The spatial interaction model is an effective way to explore the geographical disparities inherent in the Belt and Road Initiative(BRI) by simulating spatial flows. The traditional gravity model implies the hypothesis...The spatial interaction model is an effective way to explore the geographical disparities inherent in the Belt and Road Initiative(BRI) by simulating spatial flows. The traditional gravity model implies the hypothesis of equilibrium points without any reference to when or how to achieve it. In this paper, a dynamic gravity model was established based on the Maximum Entropy(MaxEnt) theory to estimate and monitor the interconnection intensity and dynamic characters of bilateral relations. In order to detect the determinants of interconnection intensity, a Geodetector method was applied to identify and evaluate the determinants of spatial networks in five dimensions. The empirical study clearly demonstrates a heterogeneous and non-circular spatial structure. The main driving forces of spatial-temporal evolution are foreign direct investment, tourism and railway infrastructure construction, while determinants in different sub-regions show obvious spatial differentiation. Southeast Asian countries are typically multi-island area where aviation infrastructure plays a more important role. North and Central Asian countries regard oil as a pillar industry where power and port facilities have a greater impact on the interconnection. While Western Asian countries are mostly influenced by the railway infrastructure, Eastern European countries already have relatively robust infrastructure where tariff policies provide a greater impetus.展开更多
Background: Hylurgus ligniperda(Fabricius) is native to Europe but has established populations in many countries and regions. H. ligniperda mainly infests Pinus species, and can cause severe weakness and even death of...Background: Hylurgus ligniperda(Fabricius) is native to Europe but has established populations in many countries and regions. H. ligniperda mainly infests Pinus species, and can cause severe weakness and even death of the host through its boring activity;it can also be a vector of various pathogenic fungi. This study was conducted to investigate the environmental variables limiting the distribution of H. ligniperda and the change trend of its suitable areas under climate change.Results: We used a maximum entropy model to predict the potential geographical distribution of H. ligniperda on a global scale under near current and future climatic scenarios using its occurrence data and environmental variables. The result shows that the areas surrounding the Mediterranean region, the eastern coastal areas of Asia, and the southeastern part of Oceania are highly suitable for H. ligniperda. The environmental variables with the greatest effect on the distribution of H. ligniperda were determined using the jackknife method and Pearson’s correlation analysis and included the monthly average maximum temperature in April, precipitation of driest quarter, the monthly average minimum temperature in December, precipitation of coldest quarter, mean temperature of driest quarter and mean diurnal range.Conclusions: Excessive precipitation in winter and low temperatures in spring had a great effect on the distribution of H. ligniperda. The potential geographical distribution of H. ligniperda was predicted to change under future climatic conditions compared with near current climate conditions. Highly suitable areas, moderately suitable areas and low suitable areas were predicted to increase by 59.99%, 44.43% and 22.92%, respectively, under the2081–2100 ssp245 scenario.展开更多
The maximum entropy model was introduced and a new intrusion detection approach based on the maximum entropy model was proposed. The vector space model was adopted for data presentation. The minimal entropy partitioni...The maximum entropy model was introduced and a new intrusion detection approach based on the maximum entropy model was proposed. The vector space model was adopted for data presentation. The minimal entropy partitioning method was utilized for attribute diseretization. Experiments on the KDD CUP 1999 standard data set were designed and the experimental results were shown. The receiver operating eharaeteristie(ROC) curve analysis approach was utilized to analyze the experimental results. The analysis results show that the proposed approach is comparable to those based on support vector maehine(SVM) and outperforms those based on C4.5 and Naive Bayes classifiers. According to the overall evaluation result, the proposed approach is a little better than those based on SVM.展开更多
