Search Results (31,163)

Traditional hardware-based approaches for depth-of-field extension (DOF-E), such as optimized lens design or focus-stacking via layer scanning, are often plagued by bulkiness and prohibitive costs. Meanwhile, conventional multi-focus image fusion algorithms demand precise spatial alignment, a challenge that becomes particularly acute in applications like microscopy. To address these limitations, this paper proposed a novel single-image DOF-E method termed SDENet. The method adopts an encoder –decoder architecture enhanced with multi-scale self-attention and depth enhancement modules, enabling the transformation of a single partially focused image into a fully focused output while effectively recovering regions outside the original depth of field (DOF). To support model training and performance evaluation, we introduce a dedicated dataset (MSED) containing 1772 pairs of single-focus and all-focus images covering diverse scenes. Experimental results on multiple datasets verify that SDENet significantly outperforms state-of-the-art deblurring methods, achieving a PSNR of 26.98 dB and SSIM of 0.846 on the DPDD dataset, which represents a substantial improvement in clarity and visual coherence compared to existing techniques. Furthermore, SDENet demonstrates competitive performance with multi-image fusion methods while requiring only a single input. Full article

Percutaneous computed tomography (CT) -guided cryoablation is an effective curative treatment for renal cell carcinoma. Improvements in treatment efficacy reflect not only the learning curve but also the integration of multiple adjunctive techniques that can be implemented at different stages of the procedure. Tumour targeting can be enhanced by intravenous contrast administration, or by intra-arterial delivery of contrast medium or iodized oil. Fusion imaging is another option to improve tumour delineation by registering intraprocedural CT with prior cross-sectional imaging. Probe placement for difficult-to-access lesions may be facilitated by alternative access routes, while electromagnetic navigation and robotic systems are being developed as alternatives to manual advancement. To mitigate the cold-sink effect and reduce bleeding risk, transarterial techniques such as embolization or temporary arterial occlusion can be added. Finally, thermoprotective manoeuvres are increasingly used to displace adjacent organs, thereby improving the feasibility, safety, and efficacy of renal cryoablation. Full article

Background: Posterior spinal fusion (PSF) for adolescent idiopathic scoliosis causes significant postoperative pain and high opioid requirements. Methadone, with dual μ- and κ-opioid agonism and NMDA antagonism, provides long-acting analgesia and may reduce perioperative opioid use. This study evaluated whether perioperative low-dose methadone (0.1 mg/kg) improves postoperative pain and opioid outcomes after pediatric PSF. Methods: In this single-center retrospective cohort study (January 2019–June 2023), pediatric patients <23 years old undergoing PSF were categorized by perioperative methadone exposure (intraoperative and/or postoperative) versus no methadone. The primary outcome was total postoperative opioid consumption (morphine milligram equivalents per kilogram, MME/kg) over postoperative days (POD) 0–3. Secondary outcomes were average daily pain scores and hospital length of stay (LOS). Inverse probability weighting (IPW) adjusted for age, sex, and protocol period. Results: A total of 339 patients (51% no methadone, 49% methadone; mean age 14.6 ± 2.5 years; 76% female) were analyzed. Methadone patients had longer anesthesia (392 vs. 372 min, p = 0.042) and surgery times (287 vs. 266 min, p = 0.01). IPW-adjusted associations show postoperative opioid use was significantly higher in the methadone group on POD 0 (median 2.5 vs. 2.1 MME/kg in no methadone group; p = 0.005). No significant differences were found in postoperative average pain scores (e.g., mean NRS: 2.3 vs. 2.5 on POD 0, p = 0.12) and LOS (3.3 vs. 3.1 days, p = 0.38) between methadone group and no methadone group. Discussion: Perioperative methadone provided similar analgesia for pain management and recovery without prolonging hospitalization, despite higher early opioid use on POD 0. Retrospective design limits causal inference, and residual confounding may persist despite propensity score-based adjustments. Further prospective trials are required to establish safety and dosing. Conclusions: In this retrospective cohort, perioperative low-dose methadone was associated with higher early postoperative opioid use but no significant differences in pain scores or length of stay compared with standard regimens. Methadone did not demonstrate an opioid-sparing effect in this real-world setting. Prospective studies are needed to better define its role and safety in pediatric posterior spinal fusion. Full article

