Search Results (25,755)

To address the challenges of accurately extracting features from elongated scratches, irregular defects, and small-scale surface flaws on high-precision linear guide rails, this paper proposes a novel instance segmentation algorithm tailored for guide rail surface defect detection. The algorithm integrates the YOLOv8 instance segmentation framework with deformable convolutional networks and multi-scale feature fusion to enhance defect feature extraction and segmentation performance. A dedicated guide rail surface Defect (GSD) segmentation dataset is constructed to support model training and evaluation. In the backbone, the DCNv3 module is incorporated to strengthen the extraction of elongated and irregular defect features while simultaneously reducing model parameters. In the feature fusion network, a multi-scale feature fusion module and a triple-feature encoding module are introduced to jointly capture global contextual information and preserve fine-grained local defect details. Furthermore, a Channel and Position Attention Module (CPAM) is employed to integrate global and local features, improving the model’s sensitivity to channel and positional cues of small-target defects and thereby enhancing segmentation accuracy. Experimental results show that, compared with the original YOLOv8n-Seg, the proposed method achieves improvements of 3.9% and 3.8% in Box and Mask mAP₅₀, while maintaining a real-time inference speed of 148 FPS. Additional evaluations on the public MSD dataset further demonstrate the model’s strong versatility and robustness. Full article

Chatter is a self-excited vibration that limits productivity, accelerates roll wear and compromises strip surface quality in high-speed tandem cold-rolling. This work presents a predictive hybrid model that couples the strip-deformation physics to the structural dynamics of a five-stand, 4-high mill, providing a fast decision tool for process optimization and real-time control. The model represents each stand as a four-degree-of-freedom mass–spring–damper system whose parameters are extracted from manufacturing automation datasheets and roll-gap sensing. Linearization about the nominal point yields analytical sensitivity matrices that close the electromechanical loop; the delay between stands is also included in the model. Implemented in MATLAB/Simulink, the computational model, based on data provided by Danieli & C. Officine Meccaniche S.p.A., reproduces the onset of chatter for two types of steel. The framework therefore supports automation-ready scheduling, active vibration mitigation and design-space exploration for next-generation mechatronic cold-rolling systems. Full article

This paper presents a real-time pedestrian situation detection framework for autonomous mobility platforms. The proposed approach extracts pedestrians from images acquired by a camera mounted on an autonomous mobility system, classifies their postures, tracks their trajectories, and subsequently detects pedestrian situations. A convolutional neural network (CNN) is employed for pedestrian detection and posture classification, where the YOLOv12 model is fine-tuned via transfer learning for this purpose. To improve detection and classification performance, a region of interest (ROI) is defined using camera calibration data, enabling robust detection of small-scale pedestrians over long distances. Using a custom-labeled dataset, the proposed method achieves a precision of 96.6% and a recall of 97.0% for pedestrian detection and posture classification. The detected pedestrians are tracked using the DeepSORT algorithm, and their situations are inferred through a rule-based analysis module. Experimental results demonstrate that the proposed system operates at an execution speed of 58.11 ms per frame, corresponding to 17.2 fps, thereby satisfying the real-time requirements for autonomous mobility applications. These results confirm that the proposed framework enables reliable real-time pedestrian extraction and situation awareness in real-world autonomous mobility environments. Full article

Background/Objectives: Obstructive sleep apnea (OSA) is often accompanied by metabolic syndrome (MetS), forming a high-risk phenotype with elevated cardiometabolic burden. The contribution of lifestyle behaviors—particularly eating mechanics and psychological eating cues—to disease severity remains unclear. This study examined independent associations of dietary behaviors and physical activity (PA) with OSA severity, sleep quality, and metabolic health. Methods: Forty-four OSA patients (mean age 38.3 ± 9.1 years; 89% male) underwent attended polysomnography, dual-energy X-ray absorptiometry, and metabolic profiling. Validated questionnaires assessed dietary behaviors, PA, and sleep quality. Hierarchical logistic regression identified predictors of MetS, severe OSA, and poor sleep quality. Results: The prevalence of MetS was 45%. Compared with those with OSA alone, participants with MetS demonstrated significantly greater central adiposity and more severe nocturnal hypoxemia, despite similar apnea–hypopnea indexes. In multivariable models, MetS was independently associated with higher body mass index (adjusted odds ratio [aOR] = 1.64; p = 0.008) and reward eating (aOR = 3.34; p = 0.041), whereas higher total PA was associated with reduced odds (aOR = 0.96; p = 0.026). Poor subjective sleep quality was significantly associated with younger age (aOR = 0.91; p = 0.037). For severe OSA, slow chewing was associated with significantly reduced odds (aOR = 0.24; p = 0.038), while emotional eating was associated with increased odds (aOR = 2.40; p = 0.048). Conclusions: This hypothesis-generating study identifies a high-risk OSA phenotype marked by metabolic dysfunction and hypoxemia. Eating speed (a proxy for mindful eating), emotional and reward-driven eating, and PA independently shape metabolic and respiratory outcomes. These findings support incorporating behavioral nutrition into multidisciplinary OSA management. Full article

