Search Results (922)

In Liquid Composite Molding (LCM), the high variability present in reinforcement properties such as permeability creates additional challenges during the injection process, such as void formation. Although improved injection strategy designs can mitigate the formation of defects, these processes can benefit from real-time process monitoring and control to adapt the injection conditions when needed. In this study, a machine vision algorithm is proposed, with the objective of detecting and tracking both fluid flow and bubbles in an LCM setup. In this preliminary design, 3D-printed porous geometries are used to mimic the architecture of textile reinforcements. The results confirm the applicability of the proposed approach, as the detection and tracking of the objects of interest is possible, without the need to incur in elaborate experimental preparations, such as coloring the fluid to increase contrast, or complex lighting conditions. Additionally, the proposed approach allowed for the formulation of a new dimensionless number to characterize bubble transport efficiency, offering a quantitative metric for evaluating void transport dynamics. This research underscores the potential of data-driven approaches in addressing manufacturing challenges in LCM by reducing the overall process monitoring complexity, as well as using the acquired reliable data to develop robust, data-driven models that offer new understanding of process dynamics and contribute to improving manufacturing efficiency. Full article

Natural latex harvest is pushing the boundaries of unmanned agricultural production in rubber milk collection via integrated robots in hilly and mountainous regions, such as the fixed and mobile tapping robots widely deployed in forests. As there are bad working conditions and complex natural environments surrounding rubber trees, the real-time and precision assessment of rubber milk yield status has emerged as a key requirement for improving the efficiency and autonomous management of these kinds of large-scale automatic tapping robots. However, traditional manual rubber milk yield status detection methods are limited in their ability to operate effectively under conditions involving complex terrain, dense forest backgrounds, irregular surface geometries of rubber milk, and the frequent occlusion of rubber milk bowls (RMBs) by vegetation. To address this issue, this study presents an unmanned aerial vehicle (UAV) imagery rubber milk yield state detection method, termed YOLOv8n-RMB, in unstructured field environments instead of manual watching. The proposed method improved the original YOLOv8n by integrating structural enhancements across the backbone, neck, and head components of the network. First, a receptive field attention convolution (RFACONV) module is embedded within the backbone to improve the model’s ability to extract target-relevant features in visually complex environments. Second, within the neck structure, a bidirectional feature pyramid network (BiFPN) is applied to strengthen the fusion of features across multiple spatial scales. Third, in the head, a content-aware dynamic upsampling module of DySample is adopted to enhance the reconstruction of spatial details and the preservation of object boundaries. Finally, the detection framework is integrated with the BoT-SORT tracking algorithm to achieve continuous multi-object association and dynamic state monitoring based on the filling status of RMBs. Experimental evaluation shows that the proposed YOLOv8n-RMB model achieves an AP@0.5 of 94.9%, an AP@0.5:0.95 of 89.7%, a precision of 91.3%, and a recall of 91.9%. Moreover, the performance improves by 2.7%, 2.9%, 3.9%, and 9.7%, compared with the original YOLOv8n. Plus, the total number of parameters is kept within 3.0 million, and the computational cost is limited to 8.3 GFLOPs. This model meets the requirements of yield assessment tasks by conducting computations in resource-limited environments for both fixed and mobile tapping robots in rubber plantations. Full article

Cross-country (XC) skiing imposes high physiological demands under hypoxic conditions at altitude. Lactate thresholds such as Onset Blood Lactate Accumulation at 4 mmol/L (OBLA4) and lactate plus 1 mmol/L above baseline (Bsln+1.0) are crucial for tracking performance. This study investigates physiological responses in junior XC skiers under normoxic and hypoxic conditions before (PreCs) and after (PosCs) the competitive season. Nine national-level XC skiers performed a Graded Exercise Test (GXT) on a treadmill using roller skis under both normoxic and hypoxic conditions in PreCS and PosCS. Heart rate, slope (treadmill inclination), and lactate thresholds (Bsln+1.0 and OBLA4) were measured. Significant differences were found between PreCs and PosCs under hypoxia for maximum heart rate (p < 0.05). Estimated slopes at Bsln+1.0 and OBLA4 were lower under hypoxia compared to normoxia in PreCs (p = 0.005, d = −1.29 for Bsln+1.0 and p = 0.013, d = −1.06 for OBLA4). In PosCs, a lower impairment effect of hypoxia exposure under slope at OBLA4 was found (p = 0.02, d = −0.95). Positive correlations were found between heart rate and slope for Bsln+1.0 and OBLA4 in PreCs under normoxia and hypoxia, becoming stronger at PosCs, especially under hypoxia. Delta values showed that the higher the slope at Bsln+1.0 and OBLA 4 under normoxia was, the greater the decrease between normoxia and hypoxia was. Physiological changes in junior XC skiers after training and competition in normoxic and hypoxic conditions highlight the importance of hypoxic environments for assessing and monitoring performance throughout the season. Full article

