Search Results (14,565)

The accurate calculation of excavation volume is critical for open-pit mine planning and management. Traditional methods are often inefficient and constrained by operational conditions. In contrast, digital surface model (DSM) differential analysis using stereophotogrammetry enables rapid acquisition of excavation volume, which holds significant value for retrospective excavation process. However, the actual mining process is not a simple matter of “excavation” or “backfilling”, but rather a complex mining pattern involving repeated excavation as new coal seams are exposed. This study utilized multi-source stereo remote sensing data (ZY-3, GF-7 satellite and UAV data) to construct a high-precision DSM time series spanning 2013 to 2025, focusing on analyzing the topographical evolution patterns of three representative mining pits. Research indicates that constructing DSMs during summer and autumn yields higher conformity with actual terrain, RMSE = 1.67 m and ME = −0.07 m. To address diverse mining patterns, we propose two calculation methods: the Cumulative Method (CM), which captures iterative excavation-backfilling cycles, and the First-Last Subtraction Method (FLSM), which mitigates cumulative DSM errors during continuous excavation. For phased mining operations, a hybrid method combining both approaches yields optimal results. Validation in three typical pits showed relative calculation errors of 1.36%, −0.49%, and 1.68%, respectively. The study indicates that the surface morphology changes in open-pit mines exhibit distinct non-linear characteristics. The method proposed herein not only enhances computational accuracy but also provides technical support for tracing historical coal excavation volumes. Full article

Orthopaedic plates are long-established medical devices conventionally manufactured from metals, most notably titanium alloys. The introduction of Additive Manufacturing (AM) has created new opportunities to design implants with complex internal architectures, enabling precise control over infill patterns and densities that directly influence mechanical properties and fatigue performance. Biodegradable polymers such as polylactic acid (PLA) have attracted growing interest in biomedical engineering, potentially reducing the need for secondary implant-removal surgery if degradation rates are carefully controlled and clinically approved. Additionally, AM offers the ability to customise internal structure for improved mechanical performance and load-bearing, while also providing the possibility of integrating advanced functionalities, such as controlled drug delivery. Building on previous work by our research group at the University of Belgrade, this study investigates the fatigue behaviour of the best-performing AM-optimised orthopaedic plate design. Numerical models incorporating honeycomb infill structures with the full range of achievable densities were developed to assess structural integrity under fatigue loading. Fatigue crack growth was simulated in ANSYS Mechanical (ANSYS Inc., Canonsburg, PA, USA) software, employing a four-point bending configuration in accordance with the ASTM F382 standard. A validated PLA material model was implemented at a reduced load level (10%) relative to previous studies. Direct comparison with titanium plates was avoided due to fundamentally different material properties, focusing instead on infill architecture to identify optimal AM design strategies for orthopaedic plates. Full article

Visual perception serves as a crucial interface connecting human psychology with the built environment. However, current studies on urban riverscapes often rely on static 2D imagery, failing to capture the spatial depth and immersive experience essential for ecological validity. Furthermore, the “black box” nature of traditional machine learning models hinders the understanding of how specific environmental features drive public perception. To address these gaps, this study proposes an innovative framework integrating high-fidelity 3D models, computer vision (CV), and interpretable artificial intelligence (XAI). Using the River Thames (London) and the River Seine (Paris) as diverse case studies, we constructed high-precision 3D digital twins to quantify 3D spatial metrics (e.g., Viewshed Area, H/W Ratio) and applied the SegFormer model to extract 2D visual elements (e.g., Green View Index) from water-based panoramic imagery. Subjective perception data were collected via immersive Virtual Reality (VR) experiments. Random Forest models combined with SHAP were employed to decode the non-linear driving mechanisms of perception. The results reveal three universal principles: (1) Sense of Affluence and Vibrancy are primarily driven by high building density and vertical enclosure, challenging the traditional preference for openness in waterfronts; (2) Scenic Beauty is determined by a synergy of high Green View Index and quality artificial interfaces, suggesting a preference for nature-culture integration; (3) Sense of Boredom is significantly positively correlated with Viewshed Area, indicating that empty prospects without visual foci lead to monotony. This study demonstrates the efficacy of integrating Digital Twins and XAI in revealing robust perception mechanisms across different urban contexts, providing a scientific, evidence-based tool for precision urban planning and riverside regeneration. Full article

