简介：Inrecentyears,populationgrowthmodelswithspatialdiffusionhavebeenextensivelystudiedbymanyauthors(forexample,see[1-5]).Inthispaper,apopulationgrowthmodelisconsideredwithadiscreteage-dependenceandspatialdiffusion,andisinvestigatedinasemigroupframework.Thespectralpropertiesofthepopulationoper-atoraregiven.Onthebasisofsuchspectralconsideration,theasymptoticbehaviourofthesemigroupgeneratedbythepopulationoperatorisobtained.Finally,anonlinearpopulationgrowthmodelisconsideredanditsstabilityisanalyzed.

简介：SpatialobjectandspatialrelationshiparetwobasicconceptsofGIS.Spatialobjectisthedigitalrepresentationofgeographicalentityorphenomenon,whichformsthebasisfordatamanagementandanalysis;spatialrelationshipistheconnexionbetweenspatialobjectswhengeometricpropertiesareconsidered.Thecommonlyusedclassificationofspatialobjectsaspoints,linesandareasismathe-maticallystrict,andsuitablefordatamanagement,butabittoogeneralizedforrepresentingrealentitiesandextractingspatialrelationships.Agoodclassificationmodelshouldnotonlybegoodforrepresentingrealentities,butalsogoodforre-vealingspatialrelationships,thereforegoodforformalizingspatialanalyses.

简介：在这份报纸，我们与一条很一般的生长法律和驾驶M的散开$$\frac应付模特儿{{\partialu(t，x)}}{{\partialt}}=D\Delta(\frac{{u(t，吗x)}}{{M(t，x)}})+\mu(t，x)f(u(t，x)，M(t，x))为时间依赖者功能M的一般盒子的.$$并且????？？

简介：Thispaperattemptstoregardspatialrelationtransformationasanimportantprocessinmapgener-alization.Thespatialrelationgenerali-zationcanbedividedintothecompo-nentsofabstraction:topology,dis-tanceandorientation.Theconcept‘spatialrelationresolution'isintro-ducedtodescribetheconstraintsofrelativespatialrelation.Onthebasisofnineintersectionmodels,thecardi-naldirectionmodelsandtheiso-dis-tance-relationmodels,thispapergivesthreesortsofrelationresolutionrepre-sentationsfortopological,distanceandorientationrelation,respectively.Twomappingimplementationsinmapgen-eralizationisdiscussed.

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简介：SomelaboratorydiffusiontestswereconductedwithdiffusiondevicetodeterminethediffusioncoefficientofCr(Ⅵ)ionpassingthroughDalianredclaysamples.TheconcentrationsofCr(Ⅵ)atdifferentplacesofthesampleswerethenmeasuredspectrophotometricallyafterastandingtimeof1000d.Aone-dimensionalsolutetransportequationwasusedtosimulatethetransportofCr(Ⅵ)throughclaysamples.Back-calculationofdiffusioncoefficientofCr(Ⅵ)wasmadewithfinitedifferencemethod.Parametricanalysiswasconductedtosimulatevariationsinsoildrydensity,temperature,pHandstandingtime.Theresultsshowthatthemethodusedinthispaperissimpleandeffective.ThediffusioncoefficientofCr(Ⅵ)inDalianredclayvariesfrom1.50×10-7cm2/sto2.08×10-7cm2/s.After1000ddiffusion,theconcentrationofthesourcesolutiondropsdownto1.27mg/Lfrom62.5mg/L,andthediffusiondistanceisonly3.5cm.Undertheassumptionthatdiffusioncoefficientisconstant,thediffusioneffectbecomesmoreobviouswithlowerdensity,lowertemperature,higherpHvalue,andmuchmoretime.

简介：海洋的循环和气候的学习要求能精确地模仿tracer旋涡散开和移流的模型。传统的Eulerian坐标能由于轴的不正确的排列介绍大人工的水平扩散性/粘性，这被显示出。因此，如此的模型能涂锋利的前面并且介绍另外的数字人工制品。为有相对低的分辨率的模拟，大侧面的散开明确地在模型被使用；因此，如此的数字散开不能是一个问题。随水平分辨率的增加，然而，在通常使用的Eulerian坐标与水平移流联系的人工的扩散性/粘性可以为精确地为海洋发行量建模成为最挑战性的障碍之一。Isopycnal旋涡散开(混合)广泛地在数字模型被使用了。普通智慧是沿着isopycnal混合是精力免费。然而，小心的考试表明这不是事实。事实上，旋涡散开能概念上被分开成二步：激动人心并且subscale散开。由于thermobaric效果，激动人心，或交换水集中，沿着isopycnal，表面处于吝啬的状态与GPE的变化被联系。这是不稳定性的一种新类型，叫了thermobaric不稳定性。另外，由于cabbelingsubscale，水包裹的散开总是导致GPE的版本。GPE的版本可以由于激动人心的isopycnal和subscale散开导致thermobaric不稳定性。

