Search Results (659)

This study evaluates the impact of assimilating the Crustal Movement Observation Network of China (CMONOC) global navigation satellite system (GNSS) tropospheric products on heavy-rainfall simulation over the complex terrain of the Sichuan Basin. Using the Weather Research and Forecasting model with the WRF Data Assimilation (WRF/WRFDA) three-dimensional variational (3DVar) system, we conducted a control (CTRL) experiment and a data-assimilation (DA) experiment for a primary heavy-rainfall event during 10–12 August 2020. The DA experiment applied 6 h cycling assimilation of station-based zenith total delay (ZTD) and precipitable water vapor (PWV). Compared with CTRL, DA improved the placement of the primary rainband and the depiction of peak rainfall. On 10 August, the observed rainfall core (~40 mm) over the northwestern basin was underestimated in CTRL (~15 mm) but was strengthened in DA (~25 mm). Hourly verification at a threshold of 2 mm h⁻¹ showed a higher maximum Threat Score (TS) in DA (0.292) than in CTRL (0.250), and the largest instantaneous gain reached 0.061. For 72 h accumulated precipitation, TS was higher in DA across multiple thresholds (≥10, ≥25, ≥50, and ≥100 mm), with the most pronounced improvement for heavier rainfall categories. Diagnostic analysis indicates that GNSS assimilation introduces dynamically consistent low-level moistening and strengthened convergence at 850 hPa, together with a better-aligned vertical ascent structure during the key stage of the event. An additional heavy-rainfall event during 21–23 August 2021 was further examined as a compact robustness test, and the results showed a generally consistent improvement in precipitation distribution and TS after GNSS assimilation. Overall, the present results suggest that cycling assimilation of CMONOC GNSS ZTD/PWV products can provide effective moisture constraints and improve heavy-rainfall simulation over the Sichuan Basin in the examined cases. Full article

Background/Objectives: Specific Learning Disorders are lifelong neurodevelopmental conditions that persist in adulthood, yet research has traditionally focused on children. In adults, there is significant heterogeneity in cognitive profiles and a lack of consensus on how to operationalize these disorders. This study aims to map the variability in cognitive functioning in university students with Specific Learning Disorders and investigate whether cognitive profiles differ across diagnostic categories and comorbidities. Methods: A retrospective analysis was conducted on the clinical documentation of 166 university students with a diagnosis of Specific Learning Disorders. Participants were categorized into three subgroups: predominant reading disorder, predominant arithmetic disorder, and mixed learning disorder. Cognitive functioning was assessed using Wechsler scales indices. Data were analyzed using linear mixed-effects models and Latent Profile Analysis. Results: Across the sample, reasoning abilities were significantly higher than cognitive efficiency, with working memory consistently emerging as a core weakness. The mixed-disorder group exhibited the lowest cognitive scores and the greatest working memory deficits. Latent Profile Analysis identified two distinct latent subgroups: a “Low Profile” characterized by weaker working memory and a “High Profile” characterized by stronger reasoning and balanced efficiency. Diagnostic labels were only partially aligned with these profiles; while the mixed-disorder group was overrepresented in the “Low Profile,” substantial intra-individual variability existed across all diagnostic categories. Conclusions: The findings suggest that traditional categorical labels for Specific Learning Disorders have limited explanatory power in adulthood, given the high heterogeneity of cognitive functioning. Cognitive weaknesses, particularly in working memory, persist even in high-achieving university students. Clinical and educational support should shift from a label-based approach toward a dimensional, profile-based model to better address the unique strengths and vulnerabilities of adults with Specific Learning Disorders. Full article

To address the challenges of difficult feature extraction and suboptimal parameter configuration for cyclotron ion source fault diagnosis in complex environments, this study proposes an intelligent diagnostic framework integrating Kernel Principal Component Analysis (KPCA), an Improved Sparrow Search Algorithm (ISSA), and a Support Vector Machine (SVM). The KPCA algorithm is employed for dimensionality reduction to handle the highly nonlinear nature of fault data. Regarding algorithmic evolution, the basic SSA is enhanced by integrating dynamic weights, opposition-based learning, and Cauchy mutation strategies, which effectively overcome the diagnostic bottlenecks inherent in cyclotron scenarios. Furthermore, the ISSA facilitates the global adaptive optimization of key SVM parameters, eliminating the stochasticity of empirical tuning and fundamentally enhancing the model’s robustness. Experimental results across 30 independent tests demonstrate that the KPCA-ISSA-SVM model achieves an average accuracy of 97.6% in multi-class fault detection. Compared with other classic diagnostic models, the proposed framework exhibits superior precision and stability, providing an effective technical approach with significant engineering value for the precise monitoring of ion source statuses. Full article

