Allocation of Resources to Innovation Transfer Projects within the Multinational Enterprise.docx
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Allocation of Resources to Innovation Transfer Projects within the Multinational Enterprise.docx
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AllocationofResourcestoInnovationTransferProjectswithintheMultinationalEnterprise
Greycomprehensivecorrelationemployedtodeterminetheproductioncharacteristics.►Greyclusteringassessmentadoptedtoassessthesystemfunctionalrequirements.►CasestudiesfocusingontheprocessenterprisesinsouthwestChinaareillustrated.
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Fabricationofoff-axisasphericsurfacesusingaslowtoolservo OriginalResearchArticle
InternationalJournalofMachineToolsandManufacture,Volume51,Issue5,May2011,Pages404-410
Z.Q.Yin,Y.F.Dai,S.Y.Li,C.L.Guan,G.P.Tie
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AbstractAbstract|Figures/TablesFigures/Tables|ReferencesReferences
Abstract
Off-axisasphericsurfaceshavebeenwidelyappliedinopticalsystems,butitisdifficulttofabricatethiskindofopticalsurfacewithhighquality.Slowtoolservoisoneofthemethodsforturningoff-axisasphericsurfaceson-axis,whichhavealargetravelrangewithoutanadditionaldevice.Thispaperpresentsatheoreticalandexperimentalinvestigationonthe3Dsurfacegenerationinsinglepointdiamondtuningusingslowtoolservo.Theformerrorofoff-axisparaboloidcausedbytoolcenteringerrorisanalyzedandverifiedbycuttingexperiment.
ArticleOutline
1.Introduction
2.Toolpathinslowtoolservocutting
2.1.Cuttingstrategies
2.2.Interpolationforslowtoolservo
2.3.Toolpathgeneration
3.3Dsurfacegeneration
4.Formerrorcausedbytoolcenteringerror
5.Experiments
6.Conclusions
Acknowledgements
References
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AServoSystemControlwithTime-varyingandNonlinearLoadConditionsUsingType-2TSKFuzzyNeuralSystem OriginalResearchArticle
AppliedSoftComputing,InPress,AcceptedManuscript,Availableonline4April2011
ErdalKayacan,YesimOniz,AyseCiselAras,OkyayKaynak,RahibAbiyev
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AbstractAbstract
Abstract
Atype-2Takagi-Sugeno-Kangfuzzyneuralsystemisproposedanditsparameterupdaterulesarederivedusingfuzzyclusteringandgradientlearningalgorithms.Theproposedtype-2fuzzyneuralsystemisusedforthecontrolandtheidentificationofareal-timeservosystem.Fuzzyc-meansclusteringalgorithmisusedtodeterminetheinitialplacesofthemembershipfunctionstoensurethatthegradientdescentalgorithmusedafterwardsconvergesinashortertime.Anumberofdifferentloadconditionsincludingnonlinearandtime-varyingonesareusedtoinvestigatetheperformanceoftheproposedcontrolalgorithm.Thecontrolstructurehastheabilitytoregulatetheservosystemwithreducedoscillationswhencomparedwiththeresultsofitstype-1counterpartaroundthesetpointsignalinthepresenceofloaddisturbances.
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14
Miseenplacedelaventilationautoasservie
MédecineduSommeil,Volume8,Issue1,January-March2011,Pages39-46
C.Philippe,F.Viau
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Résumé
Laventilationautoasservie(VAA)estunmodeventilatoirequiassocieuneaideinspiratoirevariableetunepressionexpiratoirefixeouautopilotée.Cetteaideinspiratoirevariecycleàcycleenfonctiondelaventilationmesuréechezlepatient.Elleaétéinitialementconçuepourcorrigerlarespirationpériodiquedespatientsprésentantuneinsuffisancecardiaque(IC)congestivestable,soustraitementmédicaloptimal.Aujourd’hui,sesindicationssesontélargiesauxSAScentrauxnormo-hypocapniques,mêmesileniveaudepreuvedesonefficacitéànormaliserl’indexd’apnées/hypopnéesestplusfaible.Lorsdel’instaurationd’uneVAAoudemodificationsdespressions,lespatientsinsuffisantscardiaques,etced’autantplusquel’ICestsévère,doiventbénéficierd’unesurveillancecliniqueettensionnelleparticulière.Commetouteventilation,lechoixdumasqueetlagestiondesfuitesdoiventêtreminutieux,lespressionsdedébutdetraitementdoiventpermettrelemeilleurconfortpossible.Lesmodificationsdesautresparamètresnécessitentuneanalysefinedestracéspoly(somno)graphiquesetdesdonnées« machine ».Lapersistanced’anomaliesobstructivesseragéréeparl’augmentationdelapressionexpiratoire.Pourcelled’anomaliescentrales,ilfautsavoirêtrepatient.Lesmodificationsdesbornesdel’aideinspiratoire,et/ouunemodificationdelafréquencerespiratoiredesécuritépeuventêtreproposées.
