简介：本文选择天山开都河流域为研究区,基于巴音布鲁克和大山口2个水文站1957—2011年的日径流量观测资料,采用年最大值法（AM）抽取径流序列样本,用线性趋势法、Mann-Kendall趋势检验和Pettitt检验分析年最大日流量、春季最大日流量和夏季最大日流量序列的变化规律;并运用广义极值分布（GEV）对标准他的最大日流量序列进行拟合,分析洪水频率的变化特征。结果表明：提取的6个最大日流量序列均不存在明显的趋势性,且突变点不显著,其中巴音布鲁克站年最大日流量、春季最大日流量和大山口站年最大日流量序列近似服从Frechet分布,而大山口站春季最大日流量、夏季最大日流量和巴音布鲁克站夏季最大日流量序列则服从Gumbel分布。1980年代以来,开都河流域洪水的发生频次明显增加;巴音布鲁克站夏季洪水次数持续增加,大山口站春季和夏季洪水次数均呈增加趋势,且春季洪水出现时间均有所提前。春季显著升温与冬季降水增加,是春季融雪性洪水出现时间和水量变化的主要原因;而夏季降雨量和降雨频率显著增加,是夏季洪水形成与频率变化的主导因素。

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简介：Althoughvariousgenomeprojectshaveprovidedusenormousstaticsequenceinformation,understandingofthesophisticatedbiologycontinuestorequireintegratingthecomputationalmodeling,systemanalysis,technologydevelopmentforexperiments,andquantitativeexperimentsalltogethertoanalyzethebiologyarchitectureonvariouslevels,whichisjusttheoriginofsystemsbiologysubject.Thisreviewdiscussestheobject,itscharacteristics,andresearchattentionsinsystemsbiology,andsummarizestheanalysismethods,experimentaltechnologies,researchdevelopments,andsooninthefourkeyfieldsofsystemsbiology-systemicstructures,dynamics,controlmethods,anddesignprinciples.

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简介：Self-affinemultiplicityfluctuationisinvestigatedbyusingthetwo-dimensionalfactorialmomentmethodologyandtheconceptoftheHurstexponent(H).Investigationontheexperimentaldataofcompoundparticlesandtargetfragmentsemittedin84Kr-AgBrinteractionsat1.7AGeVrevealsthatthebestpowerlawbehavioursareexhibitedatH=0.7and0.6respectively,andthedataforshowerparticlesproducedin84Kr-emulsioninteractionsat1.7AGeVindicatethatthebestpowerlawbehaviouroccursatH=0.6,allofwhichshowtheself-affinemultiplicityfluctuationpatterns.Themultifractalityandthenon-thermalphasetransitionoccurringduringproducingthecompoundparticles,thetargetfragments,andtheshowerparticlesinthe84Kr-AgBrinteractionandthe84Kr-emulsioninteractionarealsodiscussed.Themultifractalityisobservedduringproducingcompoundparticles,targetfragments,andshowerparticles.Inthetargetfragmentproduction,anevidenceofnon-thermalphasetransitionisobserved,butintheshowerparticleproductionandthecompoundparticleproduction,noevidenceofnon-thermalphasetransitionisobserved.

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简介：IntroductionErrorsareunavoidableinlanguagelearning,however,toagreatextent,teachersinmostmiddleschoolsinChinaregarderrorsasundesirable,asignoffailureinlanguagelearning.Mostmiddleschoolsarestillusingthegrammar-translationmethodwhichaimsatencouragingstudentstoreadscientificworksandenjoyliteraryworks.Theothergoalsofthismethodaretogainagreaterunderstandingofthefirstlanguageandtoimprovethestudents’abilitytocopewithdifficultsubjectsandmaterials,i.e.todevelopthestudents’minds.Thepracticalpurposeofusingthismethodistohelplearnerspasstheannualentranceexamination."Toachievethesegoals,thestudentsmustfirstlearngrammarandvocabulary,...Grammaristaughtdeductivelybymeansoflongandelaborateexplanations...studentslearntherulesofthelanguageratherthanitsuse."(TangLixing,1983:11-12)

简介：Whenpeopleadviseyourchildrento“getaneducation”,ifyouwanttoraiseyourpayment,theytellyouonlyhalfthetruth.Whattheyreallymeanistogetjustenougheducationtogivemanpower(人力)foryoursociety,butnotsomuchthatyouproveadifficultytoyoursociety.

