Search Results (12,812)

The dynamic load caused by vehicle vibration due to an uneven pavement surface is a primary factor affecting the structural performance and service life of asphalt pavement. As the principles of vibration mechanics, in conjunction with the coherence function of the vehicle’s left and right wheels, along with the lag between front and rear wheels, the entire vehicle vibration model for three-axle and four-axle heavy-load vehicles was developed using Simulink software. Through simulation, the root-mean-square value of the dynamic load and the dynamic load coefficient of the vehicle with different pavement roughness grades, speeds, loads, and cornering radii were analyzed. The outcomes demonstrate that a nonlinear rise in the wheel dynamic load occurs when pavement roughness increases. The greater the speed, the greater the impact of pavement roughness on the dynamic load. An increase in vehicle load tends to reduce vehicle vibrations. The interaction between vehicle vibration frequency and road excitation frequency is essential in figuring out the loads, and a negative influence on the pavement structure should be given more attention when the vehicle is driving at low speed. The dynamic load coefficient of the left and right wheels is greatly affected when the vehicle is in a lateral tilt. The findings offer valuable insights for selecting appropriate loads in pavement structure design. By constructing 11 degrees of freedom for a three-axle vehicle and 16 degrees of freedom for a four-axle heavy-duty vehicle model, the dynamic load variation law under different roughness excitation conditions is systematically analyzed. The results can be applied to the selection of vehicle load in asphalt pavement design to make it closer to the actual driving state, which will be helpful for improving accuracy in the design of pavement structure and avoiding early damage to the pavement. Full article

Mine water inrush is a significant environmental catastrophe during the coal mining process, and the timely discrimination of the source of water inrush is the key to ensuring safe production in coal mines. This work suggests a mine water inrush—belief rule base (MWI-BRB) source discrimination model to overcome the interpretability and performance issues with conventional models. MWI-BRB firstly automatically constructs the reference values of prerequisite attributes using the Sum of Squared Errors—K-means++ algorithm, which effectively combines expert knowledge and data-driven methods, and solves the limitation of the traditional belief rule base model relying on specialist knowledge. Secondly, the hierarchical incremental structure solves the rule explosion problem caused by complex features while using XGBoost to select features. Finally, in the inference process, the model adopts an evidential reasoning algorithm to realize transparent causal inference, guaranteeing the model’s interpretability and transparency. The Penalized Covariance Matrix Adaptation Evolution Strategy algorithm optimizes the model parameters to increase the discriminative accuracy of the model even more. Experimental results on a real coal mine dataset (a total of 67 samples from Hebei, China, covering four water inrush sources) demonstrate that the proposed MWI-BRB achieves 95.23% accuracy, 95.23% recall, and 95.36% F1-score under a 7:3 training–testing split with parameter tuning performed via leave-one-out cross-validation. The near-identical values across accuracy, recall, and F1-score reflect the balanced nature of the dataset and the robustness of the model across different evaluation metrics. Compared with baseline models, MWI-BRB’s accuracy and recall are 4.78% higher than BPNN and 9.52% higher than KNN, RF, and XGBoost; its F1-score is 4.85% higher than BPNN, 10.64% higher than KNN, 10.19% higher than RF, and 9.65% higher than XGBoost. Moreover, the model maintains high interpretability. In conclusion, the MWI-BRB model can realize efficient and accurate water inrush source discrimination in complex environments, which provides a feasible technical solution for the prevention and control of mine water damage. Full article

