Spatial-temporal Evolution and Determinants of the Belt and Road Initiative: A Maximum Entropy Gravity Model Approach

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.

Maximum entropy spectral characteristics of seismic activity for great earthquakes in China

The maximum entropy spectral characteristics of seismicity in the seismic enhanced region of 11 great earthquakes is analysed in this paper to seek the difference of seismic period spectral structure between the normal and the abnormal stage of seismic activity in this paper. The results show that, during decades or even one hundred years before great earthquakes, only short periods with 6.5～24.3 years appear, and long ones disappear. Otherwise, long periods with 18.5～38.5 years exist chiefly within the normal stages. Decades years after great earthquakes, the period spectra of seismicity are generally about several or ten years. Then the characteristics of great earthquakes is explained physically by applying the strong body seismogenic model, so a method of studying and predicting great earthquakes is offered.

Prediction of the global potential geographical distribution of Hylurgus ligniperda using a maximum entropy model

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.

A New Detection Approach Based on the Maximum Entropy Model

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.

基于MaxEnt和GARP的阿蒙森海域南极磷虾(EUPHAUSIA SUPERBA)的分布区预测

南极磷虾是南大洋生态系统的关键物种,在南极碳汇过程中起到重要作用,近年来受到越来越多的关注。针对位于南大洋太平洋扇区的阿蒙森海域,运用最大熵模型(maximum entropy modeling,MaxEnt)和预设规则的遗传算法(genetic algorithm for rule-set production,GARP)两种生态位模型,基于已采集的南极磷虾分布点的数据,对其在阿蒙森海域的潜在分布区进行了预测和分析,并采用受试者工作特征曲线(receiver operating characteristic curve,ROC)下的面积(area under curve,AUC)和真实技巧统计法(true skill statistic,TSS)对模型结果进行评估。结果表明:MaxEnt模型中的高适生区刻画细致,GARP模型预测的高适生区分布范围更广。为克服单个模型的不确定性得到更佳结果,将两个模型的预测结果进行集合。集合后的结果模拟精度显著提高(AUC为0.946,TSS为0.78),达到了极好的预测效果。磷虾的高适生区集中分布在65°~73°S,占总面积的6.2%,中适生区占总面积的5.7%。海冰、平均海平面气压最小值和纬向流速最大值是MaxEnt中贡献最高的3个变量,3个变量贡献达81.3%。相较于MaxEnt模型,GARP模型中各个变量遗漏误差相对较平均。研究表明,集合的结果能够提高物种分布预测的准确性,阿蒙森海域南极磷虾的分布预测结果可以为磷虾保护、利用提供科学参考。

Distribution Prediction of Suitable Growth Area for Eucommia ulmoides in China under Climatic Change Background

[ 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.

Resolution of overlapping ambiguity strings based on maximum entropy model

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.

Spatial Heterogeneity and Influencing Factors of HFRS Epidemics in Rural and Urban Areas:A Study in Guanzhong Plain of Shaanxi Province,China

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.

Evaluation and prediction of ecological suitability of medicinal plant American ginseng (panax quinquefolius)

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.

Multi-Level Max-Margin Analysis for Semantic Classification of Satellite Images

The performance of scene classification of satellite images strongly relies on the discriminative power of the low-level and mid-level feature representation. This paper presents a novel approach, named multi-level max-margin analysis （M 3 DA） for semantic classification for high-resolution satellite images. In our M 3 DA model, the maximum entropy discrimination latent Dirichlet allocation （MedLDA） model is applied to learn the topic-level features first, and then based on a bag-of-words repre- sentation of low-level local image features, the large margin nearest neighbor （LMNN） classifier is used to optimize a multiple soft label composed of word-level features （generated by SVM classifier） and topic-level features. The categorization performances on 21-class land-use dataset have demonstrated that the proposed model in multi-level max-margin scheme can distinguish different categories of land-use scenes reasonably.