The fusion of data from visible (RGB) and infrared (IR) sensors is essential for robust all-day and all-weather object detection. However, existing methods often suffer from modality redundancy and noise interference. To address these challenges, we propose the Decoupled and Differentiated Attention Fusion Network (DDAF-Net). Architecturally, DDAF-Net employs a decoupled backbone with a Siamese weight-sharing strategy to extract modality-common features, while parallel branches capture modality-specific features. To effectively integrate these features, we design the Differentiated Attention Fusion Module (DAFM). First, we introduce Spatial Residual Unshuffle Embedding (SRUE) to achieve lossless downsampling while preserving global semantic information. Second, differentiated attention mechanisms are applied for feature enhancement: Dual-Norm Alignment Attention (DNAA) facilitates effective modal alignment and enhances semantic consistency in modality-common features, while Sparse Purification Attention (SPA) enables selective utilization of complementary information by suppressing noise and focusing on salient regions in modality-specific features. Finally, the Adaptive Complementary Fusion Module (ACFM) integrates these components by using modality-common features as a baseline and dynamically weighting the complementary modality-specific information. Extensive experiments on public datasets such as LLVIP and M³FD demonstrate that DDAF-Net achieves state-of-the-art performance. These results validate the effectiveness of our proposed decoupling–enhancement–fusion paradigm. Full article

Accurate field-scale crop disease detection is crucial for precise decisions and for highly efficient multi-scale collaboration. UAV-based multispectral imaging technology offers advantages in terms of high efficiency and low cost. Deep learning shows potential for deep representation and fusion of spectral and spatial features. However, traditional manual disease surveys are limited by efficiency and cost, making it difficult to meet the large sample sizes required by deep learning. Therefore, we proposed a method for rice bacterial leaf blight detection using UAV-based multispectral imagery. This method integrates a cross-scale sample-label transfer, and a spectral–spatial dual-branch feature fusion architecture (DualRiceNet). We first used RTK positioning to transfer disease labels from near-ground RGB images to high-altitude multispectral images, effectively expanding the dataset and alleviating the scarcity of labeled samples. DualRiceNet employed a cross-attention mechanism to couple its spectral and spatial branches, thereby isolating disease-specific spatial–spectral patterns from complex interference from the farmland background. DualRiceNet achieved an overall accuracy (OA) of 92.3% on the same-distribution test set. In an independent scenario test set spanning multiple differences in geography, time, phenology, and variety, the model maintained the highest OA of 80.0%. Our method demonstrated an excellent generalization ability to real-world environmental variations in rice fields. Full article

Mitochondrial dysfunction profoundly alters cellular metabolism, yet its systems-level consequences remain incompletely characterized. Here, we present a comprehensive untargeted metabolomics analysis of respiratory-deficient (ρ⁰) and competent (ρ⁺) Saccharomyces cerevisiae prototrophic cells using ultra-high-performance liquid chromatography coupled to Orbitrap Fusion™ Tribrid™ high-resolution mass spectrometry. By integrating hydrophilic interaction and reversed-phase chromatography in both ionization modes, we detected ~7000 features per chromatographic condition, of which ~12% were structurally annotated through MSⁿ fragmentation and in silico spectral matching. Principal component analysis revealed distinct metabolic signatures between ρ⁰ and ρ⁺ cells, with ~73% of total variance explained by the first two components. Volcano plot and hierarchical clustering analyses identified a marked accumulation of phosphate-containing metabolites, sphingolipids, ceramides, and fatty acid residues in ρ⁰ cells, whereas amino acids, excluding arginine, cysteine, and aromatics, were enriched in ρ⁺ cells. Notably, branched-chain amino acid depletion in ρ⁰ cells correlated with impaired growth and mitochondrial stress. Pathway enrichment analysis, supported by transcriptomic integration, prompted us to further investigate reprogramming of polyamine biosynthesis and aromatic amino acid metabolism. Calibration curves constructed from certified standards validated the accuracy of the LC–MS platform and reinforced annotation confidence. Our findings demonstrate that advanced untargeted metabolomics, coupled with MS³ fragmentation and multi-omics integration, enables high-resolution mapping of metabolic reconfiguration under mitochondrial dysfunction, offering mechanistic insights into mitochondrial retrograde signaling and adaptation. Full article

