Search Results (14,739)

Background/Objectives: Although discrepancies between bioelectrical impedance analysis (BIA) devices are well documented, their clinical relevance in vulnerable populations remains unclear. This study aims to assess the impact of device choice on muscle mass classification criteria in patients with cancer or obesity and to identify modifiers of device variability. Methods: BIA data from 224 adults (85 with cancer, 139 with obesity) measured with two segmental multi-frequency devices (seca mBCA 515 and InBody 970) were analyzed. Device differences were assessed using the Wilcoxon signed-rank test and agreement analyses. Differences in classification of body composition cut-offs cited in the GLIM criteria for malnutrition and the ESPEN and EASO criteria for sarcopenic obesity were evaluated using McNemar’s test. The impact of disease type, sex, and age on device differences was examined through multivariable models. Results: Significant device differences were found for all parameters (all p ≤ 0.0050). Discrepancies were largest for skeletal muscle mass (kg and %), with effect sizes r > 0.8 and poor agreement (Lin’s CCC < 0.90). A significant impact of device choice on muscle mass classification was observed for both cancer and obesity patients (p < 0.001), with seca classifying more patients as having low fat-free mass (50% vs. 20%) and as having a body composition consistent with sarcopenic obesity (90% vs. 50%) than InBody. Discrepancies were more pronounced in cancer patients and females. Conclusions: Muscle mass assessment by BIA is highly dependent on device choice, potentially leading to clinically relevant discrepancies in classification when rigid cut-offs are applied. An individualized interpretation of BIA data and further validation of prediction equations in disease-specific subpopulations is warranted. Full article

Background: Osteotomy around the knee (OAK) is a joint-preserving surgery for knee osteoarthritis, yet some patients experience suboptimal outcomes. Preoperative identification of high-risk patients remains challenging. This study aimed to develop a machine learning model to predict clinical outcomes after OAK using preoperative gait acceleration data from inertial measurement units (IMUs). Methods: This multicenter prospective study enrolled patients undergoing OAK. Preoperative gait was recorded using synchronized IMUs placed on the lumbar spine and tibia. Lumbar and tibial signals were used for gait-cycle segmentation, while wavelet-based time–frequency features were extracted from tibial acceleration only. Outcomes were defined by achievement of the minimal clinically important difference in ≥3 KOOS subscales at 2-year follow-up (Good vs. Poor). Continuous wavelet transform features (5–20 Hz) were summarized as mean and standard deviation across six stance subphases. A Random Undersampling Boost classifier was trained and evaluated using nested leave-one-subject-out cross-validation. A sensitivity analysis using logistic regression confirmed that the IMU-based prediction score was independently associated with outcome after adjustment for baseline KOOS (p = 0.047). Results: Of 67 enrolled patients, 37 were classified as Good and 30 as Poor outcome. For machine learning analysis, 1173 tibial acceleration gait-cycle waveforms were usable. The model achieved an AUC of 0.744 (95% CI, 0.610–0.860) using a median of 15 features (range, 5–25) with sensitivity of 0.69 and specificity of 0.72. The most informative predictors were the mean magnitude in the 5–8 Hz band during loading response (0–17%) and variability in the 5–8 Hz band during late stance (67–83%). No significant differences in baseline demographics or radiographic parameters were found between outcome groups. Conclusions: Preoperative IMU-derived gait acceleration features showed moderate-to-good discrimination between outcome groups and may support preoperative risk stratification and individualized perioperative management. Full article

One major challenge faced by deep learning-based methods that detect target objects in the form of bounding boxes is object occlusion. High degrees of occlusion significantly diminish the accuracy of instance segmentation. Nonetheless, complex-valued Fourier descriptors can robustly represent object boundaries using minimal information. In this study, the impact of integrating Fourier descriptors—renowned for their strong representational capacity—with deep network models (UNet) that exhibit high generalization performance on instance segmentation accuracy was investigated. Within the scope of the research, nine network models were designed based on different strategies for utilizing frequency components. These variants fall into four strategy families: (i) UNet-style spectrum regression on fixed low-frequency windows (FUNet), (ii) magnitude-guided frequency selection/ROI construction (FUNet–Thr, FUNet–BBox), (iii) sequence models over tokenized FFT coefficients (BiLSTM Patch/Sorted), and (iv) encoder-only spectrum predictors with different depth/capacity (EncoderFFT1/2). To fairly evaluate the models’ performance in segmenting objects subjected to disruptive factors (e.g., occlusion, blurring, noise), a specialized synthetic dataset was prepared. The task is formulated as single-target (single-instance), single-class segmentation. This dataset, automatically generated according to initial parameter values, contains images of objects moving at various speeds within a single frame. Among these models, the one termed FUNet, which relies on partial matching of central frequency components, achieved the highest segmentation accuracy despite the disruptive effects. Under the challenging Dataset 8 setting, the proposed FUNet achieved the highest overlap-based performance (Dice = 0.9329, IoU = 0.8842) among Attention U-Net, U-Net, and FourierNet, with statistically significant gains confirmed by paired per-image tests. Full article