In this paper,an effective algorithm for optimizing the subarray of conformal arrays is proposed.The method first divides theconformal array into several first-level subarrays.It uses the X algorithm to solve the feas...In this paper,an effective algorithm for optimizing the subarray of conformal arrays is proposed.The method first divides theconformal array into several first-level subarrays.It uses the X algorithm to solve the feasible solution of first-level subarray tiling and employs the particle swarm algorithm to optimize the conformal array subarray tiling scheme with the maximum entropy of the planar mapping as the fitness function.Subsequently,convex optimization is applied to optimize the subarray amplitude phase.Data results verify that the method can effectively find the optimal conformal array tiling scheme.展开更多
[ Objective] The research aimed to study distribution prediction of suitable growth area for Eucommia ulmoides in China under climatic change background. [ Method] By using the maximum entropy model and many kinds of ...[ Objective] The research aimed to study distribution prediction of suitable growth area for Eucommia ulmoides in China under climatic change background. [ Method] By using the maximum entropy model and many kinds of climate change scenarios, we predicted current and future distribution pattems of suitable growth area for Eucommia ulmoides in China and its change process. [ Result ] At present, highly suitable growth area of E. ulmoides mainly distributed in Sichuan, Shaanxi and Chongqing, Under climate change background, total suitable growth areas in future three decades all drastically reduced when compared with that at present. It was noteworthy that moderately and highly suitable growth areas of wild E. ulmoides all disappeared, and junction between Shaanxi and Gansu and Taibai Mountain would be stable suitable growth area of wild E. ulmoides. [ Condusioa] The research could provide useful reference data for investigation, protection and sustainable development of the wild E. ulmoides resources.展开更多
The resolution of overlapping ambiguity strings(OAS)is studied based on the maximum entropy model.There are two model outputs,where either the first two characters form a word or the last two characters form a word.Th...The resolution of overlapping ambiguity strings(OAS)is studied based on the maximum entropy model.There are two model outputs,where either the first two characters form a word or the last two characters form a word.The features of the model include one word in con-text of OAS,the current OAS and word probability relation of two kinds of segmentation results.OAS in training text is found by the combination of the FMM and BMM segmen-tation method.After feature tagging they are used to train the maximum entropy model.The People Daily corpus of January 1998 is used in training and testing.Experimental results show a closed test precision of 98.64%and an open test precision of 95.01%.The open test precision is 3.76%better compared with that of the precision of common word probability method.展开更多
Objective The Guanzhong Plain of Shaanxi Province is a severely afflicted hemorrhagic fever with renal syndrome(HFRS)epidemic area,while HFRS prevalence has decreased in most epidemic areas in China.Little information...Objective The Guanzhong Plain of Shaanxi Province is a severely afflicted hemorrhagic fever with renal syndrome(HFRS)epidemic area,while HFRS prevalence has decreased in most epidemic areas in China.Little information is available regarding the leading fine-scale influencing factors in this highly HFRSconcentrated area and the roles of natural environmental and socioeconomic factors.To investigate this,two regions in the Guanzhong Plain,that is,the Chang’an District and Hu County,with similar geographical environments,different levels of economic development,and high epidemic prevalence,were chosen as representative areas of the HFRS epidemic.Methods Maximum entropy models were constructed based on HFRS cases and fine-scale influencing factors,including meteorological,natural environmental,and socioeconomic factors,from 2014 to 2016.Results More than 95% of the HFRS cases in the study area were located in the northern plains,which has an altitude of less than 800 m,with topography contributed 84.1% of the impact on the spatial differentiation of the HFRS epidemic.In the northern plains,precipitation and population density jointly affected the spatial differentiation of the HFRS epidemic,with contribution rates of 60.7% and 28.0%,respectively.By comparing the influencing factors of the northern plains of Chang’an District and Hu County,we found that precipitation and the normalized difference vegetation index(NDVI)dominated the HFRS epidemic in the relatively developed Chang’an District,while land-use type,temperature,precipitation and population density dominated the HFRS epidemic in the relatively undeveloped Hu County.Conclusion Topography was the primary key factor for HFRS prevalence in the Chang’an District and Hu County,and the spatial differentiation of HFRS was dominated by precipitation and population density in the northern plains.Compared with the influencing factors of the relatively developed Chang’an District,the developing Hu County was more affected by socioeconomic factors.When formulating targeted HFRS epidemic prevention and control strategies in the targeted areas,it is crucial to consider the local economic development state and combine natural environmental factors,including the meteorological environment and vegetation coverage.展开更多
