理工类英语学术论文写作教程 课件 第5-8章 Writing the Results- Writing the Abstract

Chapter5

WritingtheResults

理工类英语学术论文写作教程AcademicWritingforStudentsofScienceandEngineeringContents01Pre-assessment02ReadingandAnalyzing03LanguageStrategy04WritingSkills05GenerativeAIEmpowerment06Summary&Post-assessmentPre-assessmentPART01PartIPre-assessmentUsethechecklistbelowtoassessyourknowledgeandskillsinthischapter.1.IknowthepurposeandsignificanceoftheResultssectionofaresearchpaper.①StronglyDisagree ②Disagree ③Neutral ④Agree ⑤StronglyAgree2.Iknowhowtousevariousvisualelementseffectively.①StronglyDisagree ②Disagree ③Neutral ④Agree ⑤StronglyAgree3.Icandifferentiatebetweenimportantandunimportantdata.①StronglyDisagree ②Disagree ③Neutral ④Agree ⑤StronglyAgree4.IknowthecommonstructureoftheResultssection.①StronglyDisagree ②Disagree ③Neutral ④Agree ⑤StronglyAgree5.Iknowthatalltables,charts,andfigures,etc.,shouldbecorrectlylabeledandreferredtointhetext.①StronglyDisagree ②Disagree ③Neutral ④Agree ⑤StronglyAgree6.Iknowhowtowriteastrongfigurecaption.①StronglyDisagree ②Disagree ③Neutral ④Agree ⑤StronglyAgree7.TheResultssectionshouldbewritteninanobjective,logical,andconcisemanner.①StronglyDisagree ②Disagree ③Neutral ④Agree ⑤StronglyAgree8.IknowwhattensesshouldbeusedintheResultssection.①StronglyDisagree ②Disagree ③Neutral ④Agree ⑤StronglyAgree9.OnlydatathataredirectlyrelevanttotheresearchquestionsshouldbepresentedintheResultssection.①StronglyDisagree ②Disagree ③Neutral ④Agree ⑤StronglyAgreeReadingandAnalyzingPART02WhatistheResultsSection?DefinitionTheResultssectionsummarizesandpresentsthefindingsofthestudybasedontheinformationgatheredasaresultofthemethodology.Itreportsresearchfindingsandisoftenconsideredoneofthemostimportantsectionsbecauseitdrawsthegreatestattentionfromreviewers,peers,andreaders.CorePurposePresentfindingsconciselyStatefindingslogicallyReportfindingsobjectivelySupportwithvisualelements“Theresearchfindingsshouldbestatedinaconcise,logical,andobjectivemanner.”—AcademicWritingGuidelinesPresentationMethodsWrittenTextNarrativedescriptionoffindingswithclearexplanationsVisualElementsTables,graphs,andillustrationsTablesFiguresChartsKeyPoint:Visualelementsenhanceratherthanreplacethenarrative.PartIIReadingandAnalyzingKeyPrinciplesforWritingResults1RelevanceFirstOnlysharedataandfindingsthataredirectlyrelevanttoansweringtheresearchquestions.Toomanydetailsmayoverwhelmreaders.Considerputtingadditionaldetailsinanappendix2SystematicPresentationPresentfindingsinasystematicmanner,suchasbytheme,chronology,ordegreeofimportance.ThemeChronologyImportance3Don’tIgnoreNegativeResultsIffindingsdon'talignwiththehypothesis,don'toverlookthem.RecordtheseresultsandelaborateintheDiscussionsection.Important:Negativeresultscanleadtoamorecompellingdiscussion.4Analysis&DiscussionTheResultssectionistheplacetonotonlypresentfindingsbutalsoengageinanalysisandinterpretationofthedata.ClarifypatternsandtrendsExplainrelationshipsobservedOfferinsightsintoreasonsGoldenRule:Startwithgeneralresultsfirst,thenproceedtomorespecific(butstillrelevant)ones.PartIIReadingandAnalyzingPurposeTosummarizeandpresentthefindingsofyourstudybasedonthemethodologyused,inaclearandunbiasedway.SignificanceOftenconsideredthemostimportantsectionasitdrawsthegreatestattentionandrepresentsthecoreofyourresearchcontribution.KeyCharacteristicsThewritingmustbeconcise,logical,andobjectivetoensurethefindingsarepresentedaccurately.TextbookCitation“TheResultssection...reportsresearchfindings,anditisoftenconsideredoneofthemostimportantsectionsbecauseitdrawsthegreatestattentionfromreviewers,peers,andreaders.”WhyistheResultsSectionImportant?PartIIReadingandAnalyzing1.RelevanceOnlypresentdatadirectlyrelevanttoansweringresearchquestions.Avoidraworinitialdataunlessnecessary.2.SystematicOrganizefindingslogically(theme,chronology,importance).Startgeneralthenmovetospecifics.3.ObjectivityReportallfindingsfactually,evenifcontradictory.Negativeresultsarevaluableandshouldbeincluded.4.ConcisenessBebriefandavoidoverwhelmingdetails.Ensurelogicalflowfromonepointtothenext.Source:SummarizedfromtextbookPartII,ReadingandAnalyzing,secondandthirdparagraphs.KeyPrinciplesforPresentingResultsPartIIReadingandAnalyzingPartIIReadingandAnalyzingTable5.1MovesoftheResultsMovesDescriptionsImpliedQuestionsRevisittheresearchquestionsand/orthemethodologyBrieflysummarizetheresearchques-tion(s)ofthecurrentstudy,and/orthemethodologyadoptedWhatis/aretheresearchquestion(s)?Whatmethodologyisadoptedtoaddresstheresearchquestion(s)?DescribethemethodsforgatheringoranalyzingdataBrieflystatehowtheresearchdataarecollectedandanalyzedHowarethedatagathered?Howwilltheybeanalyzed?LocatethevisualrepresentationswherethefindingsarepresentedStatewherethereaderscanfindtherelevantdataWhere,andinwhatform,aretherelevantdatapresented?HighlightmajorresultsPresentthemajorresultsWhatarethefindingsdrawnfromthedatapresented?ProvidespecificdatatosupporttheresultsorfindingsDiscussindetailhowthefindingsarederivedfromthepresenteddataHowdotherelevantdatasupporttheresultsorfindings?CommentontheresultsProvideevaluation,comparison,orexplanationonsomeoftheresultsHowarethefindingsofthecurrentstudysimilartoordifferentfromthoseofpreviousstudies?Whatmaybethereasonsforcertainresults?1.RevisitQs/MethodologyBrieflyrestateyourresearchquestion(s)and/orthemethodologyused.2.DescribeDataMethodsExplainhowyougatheredoranalyzedyourdata.3.LocateVisualsTellthereaderwheretofindtherelevantfiguresortables.4.HighlightMajorResultsPresentthemainfindingsorkeyresultsofyouranalysis.5.ProvideSpecificDataSupportyourmajorresultswithspecificnumbers,details,orstatistics.6.CommentonResultsOfferabriefevaluation,comparison,orexplanationoftheresults.Source:ThefollowingtableoutlinesthemosttypicalmovesoftheResultssectionofaresearchpaper(seeTable5.1).TheSixTypicalMoves(Table5.1)ExamplesMove1:DiEmphisimplementedonIDAPro[23]andPyTorch[48].Weevaluateourtechniqueviathefollowingresearchquestions(RQs):RQ1:CanDiEmphhelpbinarysimilaritymodelsachievebetterperformancewhenthecompilerconfigurationsofthetestdatasetaredifferentfromthetrainingdataset?Wesaythatthetestdataareout-of-distribution.Itdenotesarealisticandchallengingusescenarioforbinaryanalysistools.RQ2:IsDiEmpheffectivewithdifferentpoolsizesofthecandidatefunctions?RQ3:HowdoesDiEmphaffectperformanceofmodelswhenthecompilerconfigurationsofthetestdatasetalignwiththoseofthetrainingdataset?Inthiscase,wesaythetestdataarein-distribution.RQ4:HowmuchtimedoesDiEmphtaketoanalyzefunctionsintrainingdatasets?RQ5:HowdoeseachcomponentofDiEmphaffecttheperformance?WealsoconductacasestudytodemonstratetheeffectivenessofDiEmphonout-of-distributiontestdata.(Citedfrom“ImprovingBinaryCodeSimilarityTransformerModelsbySemantics-DrivenInstructionDeemphasis”inConferenceProceedings,ISSTA2023)AseriesofnumericalexperimentswereconductedtoassesstheeffectivenessoftheproposedFSTSPandPDSTSPheuristics,andtogaininsightsintopotentialstrategiesforfutureenhancements.Inadditiontoassessingtheproposedheuristics,astudywasconductedtoexplorethetrade-offsbetweenincreasedUAVflightspeedandlongerflightendurance.AllcomputationalworkwasconductedonanHP8100ElitedesktopPCwithaquad-coreInteli7–860processorand4GBRAMrunningUbuntuLinux14.04in64-bitmode.Whereapplicable,mixedintegerlinearprogrammingmodelsweresolvedviaGurobiversion5.6.0,apopularsolversoftwarepackage.HeuristicswerecodedinPythonversion2.7.5.