type II learning is intended to reduce possibiliti

Thesepatternsareimportantbecausemanyecosystemservicesrequirelandscapescalepolicyinterventions,yetpoliciestraditionallytargetonlyindividualfarmers(thusdeliveringfieldscaleorfarmscaleinterventions).►Thesepatternsarealsoimportantbecausetheyprovideaproxyindicatorfor(thestrengthof)existingneighbourhoodnetworks,throughwhichpoliciescanbecommunicatedmoreeffectivelyandfarmerscanbeenticedtojoinmorequickly(thusmakingthepolicymoreefficient).►Casestudyapplicationidentifiesspatio-temporalpatternsofuptakeindifferentpartsofScotland.Muchstrongerpatternsarefoundinmountainousareasandonsmallislands,whichisconsistentwiththeexpectationofstrongercommunitiesofplaceandneighbourhoodnetworksinmoreremoteplaces.103Technology-baseddesignandscalingforRTGsforspaceexplorationinthe100WrangeOriginalResearchArticleActaAstronautica,Volume68,Issues7-8,April-May2011,Pages873-882LeopoldSummerer,JeanPierreRoux,AlexeyPustovalov,ViacheslavGusev,NikolaiRybkinClosepreview|Relatedarticles|RelatedreferenceworkarticlesAbstract|Figures/Tables|ReferencesAbstractThispaperpresentstheresultsofastudyondesignconsiderationsfora100Wradioisotopethermo-electricgenerator(RTG).Specialemphasishasbeenputondesigningamodular,multi-purposesystemwithhighoverallTRLlevelsandmakingfulluseoftheextensiveRussianheritageinthedesignofradioisotopepowersystems.ThemodularapproachallowedinsightintothescalingofsuchRTGscoveringtheelectricpowerrangefrom50to200We(EoL).Theretainedconceptisbasedonamodularthermalblockstructure,aradiativeinner-RTGheattransferandusingatwo-stagethermo-electricconversionsystem.ArticleOutlinePurchase$31.501.Introduction1.1.Scopeandobjectives2.Methodology2.1.Administrativeandlegaldesignrequirements2.2.Safetydesignrequirements2.3.Physicaldesignrequirements3.Physicaldesignchoices3.1.Radioisotopeselection3.2.ChoiceofPuO24.Technicaldesignchoices4.1.Radioisotopefuelconsiderations4.2.Modularapproach4.3.He-ventedRHS4.4.ChoiceofstructuralmaterialforRHSshells4.5.RHUdesign4.6.Thermaltoelectricconversionsystem4.7.Thermalinsulation4.8.RTGoutercasingchoices4.9.CorrelationofRHUandTECstructureswithintheRTG4.10.FinalRTG-100Wdesign4.11.RTGscaling5.ConclusionsReferences104Semantic-basedinformationretrievalinsupportofconceptdesignOriginalResearchArticleAdvancedEngineeringInformatics,Volume25,Issue2,April2011,Pages131-146RossitzaSetchi,QiaoTang,IvanStankovClosepreview|Relatedarticles|RelatedreferenceworkarticlesAbstract|Figures/Tables|ReferencesPurchase$39.95AbstractThisresearchismotivatedbytherealisationthatsemantictechnologycanbeusedtodevelopcomputationaltoolsinsupportofdesigners’creativitybyfocusingontheinspirationalstageofdesign.Thepaperdescribesasemantic-basedimageretrievaltooldevelopedfortheneedsofconceptcarsdesignersfromtworenownedEuropeancompanies.Itiscreatedtohelpthemfindandinterpretsourcesofinspiration.Thecoreinnovationofthetoolisitsabilitytoprovideadegreeofdiversity,ambiguityanduncertaintyintheinformationgatheringandideagenerationprocess.Thetoolisbasedontheassumptionthatthereisasemanticlinkbetweentheimagesinawebpageandthetextaroundthem.Furthermore,itusestheideathatthemorefrequentlyatermoccursinadocumentandthefewerdocumentsitoccursin,themorerepresentativethistermisofthatdocument.Thenewcontributionislinkingthemostmeaningfulwordsinadocumentwithontologicalconcepts,andthenfindingthemostpowerfulsetofconceptsrepresentingthatdocumentandconsequentlytheimagesinit.Thisisbasedontheobservationthatmonosemicwords(withasinglemeaning)aremoredomain-orientedthanpolysemicones(thathavemultiplemeanings),andprovideagreateramountofdomaininformation.Thetooltagsimagesbyfirstprocessingallsignificantwordsinthetextaroundthem,extractingallkeywordsandkeyphrasesinit,rankingthemaccordingtotheirsignificance,andlinkingthemtoontologicalconcepts.Itgeneratesasetofconceptnumbersforeachtext,whichisthenusedtoretrieveinformationinaprocesscalledsemanticexpansion,whereakeywordqueryisalsoprocessedsemantically.TheproposedapproachisillustratedwithexamplesusingthetooldevelopedfortheneedsofStileBertoneandFiat,Italy,twooftheindustrialpartnersintheTRENDSprojectsponsoredbytheEuropeanCommunity.ArticleOutline1.Introduction2.Informationrequirementsofconceptdesigners3.State-of-the-artreview3.1.Content-basedimageretrieval3.2.Text-basedimageretrieval4.Semantic-basedimageretrieval4.1.Algorithm4.2.Ontologies4.3.Illustrativeexample4.4.Industrialimplementationandevaluation5.ConclusionsandfutureworkAcknowledgementsReferences105Snowcover,snowmelttimingandstreampowerintheWindRiverRange,WyomingOriginalResearchArticleGeomorphology,InPress,AcceptedManuscript,Availableonline27March2011DorothyK.Hall,JamesL.Foster,NicoloE.DiGirolamo,GeorgeA.RiggsClosepreview|Relatedarticles|RelatedreferenceworkarticlesAbstractAbstractEarlieronsetofspringtimeweather,includingearliersnowmelt,hasbeendocumentedinthewesternUnitedStatesoveratleastthelast50years.Becausethemajority(70%)ofthewatersupplyinthewesternU.S.comesfromsnowmelt,analysisofthedecliningspringsnowpack(andshrinkingglaciers)hasimportantimplicationsforthemanagementofstreamflow.ThePurchase$31.50amoun