With temperatures increasing as a result of global warming,extreme high temperatures are becoming more intense and more frequent on larger scale during summer in China.In recent years,a variety of researches have exam...With temperatures increasing as a result of global warming,extreme high temperatures are becoming more intense and more frequent on larger scale during summer in China.In recent years,a variety of researches have examined the high temperature distribution in China.However,it hardly considers the variation of temperature data and systems when defining the threshold of extreme high temperature.In order to discern the spatio-temporal distribution of extreme heat in China,we examined the daily maximum temperature data of 83 observation stations in China from 1950 to 2008.The objective of this study was to understand the distribution characteristics of extreme high temperature events defined by Detrended Fluctuation Analysis(DFA).The statistical methods of Permutation Entropy(PE)were also used in this study to analyze the temporal distribution.The results showed that the frequency of extreme high temperature events in China presented 3 periods of 7,10—13 and 16—20 years,respectively.The abrupt changes generally happened in the 1960s,the end of 1970s and early 1980s.It was also found that the maximum frequency occurred in the early 1950s,and the frequency decreased sharply until the late 1980s when an evidently increasing trend emerged.Furthermore,the annual averaged frequency of extreme high temperature events reveals a decreasing-increasing-decreasing trend from southwest to northeast China,but an increasing-decreasing trend from southeast to northwest China.And the frequency was higher in southern region than that in northern region.Besides,the maximum and minimum of frequencies were relatively concentrated spatially.Our results also shed light on the reasons for the periods and abrupt changes of the frequency of extreme high temperature events in China.展开更多
Many complex networks in real life are embedded in space and most infrastructure networks are interdependent,such as the power system and the transport network.In this paper,we construct two cascading failure models o...Many complex networks in real life are embedded in space and most infrastructure networks are interdependent,such as the power system and the transport network.In this paper,we construct two cascading failure models on the multilayer spatial network.In our research,the distance l between nodes within the layer obeys the exponential distribution P(l)~exp(-l/ζ),and the length r of dependency link between layers is defined according to node position.An entropy approach is applied to analyze the spatial network structure and reflect the difference degree between nodes.Two metrics,namely dynamic network size and dynamic network entropy,are proposed to evaluate the spatial network robustness and stability.During the cascading failure process,the spatial network evolution is analyzed,and the numbers of failure nodes caused by different reasons are also counted,respectively.Besides,we discuss the factors affecting network robustness.Simulations demonstrate that the larger the values of average degree<k>,the stronger the network robustness.As the length r decreases,the network performs better.When the probability p is small,asζdecreases,the network robustness becomes more reliable.When p is large,the network robustness manifests better performance asζincreases.These results provide insight into enhancing the robustness,maintaining the stability,and adjusting the difference degree between nodes of the embedded spatiality systems.展开更多
Relative to hospitalized patient information, outpatient admission information is relatively simple. It only includes the patient admission time, place of residence and other information. Traditionally, the excavation...Relative to hospitalized patient information, outpatient admission information is relatively simple. It only includes the patient admission time, place of residence and other information. Traditionally, the excavation of this information is not sufficient. However, when a large number of patients admitted time and residence information combined to consider, and add some data mining technology, some of the previously ignored regular information is likely to be found. Using 5 years of data mining research and admission data from a paediatric department at a large women’s and children’s hospital in China, we found important fluctuation rules regarding admissions using wavelet analysis on hospital admission data among different scales of cyclical fluctuations. Method: Seasonal distribution of patient number was analysed based on Haar wavelet transformation, and level 3 and level 2 of wavelets were extracted out to fit the data. The distribution function of hospitalized patients was visualized by kernel density estimation. Using linear regression and ARIMA (autoregressive integrated moving average model) predict the seasonally number of patients in the future. Results: The data analysis demonstrates the total surge of inpatients was decomposed into one mother wavelet and five small wavelets, each of which represents different time frequency. Besides, as distance from hospital increases, the number of patients decreased exponentially. The seasonal factors are the largest time factor influencing the number changes of patients. Conclusion: By wavelet analysis and the improved prediction model, we could make forecast on the future inpatient number trend and prove factors such as geographic position is influential on inpatient amount. Additionally, the concept of data mining based on spatial distribution and spectral analysis could be applied to other aspects of social management.展开更多
为描述单一景观的空间分布离散性和广度特征,引入基于香农熵的空间分布指数(MSHDI,Modified Shannon's Distribution Index),利用河南省中南部地区1990、2001和2007年3期TM/ETM+遥感影像,进行了MSHDI与经典景观指数的比较,并分析了M...为描述单一景观的空间分布离散性和广度特征,引入基于香农熵的空间分布指数(MSHDI,Modified Shannon's Distribution Index),利用河南省中南部地区1990、2001和2007年3期TM/ETM+遥感影像,进行了MSHDI与经典景观指数的比较,并分析了MSHDI与面积指数(PLAND,Percentage of Landscape)在多网格尺度下的相关关系。结果表明:(1)MSHDI在描述单一景观的空间分布广度和离散程度特征方面具有较好的适用性。MSHDI能适应不同制图综合程度的影响,并能反映出景观斑块边缘的细微差别。(2)通过分析MSHDI与PLAND之间相关系数的大小和变化程度可以分别验证MSHDI的表征性和稳定性。(3)MSHDI适用于基底和散布状景观,两种指数之间有显著的正相关性(农用地=0.939,=0.000;城市建设=0.877,=0.004;工矿仓储=0.870,=0.002;自然绿地=0.966,=0.001),且随网格粒度变化不大(农用地r=0.921±0.054;城市建设r=0.867±0.107;工矿仓储r=0.883±0.052;自然绿地r=0.964±0.024);而对网状景观则缺乏稳定性。展开更多
