Fast assignment reduction in inconsistent incomplete decision systems

This paper focuses on fast algorithm for computing the assignment reduct in inconsistent incomplete decision systems. It is quite inconvenient to judge the assignment reduct directly ac-cording to its definition. We propose the judgment theorem for the assignment reduct in the inconsistent incomplete decision system, which greatly simplifies judging this type reduct. On such basis, we derive a novel attribute significance measure and construct the fast assignment reduction algorithm （F-ARA）, intended for com-puting the assignment reduct in inconsistent incomplete decision systems. Final y, we make a comparison between F-ARA and the discernibility matrix-based method by experiments on 13 Univer-sity of California at Irvine （UCI） datasets, and the experimental results prove that F-ARA is efficient and feasible.

Ideas of establishing the information system for emergence decisions on the protection against earthquakes and disaster reduction in cities (application of GIS)

ith urban reformation and opening becoming deeper,the work of protection against earthquake and disaster reduction would be more important.In this paper,some ideas are suggested about establishing the information system for emergency decisions on protection against earthquake and disaster reduction in cities .The information system mainly includes a subsystem for rapid evaluation of damage loss from earthquake (which includes input of seismic information,distribution of earthquake intensity,evaluation of seismic fragility on all social factors and etc.) and a subsystem for the decisive information of seismic emergency(which mainly includes project of disaster relief,project of personnel evacuation,dangerous degree warning for the dangerous articlesstoring places and protection measures against them,assistant decision on fire due to earthquake,location of headquarter for providing disaster relief,and etc.). It is thought that the data investigation and collection about all kinds of buildings(including lifeline engineering)are the most important and difficult work as establishing this system.

Risk-based water quality decision-making under small data using Bayesian network

A knowledge-based network for Section Yidong Bridge,Dongyang River,one tributary of Qiantang River,Zhejiang Province,China,is established in order to model water quality in areas under small data.Then,based on normal transformation of variables with routine monitoring data and normal assumption of variables without routine monitoring data,a conditional linear Gaussian Bayesian network is constructed.A "two-constraint selection" procedure is proposed to estimate potential parameter values under small data.Among all potential parameter values,the ones that are most probable are selected as the "representatives".Finally,the risks of pollutant concentration exceeding national water quality standards are calculated and pollution reduction decisions for decision-making reference are proposed.The final results show that conditional linear Gaussian Bayesian network and "two-constraint selection" procedure are very useful in evaluating risks when there is limited data and can help managers to make sound decisions under small data.

Global Community of Disaster Risk Reduction Scientists and Decision Makers Endorse a Science and Technology Partnership to Support the Implementation of the Sendai Framework for Disaster Risk Reduction 2015–2030

At the first gathering of its kind on the role of science in implementing the Sendai Framework for Disaster Risk Reduction 2015–2030,over 750 scientists,policymakers,business people,and practitioners met in Geneva from January 27–29,2016.The UNISDR Science and Technology Conference on the Implementation of the Sendai Framework for Disaster Risk Reduction 2015–2030 fea-