Search Results (2,262)

Understanding how climate change alters the thermal environment experienced by dairy cattle is critical for guiding adaptation in rapidly warming regions. Using meteorological data from 1961 to 2020, this study quantified long-term trends in temperature, precipitation, relative humidity, and wind speed across Jiangsu Province, China, and assessed their impacts on thermal stress using a temperature–humidity index (THI). The results reveal pronounced spatial heterogeneity in climatic change, with accelerated warming in southern and coastal prefectures, and continued winter cold stress in the northern plain. Over the past six decades, the annual number of heat-stress days (THI > 72) increased substantially and expanded northward, while cold-stress days (THI ≤ 38) declined but remained non-negligible in northern Jiangsu. Although the total number of thermally comfortable days changed little at the provincial scale, their seasonal distribution became increasingly compressed between longer summer heat-stress periods and shorter winter cold-stress windows, indicating a narrowing of the effective comfort range for dairy cattle. To link historical analysis with operational applications, a forecasting platform was developed to generate short-term predictions of THI and associated meteorological conditions, enabling spatially explicit visualization and early identification of periods with elevated thermal risk. Overall, these findings demonstrate an intensification and redistribution of thermal stress in Jiangsu, while also illustrating a transferable climate-risk mechanism relevant to other warming, humid dairy regions worldwide. Full article

Monitoring the condition of rolling bearings is critical for industrial reliability, yet traditional contact-based accelerometers can be impractical in confined or hazardous environments. This study investigates the use of microphones as a non-invasive diagnostic alternative, focusing on the impact of sensor distance and spatial placement on fault detection sensitivity across various rotational speeds and load conditions. Using an accelerometer mounted directly on the bearing as a benchmark, acoustic data were acquired on a test bench under different speed and load conditions. The experimental setup evaluated three distinct microphone positions and five distances relative to the source to assess spatial influence. Analysis was conducted comparing scalar indicators, such as Root Mean Square (RMS), kurtosis and Crest Factor (CF) values, with advanced diagnostic techniques, specifically the High-Frequency Resonance Technique (HFRT) for envelope spectrum extraction. Results indicate that while the signal-to-noise ratio (SNR) predictably decreases with distance, diagnostic performance is significantly compromised by acoustic shielding effects caused by bearing housing. Moreover, while simple statistical factors (RMS, kurtosis, CF) show limited reliability across varying distances and noise floors, HFRT-based envelope analysis yields robust fault identification even at the maximum sensor distance. The study concludes that optimal microphone placement is essential for reliable remote monitoring. Particularly, these findings suggest that a preliminary spatial characterization of the acoustic field can significantly enhance the effectiveness of non-contact diagnostic systems in industrial applications. Full article

Accurate prediction of a ship’s Estimated Time of Arrival (ETA) is of great significance for port scheduling, logistics management, and navigation safety. Traditional ETA prediction approaches often rely on manual experience for parameter tuning, which tends to be inefficient and susceptible to subjective factors. To address this issue and improve prediction accuracy, this study proposes a hybrid modeling framework, integrating Bayesian Optimization (BO), Convolutional Neural Networks (CNNs), and Long Short-Term Memory (LSTM) networks. In this approach, Automatic Identification System (AIS) data is leveraged to predict the total voyage duration before departure, thereby deriving the vessel’s ETA. The model, referred to as BO-CNN-LSTM, utilizes BO for automatic hyperparameter tuning, employs CNN for extracting local features, and applies LSTM network to capture temporal dependencies. The model is developed using a dataset of 32,972 distinct voyage records, among which 23,947 are retained as valid samples after data cleaning. Pearson correlation analysis is conducted to select key input variables, including navigation speed, ship type, sailing distance, and deadweight tonnage. Additionally, sailing distance is processed using the Ramer–Douglas–Peucker algorithm. Experimental evaluation indicates that the BO-CNN-LSTM model achieves a coefficient of determination of 0.987, along with a mean absolute error and root mean square error of 6.078 and 8.730, respectively. These results significantly outperform comparison models such as CNN, LSTM, CNN-LSTM, random forest, AdaBoost, and Elman neural networks. Overall, this study validates the effectiveness and superiority of the proposed BO-CNN-LSTM model in ship ETA prediction, providing an efficient and effective prediction solution for intelligent maritime transportation systems. Full article

