Search Results (17,767)

Earthwork operations constitute a substantial share of infrastructure project costs and are critical to overall project efficiency. However, the construction industry still relies on conventional approaches and there is a lack of integrated fleet management systems for collaboratively working equipment. While telematics is widely used in other industries, its applications to monitor the complex interactions between excavators, dump trucks, and dozers in real time remain limited. This study proposes an intelligent fleet monitoring system that utilizes only satellite navigation data (GNSS) to analyze the real-time productivity of multiple earthwork machines without relying on additional sensors, such as IMU or accelerometers, thereby eliminating the need for separate measurement procedures. A lightweight site configuration step is required to define the work area/loading/dumping geofences on an existing site map. This research provides novel developed algorithms that facilitate a real-time productivity assessment for several earthwork equipment and provide planning-level recommendations for equipment deployment combinations. Dedicated motion classification algorithms were developed for excavators, dump trucks, and dozers to distinguish activity states, to compute working and idle times, and to quantify operational efficiency. The system integrates a web-based e-Fleet Management platform and a mobile e-Map application for visualization and equipment optimization. Field validation was conducted on two active earthwork projects to evaluate accuracy and feasibility. The results demonstrate that the developed algorithms achieved classification and productivity estimation errors within 2.5%, while enabling optimized equipment combinations and improved cycle time efficiency. The proposed system offers a practical, sensor-independent approach for enhancing productivity monitoring, real-time decision-making, and cost efficiency in large-scale earthwork operations. Full article

During the trial mining process, ground horizontal pipes are prone to generating hydrates due to pressure and temperature changes, leading to ice blockage. Hydrate inhibitors are usually added on-site to prevent freezing blockage. However, existing addition methods have limitations, including poor real-time performance, insufficient accuracy in the addition amount, and dependence on manual adjustment. In view of this, this paper aims to develop models to predict the throttling pressure and temperature for horizontal ground pipes, and to indicate the amount of ethylene glycol needed to prevent freezing blockage, thereby laying the foundation for accurate, real-time prediction of fluid pressure and temperature and for controlling the addition amount. By integrating data-driven technologies and mechanism models, this study developed intelligent prediction systems for ground horizontal pipe throttling pressure and temperature, and for suppression of freeze-blocking ethylene glycol addition. First, a three-phase throttling mechanism model for oil, gas, and water is established using the energy conservation equation to accurately predict the pressure and temperature at the throttling points along the process. At the same time, HYSYS software is used to simulate various operating conditions and to fit the ethylene glycol addition amount prediction model. Finally, edge computing equipment is integrated to enable real-time data collection, prediction, and dynamic adjustment and optimization. The field measurement data of Well A showed that the model’s prediction error of pressure and temperature before and after throttling is less than 6%, and the prediction error of the ethylene glycol addition amount is less than 5%, which provides key technical support for safe and efficient operation of the trial mining process as well as for cost reduction and efficiency improvement. Full article

Accurate measurement of sheep body dimensions is fundamental for growth monitoring and breeding management. To address the limited segmentation accuracy and the trade-off between lightweight design and precision in existing non-contact measurement methods, this study proposes an improved model, YOLOv12n-Seg-SSM, for the automatic measurement of body height, body length, and chest circumference from side-view images of sheep. The model employs a synergistic strategy that combines semantic segmentation with 3D point cloud geometric fitting. It incorporates the SegLinearSimAM feature enhancement module, the SEAttention channel optimization module, and the ENMPDIoU loss function to improve measurement robustness under complex backgrounds and occlusions. After segmentation, valid RGB-D point clouds are generated through depth completion and point cloud filtering, enabling 3D computation of key body measurements. Experimental results demonstrate that the improved model outperforms the baseline YOLOv12n-Seg: the mAP@0.5 for segmentation reaches 94.20%, the mAP@0.5 for detection reaches 95.00% (improvements of 0.5 and 1.3 percentage points, respectively), and the recall increases to 99.00%. In validation tests on 43 Hu sheep, the R² values for chest circumference, body height, and body length were 0.925, 0.888 and 0.819, respectively, with measurement errors within 5%. The model requires only 10.71 MB of memory and 9.9 GFLOPs of computation, enabling real-time operation on edge devices. This study demonstrates that the proposed method achieves non-contact automatic measurement of sheep body dimensions, providing a practical solution for on-site growth monitoring and intelligent management in livestock farms. Full article

