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.

基于MaxEnt模型的科尔沁沙地刺萼龙葵适宜生长区预测

为明确外来入侵植物刺萼龙葵(Solanum rostratum)在科尔沁沙地的适生区分布,利用刺萼龙葵的记录点和19个环境变量,用最大熵模型(MaxEnt)和地理信息系统(ArcGIS)相结合对分布在科尔沁沙地的刺萼龙葵的地理数据进行分析,预测其适生区,并采用受试者操作特征曲线(ROC)分析法对预测结果进行验证,训练数据和验证数据在特性曲线下的面积值(AUC)分别为0.849和0.950,预测结果较为准确。结果表明,气象因子中最暖月高温、最冷季平均温度、最干月降水量、昼夜温差日均温是影响刺萼龙葵分布的主要环境变量;刺萼龙葵在科尔沁沙地的高适生区约占3.9万km^(2),主要集中分布于科尔沁沙地中部地区。

基于Maxent模型的湖南省白颈长尾雉适宜栖息地预测

湖南省是白颈长尾雉(Syrmaticus ellioti)的主要栖息地之一,分析其潜在适生区对该物种保护具有现实指导意义。本研究综合考虑地形、水源、气候、植被、土地类型以及人为干扰等多项因子,结合有效的白颈长尾雉分布点,利用最大熵(maximum entropy, Maxent)模型预测湖南省白颈长尾雉的适宜栖息地。结果显示, Maxent模型10次重复运行训练集的平均曲线下面积(area under the curve, AUC)值为0.964,预测结果优秀;到县道距离(dis_x)、最暖季度降水量(bio18)、到混交林距离(dis_mxf)和平均气温日较差(bio2)是影响白颈长尾雉适宜栖息地分布的主要因子,到稀树草原距离(dis_sav)、海拔(altitude)和到荒地距离(dis_bar)对其分布的影响也较大,其余因子具有一定影响但并不显著;白颈长尾雉适生区面积为4.76×10^(4)km^(2),主要位于湘西、湘南的山地,但适宜栖息地破碎化程度较高,整体较为分散。

Predicting Potential Distribution of Tibetan Spruce (Picea smithiana) in Qomolangma (Mount Everest) National Nature Preserve Using Maximum Entropy Niche-based Model

Tibetan spruce (Picea smithiana) is an endemic species of the Himalayas,and it distributes only in a re-stricted area with very low number.To address the lack of detailed distributional information,we used maximum en-tropy (Maxent) niche-based model to predict the species' potential distribution from limited occurrence-only records.The location data of P.smithiana,relative bioclimatic variables,vegetation data,digital elevation model (DEM),and the derived data were analyzed in Maxent.The receiver operating characteristic (ROC) curve was applied to assess the prediction accuracy.The Maxent jackknife test was performed to quantify the training gains from data layers and the response of P.smithiana distribution to four typical environmental variables was analyzed.Results show that the model performs well at the regional scale.There is a potential for continued expansion of P.smithiana population numbers and distribution in China.P.smithiana potentially distributes in the lower reaches of Gyirong Zangbo and Poiqu rivers in Gyirong and Nyalam counties in Qomolangma (Mount Everest) National Nature Preserve (QNNP),China.The species prefers warm temperate climate in mountain area and mainly distributes in needle-leaved evergreen closed to open forest and mixed forest along the river valley at relatively low altitudes of about 2000-3000 m.Model simulations suggest that distribution patterns of rare species with few species numbers can be well predicted by Max-ent.

Fourth-Order Predictive Modelling: I. General-Purpose Closed-Form Fourth-Order Moments-Constrained MaxEnt Distribution

This work (in two parts) will present a novel predictive modeling methodology aimed at obtaining “best-estimate results with reduced uncertainties” for the first four moments (mean values, covariance, skewness and kurtosis) of the optimally predicted distribution of model results and calibrated model parameters, by combining fourth-order experimental and computational information, including fourth (and higher) order sensitivities of computed model responses to model parameters. Underlying the construction of this fourth-order predictive modeling methodology is the “maximum entropy principle” which is initially used to obtain a novel closed-form expression of the (moments-constrained) fourth-order Maximum Entropy (MaxEnt) probability distribution constructed from the first four moments (means, covariances, skewness, kurtosis), which are assumed to be known, of an otherwise unknown distribution of a high-dimensional multivariate uncertain quantity of interest. This fourth-order MaxEnt distribution provides optimal compatibility of the available information while simultaneously ensuring minimal spurious information content, yielding an estimate of a probability density with the highest uncertainty among all densities satisfying the known moment constraints. Since this novel generic fourth-order MaxEnt distribution is of interest in its own right for applications in addition to predictive modeling, its construction is presented separately, in this first part of a two-part work. The fourth-order predictive modeling methodology that will be constructed by particularizing this generic fourth-order MaxEnt distribution will be presented in the accompanying work (Part-2).