Understanding the spatial distribution of plant species and their dynamic changes in arid areas is crucial for addressing the challenges posed by climate change.Haloxylon ammodendron shelterbelts are essential for the...Understanding the spatial distribution of plant species and their dynamic changes in arid areas is crucial for addressing the challenges posed by climate change.Haloxylon ammodendron shelterbelts are essential for the protection of plant resources and the control of desertification in Central Asia.Thus far,the potential suitable habitats of H.ammodendron in Central Asia are still uncertain in the future under global climate change conditions.This study utilised the maximum entropy(MaxEnt)model to combine the current distribution data of H.ammodendron with its growth-related data to analyze the potential distribution pattern of H.ammodendron across Central Asia.The results show that there are suitable habitats of H.ammodendron in the Aralkum Desert,northern slopes of the Tianshan Mountains,and the upstream of the Tarim River and western edge of the Taklimakan Desert in the Tarim Basin under the current climate conditions.The period from 2021 to 2040 is projected to undergo significant changes in the suitable habitat area of H.ammodendron in Central Asia,with a projected 15.0% decrease in the unsuitable habitat area.Inland areas farther from the ocean,such as the Caspian Sea and Aralkum Desert,will continue to experience a decrease in the suitable habitats of H.ammodendron.Regions exhibiting frequent fluctuations in the habitat suitability levels are primarily found along the axis stretching from Astana to Kazakhskiy Melkosopochnik in Kazakhstan.These regions can transition into suitable habitats under varying climate conditions,requiring the implementation of appropriate human intervention measures to prevent desertification.Future climate conditions are expected to cause an eastward shift in the geometric centre of the potential suitable habitats of H.ammodendron,with the extent of this shift amplifying alongside more greenhouse gas emissions.This study can provide theoretical support for the spatial configuration of H.ammodendron shelterbelts and desertification control in Central Asia,emphasising the importance of proactive measures to adapt to climate change in the future.展开更多
Objective: American ginseng is a medicinal plant with large market demands,however,its producing areas are shrinking because of the continuous cropping obstacles in China.Therefore,it is urgent to establish a suitabl...Objective: American ginseng is a medicinal plant with large market demands,however,its producing areas are shrinking because of the continuous cropping obstacles in China.Therefore,it is urgent to establish a suitable model to determine the new producing areas.Here we evaluated and predicted the suitable areas of American ginseng using the maximum entropy model(Max Ent).Methods: Based on the 37 environmental variables over thirty years from 1970 to 20 0 0 and 226 global distribution points of American ginseng,Max Ent was used to determine the global ecological suitable areas for American ginseng.The Receiver Operating Curve(ROC)was used to evaluate the model prediction accuracy.Meanwhile,an innovative ecological variable,the precipitation–temperature ratio,was established to indicate the climate characteristic in the American ginseng suitable areas based on the monthly precipitation and temperature.Results: The potential ecological suitable areas of American ginseng were primarily in Appalachian Mountain in America and Changbai Mountain in China,about in the range of 35 °N–50 °N,60 °W–120 °W and 35 °N–50 °N,110 °E–145 °E,respectively,including the United States,Canada,China,North Korea,South Korea,Russia and Japan.South Korea and Japan were the potential producing regions.The precipitation–temperature ratios were stable at(0.22,0.56)of the vigorous growth period(April–October)in the best suitable areas of American ginseng,serving as characteristic parameters to optimize the prediction model.The model showed that the common soil parameters were pH 4.5–7.2,Base Saturation(BS)above 80%,Cation Exchange Capacity(CEC)10–20 cmol/kg,organic carbon(OC)〈 1.4%,and the soil types were sandy loam or loam.Conclusion: An optimized Max Ent model was established to predict the producing area for American ginseng that needed to be validated by a field test.展开更多