Museums are key institutions for cultural communication and public education, and their operating concept is shifting from exhibit-centered to experience-centered. As expectations for exhibition experience rise, museum fatigue has become a major constraint on visitors. Existing studies rely on questionnaires and other subjective measures, which makes it difficult to locate fatigue in specific spaces. At the same time, body pose detection and fatigue recognition techniques remain hard to apply in museums because of complex spatial configurations and dense visitor flows. Effective methods for quantifying and mitigating museum fatigue are still lacking. This study proposes a contact-free sensing scheme based on computer vision and builds a coupled analytical framework with three stages: Human Pose Estimation (HPE) for visitor posture detection, fatigue assessment, and fatigue mitigation. A Fatigue Index (FI) quantifies bodily fatigue. Applying this index to the exhibition space in both the baseline and adjusted configurations guides the formulation of mitigation strategies and shows a consistent reduction in FI, which indicates that the adopted measures are effective. The proposed approach establishes a complete frame from fatigue quantification to fatigue mitigation, supports evaluation of exhibition space design, and provides theoretical and methodological support for future improvements to museum experience. Full article

The increasing share of heavy and high-sulfur crude oils in refinery feed slates worldwide highlights the need for models of delayed coking units (DCUs) that are both physically meaningful and computationally efficient. In this study, we develop and calibrate a simplified yet dynamic one-dimensional model of an industrial coke drum intended for integration into digital twin frameworks. The model includes a three-phase representation of the drum contents, a temperature-dependent global kinetic scheme for vacuum residue cracking, and lumped descriptions of heat transfer and phase holdups. Only three physically interpretable parameters—the kinetic scaling factors for distillate and coke formation and an effective wall temperature—were calibrated using routinely measured plant data, namely the overhead vapor and drum head temperatures and the final coke bed height. The calibrated model reproduces the temporal evolution of the top head and overhead temperatures and the final bed height with mean relative errors of a few percent, while capturing the more complex bottom-head temperature dynamics qualitatively. Scenario simulations illustrate how the coking severity (represented here by the effective wall temperature) affects the coke yield, bed growth, and cycle duration. Overall, the results indicate that low-order dynamic models can provide a practical balance between physical fidelity and computational speed, making them suitable as mechanistic cores for digital twins and optimization tools in delayed coking operations. Full article

Background: Computed tomography (CT)-derived body composition parameters, including skeletal muscle and fat indices, are prognosticators in oncology. Most studies focus on baseline body-composition parameters; however, changes during treatment may provide better prognostic value. Standardized methods for measuring/reporting these parameters remain limited. Methods: This retrospective study included patients who were treated with immunotherapy for non-small cell lung cancer (NSCLC), renal cell carcinoma (RCC), or melanoma between 2017 and 2024 and had technically adequate baseline and follow-up CT scans. Body composition was analyzed using a novel, fully automated software (CompoCT) for L3 slice selection and segmentation. Body composition indices (e.g., skeletal muscle index [SMI]) were calculated by dividing the cross-sectional area by the patient’s height squared. Results: The cohort included 376 patients (mean [SD] age 66.4 [11.4] years, 67.3% male, 72.6% NSCLC, 14.6% RCC, and 12.8% melanoma). During a median follow-up of 21 months, 220 (58.5%) died. Baseline body composition parameters were not associated with mortality, except for a weak protective effect of higher SMI (HR = 0.98, p = 0.043). In contrast, longitudinal decreases were strongly associated with increased mortality. Relative decreases in SMI (HR, 1.17; 95% CI, 1.07–1.27) or subcutaneous fat index (SFI) (HR, 1.11; 95% CI, 1.07–1.15) significantly increased mortality risk. Multivariate models showed similar concordance (0.65) and identified older age, NSCLC tumor type, and relative decreases in SMI and SFI (per 5% units) as independent predictors of mortality. Conclusions: Longitudinal decreases in skeletal muscle and subcutaneous fat were independent predictors of mortality in immunotherapy-treated patients. Automated CT-based body composition analysis may support treatment decisions during immunotherapy. Full article

