Wire arc directed energy deposition (WA-DED) is a cost-effective technique for fabricating large metallic components. However, the inherent layer-by-layer deposition process leads to substantial heat accumulation, which significantly influences the resulting microstructure and mechanical properties. In this study, the effects of thermal cycling histories, at different interlayer temperatures, on the microstructural evolution and mechanical behavior of WA-DED fabricated H13 steel thin walls were systematically investigated, using an experimentally calibrated transient thermal model combined with experimental validation. Microstructural analysis revealed that at an interlayer temperature of 200 °C, the deposited material primarily consisted of coarse martensite with a low dislocation density and relatively large precipitates at a moderate volume fraction, resulting in an ultimate tensile strength of 1103 ± 28 MPa and an elongation of 14.6%. Increasing the interlayer temperature to 400 °C facilitated the formation of finer martensite with a higher dislocation density and smaller precipitates of slightly increased volume fraction. These microstructural refinements enhanced the tensile strength to 1549 ± 43 MPa, albeit at the expense of ductility, reducing elongation to 8.3%. When the interlayer temperature was further raised to 600 °C, fine martensite and a moderate dislocation density were retained; however, precipitate coarsening and a reduced volume fraction led to a decline in tensile strength to 1434 ± 33 MPa, accompanied by a slight recovery in elongation to 8.6%. Quantitative analysis based on classical strengthening models confirmed that dislocation strengthening is the dominant mechanism governing the variation in mechanical properties with changing interlayer temperature.

Walking with heavy loads is a common task in military affairs and daily life. Considering that the shoulder and leg muscles fatigue will be caused during walking, which will affect the walking endurance and physical health. However, the suspended backpack is found to improve the energy efficiency of walking with a load. In this study, A lightweight suspended backpack is designed and proposing a model for estimating the metabolic cost of a suspended backpack based on gait phase. In this study, four inertial measurement units (IMUs) are fixed on the thigh and shank, six flexible pressure sensors are mounted on the soles of the feet and shoulders, respectively. The gait is defined as four successive phases. For each phase, the muscle tension is solved based on the muscle moment balance theory. Based on the phase segmentation method, the ECCF index is calculated by adding the gait phase constraint and backpack data calculation into the energy prediction model, and the relatively accurate data is obtained. In addition, In order to study the effects of the suspended backpack with different parameters on the cost metabolism, gait phase and biomechanics, the subjects need to carry the same load of 16.5 kg to walk 400 m at the different speeds, respectively. A group of seven healthy subjects in the same walking condition need to conduct two experiments: suspended backpack work (SB) and ordinary backpack (OB). The experimental results show that the suspended backpack can reduce plantar pressure and shoulder pressure in the SB condition. And at the speed of 5.0 km/h, ground reaction force (GRF) and shoulder reaction force (SRF) were reduced by 11.59 % and 13.22 % in the SB condition compared to the OB condition, respectively.

In large-scale cattle farming, accurate behavior recognition is central to achieving intensive health monitoring and animal welfare assessment. To address challenges such as background interference from fences, feed troughs, and stains in real-world barns, and overlapping of cattle coupled with the inability of single-RGB modalities to capture physical spatial structure, which leads to issues like blurred detection boundaries and significant noise interference—we propose a cattle behavior detection method based on RGB-Depth dual-modal information fusion. This approach jointly models the texture information from RGB images and the spatial structural information from depth images. Within this framework, this paper constructs three collaborative optimization modules: first, the CDSAM module is developed, which evaluates neuron importance through a parameter-free attention mechanism and combines dynamic convolutions to adapt to the cattle’s variable postures, effectively suppressing complex background noise. Second, we propose the C2BRA module based on a two-layer routed attention mechanism. By adopting a two-stage modeling approach of “region-level routing—intra-region fine-grained attention,” it adapts to changes in target scale and enhances the model’s ability to represent spatial context for multi-scale semantic information. Finally, in the prediction stage, a lightweight shared convolutional detection head (LSCD) is introduced. By sharing convolutional parameters across scales and decoupling the classification and regression architectures, it reduces computational overhead while maintaining accuracy. Experimental results show that the improved model achieves a mAP@0.5 of 90.3% on our self-built cattle behavior dataset, representing a 4.3 percentage point increase compared to the baseline model, while reducing GFLOPs from 11.0 G to 9.6 G, a decrease of 12.7%; Visualization results indicate that the improved model can focus more accurately on cattle body contours and key behavioral regions, thereby reducing false negatives and enhancing detection accuracy. Concurrently, the model achieves an optimal balance between detection performance and computational complexity, providing robust technical support for automated cattle behavior monitoring on smart farms.