Investigating hydrogen evolution reaction properties of a new honeycomb 2D AlC

The hydrogen due to its high mass energy density is a new renewable, economically viable and clean resource. The most eco-friendly and economical approaches for the generation of hydrogen through hydrogen evolution is electrochemical water splitting. The two-dimensional (2D) nanomaterials have been recently found as potential candidates as non-noble metal catalyst for hydrogen evolution. In this work, we have systematically studied the structural and electronic properties of the newly predicted hexagonal-aluminium carbide monolayer (h-AlC ML) under the framework of dispersion-corrected density functional theory (DFT) calculations. The calculated electronic total density of states (TDOS) of h-AlC ML predict its metallic nature in contrast to other polar honeycomb 2D materials which are either semiconducting or semimetallic. The metallic behavior of h-AlC monolayer which motivates us to investigate its HER activity results due to the presence of delocalized charge density near Fermi level. Thus, we have investigated the HER activity of h-AlC ML by calculating hydrogen (H) adsorption energy (ΔE_H) and Gibbs free energy (ΔG_H) at three different sites of the 3 × 3 and 4 × 4 supercells of h-AlC ML; top of carbon atom (E_H-C), top of aluminium atom (E_H-Al) and hollow site (E_H-Hollow). Our results show that the hollow site is most catalytically active site in both supercells of h-AlC ML. We believe that our results will inspire experimentalists to fabricate this new 2D material for achieving the desired range of HER activity.     

传统村落码头空间改造的适宜性评价研究

综合运用Yaahp软件分析、层次分析法、Delphi法、文献解析与问卷调研等方法,对功能改造活动的调研数据建立判断矩阵,选取22个指标并计算推演指标权重,建立传统村落码头的功能改造的适宜性评价模型,并提出相应的量化评价方法,推导出传统村落码头空间功能改造适应性的七个关键要素。为传统村落的文化传承与功能提升提供决策依据。     

基于VMD瞬时能量与GA- RBF的滚动轴承故障诊断

针对滚动轴承早期故障的有效识别,提出了一种基于VMD瞬时能量与GA优化的RBF神经网络的滚动轴承故障诊断方法,可以有效对滚动故障做出诊断。首先,VMD将滚动轴承振动信号进行分解成合适数目的本证模态函数;其次,计算本证模态函数分量的瞬时能量并组成特征向量;最后,将特征向量输入到GA优化的RBF神经网络实现轴承故障识别。通过滚动轴承故障诊断实验对该方法进行验证。结果表明,该方法识别滚动轴承故障的准确率为96.43%,较默认参数的RBF神经网络和EEMD瞬时能量与GA-RBF神经网络有明显的提高,证明了所提方法的可行性。     

Milk from cows fed a diet with a high forage:concentrate ratio improves inflammatory state,oxidative stress,and mitochondrial function in rats

Excessive energy intake may evoke complex biochemical processes characterized by inflammation, oxidative stress, and impairment of mitochondrial function that represent the main factors underlying noncommunicable diseases. Because cow milk is widely used for human nutrition and in food industry processing, the nutritional quality of milk is of special interest with respect to human health. In our study, we analyzed milk produced by dairy cows fed a diet characterized by a high forage:concentrate ratio (high forage milk, HFM). In view of the low n-6:n-3 ratio and high content of conjugated linoleic acid of HFM, we studied the effects of this milk on lipid metabolism, inflammation, mitochondrial function, and oxidative stress in a rat model. To this end, we supplemented for 4 wk the diet of male Wistar rats with HFM and with an isocaloric amount (82 kJ, 22 mL/d) of milk obtained from cows fed a diet with low forage:concentrate ratio, and analyzed the metabolic parameters of the animals. Our results indicate that HFM may positively affect lipid metabolism, leptin:adiponectin ratio, inflammation, mitochondrial function, and oxidative stress, providing the first evidence of the beneficial effects of HFM on rat metabolism.     

带延迟索赔和随机收入的离散风险模型的Gerber-Shiu分析（英）

破产理论是保险数学中的重要问题，它可以为保险公司决策者提供一个非常有用的早期风险预警手段．本文研究了一个带潜在延迟索赔和随机保费收入的复合二项风险模型．利用矩母函数的技巧，得到了 Gerber-Shiu 期望折罚函数的递推公式．特别地，还得到了贴现因子为 1 的特殊情形下的 Gerber-Shiu 期望折罚函数的解析表达式．最后还得到了实际应用中的一些重要的破产特征量，包括破产概率，破产时赤字的密度函数，破产前盈余与破产时赤字的联合密度函数，以及导致破产的索赔密度函数等．     

[学科发展]5D打印——生物功能组织的制造

生物制造是制造技术与生命科学技术交叉融合产生的新兴学科方向，这一学科方向的发展将为巨大的人体组织与器官市场提供新技术，同时也给制造技术变革带来新机遇。面向生物制造未来发展，提出5D打印制造概念，论述了5D打印的内涵，分析了其关键技术。结合生物制造技术的现有进展，介绍了研究团队在心肌组织支架的制造、类脑神经组织制造、爬行生命机械混合机器人方面取得的初步研究进展，为生物制造技术拓展新方向提供新思路。     

Statistics of ceramic strength: Use ordinary Weibull distribution function or Weibull statistical fracture theory?