A hierarchical cooperative control strategy guided by a virtual leader is proposed to enhance the speed regulation and robustness of dual permanent magnet synchronous motor (PMSM) systems. The upper layer employs a virtual leader with model predictive speed control (MPSC) to achieve coordinated tracking, while the lower layer utilizes model predictive current control (MPCC) for regulation. A theoretical complexity analysis demonstrates that this decoupled architecture reduces the computational burden by approximately 75% compared to centralized MPC. Furthermore, a load disturbance observer is designed to estimate and compensate for external torques. Simulation and experimental results, covering both forward and reverse rotations, validate the effectiveness of the proposed strategy. Comparative results show that, compared with a conventional PI controller, the proposed method reduces speed overshoot by approximately 20% under sudden load changes, exhibiting superior steady-state performance and strong robustness against load variations. Full article

The reverse circulation pneumatic down-the-hole (DTH) drilling system employs percussive drilling to achieve high efficiency and strong adaptability across diverse rock formations. However, its cutting removal efficiency remains suboptimal. To enhance reverse circulation performance, a comprehensive understanding of airflow and solid particle dynamics at the borehole bottom is essential. This study investigates rock cutting transportation and distribution under varying drilling parameters and evaluates reverse circulation flow ratio using a Computational Fluid Dynamics (CFD) multiphase flow model, coupled with finite volume analysis of the reverse circulation bit. Simulation results reveal that increasing the input gas flow rate (Q), reducing the equivalent particle diameter (D), and minimizing the borehole enlargement ratio (E) significantly improve cutting removal efficiency, with optimal values identified for each parameter. Additionally, solid volume fraction contours at the borehole bottom indicate that the arrangement of spherical teeth influences the flow field. Optimal values for rock cutting density (ρ), rate of penetration (ROP), and rotational speed (N) were also determined to maximize reverse circulation flow ratio. The Genetic Algorithm–Least Squares Support Vector Machine (GA-LSSVM) method was used to train the response surface data and construct a predictive model, which was then further optimized using Particle Swarm Optimization (PSO) to determine accurate parameter settings. These findings provide operational insights into optimizing drilling parameters to advance efficient drilling performance. Full article

Examining pedestrian crossing violations at high-risk road midblock crossings has become essential, particularly in high-speed corridors, as a result of accidents at crossings resulting in fatalities. Hence, this article investigates such behavior in Alexandria, Egypt, as a credible case study in a developing country. According to our research methodology, a comprehensive dataset of over 2400 field-observed video recordings was used for real-life data collection. Machine learning (ML) models, such as CatBoost and gradient boosting (GB), were employed to predict crossing decisions. The models showed that risky behavior is strongly influenced by waiting time, crossing time, and the number of crossing attempts. The highest predictive performance was achieved by CatBoost and gradient boosting, indicating strong interpersonal influence within small groups engaging in unsafe road-crossing behavior. In the same context, the Shapley additive explanation (SHAP) values for these variables were 3, 2, and 0.60, respectively. Subsequently, based on SHAP sensitivity analysis, the results show that pedestrian crossing time (s) had the highest tendency to push the model towards class 1 (e.g., crossing illegally), while total time (s) and age group (40–60 Y) had a significant negative influence on model prediction converging to class 0 (e.g., crossing illegally). The results also showed that shorter exposure times increase the likelihood of crossing illegally. This research work is among the few studies that employ a behavior-based approach to understanding pedestrian behavior at midblock crossings. This study offers actionable insights and valuable information for urban designers and transportation planners when considering the design of midblock crossings. Full article

Long underground expressways have emerged as an alternative to surface highways in densely urbanized areas; however, their enclosed geometry, extended length, and steep longitudinal gradients introduce traffic-flow dynamics distinct from those of surface roads. This study investigates the combined and interaction effects of traffic volume, heavy-vehicle ratio, longitudinal gradient, lane number, and lane-changing policy on traffic performance in long underground expressways using microscopic traffic simulation. A hypothetical 20 km underground expressway network was evaluated under 72 systematically designed scenarios. Weighted average speed and throughput were analyzed using nonparametric statistics, generalized linear models with interaction terms, and machine learning-based sensitivity analysis. While traffic volume and heavy-vehicle ratio were confirmed as dominant determinants of performance, a key contribution of this study is the identification of the density-dependent role of lane-changing policies. Under moderate traffic density, permissive lane-changing improves efficiency by enabling vehicles to bypass localized disturbances caused by heavy vehicles and longitudinal gradients, thereby enhancing capacity utilization. In contrast, under high-density conditions, permissive lane-changing amplifies lane-change conflicts and shockwave propagation within the confined underground environment, accelerating traffic instability and performance breakdown. These adverse effects are further intensified by steep uphill gradients. The findings demonstrate that lane-changing policies on long underground expressways should be designed in a context-sensitive manner, balancing efficiency and stability across traffic states. Full article