Nonlinear friction in the mechanical transmission system of machine tools induces transient stagnation of the feed axis as its velocity crosses zero, thereby giving rise to contouring errors in multi-axis machining and significantly degrading machining accuracy. To address this issue, a feedforward compensation strategy is proposed based on a simplified static friction model (SSFM) with dual-segment and dual-parameter characteristics. The nonlinear friction is represented by a combination of a linear segment and an exponential segment, while the model incorporates two essential parameters that characterize the maximum friction force and the negative damping effect. Experimental results from two-axis circular trajectory tests show that the proposed SSFM reduces contour errors by approximately 73.4% and 79.2% at 600 mm/min and 2100 mm/min, respectively. To improve compensation under high-speed conditions, an acceleration-dependent dynamic correction is further introduced to establish the SDFM. The results show that the maximum contour error is further reduced to 1.44 μm and 1.49 μm at 3600 mm/min and 5000 mm/min, respectively. Compared with many existing reduced-order or hybrid friction models that rely on more parameters or more complex identification procedures, the proposed method provides a more compact and compensation-oriented modeling strategy for the velocity-reversal region of CNC feed systems. Full article

The synchronous continuum robot (SCR) was developed to emulate biological structures, such as animal tails and elephant trunks, based on continuum robot principles. By synchronizing disk motions, the SCR generates biologically inspired continuous movements while maintaining precise trajectory control. However, its synchronization-based architecture limits adaptability during physical interaction due to rigid trajectory-following characteristics. To address this limitation, this paper proposes a sensorless variable admittance control (VAC)-based compliant motion generation framework for the SCR. A dynamic model-based sensorless disturbance observer is designed to estimate external torques without additional force sensors. To compensate for uncertainties inherent in the cable-driven transmission mechanism, an adaptive term is incorporated into the parameter identification process, improving disturbance estimation accuracy. Based on the estimated external torques, admittance parameters are adaptively modulated according to joint angles, angular velocities, and robot posture, enabling interaction-aware motion speed regulation. Furthermore, the proposed method simultaneously enforces constraints on both joint angles and angular velocities through the adaptive regulation of target positions and velocities, ensuring safe and physically feasible motion. Experimental results under various interaction scenarios demonstrate reliable contact-independent force estimation and effective compliant motion generation. The proposed framework provides an integrated solution for robust force estimation, adaptive compliance control, and simultaneous constraint enforcement in mechanically synchronized continuum robots. Full article

In water injection development of tight oil reservoirs (TORs), the complex fracture network formed by hydraulic fracturing and water injection induction is the key factor determining the development effectiveness. Accurate inversion of water injection-induced fracture parameters holds significant importance for enhancing reservoir development outcomes. This paper innovatively proposes a parameter inversion framework that integrates the Embedded Discrete Fracture Model (EDFM) with intelligent optimization algorithms. EDFM efficiently characterizes complex unstructured fracture systems while maintaining mass conservation between the matrix and fractures; intelligent optimization algorithms automatically invert parameters such as fracture half-length, orientation, and conductivity. First, a three-dimensional geological model of the TOR is constructed, utilizing EDFM to handle the impact of fractures on the seepage field. Based on considerations of fracture geometry, conductivity, and stress sensitivity, a coupled fluid dynamics model for fractures and matrix is developed. Subsequently, an objective function is built based on water injection production dynamic data, and the Projection-Iterative-Methods-based Optimizer (PIMO) algorithm is employed to achieve efficient inversion of fracture parameters. Taking a TOR in the Ordos Basin as an example for verification, through synthetic model validation, this method significantly improves the accuracy and efficiency of history matching, with inversion results reliably guiding numerical simulation predictions. The results demonstrate that this method can effectively enhance the precision of fracture parameter identification, offering clear advantages in inversion speed and accuracy over traditional trial-and-error approaches. This study provides new insights for modeling induced fractures in TORs and optimizing water injection development strategies. Full article

