Search Results (147,879)

Defect detection in the hot rolling process is closely linked to the quality of the final product. Among these defects, slab camber during the intermediate rolling stage is one of the primary manifestations of asymmetry, which significantly impairs both the quality of the finished strip and the stability of subsequent rolling processes. Conventional image-based edge detection methods for slab camber are prone to detection deviations in complex industrial environments, mainly due to their weak noise robustness. To address the scientific challenge of low accuracy and poor robustness in feature extraction for hot-rolled intermediate slab camber detection, which is induced by environmental interference in complex industrial settings, we break through the technical bottlenecks of traditional edge detection methods and existing deep learning models in terms of channel–spatial feature collaborative optimization and anti-interference fusion of multi-scale features. We establish a dense perception network model integrated with a channel–spatial attention mechanism, realize robust feature recognition of slab edges under complex working conditions, and provide theoretical and technical support for the real-time quantitative detection of slab shape defects in the hot rolling process. The proposed model significantly improves detection accuracy and robustness through multi-scale feature enhancement and noise suppression, effectively meeting the requirements for real-time quantitative detection of slab camber in the roughing rolling stage. Field experiments verify that the method increases detection accuracy by 36.55% and achieves favorable performance on evaluation metrics, including ODS and OIS. Full article

This manuscript presents a transferable and reproducible methodology for quantitative 3D tree-structure mensuration and transparent, rule-based decision support for tree risk management. The workflow integrates (i) Structure-from-Motion/Multi-View Stereo (SfM–MVS) reconstruction from multi-view imagery, (ii) independent referencing to ensure metric scaling and a consistent local frame, and (iii) point cloud analytics to derive branch-level geometric descriptors (e.g., base diameter, length, inclination, slenderness, and projected reach). A clear rule-based layer operationalizes Tree Risk Assessment Qualification (TRAQ)-style risk components and As Low As Reasonably Practicable (ALARP) principles to map geometry and exposure into auditable management recommendations (e.g., monitoring intervals, pruning/weight reduction, supplemental support, and exclusion-zone planning). To provide a real-data example, the demonstration uses the public Fuji-SfM apple orchard dataset, including three neighboring trees with partially overlapping crowns for tree instance extraction and subsequent TRAQ/ALARP scenarios on an outer tree. The proposed decision layer is intentionally based on external geometry and exposure; internal decay indicators and species-specific mechanical properties (e.g., Modulus of Elasticity (MOE), Modulus of Rupture (MOR)) are outside this demonstration and should be incorporated via complementary diagnostics in operational deployments. Full article

Adenosine triphosphate (ATP) serves as a central energy currency and signaling molecule in cellular processes, with ATP-binding sites in proteins playing critical roles in enzymatic catalysis, signal transduction, and gene regulation. The accurate identification of ATP-binding sites is essential for understanding protein function mechanisms and facilitating drug discovery, enzyme engineering, and disease pathway analysis. In this study, we present a novel hybrid deep learning framework that synergizes heterogeneous learning paradigms based on protein sequence information for accurate ATP-binding site prediction. Our approach integrates two complementary base classifiers. One is a Transformer-based model, which leverages high-level contextual embeddings generated by Evolutionary Scale Modeling 2 (ESM-2), a state-of-the-art protein language model, combined with a local–global dual-attention mechanism that enables the model to simultaneously characterize short-segment and long-range contextual dependencies across the entire protein sequence. The other is a deep Q-network (DQN)-inspired classifier that achieves residue-level prediction as a sequential decision-making process. The final predictions are generated using a weighted ensemble strategy, where optimal weights are determined via cross-validations to leverage the strengths of both models. The prediction results on benchmark independent testing sets indicate that our method achieves satisfactory performance on key metrics. Beyond predictive efficacy, this work uncovers the intrinsic biological mechanisms underlying protein–ATP interactions, including the synergistic roles of local structural motifs and global conformational constraints, as well as family-specific binding patterns, endowing the research with substantial biological significance. The research in this work offers a deeper understanding of the protein–ligand recognition mechanisms and supportive efforts on large-scale functional annotations that are critical for system biology and drug target discovery. Full article