Accurate stockpile volume estimation is essential for industries that manage bulk materials across various stages of production. Conventional ground-based methods such as walking wheels, total stations, Global Navigation Satellite Systems (GNSSs), and Terrestrial Laser Scanners (TLSs) have been widely used, but often involve significant safety risks, particularly when accessing hard-to-reach or hazardous areas. Unmanned Aerial Systems (UASs) provide a safer and more efficient alternative for surveying irregularly shaped stockpiles. This study evaluates UAS-based methods for estimating the volume of coal stockpiles at a storage facility near Cadiz, Ohio. Two sensor platforms were deployed: a Freefly Alta X quadcopter equipped with a Real-Time Kinematic (RTK) Light Detection and Ranging (LiDAR, active sensor) and a WingtraOne UAS with Post-Processed Kinematic (PPK) multispectral imaging (optical, passive sensor). Three approaches were compared: (1) LiDAR; (2) Structure-from-Motion (SfM) photogrammetry with a Digital Surface Model (DSM) and Digital Terrain Model (DTM) (SfM–DTM); and (3) an SfM-derived DSM combined with a kriging-interpolated DTM (SfM–intDTM). An automated boundary detection workflow was developed, integrating slope thresholding, Near-Infrared (NIR) spectral filtering, and Canny edge detection. Volume estimates from SfM–DTM and SfM–intDTM closely matched LiDAR-based reference estimates, with Root Mean Square Error (RMSE) values of 147.51 m³ and 146.18 m³, respectively. The SfM–intDTM approach achieved a Mean Absolute Percentage Error (MAPE) of ~2%, indicating strong agreement with LiDAR and improved accuracy compared to prior studies. A sensitivity analysis further highlighted the role of spatial resolution in volume estimation. While RMSE values remained consistent (141–162 m³) and the MAPE below 2.5% for resolutions between 0.06 m and 5 m, accuracy declined at coarser resolutions, with the MAPE rising to 11.76% at 10 m. This emphasizes the need to balance the resolution with the study objectives, geographic extent, and computational costs when selecting elevation data for volume estimation. Overall, UAS-based SfM photogrammetry combined with interpolated DTMs and automated boundary extraction offers a scalable, cost-effective, and accurate approach for stockpile volume estimation. The methodology is well-suited for both the high-precision monitoring of individual stockpiles and broader regional-scale assessments and can be readily adapted to other domains such as quarrying, agricultural storage, and forestry operations. Full article

Background: Atrial fibrillation (AF) is the most common sustained cardiac arrhythmia, increasingly prevalent worldwide. Type 2 diabetes mellitus (T2DM) is a major chronic disorder and a significant risk factor for AF, contributing to high morbidity and mortality. Metformin monotherapy can contribute to the reduced occurrence of adverse cardiovascular outcomes in patients with T2DM, but its effects on AF are understudied. This meta-analysis evaluates the association of metformin with the risk of incident AF among patients with T2DM on metformin. Methods: Databases, including PubMed, Google Scholar, and EMBASE, were screened through November 2024 for studies evaluating the association between metformin and new-onset AF in patients with T2DM. Comprehensive Meta-Analysis (CMA) version 4, by Biostat, Inc., utilizing a random effects model, was used to pool hazard ratios (HR) and 95% confidence intervals (CI). A meta-regression analysis was also performed to identify factors that may have influenced the results. A p-value < 0.05 was considered statistically significant. Results: A total of seven studies, comprising 4,017,929 patients with T2DM, having a mean age of 62.82 years and 52.5% males, were included. Metformin was associated with a statistically significantly lower risk of new-onset AF among patients with T2DM compared to other hypoglycemic agents (aHR: 0.85; 95% CI 0.76–0.94; p = 0.002). Meta-regression analysis identified age as a significant moderator of the treatment effect (β = −3.15, p = 0.001). Conclusions: Metformin is associated with a lower risk of new-onset AF among patients with T2DM compared to other hypoglycemic agents. Furthermore, age-related attenuation of this association was observed, with older patients with T2DM showing a weaker association. Full article