Hydrogen bonding, a prevalent molecular interaction in nature, is crucial in biological and chemical processes. The emergence of single-molecule techniques has enhanced our microscopic understanding of hydrogen bonding. However, it is still challenging to track the dynamic behaviour of hydrogen bonding in solution, particularly under physiological conditions where interactions are significantly weakened. Here, we present a nanoscale-confined, functionalised quantum mechanical tunnelling (QMT) probe that enables continuous monitoring of electrical fingerprints of single-molecule hydrogen bonding interactions for over tens of minutes in diverse solvents, including polar physiological solutions, which reveal reproducible multi-level conductance distributions. Moreover, the functionalised QMT probes have successfully discriminated between L(+)- and D(−)-tartaric acid enantiomers by resolving the conductance difference. This work uncovers dynamic single-molecule hydrogen bonding processes within confined nanoscale spaces under physiological conditions, establishing a new paradigm for probing molecular hydrogen-bonding networks in supramolecular chemistry and biology. Full article

Although laboratory testing to assess aerobic capacity has been a ‘gold standard’ in sports science, its high costs and time-consuming protocols may not be feasible for monitoring and tracking progress in limited conditions. In dancesport athletes, several field-based aerobic tests have been proposed, but the majority of them have been developed for ballet or contemporary dancers at the individual level, while the data among dance couples engaging in standard dance styles is lacking. Therefore, the main purpose of this study was to validate a newly developed Ballroom Aerobic Test (BAT) protocol against objective methods. Twelve standard dancesport couples (age: 20.4 ± 3.9 years; height: 172.1 ± 8.7 cm; weight: 60.1 ± 9.4 kg) with 8.2 ± 3.4 years of training and competing experience participated in this study. Ventilatory and metabolic parameters were generated using the MetaMax^® 3B portable gas analyzer (the BAT), while the KF1 (an increase in speed by 0.5 km * h⁻¹ by every minute) and Bruce protocols were followed in laboratory-based settings on the running ergometer. Large to very large correlations were obtained between the BAT and KF1/Bruce protocols for the absolute maximal oxygen uptake (VO₂max; r = 0.88 and 0.87) and relative VO₂max (r = 0.88 and 0.85), respiratory exchange ratio (RER; r = 0.78 and 0.76), expiratory ventilation (VE; r = 0.86 and 0.79), tidal volume (VT; r = 0.75; 95% CI = 0.57–0.87; p < 0.001), ventilatory equivalent for O₂ (VE/VO₂; r = 0.81 and 0.80) and CO₂ (VE/VCO₂; r = 0.78 and 0.82), and dead space (VD/VT; r = 0.70 and 0.74). The Bland–Altman plots indicated no systematic and proportional biases between the BAT and KF1 protocols (standard error of estimate; SEE = ± 3.36 mL * kg⁻¹ * min⁻¹) and the BAT and Bruce protocols (SEE = ± 3.75 mL * kg⁻¹ * min⁻¹). This study shows that the BAT exhibits satisfactory agreement properties against objective methods and is a valid dance protocol to accurately estimate aerobic capacity in dancesport athletes participating in standard dance styles. Full article

Flexible and wearable sensors have emerged as transformative technologies in the field of real-time health monitoring, offering non-invasive, continuous, and personalized healthcare solutions. These devices are designed to conform intimately to the human body, enabling seamless detection of vital physiological and biochemical signals under dynamic conditions. Recent advancements in material science and device engineering have led to the development of sensors with enhanced sensitivity, biocompatibility, and wearability, addressing the growing demand for preventive healthcare and remote patient monitoring. This review provides a comprehensive overview of the progress in flexible wearable sensors, including novel materials, sensor designs, and system integration strategies. It begins by surveying the latest advances in substrate and functional materials and hybrid structures that enable mechanical flexibility, skin conformability, and high sensitivity. The review then examines various sensor mechanisms and their implementation in monitoring vital signs, physical activity, and chronic diseases. Real-world applications are explored in depth, covering scenarios from cardiovascular and respiratory monitoring to motion tracking and rehabilitation support. Despite the significant strides made, challenges related to material robustness, sensor accuracy, and multi-modal integration remain, and this review discusses these challenges alongside potential future directions for enhancing the functionality and adoption of flexible wearable sensor systems. Full article

