high performance catalysts for CO oxidation using databased modeling.docx
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high performance catalysts for CO oxidation using databased modeling.docx
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highperformancecatalystsforCOoxidationusingdatabasedmodeling
mobilityintheMANETs’topologyandoverallconectivity.►Thisisdonethroughtheperspectiveofcomplexnetworksandtheirproperties.►BesidesgeneralMANETs,wealsoinvestigatebothurbanandhighwayVANETS.►Mobilityactsasarewiringprocessshapingstaticlatticesintomobilesmallworlds.
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Apathanalyticmodelandmeasurementofthebusinessvalueofe-government:
Aninternationalperspective OriginalResearchArticle
InternationalJournalofInformationManagement,Volume28,Issue6,December2008,Pages524-535
MasoodAbdullaBadri,KhaledAlshare
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Abstract
Thepapertestsamodelofmeasuringthebusinessvalueofe-government.ItprovidesresultsofitsapplicationtobusinessfirmsoperatinginDubaiandusingitsgovernmentelectronicservices(e-government).Thedimensionsofthee-governmentbusinessvaluemodelincludeinformationtechnology(IT)capability,searchandtransaction-orientedusesofe-government,intelligencegeneration,newbusinessdevelopment,timesavings,andfirmprofitability.Thedimensionsofe-governmentuseande-governmentbenefitsonfirms’profitabilityaretested.Thestudyusesself-reporteddataandisbasedonasampleof1859firmsoperatinginDubai.UsingLISREL,weemployedconfirmatoryfactoranalysistoassessthefactorstructureofeachofourlatentconstructs.Pathanalysiswithcompositevariablesisusedtotestthehypothesizedrelationshipsamongtheconstructs.Severalrivalpathmodelsaretestedbycomparingthegoodness-of-fitofeach.Theresultsconfirmthatfirm'sITcapabilitiesarepositivelyanddirectlyassociatedwiththeuseofe-governmentservices,enhancedfirmintelligencegeneration,andfirmprofitability.ITcapabilitiesarealsoassociatedindirectlywiththefirm'stimesavingsfrome-governmentuse.Otherresultsshowthattheuseofe-government,searchortransaction-oriented,ispositivelyrelatedtoenhancedintelligencegeneration,newbusinessdevelopment,andtimesavings.Thepaperprovidesempiricalevidenceaboutthepositiveeffectofuseofe-governmentservicesontheperformanceoffirms.Resultsalsosupportthepositiveeffectofnewbusinessgeneratedfrome-governmentuseandtimesavings.Therelationshipbetweenuseofe-governmentandprofitabilityisalsomediatedbyothervariables.Ingeneral,meanscoresshowthattheaveragelevelofuseofe-governmentinoursamplewasstilllow.ThestudystronglyimpliesthatfirmsinDubaishouldusee-governmentasaninformationsourcetoenhancetheirmarketintelligenceandbuildrevenues.Inaddition,ITsuppliersshouldemphasizee-governmentservicesthatlinkfirmstocustomersandcollaborators,andfacilitateaccesstokeyinformationresources.Governmentagenciesshouldenhancetheire-governmentinformationservicestoimprovebusinessperformance.Thestudyprovidessomepreliminaryevidenceaboutthecriteriathatbusinessesusetoevaluatetheiradoptionofe-governmentservices.Thiscouldbevaluableforgovernmentsindesigningandimplementingtheire-governmentservices.ThefindingsmighthavepolicyimplicationsfortheimplementationofDubaie-governmentservices.
ArticleOutline
1.Introduction
2.Literaturereview,conceptualfoundation,andhypotheses
2.1.Informationtechnologycapabilityande-governmentuse
2.2.E-governmentuseandfirm'srevenueexpansion
2.3.E-governmentuseandcostreduction
2.4.Informationtechnologycapabilityandfirm'sintelligencegeneration
2.5.Informationtechnologycapabilityandprofitability
2.6.Firm'srevenueexpansionandprofitability
2.7.Firm'srevenueexpansionandcostreduction
2.8.Firm'scostreductionandprofitability
2.9.BusinessfirmsinDubaiandtheeffectofe-government
3.Studymethods
3.1.Samplinganddatacollectionprocedures
3.2.Developmentofmeasures
3.2.1.ITcapability
3.2.2.E-governmentuse
3.2.3.Intelligencegeneration
3.2.4.Newbusinessdevelopment
3.2.5.Costreduction(timesavings)
3.2.6.Firm'sprofitability
3.3.Effectofvariablesoutsidethestructuralmodel
3.4.Analysis
3.5.Confirmatoryfactoranalysis
4.Results
4.1.ITcapabilityande-governmentuse
4.2.E-governmentuseandrevenueexpansion
4.3.E-governmentuseandcostreduction
4.4.Informationtechnologycapabilityandfirm'sintelligencegeneration
4.5.Informationtechnologycapabilityandfirm'sprofitability
4.6.Revenueexpansionandprofitability
4.7.Revenueexpansionandcostreduction
4.8.Timesavingsfrome-governmentandprofitability
