简介：Purpose:Thisstudyaimstocompareadolescents’cardiometabolicriskscorethroughanintegrativeclassificationofphysicalactivity(PA),whichinvolvesthecombinationofmoderate-to-vigorousphysicalactivity(MVPA)andsedentarybehavior(SB).Methods:Across-sectionalstudyderivedfromtheHealthyLifestyleinEuropebyNutritioninAdolescenceCross-SectionalStudydatabase(2006-2008)wasconductedinadolescents(n=548;boys,47.3%;14.7±1.2years)from10Europeancities.MVPAandSBwereobjectivelymeasuredusingaccelerometry.Adolescentsweredividedinto4categoriesaccordingtoMVPA(meetingornotmeetingtheinternationalrecommendations)andthemedianofSBtime(aboveorbelowsex-andage-specificmedian)asfollows:High-SB&Inactive,Low-SB&Inactive,High-SB&Active,andLow-SB&Active.Aclusteredcardiometabolicriskscorewascomputedusingthehomeostaticmodelassessment,systolicbloodpressure,triglycerides,totalcholesterol/high-densitylipoproteincholesterol,sum4skinfolds,andcardiorespiratoryfitness(CRF).AnalysesofcovariancewereperformedtodiscerndifferencesoncardiometabolicriskscoresamongPAcategoriesandeachhealthcomponent.Results:ThecardiometabolicriskscorewaslowerinadolescentsmeetingtheMVPArecommendationandwithlesstimespentinSBincomparisontothehigh-SB&Inactivegroup(p<0.05).However,nodifferenceincardiometabolicriskscorewasestablishedbetweenHigh-SBorLow-SBgroupsininactiveadolescents.ItisimportanttonotethatCRFwastheonlyvariablethatshowedasignificantmodification(higher)whenchildrenwerecomparedfromthecategoryofphysicallyinactivewith"active"butnotfromhigh-tolow-SB.Conclusion:Beingphysicallyactiveisthemostsignificantandprotectiveoutcomeinadolescentstoreducecardiometabolicrisk.LowerSBdoesnotexhibitasignificantandextrabeneficialdifference.

简介：Asanimportantnon-ferrousmetalstructuralmaterialmostusedinindustryandproduction,aluminum(Al)alloyshowsitsgreatvalueinthenationaleconomyandindustrialmanufacturing.HowtoclassifyAlalloyrapidlyandaccuratelyisasignificant,popularandmeaningfultask.Classificationmethodsbasedonlaser-inducedbreakdownspectroscopy(LIBS)havebeenreportedinrecentyears.AlthoughLIBSisanadvanceddetectiontechnology,itisnecessarytocombineitwithsomealgorithmtoreachthegoalofrapidandaccurateclassification.Asanimportantmachinelearningmethod,therandomforest(RF)algorithmplaysagreatroleinpatternrecognitionandmaterialclassification.ThispaperintroducesarapidclassificationmethodofAlalloybasedonLIBSandtheRFalgorithm.TheresultsshowthatthebestaccuracythatcanbereachedusingthismethodtoclassifyAlalloysamplesis98.59%,theaverageofwhichis98.45%.ItalsorevealsthroughtherelationshiplawsthattheaccuracyvarieswiththenumberoftreesintheRFandthesizeofthetrainingsamplesetintheRF.Accordingtothelaws,researcherscanfindouttheoptimizedparametersintheRFalgorithminordertoachieve,asexpected,agoodresult.TheseresultsprovethatLIBSwiththeRFalgorithmcanexactlyclassifyAlalloyeffectively,preciselyandrapidlywithhighaccuracy,whichobviouslyhassignificantpracticalvalue.