Background/Objectives: Cast walkers (CWs) are often prescribed to offload diabetic foot ulcers (DFUs). However, their mass, the degree of ankle immobilization and the limb length discrepancy they induce may increase the energetic demands of walking, contributing to lower adherence and poorer healing. The purpose of this study was to evaluate the effects of different commercially available CW options on the metabolic costs and perceived exertion of walking, and on related spatiotemporal kinematics, in healthy young participants as an initial step to understanding factors that impact adherence in patients with DFUs. Methods: Participants walked on an instrumented treadmill at a standardized speed for six minutes under five footwear conditions: (1) athletic shoes only (control); (2) ankle-high CW on the dominant limb with athletic shoe on the contralateral limb; (3) condition two with an external lift on the athletic shoe; (4 and 5) conditions two and three with a knee-high CW. Condition 1 was performed first, after which the CW conditions were randomized. During all conditions, a portable calorimeter recorded gas exchange on a breath-by-breath basis. The metabolic cost of transport (MCoT) was quantified as the mean oxygen consumed per meter walked per kilogram body mass, after accounting for standing. After walking, participants reported perceived exertion using the Borg Rating of Perceived Exertion scale (RPE). From the treadmill data, we extracted the mean step width (SW) as well as absolute values for symmetry indices (SIs) for step length (SL) and step time (ST), all of which have associations with MCoT. For each outcome, linear mixed models compared each CW condition with the control and tested for effects of CW height (ankle-high vs. knee-high) and of the lift. Results: A total of 14 healthy young adults without diabetes participated. MCoT, RPE and SW were significantly higher for all CW conditions compared to the control, with less consistent results for asymmetry measures. MCoT was not significantly different across CW height or lift condition although an unexpected interaction between limb and CW height n was observed; MCoT was lower in the knee-high CW with vs. without a lift but did not change in the ankle-high CW based on lift status. Similarly, neither SW nor SIs changed in expected fashions across conditions. In contrast, RPE was significantly lower using the ankle- vs. knee-high CW and when using a lift vs no lift, with no significant interaction. Conclusions: Although metabolic costs were unaffected by CW design changes, which may reflect the absence of anticipated changes in kinematics that impact MCoT, perceived exertion was reduced through such changes. Unanticipated biomechanical changes may reflect a complex interaction among a number of competing factors that dictate behavior and MCoT. The differing results in perception of exertion and metabolic costs might be due to participants’ perceived exertion being sensitive to the collective impact of interacting biomechanical factors, including those not quantified in this study. Future work should seek to directly evaluate the impact of CW design changes in patients with DFU and the relationship to adherence. Full article

Background/Objectives: Follicular lymphoma (FL) and marginal zone lymphoma (MZL) are low-grade B-cell lymphomas (LGBCLs) with indolent clinical courses but a lifelong risk of histologic transformation (HT) to aggressive lymphomas, particularly diffuse large B-cell lymphoma. Predicting HT can be challenging due to class imbalances and the inherent complexity of time-dependent events. While there are current prognostic indices for survival, they do not specifically address HT risk. This study aimed to develop and validate survival-based and traditional classification machine-learning models to predict HT in cohorts. Methods: Using a multicenter retrospective dataset (n = 1068), survival models (Cox proportional hazards, Lasso-Cox, Random Survival Forest, Gradient-boosted Cox [GBM-Cox], eXtreme Gradient Boosting [XGBoost]-Cox), and classification models (Logistic regression, Lasso logistic, Random Forest, Gradient Boosting, XGBoost) were compared. The best-performing survival models—XGBoost-Cox, Lasso-Cox, and GBM-Cox—were assessed on an independent test set (n = 92). Model sensitivity was maximized using optimal binary risk cutoff points based on Youden’s index. Results: Survival models showed superior predictive performance than classical classifiers, with XGBoost-Cox exhibiting the highest mean accuracy (85.3%), time-dependent area under the curve (0.795), sensitivity (98%), specificity (83.9%), and concordance index (0.836). Incorporating next-generation sequencing (NGS) data improved model accuracy and specificity, indicating that genetic factors improve HT prediction. Principal component analysis revealed distinct gene mutation patterns associated with HT risk, highlighting DNA-repair genes such as TP53, BLM, and RAD50. Conclusions: This study highlights the clinical value of survival-based machine-learning methods integrated with NGS data to personalize HT risk stratification for patients with FL and MZL. Full article