Accurate power prediction is essential for assessing wind turbine performance under real-world operating conditions and for supporting condition monitoring and maintenance planning using SCADA data. Most existing approaches rely directly on raw SCADA signals, which may limit their ability to capture complex spatiotemporal dependencies among operational variables. To address this limitation, this paper proposes a novel SCADA-driven power prediction framework that transforms selected SCADA variables into multi-channel grayscale images and leverages an optimized LeNet-5–LSTM hybrid neural network for active and reactive power prediction. First, the SCADA dataset is analyzed to identify the most influential variables affecting power output. Six key variables are then selected, segmented, and encoded as 2D grayscale images, enabling the model to learn richer feature representations compared to conventional raw SCADA data-based methods. The proposed network combines convolutional layers for spatial feature extraction from SCADA data-based grayscale images with LSTM layers to capture temporal dependencies. Model training incorporates a customized loss function that integrates both data-driven supervision and physics-based constraints. The model is trained using 70% of the image-based dataset, with five independent runs to ensure robustness and reproducibility, while the remaining 30% is used for testing. The proposed approach is validated using SCADA data from three real-world cases: (i) a 2 MW Siemens wind turbine in Poland, (ii) a Vestas V52 wind turbine in Ireland, and (iii) the La Haute Borne wind farm in France, consisting of four wind turbines. The results demonstrate that the SCADA-based image representation enables the proposed LeNet-5–LSTM model to effectively learn discriminative feature patterns and achieve accurate active and reactive power predictions across different turbine types and operating conditions. Full article

Remote sensing images are often degraded by atmospheric effects, low illumination, and off-nadir viewing, which reduces the segmentation performance of deep models. KOMPSAT (Korea Multi-Purpose Satellite) imagery suffers from quality degradation because the Korean Peninsula is surrounded by sea on three sides and is subject to frequent weather and atmospheric variations. In practice, operators apply heuristic image enhancement techniques by hand, but these approaches are labor-intensive and inconsistent. To address this issue, we have proposed Adaptive Remote Sensing Image Enhancement (ARSIE), an automated reinforcement learning–based framework that improves segmentation performance on degraded KOMPSAT imagery. ARSIE takes only an existing segmentation network and training data as input, and learns, for each image, a sequence of enhancement operations selected from a filter pool. The policy network uses intermediate feature maps from the segmentation model to choose the next operation, ensuring that enhancement decisions directly support downstream segmentation performance. Experimental results show that ARSIE automatically discovers image-specific enhancement combinations and consistently improves segmentation accuracy on degraded KOMPSAT imagery. We demonstrate that ARSIE has the potential to be extended to improving the quality of other satellite imagery. Full article

The objective of the study was to undertake a preliminary analysis of the operational accuracy of a prototype suspension therapy apparatus. This entailed the establishment of the kinematic relationship between the movements imposed by the actuators and the movements of the participants’ body segments. The experimental procedure involved the taking of measurements on six participants (average age 32 ± 8 years, weight 67 ± 7 kg, height 178 ± 7 cm). Five movement sequences were observed, including rotation of the head, shoulders, and pelvis, and alternating movement of the shoulders, relative to the pelvis, and the head, relative to the shoulders. The movement of body segments and actuators was recorded using a Vicon optoelectronic system, based on passive markers. A virtual kinematic model was prepared for each of the measurements. It was found that the relationship between the actuator-imposed rotations and the resulting segmental rotations depended on the movement sequence and the body segment involved. The mean head rotation was 46.4° ± 1.2° (27.8% greater than the actuator setting) and the mean shoulder rotation was 23.8° ± 2.4° (11.1% greater), whereas the mean pelvic rotation (20.1° ± 0.9°) showed near agreement with the actuator-imposed value. In alternating movement sequences, distinct directional patterns were observed: head rotation remained greater than the actuator setting, shoulder rotation showed near-agreement or moderate increases, and pelvic rotation in the shoulder–pelvis sequence was markedly lower than the actuator-imposed rotation. The device demonstrates a high level of efficacy in mapping movements, particularly with regard to pelvic rotation. Differences in head rotation indicate the need for further optimisation of movement sequences. The results suggest mapping stability for the majority of participants, with isolated deviations requiring further investigation. Full article