基金the auspices of A Category of Strategic Priority Research Program of Chinese Academy of Sciences(No.XDA20010101)。
文摘The spatial interaction model is an effective way to explore the geographical disparities inherent in the Belt and Road Initiative(BRI) by simulating spatial flows. The traditional gravity model implies the hypothesis of equilibrium points without any reference to when or how to achieve it. In this paper, a dynamic gravity model was established based on the Maximum Entropy(MaxEnt) theory to estimate and monitor the interconnection intensity and dynamic characters of bilateral relations. In order to detect the determinants of interconnection intensity, a Geodetector method was applied to identify and evaluate the determinants of spatial networks in five dimensions. The empirical study clearly demonstrates a heterogeneous and non-circular spatial structure. The main driving forces of spatial-temporal evolution are foreign direct investment, tourism and railway infrastructure construction, while determinants in different sub-regions show obvious spatial differentiation. Southeast Asian countries are typically multi-island area where aviation infrastructure plays a more important role. North and Central Asian countries regard oil as a pillar industry where power and port facilities have a greater impact on the interconnection. While Western Asian countries are mostly influenced by the railway infrastructure, Eastern European countries already have relatively robust infrastructure where tariff policies provide a greater impetus.
基金funded by National Key R&D Program of China(No. 2021YFC2600400)National Natural Science Foundation of China(No. 32171794)Forestry Science and Technology Innovation Special of Jiangxi Forestry Department (No. 201912)
文摘Background: Hylurgus ligniperda(Fabricius) is native to Europe but has established populations in many countries and regions. H. ligniperda mainly infests Pinus species, and can cause severe weakness and even death of the host through its boring activity;it can also be a vector of various pathogenic fungi. This study was conducted to investigate the environmental variables limiting the distribution of H. ligniperda and the change trend of its suitable areas under climate change.Results: We used a maximum entropy model to predict the potential geographical distribution of H. ligniperda on a global scale under near current and future climatic scenarios using its occurrence data and environmental variables. The result shows that the areas surrounding the Mediterranean region, the eastern coastal areas of Asia, and the southeastern part of Oceania are highly suitable for H. ligniperda. The environmental variables with the greatest effect on the distribution of H. ligniperda were determined using the jackknife method and Pearson’s correlation analysis and included the monthly average maximum temperature in April, precipitation of driest quarter, the monthly average minimum temperature in December, precipitation of coldest quarter, mean temperature of driest quarter and mean diurnal range.Conclusions: Excessive precipitation in winter and low temperatures in spring had a great effect on the distribution of H. ligniperda. The potential geographical distribution of H. ligniperda was predicted to change under future climatic conditions compared with near current climate conditions. Highly suitable areas, moderately suitable areas and low suitable areas were predicted to increase by 59.99%, 44.43% and 22.92%, respectively, under the2081–2100 ssp245 scenario.
基金Supported bythe National Research Foundationforthe Doctoral Program of Higher Education of China(20030145029) the Teaching and Research Award Program for Outstanding Young Teachers in Higher Education Institutions of the Ministry ofEducation
文摘The maximum entropy model was introduced and a new intrusion detection approach based on the maximum entropy model was proposed. The vector space model was adopted for data presentation. The minimal entropy partitioning method was utilized for attribute diseretization. Experiments on the KDD CUP 1999 standard data set were designed and the experimental results were shown. The receiver operating eharaeteristie(ROC) curve analysis approach was utilized to analyze the experimental results. The analysis results show that the proposed approach is comparable to those based on support vector maehine(SVM) and outperforms those based on C4.5 and Naive Bayes classifiers. According to the overall evaluation result, the proposed approach is a little better than those based on SVM.
