Search Results (10,138)

Background: Spectroscopic MRI (sMRI) is a quantitative imaging technique that maps infiltrated tumors in the brain without contrast injections. In a previous study (NCT03137888), sMRI-guided radiation treatment extended patient survival, showing promise for clinical translation. The spectral fitting of individual voxels in an sMRI dataset generate metabolite concentration maps that guide treatment. The established spectral analysis methods use iterative least-squares fitting (FITT) that are computationally demanding. This study compares the performance of NNFit, a neural network-based, accelerated spectral fitting model, to the established FITT for metabolite quantification and radiation treatment planning. Methods: NNFit is a self-supervised deep learning model trained on 50 ms echo-time (TE) sMRI data to estimate metabolite levels of choline (Cho), creatine (Cr), and NAA. We trained the model on 30 GBM patients (56 scans) and tested it on 17 GBM patients (29 scans). NNFit’s performance was compared to the FITT using structural similarity indices (SSIM) and the Dice coefficient. Results: NNFit significantly improved processing speed while maintaining strong agreement with FITT. The radiation target volumes defined by Cho/NAA ≥ 2x were visually comparable, with fewer artifacts in NNFit. Structural similarity indices (SSIM) indicated minimal bias and high consistency across methods. Conclusions: This study highlights NNFit’s potential for rapid, accurate, and artifact-reduced metabolic imaging, enabling faster radiotherapy planning. Full article

The proper implementation of the monitoring process in construction projects helps strengthen sustainable development indicators. The aim of this study is to investigate the moderating effects of education and income on the relationship between job performance and effective factors on supervising engineers. Data gathering was performed by questionnaire, and analysis was performed with the PLS-SEM approach. According to the results of the moderator analysis, demographic variables of education and income levels, on the relationship between feedback and competence, as well as the relationship between job identity and job performance, had a significant moderating role. In addition, the moderating effect of demographic variables on the relationship between autonomy and competence, as well as the relationship between affective commitment and job performance, was confirmed. The moderating effect of income demographic variables on the relationship between independence and affective commitment and the relationship between competence and job performance was meaningful. To achieve a better understanding of the effects of variables on job performance, the mediation role of competence and affective commitment was studied in the research model. By using the results of this research, officials and managers of construction organizations can adopt appropriate payment and training policies, to better management of the performance of the supervising engineers. Full article

Background and Objective: Stroke, a major contributor to long-term disability worldwide, often results in significant impairments in motor function. These impairments can include weakness, impaired balance, and decreased coordination, which can have a significant influence on one’s quality of life and independence. Finding an effective protocol for rehabilitation to improve these points will decrease the impact of stroke and its coast of rehabilitation. Materials and Methods: This study was conducted to assess the effect of lower limb plyometric exercises on strength, postural control, and risk of falling in stroke patients. Materials and Methods: This study involved 40 chronic left stroke patients randomly divided into two equal groups. The experimental group participated in a 12-week supervised plyometric training program, while the control group received conventional physical therapy program. Lower limb muscle strength was measured using a handheld dynamometer, and balance and fall risk were assessed via the Biodex Balance System (BBS). These measurements were conducted before and after the intervention period to evaluate treatment effects. Results: The results of this study demonstrated significant improvements in muscle strength and balance parameters among stroke patients who underwent plyometric exercise compared to those receiving a conventional program. The plyometric group exhibited significantly greater increases in knee extension strength (p < 0.05), hip abduction strength (p < 0.05), ankle dorsiflexion strength (p < 0.05), and ankle eversion strength (p < 0.05). Furthermore, the plyometric group showed significant improvements in overall stability (p < 0.05), mediolateral stability (p < 0.05), and anteroposterior stability (p < 0.05), as measured by the Biodex Balance System (BBS). Conclusions: The results of this study suggest that plyometric exercise may be an effective intervention for decreased risk of falling and enhancing muscle strength and balance during recovery from stroke. Full article