This study presents the development and implementation of an AI-driven control system for dynamic regulation of hydrogen blending in natural gas networks. Leveraging supervised machine learning techniques, a Random Forest Classifier was trained to accurately identify the origin of gas blends based on compositional fingerprints, achieving rapid inference suitable for real-time applications. Concurrently, a Random Forest Regression model was developed to estimate the optimal hydrogen flow rate required to meet a user-defined higher calorific value target, demonstrating exceptional predictive accuracy with a mean absolute error of 0.0091 Nm³ and a coefficient of determination (R²) of 0.9992 on test data. The integrated system, deployed via a Streamlit-based graphical interface, provides continuous real-time adjustments of gas composition, alongside detailed physicochemical property estimation and emission metrics. Validation through comparative analysis of predicted versus actual hydrogen flow rates confirms the robustness and generalizability of the approach under both simulated and operational conditions. The proposed framework enhances operational transparency and economic efficiency by enabling adaptive blending control and automatic source identification, thereby facilitating optimized fuel quality management and compliance with industrial standards. This work contributes to advancing smart combustion technologies and supports the sustainable integration of renewable hydrogen in existing gas infrastructures. Full article

Astrocyte-derived extracellular vesicles (ADEVs) have emerged as promising neuroprotective agents for ischemic stroke. In this study, we evaluated the therapeutic potential of hypoxia-conditioned ADEVs (HxEVs) administered intracerebroventricularly in a rat model of transient middle cerebral artery occlusion (tMCAO). Serial magnetic resonance imaging (MRI) with diffusion tensor imaging (DTI) was performed at 1, 7, 14, and 21 days post-stroke. HxEV treatment produced a significant reduction in infarct volume from day 1, sustained through day 21, and was accompanied by improvements in motor and sensory recovery. DTI analyses showed progressive normalization of fractional anisotropy (FA) and radial diffusivity (RD), particularly in the corpus callosum and striatum, reflecting microstructural repair. In contrast, mean diffusivity (MD) was less sensitive to these treatment effects. Regional differences in therapeutic response were evident, with earlier and more sustained recovery in the corpus callosum than in other brain regions. Histological findings confirmed greater preservation of dendrites and axons in HxEV-treated animals, supporting the role of these vesicles in accelerating post-stroke neurorepair. Together, these results demonstrate that hypoxia-conditioned ADEVs promote both structural and functional recovery after ischemic stroke. They also highlight the value of DTI-derived biomarkers as non-invasive tools to monitor neurorepair. The identification of region-specific therapeutic effects and the validation of reliable imaging markers provide a strong foundation for future research and development. Full article

Understanding discourse context is fundamental to human language comprehension. Despite the remarkable progress achieved by Large Language Models, they still struggle with discourse-level anaphora resolution, particularly in Chinese. One major challenge is zero anaphora, a prevalent linguistic phenomenon in which referential elements are omitted, increasing complexity and ambiguity for computational models. To address this issue, we introduce CDAMR (Chinese Discourse Abstract Meaning Representation), a novel annotated corpus that systematically labels zero pronouns across diverse syntactic positions along with their discourse-level coreference chains. In addition, we present a hierarchical discourse-semantic enhanced model that separately encodes local discourse semantics and global discourse semantics, and models their interactions via structured multi-attention mechanisms. Experiments on both CDAMR and OntoNotes demonstrate the approach’s cross-corpus generalizability and effectiveness, achieving F1 scores of 59.86% and 60.54%, respectively. Ablation studies further confirm that discourse-level semantics significantly enhance zero pronoun resolution. These findings highlight the value of cognitively inspired discourse modeling and the importance of comprehensive discourse annotations for languages with limited explicit syntactic cues such as Chinese. Full article

