Search Results (29,557)

To address the challenges of high miss detection rates and accuracy degradation in UAV-based insulator defect detection—primarily stemming from complex background interference and the loss of fine-grained features—this paper presents an optimized lightweight detection framework based on an improved YOLOv8 model. The integration of a Spatial-to-Depth Convolution (SPDConv) module strengthens the extraction of fine-grained features for microscopic defects, while the incorporation of an SCConv module suppresses computational redundancy, leading to a 2.80% accuracy improvement. This architecture is further enhanced by a Channel and Spatial Reconstruction Attention Module (CSRAM), which dynamically prioritizes target-related regions and mitigates noise from vegetation and infrastructure. To improve regression robustness against low-quality annotations and blurred boundaries, a Focal-WIoU loss function utilizing a dynamic non-monotonic focusing mechanism is introduced. Experimental results on complex insulator datasets demonstrate that the proposed model achieves an mAP@0.5 of 91.75% and an mAP@0.5:0.95 of 59.86%, representing a 4.40% and 5.04% increase over the YOLOv8 baseline, respectively. Notably, while maintaining a lightweight profile with only 11.14 M parameters and 28.66 G FLOPs, the model achieves a high inference speed of 376.56 FPS, effectively enabling precise multi-scale defect recognition under extreme operational conditions. Full article

The performance of remote sensing-based mineral alteration extraction is significantly restricted in the vegetation area. Spectral unmixing is one of the effective methods to address the vegetation problem during mineral alteration extraction. However, the spectral curves of different tree species vary a lot; if multiple tree species are regarded as a whole during the spectral unmixing stage, the proportions of vegetation would be estimated with more errors. The purpose of this study was to verify the effects of dominant tree species classification on spectral unmixing and reconstruction, and to apply the proposed method to the mineral alteration extraction practice. To accomplish this, the Shabaosi gold deposit region in Mohe City, China, with an area of 650 km², was selected as the study area. Firstly, reference spectral curves, GaoFen-1/6 (GF-1/6) satellite imageries, ZiYuan-1F (ZY-1F) satellite imageries, Sentinel-1B satellite synthetic aperture radar (SAR) data, the ALOS digital elevation model (DEM), and sub-compartment dominant tree species data were collected; subsequently, simulated mixed-pixel reflectance images of ZY-1F, reflectance images of GF-1/6, ZY-1F, backscattering data of Sentinel-1B, slope, aspect, and 5484 tree species samples were derived from the collected data. Secondly, to verify the effect of dominant tree species classification on mineral alteration extraction, the reference spectra of pine, oak, goethite, and kaolinite were used to construct a simulated ZY-1F mixed-pixel image, and spectral unmixing and reconstruction experiments were conducted. Thirdly, fourteen independent variables were selected from the derived data, five dominant tree species classification models were trained and tested using tree species samples via the ResNet50 algorithm, and the pine- and birch-dominated parts were segmented from the ZY-1F images. Fourthly, minimum noise fraction (MNF), pixel purity index (PPI), n-dimensional visualizer auto-clustering, and spectral angle mapper (SAM) methods were separately applied to the pine- and birch-dominated parts of ZY-1F images to extract and identify endmembers; subsequently, the fully constrained least squares (FCLS) and linear spectral unmixing (LSU) methods were separately applied to the pine- and birch-dominated parts to estimate endmember proportions and generate spectrally reconstructed ZY-1F images. Fifthly, the pine- and birch-dominated parts of spectrally reconstructed ZY-1F images were mosaiced, and the SAM was utilized to extract mineral alteration in the study area. The result showed that in the spectral unmixing and reconstruction experiment, the spectral reconstruction error declined from 0.0594 (simulated ZY-1F image without segmentation) to 0.0292 and 0.0388 (simulated ZY-1F image that was segmented by pine- and oak-dominated parts), suggesting that dominant tree species classification could improve the accuracy of spectral unmixing and reconstruction and help obtain a more reliable mineral alteration extraction result. In the study area, the tested overall accuracies (OA) and Kappa coefficients of the five dominant tree species classification models were 0.75 ± 0.03 and 0.50 ± 0.05, respectively, suggesting that conducting dominant tree species classification was feasible in dense vegetation areas and could facilitate mineral alteration extraction. After segmenting the ZY-1F image by pine- and birch-dominated parts and spectral reconstruction, eight main types of alteration, including kaolinite, vesuvianite, montmorillonite, rutile, limonite, mica, sphalerite, and quartz, were identified, and nine mineral alteration areas (MA) were delineated accordingly. Full article