简介：ＯＮＡＣＬＡＳＳＯＦＡＮＩＳＯＴＲＯＰＩＣＤＩＦＦＵＳＩＯＮＥＱＵＡＴＩＯＮＳＯＮＡＣＬＡＳＳＯＦＡＮＩＳＯＴＲＯＰＩＣＤＩＦＦＵＳＩＯＮＥＱＵＡＴＩＯＮＳ￥ＧＡＯＨａｎｇ（ＤｅｐａｒｔｍｅｎｔｏｆＭａｔｈｅｍａｔｉｃｓ，ＦｕｄａｎＵｎｉｖｅｒｓｉｔ...

简介：这份报纸的目的是与散开学习一个SIS流行模型的动力学。我们首先学习模型的well-posedness。然后由使用linearization方法并且构造合适的Lyapunov功能，我们分别地建立没有疾病的平衡和地方性的平衡的本地、全球的稳定性。而且，鉴于修理的Schauder，削尖定理，当时，我们证明模型承认连接没有疾病的平衡和地方性的平衡的旅行波浪答案R0>1并且c>c*。并且由两方面的Laplace变换的优点，也，我们证明模型没有旅行波浪答案连接二equilibria什么时候R0>1并且c[0，c*)。

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简介：Inthispaper,twofinitedifferencestreamlinediffusion(FDSD)schemesforsolvingtwo-dimensionaltime-dependentconvection-diffusionequationsareconstructed.Stabilityandoptimalordererrorestimati-ionsforconsideredschemesarederivedinthenormstrongerthanL~2-norm.

简介：海水热力学的二个重要非线性的性质连接了到水密度，cabbeling和弹性(压缩的可能性)的变化，被讨论。埃迪散开和移流在密度导致变化；作为结果，系统的重力的势能被改变。因此，cabbeling和弹性玩在侧面的旋涡散开和移流的energetics给角色调音。垂直旋涡散开是处于全球海洋的机械精力平衡的关键元素之一。垂直旋涡散开能概念上被分开成二步：激动人心并且subscale散开。垂直旋涡向上、温暖/轻的激动人心的推冷/稠密水向下流水；因此，重力的势能被增加。在第二步期间，来自不同地方的水群众通过subscale混合散开，和水密度由于cabbeling被增加。使用WOA01气候学并且假定垂直旋涡扩散性等于2慭楲敮瀠慨潥桰瑹獥?有洠牡湩?桲摯灯票整?ㄠ?挠汨牯灯票整?的经常的价值?挠祲瑰灯票整??栠灡潴桰瑹獥?湡?‵汧畡潣桰瑹獥眠牥?敳畱湥散?眠?獵摥愠朠湥?湡污獹獩洠瑥潨?潴愠慮祬敺琠敨倠?朠湥?敳畱湥散?湩愠杬敡愠摮挠湯楦浲琠敨攠楸瑳湥散漠?桴???敧敮椠?桴?牴湡'諛L瑰浯捩猠煥敵据湩?慤慴漠?桒摯灯票慴愠摮传档潲桰瑹吗？

简介：重力的势能(GPE)变化由于horizontal/isopycnal旋涡散开和移流被检验。Horizontal/isopycnal旋涡散开概念上被分开成二步：激动人心并且subscale散开。与这二步联系的GPE变化被分析。另外，GPE变化由于激动人心并且在Eulerian坐标与horizontal/isopycnal移流联系的subscale散开被分析。这些公式为世界海洋被用于SODA数据。我们的分析显示在Eulerian坐标的horizontal/isopycnal移流能在模型介绍大人工的散开。在isopycnal坐标的GPE来源/水池仔细被连接到物理性质分发，这被显示出，例如温度，咸度和速度。与z协调比较，GPE来源/水池由于与isopycnal散开/移流联系的stirring/cabbeling是小得多的。尽管isopycnal坐标可以以处理侧面的散开是一种更好的选择，在传统的Eulerian坐标的移流术语能由于与移流联系的cabbeling生产GPE的人工的来源。减少如此的数字错误仍然是宏大挑战。