Background: Physical literacy is a multidimensional construct encompassing physical competence, confidence, motivation, knowledge, and lifelong engagement in physical activity. The Perceived Physical Literacy Instrument (PPLI) has been widely used internationally; however, previous adolescent validations have been based on a reduced 9-item version originally developed for teachers. This study aims to evaluate the validity and test–retest reliability of a Spanish adaptation of the original 18-item PPLI in Spanish adolescents aged 11–18 years. Methods: A multi-phase validation study was conducted with 869 Spanish adolescents (421 females). The procedure included: (1) translation and cultural adaptation, (2) Exploratory Factor Analysis (EFA; n = 290), Confirmatory Factor Analysis (CFA; n = 579) and invariance analyses, and (3) test–retest reliability assessment. Results: EFA supported a three-factor solution comprising 15 items. CFA showed standardized factor loadings ranging from 0.62 to 0.89, indicating that the latent constructs were adequately represented. Although the 15-item model showed acceptable fit, a 5-item unidimensional short form was developed due to limitations in the three-dimensional models. This short form demonstrated good model fit (scaled RMSEA = 0.073; scaled CFI = 0.992; SRMR = 0.026), adequate convergent validity (AVE = 0.558), high reliability (ω = 0.821), moderate test–retest stability (ICC = 0.69), and full configural, metric, and scalar longitudinal invariance. Conclusions: The 15-, 9-, and 5-item versions of the PPLI are valid and reliable options. The 15-item version allows comprehensive assessment and domain-level interpretation. The 9-item version facilitates comparability with previous international research. The 5-item version may be useful in contexts with time constraints but may not be the preferred choice for comprehensive assessment of physical literacy in clinical or detailed pedagogical diagnostic settings. Full article

Reservoir water level fluctuations alter the saturation line in earth-rock dams, thereby affecting the accuracy of electrical leakage detection. To quantitatively investigate this influence, a three-dimensional (3D) geoelectric model of a concentrated leakage pathway was constructed using COMSOL Multiphysics based on parameters from a reservoir in Zhejiang Province. Numerical simulations were performed under unsaturated, partially saturated, and fully saturated conditions with respect to the leakage zone, and a fixed-electrode monitoring system was deployed for in situ validation. The results show that 3D resistivity slices can approximately delineate the leakage hazard center. Under fully saturated conditions, the low-resistivity anomaly center shifts upward by 0.7 m. Under unsaturated conditions, the high-resistivity anomaly center shifts upward by 1.7 m. Under partially saturated conditions, the high-resistivity anomaly center exhibits the most pronounced upward shift (3.0 m). Notably, under partially saturated conditions, the boundary point between the high- and low-resistivity anomalies is located close to the central depth of the leakage pathway (deviation of approximately 0.7 m above the center), serving as a useful diagnostic indicator. In situ tests corroborate these findings, with identified anomaly zones matching the actual leakage points. This study provides a quantitative framework for interpreting geoelectrical data in earth-rock dams under fluctuating reservoir levels. Full article

Background/Objectives: Resin 3D printing is widely used to fabricate orthodontic diagnostic models, but the practical performance of commercial desktop workflows compared to dental-certified workflows is still debated. This study compared the dimensional accuracy and 7-day stability of maxillary orthodontic models printed from the same master STL file using a dental-certified workflow versus two commercial desktop workflows. Methods: An ISO 20896-1:2019-based reference cast with four 6 mm calibration spheres was used to generate a master STL file. Fifteen models were printed (n = 5 per workflow) using Primeprint™ (dental-certified workflow) and two commercial desktop printers (Anycubic Photon Mono M5s; Phrozen Sonic Mighty 14K REVO). The models were digitized at baseline (T0, ≤48 h) and after 7 days (T7) using a laboratory scanner. Surface superimposition in CloudCompare^® calculated the RMS (root mean square) surface deviation and mean signed deviation, and two calibrated operators performed independent extractions. Results: The mean RMS deviations were <0.10 mm for all workflows at both time points. No between-workflow differences were detected at T0 (H = 2.000; p = 0.368) or T7 (H = 1.520; p = 0.468), no within-workflow T0–T7 changes were significant (all p > 0.05), and the inter-operator agreement was excellent (ICC 0.991–0.999). Conclusions: Under the tested workflows, dental-certified and commercial desktop resin printing produced orthodontic models with a comparable global surface accuracy and short-term dimensional stability. Full article