(Citedfrom“Theflyingsidekicktravelingsalesmanproblem:Optimizationofdrone-assistedparceldelivery”inTransportationResearchPartC54,May2015)ExamplesMove2:Toseetheeffectsofnudge,weperformnumericalsimulationsofthebasicmodelinEq.(2)usingtheinteractionprobabilitygiveninEq.(3)andtheinterventionmodelinEq.(4).ThesimulationsareperformedwithN=5000agentsfor1000timestepswithdt=0.01.Atinitialtime,xiisuniformlychosenfromasmallinterval,i.e.,xi∈[−1,1]fori=1,2,...N.Themodelparametersarechosentobeα=3,β=3,K=3,m=10,γ=2.1,ε=0.01,andr=0.5forallthesimulationsunlessmentionedotherwise.Theparameterschosenforthesimulationsleadtoapolarizedstateintheoriginalmodelwithoutintervention.(Citedfrom“Depolarizationofopinionsonsocialnetworksthroughrandomnudges”inPHYSICALREVIEWE108,September2023)Inthissection,weconductextensivenumericalanalysistoevaluatetheeffectivenessoftheproposedJOCRmodels.Numeroustestinstancesofdifferentsizesaregeneratedto(i)assesstheimpactofunrestrictedfocalpointsontotalcostanddeliverycompletiontimeasopposedtorestrictedtruckstops,(ii)comparetheperformanceofourjointoptimizationapproachwithasequentialheuristicmethodproposedintheliterature(ChangandLee,2018),(iii)analyzetheimpactofchangingthemaximumallowablenumberoftruckstops,and(iv)exploretheperformanceofJOCR-Uunderdifferentwarm-startstrategies.Asensitivityanalysisisconductedtoascertaintheinfluenceofcriticalparametersontheperformancemeasures.Also,weillustratethepossibilityofobtainingParetooptimalsolutions(costandtimetrade-off)inthecaseofconflictingobjectives.Finally,weinvestigatetheimpactofallowingmultipledronedeliveriespercluster.TheproposedIPandMILPmodelshavebeendevelopedusingGeneralAlgebraicModelingSystem(GAMS24.5.6)andsolvedusingCPLEX12.8optimizer,whiletheheuristicalgorithmswerecodedandsolvedusingPythonprogramminglanguage.Further,allthecomputationalinstanceswereexecutedonacomputerwithIntelCore-i7@3.9GHzprocessorand8GBRAM.(Citedfrom“Jointoptimizationofcustomerlocationclusteringanddrone-basedroutingforlast-miledeliveries”,TransportationResearchPartC114,May2020)Move1:RevisitQs/MethodologyPurpose:Remindthereaderofthestudy’sfocusandwhatyousetouttoinvestigate.Example:“Weevaluateourtechniqueviathefollowingresearchquestions(RQs):RQ1:CanDiEmphhelpbinarysimilaritymodelsachievebetterperformance...?”Move2:DescribeDataMethodsPurpose:Explainthespecificproceduresusedtogenerateoranalyzethedatabeingpresented.Example:“Toseetheeffectsofnudge,weperformnumericalsimulationsofthebasicmodelinEq.(2)usingtheinteractionprobabilitygiveninEq.(3)...”Moves1&2:SettingtheStageExamplesMove3:Figure1summarizesthecharacteristicsofthe36,678participants(52.8%female),includingthedistributionsofage,dailyalcoholunits,andglobalGMVandWMV.Table4showsacomparisonoftheshearconnectorresistanceobtainedexperimentallyandnumerically.Move4:ResultsindicatethatthenumberofedgesEr*andservicesetS*afterimplementingProposition1ispositivelycorrelatedwiththeincreasingdemanddensityfromruraltourbanregions.Fig.6comparestheaccuracyoftheFLmodelwiththatofthedefaultmodel.TheresultsshowthattheFLmodelachievescomparableaccuracy,albeitslightlylowerthantheaccuracyattainedbythedefaultmodel.ExamplesMove3andMove4canbecombined,asillustratedinthefollowingexamples:AscanbeappreciatedinFig.9e,VCisconsiderablydeformedinitsbaseduringthepush-outtests.AscanbeseenfromFig.3d,theincreaseinparcelhandingpromotesautonomousnavigationforADRwhiletheproportionofvehicletravellingdecreases.Move3:Figure1summarizesthecharacteristicsofthe36,678participants(52.8%female),includingthedistributionsofage,dailyalcoholunits,andglobalGMVandWMV.Table4showsacomparisonoftheshearconnectorresistanceobtainedexperimentallyandnumerically.Move4:ResultsindicatethatthenumberofedgesEr*andservicesetS*afterimplementingProposition1ispositivelycorrelatedwiththeincreasingdemanddensityfromruraltourbanregions.Fig.6comparestheaccuracyoftheFLmodelwiththatofthedefaultmodel.TheresultsshowthattheFLmodelachievescomparableaccuracy,albeitslightlylowerthantheaccuracyattainedbythedefaultmodel.Move3:LocateVisualsPurpose:Directthereadertothefigure/tablewheredataisshown.Example:“Figure1summarizesthecharacteristicsofthe36,678participants...”Move4:HighlightMajorResultsPurpose:Statethemainfindingorresultderivedfromthedata/visual.Example:“Resultsindicatethatthenumberofedges...ispositivelycorrelatedwiththeincreasingdemanddensity...”CombinedStrategy(Efficiency)CombinedExample:“AscanbeseenfromFig.3d,theincreaseinparcelhandingpromotesautonomousnavigationforADRwhiletheproportionofvehicletravellingdecreases.”Moves3&4:PresentingtheFindingsExamplesMove5:OurPointNeXt-S,thesmallestvariant,outperformsPointNet++by13.5%,6.4%,and10.2%intermsofmeanIoU(mIoU),overallaccuracy(OA),andmeanaccuracy(mAcc),respectively,whilebeingfasterintermsofthroughput.ThesizeoftheoptimisednetworkdegreeandservicesetS*isreducedbyanaverageof90.1%and96.89%,respectively,comparedwiththemaximum.Move6:TheincreasedspeedisduetothereducednumberoflayersintheSAblockforPointNeXt-S(seeSec.3.2.2).Ourproposedmodelscalingstrategyachievesmuchhigherperformancethanthesenaivescalingstrategies,whilebeingmuchfaster.Move5:ProvideSpecificDataPurpose:Supportyourmajorresultwithspecificnumbers,percentages,ordetailstoestablishcredibility.Example:“OurPointNeXt-S...outperformsPointNet++by13.5%,6.4%,and10.2%intermsofmeanIoU(mIoU),overallaccuracy(OA),andmeanaccuracy(mAcc),respectively...”Move6:CommentonResultsPurpose:Offerabriefexplanation,comparison,orevaluation(savedetailedinterpretationforDiscussion).Example:“TheincreasedspeedisduetothereducednumberoflayersintheSAblockforPointNeXt-S(seeSec.3.2.2).”Moves5&6:SupportingandCommentingTask1BelowaretwoexcerptsoftheResultssectionofresearchpaperspublishedintheinternationaljournals.Readtheexcerpts,identifythekeysteps,fillintheformandthendiscussthefollowingquestions(write0ifnosentenceisdedicatedtoacertainstep):StepsSentencenumbers(example1)Sentencenumbers(example2)Revisittheresearchquestionsand/orthemethodologyDescribethemethodsforgatheringoranalyzingdataindetailLocatethevisualrepresentationswherethefindingsarepresentedHighlightmajorresultsProvidespecificdatatosupporttheresultsorfindingsCommentontheresultsTask1Excerpt1:①WequantifiedandestimatedthecarbonemissionsofmobilenetworkoperationsinNanchangfrom2020to2023.②AsillustratedinFig.1a,thelaunchof5Gresultedinanincreaseindailynetworkcapacitiesfrom12PB(12,2644Gbasestations)to22PB(12,2644Gbasestationsand2,1595Gbasestations).③Theoperationofthenewlylaunched5Gbasestationshasledtoasharpincreaseinenergyconsumptionandadeclineinenergyefficiency(SupplementaryFig.1).④Correspondingly,therehasbeenadramaticincreaseindailycarbonemissionsof178tafterlaunching5G(Fig.1b).⑤Carbonefficiency,theamountofnetworktrafficthatcanbedeliveredforoneunitofcarbonemissions,decreasedfrom2.98TBpertCO2to2.08TBpertCO2(Fig.1c).⑥Afterthelaunchofthe5Gnetwork,therewasalargeandrapidincreaseincarbonemissions.⑦However,thetrafficloaddidnotgrowtothesameextent,exacerbatingthemisalignmentbetweencellularnetworktrafficandenergyconsumption;thisisthecriticalreasonfortheloweredcarbonefficiencyofthemobilenetwork.