Molecular fingerprint and machine learning to accelerate design of high-performance homochiral metal–organic frameworks

Computational screening was employed to calculate the enantioseparation capabilities of 45 functionalized homochiral metal–organic frameworks (FHMOFs), and machine learning (ML) and molecular fingerprint (MF) techniques were used to find new FHMOFs with high performance. With increasing temperature, the enantioselectivities for (R,S)-1,3-dimethyl-1,2-propadiene are improved. The “glove effect” in the chiral pockets was proposed to explain the correlations between the steric effect of functional groups and performance of FHMOFs. Moreover, the neighborhood component analysis and RDKit/MACCS MFs show the highest predictive effect on enantioselectivities among the four ML classification algorithms with nine MFs that were tested. Based on the importance of MF, 85 new FHMOFs were designed, and a newly designed FHMOF, NO₂-NHOH-FHMOF, with high similarity to the optimal MFs achieved improved chiral separation performance, with enantioselectivities of 85%. The design principles and new chiral pockets obtained by ML and MFs could facilitate the development of new materials for chiral separation.     

等通道挤压纯铝的冷轧织构与组织演变

研究了冷轧形变量对等通道挤压纯铝的形变织构与显微组织的影响。结果表明：当形变量较小时，冷轧使挤压高纯铝的显微组织分布更加均匀。然而，随着形变量的增加，晶粒由等轴向纤维状转变。织构的演变规律为旋转立方织构和{111}<110>向冷轧织构（铜织构、黄铜织构和S织构）转变，同时出现了R织构。由此可见，等通道挤压变形对高纯铝的冷轧组织和织构存在一定的影响。     

The Propagation Background in Social Networks: Simulating and Modeling

Machine Intelligence Research - Recent years have witnessed the booming of online social network and social media platforms, which leads to a state of information explosion. Though extensive...     

Formation of Uniform Water Microdroplets on Wrinkled Graphene for Ultrafast Humidity Sensing

Portable humidity sensors with ultrafast responses fabricated in wearable devices have promising application prospects in disease diagnostics, health status monitoring, and personal healthcare data collecting. However, prolonged exposures to high‐humidity environments usually cause device degradation or failure due to excessive water adsorbed on the sensor surface. In the present work, a graphene film based humidity sensor with a hydrophobic surface and uniformly distributed ring‐like wrinkles is designed and fabricated that exhibits excellent performance in breath sensing. The wrinkled morphology of the graphene sensor is able to effectively prevent the aggregation of water microdroplets and thus maximize the evaporation rate. The as‐fabricated sensor responds to and recovers from humidity in 12.5 ms, the fastest response of humidity sensors reported so far, yet in a very stable manner. The sensor is fabricated into a mask and successfully applied to monitoring sudden changes in respiratory rate and depth, such as breathing disorder or arrest, as well as subtle changes in humidity level caused by talking, cough and skin evaporation. The sensor can potentially enable long‐term daily monitoring of breath and skin evaporation with its ultrafast response and high sensitivity, as well as excellent stability in high‐humidity environments.     

基于信息熵—集对分析的煤矿隐患排查治理风险评估模型

以煤矿安全隐患排查治理风险为研究对象，分析隐患排查闭环管理全过程，并基于此建立了隐患排查治理能力纵向评估指标体系，融合熵值法和集对分析理论，建立了煤矿安全隐患排查治理风险纵向评估模型，为企业更好地了解自身隐患排查治理能力及变化发展趋势提供参考，最后进行了实例分析。     

Optimal design of Raman fibre amplifier based on terminal value optimization strategy and shuffled frog leaping algorithm

This paper introduces an evolutionary algorithm, Shuffled Frog Leaping Algorithm (SFLA), to solve the optimization problem in designing the multi-pumped Raman Fibre Amplifier (RFA). SFLA is a powerful optimizer tool because of its efficient mathematical expressions and global search capability. We utilize SFLA to determine the optimal pump wavelengths and pump powers by minimizing the gain ripple of RFA. To accelerate calculations, a terminal value optimization strategy (TVOS) is incorporated into the evolution of SFLA. This proposed strategy takes the terminal power values of pumps as the decision variables in optimization. Then, the optimal original power values of the pumps are obtained by solving the Power Coupled Equations once, without using the traditional method of repetitive guesses.The combination of SFLA and TVOS enhances the efficiency of optimization and accelerates calculation, while satisfying the design requirements of RFA.The simulation results show that nearly 65% of computational time has been saved compared with the traditional average power analysis. The 4-pumped C+L band of backward multi-pumped RFA with the average net gain of 0 dB, 1 dB and 2 dB are designed individually, where the gain ripple is less than 0.64 dB. The combination of SFLA and TVOS enhance the optimization efficiency and improve the performance of RFA with good gain profile.