基于MaxEnt模型的公路项目环评中重要物种适宜生境评价及预测

公路建设项目属于线性项目,其对生态环境的主要影响是对物种生境的阻隔和切割,致使物种适宜生境面积减少,并伴有光污染、声环境质量下降。基于拟建的穿越国家一级保护动物黑嘴松鸡生境的公路,以MaxEnt为预测模型,模拟公路建设前后黑嘴松鸡适宜生境的分布变化。结果显示:基于最大熵模型的黑嘴松鸡受试者工作特征曲线AUC值为0.945,可信度较高。适生区分析显示:黑嘴松鸡集中分布在保护区核心区和缓冲区,项目建设后不适生区面积大幅度增加。研究认为,最大熵模型能很好地模拟项目建设前后研究区黑嘴松鸡适生区的分布,明确公路项目建设对黑嘴松鸡分布的影响,研究结果可为环评中对适宜生境的评价及预测提供参考。

Droplets diameter distribution using maximum entropy formulation combined with a new energy-based sub-model

The maximum entropy principle(MEP) is one of the first methods which have been used to predict droplet size and velocity distributions of liquid sprays. This method needs a mean droplets diameter as an input to predict the droplet size distribution. This paper presents a new sub-model based on the deterministic aspects of liquid atomization process independent of the experimental data to provide the mean droplets diameter for using in the maximum entropy formulation(MEF). For this purpose, a theoretical model based on the approach of energy conservation law entitled energy-based model(EBM) is presented. Based on this approach, atomization occurs due to the kinetic energy loss. Prediction of the combined model(MEF/EBM) is in good agreement with the available experimental data. The energy-based model can be used as a fast and reliable enough model to obtain a good estimation of the mean droplets diameter of a spray and the combined model(MEF/EBM) can be used to well predict the droplet size distribution at the primary breakup.

基于MaxEnt模型的石柱县马尾松毛虫害风险空间分布估测

【目的】精准掌握重庆市石柱土家族自治县马尾松毛虫害的风险范围,为及时有效地防治虫灾提供有力依据。【方法】选取60个马尾松毛虫分布点的数据,结合该县马尾松区域的地形、气候和人为因子,用MaxEnt模型估测该县的马尾松毛虫害风险。通过环境因子的百分比贡献率、置换重要性和环境因子响应曲线,探讨该县马尾松毛虫适宜生境条件。利用该模型的“训练遗漏、预测面积和阈值达到平衡”的阈值和“阈值熵与原始分布熵相等”的阈值对风险区进行风险分级,将该区域的马尾松毛虫害风险等级划分为低风险、中风险和高风险,并通过地理信息系统统计各个风险区的区域面积。通过该模型AUC均值验证估测精度,以及对比该县2022年马尾松毛虫飞防的区域内各风险区面积占比,对风险分级效果进行验证。【结果】1)年均降水量、月均潜在蒸散发量、距居民点距离和海拔为影响该县马尾松毛虫生存的主要环境因子。2)MaxEnt模型的训练和测试的AUC均值分别为0.92与0.87,标准差为0.013,小于0.05,表明模型的结果为优,预测结果的可靠性高,可用于该县马尾松毛虫害风险估测;通过对比2022年石柱县春季马尾松毛虫的飞防区域,面积占比高达99.05%,表明高风险区分布与其实际分布高度一致,分级效果好。3)该县马尾松毛虫害高风险区主要集中于临溪镇、悦崃镇、鱼池镇、河嘴乡和万朝镇等;王场镇、龙沙镇、王家乡、西沱镇、黎场乡、沿溪镇和万朝镇的高风险区面积占各自乡镇马尾松区域面积高达90%以上。【结论】研究表明,石柱县的马尾松毛虫害高风险区主要集中在西部和北部的乡镇,通过对乡镇间与乡镇内高风险区分布的统计分析,更全面地了解了马尾松毛虫的风险区分布,可为马尾松毛虫的针对性防治与监管提供科学依据。

A MAXIMUM ENTROPY CHUNKING MODEL WITH N-FOLD TEMPLATE CORRECTION

This letter presents a new chunking method based on Maximum Entropy (ME) model with N-fold template correction model.First two types of machine learning models are described.Based on the analysis of the two models,then the chunking model which combines the profits of conditional probability model and rule based model is proposed.The selection of features and rule templates in the chunking model is discussed.Experimental results for the CoNLL-2000 corpus show that this approach achieves impressive accuracy in terms of the F-score:92.93%.Compared with the ME model and ME Markov model,the new chunking model achieves better performance.

