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Proportional navigation guidance law (PNG) is widely used for passive homing missiles. But PNG often suffers from inaccurate tracking of target or even failure when the target is maneuvering quickly or releasing artificial decoys. In order to solve this problem, the angular acceleration compensation guidance law (AACG) is constructed by using line of sight (LOS) angular velocity and LOS angular acceleration. The stability analysis with Lyapunov stability theory shows that AACG system is asymptotically stable on a large scale under certain stability constraint conditions. AACG command is designed to be perpendicular to the LOS and optimized by gravity compensation. AACG is neither dependent on target acceleration nor the distance between the missile and the target. The numerical simulation results indicate that AACG has good guidance performance on air-to-air missiles when the target maneuvers violently or releases artificial decoys in the endgame term.

Methanol is widely existed in fermented fruit wines (FFWs), and the concentration is excessive at times due to inappropriate fermentation conditions. Methanol is neurotoxic, and its metabolites of formaldehyde and formic acid can cause organic lesions and central respiratory system disorders. FFWs with unspecified methanol limits are often produced with reference to grape wine standards (250/400 mg/L). To clarify the causes of methanol production in FFWs and minimize the methanol content, this study summarizes the current process methods commonly applied for methanol reduction in FFWs and proposes novel potential controlling strategies from the perspective of raw materials (pectin, pectinase, and yeast), which are mainly the low esterification modification and removal of pectin, passivation of the pectinase activity, and the gene editing of yeast to target the secretion of pectinases and modulation of the glycine metabolic pathway. The modified raw materials combined with optimized fermentation processes will hopefully be able to improve the current situation of high methanol content in FFWs. Methanol detection technologies have been outlined and combined with machine learning that will potentially guide the production of low‐methanol FFWs and the setting of methanol limits for specific FFW. [ABSTRACT FROM AUTHOR]

The different geometric features of point clouds affect the difficulty of point cloud registration. However, most point clouds have partial overlap, geometric features are disturbed by noise, and there are some indistinguishable features on the point cloud surface, which makes it more difficult to extract representative geometric features. Combining the above reasons, this paper proposes a point cloud registration network GMNet that fuses geometric attention and multiscale features, using geometric Transformer to extract geometric features and encode point cloud point pairs with distances and angles to make it more robust under low overlap. Using multi- scale feature architecture to aggregate abundant semantic information at different scales to improve the accuracy of point cloud registration. Finally the features are selected by consistent voting algorithm with the appropriate domain size. The experimental results show that the overall registration accuracy of GMNet is higher, and the registration recall is improved to 93.4% and 76.0% for 3DMatch and 3DLoMatch datasets respectively. The relative rotation error and relative translation error are reduced to 6.2 cm and 0.26° on the KITTI dataset, respectively. The geometric Transformer used in this method extracts representative geometric features and combines multi-scale features to learn different levels of geometric information in the point cloud, which effectively improves the point cloud registration accuracy. [ABSTRACT FROM AUTHOR]

The microbial community structure associated with wine in a wine-growing region is shaped by diverse ecological factors within that region, profoundly impacting the wine flavor. In wine fermentation, fungi contribute more sensory-active biochemical compounds than bacteria. In this study, we employed amplicon sequencing to measure samples from the spontaneous fermentation process of cabernet sauvignon wines from two wine-growing regions in China to study the diversity and structural evolution of fungi during spontaneous fermentation and analyze the correlation between fungi and volatile compounds. The results showed significant differences in fungal community structure and diversity in cabernet sauvignon musts from different geographical origins, and these differences affected the flavor quality of the wines. As alcoholic fermentation progressed, Saccharomyces became the dominant fungal genus and reshaped the fungal community structure, and the diversity of the fungal community decreased. However, the fungal communities of each wine-growing region remained distinct throughout the fermentation process. Furthermore, the correlation between the fungal community and volatile compounds indicated that wine is a product of fermentation involving multiple fungal genera, and the flavor is influenced by a variety of fungi. Our study enhances the comprehension of fungal communities in Chinese wine-growing regions, explaining the regulatory role of wine-related fungal microorganisms in wine flavor. [ABSTRACT FROM AUTHOR]