Machine Learning Methods as a Tool for Predicting Risk of Illness Applying Next‐Generation Sequencing Data

Next‐generation sequencing (NGS) data present an untapped potential to improve microbial risk assessment (MRA) through increased specificity and redefinition of the hazard. Most of the MRA models do not account for differences in survivability and virulence among strains. The potential of machine learning algorithms for predicting the risk/health burden at the population level while inputting large and complex NGS data was explored with Listeria monocytogenes as a case study. Listeria data consisted of a percentage similarity matrix from genome assemblies of 38 and 207 strains of clinical and food origin, respectively. Basic Local Alignment (BLAST) was used to align the assemblies against a database of 136 virulence and stress resistance genes. The outcome variable was frequency of illness, which is the percentage of reported cases associated with each strain. These frequency data were discretized into seven ordinal outcome categories and used for supervised machine learning and model selection from five ensemble algorithms. There was no significant difference in accuracy between the models, and support vector machine with linear kernel was chosen for further inference (accuracy of 89% [95% CI: 68%, 97%]). The virulence genes FAM002725, FAM002728, FAM002729, InlF, InlJ, Inlk, IisY, IisD, IisX, IisH, IisB, lmo2026, and FAM003296 were important predictors of higher frequency of illness. InlF was uniquely truncated in the sequence type 121 strains. Most important risk predictor genes occurred at highest prevalence among strains from ready‐to‐eat, dairy, and composite foods. We foresee that the findings and approaches described offer the potential for rethinking the current approaches in MRA.     

A limit approaching pop-up message reduces gambling expenditures,except among players with a financially focused self-concept

Responsible gambling (RG) tools that guide electronic gaming machine (EGM) players to set a pre-set money limit on their gambling expenditures are known to reduce excessive gambling. However, not all EGM players who use a limit-setting RG tool will adhere to their limit. We hypothesized that limit adherence is facilitated by informing players that their limit is approaching (and when their limit is reached), but undermined by a financially focused self-concept (FFS). Accordingly, EGM players (N = 88) were provided seed funds to gamble with on a slot machine in a simulated virtual reality casino. They were randomly assigned to receive a limit reminder both when their limit was approaching and again when their limit was reached (experimental condition) or just when their limit was reached (control condition). Players in the experimental condition were more likely to stop playing before reaching their money limit compared to players in the control condition. However, this was observed among players who are low, but not high, in FFS. Unexpectedly, condition (control vs experimental) was unrelated to playing beyond the money limit and FFS did not moderate this relation. Results suggest that individual difference factors, like FFS, can undermine the utility of RG tools.     

Moore机表示的系统迭代设计动态CTL模型检验的不变性研究(英文)

模型检验是系统级设计中验证可信计算系统安全性性质的有效方法。动态模型检验是模型随设计过程而变化的模型检验,动态模型检验过程中遇到的最严重问题之一是模型变化所带来的重复检验代价太高。因此,寻找不变性以避免重复检验显得尤为重要。不变性是一种贯穿系列模型检验而保值为真的性质。该文构建动态模型检验的形式化框架,进而提出基于Moore机描述的流控制系统迭代设计过程的不变性理论,该系统是一种嵌入式控制系统,在可信通信中用以处理数据转换,最后展示了若干非平凡CTL性质在迭代过程中的可保持性。     

机床导轨液压爬行的原因及解决措施

分析了机床导轨爬行运动的危害 ,产生爬行运动的原因及解决措施     

一种立式回转台钻孔专机的设计

介绍了一种加工Buick轿车无级变速离合器总成中二档轴零件的立式回转台钻孔专机的结构、夹具及气动控制原理 ,其效率高于钻模加工 ,且加工精度稳定     

基于支持向量机的新经济增长点选择研究

针对现阶段新经济增长点选择模型无法区分“已有的”增长点与“新的”增长点的问题,使用支持向量机挖掘新经济增长点的潜在性.研究显示:陕西省2010年38个工业行业可划分为“新经济增长点”与“非新经济增长点”两类,新经济增长点一类中前十位行业与陕西省“十二五”规划中的文化产业、高新技术产业、新能源产业发展相一致,可见支持向量机在新经济增长点选择中的可行性和可靠性.     

Negotiating With the Growth Machine: Community Benefits Agreements and Value‐Conscious Growth

Scholars have extended, challenged, and molded growth machine theory to examine growth—in terms of population, basic industry, labor force participation, commerce, financial activity, and land development—in a variety of contexts. The theory's core, however, has remained the same: cities are conceptualized as growth machines, which consist of unified and powerful growth coalitions. These coalitions pursue a pro‐growth agenda, seeking to enhance the exchange value of local land and property. They often face opposition from local residents, who are more oriented toward use values of land. Resident opposition, however, tends to be unsuccessful in the face of large‐scale commercial development. Aware of this, communities across the country are pursuing new strategies to address development projects in their backyards. In particular, some have formed coalitions to negotiate for benefits from developers through legally binding community benefits agreements (CBAs). Drawing from a case study of Pittsburgh's first CBA, this article analyzes the implications of CBAs for pro‐growth agendas. Pittsburgh's CBA surrounded the construction of a professional sports facility, a development project that presents an ideal example of growth processes in today's cities. Ultimately, CBAs can achieve “value‐conscious” growth, but they do not fundamentally alter dominant standards of growth or growth machine processes.     

机体哲学论纲

机体哲学是对各种机体结构、功能和演化规律的哲学反思。西方机体哲学的影响曾受到一定限制,但近现代机体哲学仍包含很多深刻思想理论成果。中国传统哲学有着注重直观体验的机体哲学研究特征,但不够精细,其社会影响也很有限。经济全球化、高新技术对人类生活的影响和可持续发展面临的挑战,突显了机体哲学研究的重要价值。机体哲学可以为我国解决很多重大社会现实问题提供新的思路和途径。     

用操作分析法优化数控加工培训流程

文章介绍了为应对实习学生人数增加,课时减少,内容提高的新要求,运用IE操作分析法优化CNC数控操作培训流程,完成教学培训任务的经验具体实践。