基于MaxEnt模型的福建福鼎白茶种植适宜性区划

【目的】探明福鼎白茶的种植适宜区域,为福建省福鼎白茶的种植规划布局提供科学依据。【方法】应用福鼎白茶地理分布数据与气象资料(热量、光照和水分条件),采用MaxEnt模型研究影响福鼎白茶在福建分布的关键气象因子,并对其潜在分布进行适宜性区划。【结果】MaxEnt模型适用于福鼎白茶种植分布模拟,影响福鼎白茶种植分布的关键气象因子为3月平均气温、最冷月平均气温、≥10℃活动积温、多年平均极端最低气温和年降水量。福鼎白茶在福建的种植分布对生长关键期气温最敏感。福建适宜种植区域关键气象条件为3月平均气温低于15.0℃,最冷月平均气温10.7~11.9℃。福鼎白茶在福建的适宜区分布于24.76°~28.27°N、116.16°~120.45°E。【结论】福鼎白茶适宜及高适宜区域主要分布在宁德市各县(市)、南平市西部和中部、福州市东部和北部,约占全省面积的16.41%;次适宜区域主要分布在南平和三明市大部、福州市南部及莆田、泉州和龙岩市的北部地区,约占全省面积的46.00%。

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.

RESEARCH OF PINYIN-TO-CHARACTER CONVERSION BASED ON MAXIMUM ENTROPY MODEL

This paper applied Maximum Entropy (ME) model to Pinyin-To-Character (PTC) conversion in-stead of Hidden Markov Model (HMM) that could not include complicated and long-distance lexical informa-tion. Two ME models were built based on simple and complex templates respectively, and the complex one gave better conversion result. Furthermore, conversion trigger pair of y A → y B cBwas proposed to extract the long-distance constrain feature from the corpus; and then Average Mutual Information (AMI) was used to se-lect conversion trigger pair features which were added to the ME model. The experiment shows that conver-sion error of the ME with conversion trigger pairs is reduced by 4% on a small training corpus, comparing with HMM smoothed by absolute smoothing.

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.

基于MaxEnt与ArcGIS的中国角茴香生态适宜性区划研究

利用最大熵模型(MaxEnt)和地理信息系统(ArcGIS),结合相关生态因子,对角茴香的生态适宜性范围进行预测和分析。收集角茴香在中国的205个经纬度信息,结合19项生态因子应用MaxEnt模型和ArcGIS对角茴香的潜在适生分布区进行分析。影响角茴香潜在分布的主要生态因子包括,最冷季节降水量、最冷季节平均温度、最暖季节降水量、昼夜温差与年温差比值等。当前气候条件下,角茴香高度适宜生长的区域主要集中在北京、河北北部、山西、河南、陕西和内蒙古南部等,面积约为77.4×104 km2;未来气候条件下,适生区将向东北部和西部扩张。该研究预测的结果与角茴香实际分布区域相符,为角茴香的可持续利用和角茴香的引种栽培提供重要的理论依据。

基于MaxEnt模型预测未来气候变化情境下红树秋茄(Kandelia obovata)在中国潜在适生区的变化

红树秋茄为自然分布于我国南方热带与亚热带潮间带的木本植物群落,是淤泥质滩涂上特有的植被类型,由其构成的红树林生境在维持海岸生态系统平衡、湿地生物多样性等方面具有重要作用。全球气候变化影响了红树秋茄的地理分布范围,尤其是其分布北界的变化一直是当前红树研究领域的争论焦点之一。基于MaxEnt模型,筛选了141个秋茄现有分布点,并结合筛选后的14个陆地气候变量和2个海洋表层海洋环境因子,模拟了秋茄种群在不同碳排放情景下的潜在适生分布区域。分析了影响秋茄自然分布的主要环境因子,揭示了其在中国内地分布北界的演化趋势。根据模型结果,当前模拟的潜在分布区与现有分布范围吻合度较高,受试者工作特征曲线下面积(AUC值)为0.990,预测结果优良。通过广义加性模型回归验证表明,主要环境因子与秋茄的生境适生性指数呈显著相关,整体方差解释率为94.7%(R^(2)=0.915),说明MaxEnt模型预测结果可靠稳定。基于环境变量贡献率和刀切法的结果表明海洋表层平均水温、等温性、最暖季度降水量、年平均气温、最热月份最高温度等可能是影响秋茄分布的主要环境因子;等温性(bio_03)为23.43—33.99,最热月份最高温度(bio_05)>31.7℃,最暖季度降水量(bio_18)>740.61mm,海洋表层平均水温(temperature)>24.9℃,将有利于秋茄种群的发育与扩散。根据模型预测,在21世纪中叶和21世纪末,秋茄的潜在分布区发生集中变化的区域在浙江与江苏一带,以秋茄自然分布区域北界变化最为显著。不同代表性浓度路径(Representative Concentration Pathways, RCPs2.6、4.5、8.5)下,秋茄高适生区域均发生北移,低适生区域可至长江口附近,甚至到达江苏沿岸。这为上述区域未来利用红树秋茄进行生态系统修复和增加碳汇储备提供了理论依据。