Accurate measurement of semantic similarity between geographic terms is a fundamental challenge in geographic information science, directly influencing tasks such as knowledge retrieval, ontology-based reasoning, and semantic search in geographic information systems (GIS). Traditional ontology-based approaches primarily rely on a narrow set of features (e.g., semantic distance or depth), which inadequately capture the multidimensional and context-dependent nature of geographic semantics. To address this limitation, this study proposes an ontology-driven semantic similarity model that integrates a backpropagation (BP) neural network with multiple ontological features—hierarchical depth, node distance, concept density, and relational overlap. The BP network serves as a nonlinear optimization mechanism that adaptively learns the contributions of each feature through cross-validation, balancing interpretability and precision. Experimental evaluations on the Geo-Terminology Relatedness Dataset (GTRD) demonstrate that the proposed model outperforms traditional baselines, including the Thesaurus–Lexical Relatedness Measure (TLRM), Word2Vec, and SBERT (Sentence-BERT), with Spearman correlation improvements of 4.2%, 74.8% and 80.1%, respectively. Additionally, comparisons with Linear Regression and Random Forest models, as well as bootstrap analysis and error analysis, confirm the robustness and generalization of the BP-based approach. These results confirm that coupling structured ontological knowledge with data-driven learning enhances robustness and generalization in semantic similarity computation, providing a unified framework for geographic knowledge reasoning, terminology harmonization, and ontology-based information retrieval. Full article

Background: Diagnosis is the fundamental basis for understanding biomechanics in orthodontic treatment and for accurately designing the treatment plan. Traditionally, the sagittal plane has been the primary focus of assessment; however, it is essential to consider the patient in all three spatial planes. Therefore, it is necessary to explore the transverse plane, which is equally as crucial as the sagittal and vertical planes. With current technological advances, it is now possible to obtain three-dimensional images of the patient using cone-beam computed tomography (CBCT), allowing evaluation of all planes in a single diagnostic test. This study aimed to assess the diagnostic methods used for transverse analysis and the usefulness of CBCT for this purpose. Material and Methods: To select the studies for this review, we searched the PubMed, Scopus, and Cochrane databases for publications between 1965 and 2021. Our inclusion criteria targeted studies that evaluated the transverse plane using CBCT or CT. We assessed the level of evidence according to the OCEBM classification and evaluated the risk of bias using the QUADAS-2 scale. Results: After reviewing 535 articles, we selected 16 that met the established criteria. These studies compared various diagnostic methods for transverse analysis and their reproducibility indices. We identified the absence of a gold standard for measuring transverse discrepancies and high variability among diagnostic methods as the main limitations. Conclusions: Based on the available evidence, it can be concluded that dental and skeletal transverse discrepancies can be reliably differentiated using the diagnostic techniques evaluated in this study, particularly through CBCT-based assessment. Therefore, the diagnosis of transverse discrepancies should not be considered unclear, as it can be established using objective and measurable criteria. These findings reinforce the clinical value of current diagnostic tools and highlight the importance of accurate three-dimensional interpretation for informed and effective treatment decision-making. Full article

An accurate in situ stress field is a prerequisite for evaluating the stability of surrounding rock in underground caverns of a pumped-storage power station (PSPS) and ensuring the long-term safe operation of underground powerhouses. However, in situ stress measurements in the field are typically characterized by a limited number of measurement points, strong data randomness, and high testing costs. Meanwhile, conventional regression inversion methods often yield stress fields with insufficient accuracy or unstable spatial distributions. To address these issues, this paper proposes an in situ stress field inversion method based on the particle swarm optimization–support vector regression (PSO-SVR) algorithm. Stress boundary conditions are formulated in terms of lateral stress coefficients combined with shear stresses, and PSO is employed to optimize the hyperparameters of the SVR model. The stress boundary conditions predicted by the PSO-SVR algorithm are then imposed on a numerical model to compute the stresses at the measurement points, and the optimal boundary conditions are identified by minimizing the root mean square error (RMSE) between the inverted and measured in situ stresses. On this basis, the stress components at the measurement points and the in situ stress field in the study area are obtained. The results demonstrate that the inverted in situ stresses agree well with the field measurements, exhibiting good consistency and spatial regularity. Specifically, compared with the traditional multiple linear regression (MLR) method, the PSO-SVR algorithm reduces the RMSE and mean absolute error (MAE) of the in situ stress measurement data by 48.21% and 47.01%, respectively, and produces inversion results with higher accuracy, more stable spatial patterns, and markedly fewer anomalous zones. Consequently, the PSO-SVR algorithm is well suited for in situ stress inversion in PSPSs and provides a reliable stress-field basis for subsequent optimization of underground cavern excavation and support. Full article

