The Global Aviation Industry Dynamics GAID model captures the systemic interactionsWord文件下载.docx
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The Global Aviation Industry Dynamics GAID model captures the systemic interactionsWord文件下载.docx
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BasedondynamictriaxialtestatlowtemperatureofthefrozenclayfromtheBeiluhepermafrostsubgradealongtheQinghai–TibetRailway(QTR),residualdeformationlawsanddynamicsubsidencepredictionmodelofpermafrostsubgradeundertraintrafficwerestudied.First,timehistorycurvesofresidualstrainoffrozensoilareobtainedandanalyzedunderdifferenttemperatures,frequency,confiningpressureandmoisturecontent.Andconclusionscanbedrawnthattheaxialstrainrateisgreatlyaffectedbytheamplitudeofdynamicstress,aswellasincreaseswithdynamictostaticstressratioandtemperaturerising,whileitdecreaseswiththeraiseoffrequencyandmoisturecontent.Hereby,thepowerfunctionswereadoptedtofittherelationshipsofaxialdynamicstrainratevs.stressratio,temperature,frequency,andmoisturecontent,respectively.Simultaneously,thepermafrostdynamicsubsidencemechanismwasinterpretedrationallyandthevariationoffittingparameterswasdiscussed.Furthermore,thelong-termtrafficloadingsubsidencemodelwasestablishedthroughobservingtheaxisresidualstraintimehistoriesoffrozenspecimensunderthelongtimecyclicloadingandadoptingtheamendmentofvibratorynumberoftimes.Themodelcancomprehensivelyconsidertheeffectsofstressstate,temperature,moisturecontent,andconfiningpressureoffrozensoil,aswellasthevibratoryfrequencyandvibrationnumberoflongtimecyclicloading.Consequently,themodelcanbeappliedtothetrain-inducedsubsidenceforecastinvestigationofpermafrostsubgrade.Thepaperhasanimportantsignificanceforrationalsafetyevaluationonlong-termoperationofpermafrostregionsrailwaysuchasQTR.Meanwhile,theinvestigationprovidesbasicdataforthefurtherresearchondynamicdamageconstitutivemodeloffrozensoilundertraintrafficandthegradualimprovementofrailroadingcriterionincoldregions.
ArticleOutline
1.Introduction
2.Experimentalsection
2.1.Experimentalequipmentandmaterial
2.2.Experimentalprocedure
3.Definitionofbasicconcepts
3.1.Permafrostaccumulativedynamicresidualstrain
3.2.Growthrateofpermafrostaccumulativedynamicresidualstrain
3.3.Stressratio
4.Analysisofthepermafrostaccumulativedynamicresidualstrainratetestresults
4.1.Temperature
4.2.Moisturecontent
4.3.Frequency
4.4.Confiningstress
5.Permafrostsubsidencepredictionunderlongtimetraintraffic
6.Concludingremarks
Acknowledgements
References
Purchase
$31.50
952
Cryoprotectanttransportthrougharticularcartilageforlong-termstorage:
experimentalandmodelingstudies
OriginalResearchArticle
OsteoarthritisandCartilage,Volume16,Issue11,November2008,Pages1379-1386
I.N.Mukherjee,Y.Li,Y.C.Song,R.C.LongJr.,A.Sambanis
Summary
Objective
Long-termstorageofarticularcartilage(AC)remainschallengingduetopoorpost-thawviability.Aninitialsteptowardsaddressingthisissueischaracterizingcryoprotectant(CPA)transport,sinceensuringadequateCPAequilibrationthroughoutthetissueoffersprotectionduringcooling.ThisstudytakesasystematicapproachindeterminingCPAtransportratesthroughbovineACandusesthatinformationinmathematicalmodelstodetermineCPAequilibrationtimes.
Design
Diffusionofhighconcentrationsingle(6.9
Mdimethylsulfoxide(DMSO))andmulti-componentCPAsolutions(VS55,3.1
MDMSO
+
2.2
M1,2-propanediol(PD)
3.1
Mformamide(FM))wasmeasuredthroughACusing1Hnuclearmagneticresonance(NMR)imagingandlocalizedspectroscopy,respectively.Usingexperimentallycalculatedeffectivediffusivities,diffusionmodelsdescribingCPAtransportthroughthetissuematrixandacrosschondrocytemembraneswerecombinedtodesignaCPAadditionandremovalschemeforacartilageplugofclinicallyrelevantdimensions.
Results
1HNMRimagingandlocalizedspectroscopyexperimentssuggestedthatthepermeationofCPAsthroughAC(5
mmdiameter,5–10
mminthickness)tookontheorderof4
hforfullequilibrationat22°
C.ImagingclearlyshowedthepermeationofDMSOintocartilageovertimeandlocalizedspectroscopywasabletodistinguishthepermeationratesoftheindividualVS55componentsandwater.ExperimentallymeasureddiffusivityvalueswereusedinCPAaddition/removalsimulationswithacartilageplugofclinicallyrelevantdimensions(5
mmdiameter,2
mminthickness).Resultssuggestedamulti-stepapproachforaddingandremovinghighconcentrationCPAs,withtheadditionandremovaleachtakingapproximately2
htocomplete.
