Position of chargedpolar amino acids affects the degree of their hydration.docx

Position of chargedpolar amino acids affects the degree of their hydration.docx

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Positionofchargedpolaraminoacidsaffectsthedegreeoftheirhydration

Lanemarkingdesignswithoptimisedusagesoftrafficlanestoserveapproachingorleavingtrafficisachieved.►Theoveralljunctioncapacityhasbeenimprovedwithbetterlaneusages.►Asymmetricalandcomplexlanemarkingpatternscanbeobtainedtofitindifferentjunctiongeometries.

156

Driverworkloadandeyeblinkduration  OriginalResearchArticle

TransportationResearchPartF:

TrafficPsychologyandBehaviour,Volume14,Issue3,May2011,Pages199-208

SimoneBenedetto,MarcoPedrotti,LucaMinin,ThierryBaccino,AlessandraRe,RobertoMontanari

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AbstractAbstract|Figures/TablesFigures/Tables|ReferencesReferences

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

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Researchhighlights

►In-vehicleinformationsystems（IVIS）impairdriverattentionandroadsafety.►DriverdistractionresearchneedsstandardizedmethodsliketheLaneChangeTest（LCT）.►WithintheLCTeyemovementmetricsdeservefurtherinvestigation.►WestudiedeyeblinkdurationduringdriverinteractionwithanIVIS.►EyeblinkdurationindexestheeffectofIVISusageondrivervisualworkload.

157

Psychologicaldeterminantsoffuelconsumptionofpurchasednewcars  OriginalResearchArticle

TransportationResearchPartF:

TrafficPsychologyandBehaviour,Volume14,Issue3,May2011,Pages229-239

AnjaPeters,HeinzGutscher,RolandW.Scholz

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Abstract

WithregardtoreducingfuelconsumptionandCO2emissionsofroadtransportconsumers’adoptionoffuel-efficientvehiclesiscrucial.However,facingtheongoingtrendofincreasingcarsizeandpower,fuelconsumptionisapparentlyoflesserimportancetomostbuyers.Forthedesignofeffectivemeasurestochangebehaviorandpromotefuel-efficientcars,psychologicalfactorsshouldbeconsidered.Drawingfrompsychologicalresearchonenvironmentalbehavior,weproposeamodelwhichintegratespsychologicalvariablestoexplainthepurchaseoffuel-efficientvehiclesbyprivateconsumers.Thismodelistestedwithsurveydatafrom302Swissrespondentswhosehouseholdshaveboughtanewcarsince2002.SEManalysesconfirmvalenceoflesspowerandsmallersize,andperceivedbehavioralcontrolasdirectpredictorsofthepurchaseofafuel-efficientvehicle.Problemawareness,symbolicmotives,andresponseefficacyinfluencetherespectivebehaviorindirectlyviaaffectingthedirectpredictors.Thedesign,implementationandevaluationofmeasuresaimedatchangingcarchoicebehaviorwithrespecttofuelconsumptionshouldaccountforthesefactors.

ArticleOutline

1.Introduction

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

References

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Researchhighlights

►Apsychologicalmodeltoexplainthepurchaseoffuel-efficientvehiclesistested.►Surveydatafrom302Swissrespondentswhohaveboughtanewcarsince2002isused.►Attitudinalaspectsandbehavioralcontroldirectlyinfluencepurchasebehavior.►Problemawareness,symbolicmotives,andresponseefficacyareindirectpredictors.

158

Predictinglocalpopulationdistributionsaroundacentralshelterbasedonapredationrisk-growthtrade-off  OriginalResearchArticle

EcologicalModelling,Volume222,Issue8,24April2011,Pages1448-1455

ZyBiesinger,BenjaminM.Bolker,WilliamJ.Lindberg

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AbstractAbstract|Figures/TablesFigures/Tables|ReferencesReferences

Abstract

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.

ArticleOutline

1.Introduction

2.Methods

2.1.Predationmortalityrisk

2.2.Foodacquisitionandgrowth

2.3.Habitatquality

2.4.Localpopulationdistribution

3.Results

3.1.Effectsofriskdilutionandforagingcompetition

3.1.1.bμ = bg = 0

3.1.2.bμ = bg > 0

3.1.3.bμ > bg ≥ 0

3.1.4.bg > bμ ≥ 0

3.2.Parametereffectsonpopulationdistribution

3.2.1.Strengthofforagingcompetition

3.2.2.Reactiondistance

3.2.3.Growingpopulationsintwolandscapes

4.Discussion

5.Conclusions

Acknowledgements

AppendixA.Supplementarydata

References

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Researchhighlights

►Wemodelpreyspace-usearoundacommonshelter.►Predationriskandforagingcompetitionsetrealizedhabitatqualityandspace-use.►Whencompetitionisstrongerthanriskdilutionpreyspendtimeawayfromshelter.►Preyreactiondistanceaffectslocalpopulationrangeinunexpectedways.►Range,density,andqualityrespondtogrowingnumbersdifferentlyintwolandscapes.

159

Theroleofagent-basedmodelsinwildlifeecologyandmanagement  ReviewArticle

EcologicalModelling,Volume222,Issue8,24April2011,Pages1544-1556

AdamJ.McLane,ChristinaSemeniuk,GregoryJ.McDermid,DanielleJ.Marceau

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Abstract

Conservationplanningofcriticalhabitatsforwildlifespeciesatriskisaprioritytopicthatrequirestheknowledgeofhowanimalsselectandusetheirhabitat,andhowtheyrespondtofuturedevelopmentalchangesintheirenvironment.Thispaperexplorestheroleofahabitat-modelingmethodologicalapproach,agent-basedmodeling,whichweadvocateasapromisi