4.9.DescriptivefindingsofbusinessresultsofDubaie-government
5.Discussion
6.Implicationsandconclusions
Appendix.Constructs,items,andthecorrespondingmeansandstandarddeviations
References
Vitae
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IntegratedRF-opticalTT&Cforinterplanetarytelecomms OriginalResearchArticle
ActaAstronautica,Volume65,Issues11-12,December2009,Pages1772-1782
T.Dreischer,M.Tuechler,K.Kudielka,G.Baister
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Abstract
Sciencereturnandhighbandwidthcommunicationsareoneofthekeyissuestosupporttheforeseenendeavoursonnextgenerationmissions[J.L.Gerner,Telemetry,trackingandcommandofsatellites—aperspective,TT&C2004Workshop,7–9September2004].Interplanetarytelecommunicationsystemsarerequiredthatsupporttheforeseenendeavours.Giventhesameconstraintsintermsofmass,powerandvolumealasercommunicationsterminalcanofferanincreaseintelemetrybandwidthoverclassicalRFtechnologyallowingforavarietyofnewoptions,specificallytomissionsthatrequirelargedistances,suchastotheMoon,toliberationpointsL1andL2,ultimatelyaimingatdeepspacemissions.Anincreaseintelemetrydatarateallowsthemissiontoconsidertheprocessingofrawscientificdatatotakeplaceonground,makinguseoflatesttechnologyfurtherdevelopedduringthecruisephaseoftheprobe,ratherthanapplyingdatapre-processingon-boardthesatellite.Enhancedsensingtechniquesthatgeneratemoresciencedatareturncouldbeusedandaccesstodataduringflightcouldbefaster.Resultsofon-goingactivitieswillbepresented,comprisingPPMlasercommunicationsandadvancedtrackingconcepts.AnoverviewwillbegivenofthesystemconceptforanintegratedRF-opticalTT&Ctransponder.Resultswillbeshownfromhardwaretestsoncommunicationsperformanceininter-islandtestcampaigns.
ArticleOutline
Nomenclature
1.Introduction
2.SystemconceptforRF-opticalTT&C
3.FieldtestresultsfromESAprojectDOLCE
3.1.DOLCEmissionscenario
3.2.DOLCETT&Crequirements
3.3.OpticalbeamsteeringconceptforDOLCE
3.4.Lasercommsystemforliberationpoints
3.5.DOLCEtestresults
4.MissionexampleMarssamplereturn
5.MSRsubsystemrequirementsforopticalTM
5.1.Systematicapproachtoon-boardopticaltelemetrytransponderusingexistingtechnologies
5.2.OpticalbeamsteeringconceptforMSR
5.3.LasercomsystemparametersforMSR
5.4.Atmosphericchannelcharacteristics
5.5.Opticalbackgroundconditions
5.6.Accessstatistics—highcoverageviasitediversitystrategy
5.7.OTMperformancesummaryforMSR
5.8.PreliminaryintegratedRF-opticalTT&Carchitecture
6.Conclusions
Acknowledgements
References
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Integratingwebminingandneuralnetworkforpersonalizede-commerceautomaticservice OriginalResearchArticle
ExpertSystemswithApplications,Volume37,Issue4,April2010,Pages2898-2910
Pao-HuaChou,Pi-HsiangLi,Kuang-KuChen,Menq-JiunWu
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Abstract
Electroniccommerce(EC)hasbecomeatrendintheworldnowadays.However,mostresearchesneglectafundamentalissue–theuser’sproduct-specificknowledgeonwhichtheusefulintelligentsystemsarebased.Thisresearchemploystheuser’sproduct-specificknowledgeandminehis/herinteriordesireonappropriatetargetproductsasapartofpersonalizationprocesstoconstructtheoverallECstrategyforbusinesses.
Thispaperillustratesanovelwebusageminingapproach,basedonthesequenceminingtechniqueappliedtouser’snavigationbehaviour,todiscoverpatternsinthenavigationofwebsites.Threecriticalcontributionsaremadeinthispaper:
(1)usingthefootstepgraphtovisualizetheuser’sclick-streamdataandanyinterestingpatterncanbedetectedmoreeasilyandquickly;
(2)illustratinganovelsequenceminingapproachtoidentifypre-designatedusernavigationpatternsautomaticallyandintegratesback-propagationnetwork(BPN)modelsmoothly;and(3)applyingtheempiricalresearchtoindicatethattheproposedapproachcanpredictandcategorizetheusers’navigationbehaviourwithhighaccuracy.
ArticleOutline
1.Introduction
2.Preliminary
2.1.Marketingtechniques
2.1.1.Marketingstrategies
2.1.2.Productknowledge
2.2.Webmining
2.3.Artificialneuralnetwork
3.Researchmethodology
4.Experimentresultsandperformanceevaluation
4.1.Datadescription
4.2.Webmining
4.2.1.Clickstreamdatapre-processing
4.2.2.Users’navigationroutetransformation
4.2.3.Users’navigationroutesegmentation
4.2.4.Users’navigationpatterndetectionanddefinition
4.2.5.Webminingexample
4.3.BPNmodeltrainingandperformanceevaluation
4.4.Interestingpatternstorecomme
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