The actual power curve of a wind turbine is essential for performance evaluation and operational optimization. However, SCADA data frequently contain various abnormal data points that limit their direct and effective use. Existing methods often fail to provide high-quality data for accurate power-curve fitting. Therefore, this paper proposes a comprehensive outlier cleaning method (QRD). This method incorporates the operational mechanisms of wind turbines and establishes preprocessing rules to effectively remove extreme outliers and bottom horizontal accumulation exhibiting distinct numerical characteristics. By leveraging the data distribution features in pitch angle–power and wind speed–power relationships, it implements horizontal and vertical quartile methods to eliminate mid-level accumulation and discrete outliers. A segmented regression-based outlier detection method with metrics adaptive to the power-curve distribution characteristics is proposed to clean residual outliers. Comparative results demonstrate that, relative to the Bins, CPQ, CIF, and TTLOF methods, the QRD method achieves a cleaning speed of 0.152 s per 10,000 data points, improving the average dispersion difference by 32.94%, 11.74%, 13.05%, and 9.67%, respectively. In terms of power-curve fitting accuracy, the average NMAE decreases by 8.65%, 5.07%, 7.57%, and 4.06%, while the average NRMSE decreases by 10.78%, 7.99%, 7.66%, and 5.16% and R² increases by 1.74%, 1.62%, 1.57%, and 1.03%, respectively. Overall, QRD demonstrates superior efficiency and accuracy in identifying abnormal wind power values, providing reliable support for high-quality power-curve modeling. Full article

Tomatoes are highly vulnerable to a wide range of leaf diseases, which significantly reduce agricultural yield and quality. Timely and precise detection of these diseases is essential for sustainable crop management and food security. This study analyzes configuration-level bidirectional multi-scale feature propagation within the native YOLOv12-s architecture, with emphasis on architectural behavior under controlled experimental conditions. The computational topology and parameterization of YOLOv12 are preserved, while bidirectional feature aggregation is activated at configuration level to examine its influence on cross-scale semantic consistency and localization reliability. The framework was trained and evaluated on a curated dataset of 4030 annotated RGB images spanning ten tomato leaf disease categories. All models were trained under an augmentation-free protocol and unified evaluation settings to isolate architectural effects from data-driven performance inflation. Under these controlled conditions, configuration-level bidirectional activation yields measurable improvements in detection consistency and spatial agreement while maintaining identical model complexity. Performance is evaluated using mAP, precision, recall, F1-score, and error-type decomposition within a measurement-consistency framework. The proposed configuration achieves 95.9% mAP@50 and 87.1% mAP@50–95 under identical experimental conditions, providing empirical evidence that topology-preserving feature routing influences multi-scale semantic stability in lesion detection. Full article

Accurate quantification of vegetation vertical structure is crucial for analyzing the ecological functions of urban green spaces. However, constrained by the complexity of vegetation structure and spatial heterogeneity, current approaches for extracting vegetation vertical structure by airborne LiDAR have limitations in terms of layer boundary identification stability, threshold dependency, and ecological plausibility. This study developed two integrated UAV LiDAR-based stratification frameworks for identifying urban riparian vegetation vertical structure by combining established statistical modeling and signal processing techniques: (1) a Gaussian Mixture Model with Bayesian Information Criterion (GMM-BIC)-based probabilistic stratification framework; (2) a Savitzky–Golay filtering and Pruned Exact Linear Time (SG-PELT)-based change-point detection framework. Furthermore, the ecological height constraint was incorporated into the model to achieve biological adjustments. Two models were applied in the study area and compared using reference data. The results showed that the GMM-BIC method achieved an overall classification accuracy of 91.06%, with a macro-averaged F1-score of 87.77%, while the SG-PELT method attained an overall accuracy of 84.57%, with a macro-averaged F1-score of 79.20%. These results demonstrate that both models can effectively identify the vertical structure of urban vegetation. In particular, the two models exhibited distinct characteristics across different scenarios. The GMM-BIC model showed superior stratification accuracy in regions where vegetation height distribution displayed pronounced multi-peak characteristics and distinct differences among height segments. In comparison, the SG-PELT model demonstrated greater sensitivity in areas with significant height variation and clearly defined abrupt transitions between layers. These models could provide new methodologies for monitoring vegetation vertical structure and offer data support for biodiversity monitoring and ecological function assessment within urban ecosystems. Full article