To overcome the low accuracy of conventional methods for estimating liquid volume and food nutrient content in bowl-type tableware, as well as the tool dependence and time-consuming nature of manual measurements, this study proposes an integrated approach that combines geometric reconstruction with deep learning–based segmentation. After a one-time camera calibration, only a frontal and a top-down image of a bowl are required. The pipeline automatically extracts key geometric information, including rim diameter, base diameter, bowl height, and the inner-wall profile, to complete geometric modeling and capacity computation. The estimated parameters are stored in a reusable bowl database, enabling repeated predictions of liquid volume and food nutrient content at different fill heights. We further propose Bowl Thick Net to predict bowl wall thickness with millimeter-level accuracy. In addition, we developed a Geometry-aware Feature Pyramid Network (GFPN) module and integrated it into an improved Mask R-CNN (Region-based Convolutional Neural Network) framework to enable precise segmentation of bowl contours. By integrating the contour mask with the predicted bowl wall thickness, precise geometric parameters for capacity estimation can be obtained. Liquid volume is then predicted using the geometric relationship of the liquid or food surface, while food nutrient content is estimated by coupling predicted food weight with a nutritional composition database. Experiments demonstrate an arithmetic mean error of −3.03% for bowl capacity estimation, a mean liquid-volume prediction error of 9.24%, and a mean nutrient-content (by weight) prediction error of 11.49% across eight food categories. Full article

Effectively fusing the rich semantic information from camera images with the high-precision geometric measurements provided by LiDAR point clouds is a key challenge in mobile robot environmental perception. To address this problem, this paper proposes a highly extensible instance-aware fusion framework designed to achieve efficient alignment and unified modeling of heterogeneous sensory data. The proposed approach adopts a modular processing pipeline. First, semantic instance masks are extracted from RGB images using an instance segmentation network, and a projection mechanism is employed to establish spatial correspondences between image pixels and LiDAR point cloud measurements. Subsequently, three-dimensional bounding boxes are reconstructed through point cloud clustering and geometric fitting, and a reprojection-based validation mechanism is introduced to ensure consistency across modalities. Building upon this representation, the system integrates a data association module with a Kalman filter-based state estimator to form a closed-loop multi-object tracking framework. Experimental results on the KITTI dataset demonstrate that the proposed system achieves strong 2D and 3D detection performance across different difficulty levels. In multi-object tracking evaluation, the method attains a MOTA score of 47.8 and an IDF1 score of 71.93, validating the stability of the association strategy and the continuity of object trajectories in complex scenes. Furthermore, real-world experiments on a mobile computing platform show an average end-to-end latency of only 173.9 ms, while ablation studies further confirm the effectiveness of individual system components. Overall, the proposed framework exhibits strong performance in terms of geometric reconstruction accuracy and tracking robustness, and its lightweight design and low latency satisfy the stringent requirements of practical robotic deployment. Full article

Background: The original Closed Kinetic Chain Upper Extremity Stability Test (CKCUEST) is a simple assessment tool but does not account for individual differences in hand starting position and fails to provide information on limb asymmetries. The purpose of the study is to evaluate the test–retest reliability of a new single-arm CKCUEST as well as the reliability of the limb symmetry index (LSI). This version normalizes the test based on the participant’s arm length and allows for the assessment of limb symmetry since it is performed one arm at a time. Methods: Twelve healthy young adults provided both verbal and written consent to participate. Participants were excluded if they had sustained an injury in the past three months requiring medical attention and/or resulting in decreased activity for more than three days. Testing was conducted in the push-up position with participants’ thumbs placed parallel and at a distance equal to the length of their dominant arm (measured from the acromion to the tip of the middle finger), and feet positioned shoulder-width apart. Participants were instructed to keep the testing hand stable on the floor while the opposite hand reached across the body to touch the stationary hand and then return to the starting position marked with athletic tape. The goal was to complete as many touches as possible in 15 s, with each touch counted only if the participant touched the stationary hand, returned to the starting position, and maintained the shoulder-width stance. The average number of touches from the three trials was used for analysis. Intraclass Correlation Coefficients (ICC_(3,1)) were computed to determine test–retest reliability. Results: Test–retest reliability of the single-arm CKCUEST individual tests was good to excellent. The ICC_(3,1) was 0.88 (95% CI: 0.74–0.95) for all tests, 0.89 (95% CI: 0.66–0.96) for the dominant arm, and 0.93 (95% CI: 0.78–0.98) for the non-dominant arm. In contrast, the reliability of the Limb Symmetry Index (LSI) was questionable, showing substantial variability with an ICC_(3,1) of 0.53 (95% CI: −0.03–0.83) between Day 1 and Day 2, despite similar mean values (Day 1: 93.6 ± 8.46; Day 2: 94.8 ± 5.77). The Kappa coefficient suggested a substantial level of agreement for the direction of the asymmetry (preferred limb) (Kappa coefficient = 0.62). Conclusions: The new single-arm CKCUEST, which personalizes the hand starting position and measures limb symmetry, demonstrates high reliability among healthy young adults. Full article

