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Researchhighlights
►Demonstrationofinfrastructureforurgentemergencyresponsedecisionsupport.►Asimulationmodelinfersincidentstatethatisinterpretedbyknowledgereasoning.►Densesensornetworksprovidelivedataforsteeringsimulationsinrealtime.►TheintegrationofGridandHPCprovidesrequisitecomputationalpower.►AItechniquesrationalizeandpresentcomplexsimulationresultsinaconcisemanner.
287
Onthenatureofsupportincomputer-supportedcollaborativelearningusinggStudy–January17,2009
ComputersinHumanBehavior,Volume26,Issue5,September2010,Pages835-839
PhilipC.Abrami
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AbstractAbstract|ReferencesReferences
Abstract
TheauthorsofthearticlesinthisspecialissueofComputersinHumanBehaviorexplorethenatureofsupportingStudy,acomputer-supportedcollaborativelearning(CSCL)environment,especiallyfromtheperspectiveofthetheoryofself-regulation[e.g.,Zimmerman,B.J.(2000).Attainmentofself-regulation:
Asocialcognitiveperspective.InM.Boekaerts,P.Pintrich,&
M.Zeidner(Eds.),Handbookofself-regulation,researchandapplications(pp.13–39).Orlando,FL:
AcademicPress].Tocommentcriticallyonthesystematicandcomprehensiveresearchthiscollectionofarticlesrepresentsisadauntingtask.Therefore,Iwanttobeginbyinsuringthatthereaderhastheappropriateimpressionofthequalityandimportanceofthecollectionofstudiesandthetoolitself.
ArticleOutline
1.Commentsonthearticles
2.Commentsonlearnerneedsandwants
3.Concludingsuggestions
References
$19.95
288
Optimisationbaseddesignofadistrictenergysystemforaneco-townintheUnitedKingdom
Energy,Volume36,Issue2,February2011,Pages1292-1308
C.Weber,N.Shah
AbstractAbstract|Figures/TablesFigures/Tables|ReferencesReferences
ThereductionofCO2emissionslinkedwithhumanactivities(mainlyenergyservicesandtransport),togetherwiththeincreaseduseofrenewableenergies,remainhighprioritiesonvariouspoliticalagendas.However,consideringtheincreasedconsumptionofenergyservices(especiallyelectricity),andthestochasticnatureofsomeofthemostpromisingrenewableenergies(windforinstance),thechallengeistofindtheoptimalmixoftechnologiesthatwillprovidetheenergyservices,withoutincreasingtheCO2emissions,butnonethelessensuringreliabilityofsupply.ThefocusofthispaperistopresenttheDESDOPtool,basedonmixedintegerlinearoptimisationtechnics,thathelpsgivinginsightintheoptimalmixoftechnologiesthatwillsimultaneouslyhelpdecreasetheemissionswhileatthesametimeguaranteeresilienceofsupply.Theresultsshowthatwhileitisnotyetpossibletoavoidelectricityfromthegridcompletely(hencenuclearorfossilfuel),CO2reductionsupto20%,atnoextracostscomparedtothebusiness-as-usualcase,areeasilyachievable.
Nomenclature
1.Introduction
2.Descriptionofthetool
2.1.Objectivefunction
2.2.Costfunctions
2.3.Energybalanceateachnode
2.4.Locationofthecentralisedtechnologies
2.5.Energybalanceattheplantnode
2.6.Energycascade
2.7.Networkconfiguration
2.8.Designsizeofthedistributedtechnologies
2.9.Designsizeofthecentralisedtechnologies
2.10.Partloadoperationofthecentralisedtechnologies
3.Modelsofthetechnologies
3.1.Heatpump
3.2.Combinedheatandpower
3.3.Windturbines
3.4.PVcells
3.5.Solarthermalcollectors
3.6.Boilers
4.Descriptionofthetestcase
5.Assumptions
6.Results
6.1.RestrictionsonCO2emissions
6.2.PVpricedecrease
6.3.Stochasticwindconditionsandlesswind
6.4.Higherheatingdemands
6.5.Electricitystorage
7.Conclusionsandfuturework
Acknowledgements
Appendix.Consumptionprofiles
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►Mixedintegerlinearoptimisationtechniquesareapowerfultooltodesignandoptimisedistrictenergysystems.►Integratedenergyconversionsystems(especiallycombinationofCHPsandheat-pumps)allowCO2reductionsforenergyservicesofatleast20%atnoextra-costscomparedtobusiness-as-usual(gridandboiler).►Whilethegrid(hencenuclearand/orfossilfuels)cannotbeavoidedaslongaselectricitystoragedoesn’tcomeofage,thecriticismofanti-windlobbyistsregardingtheineffectivenessofwindpowercouldnotbeverified.