Background: Tuberculosis (TB) has long been suspected to contribute to lung carcinogenesis through chronic inflammation and immune dysregulation. However, contemporary controlled evidence quantifying this association remains limited. We aimed to systematically evaluate the relationship between prior TB and subsequent lung malignancy, using recent observational studies and complementary case reports. Methods: A systematic review and random-effects meta-analysis were conducted, including controlled cohort and case–control studies published from 2020 onward. Adjusted effect estimates were converted to the logarithmic scale for pooling. Heterogeneity and small-study effects were assessed using standard meta-analytic techniques. Additionally, published case reports were descriptively analyzed to explore clinicopathological patterns. Results: Across eligible studies, prior TB was consistently associated with an increased risk of subsequent lung cancer (LC). The pooled estimate demonstrated a statistically significant positive association, despite moderate heterogeneity. Larger nationwide cohorts contributed greater statistical weight, while smaller studies showed wider variability. Case reports revealed heterogeneous temporal patterns, including long-latency scar-associated carcinoma and concurrent inflammatory–malignant presentations. Conclusions: Contemporary controlled evidence supports an association between prior tuberculosis and increased risk of subsequent lung malignancy. However, despite strong biological plausibility and the abundant literature on cancer-associated tuberculosis, modern longitudinal studies specifically evaluating tuberculosis as a preceding independent risk factor remain limited. The small number of eligible post-2020 investigations identified in this meta-analysis highlights a significant contemporary research gap and underlines the need for well-designed prospective studies to clarify causality and guide surveillance strategies in TB-exposed populations. Full article

Gas-filled discharge capillaries are widely used in the field of plasma-based particle accelerators, due to their compactness, cost-effectiveness and versatility for different applications. Technological improvement of such plasma sources is necessary to enable high energy gain acceleration at the meter scale, as required for next-generation particle colliders and light sources. Beam quality preservation within such an acceleration length involves accurate tuning of the plasma properties. In particular, precise tailoring of the plasma density distribution is required to control the emittance growth of particle bunches during the acceleration process. In this context, this paper presents a scalable and versatile approach for the design of meter-scale discharge capillaries, aimed at achieving fine tuning of the plasma density distribution, with the possibility of locally controlling the density profile by acting on the source geometry. Forty-centimeter-long capillaries are designed using numerical fluid dynamics simulations and tested in a dedicated plasma module. Different arrangements of the gas inlets are tested, with their number and diameter varied, to assess the effect of the capillary geometry on the plasma properties. Plasma density measurements show that a higher number of inlets with variable diameter along the plasma formation channel provides an enhancement in the homogeneity of the electron plasma density distribution. Longitudinal density plateaus are observed along most of the plasma channel length, with a center-to-end density uniformity of up to 80%. The experimental results highlight the proposed approach’s capability to modulate the longitudinal plasma density distribution by acting on the capillary geometry, thus providing uniform density profiles over the meter scale, as required for plasma-based acceleration experiments. Full article

Low-dose computed tomography (LDCT) can effectively reduce ionizing radiation; however, the associated image noise and artifacts can severely compromise the accuracy of clinical diagnosis. To address the challenge of balancing noise suppression and detail preservation in LDCT images, this study proposes a deep learning (DL)-based image denoising method termed Progressive Fusion Distillation Network (PFDN). Building upon the Information Multi-distillation Network (IMDN), the proposed method incorporates a pixel attention (PA) mechanism and a progressive fusion strategy, and further designs a Pixel Parallel Extraction Block (PPEB) together with a Progressive Fusion Distillation Block (PFDB) to fully exploit multi-scale and multi-channel features, thereby optimizing the image denoising network through efficient feature separation and re-fusion. In addition, by explicitly leveraging the noise characteristics specific to LDCT images, the method establishes an end-to-end training framework suitable for medical imaging. Experimental results demonstrate that PFDN not only effectively reduces image noise and artifacts, but also enhances overall image quality while preserving diagnostically relevant image structures under the adopted evaluation setting. Full article

