Indoor space cooling represents a large potential for solar energyWord文件下载.docx

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generatingstaticinformation

3.2.Generatingdynamicinformation

3.3.Learningmodule

4.Resultsandevaluation

4.1.Thedataset

4.2.Quantitiesofinterestinadmissioncontrol

4.3.Classifierperformance

4.4.Performanceinreal-timeprediction

5.Theeffectofdynamicserveractivation

6.Otherpotentialapplications

7.Conclusionsandfuturework

Acknowledgements

References

Vitae

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$31.50

912

Bankruptcypredictionwithneurallogicnetworksbymeansofgrammar-guidedgeneticprogramming 

OriginalResearchArticle

ExpertSystemswithApplications,Volume30,Issue3,April2006,Pages449-461

AthanasiosTsakonas,GeorgeDounias,MichaelDoumpos,ConstantinZopounidis

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AbstractAbstract|Figures/TablesFigures/Tables|ReferencesReferences

Abstract

Thepaperdemonstratestheefficientuseofhybridintelligentsystemsforsolvingtheclassificationproblemofbankruptcy.Theaimofthestudyistoobtainclassificationschemesabletopredictbusinessfailure.Previousattemptstoformefficientclassifiersforthesameproblemusingintelligentorstatisticaltechniquesarediscussedthroughoutthepaper.Theapplicationofneurallogicnetworksbymeansofgeneticprogrammingisproposed.Thisisanadvantageousapproachenablingtheinterpretationofthenetworkstructurethroughsetofexpertrules,whichisadesirablefeatureforfieldexperts.Theseevolutionaryneurallogicnetworksareconsistedofaninnovativehybridintelligentmethodology,bywhichevolutionaryprogrammingtechniquesareusedforobtainingthebestpossibletopologyofaneurallogicnetwork.Thegeneticprogrammingprocessisguidedusingacontext-freegrammarandindirectencodingoftheneurallogicnetworksintothegeneticprogrammingindividuals.Indicativeclassificationresultsarepresentedanddiscussedindetailintermsofboth,classificationaccuracyandsolutioninterpretability.

1.1.Theproblemofbankruptcyprediction

1.2.Backgroundandliteraturereview

2.Bankruptcydatadescription

3.Methodologicalissues

3.1.Neurallogicnetworks

3.2.AutomaticinterpretationoftheneurallogicnetworksintoPrologexpertrules

3.3.Geneticprogramming

4.Designandimplementationofthehybridintelligentsystem

4.1.Evolutionaryneurallogicnetworks

4.2.Datapreprocessingandsystemconfiguration

5.Resultsanddiscussion

6.Conclusionsandfurtherwork

$41.95

913

AnoverviewoftheSeaWiFSprojectandstrategiesforproducingaclimateresearchqualityglobaloceanbio-opticaltimeseries 

DeepSeaResearchPartII:

TopicalStudiesinOceanography,Volume51,Issues1-3,January-February2004,Pages5-42

CharlesR.McClain,GeneC.Feldman,StanfordB.Hooker

TheSea-viewingWideField-of-viewSensor（SeaWiFS）ProjectOfficewasformallyinitiatedattheNASAGoddardSpaceFlightCenterin1990.Sevenyearslater,thesensorwaslaunchedbyOrbitalSciencesCorporationunderadata-buycontracttoprovide5yearsofsciencequalitydataforglobaloceanbiogeochemistryresearch.Todate,theSeaWiFSprogramhasgreatlyexceededthemissiongoalsestablishedoveradecadeagointermsofdataquality,dataaccessibilityandusability,oceancommunityinfrastructuredevelopment,costefficiency,andcommunityservice.TheSeaWiFSProjectOfficeanditscollaboratorsinthescientificcommunityhavemadesubstantialcontributionsintheareasofsatellitecalibration,productvalidation,near-realtimedataaccess,fielddatacollection,protocoldevelopment,insituinstrumentationtechnology,operationaldatasystemdevelopment,anddesktoplevel-0tolevel-3processingsoftware.OneimportantaspectoftheSeaWiFSprogramisthehighlevelofsciencecommunitycooperationandparticipation.ThisarticlesummarizesthekeyactivitiesandapproachestheSeaWiFSProjectOfficepursuedtodefine,achieve,andmaintainthemissionobjectives.TheseachievementshaveenabledtheusercommunitytopublishalargeandgrowingvolumeofresearchsuchasthosecontributedtothisspecialvolumeofDeep-SeaResearch.Finally,someexamplesofmajorgeophysicalevents（oceanic,atmospheric,andterrestrial）capturedbySeaWiFSarepresentedtodemonstratetheversatilityofthesensor.

