Search Results (513)

Due to phenomena, such as refraction, reflection, and light scattering, the three-dimensional (3D) reconstruction of transparent objects with complex geometric symmetry or contours is confronted with the challenges of insufficient extraction of feature points and recognition of contour detail. To solve this challenge, a novel reconstruction method based on multi-cooperative Neural Radiance Fields (NeRF) is proposed in the paper. This method incorporates angular offset fields and local reconstruction fields, explicitly modeling the effects of refraction and reflection during light propagation. The angular offset field simulates the internal refractive deflection within transparent materials, while the localized reconstruction field performs secondary reconstruction in regions affected by specular reflection. This approach effectively captures surface contours of transparent objects and accurately reconstructs scene details. Experimental results demonstrate that our method achieves approximately 10% improvement in reconstruction accuracy compared to traditional neural radiance field techniques, with a PSNR of 25, an increased SSIM of 0.87, and a reduced LPIPS value of 0.365. The proposed method offers a new perspective for reconstructing transparent objects and scenes containing such materials, holding significant theoretical and practical value. Full article

In response to the challenges of target recognition and misjudgment caused by varying target scales, diverse shapes, and interference such as lake surface reflections in river and lake scenarios, this paper proposes the YOLO v11n-DDH model for fast and detection of spatial targets in river and lake environments. The model builds upon YOLO v11n by introducing the Dynamic Snake Convolution (DySnakeConv) to enhance the ability to extract detailed features. It integrates the Deformable Attention Mechanism (DAttention) to strengthen key features and suppress noise, while combining the improved High-Level Screening Feature Pyramid Network (HSFPN) structure for multi-level feature fusion, thus improving the semantic representation of targets at different scales. Experiments on a self-constructed dataset show that the precision, recall, and mAP of the YOLO v11n-DDH model reached 88.4%, 78.9%, and 83.9%, respectively, with improvements of 3.4, 2.9, and 2.5 percentage points over the original model. Specifically, DySnakeConv increased mAP@50 by 0.6 percentage points, DAttention improved mAP@50 by 0.3 percentage points, and HSFPN contributed to a 0.9 percentage point rise in mAP@50. This patrol system can effectively identify and visualize various pollutants in river and lake areas, such as underwater waste, water quality pollution, illegal swimming and fishing, and the “Four Chaos” issues, providing technical support for intelligent river and lake management. Full article

The escalation of thermal runaway in lithium-ion batteries presents severe safety hazards that necessitate advanced monitoring protocols to ensure early warning of potential failures. Carbon dioxide (CO₂) is released during preliminary decomposition well before catastrophic failure occurs, thereby providing a strategic advantage for early-stage warning. Consequently, identifying materials with high-selective CO₂ recognition is an essential prerequisite for developing reliable sensing platforms. This study integrates Grand Canonical Monte Carlo simulations with Random Forest (RF) models to systematically screen 1470 MOFs from the CoRE-MOF 2019 database. The screening process evaluates selective CO₂ recognition under multicomponent competitive adsorption conditions involving CO₂, C₂H₄, and O₂. The performance evaluation is based on working capacity, selectivity, and the trade-off between working capacity and selectivity (TSN). The RF model achieves high predictive accuracy, with tested R² exceeding 0.92 on the test samples. Shapley Additive Explanations (SHAP) interpretability analysis identifies Q⁰_st(CO₂), Q⁰_st(C₂H₄), WEPA, K_H(C₂H₄), and ETR as key performance drivers. The results indicate that CO₂ selectivity is constrained by the binding strength of competing C₂H₄. Optimal materials tend to have hard Lewis acid centers and polar inorganic clusters to minimize non-specific π-interactions with interfering species. Top-performing MOFs require balanced structural features, concentrating in moderate surface areas (965–1975 m²/g), narrow pore windows (PLD ≈ 4–7 Å, LCD ≈ 5.5–9.6 Å), high void fractions above 0.6, and low densities below 1.3 g/cm³. AJOTEY emerges as the optimal candidate with a TSN of 6.43 mol/kg, combining substantial working capacity (4.57 mol/kg) with strong selectivity (25.52). These results will accelerate the discovery of sensing materials and provide a practical pathway for MOF-based CO₂ sensor development to enhance lithium-ion battery safety. Full article

