Search Results (12,399)

Aiming at the challenges of class imbalance, tiny defect scales, and complex brushed background interference in the surface defect detection of electrical commutators, this paper proposes a high-precision and lightweight improved instance segmentation algorithm named WG-YOLOv11. Firstly, to overcome the barrier of highly imbalanced positive and negative samples in actual industrial data collection, a Balanced Defect Synthesis (BDS) data augmentation strategy is introduced to effectively enrich the morphological diversity of tiny defects. Secondly, a Wavelet Transform Convolution (WTConv) module is collaboratively integrated into the feature extraction network to expand the receptive field while preserving the high-frequency edge details of hairline cracks. Thirdly, a Group CBAM Enhancer (GCE) module is introduced to filter out high-reflection and brushed background noise through grouped attention and weight re-calibration mechanisms. Finally, addressing the difficulty of pixel-level alignment for tiny defects, an $\alpha$-IoU loss function is utilized to improve the high-precision segmentation and localization capabilities by dynamically adjusting the gradient distribution. Comprehensive evaluations are conducted on two real-world electrical commutator surface defect datasets: KolektorSDD2 and KolektorSDD. Experimental results show that on the KolektorSDD2 dataset, compared to the YOLOv11 baseline, the Mask mAP@50 of WG-YOLOv11 increases from 85.2% to 89.2%, and the stringent metric Mask mAP@50:95 improves from 52.7% to 56.9%. Additional computational analysis on the same dataset validates that the proposed method maintains high efficiency, matching the baseline computational cost without compromising real-time inference speed. Furthermore, evaluations on the public MSD dataset confirm the model’s cross-domain generalization capabilities. The proposed framework effectively achieves a balance between detection accuracy, anti-interference robustness, and a lightweight architecture. Full article

This paper introduces CMA-YOLO, a network that integrates multi-scale features with dual attention mechanisms to address weak feature representation, low detection accuracy, and loss of fine-grained details in deep networks for wind turbine blade surface defect detection. First, we construct the C2MSA module by designing a Multi-scale Feature-enhanced Attention Convolution Mix (MS-ACmix) based on ACmix and embedding it into the C2PSA block. This lets the network capture local and global contextual features, strengthening multi-scale target recognition and lowering missed detections. Second, we devise a Monte Carlo Dual Attention (MCDA) mechanism combining random sampling with dual attention. This approach retains the regularization benefits of the Monte Carlo method while leveraging dual attention selection, enabling improved detection accuracy with low computational cost. Finally, we substitute the original downsampling layers in the backbone and neck with the ADown module. This lightweight design, together with efficient feature extraction and fusion, reduces fine-grained detail loss and improves defect detection capability. Quantitative results reveal that, compared to YOLO11n, CMA-YOLO yields improvements of 3.4% in mAP@0.5, 6.1% in mAP@0.5:0.95, and 8.8% in recall, with a 0.7 GFLOPs reduction in computational cost, thus validating the proposed algorithm. Overall, CMA-YOLO provides a lightweight and effective approach for inspecting blade surface defects on wind turbines operating in resource-limited settings. Full article

As a critical component of ice-snow tourism, land cover classification for ski resorts is crucial to ice-snow resource management. However, there is currently a scarcity of datasets and methods capable of high-precision mapping for such fine-grained scenarios. Although Transformers with long-sequence interactions and convolutional neural networks (CNNs) have emerged as mainstream solutions, their performance remains limited on high-resolution remote sensing data characterized by small datasets and high heterogeneity. Targeting land cover classification in ski resort areas, this study proposes a triple-branch segmentation framework integrating CNNs and Transformers to extract global, detail and boundary features (GDBNet), and constructs the first high-resolution ski resort land cover dataset with a resolution of 0.75 m using JiLin-1 satellite constellation (LULC_SKI). The framework employs a backbone combining SegFormer with dual CNN branches. SegFormer captures global semantic context, while dual ResNet-18 branches extract local semantics and edge details respectively. The neck integrates two specialized feature interaction modules, the proposed Pixel-Guided Feature Attention (PG-AFM) and Boundary-Guided Feature Attention (BG-AFM), which synergistically fuse these heterogeneous feature representations for enhanced multi-scale modeling. For the segmentation head, a multi-task learning approach supervises both semantic and edge outputs. LULC_SKI covers seven representative ski resorts in Jilin Province, China, comprising 10,000 multi-seasonal images annotated with six land cover classes, including roads, vegetation, built-up areas, ski runs, water bodies, and cropland. Experiments demonstrate GDBNet achieves 85.44% mIoU and 91.84% mF1 on LULC_SKI, outperforming other advanced models with particularly significant improvements for linear objects like roads and ski runs. Extensive experimental comparisons show that GDBNet delivers consistently excellent performance on both the iSAID and LoveDA datasets, underscoring the superiority of our proposed method. Ablation studies validate the effectiveness of the triple-branch architecture, attention modules, and multi-task supervision. This work proposes a modular framework for land cover classification in complex ski resort scenarios. Full article