基金supported by the Advanced Functional Composites Technology Key Laboratory Fund under Grant No.6142906220404Sichuan Province Centralized Guided Local Science and Technology Development Special Project under Grant No.2022ZYD0121。
文摘In this paper,an effective algorithm for optimizing the subarray of conformal arrays is proposed.The method first divides theconformal array into several first-level subarrays.It uses the X algorithm to solve the feasible solution of first-level subarray tiling and employs the particle swarm algorithm to optimize the conformal array subarray tiling scheme with the maximum entropy of the planar mapping as the fitness function.Subsequently,convex optimization is applied to optimize the subarray amplitude phase.Data results verify that the method can effectively find the optimal conformal array tiling scheme.
基金Supported by National Basic Science Talent Culture Fund Item,China(J1103511)
文摘[ Objective] The research aimed to study distribution prediction of suitable growth area for Eucommia ulmoides in China under climatic change background. [ Method] By using the maximum entropy model and many kinds of climate change scenarios, we predicted current and future distribution pattems of suitable growth area for Eucommia ulmoides in China and its change process. [ Result ] At present, highly suitable growth area of E. ulmoides mainly distributed in Sichuan, Shaanxi and Chongqing, Under climate change background, total suitable growth areas in future three decades all drastically reduced when compared with that at present. It was noteworthy that moderately and highly suitable growth areas of wild E. ulmoides all disappeared, and junction between Shaanxi and Gansu and Taibai Mountain would be stable suitable growth area of wild E. ulmoides. [ Condusioa] The research could provide useful reference data for investigation, protection and sustainable development of the wild E. ulmoides resources.
文摘The resolution of overlapping ambiguity strings(OAS)is studied based on the maximum entropy model.There are two model outputs,where either the first two characters form a word or the last two characters form a word.The features of the model include one word in con-text of OAS,the current OAS and word probability relation of two kinds of segmentation results.OAS in training text is found by the combination of the FMM and BMM segmen-tation method.After feature tagging they are used to train the maximum entropy model.The People Daily corpus of January 1998 is used in training and testing.Experimental results show a closed test precision of 98.64%and an open test precision of 95.01%.The open test precision is 3.76%better compared with that of the precision of common word probability method.
基金funded by the National Natural Science Foundation of China[grant number 41901337 and 42071136]。
文摘Objective The Guanzhong Plain of Shaanxi Province is a severely afflicted hemorrhagic fever with renal syndrome(HFRS)epidemic area,while HFRS prevalence has decreased in most epidemic areas in China.Little information is available regarding the leading fine-scale influencing factors in this highly HFRSconcentrated area and the roles of natural environmental and socioeconomic factors.To investigate this,two regions in the Guanzhong Plain,that is,the Chang’an District and Hu County,with similar geographical environments,different levels of economic development,and high epidemic prevalence,were chosen as representative areas of the HFRS epidemic.Methods Maximum entropy models were constructed based on HFRS cases and fine-scale influencing factors,including meteorological,natural environmental,and socioeconomic factors,from 2014 to 2016.Results More than 95% of the HFRS cases in the study area were located in the northern plains,which has an altitude of less than 800 m,with topography contributed 84.1% of the impact on the spatial differentiation of the HFRS epidemic.In the northern plains,precipitation and population density jointly affected the spatial differentiation of the HFRS epidemic,with contribution rates of 60.7% and 28.0%,respectively.By comparing the influencing factors of the northern plains of Chang’an District and Hu County,we found that precipitation and the normalized difference vegetation index(NDVI)dominated the HFRS epidemic in the relatively developed Chang’an District,while land-use type,temperature,precipitation and population density dominated the HFRS epidemic in the relatively undeveloped Hu County.Conclusion Topography was the primary key factor for HFRS prevalence in the Chang’an District and Hu County,and the spatial differentiation of HFRS was dominated by precipitation and population density in the northern plains.Compared with the influencing factors of the relatively developed Chang’an District,the developing Hu County was more affected by socioeconomic factors.When formulating targeted HFRS epidemic prevention and control strategies in the targeted areas,it is crucial to consider the local economic development state and combine natural environmental factors,including the meteorological environment and vegetation coverage.
