简介：Thispaperinvestigatesthelead-lagrelationshipbetweenthestockindexfutures(knownasFKLI)anditsunderlyingindex,theKualaLumpurCompositeIndex(KLCI)intheemergingMalaysianmarket.Using15-secondintervaldata,cross-correlation,andthepartialadjustmentmodel,wefindabi-directionalasymmetriclead-lagrelationshipandthattheKLCI’sleadoverFKLIismuchstronger.TheevidencealsosuggeststhattheKLCIreturnsover-reacttoinformation,moresooncethintradingeffectsareconsidered.Overall,theevidencessuggestthattradersprefertoexploitstockspecificinformationintheunderlyingmarketdespitetheadvantagesoftradingtheindexfutures.

简介：Theq-Wienerindexofunicyclicgraphsaredeterminedinthiswork.Asanexampleofitsapplications,anexplicitexpressionofq-Wienerindexofcaterpillarcyclesispresented.

简介：Inthecontextofglobalwarming,Chinaisfacingwithincreasingclimaterisks.Itisimperativetodevelopquantitativeindicestoreflecttheclimateriskscausedbyextremeweather/climateeventsandadverseclimaticconditionsinassociationwithdifferentindustries.Basedontheobservationsat2288meteorologicalstationsinChinaandthemeteorologicaldisastersdata,asetofindicesaredevelopedtomeasureclimaterisksduetowater-logging,drought,hightemperature,cryogenicfreezing,andtyphoon.Astatisticalmethodisthenusedtoconstructanoverallclimateriskindex(CRI)forChinafromtheseindividualindices.Thereisagoodcorrespondencebetweentheseindicesandhistoricalclimaticconditions.TheCRI,theindexofwater-loggingbyrain,andthehightemperatureindexincreaseatarateof0.28,0.37,and0.65perdecade,respectively,from1961to2016.Thecryogenicfreezingindexiscloselyrelatedtochangesintheconsumerpriceindexforfood.Thehightemperatureindexiscorrelatedwiththeconsumptionofenergyandelectricity.Thecorrelationbetweentheyearlygrowthinclaimsonhouseholdpropertyinsuranceandthesumofthewater-loggingindexandthetyphoonindexinthesameyearisashighas0.70.BoththegrowthrateofclaimsonagriculturalinsuranceandtheannualgrowthrateofhospitalinpatientsarepositivelycorrelatedwiththeCRI.Theyear-on-yeargrowthinthenumberofdomestictouristsissignificantlynegativelycorrelatedwiththeCRIinthesameyear.MoreeffortsareneededtodevelopregionalCRIs.

简介：Inthispaper,aplasmonicsrefractiveindexsensorwiththesemiring-stubsystemcoupledtoabuswaveguideispro-posed.Thestructurecanachievewavelengthbandtunableandultra-sharpFanoresonance.Thecouplingmechanismbetweenthebrightmodeanddarkmodeisinvestigatedindetail,andapparentplasmoninducedtransparency(PIT)isrealizedontheresonancewavelength.Thefullwidthathalfmaximum(FWHM)oftheresonantwavelengthismodulatedbyadjustingthecoupleddistance.Thesensitivityandfigureofmerit(FOM)oftheproposedsensor'canreachupto600nm/RIUand120onthevisibleregion,respectively,andthisperformancecanbehelpfulfordesigningthephotonicintegratedcircuitandopticalcommunication,inaddition,itcanbeappliedforbio-sensing.

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简介：时间间隔经常与元组被联系在时间的关系代表他们的有效时间，在的地方重叠加入为询问的各种各样的类型是关键的。许多存在重叠基于象空铅树，B+-tree和间隔树那样的树结构加入算法使用索引。自从深路径遍历是不可避免的，这些算法通常不象数据分区或飞机打扫基于算法一样让高中央处理器花费，它使他们竞争。这份报纸建议有效重叠基于作为重叠间隔称为转换索引的一个新二层的扁平的索引加入算法(即，O2i索引)。记录间隔的结束点使用一个数组并且接近经由在第一层的二功能的间隔的嵌套的结构，和第二层使用转换了跟踪所有间隔的表令人满意接近的嵌套的结构。在新索引的帮助下，join算法仅仅访问must-be-scanned表并且跳过所有其它。真实、合成的数据集上的分析和实验证明建议算法象最先进的算法一样竞争。

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简介：High-frequencystocktrendpredictionusingmachinelearnershasraisedsubstantialinterestinliterature.Nevertheless,thereisnogoldstandardtoselecttheinputsforthelearners.Thispaperinvestigatestheapproachofadaptiveinputselection(AIS)forthetrendpredictionofhigh-frequencystockindexpriceandcomparesitwiththecommonlyuseddeterministicinputsetting(DIS)approach.TheDISapproachisimplementedthroughcomputationoftechnicalindicatorvaluesondeterministicperiodparameters.TheAISapproachselectsthemostsuitableindicatorsandtheirparametersforthetime-varyingdatasetusingfeatureselectionmethods.Twostate-of-the-artmachinelearners,supportvectormachine(SVM)andartificialneuralnetwork(ANN),areadoptedaslearningmodels.AccuracyandF-measureofSVMandANNmodelswithboththeapproachesarecomputedbasedonthehigh-frequencydataofCSI300index.TheresultssuggestthattheAISapproachusingt-statistics,informationgainandROCmethodscanachievebetterpredictionperformancethantheDISapproach.Also,theinvestmentperformanceevaluationshowsthattheAISapproachwiththesamethreefeatureselectionmethodsprovidessignificantlyhigherreturnsthantheDISapproach.