Real-time coal–rock identification is essential for intelligent mining, enabling hazard prevention and geological modeling. However, existing methods often suffer from unclear bit–rock interaction mechanisms, signal distortion, sensor remoteness, or delayed data acquisition, limiting their effectiveness in continuous operations. This study proposes a novel approach for real-time coal–rock identification based on multi-source near-bit drilling data. A near-bit data acquisition system was developed and positioned directly behind the drill bit, integrating sensors to capture high-fidelity parameters—including weight on bit (WOB), torque, rotational speed, rate of penetration (ROP), natural gamma ray, and borehole trajectory—thereby eliminating frictional interference from the drill string. A data-driven theoretical model was established to derive a near-bit drillability index (NDI) for rock strength and to correlate gamma ray responses with lithology. Field trials were conducted in a coal mine in northern Shaanxi, involving over 30 boreholes and systematic core validation. The results demonstrate that the method enables continuous, high-resolution identification of coal–rock interfaces and strength variations along the borehole trajectory, with interpreted results aligning well with core logs and achieving approximately 85% accuracy in strength estimation. By ensuring compatibility with conventional drilling rigs and supporting real-time data transmission and 3D geological updating, this study offers a practical and robust technical pathway for achieving geological transparency and real-time steering in underground coal mining. Full article

High-precision identification and three-dimensional (3D) positioning of cabbage plants across their entire growth cycle are fundamental prerequisites for automated agricultural management. To overcome field challenges like extreme morphological variations, severe leaf occlusion, and bounding box jitter, we introduce a camera-LiDAR fusion perception system. First, an advanced SCEW-YOLOv8 architecture is proposed, sequentially integrating SPD-Conv downsampling, a C2f-CX global feature enhancement module, an EMA cross-space attention mechanism, and the WIoU v3 loss function. Evaluated on a comprehensive whole-growth-cycle cabbage dataset, the model achieves 95.8% mAP@0.5 and 90.8% recall with a real-time inference speed of 64.2 FPS. Furthermore, a visual semantic-driven camera-LiDAR fusion ranging algorithm is developed. Through rigorous spatiotemporal synchronization and cascaded outlier filtering, the integrated system achieves millimeter-level 3D localization within the typical 1.0–2.0 m operating range of agricultural robots. It maintains a Mean Absolute Error (MAE) of only 1.45 mm in the longitudinal direction at a stable processing throughput of 20 FPS. Compared to traditional pure vision depth estimation, this heterogeneous fusion approach achieves a remarkable 96.3% reduction in spatial positioning error at extended distances, fundamentally eliminating depth degradation caused by complex illumination. Ultimately, this system provides a highly robust, full-cycle geometric perception framework for the autonomous management of open-field green cabbage. Full article

A multibody simulation model for deep-groove ball bearings is presented. The model considers friction in both the raceway and cage contacts, resulting from radial and axial loads. The model is validated against experimental measurements for a 6319 bearing under oil-bath lubrication over a speed range of 500–3000 min⁻¹ and two load ratios ($C/P=10$ and 6.5). Predicted friction torques show good agreement with measurements, with deviations between 5.5% and 22% at moderate speeds. In addition, electrical contact capacitances are calculated for a 6208 bearing and compared with an analytical approach, showing deviations in the range of 10–14%. Beyond friction prediction, the fully dynamic approach enables time-resolved analysis of roller kinematics and the identification of instability limits under axial excitation. The developed tool therefore enables reliable bearing loss prediction, supports efficiency-oriented drivetrain design, and provides a basis for electro-tribological and stability investigations. Full article