In urban environments, autonomous vehicles face critical challenges in localization and perception under extreme lighting conditions, including rapid illumination changes, high contrast, and nighttime low-light scenarios. To address the performance degradation of traditional LiDAR-inertial odometry systems under such conditions, this study proposes a high-precision FAST-LIO2-EC algorithm that fuses event cameras into the FAST-LIO2 framework. Event cameras, with their microsecond temporal resolution and 140 dB dynamic range, provide asynchronous edge information that complements LiDAR point clouds and IMU measurements. We validate the proposed system through real-world road tests conducted on public roads and closed test tracks, covering three typical extreme lighting scenarios: tunnel entrance/exit transitions, high-contrast shadow boundaries, and nighttime sparse-lighting conditions. The experimental platform is equipped with a 32-beam LiDAR, a 6-axis IMU, a DVS event camera, and an RTK-GNSS system for ground truth trajectory acquisition. Real-world results demonstrate that the FAST-LIO2-EC system achieves significant improvements in localization accuracy and robustness. In illumination change scenarios, the Absolute Trajectory Error (ATE) is reduced by 32.5% compared to the baseline FAST-LIO2 system, with zero tracking loss events. The point cloud quality is substantially enhanced, with more uniform distribution and clearer obstacle boundaries. In high-contrast scenarios, both systems maintain comparable performance with ATE below 0.15 m. However, in nighttime scenarios, the fusion system shows moderate improvement (15.3% ATE reduction) but reveals sensitivity to event camera noise, indicating the need for adaptive thresholding strategies. Supplementary simulation experiments validate the system’s robustness under varying speeds and sensor noise levels. This work provides a practical solution for autonomous vehicle deployment in complex urban lighting environments, with a comprehensive analysis of real-world performance boundaries and deployment considerations. Full article

Precision Livestock Farming (PLF) relies on accurate, high-frequency data to optimize production efficiency. Traditional assessments of feeding behavior remain manual and invasive, lacking the kinematic resolution required for automated control systems. This study developed and validated a novel computer vision framework integrating YOLOv8 and the Segment Anything Model (SAM) to address this gap. The objective was to engineer a non-invasive, automated pipeline to quantify high-speed broiler biomechanics in real time. The system was validated using video data from broilers across three growth stages and varying feed granulometries (fine mash, coarse mash, and pellets) to test its robustness in detecting subtle kinematic variations. The hybrid YOLO-SAM pipeline achieved high performance, with a precision of 0.95 and a recall of 0.91, confirming its reliability as a scalable sensor for smart farming platforms. Biomechanical analysis demonstrated the system’s sensitivity, showing that larger feed particles induce greater beak gape and displacement while significantly improving ingestion efficiency (0.6 effort ratio for pellets vs. 3.0 for mash). This research provides a validated technical foundation for digital phenotyping in poultry, offering a hands-free, quantitative tool that supports data-driven decision-making in feed formulation and production management. Full article

In the cold and arid regions of northern China, efficient water and nitrogen (N) management is critical for the sustainable production of leafy vegetables. Simplified models that estimate crop N and water transpiration demands using simple inputs based on climate parameters become an important method for making precise suggestions on N and irrigation application at a regional scale. This study developed and validated a regionally adapted version of the VegSyst model, named VegSyst-CH, based on a multi-year open-field experiment from 2021 to 2023. Model parameters were calibrated using data from the 2021 growing season and validated with independent datasets from 2022 and 2023. A critical N concentration (CNC) curve was established to describe the relationship between biomass accumulation and N content. VegSyst-CH, with a radiation use efficiency of 1.94 g MJ⁻¹, demonstrated high simulation accuracy for crop growth. The model showed a good predictive performance of N uptake under medium (N1) and high (N2) N treatments, with coefficients of determination (R²) above 0.80 across years and normalized root mean square error (NRMSE) values generally below 30%. The VegSyst-CH model also showed high accuracy in simulating crop evapotranspiration (ETc) over three consecutive growing seasons (2021–2023), with the dynamic trends of cumulative ETc closely aligning with measured values and the coefficients of determination (R²) consistently exceeding 0.90. These results validate the model’s robustness and applicability across different years. In conclusion, the VegSyst-CH model has strong spatiotemporal regulation capacity and climatic responsiveness, offering a robust decision support tool for precision fertilization and irrigation in open-field lettuce production in cold and arid regions. Full article