Biofilms formed by Staphylococcus aureus on medical devices and tissue surfaces are a major contributor to persistent infections due to their resistance to antibiotics. Hydrodynamic forces in physiological and device-associated environments significantly influence biofilm development, yet the dynamics of detachment and regrowth under flow remain poorly quantified. In this study, biofilm surface coverage was measured in microfluidic flow assays across combinations of shear rates and nutrient concentrations. A computational workflow was used to segment biofilm trajectories into three kinetic phases—growth, exodus, and regrowth—based on surface coverage dynamics. Each phase was modeled using parametric functions, and fitted parameters were interpolated across experimental conditions to reconstruct biofilm lifecycles throughout the flow–nutrient conditions. The analysis revealed that intermediate shear rates triggered early detachment events while suppressing subsequent regrowth, whereas lower and higher shear regimes favored biofilm persistence. The resulting model enables quantitative comparison of condition-specific biofilm behaviors and identifies key thresholds in mechanical and nutritional inputs that modulate biofilm stability. These findings establish a phase-resolved framework for studying S. aureus biofilms under hydrodynamic stress and support future development of targeted strategies to control biofilm progression in clinical and engineered systems. Full article

Bioelectrical impedance analysis (BIA) is a widely used and an easy-to-apply method for determining body composition. However, its accuracy depends on population-specific equations. The aim of the present study is to identify the most appropriate fat-free mass (FFM) prediction equation for an Eastern European population using air-displacement plethysmography (ADP) as the reference method. The study group included 101 Caucasian subjects (56 women and 45 men) with an average body mass index (BMI) of 25.37 ± 5.45 kg/m². One set of FFM_BIA values was automatically calculated with the analyzer (Maltron BioScan 920-2), and four others were computed using the published equations of Kyle, Kanellakis, Heitman, and Deurenberg. The results were compared to FFM_ADP values measured using a BOD POD Gold Standard Body Composition Tracking System by Bland–Altman analysis. The smallest bias was obtained with the equation by Deurenberg, which underestimated FFM by only −0.22 ± 4.52 kg. The largest bias was obtained with the equation by Kyle (5.92 ± 4.73 kg), followed by the formula of Kanellakis (3.04 ± 4.65 kg). The equations by Heitman and the Maltron inbuilt formula overestimated FFM by 2.15 ± 4.27 kg and, respectively, 1.95 ± 4.3 kg. Although Maltron’s automatically generated values were very strongly correlated with ADP results (CCC = 0.93, SEE = 4.7), the formula by Deurenberg provided the most reliable estimates in the studied population. Full article

Background/Objectives: Early detection of hepatocellular carcinoma (HCC), particularly small lesions (<2 cm), which is crucial for curative treatment, remains challenging with conventional liver dynamic computed tomography (LDCT). We aimed to develop a deep learning algorithm to generate synthetic CT during hepatic arteriography (CTHA) from non-invasive LDCT and evaluate its lesion detection performance. Methods: A cycle-consistent generative adversarial network with an attention module [Unsupervised Generative Attentional Networks with Adaptive Layer-Instance Normalization (U-GAT-IT)] was trained using paired LDCT and CTHA images from 277 patients. The model was validated using internal (68 patients, 139 lesions) and external sets from two independent centers (87 patients, 117 lesions). Two radiologists assessed detection performance using a 5-point scale and the detection rate. Results: Synthetic CTHA significantly improved the detection of sub-centimeter (<1 cm) HCCs compared with LDCT in the internal set (69.6% vs. 47.8%, p < 0.05). This improvement was robust in the external set; synthetic CTHA detected a greater number of small lesions than LDCT. Quantitative metrics (structural similarity index measure and peak signal-to-noise ratio) indicated high structural fidelity. Conclusions: Deep-learning–based synthetic CTHA significantly enhanced the detection of small HCCs compared with standard LDCT, offering a non-invasive alternative with high detection sensitivity, which was validated across multicentric data. Full article