Parallel robots are widely applied in many fields because of their unique advantages. To ensure their operational safety and reduce maintenance costs, designing an accurate and reliable fault diagnosis method is essential. Focusing on the 2RRU-RRS parallel robot, this paper proposes an intelligent fault diagnosis method based on a multi-scale convolutional residual network integrated with an Efficient Channel Attention mechanism (MS-ECA-ResNet). Firstly, to fully retain the time-frequency features of the signals, the one-dimensional vibration signals are converted into two-dimensional images using the Continuous Wavelet Transform (CWT). Secondly, a multi-scale convolutional feature extraction structure is designed to enhance the model’s feature extraction ability at different time scales. Furthermore, the ECA mechanism is introduced into the residual network to reinforce important feature channels and suppress noise interference. Comparative experiments, noise environment experiments, and ablation experiments were conducted on a 2RRU-RRS parallel robot experimental platform with a vibration signal dataset. The results demonstrate that the proposed method achieves superior diagnostic accuracy and robustness compared to typical deep learning models, particularly in maintaining high performance under simulated noise conditions. This provides a preliminary validation of the method’s effectiveness in capturing fault-related impacts, offering a potential technical reference for the health monitoring of parallel robots in real-world scenarios. Full article

Ultra-precision thinning of hard and brittle materials like monocrystalline silicon demands high dynamic stability in thinning spindle. To address the challenge of accurately detecting subtle spindle chatter anomalies in industrial environments characterized by high noise and limited data, this paper proposes a physics-guided dual-stream attention fusion transfer network (PG-AFNet). First, a physics-guided signal preprocessing method was developed. Using variational mode decomposition (VMD) and continuous wavelet transform (CWT) masking, one-dimensional dynamic features and high-frequency regions of interest (ROIs) rich in transient impact features were extracted. Second, the PG-AFNet architecture was designed. By introducing an attention mechanism, it achieves deep integration of one-dimensional purely dynamic sequences with two-dimensional spatiotemporal visual textures to capture surface damage features caused by subtle vibrations. Finally, systematic validations were conducted using a real silicon wafer thinning dataset with 197 real samples. By overcoming small-sample limitations via physical augmentation, PG-AFNet achieved an 82.45% (86.64% after data augmentation) diagnostic accuracy, significantly outperforming traditional baselines. Furthermore, a large-scale cross-load validation on the diverse CWRU dataset yielded an exceptional 99.68% accuracy under mixed-load conditions, conclusively verifying the model’s robust domain generalization. Lastly, a rigorous ablation study explicitly quantified the indispensable contributions of the physics-guided dual-stream architecture and attention fusion. This research provides a feasible theoretical foundation for intelligent surface quality monitoring in semiconductor hard-brittle material processing. Full article

In near-infrared optical breast lesion screening and diagnosis systems, high-speed four-dimensional scanners can dynamically acquire tens of thousands of lesion images within a five-minute period. Currently, manual computer annotation is required to generate standard samples from these scanned breast lesion images, a process that depends heavily on physicians with clinical expertise. On average, a single physician can annotate only approximately ten samples per working day. As a result, this process is time-consuming and labor-intensive, and the collected samples often suffer from low accuracy, large variability, and limited diagnostic reliability. Several AI-based annotation tools, such as QuPath, HALO AI™, and X-AnyLabeling, have been developed to assist this process. However, these tools are primarily manual or semi-automated and are unable to provide rapid and high-precision recognition. To address these limitations, this study proposes a new AI-based method for the rapid, accurate, and fully automated detection and diagnosis of breast lesions. The proposed approach complements existing AI-based annotation and diagnostic methods by enabling automated detection and classification of breast lesion samples. The proposed system employs a deep learning–based classification framework to construct a professional-level AI diagnostic model. The system automatically generates diagnostic outputs based on the annotation criteria used by professional physicians, including positive/negative classification and accuracy metrics. Compared with conventional manual diagnostic methods, the proposed approach provides faster and more reliable diagnostic estimates for new patients. These results demonstrate the potential of the proposed AI-based method to advance automated breast lesion screening and diagnosis and to contribute to future research and clinical applications in this field. Full article