Summary
Adaptiveservo-ventilation(ASV)isanoveltherapythatprovidesinspiratorypressuresupportsuperimposedonfixedorautomaticpositiveexpiratorypressure.Thisinspiratorypressureismodifiedcyclebycycleaccordingtothepatient'sventilation.ItwasinitiallyconceivedtocorrecttheCheyne-Stokesrespirationinpatientswithstablecardiacfailureandreceivingoptimalmedicaltreatment.Today,indicationswidenedineucapnic/hypocapnicsleepcentralapnea,evenifthelevelofproofofitsefficiencytonormalizetheapnea/hypopneaindexisweaker.DuringtheinstitutionofanASVormodificationsofthepressures,HFpatients,anditespeciallyiftheHFissevere,havetobenefitfromaclinicalsupervisionandarterielpressuremonitoring.Asanyventilation,thechoiceofthemaskandthemanagementoftheleaksmustbemeticulous;thebeginningpressureshavetoallowthebestcomfort.Themodificationsoftheotherparametersrequireafineanalysisofpoly(somno)graphicdataandsoftware.Thepersistenceofobstructiveabnormalitieswillbemanagedbytheincreaseoftheexpiratorypressure.Forthecentralabnormalities,itisnecessarytobepatient.Themodificationsoftheinspiratorypressuresupportlimitsandoramodificationofthebackupratecanbeproposed.
ArticleOutline
Introduction
Définitions
Indications
Syndromed’apnéesdusommeilcentraldel’insuffisantcardiaque
Syndromed’apnéesdusommeilcentralchezunpatientprésentantdesantécédentsneurologiques
Syndromed’apnéesdusommeilcomplexes
Syndromed’apnéesdusommeilcentraldespatientsprenantdesopioïdes:
Syndromed’apnéesdusommeilcentralidiopathique
Algorithmesdesappareilsdeventilationautoasservie
AutoSetCS®(Resmed)
BiPAPautoSVAdvanced®
LaSOMNOventCR®
Miseenrouted’untraitementparventilationautoasservie
Conclusion
Conflitd’intérêt
References
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Governmentprogramscanimprovelocallabormarkets:
EvidencefromStateEnterpriseZones,FederalEmpowermentZonesandFederalEnterpriseCommunity OriginalResearchArticle
JournalofPublicEconomics,Volume95,Issues7-8,August2011,Pages779-797
JohnC.Ham,CharlesSwenson,Ayşeİmrohoroğlu,HeonjaeSong
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AbstractAbstract|Figures/TablesFigures/Tables|ReferencesReferences
Abstract
Federalandstategovernmentsspendwelloverabilliondollarsayearonprogramsthatencourageemploymentdevelopmentindisadvantagedlabormarketsthroughtheuseofsubsidiesandtaxcredits.InthispaperweuseanestimationapproachthatisvalidunderrelativelyweakassumptionstomeasuretheimpactofStateEnterpriseZones(ENTZs),FederalEmpowermentZones(EMPZs),andFederalEnterpriseCommunity(ENTC)programsonlocallabormarkets.Wefindthatallthreeprogramshavepositive,statisticallysignificant,impactsonlocallabormarketsintermsoftheunemploymentrate,thepovertyrate,thefractionwithwageandsalaryincome,andemployment.Further,theeffectsofEMPZandENTCdesignationareconsiderablylargerthantheimpactofENTZdesignation.Wefindthatourestimatesarerobusttoallowingforaregressiontothemeaneffect.Wealsofindthattherearepositive,butstatisticallyinsignificant,spillovereffectstoneighboringCensustractsofeachoftheseprograms.Thusourpositiveestimatesoftheseprogramimpactsdonotsimplyrepresentatransferfromthenearestnon-treatedCensustracttothetreatedCensustract.Ourresultsarenoteworthyforseveralreasons.First,ourstudyisthefirsttojointlylookatthesethreeprograms,thusallowingpolicymakerstocomparetheimpactsoftheseprograms.Second,ourpaper,alongwithaconcurrentstudybyNeumarkandKolko(2008),isthefirsttocarryouttheestimationaccountingforoverlapbetweentheprograms.Third,ourestimationstrategyisvalidunderweakerassumptionsthanthosemadeinmanypreviousstudies;weconsiderthreecomparisongroupsandletthedatadeterminetheappropriategroup.Fourth,inspiteofourconservativeestimationstrategy,bylookingatnationaleffectswithdisaggregateddata,weshowthatENTZdesignationgenerallyhasapositiveeffectonthelocallabormarket,whilemostpreviousresearchonENTZs,muchofwhichusedmoregeographicallyaggregateddatatolookatstate-specificeffects,didnotfindanysignificantimpacts.Fifth,wenotethatthereislittleornopreviousworkonENTCs.Overall,ourresultsstronglysupporttheefficacyoftheselabormarketinterventions.
ArticleOutline
1.Introduction
2.Abriefdescriptionofenterprisezones,empowermentzones,andenterprisecommunities
2.1.EnterpriseZones(ENTZs)
2.2.EmpowermentZones(EMPZs)andEnterpriseCommunities(ENTCs)
3.Econometricapproach
3.1.Overview
3.2.Ourbasespecification;usingthenearestNENTZasacomparisonforanENTZ
3.2.1.Estimatinganaveragenationaleffect
3.2.2.Estimatingstate-specificaverageeffects
3.3.Amorerestrictive,butpotentiallymoreefficient,estimator
3.4.TheHec
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