Highly ion-conductive solid polymer electrolytes based on polyethylene non-woven matrix

Highly ion-conductive solid polymer electrolyte (SPE) based on polyethylene (PE) non-woven matrix is prepared by filling poly(ethylene glycol) (PEG)-based crosslinked electrolyte inside the pores of the non-woven matrix. The PE non-woven matrix not only shows good mechanical strength for SPE to be a free-standing film, but also has very porous structure for high ion conductivity. The ion conductivity of SPE based on PE non-woven matrix can be enhanced by adding sufficient non-volatile plasticizer such as poly(ethylene glycol) dimethyl ether (PEGDME) into ion conduction phase without sacrificing mechanical strength. SPE with 20 wt.% crosslinking agent and 80 wt.% non-volatile plasticizer shows 3.1 × 10⁻⁴ S cm⁻¹ at room temperature (20 °C), to our knowledge, which is the highest level for SPEs. It is also electrochemically stable up to 5.2 V and has high transference number about 0.52 due to the introduction of anion receptor as an additive. The interfacial resistance between Li electrode and SPE is low enough to perform charge/discharge test of unit cell consisting of LiCoO₂/SPE/Li at room temperature. The discharge capacity of the unit cell shows 87% of theoretical value with 86% Coulombic efficiency.     

Oxidation Behavior and Flexural Strength of Aluminum Nitride Exposed to Air at Elevated Temperatures

The oxidation behavior of a sintered aluminum nitride containing 3 wt% Y₂O₃ as a sintering aid was investigated. Samples were exposed to air at elevated temperatures for times up to 100 h. The weights of the samples were continuously monitored during exposure at various temperatures and humidity levels. The effects of oxidation on room-temperature flexural strength were also determined, and correlated to the observed weight changes of the samples. At temperatures 1200°C, linear weight gains were observed. However, at temperatures above 1200°C, the weight gains became parabolic with respect to exposure time. The oxidation rates were significantly increased by water vapor in the air. The oxidation products were found by X-ray analysis to be a mixture of Al₂O₃ and 5A1₂O₃·3Y₂O₃. The oxide layer formed on the surface was severely cracked because of the thermal expansion mismatch between the oxide layer and the substrate. The cracks initiated in the oxide layer and propagated into the substrate, resulting in severe reduction in the room-temperature flexural strength of the material. When exposed to ambient air for more than 50 h at temperatures greater than 1100°C, the strengths of the samples decreased to less than half that of the as-received material.     

The optical properties of hot-pressed magnesium fluoride and single-crystal magnesium fluoride in the 0.1 to 9.0 μm range

A polycrystalline high-density magnesium fluoride, fabricated into plates or shapes by hot-pressing, exhibits high in-line transmittance from 2.5 to 6.0 m, and single-crystal magnesium fluoride extends from 0.1 to 6.0 m. The ultimate and practical transmittance of hot-pressed magnesium fluoride using intrinsic and extrinsic reflectance, absorptance and scattering mechanisms, are investigated. The intrinsic scattering mechanism due to the polycrystalline structure is basically responsible for the tremendous difference in transmittance in the short wavelength region of the spectrum. The in-line transmittance of polycrystalline and singlecrystal MgF₂ is discussed in terms of sample thickness.     

A Performance Evaluation of Classic Convolutional Neural Networks for 2D and 3D Palmprint and Palm Vein Recognition

Palmprint recognition and palm vein recognition are two emerging biometrics technologies. In the past two decades, many traditional methods have been proposed for palmprint recognition and palm vein recognition, and have achieved impressive results. However, the research on deep learning-based palmprint recognition and palm vein recognition is still very preliminary. In this paper, in order to investigate the problem of deep learning based 2D and 3D palmprint recognition and palm vein recognition in-depth, we conduct performance evaluation of seventeen representative and classic convolutional neural networks (CNNs) on one 3D palmprint database, five 2D palmprint databases and two palm vein databases. A lot of experiments have been carried out in the conditions of different network structures, different learning rates, and different numbers of network layers. We have also conducted experiments on both separate data mode and mixed data mode. Experimental results show that these classic CNNs can achieve promising recognition results, and the recognition performance of recently proposed CNNs is better. Particularly, among classic CNNs, one of the recently proposed classic CNNs, i.e., EfficientNet achieves the best recognition accuracy. However, the recognition performance of classic CNNs is still slightly worse than that of some traditional recognition methods.

     

Protein interaction networks: centrality,modularity, dynamics,and applications

In the post-genomic era, proteomics has achieved significant theoretical and practical advances with the development of high-throughput technologies. Especially the rapid accumulation of protein-protein interactions (PPIs) provides a foundation for constructing protein interaction networks (PINs), which can furnish a new perspective for understanding cellular organizations, processes, and functions at network level. In this paper, we present a comprehensive survey on three main characteristics of PINs: centrality, modularity, and dynamics. 1) Different centrality measures, which are used to calculate the importance of proteins, are summarized based on the structural characteristics of PINs or on the basis of its integrated biological information; 2) Different modularity definitions and various clustering algorithms for predicting protein complexes or identifying functional modules are introduced; 3) The dynamics of proteins, PPIs and sub-networks are discussed, respectively. Finally, the main applications of PINs in the complex diseases are reviewed, and the challenges and future research directions are also discussed.     

高分2号图像业务化大气校正查找表的设计与实现

为实现高分2号图像(GF2)业务化大气校正,利用矢量6S模型(6SV)将辐射传输散射与吸收进行分离计算,其中散射部分采用涵盖各种大气模式、气溶胶类型、成像几何等条件下的大气校正查找表(look-up tables,LUTs)表示,大气吸收部分采用6SV拟合计算的6种大气模式下大气透过率的经验公式表示,这种处理有效简化了查找表大小.在内存4 GB、主频3.10 GHz条件下,通过比较基于本查找表方法和基于FLAASH大气校正软件对北京地区一景GF2数据处理.结果表明:以6SV模型直接大气校正法结果作为真值,两者的绝对误差分别为0.8%和2.6%,处理时间分别为77 s和240 s,该大气校正查找表在处理精度和效率更加适合业务化大气校正.     

层间接触状态和横向力大小对车辙贡献率的影响

车辙贡献率是反映沥青路面各结构层车辙病害程度的一个主要参数,可用于指导沥青路面的设计和施工.利用三维有限元计算软件建立三维有限元模型用于理论分析,将有限元计算结果与理论解进行对比,验证了该三维有限元模型的可靠性.采用实测的轮胎接地压力,考虑不同层间接触状态和横向力大小,结合有限元计算的数据,分析了沥青路面结构层的车辙贡献率.结果表明:在层间完全连续状态下,横向力大小的选取不能简单地选取最大垂直轮胎接地压力的一定比例,而是要根据所研究的具体情况具体分析.在对沥青路面车辙贡献率的研究中,可以忽略层间接触状态,即假定层间完全连续.