This paper presents the design and experimental evaluation of a bio-inspired gecko robot, focusing on mechanical design, vision-based obstacle perception, and rhythmic locomotion control as enabling technologies for future obstacle avoidance in complex environments. The robot features a 17-degrees-of-freedom mechanical structure with a flexible spine and multi-jointed limbs, providing a physical basis for adaptive locomotion. For perception, a custom obstacle detection dataset was constructed from the robot’s onboard camera view and used to train a YOLOv5-based detection model. Experimental results show that the trained model achieves a mean average precision (mAP) of 0.979 and a maximum F1-score of 0.97 at an optimal confidence threshold, demonstrating reliable real-time obstacle perception under diverse indoor conditions. For motion control, a central pattern generator (CPG) based on Hopf oscillators is implemented to generate rhythmic locomotion. Experimental evaluations confirm stable diagonal gait generation, with coordinated joint trajectories oscillating at 1 Hz. The flexible spine exhibits periodic lateral deflection with peak amplitudes of ±15°, ±10°, and ±8° across spinal joints, enhancing locomotion continuity and turning capability. Physical robot experiments further demonstrate smooth straight-line crawling enabled by the coupled limb–spine motion. While visual perception and CPG-based locomotion are experimentally validated as independent subsystems, their real-time closed-loop integration is not implemented in this study. Instead, this work establishes a system-level framework and experimental baseline for future perception–motion coupling, providing a foundation for closed-loop obstacle avoidance and autonomous navigation in bio-inspired gecko robots. Full article

The integration of wearable inertial measurement units (IMU) in animal welfare Internet of Things (IoT) systems has become crucial for monitoring animal behaviors and enhancing welfare management. However, the vulnerability of IoT devices to network and hardware attacks poses significant risks, potentially compromising data integrity and misleading caregivers, negatively impacting animal welfare. Additionally, current animal monitoring solutions often rely on intrusive tagging methods, such as Radio Frequency Identification (RFID) or ear tagging, which may cause unnecessary stress and discomfort to animals. In this study, we propose a lightweight integrity and provenance-oriented security stack that complements standard transport security, specifically tailored to IMU-based animal motion IoT systems. Our system utilizes a 1D-convolutional neural network (CNN) model, achieving 88% accuracy for precise motion recognition, alongside a lightweight behavioral fingerprinting CNN model attaining 83% accuracy, serving as an auxiliary consistency signal to support collar–animal association and reduce mis-attribution risks. We introduce a dynamically generated pre-shared key (PSK) mechanism based on SHA-256 hashes derived from motion features and timestamps, further securing communication channels via application-layer Hash-based Message Authentication Code (HMAC) combined with Message Queuing Telemetry Transport (MQTT)/Transport Layer Security (TLS) protocols. In our design, MQTT/TLS provides primary device authentication and channel protection, while behavioral fingerprinting and per-window dynamic–HMAC provide auxiliary provenance cues and tamper-evident integrity at the application layer. Experimental validation is conducted primarily via offline, dataset-driven experiments on a public canine IMU dataset; system-level overhead and sensor-to-edge latency are measured on a Raspberry Pi-based testbed by replaying windows through the MQTT/TLS pipeline. Overall, this work integrates motion recognition, behavioral fingerprinting, and dynamic key management into a cohesive, lightweight telemetry integrity/provenance stack and provides a foundation for future extensions to multi-species adaptive scenarios and federated learning applications. Full article

Freezing of gait (FoG) affects 38–65% of advanced Parkinson’s disease patients, yet automated detection algorithms are often validated solely on laboratory datasets. This study quantifies the critical performance gap between laboratory and real-world performance—a prerequisite for clinical deployment. Using temporal convolutional networks (TCNs), we trained models on two public datasets representing ecological extremes: a daily living dataset (Figshare; n = 35, single-sensor) and a laboratory dataset (DAPHNET; n = 10, multi-sensor). We compared five training configurations to address class imbalance. Results showed that F1-based early stopping outperformed Area Under the Curve (AUC)-based stopping by 47% (F1: 0.55 vs. 0.37, p = 0.0008). Combining multiple imbalance corrections (focal loss, weighting, sampling) paradoxically degraded precision to 33% due to a ~60-fold over-weighting of the minority class. Most importantly, cross-dataset validation revealed an 83% performance gap: laboratory F1 reached 0.9999 ± 0.0002, whereas daily living F1 dropped to 0.55 ± 0.26 (p < 0.0001), with a 1299-fold increase in variance. These findings demonstrate that laboratory success does not guarantee real-world utility. We propose that the observed gap represents a “deployment gap” reflecting the combined influence of environmental complexity, sensor constraints, and physiological variability. These results provide an empirical framework for evaluating deployment readiness of wearable FoG detection systems and offer concrete training strategy recommendations for clinical translation. Full article