Technological innovation is driving the intelligent transformation of China’s coal mining industry, leading to significant changes in miners’ working methods and risk structures. To explore the predictors of miners’ safety citizenship behaviors in an intelligent mining environment, this study introduces regulatory focus based on the JD-R model of miners and proposes safety climate and self-efficacy as additional predictors. Using multiple methods including machine learning, response surface methodology (RSM), and latent profile analysis (LPA), data from a sample of 1168 miners were analyzed. The results indicate that the random forest model performed best, with the lowest prediction error and strongest explanatory power. In the variable importance analysis, safety climate (SAC), promotion focus (PRF), prevention focus (PF), and self-efficacy (SE) were identified as key factors influencing miners’ safety citizenship behaviors. Additionally, four distinct miner work characteristic groups were identified, showing significant differences; the more aligned the job demands and resources, the higher the safety citizenship behavior. This study aims to provide a basis for segmented and classified management in coal mine safety management from the perspective of multi-method evidence and heterogeneity. Full article

Dynamic Wireless Power Transfer (DWPT) is emerging as critical smart city infrastructure for sustainable urban mobility, enabling electric vehicle charging while driving. However, DWPT introduces complex fault scenarios requiring intelligent monitoring. Existing fault diagnosis approaches for wireless power transfer systems face three key complexities: (1) they are limited to static charging with only 2–4 fault categories, failing to address the time-varying coupling dynamics and segmented coil handover transients inherent in dynamic charging; (2) they lack integration with the host distribution grid, ignoring grid-side disturbances that propagate to charging stations; and (3) they offer only reactive detection without predictive capability for incipient fault management. This paper presents a deep neural network (DNN)-based fault diagnosis framework utilizing multi-station sensor fusion for DWPT systems integrated with the IEEE 13-bus distribution network to address these limitations. The system monitors 36 sensor features across three charging stations, employing feature-level concatenation with station-specific normalization for multi-station fusion, achieving 97.85% classification accuracy across eight fault types. Unlike static charging, the framework explicitly models time-varying coupling dynamics due to vehicle motion, including segmented coil handover effects. A digital twin provides dual-horizon prediction: long-term forecasting (24–72 h) for incipient faults and real-time detection under 50 ms for critical protection, with fault probability outputs and ranked fault lists enabling actionable maintenance decisions. The DNN outperforms SVM (92.45%), Random Forest (94.82%), and LSTM (96.54%) with statistical significance ($p<0.001$), while maintaining model inference latency of 4.2 ms, suitable for edge deployment. Circuit-based analysis provides analytical justification for fault signatures, and practical parameter acquisition methods enable real-world implementation. Five case studies validate robustness across highway, urban, and grid disturbance scenarios with detection accuracies exceeding 95%. Full article

This study evaluates, through a comparative statistical analysis, the predictive performance of empirical equations for estimating peak discharge during earthen pond failures, using a curated dataset of 78 reliable historical failure cases covering the documented period of available records, selected from an initial international database of 1893 cases. The analysis focuses on reservoirs with storage volumes below 6 hm³, a range that remains insufficiently addressed by existing breach-outflow models despite its importance for hydraulic, mining, and agricultural infrastructures. The procedure established a key comparative evaluation between equations to define the fit volume intervals. The results indicate that predictive uncertainty and error dispersion increase significantly as reservoir volume decreases, with a critical high-variability interval identified between 3.5 and 6 hm³ for both overtopping and piping failure mechanisms. A key finding is that predictive performance is strongly dependent on stored volume segmentation, as no single empirical formulation dominates the entire volume range; instead, 10 of 63 different equations achieve optimal accuracy within 5 specific storage intervals considering the RMSE, MAD and MAE error values. These findings emphasize the necessity of volume-dependent equation selection, based on comparative performance evaluation, and the development of specialized predictive models for small earthen reservoirs to ensure reliable risk assessment. Full article

Despite significant advances in digital surveying technologies, Heritage Building Information Modelling (HBIM) remains constrained by labour-intensive processing, fragmented classification systems, and limited standardised pathways for integrating Artificial Intelligence (AI). The absence of a systematic and standardised roadmap for AI adoption has limited both academic progress and industrial implementation. This paper proposes a comprehensive AI implementation roadmap for automated HBIM, developed through iterative research and empirical experimentation on UK heritage case studies. Building upon Design Science Research (DSR) principles, the roadmap delineates the critical dependencies among classification systems, data acquisition, algorithmic segmentation, and geometry generation, while embedding the Five HBIM Motivations, revival, restoration, restitution, retrofit, and resilience, as the primary structuring device for project intent. The study synthesises experimental findings into a practical, ISO 19650-aligned framework capable of guiding AI integration at both strategic and operational levels. An AI-enabled HBIM Execution Plan is presented as an implementation mechanism, enabling project teams to align digital workflows with heritage objectives, classification structures, and computational capacities. Evaluation through expert interviews confirms the roadmap’s feasibility, adaptability, and potential to enhance documentation efficiency, semantic richness, and interdisciplinary collaboration. The paper contributes a robust, scalable, and standards-compliant methodology for embedding AI in HBIM, offering a pivotal reference for the UK cultural heritage sector and a template for international replication. Full article