(Citedfrom“Carbonemissionsof5GmobilenetworksinChina”inNatureSustainability,August2023)Task1Excerpt2:①Tovalidatethemodel,weusecustomerdatasetsfrom9countiesinIllinois,whichmimicsthevaryingdemanddistributionbasedontheirpopulationdensities(fromruraltourbancounties)andwasintroducedby[6].②Foreachcounty,weselect5randomsquareserviceregionswith50customersofeachtoexecutethemodel.③Weusethefollowingruletocodedifferentcountieswhichstartfromruraltourban:(1)Cumberland,(2)Johnson,(3)Jefferson,(4)Fulton,(5)Adams,(6)LaSalle,(7)Champaign,(8)Winnebago,(9)Cook.④ThemodelisexecutedinanAppleM1processor(3.2GHz)withPython3.9usingGurobi3.10solverwith16GBRAM.⑤TheparametersettingisshowninTableⅢ.⑥SincetheserviceradiusofasingleADRisrestrictedbythebatterycapacity,inwhichtherobotmainlycoversthedemandsofasmallresidentialarea,wesetthenetworkasasmallsize(n=50)forasingledeliveryround.⑦Consideringthecompartmentsize,anADRcanonlydeliverafewparcelsperloading(q<=3).⑧Wefurtherconductasensitivityanalysisfordifferentvaluesoftherobot’scapacity(q=1,2,and3).⑨Fig.1arepresentsthetimeconsumptionofdifferentdeliveryactivitiesofAADscenariosundervaryingregions.⑩Wenoticedthatparcelsortingaccountsforthehighestproportionofthetotaldeliverytimewithanaveragepercentageof50.66%,followedbyvehicletravelling(24.40%)andADRtravelling(24.90%).⑪Specifically,theAADscenarioachievesgreaterdeliverytime-savinginurbanregionscomparedwithruralareas.⑫ThiscanbeexplainedbythefactthattheADRdeliveryoptionisusedmoreextensivelyinurbanregions(seeFig.2)thatreduceslongtraveltimecausedbyunpredictabletrafficconditionsaswellasparkingspacesearching.(Citedfrom“QuantifyingtheCapabilityofAutonomousRobot-AssistedDistributioninLast-Mile:InsightfromRuraltoUrbanConfigurations”,2023IEEE26thInternationalConferenceonIntelligentTransportationSystems[ITSC])Task1BelowaretwoexcerptsoftheResultssectionofresearchpaperspublishedintheinternationaljournals.Readtheexcerpts,identifythekeysteps,fillintheformandthendiscussthefollowingquestions(write0ifnosentenceisdedicatedtoacertainstep):StepsSentencenumbers(example1)Sentencenumbers(example2)Revisittheresearchquestionsand/orthemethodology①①Describethemethodsforgatheringoranalyzingdataindetail0②③④⑤⑥⑦⑧Locatethevisualrepresentationswherethefindingsarepresented②⑨Highlightmajorresults②③④⑤⑩⑪Providespecificdatatosupporttheresultsorfindings②④⑤⑩Commentontheresults⑥⑦⑫Task2ReadcarefullythroughtheResultssectionintworesearcharticlesfromyourtargetjournals.Payspecialattentiontothepresentationoffindings,andhowthewritersusedatatosupportthem.LanguageStrategyPART033.1VerbTensesAcademicTenseUsagePastTense(StudyActions/Results)Usedfordescribingyourspecificstudy’sactions,e.g.,“Wefoundthat...”,“Theexperimentshowed...”PresentTense(Figures/Truths)Usedfordescribingfigures/tablesorgeneraltruths,e.g.,“Figure1shows...”,“Thisresultindicates...”PartIIILanugageStrategy3.1VerbTensesintheResultsSectionPastSimplePrimaryUse:DetailtheresultsobtainedanddescribeproceduresofdatagatheringandprocessingExample:“Weusedathree-layernetwork...”PresentSimplePrimaryUse:Explaindiagrams/figures/tablesandsupportresults(indicatingtheyaretrueandrelevant)Example:“Table1presentsadditivestudies...”TenseUsagebyMove1RevisitResearchQuestionsPast/Present2DescribeDataMethodsPastSimple3LocateVisualRepresentationsPresentSimple4HighlightMajorResultsPresent/Past5ProvideSpecificDataPresentSimple6CommentonResultsPresentSimpleTip:Acombinationoftensesmaybeusedwhendifferentpartsofasentencefocusondifferentpurposes.PartIIILanugageStrategyPartIIILanugageStrategyTable5.2

