Public Policy Qualities and Training of New Engineering Talents in China-Based on the Practice and Enlightenment of EPP Professional Talent Training at Carnegie Mellon University

As a measure for higher engineering education in the new era to deal with a new round of scientific and technological revolution and industrial reform,China,s new engineering construction is an important strategic deployment to cultivate diversified and innovative engineering talents.In order to promote the new engineering construction in an all-rounded wa%talent training is the key.Compared with traditional engineering talents,new engineering talents need to have richer values and normative knowledge,especially public policy qualities,which is the ability that new engineering talents should have in this complex and changeable environment.The EPP(Engineering and Public Policy)professional training model at Carnegie Mellon University provides useful practical experience for the cultivation of public policy qualities of new engineering talents.

Development and case study of a science-based software platform to support policy making on air quality

This article describes the development and implementations of a novel software platform that supports real-time, science-based policy making on air quality through a user-friendly interface. The software, RSM-VAT, uses a response surface modeling（RSM） methodology and serves as a visualization and analysis tool（VAT） for three-dimensional air quality data obtained by atmospheric models. The software features a number of powerful and intuitive data visualization functions for illustrating the complex nonlinear relationship between emission reductions and air quality benefits. The case study of contiguous U.S.demonstrates that the enhanced RSM-VAT is capable of reproducing the air quality model results with Normalized Mean Bias 〈 2% and assisting in air quality policy making in near real time.

An Optimal Resource Provision Policy in Cloud Computing Based on Customer Profiles

Conventional resource provision algorithms focus on how to maximize resource utilization and meet a fixed constraint of response time which be written in service level agreement（SLA）.Unfortunately,the expected response time is highly variable and it is usually longer than the value of SLA.So,it leads to a poor resource utilization and unnecessary servers migration.We develop a framework for customer-driven dynamic resource allocation in cloud computing.Termed CDSMS（customer-driven service manage system）,and the framework’s contributions are twofold.First,it can reduce the total migration times by adjusting the value of parameters of response time dynamically according to customers’profiles.Second,it can choose a best resource provision algorithm automatically in different scenarios to improve resource utilization.Finally,we perform a serious experiment in a real cloud computing platform.Experimental results show that CDSMS provides a satisfactory solution for the prediction of expected response time and the interval period between two tasks and reduce the total resource usage cost.