Search Results (245)

This study interrogates whether mental toughness (MT) and self-compassion (SC)—historically framed as oppositional constructs—can coexist synergistically among NCAA Division II, III, and NAIA collegiate athletes, with mindfulness as a hypothesized mediator. A cross-sectional survey of 396 participants (mean age: 19.8 yrs ± 1.9 SD; females: 51%), revealed a robust MT–SC correlation (r = 0.46), which attenuated to 0.31 when mindfulness was modeled, signaling its role as a partial mediator. Hierarchical regression controlling for sex showed that MT and sex together explained 22% of the SC variance (ΔR² = 0.22, p < 0.001). Adding mindfulness increased the total explained variance to 39% (ΔR² = 0.17, p < 0.001). Females scored slightly lower on SC (β = –0.14, SE = 0.05, p = 0.008). Sobel testing confirmed significant partial mediation (Z = 7.22, p < 0.001), with mindfulness explaining 33% of MT’s total effect on SC. Mindfulness-based interventions that exploit athletes’ intrinsic attentional resources can simultaneously enhance mental toughness and self-compassion. By reconciling performance-oriented rigor with resilient self-regard, such strategies hold promise for athletes operating at diverse competitive levels. Full article

Background: The epidemiology, characteristics and outcomes of coinfections in COVID-19 are still poorly understood. Methods: We investigated the prevalence of coinfections in COVID-19 patients hospitalized in Switzerland over the first three epidemic waves between 1 March 2020 and 1 June 2021, as well as risk factors and outcomes. Patients were identified from six hospitals of the Swiss prospective surveillance system database (CH-SUR). Details of the type and treatment of coinfections were retrieved retrospectively from medical charts. We assessed the proportion of patients with suspected coinfections and analyzed risk factors and 90-day in-hospital survival using logistic and Cox regression. Results: Of 13,265 identified patients, 36.6% (4859/13,625) had suspected coinfections, and 44.8% (5941/13,625) received antibiotics. Respiratory coinfections (25.6%) were the most common, followed by bloodstream (19.8%) and urinary tract infections (14.6%). Escherichia coli (14.8%), Staphylococcus aureus (10.7%) and Klebsiella pneumoniae (6.1%) were the most frequently isolated pathogens. The risk factors for coinfections included increasing age, male gender, certain underlying medical conditions and immunosuppression. Suspected coinfections were associated with a longer hospital stay (13 vs. 7 days, p < 0.001), more frequent ICU admission (26% vs. 6.7%, p < 0.001) and higher rates of in-hospital death (24% vs. 9.5%, p < 0.001). Hospitalization in the ICU at the time of COVID-19 diagnosis had the strongest association with coinfections. Conclusions: A high proportion of COVID-19 patients had coinfections, particularly respiratory infections, and received antibiotics. Coinfections were associated with severe illness and worse outcomes. Full article

Background/Objectives: Panoramic radiography (PANO) is widely utilized for routine dental examinations, as a single PANO image captures most anatomical structures and clinical findings, enabling an initial assessment of overall dental health. Dentists rely on PANO images to enhance clinical diagnosis and inform treatment planning. With the advancement of artificial intelligence (AI), the integration of clinical data and AI-driven analysis presents significant potential for supporting medical applications. Methods: The proposed method focuses on the segmentation and localization of impacted third molars in PANO images, incorporating Sobel edge detection and enhancement methods to improve feature extraction. A convolutional neural network (CNN) was subsequently trained to develop an automated impacted tooth detection system. Results: Experimental results demonstrated that the trained CNN achieved an accuracy of 84.48% without image preprocessing and enhancement. Following the application of the proposed preprocessing and enhancement methods, the detection accuracy improved significantly to 98.66%. This substantial increase confirmed the effectiveness of the image preprocessing and enhancement strategies proposed in this study. Compared to existing methods, which achieve approximately 90% accuracy, the proposed approach represents a notable improvement. Furthermore, the entire process, from inputting a raw PANO image to completing the detection, takes only 4.4 s. Conclusions: This system serves as a clinical decision support system for dentists and medical professionals, allowing them to focus more effectively on patient care and treatment planning. Full article