Ground deformation monitoring in mining areas is essential for hazard prevention and environmental protection. Although interferometric synthetic aperture radar (InSAR) provides detailed phase information for accurate deformation measurement, its performance is often compromised in regions experiencing rapid subsidence and strong noise, where phase aliasing and coherence loss lead to significant inaccuracies. To overcome these limitations, this paper proposes JOTGLNet, a guided learning network with joint offset tracking, for multiscale deformation monitoring. This method integrates pixel offset tracking (OT), which robustly captures large-gradient displacements, with interferometric phase data that offers high sensitivity in coherent regions. A dual-path deep learning architecture was designed where the interferometric phase serves as the primary branch and OT features act as complementary information, enhancing the network’s ability to handle varying deformation rates and coherence conditions. Additionally, a novel shape perception loss combining morphological similarity measurement and error learning was introduced to improve geometric fidelity and reduce unbalanced errors across deformation regions. The model was trained on 4000 simulated samples reflecting diverse real-world scenarios and validated on 1100 test samples with a maximum deformation up to 12.6 m, achieving an average prediction error of less than 0.15 m—outperforming state-of-the-art methods whose errors exceeded 0.19 m. Additionally, experiments on five real monitoring datasets further confirmed the superiority and consistency of the proposed approach. Full article

High-energy (710 MeV) Bi ion track morphology in polycrystalline silicon nitride was investigated during post-irradiation annealing. Using both in-situ and ex-situ transmission electron microscopy, we monitored the recovery of crystallinity within initially amorphous ion track regions. In-situ annealing involved heating samples from room temperature to 1000 °C in 50 °C increments, each held for 10 s. We observed a steady decrease in both the size and number of tracks, with only a small number of residual crystalline defects remaining at 1000 °C. Ex-situ annealing experiments were conducted at 400 °C, 700 °C, and 1000 °C for durations of 10, 20, and 30 min. Complete restoration of the crystalline lattice occurred after 30 min at 700 °C and 20 min at 1000 °C. Due to inherent differences in geometry, heat flow, and stress conditions between thin lamella and bulk specimens, in-situ and ex-situ results cannot be compared. Molecular dynamics simulations further revealed that track shrinkage begins in cells within picoseconds, supporting the notion that recrystallization can start on very short timescales. Overall, these findings demonstrate that thermal recrystallization of damage induced by swift heavy ion irradiation in polycrystalline Si₃N₄ is possible. This study provides a foundation for future research aimed at better understanding radiation damage recovery in this material. Full article

Wind turbines face significant operational challenges due to their complex electromechanical systems, exposure to harsh environmental conditions, and high maintenance costs. Reliable structural health monitoring and condition monitoring are therefore essential for early fault detection, minimizing downtime, and optimizing maintenance strategies. Traditional approaches typically rely on either Trend Monitoring (TM) or Change Point Detection (CPD). TM methods track the long-term behaviour of process parameters, using statistical analysis or machine learning (ML) to identify abnormal patterns that may indicate emerging faults. In contrast, CPD techniques focus on detecting abrupt changes in time-series data, identifying shifts in mean, variance, or distribution, and providing accurate fault onset detection. While each approach has strengths, they also face limitations: TM effectively identifies fault type but lacks precision in timing, while CPD excels at locating fault occurrence but lacks detailed fault classification. This review critically examines the integration of TM and CPD methods for wind turbine diagnostics, highlighting their complementary strengths and weaknesses through an analysis of widely used TM techniques (e.g., Fast Fourier Transform, Wavelet Transform, Hilbert–Huang Transform, Empirical Mode Decomposition) and CPD methods (e.g., Bayesian Online Change Point Detection, Kullback–Leibler Divergence, Cumulative Sum). By combining both approaches, diagnostic accuracy can be enhanced, leveraging TM’s detailed fault characterization with CPD’s precise fault timing. The effectiveness of this synthesis is demonstrated in a case study on wind turbine blade fault diagnosis. Results shows that TM–CPD integration enhances early detection through coupling vibration and frequency trend analysis with robust statistical validation of fault onset. Full article