Station Signal Layout Plans (SSLPs) are pivotal engineering drawings used in the design of railway signaling systems. Accurate recognition of such drawings is essential for enabling intelligent railway operations and supporting digital management. However, the inherent complexity of engineering drawings—characterized by diverse object categories and significant scale variations—substantially increases the difficulty of detection tasks. To address these challenges, this paper proposes an improved YOLOv8-based algorithm for rapid and accurate object detection. First, to enhance the detection of small objects in engineering drawings, a cross-scale attention mechanism is introduced into the mid-scale detection head. During prediction, this mechanism leverages fine-grained details from lower-level features to improve small-object detection. In addition, to suppress noise and blurred edges in drawings, the YOLOv8 neck network is enhanced with a DWRSeg-based design. This structure enlarges the receptive field while preserving local details, thereby effectively reducing the impact of noise on localization. To evaluate the proposed method, a complex dataset was constructed from station signal layout plans provided by a railway bureau, featuring substantial variations in target scale, diverse categories, and densely distributed objects. Experimental results demonstrate that, compared with YOLOv8n, the proposed DCS-YOLO model improves precision, recall, and mAP@0.5 by 3.1%, 0.8%, and 2.1%, respectively, while maintaining a comparable mAP@0.5:0.95. Comparative experiments with representative object detection methods demonstrate that the proposed algorithm achieves competitive detection accuracy and real-time performance for SSLP symbol recognition, providing a practical technical solution for the intelligent analysis of engineering drawings in the railway industry. Full article

Counterfeit and unsafe medicines pose significant risks to patient safety and undermine trust in healthcare systems. This paper presents ACTMeds, a blockchain-supported pharmaceutical traceability and recall platform that considers pharmaceutical supply chain requirements and public health operational needs relevant to the Australian Capital Territory (ACT). The system integrates Ethereum smart contracts, developed using Ganache, with a React-based web application providing regulator, operator, pharmacy, and auditor interfaces, alongside a public verification portal leveraging QR and GS1 barcodes. In addition, role-based access control is enforced across the medicine lifecycle, including manufacture, custody transfer, dispensing, and recall, with immutable on-chain events generated to support auditability and accountability. To balance transparency with confidentiality, the platform prototypes a zero-knowledge (ZK) recall mechanism in which regulators can cryptographically prove that recall conditions meet predefined policy requirements without disclosing sensitive incident details. Threat modeling was conducted using the STRIDE framework, and security evaluation combined static application security testing (Solhint and ESLint) and dynamic testing. The paper further discusses deployment options, cost considerations, ZK recall performance analysis, ethical implications, and future enhancements. Security testing validated the platform’s resilience, with no high-severity vulnerabilities identified and medium-severity issues related to HTTP security headers addressed. The results indicate that a regulator-led, privacy-preserving, tamper-evident ledger can improve medicine authenticity verification and recall responsiveness while maintaining compliance and data protection obligations. Full article