Precise detection of potato diseases is critical for food security, yet traditional image segmentation methods struggle with challenges including uneven illumination, background noise, and the gradual color transitions of lesions under complex field conditions. Therefore, a collaborative segmentation framework of Otsu and Sobel edge detection based on the beluga whale optimization algorithm with a danger sensing mechanism (DSBWO) is proposed. The method introduces an S-shaped control parameter, a danger sensing mechanism, a dynamic foraging strategy, and an improved whale fall model to enhance global search ability, prevent premature convergence, and improve solution quality. DSBWO demonstrates superior optimization performance on the CEC2017 benchmark, with faster convergence and higher accuracy than other algorithms. Experiments on the Berkeley Segmentation Dataset and potato early/late blight images show that DSBWO achieves excellent segmentation performance across multiple evaluation metrics. Specifically, it reaches a maximum IoU of 0.8797, outperforming JSBWO (0.8482) and PSOSHO (0.8503), while maintaining competitive PSNR and SSIM values. Even under different Gaussian noise levels, DSBWO maintains stable segmentation accuracy and low CPU time, confirming its robustness. These findings suggest that DSBWO provides a reliable and efficient solution for automatic crop disease monitoring and can be extended to other smart agriculture applications. Full article

To address the challenges of poor instance segmentation accuracy, real-time performance trade-offs, high miss rates, and imprecise edge localization in tomato grading and harvesting robots operating in complex scenarios (e.g., dense growth, occluded fruits, and dynamic viewing conditions), an accurate, efficient, and robust visual instance segmentation network is urgently needed. This paper proposes TQVGModel (Tomato Quality Visual Grading Model), a Mask RCNN-based instance segmentation network for tomato quality grading. First, TQVGModel employs a multi-branch IncepConvV2 backbone, reconstructed via ConvNeXt architecture and large-kernel convolution decomposition, to enhance instance segmentation accuracy while maintaining real-time performance. Second, the Class Balanced Focal Loss is adopted in the classification branch to prioritize sparse or challenging classes, reducing the miss rates in complex scenes. Third, an Enhanced Sobel (E-Sobel) operator integrates boundary prediction with an edge loss function, improving edge localization precision for quality assessment. Additionally, a quality grading subsystem is designed to automate tomato evaluation, supporting subsequent harvesting and growth monitoring. A high-quality benchmark dataset, Tomato-Seg, is constructed for complex-scene tomato instance segmentation. Experiments show that the TQVGModel-Tiny variant achieves an 80.05% mAP (7.04% higher than Mask R-CNN), with 33.98 M parameters (10.2 M fewer) and 53.38 ms inference speed (16.6 ms faster). These results demonstrate TQVGModel’s high accuracy, real-time capability, reduced miss rates, and precise edge localization, providing a theoretical foundation for tomato grading and harvesting in complex environments. Full article

Wear particle analysis, which identifies failure modes caused by the wear of various machine components, is an essential technique for monitoring machinery conditions. This analysis plays a vital role in predictive maintenance by revealing component degradation in machinery. This study proposes an automated framework to classify four standard wear particle textures—rough, striated, pitted, and fatigued—using artificial neural networks (ANNs) combined with advanced image processing techniques. Images acquired via Charged-Coupled Device (CCD) microscopy were preprocessed using sharpening, histogram stretching, and four edge detection algorithms: Sobel, Laplacian, Boie–Cox, and Canny. The Laplacian and Canny methods yielded the highest classification accuracies of 97.9% and 98.9%, respectively. By minimizing human subjectivity, this automated approach enhances diagnostic consistency and represents a scalable solution for industrial condition monitoring. Full article

To address the issues of poor environmental adaptability and high costs associated with traditional methods of measuring water body boundaries, this paper proposes an innovative path planning approach for water body boundary measurement based on Unmanned Surface Vehicles (USVs)—the Chemotactic Search Traversal (CST) algorithm. This method incorporates the chemotaxis operation mechanism of the Bacterial Foraging Optimization algorithm, integrating it with high-resolution grid maps to enable efficient traversal and accurate measurement of water body boundaries within large-scale grid environments. Simulation experiments demonstrate that the CST algorithm outperforms the Brute Force Algorithm (BFA), Roberts operator, Canny operator, Log operator, Prewitt operator, and Sobel operator in terms of optimal pathfinding, stability, and path smoothness. The feasibility and reliability of this algorithm in real water environments are validated through experiments conducted with actual USVs. These findings suggest that the CST algorithm not only enhances the accuracy and efficiency of water body boundary measurement but also offers a cost-effective and practical solution for measuring water body areas. Full article