Snowpack dynamics in continental climates are important for water-resource monitoring and snow water equivalent (SWE) estimation, yet the response of the snow depth–snow water equivalent (SD-SWE) relationship to changing thermodynamic and accumulation forcing remains insufficiently understood. This study develops a process-based framework to evaluate how moderate perturbations in air temperature and precipitation influence snowpack evolution and depth–mass coupling in representative snow regimes of northeastern Kazakhstan. SNTHERM (the Snow Thermal Model) simulations were combined with regression analysis, ANCOVA diagnostics, and bulk-density evaluation under controlled delta-change perturbations of air temperature (±1–2 °C) and precipitation (±5–10%). The results show that the SD-SWE relationship remains approximately linear within the tested perturbation range (R² ≈ 0.78–0.84), although its parameters are partially sensitive to precipitation-driven accumulation. Temperature perturbations mainly affect melt timing, seasonal persistence, and snow-density redistribution, whereas precipitation modifies snowpack mass and overburden, enhancing mechanical compaction and increasing the regression slope. These findings indicate that snow density is a key integrative state variable linking energy balance, phase change, and compaction processes. Under the tested conditions, snow depth remains a physically consistent proxy for SWE, although the conclusions are limited by the one-dimensional model structure, reanalysis-based forcing, and restricted observational coverage. Full article

Background: Prostate cancer, benign prostatic hyperplasia, and prostatitis share substantial overlap in clinical symptoms and biological characteristics, which hampers non-invasive and early differential diagnosis. Untargeted metabolomics enables comprehensive profiling of disease-associated metabolic alterations; however, its high dimensionality and strong feature correlations challenge conventional statistical approaches. Methods: To address this, we analyzed serum untargeted LC–MS data following standardized preprocessing. We adopted a nested cross-validation strategy to evaluate various feature selection methods and machine learning classifiers, ultimately determining that multiclass LASSO regression was the most effective feature selection approach. Results: An optimized Random Forest model demonstrated strong, superior performance in distinguishing between prostate cancer, prostatitis, benign prostatic hyperplasia, and healthy controls (out-of-fold accuracy: 93.8%; macro-F1: 0.937). Additionally, SHAP (SHapley Additive exPlanations) analysis translated feature statistical importance into biologically meaningful modules, revealing that distinct, disease-specific patterns of metabolic reprogramming drove the model’s robust multiclass discrimination. Conclusions: This study demonstrates the value of integrating serum untargeted metabolomics with advanced explainable machine learning for effective multiclass differentiation of major prostate diseases, providing a promising non-invasive framework for diagnostic stratification and metabolic biomarker discovery. Full article

Ultra-high-field (UHF) magnetic resonance imaging, defined as imaging at field strengths of 7 Tesla (7T) and above, represents a frontier technology in neuroimaging with emerging applications in prenatal brain research. This narrative review examines the current evidence on the potential benefits of UHF-MRI for investigating embryonic and fetal brain development. Through analysis of 97 studies identified across multiple databases, we find that UHF-MRI offers substantial advantages in spatial resolution, tissue contrast, and anatomical detail compared to conventional clinical field strengths (1.5T and 3T). The primary applications to date have been in ex vivo imaging of post-mortem fetal specimens and preclinical animal models, where UHF-MRI has enabled unprecedented visualization of laminar cortical organization, early sulcation patterns, microstructural development, and subtle anatomical features critical for understanding normal and abnormal neurodevelopment. Key benefits include enhanced delineation of transient developmental zones, improved characterization of cortical folding, superior detection of subtle malformations, and the ability to create high-resolution three-dimensional atlases of fetal brain development. However, significant technical and safety challenges currently limit in utero human applications, including concerns about specific absorption rate, acoustic noise, and fetal motion artifacts. This review identifies critical knowledge gaps and future directions for translating UHF-MRI technology to clinical prenatal diagnostics. Full article