289
Anempiricalstudyofinstructoradoptionofweb-basedlearningsystems
Computers&
Education,Volume53,Issue3,November2009,Pages761-774
Wei-TsongWang,Chun-ChiehWang
Foryears,web-basedlearningsystemshavebeenwidelyemployedinbotheducationalandnon-educationalinstitutions.Althoughweb-basedlearningsystemsareemergingasausefultoolforfacilitatingteachingandlearningactivities,thenumberofusersisnotincreasingasfastasexpected.Thisstudydevelopsanintegratedmodelofinstructoradoptionofweb-basedlearningsystemsbyincorporatingexistingliteratureandmultipleempiricallyverifiedtheories,includingthetechnologyacceptancemodelandDeLoneandMcLean’sinformationsystemsuccessmodel.Surveydatacollectedfrom268universityinstructorswereexaminedusingstructuralequationmodelingtoverifytheproposedtheoreticalmodel.Theresearchresultsfurtherilluminatethefactorsthatexplainandpredicttheinstructoradoptionofweb-basedlearningsystems.Implicationsofthisstudyarealsodiscussed.
2.Literaturereview
2.1.Web-basedlearningsystems
2.2.Instructors’adoptionofweb-basedlearningsystems
2.3.Userintentiontheory
2.4.Informationsystemsuccessmodel
3.Researchmodelandhypotheses
3.1.Overviewoftheproposedresearchmodel
3.2.Theinformationsystemdimension
3.3.Thepsychologicaldimension
3.4.Theuserbehaviordimension
4.Researchmethod
4.1.Developmentofinstruments
4.2.Datacollection
4.3.Demographicsanddescriptivestatistics
5.Dataanalysisandresults
5.1.Measurementmodel
5.2.Structuralmodel
6.Discussion
7.Conclusion
AppendixA.Appendix
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290
SupportingmobilityinanIMS-basedP2PIPTVservice:
Aproactivecontexttransfermechanism
ComputerCommunications,Volume33,Issue14,1September2010,Pages1736-1751
IvanVidal,JaimeGarcia-Reinoso,AntoniodelaOliva,AlexBikfalvi,IgnacioSoto
Inrecentyears,IPTVhasreceivedanincreasingamountofinterestfromtheindustry,commercialprovidersandtheresearchcommunity,alike.Inthiscontext,standardizationbodies,suchasETSIandITU-T,arespecifyingthearchitectureofIPTVsystemsbasedonIPmulticast.AninterestingalternativetosupporttheIPTVservicedeliveryreliesonthePeer-to-Peer(P2P)paradigmtodistributeandpushthestreamingefforttowardsthenetworkedge.However,whileP2PIPTVwasstudiedinfixedaccesstechnologies,therehasbeenlittleattentionpaidtotheimplicationsarisinginmobileenvironments.Oneoftheseinvolvestheservicehandoverwhentheusermovestoadifferentnetwork.ByanalyzingpreviousworkfromtheperspectiveofanIPTVservice,weconcludedthataproactiveapproachisnecessaryforthehandlingofinter-networkhandovers.Inthispaper,weproposeanewgeneralhandovermechanismfortheIPMultimediaSubsystem(IMS),whilestudyingitsapplicabilitytoaP2PIPTVservice.Oursolution,calledproactivecontexttransferservice,incorporatestheexistingIEEE802.21technologyinordertominimizethehandoverdelay.Theproposalisvalidatedbycomparingitagainstsolutionsderivedfrompreviouswork.
2.BackgroundontheIMS-basedP2PIPTVservice
2.1.TheIPMultimediaSubsystem
2.2.P2PstreaminginIMS
3.EnablingseamlessmobilityintheP2PIPTVservice
3.1.BufferingpacketswhenroaminginIMS
3.2.AlternativesforUEmobilityinanIMS-basedIPTVservice
3.2.1.SIPmobility
3.2.2.OptimisedSIPmobility
3.2.3.MobileIPandIMS
4.Proactivecontexttransferservice
4.1.Initializingthecontexttransferservice
4.2.Transferringthecontext
5.DelayanalysisformobilestreaminginIMS
5.1.SIPmobilitydelay
5.2.SIPcontexttransfer
5.3.MobileIP
5.3.1.P-CSCFinthehomenetwork
5.3.2.P-CSCFinthevisitednetwork
5.4.PCTSAS
5.4.1.DelaywithoutMIP
5.4.2.DelaywithMIP
5.5.Summaryofthedelays
5.6.Recoveryphaseduration
6.Conclusions
291
Process,practiceandpriorities–keylessonsl
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