Accurate delineation of rural settlements at large spatial extents is fundamental to territorial spatial governance, rural revitalization, and the improvement of human living environments. However, in medium-resolution remote sensing imagery, rural settlement patches are typically small, morphologically complex, and easily confused with other impervious surfaces. As a result, existing products still fall short in characterizing these features. Here, we propose a lightweight Transformer-based semantic segmentation model, SELPFormer, and develop a multi-source fusion automatic labeling pipeline that integrates Global Impervious Surface Dynamics dataset, OpenStreetMap spatial priors, and nighttime lights constraints. Built upon SegFormer as the backbone, SELPFormer introduces a lightweight pyramid pooling module at the deepest feature level to aggregate multi-scale global context and embeds an SCSE channel–spatial attention mechanism into deep features to suppress background interference. In addition, it incorporates an efficient local attention module into multi-scale lateral connections to enhance boundary and texture representations, thereby jointly improving small-object recognition and fine boundary preservation. We evaluate the proposed method using Landsat multispectral imagery covering five provinces on the North China Plain. SELPFormer achieves IoU = 74.23%, mIoU = 86.43%, F1 = 85.21%, OA = 98.69%, and Kappa = 0.8452 under a unified training and evaluation protocol, yielding IoU gains of +1.44, +3.98, and +12.35 percentage points over SegFormer, U-Net, and DeepLabV3+, respectively. SELPFormer has 15.44 M parameters and attains a parameter efficiency of 3.93% IoU per million parameters and an ROC-AUC of 0.993, indicating strong threshold-independent discriminative capability. These results indicate that the proposed method can effectively extract rural settlements from medium-resolution imagery and provides a generic “global–channel–local” collaborative framework for model design and data construction. Full article

The rapid and accurate detection of SARS-CoV-2 biomarkers remains a critical requirement for effective outbreak control and decentralized diagnostics. Although RT-PCR is the current gold standard, its reliance on centralized laboratories and long processing times limits its applicability in point-of-care settings. In this context, optical biosensing platforms based on surface plasmon resonance (SPR) offer attractive features, including label-free, real-time, and quantitative detection. This study explores the use of synthetic receptors for the highly sensitive detection of the receptor-binding domain (RBD) of the SARS-CoV-2 spike protein. Specifically, soft molecularly imprinted polymer nanoparticles (nanoMIPs) were employed as synthetic receptors and integrated into a high-sensitivity, portable plasmonic platform based on a D-shaped plastic optical fiber (POF) SPR sensor. The nanoMIPs were selectively imprinted against the RBD, characterized by Dynamic Light Scattering (DLS), Isothermal Titration Calorimetry (ITC), and Scanning Electron Microscopy (SEM) to confirm nanoMIPs size, binding properties, and surface morphology. Next, the nanoMIPs were immobilized onto a gold-coated sensing surface, enabling enhanced specificity, affinity, and signal amplification compared to conventional biological recognition elements. The resulting RBD-SPR-nanoMIPs sensor demonstrated promising analytical performance, exhibiting high selectivity against potentially interfering proteins and an anticipated sensitivity suitable for RBD detection at femtomolar concentrations. The inherent stability of nanoMIPs suggests the potential for reusable SPR sensing platforms, paving the way for next-generation synthetic receptor-based plasmonic biosensors. Full article

Existing methods for recognizing motion intent in lower limb rehabilitation robots focus on spatial feature extraction while neglecting movement continuity, thus failing to extract temporal features. This paper proposes a movement intention recognition model based on a CNN-LSTM parallel dual-stream spatio-temporal neural network, taking surface electromyography (sEMG) signals as the core data. This model concurrently extracts temporal and spatial features from sEMG signals, integrating dual-dimensional information to comprehensively explore deep signal characteristics. By overcoming the limitations of traditional single-feature extraction, it significantly enhances recognition accuracy. Experimental results from movement intention recognition studies involving multiple subjects demonstrate an average recognition accuracy of 97%, providing reliable technical support for precise intent recognition and human–robot collaborative control in lower limb rehabilitation robots. Full article

Condition monitoring of railway track surfaces is crucial for ensuring the safety, operational efficiency, and effective maintenance of railway systems. This work presents a data-driven modelling and an experimental methodology for identifying and classifying contaminants on railway tracks using vibration analysis and artificial intelligence techniques. In this study, the railway dynamics were physically simulated using a 1:20 scaled test rig, where the rails were treated with various contaminants (oil, water, and sand), and the resulting vehicle vibrations were recorded by on-board accelerometers and gyroscopes. To construct the predictive model, a hybrid architecture was designed integrating Short-Time Fourier Transform (STFT) for time-frequency feature extraction and a multi-channel Convolutional Neural Network (CNN) for pattern recognition. Initial results indicate that accelerometer data, particularly from longitudinal and lateral vibrations, are more effective than gyroscope data for classifying certain contaminants. To enhance classification robustness, this work introduces a multi-channel CNN that simultaneously processes the most informative signals, leading to a significant improvement in detection accuracy across all tested contaminants. This study validates the effectiveness of the proposed methodology as a robust and reliable solution for contaminant detection, while also confirming the utility of the scaled testbed as a valuable platform for future research in railway dynamics. Full article

