Position of chargedpolar amino acids affects the degree of their hydrationWord文档下载推荐.docx
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Position of chargedpolar amino acids affects the degree of their hydrationWord文档下载推荐.docx
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Abstract
Theuseofstandardizedmethodsindriverdistractionresearchisessentialforcomparingresultsacrossstudies.Thisworkexaminedtheeffectsofin-vehicleinformationsystems(IVIS)usageoneyeblinksinasimulatedLaneChangeTest(LCT),asimpledrivingtaskspecificallydesignedbytheInternationalOrganizationforStandardization.FifteenparticipantsperformedtheLCTinadrivingsimulatorinbothsingle-anddual-taskconditions,thelattermanipulatedbyintroducinganIVIStaskinthecarcockpit.Resultssuggestthatblinkduration(BD),withrespecttoblinkrate(BR),isamoresensitiveandreliableindicatorofdrivervisualworkload.BesidesconsideringmeanBDvalues,adetailedanalysisrevealedthatthedistributionofBDfollowsaGaussian-likecurveinnormaldrivingconditions:
threedurationclasses(short,medium,long)wereextractedfromsuchdistribution,andchangeshappeningtoeachclasswereanalyzedwithinthedual-taskconditions.Shortandlongblinksreflect,respectively,theeffectsofvisualworkloadandtimeontask:
moreshortblinksoccurwithanIVISinteractionduringdriving,whilemorelongblinksariseastimespentdrivingincreases.Theseresultsmayhavepracticalimplicationsforsystemdesigninautomotive.
ArticleOutline
1.Introduction
2.Method
2.1.Participants
2.2.Apparatus
2.2.1.Drivingsimulation
2.2.2.Eye-tracking
2.2.3.Secondarytaskdisplaysettings
2.3.Procedure
2.3.1.Informationtoparticipants
2.3.2.Training
2.3.3.Primarytask:
LaneChangeTest(LCT)
2.3.4.Secondarytask:
IVIS
2.4.Experimentaldesign
2.4.1.Dependentvariables
2.4.1.1.Blinkrate
2.4.1.2.Blinkduration
2.4.1.3.AveragePupilSize(APS)
2.4.1.4.Reactiontime(LaneChangeDelay)
2.4.1.5.IVISperformance
2.4.1.6.NASA-TLXandRSMEscores
3.Results
3.1.Blinkduration
3.2.AveragePupilSize(APS)
3.3.Reactiontime(LaneChangeDelay)
3.4.IVISperformance
3.5.NASA-TLXandRSMEscores
4.Discussion
5.Conclusions
References
Purchase
$27.95
Researchhighlights
►In-vehicleinformationsystems(IVIS)impairdriverattentionandroadsafety.►DriverdistractionresearchneedsstandardizedmethodsliketheLaneChangeTest(LCT).►WithintheLCTeyemovementmetricsdeservefurtherinvestigation.►WestudiedeyeblinkdurationduringdriverinteractionwithanIVIS.►EyeblinkdurationindexestheeffectofIVISusageondrivervisualworkload.
157
Psychologicaldeterminantsoffuelconsumptionofpurchasednewcars
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AnjaPeters,HeinzGutscher,RolandW.Scholz
WithregardtoreducingfuelconsumptionandCO2emissionsofroadtransportconsumers’adoptionoffuel-efficientvehiclesiscrucial.However,facingtheongoingtrendofincreasingcarsizeandpower,fuelconsumptionisapparentlyoflesserimportancetomostbuyers.Forthedesignofeffectivemeasurestochangebehaviorandpromotefuel-efficientcars,psychologicalfactorsshouldbeconsidered.Drawingfrompsychologicalresearchonenvironmentalbehavior,weproposeamodelwhichintegratespsychologicalvariablestoexplainthepurchaseoffuel-efficientvehiclesbyprivateconsumers.Thismodelistestedwithsurveydatafrom302Swissrespondentswhosehouseholdshaveboughtanewcarsince2002.SEManalysesconfirmvalenceoflesspowerandsmallersize,andperceivedbehavioralcontrolasdirectpredictorsofthepurchaseofafuel-efficientvehicle.Problemawareness,symbolicmotives,andresponseefficacyinfluencetherespectivebehaviorindirectlyviaaffectingthedirectpredictors.Thedesign,implementationandevaluationofmeasuresaimedatchangingcarchoicebehaviorwithrespecttofuelconsumptionshouldaccountforthesefactors.