The rapid advancement of flexible electronics technology has endowed flexible sensors with significant application potential in fields such as wearable sensors, bionic skin, and human–machine interaction, owing to their excellent conformability, stretchability, and comfort. However, as application scenarios continue to expand and deepen, higher requirements are imposed on sensor performance in terms of sensitivity, stability, biocompatibility, environmental friendliness, and multifunctional integration. Polyurethane composites, leveraging their intrinsic characteristics, including tunable molecular structure, superior flexibility, and good biocompatibility, can effectively impart properties such as electrical conductivity, self-healing capability, and high sensitivity through compositing with various functional materials, thereby precisely aligning with the diverse demands of next-generation flexible sensors. This article systematically reviews the synthesis strategies of polyurethane composites; provides a detailed analysis of the roles of fillers—including carbon-based materials, polymers, and metal nanoparticles/nanowires—in enhancing the mechanical, electrical, and functional properties of the composites; and further summarizes the research progress of polyurethane composite-based flexible sensors in cutting-edge areas such as eco-friendly sensing, human motion monitoring, health monitoring, and bionic electronic skin. Future development trends are also discussed, aiming to provide insights for the design and development of high-performance flexible sensors. Full article

Background: Plant essential oils are extensively utilized for their antimicrobial properties; however, the specific antifungal mechanisms of certain compounds are not well characterized. Geraniol, a naturally occurring monoterpene alcohol approved for use in foods, demonstrates potential efficacy against spoilage fungi, yet detailed mechanistic insights are lacking. Methods: In this study, we determined the minimum inhibitory concentration (MIC) and minimum fungicidal concentration (MFC) of geraniol against P. polonicum. We assessed the underlying mechanisms by evaluating membrane integrity, intracellular leakage, reactive oxygen species (ROS), antioxidant enzymes (superoxide dismutase [SOD], peroxidase [POD], catalase [CAT]), malondialdehyde (MDA) levels, ATP content, and ATPase activity. Inoculated yam slices were exposed to geraniol vapor, and we monitored sensory, physicochemical, enzymatic, and microbial parameters. Results: Geraniol exhibited a minimum inhibitory concentration/minimum fungicidal concentration (MIC/MFC) of 0.3 mL/L. It disrupted cellular membranes, induced leakage, generated ROS, and caused lipid peroxidation, leading to elevated levels of malondialdehyde (MDA). Additionally, geraniol activated antioxidant enzymes and impaired energy metabolism. Fumigation with geraniol dose-dependently delayed the deterioration of yam, reduced weight loss, preserved texture and color, inhibited polyphenol oxidase (PPO) and POD activities, enhanced CAT and SOD activities, lowered MDA levels, and suppressed bacterial growth. Conclusions: Geraniol inhibits P. polonicum through multiple mechanisms, including membrane disruption, oxidative stress, and interference with energy metabolism, thereby effectively preserving the quality of fresh-cut yam and demonstrating potential as a natural preservative. Full article

The emission of hydrogen cyanide (HCN) from formaldehyde (CH₂O) during ammonia-assisted selective catalytic reduction (NH₃-SCR) remains a critical challenge for aftertreatment of bio-hybrid fuel combustion exhaust. The mechanistic details of HCN formation are still poorly understood, especially on widely deployed commercial catalysts like Cu-SSZ-13. In this work, we employed density functional theory calculations in combination with the Energetic Span Model to elucidate HCN formation pathways from CH₂O in the presence of NO₂ and H₂O over Cu-SSZ-13. The results revealed the HCN formation pathway with intermediate methylene imine as the dominant one under typical reaction conditions. These findings resonate very well with reports of hexamethylenetetramine (HMT) formation during NH₃-SCR with CH₂O, for which methylene imine is a critical intermediate. Turnover frequency (TOF) estimations highlighted the strong influence of NO₂ and H₂O: higher NO₂ concentrations promoted CO selectivity and suppressed HCN by oxidizing CH₂O to HCOOH, while lower H₂O enhanced HCN formation. These findings establish a detailed mechanistic framework for HCN emission on Cu-SSZ-13 and suggest that controlling NO₂/NO_x ratios and water content can mitigate HCN formation during NH₃-SCR. Full article