Background/Objectives: Ascending aortic aneurysm is a heterogeneous disease, with many cases of acute Stanford type A aortic dissection (ATAAD) presenting with aortic diameters below currently recommended surgical thresholds. Demographic factors such as age and sex, along with indexed aortic size groups, have been proposed to improve risk stratification. Methods: We included 65 adult patients who underwent surgical intervention for ATAAD. Morphometric measurements were obtained from computed tomography angiography (CTA) using centerline reconstruction. Maximum ascending aortic diameter and length were measured. Indexed parameters included the aortic size index (ASI), aortic height index (AHI), aortic length index (ALI) and cross-sectional aortic area indexed to height (CSA/H). Estimated pre-dissection dimensions were derived by reducing diameter by 18% and length by 2.7%. The cohort was stratified by age-, sex-, and ASI-defined groups. Results: Women were older than men (mean age 67 [SD 11] vs. 58 [SD 13] years, p = 0.01). Aortic diameter and length did not differ significantly by age or sex. At presentation, an ascending aortic diameter < 5.0 cm was observed in 37.1% of patients aged < 65 years and 26.7% of those aged ≥ 65 years. When stratified by sex, 25.0% of women and 35.6% of men presented with an ascending aortic diameter < 5.0 cm. Indexed parameters (ALI, AHI and ASI) were higher in older patients and women despite their smaller body size. In estimated pre-dissection analyses, less than 10% of the patients had diameters ≥ 5.5 cm, whereas most had estimated diameters < 5.0 cm. Conclusions: A substantial proportion of patients with ATAAD present with aortic dimensions below the current surgical thresholds. These findings underscore the limitations of diameter-based criteria and support the potential value of indexed geometric parameters in improving risk assessment in ATAAD patients. Full article

Stain variability in digital pathology affects both cross-center diagnostic consistency and the robustness of downstream computational analysis. In this work, we formulate stain normalization as a variational inverse problem and derive a Screened Poisson Normalization (SPN) model from the steady-state reaction–diffusion mechanism underlying histological staining. In the CIE $L^{*}{a}^{*}{b}^{*}$ space, the model couples a gradient-domain fidelity term with a chromatic anchoring term, yielding a screened Poisson equation that preserves tissue morphology while enforcing color consistency. We prove that the corresponding variational problem is well-posed in $H^1\left(\Omega \right)$ and stable with respect to perturbations of the input data. We further show that the screening term induces an intrinsic localization length ${\ell }_c\sim {\lambda }_c^{-1/2}$, so that boundary perturbations decay exponentially away from tile interfaces. Based on this locality, we develop a non-overlapping tiled DCT-based spectral solver for gigapixel whole-slide images, enabling consistent tile-wise stain normalization and seamless whole-slide reassembly without heuristic boundary blending. Experiments on multi-scanner, multi-protocol, and archival-fading pathology datasets show that SPN achieves stable stain normalization with competitive chromatic alignment and strong preservation of diagnostically relevant microstructure, particularly in full-slide and tiled reconstruction settings. Supplementary experiments on synthetic pathology-like images further support the robustness of SPN under controlled color perturbations and indicate good generalization across diverse staining variations. Full article

Background: Esophagectomy remains the definitive curative treatment for esophageal cancer but is historically burdened by significant procedure-related morbidity. Anastomotic leakage (AL) is still the “Achilles’ heel” of esophageal surgery, serving as a primary benchmark for surgical quality due to its profound impact on patient recovery, healthcare costs, and long-term oncological outcomes. While surgical expertise and perioperative care have matured, reported AL rates remain persistently high. This necessitates a shift in focus from purely technical modifications toward integrated, data-driven preventive strategies. Purpose: Five years after our initial review, this update synthesizes the rapid evolution in AL prevention. We evaluate the transition from empirical surgical pragmatism to evidence-based protocols, integrating recent breakthroughs in real-time perfusion monitoring, prophylactic endoluminal technologies, and multidisciplinary patient optimization. This work provides a contemporary “roadmap” for navigating the complexities of esophageal reconstruction. Conclusions: The prevention of AL has evolved into a multimodal “bundle” that begins well before the index operation. This review highlights the critical shift toward quantitative perfusion assessment via indocyanine green fluorescence angiography, which is increasingly replacing subjective visual inspection as the standard for anastomotic site selection. We discuss the emerging role of gastric ischemic preconditioning as a biological strategy to enhance conduit vascularity, alongside the paradigm of proactive management using preemptive endoluminal vacuum therapy to mitigate septic sequelae in high-risk cases. Furthermore, we examine technical refinements in conduit construction and conditioning—focusing on the ‘tension-perfusion’ relationship—and the essential role of structured prehabilitation within enhanced recovery after surgery frameworks. While the quality of evidence remains heterogeneous, the move toward standardized reporting and objective monitoring marks a new era of precision in esophageal surgery. Full article

