科创中国

目的 分析西安市雁塔区脑力劳动者健康现状.方法 对2011年13 109名脑力劳动者的体检资料进行汇总分析.结果 71.23%的脑力劳动者有各种健康问题;代谢性健康问题总检出率为68.80%;循环系统健康问题总检出率为42.52%;不同年龄、职业和性别体检者检出健康问题比较,差异具有统计学意义(P<0.01或P<0.05).结论 脑力劳动者的健康状况不容乐观,定期体检非常必要,而且要加强健康教育和健康管理.

在问卷调查、文献分析和实验的基础上,提出在我国硕士研究生招生初试中设置一般能力测试,考查考生是否具备攻读硕士学位所需要的基本素质和一般能力,以弥补目前初试重知识轻能力考查的现状.提高人才选拔的科学性和有效性.

Cyber physical systems(CPS)recently emerge as a new technology which can provide promising approaches to demand side management(DSM),an important capability in industrial power systems.Meanwhile,the manufacturing center is a typical industrial power subsystem with dozens of high energy consumption devices which have complex physical dynamics.DSM,integrated with CPS,is an effective methodology for solving energy optimization problems in manufacturing center.This paper presents a prediction-based manufacturing center self-adaptive energy optimization method for demand side management in cyber physical systems.To gain prior knowledge of DSM operating results,a sparse Bayesian learning based componential forecasting method is introduced to predict24-hour electric load levels for specific industrial areas in China.From this data,a pricing strategy is designed based on short-term load forecasting results.To minimize total energy costs while guaranteeing manufacturing center service quality,an adaptive demand side energy optimization algorithm is presented.The proposed scheme is tested in a machining center energy optimization experiment.An AMI sensing system is then used to measure the demand side energy consumption of the manufacturing center.Based on the data collected from the sensing system,the load prediction-based energy optimization scheme is implemented.By employing both the PSO and the CPSO method,the problem of DSM in the manufacturing center is solved.The results of the experiment show the self-adaptive CPSO energy optimization method enhances optimization by 5%compared with the traditional PSO optimization method.