Corporate financing decisions, particularly the choice between equity and debt, significantly impact a company’s financial health and value. This study predicts binary corporate debt levels (high or low) using supervised machine learning (ML) models and firms’ characteristics as predictive variables. Key features include companies’ size, tangibility, profitability, liquidity, growth opportunities, risk, and industry. Deterministic models, represented by logistic regression and multilevel logistic regression, and stochastic approaches that incorporate a certain degree of randomness or probability, including decision trees, random forests, Gradient Boosting, Support Vector Machines, and Artificial Neural Networks, were evaluated using usual metrics. The results indicate that decision trees, random forest, and XGBoost excelled in the training phase but showed higher overfitting when evaluated in the test sample. Deterministic models, in contrast, were less prone to overfitting. Notably, all models delivered statistically similar results in the test sample, emphasizing the need to balance performance, simplicity, and interpretability. These findings provide actionable insights for managers to benchmark their company’s debt level and improve financing strategies. Furthermore, this study contributes to ML applications in corporate finance by comparing deterministic and stochastic models in predicting capital structure, offering a robust tool to enhance managerial decision-making and optimize financial strategies. Full article

Since 2015, China has witnessed a rapid increase in new energy vehicle (NEV) market penetration, achieving global leadership in this sector. This study employs social network analysis (SNA) and Granger causality tests to examine how policy coordination has influenced China’s NEV market development from 2015 to 2023. We evaluated policy coordination using six network metrics: network density, average path length, transitivity, average clustering coefficient, number of components, and size of largest component. Our findings reveal both correlative and causal relationships between policy coordination and market performance. The analysis demonstrated strong positive correlations between network metrics and market performance indicators (ρ = 0.800–0.850, p < 0.01), while Granger causality tests identified significant temporal effects, particularly in the long term (F = 284.051–281,486.748, p < 0.001). Notably, the largest component size shows immediate causal effects on market performance (F = 4.152, p < 0.05). Based on these results, we recommend establishing a multi-level policy coordination system, optimizing the policy network structure with emphasis on core components, implementing dynamic policy adjustment mechanisms considering time-lagged effects, and strengthening collaborative supervision of policy implementation to further advance China’s NEV market development. Full article

Face images hold exceptional significance in contemporary society, serving as direct identifiers due to their rich personal attributes, enhancing daily life and work efficiency. However, advancements in deep learning and image processing have led to the proliferation of sophisticated face forgery software, rendering detection increasingly challenging. We propose a novel face tampering detection method utilizing a spatial attention-enhanced bidirectional convolutional neural network to address this. This approach synergizes the strengths of dense convolutional and depthwise separable networks for superior image feature extraction, thereby improving the accuracy of authentic and manipulated face detection. Furthermore, the network is trained to initially localize tampered regions within face images by integrating a spatial channel-based attention module as supervisory input. On three widely used public face forgery datasets, our method achieves an AUC of no less than 96.45%. The experimental results validate the effectiveness of our method in accurately detecting and initially localizing face tampering. Full article

Automatic intention recognition in financial service scenarios faces challenges such as limited corpus size, high colloquialism, and ambiguous intentions. This paper proposes a hybrid intention recognition framework for financial customer service, which involves semi-supervised learning data augmentation, label semantic inference, and text classification. A semi-supervised learning method is designed to augment the limited corpus data obtained from the Chinese financial service scenario, which combines back-translation with BERT models. Then, a K-means-based semantic inference method is introduced to extract label semantic information from categorized corpus data, serving as constraints for subsequent text classification. Finally, a BERT-based text classification network is designed to recognize the intentions in financial customer service, involving a multi-level feature fusion for corpus information and label semantic information. During the multi-level feature fusion, a shallow-to-deep (StD) mechanism is designed to alleviate feature collapse. To validate our hybrid framework, 2977 corpus texts about loan service are provided by a financial company in China. Experimental results demonstrate that our hybrid framework outperforms existing deep learning methods in financial customer service intention recognition, achieving an accuracy of 89.06%, precision of 90.27%, recall of 90.40%, and an F1 score of 90.07%. This study demonstrates the potential of the hybrid framework to automatic intention recognition in financial customer service, which is beneficial for the improvement of the financial service quality. Full article

Weakly supervised crack segmentation aims to create pixel-level crack masks with minimal human annotation, which often only differentiate between crack and normal no-crack patches. This task is crucial for assessing structural integrity and safety in real-world industrial applications, where manually labeling the location of cracks at the pixel level is both labor-intensive and impractical. Addressing the challenges of labeling uncertainty, this paper presents CrackCLIP, a novel approach that leverages language prompts to augment the semantic context and employs the Contrastive Language–Image Pre-Training (CLIP) model to enhance weakly supervised crack segmentation. Initially, a gradient-based class activation map is used to generate pixel-level coarse pseudo-labels from a trained crack patch classifier. The estimated coarse pseudo-labels are utilized to fine-tune additional linear adapters, which are integrated into the frozen image encoders of CLIP to adapt the CLIP model to the specialized task of crack segmentation. Moreover, specific textual prompts are crafted for crack characteristics, which are input into the frozen text encoder of CLIP to extract features encapsulating the semantic essence of the cracks. The final crack segmentation is determined by comparing the similarity between text prompt features and visual patch token features. Comparative experiments on the Crack500, CFD, and DeepCrack datasets demonstrate that the proposed framework outperforms existing weakly supervised crack segmentation methods, and the pre-trained vision-language model exhibits strong potential for crack feature learning, thereby enhancing the overall performance and generalization capabilities of the proposed framework. Full article