Artemether, a derivative of the natural compound artemisinin, is increasingly recognized for its multi-target anti-inflammatory and antioxidant properties. This review systematically elucidates the molecular mechanisms underlying these effects, focusing on artemether’s dual modulation of the MAPK/NF-κB and Nrf2 signaling pathways. We detail how artemether concurrently inhibits the MAPK/NF-κB axis—suppressing IKKβ phosphorylation and IκBα degradation to block NF-κB nuclear translocation—and downregulates p38/contextually modulates ERK phosphorylation. This leads to a significant reduction in key inflammatory mediators, including TNF-α, IL-6, and COX-2. Simultaneously, artemether activates the Nrf2 antioxidant pathway, upregulating HO-1 expression and enhancing the activity of SOD and GSH-Px, which effectively scavenges free radicals and reduces markers of oxidative damage such as MDA and 8-OHdG. The core therapeutic synergy arises from artemether’s disruption of the ROS-NF-κB positive feedback loop, which inhibits neutrophil infiltration and lipid peroxidation, thereby ameliorating tissue injury in experimental models of arthritis and neurodegenerative diseases. Compared to conventional NSAIDs and glucocorticoids, artemether exhibits a favorable safety profile, particularly regarding gastrointestinal effects, and demonstrates unique immunomodulatory potential. Future research directions should prioritize the development of nano-targeted delivery systems and the elucidation of pathway crosstalk at the single-cell level to advance the clinical translation of artemether for chronic inflammatory diseases. Full article

Accidents and vehicle damage caused by irregular road surfaces, such as potholes and cracks, remain a significant challenge in autonomous driving, particularly in terms of safety and trajectory reliability. Existing approaches often treat perception and control as separate processes, limiting their ability to respond effectively to road-surface anomalies in real time. In the proposed work, a unified framework for road-surface anomaly-aware control that integrates 3D point cloud perception with a Linear Parameter-Varying Model Predictive Controller (LPV-MPC) is presented. The proposed approach utilizes onboard sensors to capture detailed geometric information of the road surface and detect anomalies relevant to vehicle motion. The detected anomalies are represented in a control-oriented form and incorporated into the LPV-MPC framework, enabling adaptive trajectory planning and speed regulation. This integration allows the controller to proactively adjust vehicle behavior in response to surface irregularities, improving both safety and tracking performance. Experimental results demonstrate that the proposed method enhances robustness against road disturbances and improves trajectory tracking compared to conventional control approaches without anomaly awareness. These results highlight the effectiveness of tightly coupling perception and control for reliable autonomous driving in real-world conditions. Full article

Lane detection is a crucial perception task for autonomous driving, but existing methods often struggle with spatial information loss, feature upsampling artifacts, and prediction discontinuities under complex scenarios such as occlusions or poor lighting. To address these limitations, this paper proposes ACL-Net, an end-to-end lane detection network integrating attention mechanisms and context enhancement based on the Cross Layer Refinement Network framework. First, a coordinate attention module is embedded at the output of the backbone network to recalibrate spatial position information and mitigate depth-induced detail loss. Second, the feature pyramid network is reconstructed utilizing a dynamic upsampling operator and an additional bottom-up pathway to prevent edge distortion and preserve fine-grained geometric features. Finally, a lane-aware atrous spatial pyramid pooling module with asymmetric convolutions is designed to aggregate multi-scale global context, effectively reconnecting fragmented lane lines caused by visual occlusions. Extensive experiments on the TuSimple and CULane datasets demonstrate the superiority of the proposed approach. ACL-Net achieves an accuracy of 96.98% on TuSimple and a total F1-measure of 80.34% on CULane, outperforming the baseline Cross Layer Refinement Network while maintaining a real-time inference speed of 61.90 FPS. The results indicate that ACL-Net significantly improves the utilization of geometric features and exhibits enhanced robustness in challenging road conditions, including severe occlusions, nighttime, and large-curvature curves. Full article