Accurate and continuous outdoor pedestrian positioning using smartphones remains challenging in complex environments like urban canyons, where Global Navigation Satellite System (GNSS) signals are frequently degraded or blocked, and Pedestrian Dead Reckoning (PDR) suffers from cumulative errors. To address this, this paper proposes a novel fusion method based on a Robust Adaptive Cubature Kalman Filter (RACKF). The core of our approach is a two-stage filtering architecture: the first stage employs a quaternion-based RACKF to optimally fuse gyroscope and magnetometer data for robust heading estimation; the second stage performs the core fusion of GNSS observations with an enhanced 3D PDR solution. Key innovations include an adaptive noise estimation strategy combining fading and limited memory weighting, a robust M-estimator-based mechanism to suppress outliers, and the integration of differential barometric height measurements. Experimental results demonstrate that the proposed method achieves a horizontal positioning accuracy of 3.28 m (RMSE), outperforming standalone GNSS and improving 3D PDR by 25.97% and 10.39%, respectively. This work provides a practical, infrastructure-free solution for robust smartphone-based outdoor navigation. Full article

To address the challenges posed by foggy conditions in object detection tasks, we propose FogGate-YOLO, an enhanced YOLOv8 framework designed for robust and efficient detection in foggy environments. Unlike traditional methods that rely on image dehazing or preprocessing enhancements, our approach directly strengthens the model’s feature representation by introducing two novel modules: GroupGatedConv and C2fGated. These modules collaboratively mitigate fog-induced degradation, improving feature extraction and enhancing performance without additional inference overhead. The GroupGatedConv module focuses on coarse-grained channel selection in the early to mid-stages of the backbone, suppressing noise while preserving essential structural features. The C2fGated module refines the aggregated features in both the backbone and neck after multi-branch fusion, enhancing fine-grained feature recalibration. Together, these two modules provide a hierarchical coarse to fine channel selection strategy that significantly improves the model’s discriminative power in foggy conditions. Full article

The primary objective of this study is to develop a specialized deep learning framework specifically adapted for the unique physical characteristics of neurovascular Optical Coherence Tomography (OCT) imaging. Although Polyp-PVT, originally designed for polyp segmentation, shows promise for OCT analysis, it faces limitations in neurovascular applications. The default RGB input wastes resources on duplicated grayscale data, while its fixed-scale fusion struggles with vascular curvature variations. Furthermore, the attention mechanism fails to capture radial vessel patterns, and geometric constraints limit thin boundary detection. To address these challenges, we propose Brain-OCT-PVT with key innovations: a single-channel input stem reducing parameters by two-thirds; a Radial Intensity Module (RIM) using polar transforms and angular convolution to model annular structures; and a Deformable Cross-scale Fusion Module (D-CFM) with learnable offsets. The Boundary-aware Attention Module (BAM) combines Laplace edge detection with Swin-Transformer for sub-pixel consistency. A specialized loss function combines Dice Similarity Coefficient (Dice), BoundaryIoU on 2-pixel dilated edges, and Focal Tversky to handle extreme class imbalance. Evaluation on 13 clinical cases achieves a Dice score of 95.06% and an 95% Hausdorff Distance (HD95) of 0.269 mm, demonstrating superior performance compared to existing approaches. Full article

