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The conventional finite element method was employed to study the relationship between melt filling and air accumulation in micro-cavities. The variational equation for melt fiow with slip boundary condition and air resistance was generalized based on Galerkin principle. To investigate the effect of air accumulation, the governing equation for air fiow inside runner was established by dimensionless method. An iterative approach was proposed to solve the coupled melt fiow problem and air fiow problem. Numerical results show that air accumulation can affect the melt filling in micro-cavity, and closely depends on injection speed. Adjusting branch runner distribution can improve the fiow balance, which was convinced by a real micro-part manufacturing.

This study explored the impact of atmospheric turbulence on partially coherent light propagation. Atmospheric turbulence causes random modulation of the intensity and phase of light, resulting in a speckle pattern in the far field. This study focused on partially coherent Gaussian Schell model beams and derived an analytical expression of the cross-spectral density function for their transmission through atmospheric turbulence, based on the generalized Huygens–Fresnel principle and the Tatarski spectrum model. Numerical simulations were used to investigate the effects of the source parameters and turbulence strength on the intensity distribution, beam width, and coherence length of partially coherent light in horizontal atmospheric transmission. The results demonstrate that diffraction-induced broadening primarily affects the intensity distribution of light in free-space transmission. Short transmission distances in atmospheric turbulence have comparable characteristics to those in a vacuum; however, as the turbulence intensity and transmission distance increase, the beam broadening effect amplifies, and the coherence length is reduced. The findings are relevant to the design of acquisition, pointing, and tracking systems for wireless laser communication systems and offer insights into the optimization of optical systems for atmospheric conditions.

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The gradual development of remote sensing object tracking technology based on unmanned aerial vehicles (UAV) videos has become one of the main research directions in the field of visual tracking. However, due to characteristics of the UAV platform, typical visual tracking algorithms currently applied to natural scenes cannot be used directly. Small-scale objects in UAV remote sensing videos are difficult to detect and have the problem of tracking identity switching. In order to solve these problems, we designed the Swin transformer neck YOLOX (STN-YOLOX) object detection algorithm as the detection module, and the G-Byte data association method as the tracking module. We then combined the two into a new multi-object tracking algorithm named STN-Track. We used STN-Track to conduct experiments on the UAVDT and VisDrone MOT datasets. The experimental results show that compared with the current state-of-the-art (SOTA) methods, our STN-Track has improved detection and tracking accuracy of small-scale objects and greatly improved identification capabilities for object tracking. Compared with the SOTA ByteTrack algorithm, MOTA of STN-Track can be improved by up to 3.2%, APS can be improved by up to 4.4%, MT can be improved by up to 6.8%, and IDSW can be reduced by up to 28.0%.