Accurate fruit counting is crucial for data-driven decision-making in modern precision agriculture. In strawberry cultivation, a labor-intensive sector, automated, scalable yield estimation is especially critical. However, dense foliage, variable lighting, visual ambiguity of ripeness stages, and fruit clustering pose significant challenges. To overcome these, we developed a real-time multi-stage framework for strawberry detection and counting by optimizing a YOLOv8s detector and integrating a class-aware tracking system. The detector was enhanced with a lightweight C3x module, an additional detection head for small objects, and the Wise-IOU (WIoU) loss function, thereby improving performance against occlusion. Our final model achieved a 92.5% mAP@0.5, outperforming the baseline while reducing the number of parameters by 27.9%. This detector was integrated with the ByteTrack multiple object tracking (MOT) algorithm. Our system enabled accurate, automated fruit counting in complex greenhouse environments. When validated on video data, results showed a strong correlation with ground-truth counts (R² = 0.914) and a low mean absolute percentage error (MAPE) of 9.52%. Counting accuracy was highest for ripe strawberries (R² = 0.950), confirming the value for harvest-ready estimation. This work delivers an efficient, accurate, and resource-conscious solution for automated yield monitoring in commercial strawberry production. Full article

Background/Objectives: This systematic review aimed to evaluate the effectiveness and predictability of lower incisor intrusion with clear aligners in permanent dentition, addressing one of the most challenging aspects of vertical tooth movement control in the mandibular anterior region. Methods: A comprehensive literature search was conducted across five databases (PubMed, Scopus, Embase, and Cochrane) according to PRISMA guidelines. Eight clinical studies fulfilled the eligibility criteria. Risk of bias was assessed using ROBINS-I, and certainty of evidence was graded with GRADE. Key outcomes included the amount of achieved versus planned intrusion, predictability, treatment protocols, use of auxiliaries, and patient-related factors such as age and compliance. Results: Reported mean intrusion values ranged from 0.4 to 1.5 mm, with predictability between 35% and 65%. The effectiveness of intrusion was influenced by the magnitude of planned movement, auxiliaries (e.g., attachments, elastics), refinement strategies, and patient-specific factors. Substantial heterogeneity was present in measurement methods (CBCT, cephalometry, digital models) and clinical protocols (aligner change intervals, refinement frequency), preventing meta-analysis. Seven of the eight studies were rated as having a serious risk of bias, and the overall certainty of evidence was moderate to low. Long-term outcomes and patient-centered measures were not adequately assessed. Conclusions: Within the limitations of the available evidence, lower incisor intrusion with clear aligners may be considered a feasible orthodontic option when supported by biomechanically informed clinical management. However, conclusions should be interpreted with caution due to heterogeneity, high risk of bias, and lack of long-term data. Further standardized studies with longer follow-up are required to strengthen reliability and clinical applicability. Full article

This paper introduces the Construction 4.0 Value Proposition Score (CVPS4.0)—a structured framework that enables Architecture, Engineering, and Construction (AEC) organizations to evaluate and communicate the value proposition of Construction 4.0 decisions. Grounded in the “Futures Triangle” theory, the study draws on existing research to identify three key dimensions: past barriers constraining AEC organizations, current trends driving industry change, and future transformations toward which the sector is evolving. In total, 45 barriers, 13 trends, and four transformations were identified as the foundation of the scoring framework. The model assesses how a decision influences each dimension, producing a composite score that reflects its overall value proposition. This score incorporates three considerations: the applicability of each factor to the organization, the degree of impact the decision has on it, and the relevance of the factor to the decision. The framework was validated through proof-of-concept with a subject-matter expert, who confirmed its value in supporting strategic, data-informed decision-making. As one of the first studies to evaluate the value proposition of Construction 4.0, this research offers both a practical decision-support tool and a consolidated reference on the forces shaping organizational change. CVPS4.0 provides AEC organizations with a proactive means to guide decisions, mitigate risks, and enhance long-term value creation. Full article