“Weibull statistics” for strength distribution analysis refers to either the ordinary Weibull distribution function or the Weibull statistical fracture theory. The ordinary Weibull distribution function is an empirical distribution function on an equal footing with other type of classical empirical distributions such as normal and log-normal distributions for fitting the statistical data of various random variables nonexclusive to materials strength. It has no explicit physical meaning and cannot be used for size scaling and prediction of strength. The Weibull statistical fracture theory is a weakest-link statistical fracture model for a solid with the strength distribution of an elemental volume being described by the ordinary Weibull distribution function. It has the capability of size scaling and prediction of strength for specimens with different geometries and different loading modes. The three-parameter Weibull statistical fracture theory in uniaxial flexure of prismatic beams is reformulated and validated by both numerical and real strength experiments of different ceramics.     

Prediction of process parameters of water-assisted injection molding based on inverse radial basis function neural network

Because of the introduction of new processing parameters in water-assisted injection molding (WAIM), processes control has become more difficult. First, design of experiment (DOE) was carried out by using optimized Latin hypercubes (Opt LHS). On the basis of this, computational fluid dynamics (CFD) method was used to simulate and calculate hollowed core ratios and wall thickness differences of cooling water pipe at different positions. Then inverse radial basis function (RBF) neural network model reflecting the fitting relationship between processing parameters and molding quality was established, and accuracy of the model was detected by cross validation. Finally, expected molding quality was applied to predict processing parameters, and the obtained molding quality under the predicted processing parameters was verified by computer aided engineering (CAE) simulation and experimental methods. The results showed that mean relative precisions of processing parameters such as melt temperature, delay time, short shot size, water pressure, and mold temperature for inverse RBF model were 98.6%, 93.6%, 98.5%, 93.9%, and 97.9%, respectively, which met the accuracy requirements. Furthermore, compared with expected values of hollowed core ratios and wall thickness differences, the average errors of CAE and experiment were 2.3% and 4.9%, respectively.     

二甲双胍联合糖皮质激素治疗伴糖耐量异常的系统性红斑狼疮患者的临床研究

目的: 探究二甲双胍联合糖皮质激素治疗伴糖耐量异常(IGT)的系统性红斑狼疮(SLE)患者的临床疗效及对胰岛功能、Th17/Treg细胞失衡的影响。方法: 本院84例伴IGT的SLE患者随机分为联用组和对照组各42例,均给予生活、饮食指导,对照组给予糖皮质激素治疗,联用组联合二甲双胍治疗,1个月后观察疗效,并评估胰岛功能和Th17/Treg细胞失衡情况。结果: 联用组总有效率明显高于对照组(90.48% vs. 71.43%,P<0.05),SLE活动指数评分为(2.6±0.3)分,红细胞沉降率(ESR)为(18±4)mm/h,显著低于对照组的(3.9±0.8)分、(23±4)mm/h(P<0.05)。治疗后联用组空腹血糖(FBG)、空腹胰岛素(Fins)、稳态模型-胰岛素抵抗指数(HOMA-IR)、胰岛β功能细胞指数(HOMA-β)显著改善(P<0.05),且与对照组相比差异有统计学意义(P<0.05);联用组正常糖耐量(NGT)占比明显高于对照组(73.81% vs. 30.95%,P<0.05)。治疗后联用组Th17、Treg细胞比例为(6.2±0.9)个/μL、(31±7)个/μL,Th17/Treg为(0.20±0.05),与对照组的(7.4±1.3)个/μL、(28±7)个/μL、(0.26±0.06)相比有统计学差异(P<0.05)。治疗后SLE活动指数评分与HOMA-IR、HOMA-β及Th17细胞、Treg细胞、Th17/Treg明显相关(P<0.05),且HOMA-IR、HOMA-β与Th17细胞、Treg细胞、Th17/Treg也明显相关(P<0.05)。2组不良反应均轻微,发生率无统计学差异(P>0.05)。结论: 二甲双胍联合糖皮质激素治疗伴IGT的SLE患者疗效显著,可控制疾病活动性,降低血糖水平、改善胰岛功能及纠正Th17/Treg细胞失衡,且安全性高。