社会学视野中的公共安全分析

现阶段我国公共安全问题已延伸到社会各个领域,须以科学安全观对公共安全进行再思考。西方社会学家对公共安全有其独特分析视角。透析社会公共安全问题,诱发公共安全事故的原因是多元的;我国公共安全问题的解决路径,是要加强立法建设,建立起公共安全综合管理体系。     

Machine Learning for heat radiation modeling of bi- and polydisperse particle systems including walls

We investigated the ability of four popular Machine Learning methods i.e., Deep Neural Networks (DNNs), Random Forest-based regressors (RFRs), Extreme Gradient Boosting-based regressors (XGBs), and stacked ensembles of DNNs, to model the radiative heat transfer based on view factors in bi- and polydisperse particle beds including walls. Before training and analyzing the predictive capability of each method, an adjustment of markers used in monodisperse systems, as well as an evaluation of new markers was performed. On the basis of our dataset that considers a wide range of particle radii ratios, system sizes, particle volume fractions, as well as different particle-species volume fractions, we found that (i) the addition of particle size information allows the transition from monodisperse to bi- and polydisperse beds, and (ii) the addition of particle volume fraction information as the fourth marker leads to very accurate predictions. In terms of the overall performance, DNNs and RFRs should be preferred compared to the other two options. For particle–particle view factors, DNN and RFR are on par, while for particle–wall the RFR is superior. We demonstrate that DNNs and RFRs can be built to meet or even exceed the prediction quality standards achieved in a monodisperse system.