Background: Alemtuzumab is a recombinant DNA-derived humanized monoclonal antibody directed against the 21–28 kd cell surface glycoprotein, CD52. Alemtuzumab is used as an organ anti-rejection therapy in transplant recipients. Neuro-ophthalmic adverse effects are rarely described, and, to our knowledge, accommodative spasm has not previously been reported in a transplant recipient. Case Description: A thirty-nine-year-old woman with genetically confirmed NPHP1-associated nephronophthisis, with stage F3 fibrosis, developed persistent bilateral blurred vision 72 h following alemtuzumab administration for a biopsy-proven acute cellular rejection, approximately six to seven weeks post-transplant. Initial attribution to hyperglycaemia and tacrolimus toxicity delayed recognition. Cycloplegic refraction confirmed a marked hyperopic shift (+2.75 D right eye, +2.50 D left eye) with significant improvement in visual acuity, consistent with accommodative spasm. Systemic evaluations excluded hyperglycaemia-related lens changes, calcineurin inhibitor neurotoxicity, and cytomegalovirus retinitis. MRI was not pursued in the absence of red flag neurological features, and because a definitive ophthalmic diagnosis had been made. Management and Outcome: The patient was managed expectantly, as cycloplegic refraction had already confirmed the diagnosis, and symptoms were improving. Therapeutic cycloplegia (e.g., atropine) was withheld to avoid impairing near vision and driving ability. Full resolution occurred within 4 to 6 weeks without intervention. Drug exposure to onset of symptoms was 72 h; onset of symptoms to diagnostic confirmation was 22 days; total symptom duration was 5.5 weeks, and recovery was 2 weeks after diagnosis. Conclusions: This case represents the first reported transplant case of alemtuzumab-associated accommodative spasm. Causality assessment supports a WHO-UMC classification of “Probable”, aligning with five Bradford–Hill considerations (temporality, biological plausibility, consistency, specificity, and analogy), but without statistical “strength of association” given that this is a single case report. Early cycloplegic refraction should be incorporated into the evaluation of post-alemtuzumab visual complaints, and clinicians should contribute to pharmacovigilance through structured reporting to capture these rare but important events. Full article

Electromyography (EMG) has become essential in biomedical engineering, rehabilitation, and human–machine interfacing due to its ability to capture neuromuscular activation for control, monitoring, and diagnosis. Recent advances in sensing hardware, high-density and flexible electrodes, and embedded acquisition modules combined with modern signal processing and machine learning have significantly enhanced the robustness and applicability of EMG-based systems. This review provides an integrated overview of EMG generation, acquisition standards, and preprocessing techniques, including adaptive filtering, wavelet denoising, and empirical mode decomposition. Feature extraction methods across the time, frequency, time–frequency, and nonlinear domains are compared with respect to computational efficiency and suitability for real-time systems. The review synthesizes classical and contemporary pattern-recognition approaches, from statistical classifiers to deep architectures such as CNNs, RNNs, hybrid CNN–RNN models, transformer-based networks, and graph neural networks. Key challenges, including signal non-stationarity, electrode displacement, muscle fatigue, and poor cross-user or cross-session generalization, are examined alongside emerging strategies such as transfer learning, domain adaptation, and multimodal fusion with IMU or FMG signals. Finally, the paper surveys rapidly growing EMG applications in prosthetics, rehabilitation robotics, human–machine interfaces, clinical diagnostics, and sports analytics. The review highlights ongoing limitations and outlines future pathways toward robust, adaptive, and deployable EMG-driven intelligent systems. Full article