Background and Objectives: Coronavirus disease 2019 (COVID-19) is characterized by excessive inflammatory responses, including the so-called cytokine storm, which contributes substantially to morbidity and mortality in hospitalized patients. The vagus nerve, through the cholinergic anti-inflammatory pathway, represents a theoretically attractive therapeutic target for modulating systemic inflammation. Vagus nerve stimulation (VNS) has emerged as a potential adjunctive treatment for COVID-19, with several randomized controlled trials (RCTs) investigating its efficacy on inflammatory biomarkers and clinical outcomes. The quality of this evidence base has not been rigorously evaluated. This systematic review critically appraises all available RCT evidence for VNS in hospitalized COVID-19 patients. Materials and Methods: We systematically searched PubMed, Scopus, Cochrane (CENTRAL), and Web of Science from database inception to January 2026, for RCTs evaluating any form of VNS (invasive, non-invasive, cervical, or auricular) in hospitalized patients with confirmed acute COVID-19. Two reviewers independently screened titles, abstracts, and full texts according to pre-specified eligibility criteria. Risk of bias was assessed using the Cochrane Risk of Bias 2 (RoB 2) tool, with assessments initially performed using multiple artificial intelligence tools and subsequently validated by the authors in accordance with PRISMA 2020 guidelines. Given substantial heterogeneity and high risk of bias, narrative synthesis was performed rather than meta-analysis. Also, GRADE assessment was performed. Results: From 437 records identified, six RCTs comprising 221 patients met the inclusion criteria. Five trials (83%) were rated as high risk of bias, primarily due to inadequate blinding, substantial baseline imbalances, significant missing data and extensive multiple testing without statistical correction. The single double-blind trial with a credible sham control (Rangon et al.) found null results across all outcomes, including clinical progression, ICU transfer, and mortality, while the five “high” risk-of-bias trials generally reported positive findings on various inflammatory markers and clinical outcomes. One trial (Corrêa et al.) measured heart rate variability as a direct indicator of vagal activation and found no change despite claiming anti-inflammatory effects, contradicting the proposed mechanism of action. Significant cognitive findings from an interim analysis (Uehara et al., n = 21) disappeared in the larger completed trial (Corrêa et al., n = 52), providing empirical demonstration of false positive findings in small, underpowered studies. Conclusions: Currently available evidence supporting the use of VNS for acute COVID-19 remains scarce; however, the physiological rationale remains sound, although the absence of reliable target engagement markers in the included studies limits confidence in this treatment method. Large-scale, double-blind, sham-controlled trials are required before VNS can be firmly recommended for COVID-19 management. Full article

Wind turbine blade surface defect detection remains challenging due to large variations in defect scales, blurred edge textures, and severe interference from complex backgrounds, which often lead to insufficient detection accuracy and high false and missed detection rates. To address these issues, this paper proposes an improved RTDETR-based detection framework, termed CEA-DETR, for wind turbine blade surface defect inspection. First, a Cross-Scale Multi-Edge feature Extraction (CSME) backbone is designed by integrating multi-scale pooling and edge-enhancement units with a dual-domain feature selection mechanism, enabling effective extraction of fine-grained texture and edge features across different scales. Second, an Efficient Multi-Scale Feature Fusion Network (EMSFFN) is constructed to facilitate deep cross-level feature interaction through adaptive weighted fusion and multi-scale convolutional structures, thereby enhancing the representation of multi-scale defects. Furthermore, an adaptive sparse self-attention mechanism is introduced to reconstruct the AIFI module, strengthening global dependency modeling and guiding the network to focus on critical defect regions under complex background conditions. Experimental results demonstrate that CEA-DETR achieves mAP50 and mAP50:95 of 89.4% and 68.9%, respectively, representing improvements of 3.1% and 6.5% over the RT-DETR-r18 baseline. Meanwhile, the proposed model reduces computational cost (GFLOPs) by 20.1% and parameter count by 8.1%. These advantages make CEA-DETR more suitable for deployment on resource-constrained unmanned aerial vehicles (UAVs), enabling efficient and real-time autonomous inspection of wind turbine blades. Full article

Grammatical error correction (GEC) for Chinese learner English is still dominated by sentence-level modeling, which limits discourse-level consistency and weakens adaptation to learner-specific error profiles. From an instructional perspective, these limitations also reduce the value of automated feedback as a basis for spaced-repetition instructional structures sensitive to individual differences. This study proposes a symmetry-aware dual-encoder architecture for context-aware GEC in Chinese learner English. A context encoder captures preceding-sentence information, while a source encoder integrates BERT-based semantic representations with Bi-GRU-based syntactic features for the current sentence. A gated decoder performs asymmetric fusion of local and contextual evidence. To better reflect corpus-level tendencies in Chinese learner English, a CLEC-informed augmentation strategy generates synthetic errors using empirical category frequencies as a coarse sampling prior. Experiments on CoNLL-2014, JFLEG, and CLEC show consistent improvements over strong neural baselines in F_0.5 and GLEU under the current desktop-oriented implementation setting. Nevertheless, the integration of BERT, dual encoders, and gated decoding introduces non-negligible computational overhead, and the present system is therefore better suited to desktop writing-support scenarios than to strict real-time or large-scale online deployment. The proposed framework thus provides a practical technical basis for personalized grammar feedback and for future spaced-repetition instructional designs in ESL writing support. Full article