Differential wavefront sensing (DWS) is widely adopted for high-precision angular detection in interferometric systems, yet its measurement range is constrained by the nonlinear implicit phase–angle relationship. This paper proposes a strategy-enhanced differential evolution algorithm, termed Bi-inheritance and Tournament-Selection-based Differential Evolution (BiTsDE), to suppress nonlinear angular errors. The method introduces fitness-guided inheritance of mutation and crossover factors and tournament-based elite parent selection, enabling adaptive balance between global exploration and local exploitation. Unlike conventional DE variants that mainly tune control parameters, BiTsDE optimizes the evolutionary search strategy, enhancing early-stage diversity and late-stage convergence stability. Simulations demonstrate angular resolution better than 0.06 nrad within ±1 mrad. Experiments show that up to 600 μrad, BiTsDE reduces demodulation error by 99.9% compared with linear DWS, achieving 17.9 nrad precision and 42% faster convergence. These results validate BiTsDE as an effective solution for nonlinear error suppression in DWS-based high-precision optical metrology, particularly for space-based gravitational wave detection. Full article

Cropland constitutes a foundational resource for global food security and agricultural sustainability, and its accurate extraction from high-resolution remote sensing imagery is essential for agricultural monitoring and land management. However, existing deep learning-based segmentation methods often struggle to balance global contextual modeling and fine-grained boundary representation, leading to boundary blurring and omission of small cropland parcels. To address these challenges, this study proposes a novel CNN–Transformer dual-branch fusion network, named CAFM-Net, which integrates a convolution and attention fusion module (CAFM) and an edge-assisted supervision head (EH) to jointly enhance global–local feature interaction and boundary delineation capability. Experiments were conducted on a self-built PlanetScope cropland dataset from Suihua City, China, and the GID public dataset to evaluate the effectiveness and generalization ability of the proposed model. On the self-built dataset, CAFM-Net achieved an overall accuracy (OA) of 96.75%, an F1-score of 96.80%, and an Intersection over Union (IoU) of 93.79%, outperforming mainstream models such as UNet, DeepLabV3+, TransUNet, and Swin Transformer by a clear margin. On the GID public dataset, CAFM-Net obtained an OA of 94.58%, an F1-score of 94.19%, and an IoU of 89.02%, demonstrating strong robustness across different data sources. Ablation experiments further confirm that the CAFM contributes most significantly to performance improvement, while the EH module effectively enhances boundary accuracy. Overall, the proposed CAFM-Net provides a quantitatively validated and robust solution for fine-grained cropland segmentation from high-resolution remote sensing imagery, with clear advantages in boundary precision and small-parcel detection. Full article

The development of effective antioxidants has evolved from descriptive analysis toward a precise, mechanism-driven discipline targeting the molecular “redox switch”. This review synthesizes the critical advances reported since 2021, focusing on how the interplay between polyphenolic architecture and electronic descriptors, such as bond dissociation enthalpy and ionization potential, governs radical scavenging through the HAT, SET, and SPLET pathways. We evaluate the dual influence of metal coordination, where interactions can either enhance antioxidant stability through σ bond polarization or trigger pro-oxidant transitions via ligand-to-metal charge transfer. Central to this progress is the integration of computational models (DFT, QSAR) with advanced synchrotron methodologies (XAS, STXM, SR-FTIR, and SAXS), which provide element-specific validation of antioxidant behavior and subcellular oxidative mapping within complex matrices. Furthermore, we highlight how these molecular insights inform formulation engineering, specifically the development of organic nanocarriers and hybrid delivery systems, such as metal–phenolic networks, that shield therapeutic cargo from degradation and govern release in challenging physiological environments. These fundamental studies provide an essential physicochemical basis for medicine by enabling a better understanding and the rational design of antioxidant drugs, dietary supplements, and antioxidant strategies. Full article