High-quality observational datasets are essential for climate research and models, but validating and filtering decades of meteorological measurements is an enormous task. Advances in machine learning provide opportunities to expedite and improve quality control while offering insight into non-linear interactions between the meteorological variables. The Cabauw Experimental Site for Atmospheric Research in the Netherlands, known for its 213 m observation mast, has provided in situ observations for over 50 years. Despite high-quality instrumentation, measurement errors or non-representative data are inevitable. We explore machine-learning-assisted quality control, focusing on wind vane stalling at 10 m height. Wind vane stalling is treated as a binary classification problem as we evaluate five supervised methods (Logistic Regression, K-Nearest Neighbour, Random Forest, Gaussian Naive Bayes, Support Vector Machine) and one semi-supervised method (One-Class Support Vector Machine). Our analysis determines that wind vane stalling occurred 4.54% of the time annually over 20 years, often during stably stratified nocturnal conditions. The K-Nearest Neighbour and Random Forest methods performed the best, identifying stalling with approximately 75% accuracy, while others were more affected by data imbalance (more non-stalling than stalling data points). The semi-supervised method, avoiding the effects of the inherent data imbalance, also yielded promising results towards advancing data quality assurance. Full article

Camera arrays typically use image-stitching algorithms to generate wide field-of-view panoramas, but parallax and color differences caused by varying viewing angles often result in noticeable artifacts in the stitching result. However, existing solutions can only address specific color difference issues and are ineffective for pinhole images with parallax. To overcome these limitations, we propose a parallax-tolerant weakly supervised pixel-wise deep color correction framework for the image stitching of pinhole camera arrays. The total framework consists of two stages. In the first stage, based on the differences between high-dimensional feature vectors extracted by a convolutional module, a parallax-tolerant color correction network with dynamic loss weights is utilized to adaptively compensate for color differences in overlapping regions. In the second stage, we introduce a gradient-based Markov Random Field inference strategy for correction coefficients of non-overlapping regions to harmonize non-overlapping regions with overlapping regions. Additionally, we innovatively propose an evaluation metric called Color Differences Across the Seam to quantitatively measure the naturalness of transitions across the composition seam. Comparative experiments conducted on popular datasets and authentic images demonstrate that our approach outperforms existing solutions in both qualitative and quantitative evaluations, effectively eliminating visible artifacts and producing natural-looking composite images. Full article

Abstract: Childhood obesity has profound effects on puberty in boys and girls, altering its timing, progression, and associated hormonal changes. Also, later male fertility could be impaired by childhood and pubertal obesity in light of the impact of inflammatory markers on semen quality. The aim of this narrative review is to explore the intricate relationship between childhood obesity and its impact on pubertal development and fertility, with a specific focus on boys. Such a relationship between obesity and pubertal timing in males is highly influenced by metabolic, hormonal, genetic, epigenetic, and environmental factors. While many studies suggest that obesity accelerates pubertal onset in boys, some studies do not confirm these findings, especially in cases of severe obesity. In fact, delayed puberty has also been reported in certain instances. Obesity influences fertility through different central and peripheral processes, including an altered endocrine milieu, inflammatory environment, and epigenetic modifications that alter semen quality and vitality, leading to subfertility or infertility. The early identification and management of potential issues associated with obesity are crucial for ensuring optimal reproductive health in adulthood. Further research is essential to clarify these associations and to develop targeted interventions aimed at preventing the negative health outcomes associated with obesity-related disruptions in puberty and fertility. Full article

