2025年英語(yǔ)考研閱讀理解長(zhǎng)難句解析與解題策略試卷考試時(shí)間：______分鐘總分：______分姓名：______PartAReadingComprehensionDirections:Inthepassagebelow,eachsentenceisfollowedbyfourchoicesmarkedA),B),C)andD).YoushoulddecideonthebestchoiceandmarkthecorrespondingletteronAnswerSheet2withasinglelinethroughthecentre.Arecentstudyonurbancommutingpatternshasrevealedsomeinteresting,albeitsomewhatcounterintuitive,findingsregardingtherelationshipbetweentraveltimeandjobsatisfaction.Traditionalwisdomsuggeststhatlongercommutesleadtoincreasedstressanddecreasedjobsatisfaction,asindividualsspendmoretimetravelingandhavelesstimeforpersonalactivities.However,thestudy'sresultsindicatethatthisrelationshipisnotstraightforward.Infact,formanyprofessionals,particularlythosewhoworkinhigh-payingfieldssuchasfinanceandtechnology,longercommutescanbeassociatedwithhigherlevelsofjobsatisfaction.Theresearchershypothesizedthatthekeyfactorinfluencingjobsatisfactioninthiscontextisnotthemeredurationofthecommute,butrathertheperceivedvalueofthejob.Individualswhofeelthattheirjobprovidessignificantpersonalandfinancialrewardsmaybewillingtoendurelongercommutesiftheybelievethebenefitsoutweighthecosts.Conversely,thosewhoarelesssatisfiedwiththeirjobmayfindevenshortcommutestobeasourceoffrustrationandstress.Oneofthestudy'smostsurprisingfindingswasthepositivecorrelationobservedbetweencommutedurationandjobsatisfactionamongemployeesincertainindustries.Forinstance,softwareengineersandinvestmentbankersinmajormetropolitanareasoftenreporthighlevelsofjobsatisfactiondespitespendingtwoorthreehourseachwaycommutingtowork.Thisphenomenonappearstobedrivenbyacombinationoffactors,includingthehighdemandforskilledprofessionalsinthesefields,whichtranslatesintocompetitivesalariesandcareeradvancementopportunities.Additionally,manyoftheseworkersviewtheircommutesasanecessaryinvestmentintheircareers,ratherthanaburden.Theimplicationsofthisstudyforurbanplanningandworkplacepolicyaresignificant.Iflongercommutesdonotnecessarilyequatetolowerjobsatisfactionforcertainsegmentsoftheworkforce,thenemployersmayhavemoreflexibilityinlocatingtheirofficesinareasthatoffergreateraccesstohousingorotheramenities,withoutsacrificingemployeecontentment.Similarly,policymakersmightconsiderrevisingtransportationpoliciestofocusmoreonefficiencyandconvenience,ratherthansimplyminimizingtraveltime.Afterall,therelationshipbetweencommutingandjobsatisfactionismorecomplexthanitfirstappears.Furthermore,thestudyhighlightstheimportanceofindividualperceptionindeterminingjobsatisfaction.Twopeoplemayexperiencethesamecommute,buttheirsubjectiveevaluationsofthatexperiencecandiffergreatly.Forexample,anindividualwhoviewstheircommuteasatimetorelaxandlistentopodcastsmayperceiveitasapositiveaspectoftheirday,whereassomeonewhoviewsitasatediousandunavoidablenecessitymayexperienceitasasignificantsourceofstress.Thisunderscorestheneedforpersonalizedapproachestomanagingthechallengesofurbancommuting.Thefindingsalsoraisequestionsaboutthenatureofworkitselfinthemoderneconomy.Asmorejobsbecomeremoteorflexible,thetraditionalmodelofthedailycommutemaybecomelessrelevant.Thisshiftcouldpotentiallyleadtogreaterjobsatisfactionforsomeworkers,astheywouldbeabletoeliminatecommutesaltogetherorreducetheirdurationsignificantly.However,itcouldalsocreatenewchallenges,suchasdifficultiesinseparatingworkfrompersonallifewhenworkingfromhome.Thelong-termimpactofthesechangesonjobsatisfactionremainstobeseen,butitisclearthattherelationshipbetweencommutingandjobsatisfactionisevolvinginresponsetotechnologicalandsocietalshifts.C)Thestudy'sfindingschallengetheconventionalviewthatlongercommutesinevitablyleadtolowerjobsatisfaction.D)Theresearchersbelievethattheperceivedvalueofajobistheprimarydeterminantofjobsatisfactionforcommuters.Accordingtothepassage,whatisthemainreasonwhysomeprofessionalswithlongcommutesreporthighlevelsofjobsatisfaction?A)Theyderivesignificantpersonalenjoymentfromtheirdailytravelexperiences.B)Theybelievethattheircommutesareanecessaryinvestmentintheircareerdevelopment.C)Theyliveinareaswithbetterpublictransportationsystems.D)Theyarecompensatedwithhighersalariestooffsetthetimespentcommuting.Thepassagesuggeststhatwhichofthefollowingfactorsmaycontributetothepositivecorrelationbetweencommutedurationandjobsatisfactionincertainindustries?A)Theavailabilityofaffordablehousingnearmajortransportationhubs.B)Thehighdemandforskilledprofessionals,leadingtocompetitivesalariesandcareeropportunities.C)Theimplementationofflexibleworkschedulesbyemployers.D)Thepresenceofnumerousrecreationalactivitiesalongthecommuteroutes.Whatdoesthepassageimplyabouttheroleofurbanplannersandpolicymakersinlightofthestudy'sfindings?A)Theyshouldprioritizethedevelopmentofpublictransportationtoreducecommutetimes.B)Theyshouldfocusoncreatingmorejobopportunitiesinareaswithhighhousingcosts.C)Theyshouldconsiderthediverseneedsofcommuterswhendesigningtransportationpolicies.D)Theyshouldencourageemployerstolocatetheirofficesfartherfromresidentialareas.Accordingtothepassage,howmighttheriseofremoteworkaffecttherelationshipbetweencommutingandjobsatisfaction?A)Itcouldleadtoincreasedjobsatisfactionforallworkersbyeliminatingtheneedforcommutes.B)Itmightcreatenewchallengesforworkerswhostruggletomaintainwork-lifeboundaries.C)Itwillhavenosignificantimpactonjobsatisfactionsincemostjobswillremainintraditionalofficesettings.D)Itwillprimarilybenefitworkersinfieldsthatrequirefrequentface-to-faceinteractions.Thepassageindicatesthatwhichofthefollowingisakeyfactorinfluencinganindividual'sperceptionoftheircommute?A)Thephysicaldistancetraveledduringthecommute.B)Themodeoftransportationusedforthecommute.C)Theindividual'spersonalattitudesandexpectationsregardingtravel.D)Theavailabilityofentertainmentoptionsduringthecommute.Theresearchers'hypothesisinthestudyisbestdescribedas:A)Longercommutesleadtohigherstresslevels,whichnegativelyimpactjobsatisfaction.B)Thedurationofthecommuteisthemostsignificantfactoraffectingjobsatisfaction.C)Jobsatisfactionisprimarilydeterminedbytheperceivedvalueofthejob,regardlessofcommutelength.D)Commuterswhoaresatisfiedwiththeirjobsarewillingtoendurelongercommutesforfinancialrewards.Thepassageprovidesanexampleofsoftwareengineersandinvestmentbankerstoillustrate:A)Thenegativeeffectsoflongcommutesonmentalhealth.B)Theimportanceofflexibleworkarrangementsforhigh-earningprofessionals.C)Howcertainindustriescantoleratelongercommutesduetohighjobsatisfaction.D)Theroleofcareeradvancementopportunitiesincompensatingforlongtraveltimes.Whichofthefollowingstatementsissupportedbytheinformationinthepassage?A)Allprofessionalsinhigh-payingfieldsareequallysatisfiedwiththeirjobsdespitelongcommutes.B)Urbanplannersshouldprioritizetheconstructionofhousingnearworkplacestoreducecommutes.C)Therelationshipbetweencommutingandjobsatisfactionisinfluencedbyindividualperceptionsandjobvalue.D)Employersshouldavoidlocatingtheirofficesinareaswithlongcommutestomaintainemployeesatisfaction.Thepassagesuggeststhatwhichofthefollowingmightbeafuturetrendintheworkplace?A)Thecompleteeliminationofoffice-basedworkinfavorofremotework.B)Acontinuedemphasisonminimizingcommutetimesasakeyfactorinjobsatisfaction.C)Thedevelopmentofnewtransportationtechnologiestomakecommutesmoreenjoyable.D)Greaterflexibilityinworkarrangementstoaccommodatevaryingcommutingpatterns.PartBReadingComprehensionDirections:Thefollowingpassageisfollowedbysomequestions.Readthepassagecarefullyandthenanswerthequestionsbasedonwhatisstatedorimpliedinthepassage.WriteyouranswersneatlyonAnswerSheet2.Intherealmofartificialintelligence(AI),theconceptof"deeplearning"hasemergedasarevolutionaryapproach,enablingmachinestolearnfromexperienceandunderstandtheworldthroughahierarchyofconcepts.Unliketraditionalmachinelearningalgorithmsthatrelyonfeatureextractionbyhumanexperts,deeplearningmodelsuseneuralnetworkswithmultiplelayers(hencetheterm"deep")toautomaticallylearntherepresentationsneededfordetectionorclassification.Thisabilitytolearnhierarchicalfeatureshasmadedeeplearningparticularlysuccessfulinareassuchascomputervision,naturallanguageprocessing,andspeechrecognition.Thefoundationofdeeplearningliesinneuralnetworks,whichareinspiredbythestructureandfunctionofthehumanbrain.Thesenetworksconsistofinterconnectednodes,or"neurons,"organizedintolayers.Eachneuronreceivesinputfromthepreviouslayer,processesitusingamathematicaloperation,andpassestheoutputtothenextlayer.Theprocessisrepeatedacrossmultiplelayers,allowingthenetworktolearnincreasinglycomplexpatternsandrepresentations.The"depth"ofthenetworkreferstothenumberoflayersitcontains,anddeepernetworkscanpotentiallylearnmoreabstractandnuancedfeatures.Oneofthekeyadvantagesofdeeplearningisitsabilitytohandlelargeamountsofdataefficiently.Thehierarchicallearningprocessenablesthenetworktoautomaticallydiscoverthemostrelevantfeaturesfromtherawdata,eliminatingtheneedformanualfeatureengineering.ThishasledtosignificantimprovementsintheperformanceofAIsystemsacrossvariousdomains.Forinstance,deeplearningmodelshaveachievedstate-of-the-artresultsinimagerecognitiontasks,surpassinghuman-levelaccuracyinsomecases.However,deeplearningalsofacesseveralchallenges.Onemajorchallengeistherequirementforlargeamountsoflabeleddatatotrainthemodelseffectively.Unliketraditionalmachinelearningalgorithmsthatcansometimesperformwellwithsmallerdatasets,deeplearningmodelsneedextensivetrainingdatatogeneralizewelltonew,unseenexamples.ThishasledtoconcernsaboutdataprivacyandtheethicalimplicationsofusinglargedatasetsfortrainingAImodels.Anotherchallengeisthecomputationalcostassociatedwithtrainingdeeplearningmodels.Theprocessoftrainingadeepneuralnetworkinvolvesalargenumberofcomputations,whichcanbecomputationallyintensiveandtime-consuming.Thishasnecessitatedthedevelopmentofspecializedhardware,suchasgraphicsprocessingunits(GPUs)andfield-programmablegatearrays(FPGAs),toacceleratethetrainingprocess.Despitetheseadvancements,trainingdeepmodelsremainsasignificantbottleneckinthedeploymentofAIsystems.Recentadvancementsindeeplearninghavefocusedonaddressingthesechallenges.Techniquessuchastransferlearningandunsupervisedlearninghavebeendevelopedtoreducethedependencyonlargelabeleddatasets.Transferlearninginvolvesleveragingpre-trainedmodelsonlargedatasetsandfine-tuningthemforspecifictasks,whileunsupervisedlearningmethodsaimtolearnmeaningfulrepresentationsfromunlabeleddata.Theseapproacheshaveshownpromisingresultsinreducingtheamountoflabeleddatarequiredandimprovingthegeneralizationperformanceofdeeplearningmodels.Theimpactofdeeplearningonsocietyhasbeenprofound.Inhealthcare,deeplearningmodelshavebeenusedtoanalyzemedicalimages,assistindiagnosis,andpredictpatientoutcomes.Inautonomousvehicles,deeplearningplaysacrucialroleinobjectdetection,navigation,anddecision-making.Innaturallanguageprocessing,deeplearninghasenabledadvancementsinmachinetranslation,sentimentanalysis,andchatbots.Theseapplicationsdemonstratetheversatilityandpotentialofdeeplearningintransformingvariousindustriesandimprovinghumanlives.Despiteitssuccess,deeplearningisnotwithoutitslimitations.Onelimitationisthelackofinterpretabilityandtransparencyinthedecision-makingprocessofdeeplearningmodels.Unliketraditionalalgorithmsthatprovideclearrulesformakingpredictions,deeplearningmodelsoftenoperateas"blackboxes,"makingitdifficulttounderstandhowtheyarriveatspecificdecisions.Thislackofinterpretabilityhasraisedconcernsaboutthereliabilityandtrustworthinessofdeeplearningsystems,particularlyincriticalapplicationssuchashealthcareandfinance.Anotherlimitationisthepotentialforbiasindeeplearningmodels.Thesemodelslearnfromthedatatheyaretrainedon,andifthetrainingdatacontainsbiases,themodelwilllearnandperpetuatethesebiases.Thishasledtoissuessuchasracialandgenderbiasesinfacialrecognitionsystemsandpredictivepolicingalgorithms.Addressingthesebiasesrequirescarefuldatasetcuration,algorithmicfairnesstechniques,andongoingmonitoringtoensurethatdeeplearningmodelsarefairandequitable.FutureresearchindeeplearningislikelytofocusonaddressingtheselimitationsanddevelopingmorerobustandreliableAIsystems.EffortsarebeingmadetoimprovetheinterpretabilityofdeeplearningmodelsthroughtechniquessuchasattentionmechanismsandexplainableAI(XAI)methods.Thesemethodsaimtoprovideinsightsintothedecision-makingprocessofdeeplearningmodels,makingthemmoretransparentandunderstandabletohumans.Additionally,researchisongoingtodevelopmoreinclusiveandunbiaseddeeplearningmodels.Thisincludeseffortstodiversifytrainingdatasets,incorporatefairnessconstraintsintothetrainingprocess,andevaluatemodelsfortheirpotentialbiases.Byaddressingtheseissues,thegoalistoensurethatdeeplearningmodelsarefair,equitable,andbeneficialforallmembersofsociety.Inconclusion,deeplearninghasemergedasapowerfulandtransformativeapproachinthefieldofartificialintelligence.Itsabilitytolearnhierarchicalfeaturesfromlargedatasetshasledtosignificantadvancementsinvariousdomains.However,deeplearningalsofaceschallengesrelatedtodatarequirements,computationalcosts,interpretability,andbias.Ongoingresearcheffortsarefocusedonaddressingthesechallengesanddevelopingmorerobust,reliable,andequitableAIsystems.Asdeeplearningcontinuestoevolve,itholdsthepotentialtorevolutionizenumerousindustriesandimprovehumanlivesinprofoundways.Whatisthemainpurposeofthepassage?A)Toprovideacomprehensiveoverviewofdeeplearninganditsapplications.B)Toargueagainsttheuseofdeeplearningduetoitslimitations.C)Toexplainthetechnicaldetailsofneuralnetworksandtheirroleindeeplearning.D)Toproposenewresearchdirectionsfordeeplearninginthefuture.Accordingtothepassage,whatisoneofthekeyadvantagesofdeeplearning?A)Itsabilitytoperformwellwithsmalldatasets.B)Itsrelianceonmanualfeatureengineering.C)Itsuseofneuralnetworkswithmultiplelayerstoautomaticallylearnrepresentations.D)Itsrequirementforspecializedhardwarefortraining.Thepassagementionswhichofthefollowingasachallengefacedbydeeplearning?A)Theneedformanualfeatureextraction.B)Thehighcomputationalcostoftrainingmodels.C)Thelackofinterpretabilityinthedecision-makingprocess.D)Therelianceonsmalllabeleddatasets.Whichofthefollowingstatementsissupportedbytheinformationinthepassage?A)Deeplearningmodelsareonlyusefulinthefieldofcomputervision.B)Thedevelopmentofspecializedhardwarehaseliminatedthecomputationalchallengesofdeeplearning.C)Transferlearningandunsupervisedlearningaretechniquesusedtoaddressthechallengesofdeeplearning.D)Deeplearningmodelsarecompletelyunbiasedandfair.Thepassagesuggeststhatwhichofthefollowingmightbeafuturetrendindeeplearningresearch?A)Thecompleteabandonmentofsupervisedlearninginfavorofunsupervisedlearning.B)Agreaterfocusontheinterpretabilityandtransparencyofdeeplearningmodels.C)Thedevelopmentofdeeplearningmodelsthatareexclusivelyusedforhealthcareapplications.D)Thereductionofthecomputationalcostoftrainingdeeplearningmodelstothepointwhereitisnolongerachallenge.Thepassagementionswhichofthefollowingasalimitationofdeeplearning?A)Itsinabilitytohandlelargeamountsofdataefficiently.B)Itsrelianceoninterpretabilityandtransparencyinthedecision-makingprocess.C)Itspotentialforbiasandlackoffairness.D)Itslimitedapplicationsinvariousindustries.Thepassageindicatesthatwhichofthefollowingisagoalofongoingresearchindeeplearning?A)Toreducethedependencyonlargelabeleddatasets.B)Toeliminatetheneedforspecializedhardwareindeeplearningtraining.C)Todevelopdeeplearningmodelsthatareexclusivelyusedforfacialrecognition.D)Tomakedeeplearningmodelscompletelyunbiasedandfair.Thepassagesuggeststhatwhichofthefollowingfactorscontributestothesuccessofdeeplearninginvariousdomains?A)Itsrelianceonmanualfeatureengineering.B)Itsabilitytohandlelargeamountsofdataandautomaticallylearnrepresentations.C)Itsrequirementforsmalllabeleddatasets.D)Itsuseoftraditionalmachinelearningalgorithms.Thepassagementionswhichofthefollowingasanapplicationofdeeplearning?A)Predictivepolicingalgorithms.B)Facialrecognitionsystems.C)Machinetranslationandsentimentanalysis.D)Traditionalmachinelearningalgorithms.Thepassagein