High-precision autonomous localization remains a critical challenge for intelligent mining vehicles in GNSS-denied and unstructured coal mine roadways, where traditional odometry-based methods suffer from severe cumulative drift and perceptual aliasing. Inspired by the synergy between mammalian visual cues and cognitive neural mechanisms, this paper proposes a robust biomimetic localization framework that integrates multi-source perception with a prior cognitive map. The core contributions are three-fold: First, a semantic-enhanced biomimetic localization method is developed, leveraging roadway sign data as absolute spatial anchors to suppress long-distance cumulative errors. Second, an optimized head direction (HD) cell model is formulated by incorporating a speed balance factor, kinematic constraints, and a drift correction influence factor, significantly improving the precision of angular perception. Third, boundary-adaptive and sign-based semantic constraint terms are integrated into a continuous attractor network (CAN)-based path integration model, effectively preventing trajectory deviation into non-navigable regions. Comprehensive evaluations conducted in large-scale underground scenarios demonstrate that the proposed framework consistently outperforms conventional IMU-odometry fusion, representative 3D SLAM solutions, and baseline biomimetic algorithms. By effectively integrating semantic landmarks as spatial anchors, the system exhibits superior resilience against cumulative drift, maintaining high localization precision where standard methods typically diverge. The results confirm that our approach significantly enhances both trajectory consistency and heading stability across extensive distances, validating its robustness and scalability in handling the inherent complexities of unstructured coal mine environments for enhanced intrinsic safety. Full article

Unsteady turbulent flows are present in most engineering applications of practical relevance. In aeronautics, these applications span the speed range from subsonic to hypersonic flows. Thus, it is important that our mathematical models and numerical techniques can represent the various flow regimes in a seamless way. The latter is the main motivation of the current paper, which extends the PANS turbulence model to compressible and high-speed flows. The new model, called BPANS-CC, blends the (k,ε) and (k,ω) versions of PANS. In addition, compressibility correction is added to the new model to expand its simulation range into the compressible high-speed flow regime. The new model was implemented in various CFD software, both academic and commercial. Several well-known benchmark problems were used to test the new model, and the results are in good agreement with experimental data. Full article

Accurate mapping of land use and land cover (LULC) is crucial for various applications such as urban planning, environmental management, and sustainable development, particularly in rapidly growing urban areas. African cities such as Yaoundé, Cameroon, are particularly affected by this rapid and often uncontrolled urban growth with complex spatio-temporal dynamics. Effective modeling of LULC indicators in such areas requires robust algorithms for high-resolution images segmentation and classification, as well as reliable data with great spatio-temporal distributions. Among the most suitable data sources for these types of studies, Sentinel-2 image time series, thanks to their high spatial (10 m) and temporal (5 days) resolution, are a valuable source of data for this task. However, for an effective LULC modeling purpose in such dynamic areas, many challenges remain, including spectral confusion between certain classes, seasonal variability, and spatial heterogeneity. This study proposes a hybrid deep learning architecture combining U-Net and Convolutional Long Short-Term Memory (ConvLSTM) layers, allowing the spatial structures and temporal dynamics of the Sentinel-2 series to be exploited jointly. Applied to the Yaoundé region (Cameroon) over the period 2018–2025, the hybrid model significantly outperforms the U-Net and ConvLSTM models alone. It achieves a macro-average F1 score of 0.893, an accuracy of 0.912, and an average IoU of 0.811 on the test set. These segmentation performances reached up to 0.948, 0.953, and 0.910 for precision, F1-score, and IoU, respectively, on the built-up areas class. Moreover, despite its better performance, in terms of complexity, the figures confirm that the hybrid does not significantly penalize evaluation speed. These results demonstrate the relevance of jointly integrating space and time for robust LULC classification from multi-temporal satellite images. Full article