基金supported by the the Basic Frontier Project of Xinjiang Institute of Ecology and Geography,Chinese Academy of Sciences(E3500201)the Xinjiang Tianshan Talent Program(2022TSYCLJ0002)the Fundamental Research Funds for the Central Universities(ZY20240223).
文摘Understanding the spatial distribution of plant species and their dynamic changes in arid areas is crucial for addressing the challenges posed by climate change.Haloxylon ammodendron shelterbelts are essential for the protection of plant resources and the control of desertification in Central Asia.Thus far,the potential suitable habitats of H.ammodendron in Central Asia are still uncertain in the future under global climate change conditions.This study utilised the maximum entropy(MaxEnt)model to combine the current distribution data of H.ammodendron with its growth-related data to analyze the potential distribution pattern of H.ammodendron across Central Asia.The results show that there are suitable habitats of H.ammodendron in the Aralkum Desert,northern slopes of the Tianshan Mountains,and the upstream of the Tarim River and western edge of the Taklimakan Desert in the Tarim Basin under the current climate conditions.The period from 2021 to 2040 is projected to undergo significant changes in the suitable habitat area of H.ammodendron in Central Asia,with a projected 15.0% decrease in the unsuitable habitat area.Inland areas farther from the ocean,such as the Caspian Sea and Aralkum Desert,will continue to experience a decrease in the suitable habitats of H.ammodendron.Regions exhibiting frequent fluctuations in the habitat suitability levels are primarily found along the axis stretching from Astana to Kazakhskiy Melkosopochnik in Kazakhstan.These regions can transition into suitable habitats under varying climate conditions,requiring the implementation of appropriate human intervention measures to prevent desertification.Future climate conditions are expected to cause an eastward shift in the geometric centre of the potential suitable habitats of H.ammodendron,with the extent of this shift amplifying alongside more greenhouse gas emissions.This study can provide theoretical support for the spatial configuration of H.ammodendron shelterbelts and desertification control in Central Asia,emphasising the importance of proactive measures to adapt to climate change in the future.
基金supported by National Natural Science Foundation of China (81473304)National Science and Technology Support Program (2015BAI05B01)
文摘Objective: American ginseng is a medicinal plant with large market demands,however,its producing areas are shrinking because of the continuous cropping obstacles in China.Therefore,it is urgent to establish a suitable model to determine the new producing areas.Here we evaluated and predicted the suitable areas of American ginseng using the maximum entropy model(Max Ent).Methods: Based on the 37 environmental variables over thirty years from 1970 to 20 0 0 and 226 global distribution points of American ginseng,Max Ent was used to determine the global ecological suitable areas for American ginseng.The Receiver Operating Curve(ROC)was used to evaluate the model prediction accuracy.Meanwhile,an innovative ecological variable,the precipitation–temperature ratio,was established to indicate the climate characteristic in the American ginseng suitable areas based on the monthly precipitation and temperature.Results: The potential ecological suitable areas of American ginseng were primarily in Appalachian Mountain in America and Changbai Mountain in China,about in the range of 35 °N–50 °N,60 °W–120 °W and 35 °N–50 °N,110 °E–145 °E,respectively,including the United States,Canada,China,North Korea,South Korea,Russia and Japan.South Korea and Japan were the potential producing regions.The precipitation–temperature ratios were stable at(0.22,0.56)of the vigorous growth period(April–October)in the best suitable areas of American ginseng,serving as characteristic parameters to optimize the prediction model.The model showed that the common soil parameters were pH 4.5–7.2,Base Saturation(BS)above 80%,Cation Exchange Capacity(CEC)10–20 cmol/kg,organic carbon(OC)〈 1.4%,and the soil types were sandy loam or loam.Conclusion: An optimized Max Ent model was established to predict the producing area for American ginseng that needed to be validated by a field test.