Conventional epoxy polymers and their composites are increasingly challenged by environmental concerns, high manufacturing costs, and limited recyclability, necessitating the exploration of sustainable alternatives. Many research groups have sought to develop alternate polymers from various renewable resources, such as lignin, polyphenols, natural resins, saccharides, and plant oils. This new type of polymer has led to the emergence of bio-based polymers, which are often used with different reinforcements as bio-based composites. In this review, the synthesis of different bio-epoxy resins is discussed in detail along with their chemical structures. Subsequently, the enhancements in the properties of these bio-composites with the addition of different nanomaterials such as carbonaceous nanofillers (carbon nanotubes, graphene nanoplatelets, graphene oxide, etc.), cellulose-based nanomaterials, inorganic nano-silica (spherical and mesoporous), and nano-clay is explained. Lastly, the properties of these bio-composites and their applications in civil engineering are highlighted. This review has provided a detailed overview of the developments in bio-composites that can be used as a guide for the development of a new class of bio-composites using other alternate resources. Full article

Underwater images often exhibit low contrast and loss of detail due to light scattering and absorption, which poses significant challenges for visual analysis in aquatic environments. Polarization imaging addresses these issues by exploiting the polarization states of light, effectively reducing backscatter and enhancing image contrast. In this paper, we propose a polarization image fusion method guided by information entropy and a hierarchical-adaptive fusion strategy. Local information entropy is first employed to perform multiscale denoising on Degree of Linear Polarization (DOLP) images, enabling adaptive detail reconstruction while distinguishing texture from noise. Subsequently, a hierarchical fusion framework is applied: low-frequency components are enhanced through detail injection, while high-frequency components are fused using a structure-guided mechanism that leverages low-frequency gradient information to generate soft masks for phase-aligned detail integration and edge sharpening. Experiments conducted on self-collected underwater images, two public underwater datasets, and three general-scene datasets demonstrate that the proposed method improves objective metrics, including information entropy, average gradient, and edge strength. Subjective evaluations further confirm its effectiveness in preserving details and adapting to diverse scenes. Furthermore, rigorous ablation studies and runtime analyses demonstrate that the optimized framework achieves a highly favorable balance between robust, artifact-free detail enhancement and computational efficiency. The proposed approach provides a practical solution for underwater image enhancement, with potential applications in target detection and infrastructure inspection. Full article

Underwater object detection is important for ocean exploration and marine applications. However, underwater images are often degraded by absorption, scattering, and background interference, which weaken object contours, blur boundaries, and obscure fine texture details, thereby increasing the difficulty of detecting small objects and objects with large shape variations. To address these challenges, we propose WEC-UOD, an underwater object detector that improves structure-sensitive representation learning and multi-scale feature fusion within the detector, without relying on a separate image enhancement stage. In the backbone, the Wavelet–Edge Collaboration (WEC) module first uses wavelet-subband guidance to compensate for degraded structural and texture information and then applies edge-guided spatial correction to refine object boundaries and local geometry. In the neck, the Scale-Selective Fusion (SSF) module adaptively selects informative responses from branches with different receptive fields and further suppresses background interference through channel and spatial recalibration. Experiments on RUOD and DUO show that WEC-UOD achieves mAP@0.5 scores of 87.4% and 86.9%, respectively, consistently outperforming the YOLOv11s baseline. These results demonstrate the effectiveness of combining structural enhancement with selective multi-scale aggregation for underwater object detection. Full article

This paper proposes a multi-dimensional vision-based system for external thread defect detection, aiming to overcome the limitations of conventional 2D inspection in geometric characterization. The proposed framework integrates 2D detection and 3D reconstruction to enable both accurate localization and quantitative analysis of defects. Specifically, a YOLOv13-based detector enhanced with data augmentation is employed to detect missing teeth, scratches, and corrosion defects, achieving average precisions of 95.3%, 96.7%, and 79.7%, respectively. To further capture geometric details, a Gaussian Splatting-based reconstruction method is introduced to recover high-fidelity 3D structures from multi-view images. Based on the reconstructed point cloud, dedicated 3D analysis methods are designed to enable defect size estimation with an error of less than 1 mm. Experimental results demonstrate that the proposed system achieves a favorable balance between detection accuracy and geometric measurement capability under complex industrial conditions. In addition, a robustness analysis under image perturbations is conducted to evaluate system reliability. Full article