Solid-state welding processes, particularly friction-stir welding (FSW), offer significant advantages for joining different aluminum alloys due to their good mechanical performance, energy efficiency, and cost-effectiveness. The FSW of the AA5052-H32 and AA6261-T6 alloys has not been previously reported. In this study, the effects of the main FSW process parameters on the mechanical behavior of different AA5052/AA6261 alloy joints were systematically investigated. A full factorial experimental design was applied, considering the tool rotation speed (900–1800 rpm) and the welding speed (56–252 mm/min) as control factors, along with their ratio (Rs/Ws). The results of the tensile tests reveal that the joint strength is strongly affected by the interaction between the rotation and welding speeds, with the Rs/Ws ratio is identified as a key parameter governing material flow, plastic deformation, and defect formation. The maximum tensile strength, approximately 198 MPa, corresponding to a mechanical efficiency of 84.4%, was achieved at 1800 rpm and 7 rev/mm, a condition that favored effective material mixing and a defect-free interfacial bond (≈162–186 MPa). The microhardness profiles showed a minimum of approximately 40–50 HV within the TMAZ, on the advancing side. In general, clear quantitative relationships were established between the process parameters and the mechanical properties, which allowed for the identification of optimal operating conditions to produce high-quality FSW joints between the dissimilar materials AA5052/AA6261. Full article

Wheat common bunt, caused by Tilletia foetida Liro, is a devastating disease in wheat production. In this study, the antagonistic endophytic bacteria 29-Y2 were screened based on the germination rate of teliospore and the control effect of wheat common bunt. During primary screening, 29-Y2 had the best performance, with a 96.73% inhibition on TFL spore germination. In the deep screening, the control effect of 29-Y2 on wheat common bunt was 66.12% in pots. Based on morphological, physiological, and biochemical characteristics and molecular biological identification, the antagonist 29-Y2 was identified as Paenibacillus polymyxa. The antagonist 29-Y2 promoted the germination rate of wheat seeds and the growth of wheat seedlings at a solution dilution of 10⁻⁵ cfu/mL. In different field trials, the antagonists 29-Y2 both had better control efficiencies of 62.31% and 67.62% for wheat common bunt. In order to further promote the inhibition activities of 29-Y2, the optimal culture condition was 11.1 g/L of glucose, 20 g/L of yeast extract powder, 3.8 g/L of soybean pepyone and 10 g/L of NaCl based on the response surface methodology; the liquid loading volume was 15 mL, of which the inoculant amount accounted for 2%, the pH was 6.8, the temperature was 30 °C and the rotation speed was 186 r/min for 26 h. When the fermentation broth obtained under these cultivation conditions was diluted 10,000 times, the inhibition rate of TFL teliospore germination could reach 80.32%. The fermentation broth control effect in pots improved from 57.77% to 84.17%. It was a promising endophytic bacterium for the prevention and control of wheat common bunt. Full article

The adjustable speed hydro-generating unit has a complex three-phase alternating current excitation structure. The existing rotor winding short circuit (RWSC) fault diagnosis methods are generally difficult to use to locate the fault location and identify the severity of the fault. Therefore, an offline diagnosis method for the internal RWSC of an adjustable speed hydro-generating unit is proposed in this paper. Firstly, after the unit is shut down, the low-voltage pulse signal is repeatedly injected into the rotor winding by the pulse generator. By comparing and analyzing the voltage response characteristics under different types of short circuit faults, an identification method of rotor winding short circuit fault type and fault phase based on detecting the reverse polarity sub-spike is proposed. Furthermore, the short circuit fault point can be accurately located by combining ensemble empirical mode decomposition (EEMD) with the Teager energy operator (TEO). Finally, the fault factor is constructed based on the area between the characteristic waveform and the zero line, and the quantitative evaluation of the severity of the short circuit fault is realized based on this. The effectiveness of the proposed fault diagnosis and location method is verified by the simulation results. Full article