简介：这份报纸论述一个全面区域扩大预言索引方法为地震的短逼近的预言使用GNSS。从云南省基于连续GNSS观察数据，排水量域用处理软件和地球物理的域效果的精确数据在数据周期滑倒修理以后被检测模型修正。云南区域被划分成56个格子房间让排水量地插值获得一块更一致的排水量地和一紧张地变化时间系列。每个格子房间扩大时间系列的地震前反应被评估并且综合了提取一个短逼近的地震异例鉴定索引。结果证明这个索引为大小M5的地震显示了出现时间和hypocenter。十四地震精确地被预言，并且有五份假报告。这个索引能因此被用于地震的短逼近的预言。

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简介：Weexperimentallydemonstrateforthefirsttime,tothebestofourknowledge,anall-fiberpassivelymode-lockedlaseroperationbasedonthenonlinearmultimodeinterferenceofstep-indexmultimodefiber.Suchastructurecouplesthelightinandoutofthemultimodefiberviasingle-modefibers,anditsphysicalmechanismsforsaturableabsorptionhavebeenanalyzedtheoreticallybasedonthethird-ordernonlinearKerreffectofmultimodefiber.Usingthenonlinearmultimodeinterferencestructurewith48.8mmlengthstep-indexmultimodefiber,themodulationdepthhasbeenmeasuredtobe~5%.Thepassivelymode-lockedlaseroutputpulseshaveacentralwavelengthof1596.66nm,bandwidthof2.18nm,pulsewidthof~625fs,andfundamentalrepetitionrateof8.726MHz.Furthermore,theinfluenceoftotalcavitydispersionontheopticalspectrum,pulsewidth,andoutputpowerisinvestigatedsystematicallybyaddingdifferentlengthsofsingle-modefiberanddispersioncompensationfiberinthelasercavity.

简介：Background:LeafAreaIndex(LAI)isanimportantparameterusedinmonitoringandmodelingofforestecosystems.Theaimofthisstudywastoevaluateperformanceoftheartificialneuralnetwork(ANN)modelstopredicttheLAIbycomparingtheregressionanalysismodelsastheclassicalmethodinthesepureandeven-agedCrimeanpineforeststands.Methods:OnehundredeighttemporarysampleplotswerecollectedfromCrimeanpineforeststandstoestimatestandparameters.EachsampleplotwasimagedwithhemisphericalphotographstodetecttheLAI.ThepartialcorrelationanalysiswasusedtoassesstherelationshipsbetweenthestandLAIvaluesandstandparameters,andthemultivariatelinearregressionanalysiswasusedtopredicttheLAIfromstandparameters.DifferentartificialneuralnetworkmodelscomprisingdifferentnumberofneuronandtransferfunctionsweretrainedandusedtopredicttheLAIofforeststands.Results:ThecorrelationcoefficientsbetweenLAIandstandparameters(standnumberoftrees,basalarea,thequadraticmeandiameter,standdensityandstandage)weresignificantatthelevelof0.01.Thestandage,numberoftrees,siteindex,andbasalareawereindependentparametersinthemostsuccessfulregressionmodelpredictedLAIvaluesusingstandparameters(/?;adj=0.5431).AscorrespondingmethodtopredicttheinteractionsbetweenthestandLAIvaluesandstandparameters,theneuralnetworkarchitecturebasedontheRBF4-19-1withGaussianactivationfunctioninhiddenlayerandtheidentityactivationfunctioninoutputlayerperformedbetterinpredictingLAI(SSE(12.1040),MSE(0.1223),RM5E(0.3497),AIC(0.1040),BIC(-777310)andR2(0.6392))comparedtotheotherstudiedtechniques.Conclusion:TheANNoutperformedthemultivariateregressiontechniquesinpredictingLAIfromstandparameters.TheANNmodels,developedinthisstudy,mayaidinmakingforestmanagementplanninginstudyforeststands.

简介：采用Nature公布的2014、2015、2016年生命科学学科的自然指数作为数据来源,分别整理统计42所高校的论文计数指标、分数计数指标;在此基础上对比分析了这3年中国高校生命科学学科的科研产出情况,并对如何促进科研人员发表高质量论文,提高机构科研贡献提出了建议.