Understanding the tribological properties of alloy-based sliding electrical contacts is crucial for both fundamental research and practical applications. Here, to explore the friction, wear, and contact resistance of a AgCuNi alloy/Au-electroplated layer during sliding, a ball-on-disk tribometer was coupled with a source meter. The experiments were conducted under various conditions including a current ranging from 0 to 1.0 A, a normal load ranging from 0.5 to 3.0 N, and a sliding speed of 40 mm/s. The results indicate that the wear of the friction pair is aggravated by both the current and the increase in the normal load. When the current was 0.5 A, the wear loss reached its lowest point. However, as the current increased from 0.5 A to 1.0 A, there was an intensification in Ag transfer from the alloy ball to the Au-electroplated layer, resulting in an increase in wear loss. Both the normal load and current have significant effects on both friction coefficient and contact resistance. The variation in contact resistance over time follows a similar pattern to that of the friction coefficient over time. The formation of a transfer film plays a crucial role in determining contact resistance, wear resistance, and friction coefficient. The experiment demonstrates that optimizing the normal load and current can adjust both the contact resistance and friction coefficient, thereby prolonging service life and ensuring the stability of contacts.

To avoid wear and tear of the slip ring due to electrical corrosion, the slip ring needs to undergo the running-in process under atmospheric conditions without current after assembly. To address the urgent demand for long-service capability space conductive slip rings in the aerospace field, the running-in behavior and failure mechanism between the AgCuNi alloy and Au-electroplated layer are investigated using a ball-on-disc tribometer in this paper. The results show that the transfer film composed of Au plays an important role in modifying the friction during the sliding process. With the accumulation of wear debris composed of Ag on the disc, the contact material of the friction pair changed from Au and Au to Au, Ag and Au, so the surface roughness of wear tracks increased. Finally, the transfer film broke, which made the layer fail. This paper reveals the key element failure mechanism that causes transfer film failure in the running-in contact area, which is used to reveal the friction behavior and failure mechanism of slip ring friction pair materials, and provides a basis for the selection of running-in parameters during the running-in process of slip rings before power-on operation.

Straw cover is commonly used in agriculture to reduce soil water loss and improve crop productivity. However, the effects of straw length and cover amount remain unclear. This study, conducted from 2020 to 2022 in double-cropping (Laixi County) and mono-cropping (Qingyuan County) systems, examined the impact of straw length, cover amount, and tillage methods on soil properties, crop growth, yield, and water use efficiency (WUE). Five treatments were tested: no till (NT) with long and full straw cover (LF), long and half straw cover (LH), short and full straw cover (SF), short and half straw cover (SH), and no straw cover (NS). The results showed that straw cover improved soil temperature, crop growth, yield, and WUE. Under the NS treatment, the 30-day average soil temperature was 0–1.22 °C lower than with straw cover. Meanwhile, WUE increased by 2.38% to 5.52% under straw cover treatments compared to NS. Consequently, the straw cover treatments resulted in an increase of 2.28% to 6.89% in crop yield compared with the NS treatment. In double-cropping systems, SH treatment had the best results, while LF treatment was most effective in mono-cropping systems. These findings suggest that straw cover can enhance water conservation and soil quality while improving crop yields.

This study investigates the tribological behavior of the attachment force between bio-inspired claw–spines and rough surfaces and conducts theoretical and model-based analyses of the corresponding attachment mechanism. Based on critical interfacial tribological theory, a frictional mechanics model for a single claw–spine interacting with an arbitrary rough surface was developed. Furthermore, a stiffness-matrix-based mechanical model of bio-inspired claw–spine attachment to arbitrary surfaces was established, together with a contact interaction model between the claw–spine and the contact surface. The force distribution during the contact and attachment of the claw–spine to arbitrary surfaces was analyzed in detail. Criteria for determining stable claw–spine attachment were formulated, and the safe range of frame displacement variation under the corresponding conditions was identified. Based on these analytical results, a test platform for the bio-inspired claw–spine attachment structure was designed. In the experiments, the developed attachment-force measurement system was used to measure and analyze the frictional attachment forces generated by the claw–spine foot on different rough surfaces. After the claw–spine entered the stable attachment stage, the maximum claw–spine attachment forces measured on 60-grit, 80-grit, and 120-grit sandpaper surfaces were 0.62 N, 0.54 N, and 0.61 N, respectively. The corresponding maximum attachment forces measured on horizontal and vertical brick surfaces were 0.92 and 0.99 N, respectively. The results provide a basis for the mechanical analysis and force sensing of bio-inspired claw–spine attachment states and establish theoretical and experimental foundations for subsequent research on attachment-state recognition and motion control based on attachment-force feedback.

To solve the problems of high kernel breakage and low threshing efficiency in the threshing operation of high-moisture maize, this study designs an adaptive threshing device based on the coupled working principle of vortex airflow driving and flexible collision. The adaptive threshing mode enables maize ears to move spirally upward under vortex airflow and make compliant contact with flexible components. By adopting repeated mild collisions instead of the rigid violent impact used in traditional devices, low-damage maize threshing is achieved. Preliminary experiments verify that the layout density of flexible threshing units, tangential airflow velocity, and feeding speed are the key factors affecting threshing performance. A regression orthogonal rotational combination test is conducted to systematically explore the single-factor and interactive effects on threshing efficiency, and the optimal parameter configuration is obtained. The test results show that, under the conditions of circumferential angular spacing of 21.5°, tangential velocity of 45.9 m/s and feed rate of 0.65 kg/s, the maize threshing rate reaches 96.1% while the grain breakage rate is controlled below 0.1%, which is significantly superior to conventional rigid threshing methods. This research provides a new technical scheme and experimental data reference for the low-damage threshing study of high-moisture maize.

Maize kernel damage detection is critical for grain quality assessment and post-harvest processing. However, existing deep learning methods struggle to balance accuracy, model complexity, and multi-class recognition under real-world conditions. To address these issues, this paper proposes YOLOv11n_DEG, an improved lightweight detection model based on YOLOv11n. The model uses the first ten pretrained layers as a feature extractor, replaces standard convolutions in the backbone with depthwise separable convolutions to reduce parameters, integrates an Efficient Channel Attention (ECA) module to enhance feature representation, and employs a dual-dropout strategy in the classification head to mitigate overfitting and improve generalization. Additionally, to account for potential discrepancies in damage characteristics between the obverse and reverse sides of maize kernels, a dual-sided synchronous image feature fusion method is introduced. The output layer classifies five target categories for multi-class damage detection. On an independent test set, the proposed model achieves a precision of 92.5%, a recall of 89.6%, and a mean average precision (mAP) of 93.4%, outperforming the original YOLOv11n by 6.2%, 4.0%, and 3.7%, respectively, while reducing parameter count and computational complexity by 32.9% and 6.5%. To validate practical deployability, a custom testbed with dual-camera synchronous acquisition and geometry-based matching was developed. On this platform, the model with dual-sided fusion achieves a single-side recognition accuracy of 94.1% and a dual-sided recognition accuracy of 89.3%, with an average detection time of 0.8 s per batch. These results demonstrate that YOLOv11n_DEG provides an accurate and practical solution for intelligent maize kernel damage detection, with strong potential for real-world deployment in grain inspection systems.