Tenses

in

the

Results

MovesVerbTensesExampleSentencesRevisittheresearchquestionsand/orthemethodologyPresentSimpleorPastSimpleWepresentacomprehensivecomputationalstudytoevaluatetheeffectivenessofusingtheFCPaswellastoevaluatecomp-utationalperformanceofthenovelsolutionalgorithmontworealworldroadnetworks.Tobringourevaluationsclosertopracticalscenarioswhileensuringamanageablescale,wealsoconstructedtwodatasets,WD27MandFB37M,withtwodifferentKGsrespectively.DescribethemethodsforgatheringoranalyzingdataindetailPastSimple(occasionallyPresentSimple)FortheMNISTdataset,weusedathree-layernetworkwithtwohidden,fullyconnectedlayersof256neuronsandrectifiedlinearunits.Theoutputlayerwasfullyconnectedwith10outputneuronsandutilizedsoftmaxactivation.TheexperimentsareexecutedusingCPLEXOptimizerv12.9.0intheJavaAPIwithConcertTechnologyonacomputerwithInteli7-9700K3.60GHzprocessorand16GBofRAM.TheBendersalgorithmframeworkisimplementedusinglazyconstraintcallbackfeatureofCPLEXandapredeterminedtimelimitofonehourissetforallimplementations.LocatethevisualrepresentationswherethefindingsarepresentedPresentSimpleTab.4andTab.5presentadditivestudiesfortheproposedtrainingandscalingstrategiesinScanObjectNN[44]andS3DIS[1],respectively.Fig.6highlightssomeinterestingcharacteristicsoftheproposedPDSTSPsolutionframework.PartIIILanugageStrategyTable5.2

Tenses

in

the

Results(continued)