Background: Esophageal squamous cell carcinoma (ESCC) is the predominant subtype of esophageal cancer, with poor outcomes following neoadjuvant chemoradiotherapy (NCRT). Neoadjuvant chemoimmunotherapy (NCIT) has emerged as a promising strategy, but reliable predictive biomarkers remain lacking. This study aimed to develop an AI-driven pathomic model for NCIT response prediction and explore its biological mechanisms. Methods: We analyzed 269 H&E-stained whole-slide images (WSIs) from 198 ESCC patients (104 from Tongji Hospital, 94 from TCGA). Using ResNet152, we segmented WSIs into four tissue categories (tumor cells, stroma, lymphocytes, and necrosis), extracted spatially weighted pathomic features, and constructed the ECiT score via logistic regression. An integrated model combining the ECiT score with clinical variables (T stage, P53 status) was developed. Mechanistic analyses were performed using TCGA-ESCA and GSE160269 datasets. Results: The integrated model achieved AUCs of 0.897 (training) and 0.809 (temporal validation), outperforming clinical (AUC = 0.624) and pathomic-only (AUC = 0.751) models. Mechanistically, a high ECiT score correlated with enhanced immune activation (elevated CD4⁺ memory T cell infiltration), while low scores were linked to endoplasmic reticulum (ER) stress-unfolded protein response (UPR) activation. EIF2S3 was identified as a key molecular mediator, correlating with three pathomic features, UPR activation, and poor prognosis. Conclusions: This study may offer a preliminary indicator that could assist in personalized clinical decision-making. Correlative evidence suggests that the EIF2S3-mediated ER stress–UPR axis represents a potential candidate therapeutic target to overcome NCIT resistance, generating testable hypotheses to advance precision oncology for resectable locally advanced ESCC. Full article

Building extraction from high-resolution remote sensing imagery holds significant value for urban planning, disaster assessment, and geospatial analysis. However, current semantic segmentation models still face limitations when handling complex scenarios characterized by diverse building morphologies, significant scale variations, and blurred boundaries. To address the challenges of insufficient long-range dependency modeling, suboptimal multi-scale feature representation, and weak spatial adaptability, this paper proposes a building extraction network that integrates multi-scale sequence modeling with spatial adaptive enhancement. Adopting UPerNet (equipped with ConvNeXt-Tiny) as the baseline framework, the proposed method introduces a dedicated PyramidSSM-based neck (PyramidSSMNeck) as the primary design for multi-scale feature alignment and fusion, and further integrates three enhancement components (S6 (SSM-based), LSKNet, and SAFM) that provide additional improvements mainly reflected in boundary delineation. Specifically, PyramidSSMNeck performs structured cross-scale feature projection, alignment, and aggregation to strengthen multi-scale representation; S6 enhances long-range contextual modeling, LSKNet adaptively adjusts spatial receptive fields to accommodate scale variations, and SAFM modulates feature responses with spatial cues to refine boundaries and fine details—forming a unified framework in which PyramidSSMNeck primarily drives multi-scale alignment and fusion, while S6, LSKNet, and SAFM further enhance long-range context modeling and spatial adaptivity, mainly benefiting boundary preservation and fine-detail integrity. Experiments were conducted on the WHU Building, INRIA, and a self-constructed Ganzhou urban dataset, and the results indicate that the proposed method achieved IoU scores of 91.29%, 81.96%, and 88.18% across the three datasets, outperforming the baseline UPerNet (ConvNeXt-Tiny) by 2.37%, 0.88%, and 3.68%, respectively, with F1-scores consistently exceeding 90%. Importantly, ablation results indicate that the majority of region-level gains (IoU/F1) come from PyramidSSMNeck, whereas the additional modules contribute more prominently to boundary quality, yielding a Boundary IoU increase from 63.29% to 65.63% (+2.34) from the neck-only setting to the full model. Visualization results further support the method’s advantages in boundary preservation and detail integrity, and additional cross-domain transfer experiments (zero-shot and few-shot from WHU to Ganzhou) suggest improved robustness under domain shift. Full article