长江经济带工业集聚与生态环境脱钩时空分析

工业集聚在推动我国经济增长的过程中，不时伴随资源浪费、能源高耗、污染物直排等生态问题，而长江经济带作为生态文明建设的先行示范带，其生态环境保护一直是重中之重。本文以2006—2015年为时间界限，以T1、T2、T3、T4和T55个时期为时间尺度，以长江经济带108个地级以上城市为空间尺度，从长江经济带、区域和市域视角，运用区位熵、熵值法，构建Tapio脱钩指数模型和追赶脱钩指数模型研究工业集聚与生态环境的脱钩关系，并结合空间分析方法使用ArcGIS10.4得出二者脱钩关系的空间分布图。     

Development of a novel machine learning-based weighted modeling approach to incorporate Salmonella enterica heterogeneity on a genetic scale in a dose–response modeling framework

Estimating microbial dose–response is an important aspect of a food safety risk assessment. In recent years, there has been considerable interest to advance these models with potential incorporation of gene expression data. The aim of this study was to develop a novel machine learning model that considers the weights of expression of Salmonella genes that could be associated with illness, given exposure, in hosts. Here, an elastic net-based weighted Poisson regression method was proposed to identify Salmonella enterica genes that could be significantly associated with the illness response, irrespective of serovar. The best-fit elastic net model was obtained by 10-fold cross-validation. The best-fit elastic net model identified 33 gene expression–dose interaction terms that added to the predictability of the model. Of these, nine genes associated with Salmonella metabolism and virulence were found to be significant by the best-fit Poisson regression model (p < 0.05). This method could improve or redefine dose–response relationships for illness from relative proportions of significant genes from a microbial genetic dataset, which would help in refining endpoint and risk estimations.     

基于蚁群BP神经网路算法的六维力传感器解耦研究

针对传统BP神经网络在六维力传感器解耦训练过程中,由于其初始参数的选取不确定性导致神经网络出现震 荡、收敛速度缓慢和陷入局部极值等问题,提出一种基于蚁群BP神经网络算法并应用于六维力传感器解耦研究。该算 法利用蚁群算法在种群寻优方面的优越性,通过局部和全局信息素更新相结合的方式更新信息素,提高蚁群算法搜索的 效率,为BP神经网络提供一组最优的训练初始参数,网络收敛速度得到很大地提高,同时局部极值和震荡等缺点也有一 定的改善。实验仿真结果表明,在六维力传感器神经网络模型训练过程中,达到同样的目标误差,基于蚁群BP神经网络 算法的迭代次数Ⅳ比传统算法少50%,运行时间r快60%。这说明蚁群BP种经网络算法在六维力传感器解耦研究中 有着很好的应用效果。