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Herein, a novel one pot synthesis of chiral δ-substituent caprolactam strategy which applies 1-substituent homoallylic amine as the starting material is reported. This method combines the enzymatic dynamic kinetic resolution (DKR) and ring cross metathesis. Moreover, the configuration of caprolactam can be controlled by using lipase and protease as the DKR catalyst. The enantiomeric excess value of obtained R- and S-caprolactam can reach up to 90% and 82%, the yield can also be above 82% and 70%.(报道了一种以1-取代高烯丙基胺为原料，结合酶催化动态动力学拆分与分子内烯烃复分解反应串联一锅法制备手性δ-取代己内酰胺的方法。分别用脂肪酶和蛋白酶作为动态动力学拆分催化剂，实现了手性δ-取代己内酰胺立体构型的调控。通过该方法制备的R-δ-取代己内酰胺的对映体过量值（enantiomeric excess，ee）均在90%以上，产率维持在76%以上；所制备的S-δ-取代己内酰胺的ee也在82%以上，产率均高于70%。)

In response to the issue of existing muscle force calculation methods lacking consideration for the muscle synergy and muscle force relationships during motions, a biomechanical calculation method based on the muscle force relationships and muscle synergy during walking is proposed. This method incorporates the muscle synergy and the relative force of muscles into inverse dynamics calculation based on the quadratic optimization. The proposed method is applied for the calculation of forces of major lower-extremity muscles of human walking. Muscle forces are obtained based on a musculoskeletal model, which effectively reflects the human motion mechanisms and muscle synergy during walking. This method provides significance for exploring the laws of human movement, biomechanical principles, and humanoid robot motion control. [ABSTRACT FROM AUTHOR]

Aiming at the characteristics of the UAV ad hoc network (UAANET), such as topological temporal-varying, node mobility and intermittent connection, a temporal graph embedding model was proposed to present the preprocessed UAANET. To improve the sampling efficiency, the sampling interval was calculated based on linear probability. The network structure features were mapped to the relationship between nodes, and the contextual semantic features of nodes were extracted by adversarial training. With the help of long and short-term memory network, the temporal characteristics of the UAANET were extracted to predict the connection at the next moment. AUC, MAP, and Error Rate were employed as evaluation indexes. The simulation experiments based on NS-3 show that compared with Node2vec, DDNE and E-LSTM-D, the proposed method has a better accuracy. [ABSTRACT FROM AUTHOR]