2.Factorsinfluencingenvironmentalbehaviorwithspecialregardtocarpurchase
3.Method
3.1.Participants
3.2.Vehicles
3.3.Questionnaire
3.3.1.CO2emissionsofvehiclesinpossession
3.3.2.Psychologicalconstructs
3.3.3.Socio-demographicvariables
3.4.Analyses
4.Results
4.1.Descriptiveresultsforthelatentconstructs
4.1.1.Latentconstructs
4.1.2.Attitudinalratings
4.1.3.CO2emissionsofrecentlyboughtnewvehicles
4.2.Testofthemeasurementmodels
4.2.1.Psychologicalvariables
4.3.Structuralequationmodelinganalyses
5.Discussionandconclusion
Acknowledgements
►Apsychologicalmodeltoexplainthepurchaseoffuel-efficientvehiclesistested.►Surveydatafrom302Swissrespondentswhohaveboughtanewcarsince2002isused.►Attitudinalaspectsandbehavioralcontroldirectlyinfluencepurchasebehavior.►Problemawareness,symbolicmotives,andresponseefficacyareindirectpredictors.
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EcologicalModelling,Volume222,Issue8,24April2011,Pages1448-1455
ZyBiesinger,BenjaminM.Bolker,WilliamJ.Lindberg
Supplementarycontent
Animalsfacetrade-offsbetweenpredationriskandforagingsuccessdependingontheirlocationinthelandscape;
forexample,individualsthatremainnearacommonsheltermaybesafefrompredationbutincurstrongercompetitionforresources.Despitealongtraditionoftheoreticalexplorationoftherelationshipsamongforagingsuccess,conspecificcompetition,predationrisk,andpopulationdistributioninaheterogeneousenvironment,thescenariowedescribeherehasnotbeenexploredtheoretically.Weconstructamodelofhabitatuserulestopredictthedistributionofalocalpopulation(preysharingacommonshelterandforagingacrosssurroundinghabitats).Ourmodeldescribesrealizedhabitatqualityasaratioofdensity-andlocation-dependentmortalitytodensity-dependentgrowth.Weexplorehowthepreydistributionaroundashelterisexpectedtochangeastheparametersgoverningthestrengthofdensitydependence,landscapecharacteristics,andlocalabundancevary.Withintherangeofparameterswherepreyspendsometimeawayfromshelterbutremainsite-attached,thepreydensitydecreasesawayfromshelter.Asthedistanceatwhichpreyreacttopredatorsincreases,thepopulationrangegenerallyincreases.Atintermediatereactiondistances,however,increasesinthereactiondistanceleadtodecreasesinthemaximumforagingdistancebecauseofincreasedevennessinthepopulationdistribution.Astotalabundanceincreases,thepopulationrangeincreases,averagepopulationdensityincreases,andrealizedqualitydecreases.Themagnitudeofthesechangesdiffersin,forexample,‘high-’and‘low-visibility’landscapeswherepreycandetectpredatorsatdifferentdistances.
2.Methods
2.1.Predationmortalityrisk
2.2.Foodacquisitionandgrowth
2.3.Habitatquality
2.4.Localpopulationdistribution
3.1.Effectsofriskdilutionandforagingcompetition
3.1.1.bμ
=
bg
3.1.2.bμ
>
3.1.3.bμ
≥
3.1.4.bg
bμ
3.2.Parametereffectsonpopulationdistribution
3.2.1.Strengthofforagingcompetition
3.2.2.Reactiondistance
3.2.3.Growingpopulationsintwolandscapes
AppendixA.Supplementarydata
$31.50
►Wemodelpreyspace-usearoundacommonshelter.►Predationriskandforagingcompetitionsetrealizedhabitatqualityandspace-use.►Whencompetitionisstrongerthanriskdilutionpreyspendtimeawayfromshelter.►Preyreactiondistanceaffectslocalpopulationrangeinunexpectedways.►Range,density,andqualityrespondtogrowingnumbersdifferentlyintwolandscapes.
159
Theroleofagent-basedmodelsinwildlifeecologyandmanagement
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AdamJ.McLane,ChristinaSemeniuk,GregoryJ.McDermid,DanielleJ.Marceau
Conservationplanningofcriticalhabitatsforwildlifespeciesatriskisaprioritytopicthatrequirestheknowledgeofhowanimalsselectandusetheirhabitat,andhowtheyrespondtofuturedevelopmentalchangesintheirenvironment.Thispaperexplorestheroleofahabitat-modelingmethodologicalapproach,agent-basedmodeling,whichweadvocateasapromisi
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