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In this study, the effects of the denaturation degree of whey protein (WP) and whey protein/casein (WP/CN, m/m) ratio in milk powder on the emulsion stability and whipping properties of recombined cream were investigated. The results showed that the cream prepared from milk power with 60% of WP denaturation had good emulsion stability, and the stability of the system decreased with increase in denaturation degree. An increase in the degree of WP denaturation shortened the whipping time and reduced the leakage rate of whey. The emulsion stability at a WP/CN ratio of 1:4 first increased and then decreased to a level higher than the initial one with increasing proportion of WP. In addition, the whipping time of the cream was shortened, and whey leakage was reduced, while foaming rate declined and bubble collapse rate increased. In summary, the cream prepared from milk power with 60% of WP denaturation showed good emulsion stability and whipping properties; it had the best stability at a WP/CN ratio of 2:4 and better foaming capacity and foam stability at a WP/CN ratio of 1:4. Therefore, WP/CN ratio can be adjusted according to the practical needs to improve the functional characteristics of cream products.

Owing to the climatic dryness and shortage of surface water, Pakistani farmers have been forced to rely on groundwater resources to meet the irrigation demand for crops. Pumping an excessive amount of water from the aquifers is turning it brackish. Our study aims to select a suitable depth of freshwater for installing tube wells in a study area, Tehsil Nankana Sahib. Sixteen vertical electrical soundings (VES) were arrayed in this study area, and water samples were also collected to record their electrical conductivity. The interpolation maps for different parameters were generated by using the inverse distance weighted interpolation method. The obtained results depict that the central and southeastern region has good groundwater at depths from 60 to 90 m, the northeastern region has marginally fit water, while the northwestern region is marked as having an unfit groundwater zone. This research article is not only a case study but also can be used for other areas of Punjab, Pakistan, facing similar groundwater problems. However, the comparison of VES results with borehole data is critical to achieve better accuracy. An extensive exploratory borehole study of the area is recommended to further characterize the subsurface lithology.

With the development of aerial refueling technology, autonomous aerial refueling(AAR) has become an important technology in the future battlefield, which is a promising prospective and challenging topic. Since the relative position between the receiver and the drogue is important to accomplish the AAR task, a neural network-based image recognition method is proposed to acquire the required information. Firstly, a C language-based YOLO network is used as the initial network, which meets the requirements of the on-board VxWorks system and can be run directly on the hardware. Then, considering the physical characterizes of the drogue, a multi-dimensional anchor box is designed and the network structure is optimized to adapt to the multi-dimensional situations. Finally, to address the problem of results shifts, feature maps with various sizes and the optimized loss function are used to optimize the network, where the pyramid structure suggests the design of feature maps. The experimental results indicate that the presented method can recognize the drogue more accurately and quickly.

Aerial refueling is an important technology of great military significance. It can effectively boost an aircraft's performance owing to the longer period of time and longer endurance of range an aircraft can maintain in the air. To solve the problem that it is hard for a receiver aircraft to track a drogue during its docking phase, a drogue trajectory prediction method based on the multi-head convolutional long-short-term memory network is proposed. First, the one-dimensional time sequence data of the drogue is extended to its high-dimensional space. Then its spatial features are extracted through the multi-head convolutional residual network and fused together. On this basis, a long-short-term memory network is adopted to reveal the underlying temporal correlations among the spatial features and predict the trajectory of the drogue. The simulation and experimental results show that the method presented in this paper has a higher prediction accuracy than the traditional prediction methods that use time sequence data.