Frequent extreme heat events exacerbated by global warming pose a significant threat to human health.However,the dynamic changes in human thermal comfort during such regional extremes remain understudied.This study in...Frequent extreme heat events exacerbated by global warming pose a significant threat to human health.However,the dynamic changes in human thermal comfort during such regional extremes remain understudied.This study investigates the spatiotemporal characteristics of the Universal Thermal Climate Index(UTCI)during 5-year return period extreme heat events across the Beijing-Tianjin-Hebei(BTH)region of China,utilizing 40 years of meteorological data from 174 stations.A non-stationary Generalized Extreme Value distribution model with a location parameter link function was identified as the optimal model(for 65.3%of stations)through the Akaike Information Criterion,capturing 16 regional extreme heat events.Results indicate that extreme heat thresholds rise with increasing return periods,with the highest thresholds concentrated around Beijing and Shijiazhuang.Air temperature and mean radiant temperature were found to be the dominant factors influencing UTCI,with daytime air temperature contributing 47.03%to 50.64%and nighttime mean radiant temperature contributing up to 48.55%.Spatially,“extreme heat stress”conditions,as defined by UTCI,were predominantly observed in the southeastern plains of Beijing and southern Hebei Province.Diurnally,UTCI peaked between 1200 and 1600 BT(Beijing time),generally returning to“no heat stress”levels across most areas between 0000 and 0600 BT.These findings provide crucial insights into the dynamics of human thermal comfort during extreme heat events in the BTH region,offering valuable scientific support for developing targeted heat mitigation and adaptation strategies.展开更多
With the development of IoT and 5G technologies,more and more online resources are presented in trendy multimodal data forms over the Internet.Hence,effectively processing multimodal information is significant to the ...With the development of IoT and 5G technologies,more and more online resources are presented in trendy multimodal data forms over the Internet.Hence,effectively processing multimodal information is significant to the development of various online applications,including e-learning and digital health,to just name a few.However,most AI-driven systems or models can only handle limited forms of information.In this study,we investigate the correlation between natural language processing(NLP)and pattern recognition,trying to apply the mainstream approaches and models used in the computer vision(CV)to the task of NLP.Based on two different Twitter datasets,we propose a convolutional neural network based model to interpret the content of short text with different goals and application backgrounds.The experiments have demonstrated that our proposed model shows fairly competitive performance compared to the mainstream recurrent neural network based NLP models such as bidirectional long short-term memory(Bi-LSTM)and bidirectional gate recurrent unit(Bi-GRU).Moreover,the experimental results also demonstrate that the proposed model can precisely locate the key information in the given text.展开更多
With the chaotic nature surrounds them, the understanding of of disasters and the uncertainty that how to achieve effectiveness in crisis management remains limited. This paper investigates how government applies vari...With the chaotic nature surrounds them, the understanding of of disasters and the uncertainty that how to achieve effectiveness in crisis management remains limited. This paper investigates how government applies various capabilities in the crisis management process contingent upon the nature of different disasters. By demonstrating the crisis management process from a contingent perspective, the capabilities that government should emphasise can vary according to the nature of the disaster. This study develops a framework for crisis management to serve as a guide when government is facing a crisis.展开更多
基金supported by the Hebei Provincial Key Research and Development Program[grant numbers 23375401D and22375404D]the China Meteorological Administration[grant number FPZJ2024-011]the Hebei Meteorological Bureau[grant number21ky32]。
文摘Frequent extreme heat events exacerbated by global warming pose a significant threat to human health.However,the dynamic changes in human thermal comfort during such regional extremes remain understudied.This study investigates the spatiotemporal characteristics of the Universal Thermal Climate Index(UTCI)during 5-year return period extreme heat events across the Beijing-Tianjin-Hebei(BTH)region of China,utilizing 40 years of meteorological data from 174 stations.A non-stationary Generalized Extreme Value distribution model with a location parameter link function was identified as the optimal model(for 65.3%of stations)through the Akaike Information Criterion,capturing 16 regional extreme heat events.Results indicate that extreme heat thresholds rise with increasing return periods,with the highest thresholds concentrated around Beijing and Shijiazhuang.Air temperature and mean radiant temperature were found to be the dominant factors influencing UTCI,with daytime air temperature contributing 47.03%to 50.64%and nighttime mean radiant temperature contributing up to 48.55%.Spatially,“extreme heat stress”conditions,as defined by UTCI,were predominantly observed in the southeastern plains of Beijing and southern Hebei Province.Diurnally,UTCI peaked between 1200 and 1600 BT(Beijing time),generally returning to“no heat stress”levels across most areas between 0000 and 0600 BT.These findings provide crucial insights into the dynamics of human thermal comfort during extreme heat events in the BTH region,offering valuable scientific support for developing targeted heat mitigation and adaptation strategies.
基金This work was supported by the Australian Research Council Discovery Project(No.DP180101051)Natural Science Foundation of China(No.61877051).
文摘With the development of IoT and 5G technologies,more and more online resources are presented in trendy multimodal data forms over the Internet.Hence,effectively processing multimodal information is significant to the development of various online applications,including e-learning and digital health,to just name a few.However,most AI-driven systems or models can only handle limited forms of information.In this study,we investigate the correlation between natural language processing(NLP)and pattern recognition,trying to apply the mainstream approaches and models used in the computer vision(CV)to the task of NLP.Based on two different Twitter datasets,we propose a convolutional neural network based model to interpret the content of short text with different goals and application backgrounds.The experiments have demonstrated that our proposed model shows fairly competitive performance compared to the mainstream recurrent neural network based NLP models such as bidirectional long short-term memory(Bi-LSTM)and bidirectional gate recurrent unit(Bi-GRU).Moreover,the experimental results also demonstrate that the proposed model can precisely locate the key information in the given text.
文摘With the chaotic nature surrounds them, the understanding of of disasters and the uncertainty that how to achieve effectiveness in crisis management remains limited. This paper investigates how government applies various capabilities in the crisis management process contingent upon the nature of different disasters. By demonstrating the crisis management process from a contingent perspective, the capabilities that government should emphasise can vary according to the nature of the disaster. This study develops a framework for crisis management to serve as a guide when government is facing a crisis.