This study investigates the influence of a battery’s formation process on its long-term performance and proposes a management strategy that integrates formation optimization with controlled over-discharge recovery. The results indicate that the initial formation is a critical prerequisite determining the effectiveness of subsequent capacity recovery. The proposed controlled over-discharge strategy aims to “reset” the battery degradation process by controllably oxidizing and decomposing the aged SEI and in situ reconstructing a new SEI. The efficacy of this strategy highly depends on the SEI substrate characteristics determined by the initial formation: different formation processes result in initial SEI layers with significant differences in structure and composition, which directly affect their reconstructability during over-discharge and the upper limit of performance recovery. Experiments confirm that batteries subjected to an optimized formation process achieve a maximum SOH improvement of 3.11% and a maximum cycle life extension of 19.7%. In contrast, batteries with a non-optimized formation show only a 2.01% SOH improvement and a 12.8% life extension. Therefore, optimizing the formation process is not only fundamental for enhancing the initial battery state but also a necessary prerequisite for fully unleashing the potential of subsequent recovery strategies. This study provides theoretical and technical foundations for establishing an integrated battery management strategy spanning from manufacturing formation to regeneration. Full article

The transverse beam size is a key parameter for characterizing the performance of synchrotron radiation sources. Accurate measurement of the transverse beam size is crucial for assessing beam quality. In this study, a fiber array-photomultiplier tube (PMT) beam measurement system was developed to enable high-precision sampling of beam profile information for beam-size measurement. Furthermore, a hybrid method integrating nonlinear least squares (NLLS) fitting and Gaussian basis-function fitting was proposed to reconstruct the beam intensity profile from discrete sampling data. Before performing NLLS fitting, a median absolute deviation (MAD)-based threshold filter is employed to remove outliers and suppress random noise, thereby improving the stability and robustness of the parameter estimation. The filtered data are then fitted using NLLS to obtain the reconstructed distribution. To capture potential high-order modal features in the beam profile, a Gaussian basis-function fitting model was also introduced for comparison, and its performance was evaluated under complex intensity distributions. Additionally, the relationship between the full width at half maximum (FWHM) and beam intensity was experimentally verified while accounting for measurement effects in the system. The results demonstrate that the proposed hybrid algorithm improves reconstruction accuracy and robustness, enabling precise recovery of the beam-intensity profile in the fiber-array PMT system. Full article

Accurate brain tumor segmentation from magnetic resonance imaging (MRI) remains challenging due to heterogeneous tumor morphology, intensity variability, and multi-scale structural complexity. This study proposes a DeepLabV3+-based segmentation framework integrating a VGG19-UNet encoder, Atrous Spatial Pyramid Pooling (ASPP), and low-level feature refinement to simultaneously capture hierarchical semantics and boundary-sensitive spatial details. The architecture enhances receptive field coverage without additional downsampling while preserving fine-grained contour information during reconstruction. Extensive evaluation was conducted on the Figshare Brain Tumor Segmentation (FBTS) dataset and the BraTS 2021 and BraTS 2018 benchmarks, focusing on Whole Tumor segmentation across multiple MRI modalities and tumor grades. Under five-fold cross-validation, the proposed model achieved a mean Dice Similarity Coefficient of 0.9717 and Jaccard Index of 0.9456 on FBTS, with stable and competitive performance across FLAIR, T1, T2, and T1CE modalities in both HGG and LGG cases. Boundary-level analysis further confirmed controlled Hausdorff Distance and low Average Symmetric Surface Distance. Statistical validation and ablation analysis demonstrate consistent improvements over baseline U-Net configurations. The proposed framework provides a robust and computationally efficient solution for automated brain tumor segmentation across heterogeneous datasets. Full article