简介：Ortho_imageswillfullybeintegratedinGIS.Nodoubt,GISforgeneralpurposeisusablewithouttheintegrationofortho_imagesandotherrasterdata.ForsmallGISprojectsitmaybesufficienttostoreinformationassimplefiles.Whendatavolume,however,becomeslargeandthenumberofdatausersbecomesmorethanafew,thedatabasemanagementsystembecomesthemostimportantcomponent.Forinstance,thebasicdesignofGeoStarisbasedontheClient/ServerarchitectureandusinglargeRDBSandOODBStomanagetherasterandvectordatasuchasgraphicdata,attributedata,imagedataandDEMatthesameframework,whichisathoroughandseamlessintegratedsolutionforGISapplications.Thebriefstructureofdatabase,whichisreallyhybridandintegrated,isshowninFig.1.　　Inordertoprovidemoreefficientdataaccessorbrowseforalargearea,thevectordataisorganizedaccordingtotheclassifiedspatialindexbasedonanextendedquadtreecodestoredasclusterrelatedtothefeaturesize,asshowninFig.2.TherasterdatalikeimageandDEMisorganizedinpyramidgrid_dedmanner(Fig.3).EspeciallywearefacingtheproblemofmultiscaledatabaseswithindifferentcoordinatesystemsanddifferentmapprojectionsinChina,themoresophisticateddatabasesintegrationmethodthereforehastobedevelopedtodealwiththesocalled“crossingtheprojectionzone'problem.

简介：大多数geospatial现象能被解释概率的联盟者因为我们不知道生物物理的过程和机制，那联合创造了并且观察事件。因为我们关于在在取样的地点的学习下面的现象肯定，这哲学是重要的，除了测量错误，，但是，在之间取样，我们关于现象怎么表现变得不明确。Geostatistical不确定性描述是以如此的一个方法产生随机数，他们模仿创造了存在样品数据的随机过程的结果。存在样品的这个集合被看作那个随机的功能模型的一条部分取样的认识。随机的功能的空间可变性被一张变量图或协变性模型描述。在他们的地点奖励样品数据，并且反映空间结构的认识到的表面需要由变量图模型确定了。他们应该各复制整个采样区域的样品直方图代表。这篇论文将由盖住univariate和指示物技术在随机的模拟考察基础希望他们在geospatial信息科学的应用将普遍、多产。

简介：TheInternettechnologyhasalreadychangedtheInformationSocietyinprofoundways,andwillcontinuetodoso.NowadaysmanypeopleforeseethatthereisasimilartrajectoryforthenextgenerationofInternet-GridTechnology.Asanemergingcomputationalandnetworkinginfrastructure,GridComputingisdesignedtoprovidepervasive,uniformandreliableaccesstodata,computationalandhumanresourcesdistributedinadynamic,heterogeneousenvironment.Ontheotherhand,thedevelopmentofGeographicInformationSystem(GIS)hasbeenhighlyinfluencedbytheevolutionofinformationtechnologysuchastheInternet,telecommunications,softwareandvarioustypesofcomputingtechnology.Inparticular,inthedistributedGISdomain,thedevelopmenthasmadesignificantimpactinthepastdecade.However,duetotheclosedandcentralizedlegacyofthearchitectureandthelackofinteroperability,modularity,andflexibility,currentdistributedGISstillcannotfullyaccommodatethedistributed,dynamic,heterogeneousandspeedydevelopmentinnetworkandcomputingenvironments.Hence,thedevelopmentofahighperformancedistributedGISsystemisstillachallengingtask.So,thedevelopmentofGridcomputingtechnologyundoubtedlyprovidesauniqueopportunityfordistributedGIS,andaGridComputingbasedGISparadigmbecomesinevitable.ThispaperproposesanewcomputingplatformbaseddistributedGISframework–theGridGeographicInformationSystem(G2IS).

简介：在这篇论文，在散开现象的行为的不可分的记忆术语的效果被学习。精力功能与不同模型联系了被分析，稳定性不平等被建立。为integro微分的方程的答案的计算的近似方法被构造。数字结果被包括。[从作者抽象]

简介：Inthispaper,weconsiderthedeadcoreandsomeasymptoticbehaviorsofsolutionstotheinitialboundaryvalueproblemsoftheequationu_t=△u-λupeu,where>0,0

简介：Inthispaper,thecLassicaLriskprocessperturbedbydiffusionisgenera]izedbyalLowingfor"sizefluctuation"andthertl[nprobabiLityforthisnewmodelisdlscussed.

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