Rocket launches constitute a major anthropogenic source of disturbance in the near-Earth space environment, inducing significant ionospheric perturbations through both chemical and dynamic mechanisms. This study presents a systematic analysis of ionospheric disturbances—specifically, electron density depletion and traveling ionospheric disturbances (TIDs)—triggered by four rocket launches from China’s Jiuquan Satellite Launch Center between 2023 and 2025. Using high-rate, multi-constellation GNSS data from 370 ground stations and BeiDou GEO satellites, we extracted total electron content (TEC) signals and applied advanced detection methods, including the Multi-Rolling-Multi-Image-Tracking (MRMIT) algorithm for depletion identification and a parametric integration framework for quantitative comparison. Our results reveal that all launches produced rapid TEC depletions, evolving along the rocket trajectory and peaking within approximately 30 min. Launch mass was the dominant factor controlling depletion intensity, while propellant chemistry (UDMH-based vs. liquid oxygen/methane) and local time/background TEC levels modulated the recovery rate and spatial extent. Additionally, distinct TIDs exhibiting wave-like and V-shaped structures were observed, propagating outward from the trajectory with latitudinal variations in amplitude and waveform. These findings highlight the critical roles of rocket attributes and ambient ionospheric conditions in shaping disturbance characteristics. The study underscores the value of multi-source GNSS networks and novel methodologies in monitoring anthropogenic space weather effects, with implications for GNSS performance and sustainable space operations. Full article

The daily behavioral patterns of giraffes reflect their health status and well-being. Behaviors such as licking, walking, standing, and eating are not only essential components of giraffes’ routine activities but also serve as potential indicators of their mental and physiological conditions. This is particularly relevant in captive environments such as zoos, where certain repetitive behaviors may signal underlying well-being concerns. Therefore, developing an efficient and accurate automated behavior detection system is of great importance for scientific management and welfare improvement. This study proposes a multi-behavior automatic detection method for giraffes based on YOLO11-Pose and the spatial-adaptive two-stream network (SATSN). Firstly, YOLO11-Pose is employed to detect giraffes and estimate the keypoints of their mouths. Observation-Centric SORT (OC-SORT) is then used to track individual giraffes across frames, ensuring temporal identity consistency based on the keypoint positions estimated by YOLO11-Pose. In the SATSN, we propose a region-of-interest extraction strategy for licking behavior to extract local motion features and perform daily behavior classification. In this network, the original 3D ResNet backbone in the slow pathway is replaced with a video transformer encoder to enhance global spatiotemporal modeling, while a Temporal Attention (TA) module is embedded in the fast pathway to improve the representation of fast motion features. To validate the effectiveness of the proposed method, a giraffe behavior dataset consisting of 420 video clips (10 s per clip) was constructed, with 336 clips used for training and 84 for validation. Experimental results show that for the detection tasks of licking, walking, standing, and eating behaviors, the proposed method achieves a mean average precision (mAP) of 93.99%. This demonstrates the strong detection performance and generalization capability of the approach, providing robust support for automated multi-behavior detection and well-being assessment of giraffes. It also lays a technical foundation for building intelligent behavioral monitoring systems in zoos. Full article

Cloud cover can delay landslide detection in optical satellite imagery for weeks, complicating disaster response. Synthetic Aperture Radar (SAR) backscatter imagery, which is widely used for monitoring floods and avalanches, remains underutilised for landslide detection due to a limited understanding of landslide signatures in SAR data. We developed a conceptual model of landslide expression in SAR backscatter (σ°) change images through iterative investigation of over 1000 landslides across 30 diverse study areas. Using multi-temporal composites and dense time series Sentinel-1 C-band SAR data, we identified characteristic patterns linked to land cover, terrain, and landslide material. The results showed either increased or decreased backscatter depending on environmental conditions, with reduced visibility in urban or mixed vegetation areas. Detection was also hindered by geometric distortions and snow cover. The diversity of landslide expression illustrates the need to consider local variability and multi-track (ascending and descending) satellite data in designing representative training datasets for automated detection models. The conceptual model was applied to three recent disaster events using the first post-event Sentinel-1 image, successfully identifying previously unknown landslides before optical imagery became available in two cases. This study provides a theoretical foundation for interpreting landslides in SAR imagery and demonstrates its utility for rapid landslide detection. The findings support further exploration of rapid landslides in SAR backscatter data and future development of automated detection models, offering a valuable tool for disaster response. Full article

The deployment of conditionally automated vehicles raises safety concerns, as drivers often engage in non-driving-related tasks (NDRTs), delaying takeover responses. This study investigates driver state monitoring (DSM) using multimodal physiological and ocular signals from the TD2D (Takeover during Distracted L2 Automated Driving) dataset, which includes synchronized electrocardiogram (ECG), photoplethysmography (PPG), electrodermal activity (EDA), and eye-tracking data from 50 participants across ten task conditions. Tasks were reassigned into three workload-based categories informed by NASA-TLX ratings. A unified preprocessing and feature extraction pipeline was applied, and 25 informative features were selected. Random Forest outperformed Support Vector Machine and Multilayer Perceptron models, achieving 0.96 accuracy in within-subject evaluation and 0.69 in cross-subject evaluation with subject-disjoint splits. Sensitivity analysis showed that temporal overlap had a stronger effect than window length, with moderately long windows (5–8 s) and partial overlap providing the most robust generalization. SHAP (Shapley Additive Explanations) analysis confirmed ocular features as the dominant discriminators, while EDA contributed complementary robustness. Additional validation across age strata confirmed stable performance beyond the training cohort. Overall, the results highlight the effectiveness of physiological and ocular measures for distraction detection in automated driving and the need for strategies to further improve cross-driver robustness. Full article

The evolution of the winemaking industry towards intelligent and digitalized systems is crucial for precision winemaking and ensuring product safety. In this context, the Internet of Things (IoT) provides a key strategy for real-time monitoring and data management throughout the winemaking process. However, comprehensive multi-parameter IoT-based monitoring and time-series prediction of physicochemical parameters during storage are currently lacking, limiting the ability to assess storage conditions and provide early warning of quality deterioration. To address these gaps, a multi-parameter IoT monitoring system was designed and developed to track conductivity, dissolved oxygen, and temperature in real time. Data were transmitted via a 4th-generation (4G) mobile communication module to the TLINK cloud platform for storage and visualization. An 80-day storage experiment confirmed the system’s reliability for long-term monitoring, and analysis of parameter trends demonstrated its effectiveness in assessing storage conditions and wine quality evolution. Furthermore, Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), Temporal Convolutional Network (TCN) models, and Autoregressive Integrated Moving Average (ARIMA) were implemented to predict physicochemical parameter trends. The TCN model achieved the highest predictive performance, with coefficients of determination (R²) of 0.955, 0.968, and 0.971 for conductivity, dissolved oxygen, and temperature, respectively, while LSTM and GRU showed comparable results. These results demonstrate that integrating IoT-based multi-parameter monitoring with deep learning time-series prediction enables real-time detection of abnormal storage and quality deterioration, providing a novel and practical framework for early warning throughout the wine storage process. Full article

The rehabilitation of mining environments poses significant challenges due to the contamination risks associated with hazardous materials, such as arsenic and other chemical products. This research study presents the development of a Digital Twin for the Lavrion Technological and Cultural Park (LTCP), a former mining and metalworking site that is currently undergoing environmental restoration. The Digital Twin integrates advanced technologies, including real-time sensor monitoring, geophysical methods, and 3D modeling, to provide a comprehensive tool for assessing and managing the environmental conditions of the site. Key elements of the project include the monitoring of hazardous-waste storage, the evaluation of contaminated soils, and the assessment of the Park’s infrastructure, which includes both deteriorating buildings and successfully restored structures. Real-time sensor data are collected to track critical parameters such as conductivity, temperature, salinity, and levels of pollutants, enabling proactive environmental management and mitigation of potential risks. The integration of these technologies enables continuous monitoring, historical data analysis, and improved decision making in the ongoing efforts to preserve the site’s ecological integrity. This study demonstrates the potential of using Digital Twins as an innovative solution for the sustainable management and valorization of mining heritage sites, offering insights into both technological applications and environmental conservation practices. Full article