Wind turbine blades (WTBs) are inevitably exposed to harsh environmental conditions, leading to surface damages such as cracks and corrosion that compromise power generation efficiency. While UAV-based inspection offers significant potential, it frequently encounters challenges in handling irregular defect shapes and preserving fine edge details. To address these limitations, this paper proposes DMR-YOLO, an Improved Wind Turbine Blade Surface Damage Detection Method Based on YOLOv8. The proposed framework incorporates three key innovations: First, a C2f-DCNv2-MPCA module is designed to dynamically adjust feature weights, enabling the model to more effectively focus on the geometric structural details of irregular defects. Secondly, a Multi-Scale Edge Perception Enhancement (MEPE) module is introduced to extract edge textures directly within the network. This approach prevents the decoupling of edge features from global context information, effectively resolving the issue of edge information loss and enhancing the recognition of small targets. Finally, the detection head is optimized using a Re-parameterized Shared Convolution Detection Head (RSCD) strategy. By employing weight sharing combined with Diverse Branch Blocks (DBB), this design significantly reduces computational redundancy while maintaining high localization accuracy. Experimental results demonstrate that DMR-YOLO outperforms the baseline YOLOv8n, achieving a 1.8% increase in mAP@0.5 to 82.2%, with a notable 3.2% improvement in the “damage” category. Furthermore, the computational load is reduced by 9.9% to 7.3 GFLOPs, while maintaining an inference speed of 92.6 FPS, providing an effective solution for real-time wind farm defect detection. Full article

Sea fog is a major meteorological hazard that severely disrupts maritime transportation and economic activities in the South China Sea. As China’s next-generation geostationary meteorological satellite, Fengyun-4B (FY-4B) supplies continuous observations that are well suited for sea fog monitoring, yet a satellite-specific recognition method has been lacking. A key obstacle is the radiometric inconsistency between the Advanced Geostationary Radiation Imager (AGRI) sensors on FY-4A and FY-4B, compounded by the cessation of Cloud–Aerosol Lidar with Orthogonal Polarization (CALIOP) observations, which prevents direct transfer of fog labels. To address these challenges and fill this research gap, we propose a machine learning framework that integrates cross-satellite radiometric recalibration and physical mechanism constraints for robust daytime sea fog detection. First, we innovatively apply a radiation recalibration transfer technique based on the radiative transfer model to normalize FY-4A/B radiances and, together with Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) cloud/fog classification products and ERA5 reanalysis, construct a highly consistent joint training set of FY-4A/B for the winter-spring seasons since 2019. Secondly, to enhance the model’s physical performance, we incorporate key physical parameters related to the sea fog formation process (such as temperature inversion, near-surface humidity, and wind field characteristics) as physical constraints, and combine them with multispectral channel sensitivity and the brightness temperature (BT) standard deviation that characterizes texture smoothness, resulting in an optimized 13-dimensional feature matrix. Using this, we optimize the sea fog recognition model parameters of decision tree (DT), random forest (RF), and support vector machine (SVM) with grid search and particle swarm optimization (PSO) algorithms. The validation results show that the RF model outperforms others with the highest overall classification accuracy (0.91) and probability of detection (POD, 0.81) that surpasses prior FY-4A-based work for the South China Sea (POD 0.71–0.76). More importantly, this study demonstrates that the proposed FY-4B framework provides reliable technical support for operational, continuous sea fog monitoring over the South China Sea. Full article

Dairy cow body temperature is a key physiological indicator that reflects metabolic level, immune status, and environmental stress responses, and it has been widely used for early disease recognition. Infrared thermography (IRT), as a non-contact imaging technique capable of remotely acquiring the surface radiation temperature distribution of animals, is regarded as a powerful alternative to traditional temperature measurement methods. Under practical cowshed conditions, IRT images of dairy cows are easily affected by complex background interference and generally suffer from low resolution, poor contrast, indistinct boundaries, weak structural perception, and insufficient texture information, which lead to significant degradation in target detection and temperature extraction performance. To address these issues, a lightweight detection model named YOLOv11n-CGSD is proposed for dairy cow IRT images, aiming to improve the accuracy and robustness of region of interest (ROI) detection and body temperature extraction under complex background conditions. At the architectural level, a C3Ghost lightweight module based on the Ghost concept is first constructed to reduce redundant feature extraction while lowering computational cost and enhancing the network capability for preserving fine-grained features during feature propagation. Subsequently, a space-to-depth convolution module is introduced to perform spatial rearrangement of feature maps and achieve channel compression via non-strided convolution, thereby improving the sensitivity of the model to local temperature variations and structural details. Finally, a dynamic sampling mechanism is embedded in the neck of the network, where the upsampling and scale alignment processes are adaptively driven by feature content, enhancing the model response to boundary temperature changes and weak-texture regions. Experimental results indicate that the YOLOv11n-CGSD model can effectively shift attention from irrelevant background regions to ROI contour boundaries and increase attention coverage within the ROI. Under complex IRT conditions, the model achieves P, R, and mAP50 values of 89.11%, 86.80%, and 91.94%, which represent improvements of 3.11%, 5.14%, and 4.08%, respectively, compared with the baseline model. Using T_max as the temperature extraction parameter, the maximum error (Max. Error) and mean error (MAE. Error) in the lower udder region are reduced by 33.3% and 25.7%, respectively, while in the around the anus region, the Max. Error and MAE. Error are reduced by 87.5% and 95.0%, respectively. These findings demonstrate that, under complex backgrounds and low-quality IRT imaging conditions, the proposed model achieves lightweight and high-performance detection for both lower udder (LU) and around the anus (AA) regions and provides a methodological reference and technical support for non-contact body temperature measurement of dairy cows in practical cowshed production environments. Full article