This study proposes a four-dimensional Historical Building Defect Information Modeling (HBDIM) framework designed to support the documentation, diagnosis, and conservation of deteriorated historic stucco elements. The framework integrates multi-source digital documentation techniques, including terrestrial laser scanning (TLS), high-resolution photogrammetry, and automated total station measurements with laboratory-based material diagnostics to create a unified digital environment for defect detection and conservation assessment. The approach was applied to the Baron Empain Palace in Egypt as a representative case study of complex architectural heritage affected by material deterioration. Within the HBDIM workflow, point cloud processing and defect-oriented information modeling were used to identify and spatially localize deterioration features such as cracking, erosion, and material loss. Laboratory investigations—including computed tomography (CT), scanning electron microscopy (SEM), transmission electron microscopy (TEM), and X-ray fluorescence (XRF)—were conducted to evaluate the effectiveness of calcium hydroxide nanoparticle consolidation treatments and to relate microstructural material behavior to spatially mapped defects within the digital model. Mechanical testing demonstrated a significant improvement in material performance, with treated stucco samples exhibiting an average compressive strength increase of approximately 69.06% compared to untreated specimens. The results demonstrate that integrating digital documentation, defect-oriented modeling, and material diagnostics within a four-dimensional framework provides a robust platform for linking geometric deterioration patterns with material-level conservation performance. By embedding diagnostic data and treatment outcomes within a temporally structured digital model, the HBDIM approach supports preventive conservation strategies, long-term monitoring, and data-driven decision-making in sustainable heritage management. Full article

Purpose: The purpose of this study was to evaluate the feasibility of magnetic resonance imaging (MRI)-based radiomics derived from routine T2-weighted imaging for initial risk stratification in pediatric neuroblastoma (NB) and to explore its potential role as a noninvasive adjunct to established clinical and molecular classification systems. Methods: In this retrospective, single-center pilot study, 45 children with newly diagnosed NB (2015–2024) were analyzed. Primary tumors were manually segmented on baseline axial T2-weighted MRI. A total of 107 Image Biomarker Standardization Initiative (IBSI)-compliant radiomic features were extracted. Supervised machine learning classifiers (Random Forest, XGBoost) and dimensionality reduction approaches (principal component analysis [PCA], linear discriminant analysis [LDA]) combined with K-means clustering were evaluated. Model performance was assessed using stratified cross-validation and an independent test set. Reporting adhered to the Checklist for Evaluation of Radiomics Research (CLEAR). Results: Fifteen patients (33%) were classified as high-risk (HR) and 30 (67%) as non-high-risk (NHR) according to International Neuroblastoma Risk Group (INRG) criteria. The highest classification performance was achieved using LDA followed by K-means clustering, with a test accuracy of 77.8%, sensitivity of 64.7%, and specificity of 85.7%. Radiomic classification agreed with conventional risk stratification in 77.8% of cases. The analysis relied exclusively on T2-weighted imaging, supporting workflow feasibility without requiring contrast administration or advanced MRI sequences. Conclusions: In this single-center pilot study, T2-weighted MRI radiomics demonstrated feasibility for noninvasive initial risk stratification in pediatric NB. Although limited by sample size and the lack of external validation, these findings support further multicenter investigations of radiomics as an adjunctive imaging biomarker during early diagnostic evaluation. Full article

Accurately extracting deviation features in frequency response curves, which reflect winding deformation states, and selecting appropriate machine learning algorithms are critical for achieving a precise quantitative diagnosis of winding deformation based on frequency response analysis (FRA). To address the existing challenges in transformer winding fault diagnosis, including the absence of a systematic feature evaluation framework for frequency response data and the limited recognition accuracy of machine learning models, a novel hybrid feature selection and diagnostic framework was developed. First, a high-dimensional feature pool comprising 25 numerical indices was extracted from experimental FRA curves. To eliminate feature redundancy and arbitrary selection, a hybrid mechanism integrating maximum-relevance, minimum-redundancy (mRMR) with random forest (RF) was developed to dynamically construct task-specific optimal feature subsets. Furthermore, an inverse-distance-weighted K-nearest neighbors (IKNN) model was introduced to enhance diagnostic sensitivity by accounting for feature-space distance variations. Experimental results obtained from a laboratory winding model demonstrate that the proposed mRMR-RF-IKNN model significantly outperforms traditional and optimized benchmarks across multiple macro-evaluation metrics. This study provides a systematic, intelligent screening mechanism that ensures high-precision identification of both the types and severity of faults in power transformers. Full article