Industrial volatile organic compound (VOC) emissions from large-scale petroleum storage represent a persistent environmental challenge, particularly in agricultural perimeters where atmospheric “breathing” cycles drive localized soil loading. This study investigates the thermodynamic and spatial relationship between gasoline storage emissions and chemical contamination in the Constanta South terminal area using a multi-layered analytical approach. By integrating gas chromatography (GC-MS) headspace analysis with an artificial intelligence (AI) framework utilizing high-order polynomial regression, we quantified the source–path–receptor dynamics across a thermal gradient (12 °C to 70 °C). The results reveal a non-linear surge in VOC emissions at temperatures exceeding 37 °C, characterized by a shift toward medium-weight hydrocarbons (C4–C6) that act as carriers for heavier aromatics. The AI risk model identified a significant spatial gradient, identifying a 500 m “critical zone” where the Hazard Quotient (HQ) is elevated, necessitating technological upgrades like Vapor Recovery Units (VRUs) to mitigate ecological risks. Full article

Bone marrow cell classification and nucleus segmentation in microscopic images are fundamental tasks for computer-aided diagnosis of lymphocytic leukemia. However, bone marrow cells from different subtypes exhibit high morphological similarity, and structural information is often constrained under optical microscopic imaging, posing challenges for stable and effective feature representation. To address this issue, we propose MTLL (Multitask Model on Lymphocytic Leukemia), a novel multitask approach that performs cell classification and nucleus segmentation within a unified network to exploit their complementary information. The model constructs a hybrid backbone for shared feature representation based on a CNN-Transformer architecture, in which Fuse-MBConv modules are tightly integrated with multilayer multi-scale transformers to enable deep fusion of local texture and global semantic information. For the segmentation branch, we design an AM (Atrous Multilayer Perceptron) decoder that combines atrous spatial pyramid pooling with multilayer perceptrons to fuse multi-scale information and accurately delineate nucleus boundaries. The classification branch incorporates prior knowledge of cell nuclei structures to capture subtle variations in cellular morphology and texture, thereby enhancing the model’s ability to distinguish between leukemia subtypes. Experimental results demonstrate that the MTLL model significantly outperforms existing advanced single-task and multi-task models in both lymphocytic leukemia classification and cell nucleus segmentation. These results validate the effectiveness of the multi-task feature-sharing strategy for lymphocytic leukemia diagnosis using bone marrow microscopic images. Full article

Accurate GNSS timing is fundamental to Power Time Synchronization Systems (PTSS). However, conventional substation infrastructures remain vulnerable to sophisticated spoofing attacks. In this research, a sensing-assisted array antenna-based spoofing mitigation method is proposed. The proposed architecture operates at the signal front-end and incorporates a dedicated spoofing sensing path to estimate the Direction-of-Arrival (DoA) of malicious signals, enabling adaptive null steering while preserving authentic satellite reception. To provide reliable spatial reference for DoA estimation, a unified high-precision attitude determination method is developed for compact 10 cm-scale array antennas under single-frequency and environmental error conditions. The method integrates the Constrained Least-squares AMBiguity Decorrelation Adjustment (C-LAMBDA)-based constrained ambiguity resolution, redundant antenna element-based vertical accuracy enhancement, and iterative refinement to mitigate centimeter-level environmental biases. Semi-simulated experiments demonstrate that the proposed method achieves baseline vector Root Mean Square Errors (RMSE) below 5 mm in horizontal components and approximately 10 mm in vertical components. The resulting attitude accuracies reach 2° in heading, 6° in pitch, and 4° in roll, while eliminating over 80% of systematic environmental phase errors with an average convergence within 6 iterations. These results satisfy the spatial accuracy requirements for effective spoofing suppression and front-end signal purification. Consequently, a robust technical approach is established for enhancing the anti-spoofing capabilities of PTSS without modifying existing infrastructure. Full article