简介：Aconceptualleveldatabaselanguagefortheentityrelationship(ER)modelimplicitlycontainsintegritiesbasictoERconceptsandspecialretrievalsemanticsforinheritancesofattributesandrelationships.Prolog,whichbelongstothelogicalandphysicallevel,cannotbeusedasafoundationtodirectlydefinethedatabaselanguage.ItisshownhowPrologcanbeenhancedtounderstandtheconceptsofentities,relationships,attributesandis-arelationships.TheenhancedPrologisthenusedasafoundationtodefinethesemanticsofadatabasequerylanguagefortheERmodel.Thethreebasicfunctionsofmodelspecification,updatesandretrievalsaredefined.

简介：ThispaperoutlinesthedesgnandprototypingoftheATLASHighLevelTrigger(HLT)wihchisacombinedeffortoftheDataCollectionHLTandPESA(PhysicsandEventSelectionArchitecture)subgroupswithintheATLASTDAQcollaboration.Twoimportantissues,alresdyoutlinedintheATLASHLT,DAQandDCSTechnicalProposal[1]willbehighlighted:thetreatmentoftheLVL2TriggerandEventFilterasaspectsofageneralHLTwithaviewtoeasiermigrationofalgorthmsbetweenthetwolevels;unificationoftheselectivedatacollectionforLVL2andEventBuilding.

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简介：Objective:ToidentifytheeffectofdyslipidemiaonauditoryfunctiondetectedbyPureToneAudiometry.Tocheckifdyslipidemiaworsensthehearinglevelindiabetics.Design:Thiswasacomparativestudywhere120subjectsbetweentheagegroupof20and50yearsunderwentpuretoneaudiometry,lipidprofileandbloodsugars.Group1consistedof30subjectswithtype2diabetesanddyslipidemia;Group2had30subjectswithisolateddiabetes;Group3had30withisolateddyslipidemiaandGroup4included30normalsubjectsascontrol.Results:Significanthearinglosswasseenonlyinthegroupwithisolateddiabetes(63%).Themostcommontypeofhearinglosswashighfrequencysensorineuralhearingloss.Whencomparisonwasmadebetweenthecombinationsofdifferentlipidprofiles,noassociationwasfoundtothelevelofhearing.Conclusions:Diabeticsaremorepronetohighfrequencyhearingloss.Alteredlipidprofilehasnoroleincausinghearingloss.

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简介：IncreasingICdensitiesnecessitatediagnosismethodologieswithenhanceddefectlocatingcapabilities.YetthecomputationaleffortexpendedinextractingdiagnosticinformationandthestringentstoragerequirementsconstitutemajorconcernsduetothetremendousnumberoffaultsintypicalICs.Inthispaper,weproposeanRT-leveldiagnosismethodologycapableofrespondingtothesechallenges.Intheproposedscheme,diagnosticinformationiscomputedonagroupedfaulteffectbasis,enhancingboththestorageandthecomputationalaspects.Thefaulteffectgroupingcriteriaareidentifiedbasedonamodulestructureanalysis,improvingthepropagationabilityofthediagnosticinformationthroughRTmodules.Experimentalresultsshowthattheproposedmethodologyprovidessuperiorspeed-upsandsignificantdiagnosticinformationcompressionatnosacrificeindiagnosticresolution,comparedtotheexistinggate-leveldiagnosisapproaches.

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简介：TheALICEexperiment[1]attheLargeHadronCollider(LHC)atCERNwilldetectupto20,000particlesinasinglePb-Pbeventresultinginadatarateof-75MByte/event,Theeventrateislimitedbythebandwidthofthedatastoragesystem.Higherratesarepossiblebyselectinginterestingeventsandsubevents(HighLeveltrigger)orcompressingthedataefficientlywithmodelingtechniques.Bothrequireafastparallelpatternrecognition.OnepossiblesolutiontoprocessthedetectordataatsuchratesisafarmofclusteredSMPnodes,basedonoff-the-shelfPCs,andconnectedbyahighbandwidt,lowlatencynetwork.

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