Accurate estimation of global terrestrial evapotranspiration (ET) is fundamental for understanding the Earth’s water and energy cycles, yet existing multi-source ET products inevitably contain uncertainties that require spatially explicit characterization for optimal data merging or data assimilation. While Quadruple Collocation Analysis (QCA) offers a robust and reference-free approach to quantify uncertainties, its reliability in the ET discipline remains underexplored, and algorithmic non-convergence frequently results in substantial spatial data gaps. To address these limitations, this study evaluated the accuracy of the QCA method using validation errors derived from high-quality FLUXNET sites (N = 55). Moreover, we employed a Random Forest (RF) framework that is driven by 17 environmental variables to generate spatially seamless error maps for four mainstream ET products, i.e., ERA5, GLDAS, GLEAM, and MERRA2, from 2000 to 2020. Results demonstrate that QCA-based errors strongly correlated with ground-based errors as Pearson’s correlation coefficient was >0.3 for all four ET products. Furthermore, the RF model successfully reconstructed the spatial gaps in QCA errors, achieving an exceptionally low mean prediction error of approximately 0.03 mm/day. Based on these seamless maps, the global mean ET error is estimated at roughly 0.3 mm/day, with pronounced high-error clusters emerging in regions such as central Canada and northern Argentina driven by underlying land cover heterogeneity. Ultimately, this seamless gap-filling redefined the global map of product with the lowest estimated collocation error. ERA5 emerged as the superior choice across approximately 45% of the land surface (predominantly in the tropics and mid-to-high latitudes). Meanwhile, before algorithmic gap-filling, GLEAM was optimal across approximately 28% of the valid land pixels; after spatial gap-filling, it proved most effective across approximately 30% of the globe, particularly within arid deserts and glaciated regions. Our work provides useful geographic guidance for optimizing multi-source data merging and land data assimilation frameworks in future global hydrological studies. Full article

Signals of opportunity (SoO) enable emission-free passive sensing, but low Earth orbit (LEO) satellite illumination with unmanned aerial vehicle (UAV) array receivers exhibits rapid geometry variation. As a result, the received phase evolves in a space–time coupled manner, and the array snapshots become nonstationary even within one coherent processing interval (CPI), degrading conventional stationary-snapshot direction-of-arrival (DOA) estimators. This paper proposes a decomposition-based sparse reconstruction with successive interference cancellation (D-SR-SIC) framework for dynamic DOA estimation in LEO SoO UAV passive sensing. The proposed estimator leverages a sparse-reconstruction signal model and is implemented via a computationally efficient decomposition-based search-and-cancel procedure. A short-CPI parameterized space–time phase model captures the common motion-induced phase history and the time-varying steering drift; the coupled multi-parameter estimation is decomposed into two low-dimensional correlation searches followed by least-squares amplitude estimation and multi-target peeling. Optional local refinement and multi-beam pre-screening improve robustness to off-grid mismatch, near–far interference, and wide field-of-view operation. Simulations show that the proposed method achieves about 0.11${}^{\circ }$ DOA root-mean-square error (RMSE) at $-20$ dB signal-to-noise ratio (SNR) in a representative highly dynamic setting. Full article

Missing modalities remain a major obstacle to the real-world deployment of multimodal learning systems, as incomplete inputs can substantially degrade model performance. Existing methods often suffer from biased imputation under high missing rates and lack uncertainty-aware, differentiated processing. Inspired by three-way decision, a framework for handling uncertainty by adding a deferment option to acceptance and rejection, we propose three-way multimodal learning with severely missing modalities (3WML-SMMs), a novel framework that introduces a three-way decision mechanism into both missing-modality imputation and feature regularization for the first time. Specifically, 3WML-SMM treats variance not merely as a descriptive measure of uncertainty, but as a decision signal for adaptive processing. Based on this idea, the framework incorporates (1) a variance-guided three-way imputation strategy with accept–delay–reject decisions to reduce unreliable reconstruction when only a limited number of complete samples are available and (2) a dimension-wise adaptive feature enhancement module that performs fine-grained regularization according to perturbation uncertainty. Experiments on the CMU Multimodal Opinion Sentiment Intensity (CMU-MOSI) and Multimodal Internet Movie Database (MM-IMDb) datasets show that 3WML-SMM consistently outperforms representative baselines, including reconstruction-based methods, complete-input multimodal methods, and missing-modality-specific methods under severe missing-modality settings, with statistically significant improvements over the multimodal learning with severely missing modality (SMIL) baseline ($p<0.05$). These results demonstrate the effectiveness of the proposed framework, even in extreme settings where only 10% of the text modality is available. Full article