Ultrafast laser systems, implemented at the ELI-NP, require exceptional beam quality and spatial stability due to their femtosecond pulse durations and extremely high peak powers. This work presents a diagnostic and computational framework for analyzing the ELI-NP Front-End beam characteristics, where spatial coherence and precise pulse shaping are essential for reliable amplification and experimental consistency. The methodology integrates classical beam diagnostics with image processing and machine learning tools to evaluate anomalies based on high-resolution beam profile images. We use centroid tracking to monitor pointing fluctuations, statistical intensity analysis to detect energy instabilities, and Sobel-based edge detection to evaluate beam sharpness and extract structural features from the beam image. Geometric parameters such as ellipticity, roundness, and symmetry indicators are extracted and examined over time. The system applies an unsupervised Isolation Forest algorithm to detect subtle or short-lived anomalies, identifying irregularities without relying on predefined thresholds. These diagnostics are supported by visual plots and statistical summaries, offering a clear picture of the beam’s behavior under real operating conditions. Results confirm that this integrated approach effectively captures major and minor beam instabilities, making it a practical tool for continuous monitoring and performance optimization in ultrafast laser systems. Full article

Space–Time Image Velocimetry (STIV) estimates the one-dimensional time-averaged velocity by analyzing the main orientation of texture (MOT) in space–time images (STIs). However, environmental interference often blurs weak tracer textures in STIs, limiting the accuracy of traditional MOT detection algorithms based on shallow features like images’ gray gradient. To solve this problem, we propose a deep learning-based MOT detection model using a dual-channel ResNet (DCResNet). The model integrates gray and edge channels through ResNet18, performs weighted fusion on the features extracted from two channels, and finally outputs the MOT. An adaptive threshold Sobel operator in the edge channel improves the model’s ability to extract edge features in STI. Based on a typical mountainous river (located at the Panzhihua hydrological station in Panzhihua City, Sichuan Province), an STI dataset is constructed. DCResNet achieves the optimal MOT detection at a 7:3 gray–edge fusion ratio, with MAEs of 0.41° (normal scenarios) and 1.2° (complex noise scenarios), respectively, outperforming the single-channel models. In flow velocity comparison experiments, DCResNet demonstrates an excellent detection performance and robustness. Compared to current meter results, the MRE of DCResNet is 4.08%, which is better than the FFT method. Full article

Adolescent externalizing problems are commonly linked to maternal psychological distress and family functioning, but these associations remain underexplored in the Greek sociocultural context. This study examined how maternal symptoms of depression, anxiety, and stress, along with adolescent-perceived family functioning, predict externalizing behaviors in Greek adolescents. A total of 563 adolescent–mother dyads (63.4% girls; M_age = 15.03 and SD = 0.83) participated. Mothers completed the Child Behavior Checklist (CBCL), the Depression Anxiety Stress Scales (DASS-21), and the Family Assessment Device (FAD–GF), while adolescents completed the Youth Self-Report (YSR) and FAD–GF. Hierarchical regression analysis showed that adolescent-perceived family functioning was the strongest predictor of externalizing behavior (β = 0.24 and p < 0.001), even after accounting for demographic and maternal mental health variables. The final model explained 18% of the variance in adolescent externalizing problems. Mediation analysis confirmed that family functioning partially mediated the relationship between maternal depression and adolescent externalizing problems, with a significant indirect effect (a × b = 0.088, Sobel z = 2.90, and p = 0.004). Gender differences were found for self-reported aggressive behavior (t = −2.40, p = 0.017, and d = 0.20), with girls scoring higher than boys. These findings highlight the indirect impact of maternal depression through family dynamics and underscore the importance of culturally sensitive, family-centered interventions to reduce adolescent externalizing problems. Full article