Despite the widespread adoption of BIM, information exchange across disciplines remains hindered by heterogeneous structures at the tabular data level, particularly when integrating data across multiple discipline-specific models. Manual mapping, rigid templates, or one-off programming scripts are labor-intensive and difficult to scale, limiting automated querying, cross-model aggregation, and schedule-level analytics. This study proposes a lightweight, workflow-driven approach for semantic normalization and cross-model integration of BIM schedule data, with optional script-supported workflow configuration used only to assist the configuration of deterministic, rule-guided mapping logic, rather than serving as a core analytical method. By introducing a customizable subcategory layer, the workflow enables fine-grained semantic alignment and efficient normalization across diverse schedule datasets, implemented through lightweight Python scripting and rule-guided semantic matching used solely as a supporting mechanism for deterministic field mapping. Using structural, architectural, and HVAC models, we demonstrate a stepwise process including data cleaning, hierarchical classification, consistency checking, batch analytics, and automated computation of cross-model metrics such as opening-to-wall ratios. Sample-based validation confirms the workflow’s reliability, achieving semantic mapping agreement rates above 95% and reducing manual processing time by more than 85%. The workflow is readily extensible to other disciplines and modeling conventions, supporting high-throughput data integration for tasks such as design coordination, semantic alignment, RFI reduction, accelerated design reviews, and data-driven decision making. Overall, rather than introducing a new algorithm, the contribution of this work lies in formalizing a reusable, schedule-level workflow abstraction that enables consistent semantic alignment and automated cross-model aggregation without relying on rigid ontologies or training-intensive learning-based models. Any optional tooling used during workflow configuration is auxiliary and does not constitute a standalone learning-based method requiring model training or performance benchmarking. This provides a reusable methodological foundation for scalable, schedule-level BIM data integration and cross-model analytics. Full article

The Advanced Meteorological Imager (AMI) on GEO-KOMPSAT-2A (GK-2A) lacks a 2.1 μm shortwave infrared channel, precluding the dark target surface reflectance estimation that other geostationary aerosol retrievals rely on. We propose an improved land aerosol optical depth (AOD) retrieval in which background surface reflectance (BSR) is derived entirely from pixel-level bidirectional reflectance distribution function (BRDF) inversion using the scaled Ross-Thick Li-Sparse (sRTLS) kernel model fitted to geostationary time-series observations. Unlike existing approaches, the algorithm inverts the BRDF independently at each retrieval channel without relying on spectral reflectance relationships or external surface reflectance products; it assumes a low-background AOD during an initial accumulation period and then iteratively refines both BRDF coefficients and AOD. Two aerosol models—generic and dust—are supported, with a geographic dust-zone mask activating two-model selection during spring. Validation against 74 Aerosol Robotic Network sites over 2023 yields R = 0.86, RMSE = 0.15, and bias = −0.02, compared with R = 0.59, RMSE = 0.25, and bias = −0.04 for the National Meteorological Satellite Center (NMSC) GK-2A AOD product. The largest improvements appear at AOD ≤ 0.1 (bias: +0.03 versus +0.11) and AOD > 0.8 (bias: −0.12 versus −0.85). The full March–May (MAM) evaluation yields bias = −0.06 across all 74 sites. As a separate parallel retrieval restricted to matchups inside the geographic dust-zone mask, the proposed algorithm (dust model included) gives bias = −0.03, which worsens to −0.11 when only the generic model is applied—nearly a fourfold increase. A comparison against Himawari-9/Advanced Himawari Imager (AHI)—a co-located geostationary sensor carrying a 2.3 μm shortwave infrared (SWIR) channel—shows that the proposed algorithm (R = 0.897) outperforms Himawari-9/AHI (R = 0.855) across all metrics, demonstrating competitive accuracy without relying on a SWIR channel. Full article

Cervical cancer is one of the most prevalent and easily contracted diseases among women, significantly impacting their daily lives. Computer vision-based cervical cell morphology diagnosis technology can offer robust support for cervical cell analysis at a lower cost. However, the presence of a substantial number of overlapping cells in cervical images renders existing cell segmentation methods less accurate, thereby complicating the guidance of medical diagnosis. In this paper, we introduce a tristage Progressive Refinement method (PRefiner) for overlapping cell segmentation that decouples the traditional end-to-end pipeline, with the final stage specifically correcting anomalous results to enhance precision. We achieve separable overlapping cervical cell segmentation results through a cell nucleus locator, a single-cell segmenter, and a Segmentation Result Mask Refiner. Specifically, we employ a hybrid U-Net as the primary network for the cell nucleus locator and single-cell segmenter, which determines the position of the cell nucleus and procures the initial coarse segmentation result. In the mask refiner, we incorporate a conditional generation framework to address the perception decision problem and design a local–global dual-scale discriminator to ensure that the segmentation result aligns with the prior of a single-cell mask. Experimental results on CCEDD and ISBI2015 demonstrate that PRefiner achieves optimal performance by effectively resolving abnormal segmentations. Notably, our method improves the Dice coefficient of abnormal results from five different models by an average of 2.62% (ranging from 1.0% to 5.1%). Full article