Conclusions
ThisstudyprovidesafoundationfordesigningCPAadditionandremovalprotocolsforeffectivelong-termstorageofcartilagetissueusinganovelapproachtomeasureCPApermeation.
Introduction
Materialsandmethods
Chemicals
Tissueisolation
MRimagingsetup,acquisitionandanalysis
MRspectroscopysetup,acquisitionandanalysis
Mathematicalmodelingandcalculations
Modelassumptions
CPAtransportacrosscellmembrane
CPAtransportthroughcartilageECM
DeterminationoftheeffectiveCPAdiffusivitythroughcartilagematrix
Bulksolutiondiffusivitycalculations
CPAtransportthroughmatrixandacrosscellmembrane
Results
1HNMRimagingandspectroscopy
Simulationsofcellvolumechangeandintracellularsoluteconcentration
Discussion
Conflictofinterest
953
Apracticaleco-environmentaldistributionnetworkplanningmodelincludingfuelcellsandnon-renewabledistributedenergyresources
RenewableEnergy,Volume36,Issue1,January2011,Pages179-188
AlirezaSoroudi,MehdiEhsan,HamidrezaZareipour
Thispaperpresentsalong-termdynamicmulti-objectiveplanningmodelfordistributionnetworkexpansionalongwithdistributedenergyoptions.Theproposedmodeloptimizestwoobjectives,namelycostsandemissionsanddeterminestheoptimalschemesofsizing,placementandspeciallythedynamics(i.e.,timing)ofinvestmentsondistributedgenerationunitsandnetworkreinforcementsovertheplanningperiod.Anefficienttwo-stageheuristicmethodisproposedtosolvetheformulatedplanningproblem.Theeffectivenessoftheproposedmodelisdemonstratedbyapplyingittoadistributionnetworkandcomparingthesimulationresultswithothermethodsandmodels.
2.Problemformulation
2.1.Assumptions
2.2.Constraints
2.2.1.Powerflowconstraints
2.2.2.OperatinglimitsofDGunits
2.2.3.Voltageprofile
2.2.4.Capacitylimitoffeedersandsubstation
2.3.Objectivefunctions
2.3.1.Totalcosts
2.3.2.Totalemissions
3.Theproposedsolutionmethod
3.1.Theproposedimmune-GA-basedtechniqueforfindingPareto
fronts
3.2.Theproposedtwo-stagesolutionalgorithmforsolvingthemulti-objectiveDG-planningproblem
3.2.1.StageI(findingtheParetooptimalfront)
3.2.2.StageII(selecting‘thebest’solution)
4.Simulationresults
4.1.Proposeddynamicintegratedplanning
4.2.Comparingwithotherplanningmodelsandmethods
5.Conclusion
AppendixA.Listofsymbolsandabbreviations
AppendixB.Paretooptimality
$35.95
954
Value-at-riskestimationwiththeoptimaldynamicbiofuelportfolio
EnergyEconomics,Volume33,Issue2,March2011,Pages264-272
Ting-HuanChang,Hsin-MeiSu,Chien-LiangChiu
Inthepast,petroleumcompaniesonlypaidattentiontohedgingthevariationinthecrudeoilpriceandvolatility.However,theyhavenowexpandedtheiranalysistoencompassrenewablesources,suchascornandsoybeans,underthecurrentlow-carbonbiofuelobligations.ThispaperemploysGARCH(1,1)andARJImodelstoestimatetheone-day-aheadValue-at-Risk(VaR)oftheoptimaldynamicbiofuelportfolio,whichconsistsofcrudeoil,cornandsoybeans.Theoptimalblendedstandardissubjecttotheduallimitationsofminimumproductioncostsandthelowestbiofuelusingrequirements.OurempiricalfindingsconfirmthattheARJImodelismoresuitablethantheGARCH(1,1)modelandfurthercapturesthediscontinuousjumpbehaviorfromthein-the-sampledata.Theresultsofout-of-sampleforecastsalsoarerepresentedthatourmodelsplayimportantrolesinVaRestimationandriskmanagementforbiofuelportfolio.Wethereforesuggestthatthepetroleumcompaniesshouldsimultaneouslypayattentiontojumpriskinhedgingmaterialcostsinthepricesofenergy-relatedcrops.
2.Datasourcesandcreationofoptimaldynamicportfolio
3.GARCHandARJImodel-basedVaRestimatesfordynamicportfolioandappraisalmethod
3.1.Econometricmethodology
3.2.Evaluationmethod
3.2.1.Binarylossfunction(BLF)
3.2.2.LRtestforunconditionalcoverage(LRuc)
3.2.3.LRtestforconditionalcov
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