0?1膨胀负二项回归模型在COVID?19疫情分析中的应用

在公共卫生等应用领域，经常会同时出现零观测值、一观测值较多的情况. 为更好地拟合这类数据，采用0?1膨胀负二项分布及其回归模型进行分析. 在数据扩充基础上，结合Pólya?Gamma潜变量对模型参数进行贝叶斯推断. 最后，对我国湖北省2019冠状病毒病(COVID?19)死亡数据集进行分析. 研究表明，0?1膨胀负二项回归模型能够达到更好的拟合效果.     

紫色马铃薯酒发酵工艺优化及主发酵期花青素与色泽的动态变化

以紫色马铃薯为原料，在单因素的基础上，通过响应面法对紫色马铃薯酒的发酵工艺参数进行优化。最佳发酵工艺参数为:马铃薯与面粉比1:0.6、料水比1:1 (g/mL)、α-淀粉酶添加量8.5 U/g、酒曲添加量0.45%。此工艺在28 ℃下发酵14 d，得到的紫色马铃薯酒酒精度13.5%vol，花青素含量166.34 mg/mL，酒体澄清透明呈紫红色，酸甜适中，有独特酒香，综合感官评定得分90分。测定主发酵期花青素和色泽的动态变化，分析表明，紫色马铃薯酒在发酵过程中花青素呈现先上升后下降的趋势，色度呈现先下降后趋于平缓的趋势，色调则与之相反。聚合色度和褐变指数总体均呈现逐渐上升的趋势。试验结果为工业化生产紫色马铃薯酒提供理论依据。     

结合优化支持向量机与K-means++的工控系统入侵检测方法

针对工业控制系统传统单一检测算法模型对不同攻击类型检测率和检测速度不佳的问题，提出一种优化支持向量机和K-means++算法结合的入侵检测模型。首先利用主成分分析法（PCA）对原始数据集进行预处理，消除其相关性；其次在粒子群优化（PSO）算法的基础上加入自适应变异过程避免在训练的过程中陷入局部最优解；然后利用自适应变异粒子群优化（AMPSO）算法优化支持向量机的核函数和惩罚参数；最后利用密度中心法改进K-means算法与优化后的支持向量机组合成入侵检测模型，从而实现工业控制系统的异常检测。实验结果表明，所提方法在检测速度和对各类攻击的检测率上得到明显提升。     

基于Freescale的水质氨氮检测系统的设计

针对水质污染情况的严重性和水质主要污染源之一的氨氮缺乏实时检测的情况，设计了一套基于Freescale单片机芯片的水质氨氮检测系统，可以实时和快速地计算出水质中氨氮的含量。检测系统的硬件部分设计主要包括微处理器模块、温度控制模块、存储模块、信号采集模块以及通信模块的电路设计，软件部分对不同的模块进行程序设计以及上位机软件平台的开发。最后对整个系统进行实验测试，实验结果表明检测系统可以满足氨氮检测的精度要求。     

A Dual Inhibitor of DYRK1A and GSK3β for β-Cell Proliferation: Aminopyrazine Derivative GNF4877

Loss of β-cell mass and function can lead to insufficient insulin levels and ultimately to hyperglycemia and diabetes mellitus. The mainstream treatment approach involves regulation of insulin levels; however, approaches intended to increase β-cell mass are less developed. Promoting β-cell proliferation with low-molecular-weight inhibitors of dual-specificity tyrosine-regulated kinase 1A (DYRK1A) offers the potential to treat diabetes with oral therapies by restoring β-cell mass, insulin content and glycemic control. GNF4877, a potent dual inhibitor of DYRK1A and glycogen synthase kinase 3β (GSK3β) was previously reported to induce primary human β-cell proliferation in vitro and in vivo. Herein, we describe the lead optimization that lead to the identification of GNF4877 from an aminopyrazine hit identified in a phenotypic high-throughput screening campaign measuring β-cell proliferation.     

Regulation of sensory nerve conduction velocity of human bodies responding to annual temperature variations in natural environments

The extensive research interests in environmental temperature can be linked to human productivity / performance as well as comfort and health; while the mechanisms of physiological indices responding to temperature variations remain incompletely understood. This study adopted a physiological sensory nerve conduction velocity (SCV) as a temperature‐sensitive biomarker to explore the thermoregulatory mechanisms of human responding to annual temperatures. The measurements of subjects’ SCV (over 600 samples) were conducted in a naturally ventilated environment over all four seasons. The results showed a positive correlation between SCV and annual temperatures and a Boltzmann model was adopted to depict the S‐shaped trend of SCV with operative temperatures from 5°C to 40°C. The SCV increased linearly with operative temperatures from 14.28°C to 20.5°C and responded sensitively for 10.19°C‐24.59°C, while tended to be stable beyond that. The subjects’ thermal sensations were linearly related to SCV, elaborating the relation between human physiological regulations and subjective thermal perception variations. The findings reveal the body SCV regulatory characteristics in different operative temperature intervals, thereby giving a deeper insight into human autonomic thermoregulation and benefiting for built environment designs, meantime minimizing the temperature‐invoked risks to human health and well‐being.     

Stick-Breaking Dependent Beta Processes with Variational Inference

Neural Processing Letters - The beta processes (BP) is a powerful nonparametric tool in feature learning, which is often used as the prior of Bernoulli process for choosing features from a feature...     

Study of MW-scale biogas-fed SOFC-WGS-TSA-PEMFC hybrid power technology as distributed energy system: Thermodynamic,exergetic and thermo-economic evaluation

Advanced biogas power generation technology has been attracting attentions, which contributes to the waste disposal and the mitigation of greenhouse gas emissions. This work proposes and models a novel biogas-fed hybrid power generation system consisting of solid oxide fuel cell, water gas shift reaction, thermal swing adsorption and proton exchange membrane fuel cell (SOFC-WGS-TSA-PEMFC). The thermodynamic, exergetic, and thermo-economic analyses of this hybrid system for power generation were conducted to comprehensively evaluate its performance. It was found that the novel biogas-fed hybrid system has a gross energy conversion efficiency of 68.63% and exergy efficiency of 65.36%, indicating high efficiency for this kind of hybrid power technology. The market sensitivity analysis showed that the hybrid system also has a low sensitivity to market price fluctuation. Under the current subsidy level for the distributed biogas power plant, the levelized cost of energy can be lowered to 0.02942 $/kWh for a 1 MW scale system. Accordingly, the payback period and annual return on investment can reach 1.4 year and about 20%, respectively. These results reveal that the proposed hybrid system is promising and economically feasible as a distributed power plant, especially for the small power scale (no more than 2 MW).