With the core advantages of high energy efficiency, high power density, and reliable operation, high-voltage permanent magnet motors have become the mainstream development direction of modern motor technology. However, the risk of demagnetization caused by excessive temperature increases in permanent magnets has become a key bottleneck restricting motor performance and operational reliability, which makes research on the flow and heat transfer characteristics of motor cooling systems of great engineering value. Taking the 710 kW high-voltage permanent magnet motors as the research object, this study established a global flow field mathematical model covering the internal and external air duct cooling systems of the motor based on the theories of computational fluid dynamics and numerical heat transfer, and systematically analyzed the flow characteristics and distribution laws of cooling air. The thermal–fluid coupling numerical method was employed to simulate the temperature field of the motor, and the overall temperature distribution of the motor, temperature gradient of key components, and maximum temperature value were accurately obtained. To verify the validity of the established model, a test platform for the cooling system performance was designed and built. Measuring points for wind speed, air temperature, and component temperature were arranged at key positions, such as the stator radial ventilation ducts, and experimental tests were conducted under the rated operating conditions. The results show that the flow field distribution of the internal and external air ducts of the motor is reasonable and that the cooling air flows uniformly, with the external and internal circulating air volumes reaching 1.2 m³/s and 0.6 m³/s, respectively, which meets the heat dissipation requirements. The maximum temperature of 95 °C occurs in the stator winding area, and the maximum temperature of the permanent magnets is controlled within the safe range of 65 °C. The simulation results were in good agreement with the experimental data, with an average relative error of only 4%, which fell within the engineering allowable range, thus verifying the accuracy and reliability of the established global model and thermal–fluid coupling calculation method. This study reveals the thermal–fluid coupling transfer mechanism of high-voltage permanent magnet motors and provides a theoretical basis and engineering reference for the optimal design, precise temperature rise control, and reliability improvement of motor cooling systems. Full article

The rapid expansion of distributed cloud platforms introduces critical security challenges, specifically non-deterministic race conditions like Time-of-Check to Time-of-Use (TOCTOU) vulnerabilities. Traditional passive detection methods often fail to identify these transient “Heisenbugs” due to the asynchronous nature of multi-threaded control planes. To address this, we propose a novel DAG-Guided Active Fuzzing framework. Our approach constructs a Directed Acyclic Graph (DAG) to map causal dependencies of API operations and implements deterministic proactive scheduling. By injecting microsecond-level delays into identified race windows, the system enforces adversarial interleavings to expose hidden order and atomicity violations. Validated on 32 verified vulnerabilities across six distributed systems (including Hadoop and OpenStack), our method achieves an overall Recall (Detection Rate) of 68.8% across the entire dataset and a peak Precision of 92% in reproducibility tests, significantly outperforming random fuzzing baselines ($p<0.01$). Furthermore, the framework maintains a low runtime overhead of 11.5%. These findings demonstrate a favorable trade-off between detection depth and system efficiency, establishing the approach as a robust toolchain for transforming theoretical concurrency risks into reproducible security findings in large-scale cloud infrastructure. Full article

Fall-from-height fatalities in underground construction are closely associated with formwork scaffold operations, where dense steel members cause severe non-line-of-sight (NLOS) and multipath effects that degrade positioning performance. Although ultra-wideband (UWB) technology offers high theoretical ranging accuracy, its deployment-dependent performance in metal-rich scaffold environments remains insufficiently quantified. This study focuses on physical deployment optimization rather than algorithmic compensation. A full-scale formwork scaffold was constructed, and a stepwise one-factor controlled experimental design was employed to quantify the effects of anchor height (H) and horizontal spacing (S) on 3D positioning accuracy. The results show that sub-meter accuracy can be achieved through appropriate deployment, with a minimum 3D RMSE of 0.317 m and over 80% of single-axis errors confined within a 0.2 m engineering-valid region. For this specific setup, the optimal S = 1.5 m correlates with the scaffold grid size (approximately 0.8 times the 1.8 m bay width). While we hypothesize this ratio dependency applies to other geometries, this remains a site-specific observation requiring future cross-validation. Further analysis indicates that this deployment balances vertical signal visibility and multipath suppression. In addition, while the Position Dilution of Precision (PDOP) metric reflects geometric sensitivity, it does not linearly correlate with actual positioning errors under coplanar UWB deployments. These findings provide a rigorous static error model, serving as a critical prerequisite for developing robust real-time safety monitoring systems in scaffold-intensive construction environments. Full article