Background: Glioblastoma multiforme (GBM) is a very malignant brain tumor. GBM exhibits cellular and molecular heterogeneity that can be exploited to improve patient outcomes by individually tailoring chemotherapy regimens. Objective: Our objective was to develop a predictive model of the life expectancy of GBM patients using data on tumor cells’ sensitivity to chemotherapy drugs, as well as the levels of blood cells and proteins forming the tumor microenvironment. Methods: The investigation included 31 GBM patients from the Almazov Medical Research Centre (Saint Petersburg, Russia). The cytotoxic effects of chemotherapy drugs on GBM cells were studied by an MTT test using a 50% inhibitory concentration (IC₅₀). We analyzed the data with life expectancy by a one-way ANOVA, principal component analysis (PCA), ROC, and Kaplan–Meier survival tests using GraphPad Prism and Statistica 10 software. Results: We determined in vitro the IC₅₀ of six chemotherapy drugs for GBM and 32 clinical and biochemical blood indicators for these patients. This model includes an assessment of only three parameters: IC₅₀ of tumor cells to carboplatin (CARB) higher than 4.115 μg/mL, as well as levels of band neutrophils (NEUT-B) below 2.5% and total protein (TP) above 64.5 g/L in the blood analysis, which allows predicting with 83.3% probability (sensitivity) the life expectancy of patients for 15 months or more. In opposite, a change in these parameters—CARB above 4115 μg/mL, NEUT-B below 2.5%, and TP above 64.5 g/L—predict with 83.3% probability (specificity) no survival rate of GBM patients for more than 15 months. The relative risk for CARB was 6.41 (95 CI: 4.37–8.47, p = 0.01); for NEUT-B, the RR was 0.40 (95 CI: 0.26–0.87, p = 0.09); and for TP, it was 2.88 (95 CI: 1.57–4.19, p = 0.09). Overall, the model predicted the risk of developing a positive event (an outcome with a life expectancy more than 10 months) eight times (95 CI 6.34–9.66, p < 0.01). Cross k-means validation on three clusters (n = 10) of the model showed that its average accuracy (sensitivity and specificity) for cluster 1 was 74.98%; for cluster 2, it was 66.7%; and for cluster 3, it was 60.0%. At the same time, the differences between clusters 1, 2, and 3 were not significant. The results of the Sobel test show that there are no interactions between the components of the model, and each component is an independent factor influencing the event (life expectancy, survival) of GBM patients. Conclusions: A simple predictive model for GBM patients’ life expectancy has been developed using statistical analysis methods. Full article

Railwayfreight cars operating in heavy-load and complex outdoor environments are frequently subject to adverse conditions such as haze, temperature fluctuations, and transmission interference, which significantly degrade the quality of the acquired images and introduce substantial noise. Furthermore, the structural complexity of freight cars, coupled with the small size, diversity, and complex structure of defect areas, poses serious challenges for image denoising. Specifically, it becomes extremely difficult to remove noise while simultaneously preserving fine-grained textures and edge details. These challenges distinguish railway freight car image denoising from conventional image restoration tasks, necessitating the design of specialized algorithms that can achieve both effective noise suppression and precise structural detail preservation. To address the challenges of incomplete denoising and poor preservation of details and edge information in railway freight car images, this paper proposes a novel image denoising algorithm named the Nonlinear Activation-Free Network based on Multi-Scale Edge Enhancement and Fusion (NAF-MEEF). The algorithm constructs a Multi-scale Edge Enhancement Initialization Layer to strengthen edge information at multiple scales. Additionally, it employs a Nonlinear Activation-Free feature extractor that effectively captures local and global image information. Leveraging the network’s multi-branch parallelism, a Multi-scale Rotation Fusion Attention Mechanism is developed to perform weight analysis on information across various scales and dimensions. To ensure consistency in image details and structure, this paper introduces a fusion loss function. The experimental results show that compared with recent advanced methods, the proposed algorithm has better noise suppression and edge preservation performance. The proposed method achieves significant denoising performance on railway freight car images affected by Gaussian, composite, and simulated real-world noise, with PSNR gains of 1.20 dB, 1.45 dB, and 0.69 dB, and SSIM improvements of 2.23%, 2.72%, and 1.08%, respectively. On public benchmarks, it attains average PSNRs of 30.34 dB (Set12) and 28.94 dB (BSD68), outperforming several state-of-the-art methods. In addition, this method also performs well in railway image dehazing tasks and demonstrates good generalization ability in denoising tests of remote sensing ship images, further proving its robustness and practical application value in diverse image restoration tasks. Full article