Crack detection plays a pivotal role in ensuring the safety and stability of infrastructure. Despite advancements in deep learning-based image analysis, accurately capturing multiscale crack features in complex environments remains challenging. These challenges arise from several factors, including the presence of cracks with varying sizes, shapes, and orientations, as well as the influence of environmental conditions such as lighting variations, surface textures, and noise. This study introduces DAH-YOLO (Dynamic-Attention-Haar-YOLO), an innovative model that integrates dynamic convolution, an attention-enhanced dynamic detection head, and Haar wavelet down-sampling to address these challenges. First, dynamic convolution is integrated into the YOLOv8 framework to adaptively capture complex crack features while simultaneously reducing computational complexity. Second, an attention-enhanced dynamic detection head is introduced to refine the model’s ability to focus on crack regions, facilitating the detection of cracks with varying scales and morphologies. Third, a Haar wavelet down-sampling layer is employed to preserve fine-grained crack details, enhancing the recognition of subtle and intricate cracks. Experimental results on three public datasets demonstrate that DAH-YOLO outperforms baseline models and state-of-the-art crack detection methods in terms of precision, recall, and mean average precision, while maintaining low computational complexity. Our findings provide a robust, efficient solution for automated crack detection, which has been successfully applied in real-world engineering scenarios with favorable outcomes, advancing the development of intelligent structural health monitoring. Full article

Gesture recognition is important for rehabilitation assistance and intelligent prosthetic control. However, surface electromyography (sEMG) signals exhibit strong non-stationarity, and conventional deep-learning models require long training time and high computational cost, limiting their use on resource-constrained devices. This study proposes a Frozen Multi-Convolution Dual-Branch Network (FMC-DBNet) to address these challenges. The model employs randomly initialized and fixed convolutional kernels for training-free multi-scale feature extraction, substantially reducing computational overhead. A dual-branch architecture is adopted to capture complementary temporal and physiological patterns from raw sEMG signals and intrinsic mode functions (IMFs) obtained through variational mode decomposition (VMD). In addition, positive-proportion (PPV) and global-average-pooling (GAP) statistics enhance lightweight multi-resolution representation. Experiments on the Ninapro DB1 dataset show that FMC-DBNet achieves an average accuracy of 96.4% ± 1.9% across 27 subjects and reduces training time by approximately 90% compared with a conventional trainable CNN baseline. These results demonstrate that frozen random-convolution structures provide an efficient and robust alternative to fully trained deep networks, offering a promising solution for low-power and computationally efficient sEMG gesture recognition. Full article

Aiming at the core issues of the traditional YOLO11n model in rail surface defect detection—fine-grained feature loss of small defects, insufficient micro-target recognition accuracy, and the mismatch of existing downsampling/fusion methods for micro-defect feature extraction—this paper proposes an improved YOLO11n algorithm with two-dimensional network structure innovations. First, the Adaptive Downsampling (ADown) module is introduced into the backbone network for the first time, retaining global features via 2D average pooling and extracting local details through channel-split multi-path convolution/max pooling to avoid fine texture loss. Second, the original SOEP-RFPN-MFM neck network is designed, integrating SNI, GSConvE and MFM modules to achieve dynamic weighted fusion of multi-scale features and break the bottleneck of inefficient small-target feature aggregation. Trained and verified on a 4020-image rail dataset covering four defect types (Spalling, Squat, Wheel Burns, Corrugation), the improved algorithm achieves 93.7% detection accuracy, 92.4% recall and 95.6% mAP, realizing incremental improvements of 1.2, 2.6 and 0.8 percentage points, respectively, compared with the original YOLO11n, which is particularly optimized for rail micro-defect detection scenarios. This study provides a new deep learning method for rail transit micro-defect detection and a reference for scenario-specific improvement of lightweight YOLO11n models. Full article