High renewable energy penetration creates significant operational challenges for power systems, especially during extreme weather that disrupts supply–demand balance. This study introduces a framework that integrates extreme scenario identification, data augmentation, and power balance optimization. It defines extreme wind speed events, such as sudden drops, surges, and persistent anomalies, and uses a sliding-window algorithm to extract these events from historical meteorological data. To address the scarcity of extreme samples, a new data augmentation method combines the Wasserstein Generative Adversarial Network with Gradient Penalty (WGAN-GP) and iterative distribution shifting. This approach focuses the generated data on distribution tails while preserving diversity and temporal consistency. An optimization model, which includes various generation resources, energy storage, and load shedding, is developed to assess system flexibility under extreme conditions. Case studies on the projected 2030 Northeast China Power Grid show that the augmentation method expands extreme scenario datasets from 150 to 1000 samples, maintains extremity and temporal consistency, and reveals that wind curtailment rises sharply above 70% renewable share, with storage systems providing key flexibility in high-output scenarios. Full article

Background: Sarcopenia is the age-related, progressive loss of strength, function, and skeletal muscle mass, which can be assessed with specific tests. The Growth differentiation factor 15 (GDF-15) has been proposed as a key biomarker of aging, and it has been associated with mitochondrial dysfunction, cachexia, and physical impairment. Methods: The cohort of this study comes from the SardiNIA study, an ongoing longitudinal survey focused on the identification of genetic and phenotypic variants associated with aging. We assessed hand grip strength, gait speed, and GDF-15 in all samples. Linear multivariate analysis was used to assess the correlation after adjusting for a range of potential confounders. Results: The sample consisted of 4842 subjects (57.5% female) with a median age of 48.6 years. Levels of GDF-15 were comparable between males and females and showed a strong positive association with aging (rho 0.617, p < 0.001). Linear multivariate regression analyses showed that GDF-15 was negatively associated with gait speed and grip strength in both hands (respectively, Beta −0.09, Beta −0.07, and Beta −0.08, p < 0.001 for all). Conclusions: GDF-15 was negatively associated with physical function. GDF-15 may be considered a proxy for reduced physical performance. Future research is needed to understand the pathogenetic role of GDF-15 in the reduction in skeletal muscle in aging people. Full article

The global rise in electronic waste (e-waste), especially in semiconductor components such as circuit boards and microchips, underscores a critical need for improved recycling technology. Current industrial sorters often miss small, high-value components. This leads to the loss of precious metals and inefficient recycling processes. This paper introduces an automated detection framework for detecting semiconductor components in e-waste. It assesses ensemble learning methods that leverage the strengths of multiple YOLO (You Only Look Once) object detection models, including YOLOv5, YOLOv8, YOLOv9, YOLOv10, YOLOv11, and YOLOv12. Three ensemble fusion strategies are systematically compared: standard Non-Maximum Suppression (NMS), voting-based strategies (Affirmative, Consensus, Unanimous), and Weighted Box Fusion (WBF) with both static and dynamic weight optimization. Our simulations demonstrate that using multiple models together is far more effective than a single model for the following reasons. 1. Higher Accuracy: The best configuration, Top-4 Consensus Voting ensemble strategy, achieved an mAP@0.5 of 59.63%, a 10.3% improvement over the best individual model (YOLOv8s, 54.04%); 2. Greater Reliability: It significantly reduced “false negatives” (missed detections), even in cluttered or crowded e-waste scenarios; 3. Enhanced Detection: While the individual YOLOv8 model is fast (taking only 62.6 ms), supporting real-time detection, the best ensemble configuration (Consensus Top-4) takes 384.9 ms, creating a trade-off between detection accuracy and speed; 4. Well-Balanced Performance: Some fusion strategies showed slight trade-offs in mAP for certain parts, but collectively achieved a 7% rise in F1-score, indicating a better balance between precision and recall. This research marks significant progress in smart recycling. Improved component identification allows for more efficient recovery of high-purity materials. This promotes a circular economy by ensuring that rare and strategic materials in electronics are reused instead of discarded. Full article