MovesVerbTensesExampleSentencesHighlightmajorresultsPresentSimple(occasionallyPastSimple)ItisevidentfromTable1thatthejointoptimizationapproachoutperformsthesequentialheuristicmethodforallthetestinstancesevaluated.Theexperimentalresultsdemonstrateaclosecorrelationbetweentherecognitionofthe1Asymbolandthesimulatedoutcome,whichindicatedthatthedevicesuccessfullyperformedANDlogicoperationandexhibitedtheabilitytoaccuratelyrecognizethesymbol1A.ProvidespecificdatatosupporttheresultsorfindingsPresentSimpleAsillustratedinFig.2f–h,regionswithlargemisalignmentfactorsexhibitlowenergyefficiencyandviceversa.Theaverageenergyefficiencyofthecitycentrefallsfrom2.98TBMWh−1(4Gnetworks)to1.94TBMWh−1(coveringboth4Gand5Gnetworks),correspondingtotheincreaseinthemisalignmentfactor.Thewealthiereasternprovincialregionsalsohavehigheradditionalcarbonemissionsperunitareathanthelessdevelopedwesternareas(Fig.1f).Xizanghasthelowestadditionalcarbonemissionsperunitareaof0.0155tCO2km−2,whereasShanghai’sadditionalcarbonemissionsperunitareaareroughly10,811timeshigher(167.58tCO2km−2).CommentontheresultsPresentSimpleTherefore,onthebasisoftheintensityanddensityofadditionalcarbonemissions,China(especiallytheeasternprovincialregions)mayfacesevereenvironmentalissuesthatmaycauseirreversibledamageduringthelaunchof5G.Weurgentlyneedtodigintotherootcausesandfindasolutiontolaunchandoperate5Gnetworkssustainably.Task3AnalyzetheverbtensesusedinExample1.①WequantifiedandestimatedthecarbonemissionsofmobilenetworkoperationsinNanchangfrom2020to2023.②AsillustratedinFig.1a,thelaunchof5Gresultedinanincreaseindailynetworkcapacitiesfrom12PB(12,2644Gbasestations)to22PB(12,2644Gbasestationsand2,1595Gbasestations).③Theoperationofthenewlylaunched5Gbasestationshasledtoasharpincreaseinenergyconsumptionandadeclineinenergyefficiency(SupplementaryFig.1).④Correspondingly,therehasbeenadramaticincreaseindailycarbonemissionsof178tafterlaunching5G(Fig.1b).⑤Carbonefficiency,theamountofnetworktrafficthatcanbedeliveredforoneunitofcarbonemissions,decreasedfrom2.98TBpertCO2to2.08TBpertCO2(Fig.1c).⑥Afterthelaunchofthe5Gnetwork,therewasalargeandrapidincreaseincarbonemissions.⑦However,thetrafficloaddidnotgrowtothesameextent,exacerbatingthemisalignmentbetweencellularnetworktrafficandenergyconsumption;thisisthecriticalreasonfortheloweredcarbonefficiencyofthemobilenetwork.Task3AnalyzetheverbtensesusedinExample2.①Tovalidatethemodel,weusecustomerdatasetsfrom9countiesinIllinois,whichmimicsthevaryingdemanddistributionbasedontheirpopulationdensities(fromruraltourbancounties)andwasintroducedby[6].②Foreachcounty,weselect5randomsquareserviceregionswith50customersofeachtoexecutethemodel.③Weusethefollowingruletocodedifferentcountieswhichstartfromruraltourban:(1)Cumberland,(2)Johnson,(3)Jefferson,(4)Fulton,(5)Adams,(6)LaSalle,(7)Champaign,(8)Winnebago,(9)Cook.④ThemodelisexecutedinanAppleM1processor(3.2GHz)withPython3.9usingGurobi3.10solverwith16GBRAM.⑤TheparametersettingisshowninTableⅢ.⑥SincetheserviceradiusofasingleADRisrestrictedbythebatterycapacity,inwhichtherobotmainlycoversthedemandsofasmallresidentialarea,wesetthenetworkasasmallsize(n=50)forasingledeliveryround.⑦Consideringthecompartmentsize,anADRcanonlydeliverafewparcelsperloading(q<=3).⑧Wefurtherconductasensitivityanalysisfordi