Advances in combinatorial synthesis and high-throughput screening methods have led to renewed interest in synthetic plant immunity activators as well as priming agents. 3,5-Dichloroanthranilic acid (3,5-DCAA) is a derivative of anthranilic acid that has shown potency in activating defence mechanisms in Arabidopsis and barley. Chemical biology, which is the interface of chemistry and biology, can make use of metabolomic approaches and tools to better understand molecular mechanisms operating in complex biological systems. Here we report on the untargeted metabolomic profiling of barley seedlings treated with 3,5-DCAA to gain deeper insights into the mechanism of action of this resistance inducer. Histochemical analysis revealed the production of reactive oxygen species in the leaves upon 3,5-DCAA infiltration. Subsequently, methanolic extracts from different time periods (12, 24, and 36 h post-treatment) were analysed by ultra-high-performance liquid chromatography hyphenated to a high-resolution mass spectrometer. Both unsupervised and supervised chemometric methods were used to reveal hidden patterns and highlight metabolite variables associated with the treatment. Based on the metabolites identified, both the phenylpropanoid and octadecanoid pathways appear to be main routes activated by 3,5-DCAA. Different classes of responsive metabolites were annotated with flavonoids, more specifically flavones, which were the most dominant. Given the limited understanding of this inducer, this study offers a metabolomic analysis of the response triggered by its foliar application in barley. This additional insight could help make informed decisions for the development of more effective strategies for crop protection and improvement, ultimately contributing to crop resilience and agricultural sustainability. Full article

Among the many methods of deep semi-supervised learning (DSSL), the holistic method combines ideas from other methods, such as consistency regularization and pseudo-labeling, with great success. This method typically introduces a threshold to utilize unlabeled data. If the highest predictive value from unlabeled data exceeds the threshold, the associated class is designated as the data’s pseudo-label. However, current methods utilize fixed or dynamic thresholds, disregarding the varying learning difficulties across categories in unbalanced datasets. To overcome these issues, in this paper, we first designed Cumulative Effective Labeling (CEL) to reflect a particular class’s learning difficulty. This approach differs from previous methods because it uses effective pseudo-labels and ground truth, collectively influencing the model’s capacity to acquire category knowledge. In addition, based on CEL, we propose a simple but effective way to compute the threshold, Self-adaptive Dynamic Threshold (SDT). It requires a single hyperparameter to adjust to various scenarios, eliminating the necessity for a unique threshold modification approach for each case. SDT utilizes a clever mapping function that can solve the problem of differential learning difficulty of various categories in an unbalanced image dataset that adversely affects dynamic thresholding. Finally, we propose a deep semi-supervised method with SDT called FldtMatch. Through theoretical analysis and extensive experiments, we have fully proven that FldtMatch can overcome the negative impact of unbalanced data. Regardless of the choice of the backbone network, our method achieves the best results on multiple datasets. The maximum improvement of the macro F1-Score metric is about 5.6% in DFUC2021 and 2.2% in ISIC2018. Full article

This paper presents a machine learning-based approach to grade engine health and generate a respective score ranging from 0 to 100 for tuned high-performance vehicles. It integrates the technical intricacies of automotive engineering with machine learning practices in a clear and sequential process. Data are collected from sensors monitoring revolutions per minute, boost, rail pressure, timing, and temperature. The data are processed for supervised learning and analyzed using visualizations such as a heatmap and t-SNE plots. Models are trained, innovatively tuned through hyperparameter optimization, and tested for their ability to grade new data logs. The results highlight K-Neighbors, Extra Trees, and Extreme Gradient Boosting as exceptional regressors for this task. The automated grading of engine health and performance enhances objectivity and efficiency in the tuning process and potentially serves as a basis for a digital twin. The developed methodology is discussed in the context of health evaluation for any sensor-based system, with practical applications extending across various domains and industries. Full article

Increasing vessel traffic in narrow, winding inland waterways has heightened the risk of accidents, driving the need for improved surveillance and management. This study addresses the challenge of real-time processing and synchronization of voluminous video and AIS data for effective waterway management. We developed a surveillance method utilizing smart buoys equipped with sensors and edge computing devices, enabling dynamic spatiotemporal data fusion. The integration of AIS data with advanced computer vision techniques for target detection allows for real-time traffic analysis and provides detailed navigational dynamics of vessels. The method employs an enhanced Long Short-Term Memory network for precise trajectory prediction of AIS data and a single-stage target detection model for video data analysis. Experimental results demonstrate significant improvements in ship detection accuracy and tracking precision, with an average position prediction error of approximately 1.5 m, which outperforms existing methods. Additionally, a novel regional division and a Kalman filter-based method for AIS and video data fusion were proposed, effectively resolving the issues of data sparsity and coordinate transformation robustness under complex waterway conditions. This approach substantially advances the precision and efficiency of waterway monitoring systems, providing a robust theoretical and practical framework for the intelligent supervision of inland waterways. Full article