Remanufacturing, a key element of the circular economy, enables products and parts to have new life cycles through a systematic process. Initially, used products (cores) are visually inspected and categorized according to their manufacturer and variant before being disassembled and cleaned. Subsequently, parts are manually classified as directly reusable, reusable after reconditioning, or recyclable. As demand for remanufactured parts increases, automated classification becomes crucial. However, current Deep Learning (DL) methods, constrained by the scarcity of unique parts, often suffer from insufficient datasets, leading to overfitting. This research explores the effectiveness of Data-Efficient Generative Adversarial Network (DE-GAN) optimization approaches like FastGAN, APA, and InsGen in enhancing dataset diversity. These methods were evaluated against the State-of-the-Art (SOTA) Deep Convolutional Generative Adversarial Network (DCGAN) using metrics such as the Inception Score (IS), Fréchet Inception Distance (FID), and the classification accuracy of ResNet18 models trained with partially synthetic data. FastGAN achieved the lowest FID values among all models and led to a statistically significant improvement in ResNet18 classification accuracy. At a [1:1] real-to-synthetic ratio, the mean accuracy increased from 72% ± 4% (real-data-only) to 87% ± 3% (p < 0.001), and reached 94% ± 3% after hyperparameter optimization. In contrast, synthetic data generated by the SOTA DCGAN did not yield statistically significant improvements. Full article

Data stream classification is a critical challenge in data mining, where models must rapidly adapt to evolving data distributions and concept drift in real time, while extreme learning machines offer fast training and strong generalization, most existing methods struggle to jointly address multi-class imbalance, concept drift, and the high cost of label acquisition in streaming settings. In this paper, we present the Adaptive Broad Learning System for Online Imbalanced Classification (ABLS-OIC), which introduces three core innovations: (1) a Class-Adaptive Weight Matrix (CAWM) that dynamically adjusts sample weights according to class distribution, sample density, and difficulty; (2) a Hybrid Memory Retention Mechanism (HMRM) that selectively retains representative samples based on importance and diversity; and (3) a Multi-Objective Adaptive Optimization Framework (MAOF) that balances classification accuracy, class balance, and computational efficiency. Extensive experiments on ten benchmark datasets with varying imbalance ratios and drift patterns show that ABLS-OIC consistently outperforms state-of-the-art methods, with improvements of 5.9% in G-mean, 6.3% in F1-score, and 3.4% in AUC. Furthermore, a real-world credit fraud detection case study demonstrates the practical effectiveness of ABLS-OIC, highlighting its value for early detection of rare but critical events in dynamic, high-stakes applications. Full article

Background: Neuroendocrine neoplasms (NENs) are a diverse group of malignancies in which somatostatin receptor expression can be crucial in guiding therapy. We aimed to evaluate the effectiveness of [^99mTc]Tc-EDDA/HYNIC-TOC SPECT/CT in differentiating neuroendocrine tumor histology, selecting candidates for radioligand therapy, and identifying correlations between somatostatin receptor expression and non-imaging parameters in metastatic NENs. Methods: This retrospective study included 65 patients (29 women, 36 men, mean age 61) with metastatic neuroendocrine neoplasms confirmed by histology, follow-up, or imaging, comprising 14 poorly differentiated carcinomas and 51 well-differentiated tumors. Somatostatin receptor SPECT/CT results were assessed visually and semiquantitatively, with mathematical models incorporating histological, oncological, immunohistochemical, and laboratory parameters, followed by biostatistical analysis. Results: Of 392 lesions evaluated, the majority were metastases in the liver, lymph nodes, and bones. Mathematical models estimated somatostatin receptor expression accurately (70–83%) based on clinical parameters alone. Key factors included tumor origin, oncological treatments, and the immunohistochemical marker CK7. Associations were found between age, grade, disease extent, and markers (CEA, CA19-9, AFP). Conclusions: Our findings suggest that [^99mTc]Tc-EDDA/HYNIC-TOC SPECT/CT effectively evaluates somatostatin receptor expression in NENs. Certain immunohistochemical and laboratory parameters, beyond recognized factors, show potential prognostic value, supporting individualized treatment strategies. Full article