1.Introduction

2.Projectphilosophy,functions,andapproaches

2.1.Missionoperationsanddatacapture

2.2.Calibrationandvalidationprogram

2.2.1.SeaWiFSseatruthdataaccuracyconsiderationsandadvancements

2.2.2.Fieldinstrumentdevelopmentandevaluation

2.2.3.TheSeaWiFSprojectfieldprogram

2.3.Dataprocessingsystem

2.3.1.Systemrequirements

2.3.2.Systemdesign

2.3.3.Systemevolution

2.4.Datadistribution

2.5.Outreach

2.6.SeaWiFSreprocessingsanddataqualityimprovements

3.MajorgeophysicaleventscapturedbySeaWiFS

4.Summary

914

Productionofaromavolatilesinresponsetodeficitirrigationandtocroploadinrelationtofruitmaturityfor‘Braeburn’apple 

PostharvestBiologyandTechnology,Volume24,Issue1,January2002,Pages1-11

BussakornS.Mpelasoka,M.HosseinBehboudian

Aromavolatilesareimportantqualityattributesforapplesandthereisnotenoughinformationonhowtheirproductionisaffectedbydeficitirrigation（DI）orbycropload.Weinvestigatedeffectsofirrigation,cropload,andtheirinteractiononmaturity-relatedqualityattributes（aromavolatiles,totalsolublesolids（TSS）,titratableacidity（TA）,andfirmness）andmaturityattributes（internalethyleneconcentration,‘percentripeningfruit’,andstarchpatternindex）for‘Braeburn’apple.Multivariaterelationshipsbetweenthesetwosetsofattributeswerealsoexplored.Irrigationtreatmentswerecommerciallyirrigatedcontrol（CI）andDIappliedthroughouttheseason.Croploadtreatmentswerecommercialcropload（CCL）havingsixfruitspercm2oftrunkcross-sectionalareaandlightcropload（LCL）havingfour.Therewasnointeractionbetweenirrigationandcroploadonanyindividualqualityattributes.ControlandDIfruithadsimilarmaturityatharvestbutDIfruitbecamemoreadvancedinmaturityduringstorageat20 

°

Candaftercoldstorage.DIenhancedvolatileproductionduringripeningandaftercoldstorage.FirmnessandTSSalsoincreasedinDIfruitbuttheincreasedfirmnesswaslostduringsubsequentstorage.FirmnessandTSSincreasedinLCLinsomeoccasionsbutcroploadhadnoeffectonmaturityoraromavolatiles.FruitTAwasnotaffectedbyirrigationorcropload.Aromavolatilespoorlycorrelatedwitheachindividualmaturityattribute.Canonicalcorrelationanalysisshowedhighmultivariatecorrelationbetweenmaturityattributesandmaturity-relatedqualityattributeswithtwounderlyingdimensionsthatcharacterisedtheirrelationships.QualityenhancementinDIfruitwasrelated,inpart,totheadvancementinripening.

2.Materialsandmethods

2.1.Experimentalconditionsandtreatments

2.2.Measurementsofsoilandplantwaterstatus

2.3.Determinationoffruitquality

2.4.Statisticalanalysis

3.Resultsanddiscussion

3.1.Soilandplantwaterstatus

3.2.Fruitmaturityandmaturity-relatedquality

3.3.Relationshipsbetweenmaturityattributesandmaturity-relatedqualityattributes

4.Conclusions

915

PreparationandstructureofHGdP2O7·

3H2O 

MaterialsResearchBulletin,Volume36,Issues1-2,January2001,Pages365-373

F.Chehimi-Moumen,D.BenHassen-Chehimi,M.Ferid,M.Trabelsi-Ayadi

SinglecrystalstructureofHGdP2O7·

3H2Ohasbeensolved,forthefirsttime,using2344X-rayreflectionswithafinalRvalue:

0.029.Thissaltcrystallizesinthetriclinicsystem;

thespacegroupisP

withZ=2andthefollowingunit-celldimensions:

a=6.4585（4）B=6.976

（1）C=9.803

（1）Å

α=98.48

（1）β=99.439（8）γ=88.388（9）°

V=430.93（9）Å

3TheatomicarrangementofHGdP2O7·

3H2OisbuiltbypairsofHP2O73-anionsinterconnectedbystronghydrogenbondsandlocatedaroundtheinversioncenterat（0,0,1/2）.ThecohesionbetweentheseentitiesisprovidedbyGdO8polyhedra.

2.Exp