From an epidemiological perspective, polymorphous low-grade neuroepithelial tumor (PLNTY) represents a small proportion of brain tumors encountered in epilepsy surgery series. Their rarity and relatively recent recognition likely contribute to underdiagnosis and poor prognosis. In terms of histopathological features, they are similar to oligodendrogliomas. Molecular analyses can be used to show the fusion between fibroblast growth factor receptor (FGFR3) and transforming acidic coiled coil (TACC) proteins, which most commonly results in progression towards glioblastoma (GBM). We report a case of a 62-year-old man who underwent left frontal craniotomy to remove a frontal mass. Histologically, the glial lesion consisted of elements associated with oligodendroglia-like features. Immunohistochemistry was positive for glial fibrillary acidic protein (GFAP), oligodendrocyte transcription factor 2 (OLIG2), and α-thalassemia X-linked mental retardation syndrome (ATRX) nuclear expression, but negative for isocitrate dehydrogenase 1 (IDH1) and BRAF-V600E. Next-generation sequencing showed the FGFR-TACC3 fusion, and taken together, these findings supported the final diagnosis of PLNTY. During follow-up, the patient underwent a second neurosurgery, where histological evaluation indicated a GMB. This article presents clinical and radiological data, morphology, immunohistochemistry, molecular features, and treatment to enhance the clinical and pathological understanding of PLNTY with FGFR3-TACC3 fusion for all professionals involved in medical decisions. Full article

Rotating machinery serves as a critical component in various engineering systems, making accurate prediction of its Remaining Useful Life (RUL) essential for ensuring operational stability. To address the technical limitations of mainstream RUL prediction models comprehensively capturing spatial correlations among multiple sensors, this paper proposes a multi-graph-structured spatiotemporal feature fusion model for RUL prediction of rotating machinery. Breaking through the constraints of constructing a single correlation graph, the model first builds two distinct graphs—a prior correlation graph based on the structural mechanism of the rotating machinery and a similarity correlation graph derived from monitoring data distribution characteristics. These dual-perspective graphs collectively characterize the potential spatial dependencies among multiple sensors. Subsequently, a Graph Attention Network (GAT) is introduced to aggregate spatial features from both graphs, and a feature concatenation fusion strategy is adopted to achieve a comprehensive representation of the inter-sensor spatial dependencies. Finally, a Long Short-Term Memory (LSTM) network is employed to extract temporal evolution features from the operational data. The effective fusion of these spatial and temporal features enhances the model’s RUL prediction performance. Simulation experiments conducted on the Commercial Modular Aero-Propulsion System Simulation (C-MAPSS) dataset validated the robustness of the proposed method. Full article

Tea buds are the key raw material for high-quality tea production, and their accurate perception is essential for intelligent harvesting and quality-oriented management. However, tea bud detection in mountainous large-leaf tea plantations remains challenging because small, densely distributed targets are embedded in complex field environments, significantly limiting the stability and accuracy of existing detection methods. To address these challenges, this study proposes an improved tea bud detection model, termed YOLO-LAR, for mountainous large-leaf tea plantations in Yunnan Province, China, which is developed as an enhanced framework based on the YOLOv11 baseline. YOLO-LAR improves feature representation through multi-scale feature fusion, enabling more effective detection of densely distributed small tea buds. In addition, an optimized downsampling strategy is employed to preserve critical spatial information, and a context-enhanced feature aggregation mechanism is introduced to strengthen robustness under complex backgrounds and illumination variations. The results demonstrate that YOLO-LAR achieves precision, recall, mAP@0.50, and mAP@0.50:0.95 of 0.959, 0.908, 0.961, and 0.814, respectively, outperforming mainstream YOLO-based models, including YOLOv11n, YOLOv10n, and YOLOv8n. These results indicate that YOLO-LAR provides an effective and practical solution for accurate tea bud detection, offering strong technical support for intelligent harvesting and precision management in mountainous tea plantation environments. Full article