Video segmentation using Maximum Entropy Model

Detecting objects of interest from a video sequence is a fundamental and critical task in automated visual surveillance. Most current approaches only focus on discriminating moving objects by background subtraction whether or not the objects of interest can be moving or stationary. In this paper, we propose layers segmentation to detect both moving and stationary target objects from surveillance video. We extend the Maximum Entropy (ME) statistical model to segment layers with features, which are collected by constructing a codebook with a set of codewords for each pixel. We also indicate how the training models are used for the discrimination of target objects in surveillance video. Our experimental results are presented in terms of the success rate and the segmenting precision.

基于MaxEnt模型的湖北烟区潜在适宜性分布及其环境特征分析

为全面评价生态气候因子对湖北省烟叶生长发育的影响,利用2008—2020年中国气象局陆面数据同化系统(CMA land data assimilation system,CLDAS)数据集和其他环境因子,基于最大熵模型(MaxEnt),利用湖北省西部烟区实际种植点位模拟湖北省烟区的潜在适宜性分布,并评估影响适宜性分布的主导环境因子。结果显示:MaxEnt模型对烟草适宜性分布的模拟预测精度较高(AUC=0.854)。海拔(650~1750 m)、土壤类型(黄壤)、团棵期平均最低气温(8.9~15.0℃)、旺长期平均气温(16.0~22.9℃)、成熟期平均气温(18.8~25.6℃)是影响湖北烟草分布的主导环境因子。烟草的高适生区主要分布在恩施、十堰和襄阳南部及宜昌东南部,占湖北省总面积的18.2%,80%区域的海拔为700~1350 m、团棵期平均最低气温11.2~14.5℃、旺长期平均气温19.2~22.8℃、成熟期平均气温22.0~25.4℃。在所有环境因子中,海拔高度起决定性作用,温度则是影响烟草生长发育的重要气象因子。研究表明,本研究结果与湖北烟区实际空间分布基本相符。

Maximum Entropy Distribution Function and Uncertainty Evaluation Criteria

Marine environmental design parameter extrapolation has important applications in marine engineering and coastal disaster prevention.The distribution models used for environmental design parameter usually pass the hypothesis tests in statistical analysis,but the calculation results of different distribution models often vary largely.In this paper,based on the information entropy,the overall uncertainty test criteria were studied for commonly used distributions including Gumbel,Weibull,and Pearson-III distribution.An improved method for parameter estimation of the maximum entropy distribution model is proposed on the basis of moment estimation.The study in this paper shows that the number of sample data and the degree of dispersion are proportional to the information entropy,and the overall uncertainty of the maximum entropy distribution model is minimal compared with other models.

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.

基于MaxEnt模型预测马来鳄在中国的潜在生境

为探究马来鳄在中国的潜在适宜生存区及各环境变量对其地理分布的影响,文章基于马来鳄地理分布记录和7个环境变量,应用最大熵(maximum entropy,MaxEnt)模型模拟马来鳄在中国的潜在分布,利用贡献率、置换重要性及Jackknife检验来评估制约马来鳄地理分布的环境变量。结果表明:马来鳄在中国的高度适宜区主要位于广西西南部、广东南部、福建西南部及台湾地区东北部;影响马来鳄分布的主要气候因子为最暖季度降水量、最冷月份最低温度及最干月份降水量,满足最冷月份最低温度大于21.4℃、最干月份降水量大于116 mm、年气温变化范围在6.6~11.0℃之间、最暖季度降水量保持在550~1530 mm之间的环境最适宜马来鳄的生存。该研究结果可为马来鳄生物资源的保护和在适宜分布区进行人工繁育提供理论依据。