This study introduces a novel control device, the nonlinear air-spring absorber (ASA), aimed at improving the collapse resistance of transmission tower-line systems subjected to severe wind loads. Initially, a detailed finite element (FE) model is developed for a representative transmission tower-line system, grounded in an actual engineering project, and the wind load applied to the system is obtained. Then, the working principle and design method of the ASA are introduced, and the device is embedded into the FE model. The Inter-Segment Displacement Ratio (ISDR) is employed as a collapse indicator to systematically evaluate, via fragility analysis, the effectiveness of the ASA. The effectiveness of the ASA at improving the collapse resistance of the tower-line system under different wind attack angles is systematically studied through a fragility analysis. The results show that the device effectively suppresses the structural wind-induced vibration and significantly improves the system’s collapse resistance. In particular, the vibration suppression effect is most pronounced along the transmission line (90° wind attack angle), with the critical collapse wind speed increasing by up to 23%. This study provides a practical and feasible technical approach for addressing the problem of wind-induced collapse control. Full article

Background/Objectives: Frailty and functional decline represent major challenges for aging populations, particularly among institutionalized older adults. Preserving functional capacity is essential to maintain autonomy, mobility, and quality of life. This study aimed to compare the effects of two strength training interventions—functional electromechanical dynamometer (FEMD) training and weighted vest training—on peak concentric and eccentric force during the sit-to-stand task, as well as on functional performance and body composition in frail nursing home residents. Methods: A pilot quasi-experimental study with a non-randomized control group was conducted in 19 older adults (mean age: 86.3 ± 5.8 years). Participants were allocated to FEMD training (EG1, n = 6), weighted vest training (EG2, n = 6), or a control group (CG, n = 7). Training was performed twice weekly for eight weeks. Assessments included body composition, handgrip strength, 30 s chair stand test, 3 m walking speed, and peak concentric and eccentric force during the sit-to-stand movement. Data were analyzed using mixed-model ANOVA and complementary within-group analyses. Results: No significant group × moment interactions were observed. However, EG1 demonstrated significant within-group improvements in chair stand performance (+4.8 repetitions, p = 0.006), walking speed (+0.1 m·s⁻¹, p = 0.030), concentric peak force (+46.5%, p = 0.008), and eccentric peak force (+34%, p = 0.047). EG2 showed a smaller but significant increase in eccentric peak force (+6.1%, p = 0.019), without functional improvements. Body composition changes were modest, with EG1 showing increases in weight and BMI without concomitant fat mass gains. Conclusions: In this pilot quasi-experimental study, functional electromechanical dynamometer-based training was associated with improvements in neuromuscular performance, particularly concentric peak force. However, no significant group × moment interactions were observed, indicating that differential effects between interventions cannot be established. Functional improvements should be interpreted cautiously. The present results should therefore be considered exploratory and hypothesis-generating. These findings suggest that FEMD-based training may be a feasible and potentially beneficial functional strength training strategy for frail institutionalized older adults, which should be confirmed in adequately powered randomized controlled trials. Full article

Core body temperature is a critical physiological indicator for assessing and diagnosing animal health status. In bovines, continuously monitoring this metric enables accurate evaluation of their physiological condition; however, traditional rectal measurements are labor-intensive and cause stress in animals. To achieve intelligent, contactless temperature monitoring in cattle, we proposed a non-invasive method based on thermal imaging combined with environmental data fusion. First, thermal infrared images of the cows’ faces were collected, and the You Only Look Once (YOLO) object detection model was used to locate the head region. Then, the YOLO segmentation network was enhanced with the Online Convolutional Re-parameterization (OREPA) and High-level Screening-feature Fusion Pyramid Network (HS-FPN) modules to perform instance segmentation of the eye socket area. Finally, environmental variables—ambient temperature, humidity, wind speed, and light intensity—were integrated to compensate for eye socket temperature, and a random forest algorithm was used to construct a predictive model of rectal temperature. The experiments were conducted using a thermal infrared image dataset comprising 33,450 frontal-view images of dairy cows with a resolution of 384 × 288 pixels, along with 1471 paired samples combining thermal and environmental data for model development. The proposed method achieved a segmentation accuracy (mean average precision, mAP_50–95) of 86.59% for the eye socket region, ensuring reliable temperature extraction. The rectal temperature prediction model demonstrated a strong correlation with the reference rectal temperature (R² = 0.852), confirming its robustness and predictive reliability for practical applications. These results demonstrate that the proposed method is practical for non-contact temperature monitoring of cattle in large-scale farms, particularly those operating under confined or semi-confined housing conditions. Full article

This paper outlines the design, development, and practical implementation of a rubber belt-driven dual cone continuously variable transmission (CVT) model. This model enables a demonstration of stepless changes in the transmission ratio between input and output shafts. Although the model can be operated manually via a control panel, enhanced functionality, such as automated measurement, proportional-integral-derivative (PID) speed control, and data measurement and storage, is achieved through a control application created within the LabVIEW virtual instrument environment. This work also includes a partial comparison between the practical implementation and its simulation model created in MATLAB-Simulink. Full article