNet (r = 8) at 28 × 28 with deblurred MR preprocessing—achieved the highest localization performance (Pointing Game = 0.927), indicating correct attention to the mite region in 92.7% of infested test images. Full article

In intensive aquaculture, non-invasive real-time monitoring of morphological traits and body weight of largemouth bass (Micropterus salmoides) is essential for precision feeding and yield estimation. Manual measurement is laborious and stressful, whereas vision-based methods are challenged by insufficient dual-view feature fusion, poor robustness to underwater noise, and over-parameterized models unsuitable for edge deployment. To address these issues, a lightweight framework, MECT-MobileViT, is proposed based on MobileViT-xxs. A Morphometric-Guided Multi-Scale Fusion module is designed to couple physical priors with dual-branch visual features, strengthening shape–weight association. An ECA-NL attention block employing instance normalization, GLU gating, and threshold filtering is embedded to enhance feature robustness against visual disturbances typical in aquaculture and to accentuate critical morphological features. A three-stage synergistic pruning strategy—attention head pruning, structured channel pruning, and depthwise separable attention substitution—is applied to achieve substantial compression while preserving representational capacity. Experiments on a self-built lateral–dorsal dual-view dataset show that the proposed model significantly outperforms mainstream benchmarks. The pruned version attains an R² of 0.8266 and an RMSE of 16.4201, with less than 2% accuracy degradation relative to the best unpruned model, and contains only 7.34 M parameters. This study demonstrates a promising prototype for contactless, stress-free weight estimation in largemouth bass and offers new technical insights into feature fusion, noise suppression, and collaborative model compression for aquaculture visual perception. Full article

The performance of haul trucks in open-pit mining is strongly affected by haul-road geometry, surface condition, rolling resistance, and operational traffic regimes. However, existing studies often consider road-surface mapping, vehicle dynamic response, and onboard telemetry as separate information streams, which limits the reproducible assessment of how road-related factors are associated with VIMS-derived fuel-use proxy burden, mechanical dynamic response, and transport-cycle instability. This study proposes a field-based, segment-level multisensor framework that integrates unmanned aerial vehicle/light detection and ranging (UAV/LiDAR) road-surface reconstruction, global positioning system/inertial measurement unit (GPS/IMU) trajectory and vibration data, and Caterpillar Vial Information Management System (VIMS) telemetry into a unified spatiotemporal analytical dataset. The methodological contribution consists in the synchronization of heterogeneous data sources at the road-segment level, the calculation of interpretable road-condition and vehicle-response indicators, and the statistical assessment of road-related effects while explicitly accounting for confounding factors such as longitudinal grade, payload state, speed regime, truck class, and operational variability. Unlike studies that use LiDAR mapping, vibration monitoring, or onboard telemetry as separate diagnostic channels, the proposed approach introduces a segment-level analytical framework in which road morphology, truck response, and operational penalties are aligned within the same spatial unit, interpreted under confounder-aware conditions, and verified through repeat-pass reproducibility and robustness checks. The framework was tested on haul roads around the Ekibastuz open-pit coal mine. The field analysis identifies road segments where degraded surface morphology, increased waviness, unfavorable longitudinal profile, and higher rolling resistance coincide with increased mechanical dynamic response, VIMS-derived fuel-use proxy burden, braking instability, and travel-time variability. The results are interpreted as controlled field-supported associations rather than as isolated causal effects. The proposed maintenance ranking should therefore be regarded as a decision-support output, while the operational effectiveness of specific repair interventions requires future before–after validation. Full article

Submarine pockmarks are typical seafloor micro-geomorphic landforms formed by deep fluid seepage and sediment erosional processes. Based on high-resolution multibeam bathymetric data, multi-channel seismic sections and sediment core data, the present study systematically investigates 64 pockmarks in the northwestern Xisha Uplift, focusing on their distribution, morphology and genetic mechanisms. These pockmarks exhibit a NE–SW zonal distribution, concentrated in the 1200–1600 m central slope transition zone, and are classified into circular–elliptical, crescentic and elongated types with distinct morphometric variability. Vertically, the T40 unconformity defines the stratified geological architecture: underlying carbonate uplifts and karst-fracture systems act as fluid reservoirs and migration conduits, while overlying Late Miocene–Quaternary fine-grained hemipelagic sediments form a low-permeability caprock. Fluid overpressure accumulation and hydraulic fracturing of the caprock trigger initial pockmark formation, while spatial heterogeneity of surficial sediments and bottom-current reworking control morphological differentiation. The present study clarifies the coupled controls of deep tectono-fluid activities and shallow sedimentary and hydrodynamic processes on pockmark evolution, establishing a refined dynamic model to address the research gap regarding pockmark group genesis in the study area. Full article