Recent automated search methods have improved lower bounds for several Ramsey numbers, but the strongest gains often depend on structured seeding and cell-specific heuristic discovery. This leaves open a more fundamental question: Can a useful search structure be transferred across related Ramsey cells rather than rediscovered independently for each target instance? This work proposes a teacher–student framework for transferable structural search in Ramsey graph construction, inspired by the structure-distillation logic of Physics Structure-Informed Neural Networks (Ψ-NNs). The framework builds compressed structural representations from teacher witnesses and search traces, extracts reusable motifs and relations, and reconstructs transfer candidates. These are refined by balanced search and, for weak R(3, s) cells, by exact small-cell supervision. The framework is evaluated as a proof of concept across five Ramsey cells under transfer, matched-compute, search, ablation, and interpretability settings, including a proportional shift-scaling baseline and a greedy triangle-closing baseline that probe the structure-validity frontier from complementary directions. Supplementary experiments cover seed robustness, budget sensitivity, transfer-neighborhood variation, structural-resolution changes, stronger exact supervision, cross-r teacher pooling, single-teacher configurations, and scaling behavior across graph sizes. The results show that the portfolio version of the framework is the strongest balanced transfer method in the current study, while a structure-dominant oracle achieves stronger witness-shape agreement but worse Ramsey-valid construction. These findings reveal a clear structure-validity frontier and suggest that transferable Ramsey search should be evaluated by how well structural priors survive the validity constraints of new cells. Full article

Background: Pre-pectoral direct-to-implant breast reconstruction is increasingly adopted after mastectomy because it avoids pectoralis major dissection, reduces postoperative pain, and eliminates animation deformity. However, reconstruction in patients with large or markedly ptotic breasts remains challenging because of skin envelope management, nipple–areola complex (NAC) viability, and implant stability. This study evaluated batwing skin-reducing mastectomy with immediate pre-pectoral polyurethane-coated implant reconstruction. Methods: We conducted a retrospective single-center study of consecutive patients who underwent batwing skin-reducing mastectomy with immediate pre-pectoral polyurethane-coated implant reconstruction between November 2022 and January 2025. Demographic, oncologic, operative, postoperative, and BREAST-Q data were collected. Primary outcomes included complications, oncologic events, and 12-month patient-reported outcomes. Results: Thirteen patients underwent reconstruction, accounting for 18 breasts, with a mean follow-up of 12.85 months. Mean age was 54.5 ± 9.7 years, mean body mass index was 27.0 ± 3.4 kg/m², and mean Regnault ptosis grade was 3.46 ± 0.52. No seromas or oncologic recurrences were observed. One hematoma and one late infection requiring implant removal occurred. Superficial NAC/central flap epidermolysis developed in four patients and resolved conservatively; no full-thickness NAC necrosis occurred. BREAST-Q scores improved significantly in all domains at 12 months, including satisfaction with breasts, psychosocial well-being, physical well-being, and sexual well-being (all p < 0.05). Conclusions: Batwing skin-reducing mastectomy with immediate pre-pectoral polyurethane implant reconstruction appears safe and reproducible in selected patients with advanced ptosis, with acceptable complication rates and significant improvement in patient-reported outcomes. Full article

Assembled H-shaped steel strut (AHSS) has been widely applied in deep excavation projects. In this study, the collapse failure of AHSS C1 in a deep excavation project in China was investigated. The collapse of C1 was directly attributed to the settlement of its supporting columns in the mid-span, which was triggered by a nearby pit bottom leakage through an exploration borehole. Then the implementation of the emergency measures and reconstruction works were introduced. Theoretical and numerical pre-assessments confirmed that the reconstructed C1 exhibited adequate safety for strength, in-plane stability and out-of-plane stability, with all steel components and bolts within their safe limits. The good working performance of reconstructed C1 was finally verified through the monitoring results (i.e., strut axial force, soil horizontal displacement, column vertical displacement, road settlement and building settlement) of the foundation pit during the subsequent soil excavation and basement construction. This study is believed to provide references for future excavation projects using AHSS with similar risks. Full article