基金Key Projects in the National Science & Technology Pillar Program during the Eleventh Five-Year Plan Period(2007BAC29B05)Jiangsu Key Laboratory of Meteorological Disaster(KLME05005)
文摘With temperatures increasing as a result of global warming,extreme high temperatures are becoming more intense and more frequent on larger scale during summer in China.In recent years,a variety of researches have examined the high temperature distribution in China.However,it hardly considers the variation of temperature data and systems when defining the threshold of extreme high temperature.In order to discern the spatio-temporal distribution of extreme heat in China,we examined the daily maximum temperature data of 83 observation stations in China from 1950 to 2008.The objective of this study was to understand the distribution characteristics of extreme high temperature events defined by Detrended Fluctuation Analysis(DFA).The statistical methods of Permutation Entropy(PE)were also used in this study to analyze the temporal distribution.The results showed that the frequency of extreme high temperature events in China presented 3 periods of 7,10—13 and 16—20 years,respectively.The abrupt changes generally happened in the 1960s,the end of 1970s and early 1980s.It was also found that the maximum frequency occurred in the early 1950s,and the frequency decreased sharply until the late 1980s when an evidently increasing trend emerged.Furthermore,the annual averaged frequency of extreme high temperature events reveals a decreasing-increasing-decreasing trend from southwest to northeast China,but an increasing-decreasing trend from southeast to northwest China.And the frequency was higher in southern region than that in northern region.Besides,the maximum and minimum of frequencies were relatively concentrated spatially.Our results also shed light on the reasons for the periods and abrupt changes of the frequency of extreme high temperature events in China.
基金Project supported by the National Natural Science Foundation of China(Grant No.61871046).
文摘Many complex networks in real life are embedded in space and most infrastructure networks are interdependent,such as the power system and the transport network.In this paper,we construct two cascading failure models on the multilayer spatial network.In our research,the distance l between nodes within the layer obeys the exponential distribution P(l)~exp(-l/ζ),and the length r of dependency link between layers is defined according to node position.An entropy approach is applied to analyze the spatial network structure and reflect the difference degree between nodes.Two metrics,namely dynamic network size and dynamic network entropy,are proposed to evaluate the spatial network robustness and stability.During the cascading failure process,the spatial network evolution is analyzed,and the numbers of failure nodes caused by different reasons are also counted,respectively.Besides,we discuss the factors affecting network robustness.Simulations demonstrate that the larger the values of average degree<k>,the stronger the network robustness.As the length r decreases,the network performs better.When the probability p is small,asζdecreases,the network robustness becomes more reliable.When p is large,the network robustness manifests better performance asζincreases.These results provide insight into enhancing the robustness,maintaining the stability,and adjusting the difference degree between nodes of the embedded spatiality systems.
文摘Relative to hospitalized patient information, outpatient admission information is relatively simple. It only includes the patient admission time, place of residence and other information. Traditionally, the excavation of this information is not sufficient. However, when a large number of patients admitted time and residence information combined to consider, and add some data mining technology, some of the previously ignored regular information is likely to be found. Using 5 years of data mining research and admission data from a paediatric department at a large women’s and children’s hospital in China, we found important fluctuation rules regarding admissions using wavelet analysis on hospital admission data among different scales of cyclical fluctuations. Method: Seasonal distribution of patient number was analysed based on Haar wavelet transformation, and level 3 and level 2 of wavelets were extracted out to fit the data. The distribution function of hospitalized patients was visualized by kernel density estimation. Using linear regression and ARIMA (autoregressive integrated moving average model) predict the seasonally number of patients in the future. Results: The data analysis demonstrates the total surge of inpatients was decomposed into one mother wavelet and five small wavelets, each of which represents different time frequency. Besides, as distance from hospital increases, the number of patients decreased exponentially. The seasonal factors are the largest time factor influencing the number changes of patients. Conclusion: By wavelet analysis and the improved prediction model, we could make forecast on the future inpatient number trend and prove factors such as geographic position is influential on inpatient amount. Additionally, the concept of data mining based on spatial distribution and spectral analysis could be applied to other aspects of social management.
文摘为描述单一景观的空间分布离散性和广度特征,引入基于香农熵的空间分布指数(MSHDI,Modified Shannon's Distribution Index),利用河南省中南部地区1990、2001和2007年3期TM/ETM+遥感影像,进行了MSHDI与经典景观指数的比较,并分析了MSHDI与面积指数(PLAND,Percentage of Landscape)在多网格尺度下的相关关系。结果表明:(1)MSHDI在描述单一景观的空间分布广度和离散程度特征方面具有较好的适用性。MSHDI能适应不同制图综合程度的影响,并能反映出景观斑块边缘的细微差别。(2)通过分析MSHDI与PLAND之间相关系数的大小和变化程度可以分别验证MSHDI的表征性和稳定性。(3)MSHDI适用于基底和散布状景观,两种指数之间有显著的正相关性(农用地=0.939,=0.000;城市建设=0.877,=0.004;工矿仓储=0.870,=0.002;自然绿地=0.966,=0.001),且随网格粒度变化不大(农用地r=0.921±0.054;城市建设r=0.867±0.107;工矿仓储r=0.883±0.052;自然绿地r=0.964±0.024);而对网状景观则缺乏稳定性。