The intelligent detection of asphalt pavement cracks has become increasingly important for ensuring service performance of road infrastructure. Traditional manual detection has significant safety hazards and insufficient accuracy. Furthermore, existing deep learning models still face challenges, including missed detection, false alarms, and poor performance in small target detection under complex conditions. This investigation adopts unmanned aerial vehicles (UAVs) to acquire pavement distress information and develops an intelligent detection approach for asphalt pavement crack based on improved YOLOv8s. First, the Spatial Pyramid Pooling Fast (SPPF) module is replaced with the Spatial Pyramid Pooling Fast with Cross Stage Partial Connections (SPPFCSPC) module in the backbone network to enhance the multi-scale feature fusion capability. Secondly, the Convolutional Block Attention Module (CBAM) module is introduced to the neck network to optimize the feature weights in both channel and spatial attention. Meanwhile, the Efficient Intersection over Union (EIoU) loss is adopted to improve accuracy. Finally, the Crack_Dataset is established, and the ablation experiments are conducted to verify the reliability of the detection model. The research indicates that the improved model achieves Precision, Recall, and mAP@0.5 of 83.9%, 79.6%, and 83.9%, respectively, representing increases of 1.5%, 1.3%, and 1.4%, compared with the baseline model. In comparison with mainstream object detection algorithms such as YOLOv5s and YOLOv8s, the proposed method attains an F1-score, mAP@0.5, and mAP@[0.5–0.95] of 0.82, 83.9%, and 46.6%, respectively, demonstrating a performance improvement. Based on the improved detection model, a pavement crack detection system was designed and implemented using PyQt5. This system supports image, video, and real-time camera input and detection. Full article

Visual–inertial odometry (VIO) often struggles with illumination variations, sparse visual features, and inertial drift in complex indoor settings, leading to scale uncertainties and accumulated errors. To address these issues, this paper proposes a new UWB–VIO initialization method combined with an enhanced Robust error-state Kalman filter (Robust ESKF) fusion technique for mobile robot localization. During initialization, common problems include scale drift and heading inconsistency. To solve these, a direction-consistent constrained initialization model is developed. By jointly optimizing the scale factor and yaw angle, this model ensures consistent alignment between the visual–inertial and ultra-wideband (UWB) coordinate frames. This approach removes the need for external calibration and independent coordinate transformation, which are typically required by traditional methods. In the fusion process, an improved residual-weighted robust filtering mechanism is employed to minimize the impact of abnormal UWB ranging data and noise interference. This mechanism adaptively suppresses outliers caused by UWB multipath reflections and non-line-of-sight (NLOS) propagation, thereby reducing VIO drift and improving the overall robustness and stability of the localization system. Experiments conducted in narrow-corridor environments, where both UWB and visual sensors are affected by interference, demonstrate that the proposed method significantly reduces trajectory drift and attitude jumps, resulting in better positioning accuracy and trajectory continuity. Compared to conventional UWB–VIO fusion algorithms, the proposed method enhances average localization accuracy by over 50% and maintains stable estimation even in severe multipath interference conditions, demonstrating high precision and strong robustness. Full article

Laser Powder Bed Fusion (LPBF)-processed AlSi10Mg produces highly heterogeneous microstructures, where fine α-Al cells, Si-rich networks, and melt-pool boundaries govern local mechanical behavior. Nanoindentation has emerged as a key tool for probing these variations, yet systematic understanding of the links between processing parameters, microstructure, and nano-mechanical response remains limited. This critical review examines how laser processing parameters influence local mechanical response through their impact on microstructural features. Key challenges in interpreting nanoindentation are highlighted, alongside inconsistencies in experimental protocols and reporting practices that hinder cross-study comparisons. Beyond summarizing existing findings, underexplored aspects of nanoindentation in LPBF AlSi10Mg are identified, including spatially correlated microstructure-mechanical mapping, depth-resolved measurements, and integration with advanced characterization and data-driven approaches. By synthesizing current knowledge and clarifying methodological constraints, this review positions nanoindentation not merely as a descriptive tool, but as a mechanistically informed approach for linking processing conditions, microstructural heterogeneity, and local mechanical response. These insights aim to support more rigorous interpretation of small-scale mechanical data and to guide future studies toward predictive understanding and rational process optimization in additively manufactured aluminum alloys. Full article