Hematoxylin and eosin (HE) staining is widely used in medical diagnosis. Stained slides provide crucial information to diagnose or monitor the progress of many diseases. Due to the large size of scanned images of whole tissues, a JPEG algorithm is commonly used for compression. This lossy compression method introduces artifacts visible as 8 × 8 pixel blocks and reduces overall quality, which may negatively impact further analysis. We propose a fully unsupervised Edge-Preserving method Increasing nucleI Clarity (EPIIC) for removing compression artifacts from whole-slide HE-stained images. The method is introduced in two versions, EPIIC and EPIIC Sobel, composed of stain deconvolution, gradient-based edge map estimation, and weighted smoothing. The performance of the method was evaluated using two image quality measures, $PSNR$ and $SSIM$, and various datasets, including BreCaHAD with HE-stained histopathological images and five other natural image datasets, and compared with other edge-preserving filtering methods and a deep learning-based solution. The impact of compression artifacts removal on the nuclei segmentation task was tested using Hover-Net and STARDIST models. The proposed methods led to improved image quality in histopathological and natural images and better segmentation of cell nuclei compared to other edge-preserving filtering methods. The biggest improvement was observed for images compressed with a low compression quality factor. Compared to the method using neural networks, the developed algorithms have slightly worse performance in image enhancement, but they are superior in nuclei segmentation. EPIIC and EPIIC Sobel can efficiently remove compression artifacts, positively impacting the segmentation results of cell nuclei and overall image quality. Full article

Accurate weed segmentation in Unmanned Aerial Vehicle (UAV) imagery remains a significant challenge in precision agriculture due to environmental variability, weak contextual representation, and inaccurate boundary detection. To address these limitations, we propose the Multi-Scale and Edge-Aware Network (MSEA-Net), a lightweight and efficient deep learning framework designed to enhance segmentation accuracy while maintaining computational efficiency. Specifically, we introduce the Multi-Scale Spatial-Channel Attention (MSCA) module to recalibrate spatial and channel dependencies, improving local–global feature fusion while reducing redundant computations. Additionally, the Edge-Enhanced Bottleneck Attention (EEBA) module integrates Sobel-based edge detection to refine boundary delineation, ensuring sharper object separation in dense vegetation environments. Extensive evaluations on publicly available datasets demonstrate the effectiveness of MSEA-Net, achieving a mean Intersection over Union (IoU) of 87.42% on the Motion-Blurred UAV Images of Sorghum Fields dataset and 71.35% on the CoFly-WeedDB dataset, outperforming benchmark models. MSEA-Net also maintains a compact architecture with only 6.74 M parameters and a model size of 25.74 MB, making it suitable for UAV-based real-time weed segmentation. These results highlight the potential of MSEA-Net for improving automated weed detection in precision agriculture while ensuring computational efficiency for edge deployment. Full article

Infrared imaging is based on thermal radiation and does not rely on visible light, allowing for it to operate normally at night and in low-light conditions. This characteristic is beneficial for regulatory authorities to monitor ships. Existing infrared image segmentation methods face challenges such as the absence of color information, blurred edges, weak high-frequency details, and low contrast due to the imaging principles. Consequently, the segmentation accuracy for small-sized ship targets and edges is low, influenced by the indistinct features of infrared images and the weak difference between the background and targets. To address these issues, this paper proposes an infrared image ship segmentation algorithm called the Infrared Image Edge-Enhanced Segmentation Network (IERNet) to extract ship temperature information. By using pseudo-color infrared images, the sensitivity to edges is enhanced, improving the edge features of ships in infrared images. The Sobel operator is used to obtain edge feature maps, and the Convolutional Block Attention Module (CBAM) extracts key feature information. In the Fusion Unit, edge features guide the extraction of infrared ship features in the backbone network, resulting in feature maps rich in edge information. Finally, a specialized loss function with edge weights supervises the fusion features. An eXtreme Gradient Boosting (XGBoost) machine learning model is then established to predict the ship image brightness temperature threshold, using engine brightness threshold, water area brightness threshold, boundary brightness threshold, and temperature gradient as predictive elements. In terms of image segmentation, our algorithm achieves a segmentation performance of 89.17% mIoU. Regarding the XGBoost model’s performance, it achieves high goodness of fit and small error values on both the training and testing sets, demonstrating its good performance in predicting ship temperature. The model achieves over 70% goodness of fit, and the RMSE values for both models are 3.472, indicating minimal errors. Statistical analysis reveals that the proportion of ship temperature differences predicted by the XGBoost model exceeding 2 is less than 0.020%. The proposed temperature detection method offers higher accuracy and versatility, contributing to more efficient detection of abnormal ship temperatures at night. Full article