简介：在日常生活中，我们经常和数字打交道，也常常用到百分率。那么，如何用英语表达百分率和数量，正确表达自己的意思呢？下面让我们一起来学习“百分率与数目”。

简介：FordeterminingtheoptimalpercentageofRAPmaterialincentralplanthotrecycling,binderwasrecoveredfromRAPbymeansofAbsonrecoverymethod,andpropertiestestsofmixedbindersconsistingofrecoveredasphaltandfreshasphaltatdifferentratioswereperformed.Inaddition,theperformancesofmixturewithdifferentpercentagesofRAPsuchasruttingresistance,anti-cracking,moisturesusceptibilityandfatigueresistanceweretested.ThebindertestresultsshowedthatthehightemperatureperformancewasimprovedwiththeincreaseofthepercentageoftheRAP,whilethelowtemperatureperformancewasdeclined.Whenthepercentageoftherecoveredbinderwaslessthan30%,themixedbindercouldmatchthetechnicalstandardsforfreshasphalt.TestsonthemixturesshowedthatruttingresistanceincreasedgraduallyasRAPpercentageincreased,whilethermalanti-crackingatlowtemperatureandfatiguepropertiesdeclined.TheeffectofthepercentageofRAPonmoisturesusceptibilitywaslimited.ItisindicatedthatlowtemperatureperformanceandfatiguepropertiesareimportantforselectingtheoptimalpercentageofRAP.Basedondataobtainedfrombindersandmixtures,itisconcludedthatthemaximumpercentageofRAPisapproximately30%withouttheadditionofrejuvenatingagent.

简介：我们在经历了激进的前列腺切除术(RP)的病人之中根据前列腺癌症的clinicopathological特征调查了瘤参与(PTI)的百分比的预示的意义。没有任何neoadjuvant或辅助治疗，经历了在2003年9月和2008年3月之间的RP的534个病人的回顾的研究被执行。有各种各样的clinicopathological特征和生物化学的没有复发的幸存的PTI的协会经由uni-和multivariate分析被检验。multivariate模型的预兆的精确性与在曲线下面操作导出特征的区域的接收装置被估计。PTI被表明显著地与外科手术前的前列腺特定的抗原(PSA)被联系水平(P=0.001)，病理学的格利森分数(P<；0.001)，extraprostatic瘤扩展(P<；0.001)，精囊侵略(P<；0.001)并且积极外科的边缘(P<；0.001)在univariate分析。当病人们被成层进疾病风险组时，PTI在低风险的组(P=0.033)然而并非中介之中仅仅是在multivariate分析的生物化学的没有复发的幸存的一个独立预言者--(P=0.287)或高风险的组(P=0.828)。PTI的增加显著地没增加分别地在彻底的病人(P=0.459)和低风险的组(P=0.268)之中为生物化学的没有复发的幸存的预言设计的multivariate模型的精确性。在结论，尽管PTI看起来比在有更高的风险疾病的那些之中是在有低风险的疾病的病人之中的一个更重要的预示的因素，总的来说，PTI不能提供在罐头已经经由确定的预示的因素被获得的以外的另外的预示的信息。

简介：Inthepresentpaper,twomodelsbasedonartificialneuralnetworksandgeneticprogrammingforpredictingsplittensilestrengthandpercentageofwaterabsorptionofconcretescontainingZrO2nanoparticleshavebeendevelopedatdifferentagesofcuring.Forbuildingthesemodels,trainingandtestingusingexperimentalresultsfor144specimensproducedwith16differentmixtureproportionswereconducted.Thedatausedinthemultilayerfeedforwardneuralnetworksmodelsandinputvariablesofgeneticprogrammingmodelswerearrangedinaformatofeightinputparametersthatcoverthecementcontent,nanoparticlecontent,aggregatetype,watercontent,theamountofsuperplasticizer,thetypeofcuringmedium,ageofcuringandnumberoftestingtry.Accordingtotheseinputparameters,intheneuralnetworksandgeneticprogrammingmodels,thesplittensilestrengthandpercentageofwaterabsorptionvaluesofconcretescontainingZrO2nanoparticleswerepredicted.ThetrainingandtestingresultsintheneuralnetworkandgeneticprogrammingmodelshaveshownthattwomodelshavestrongpotentialforpredictingthesplittensilestrengthandpercentageofwaterabsorptionvaluesofconcretescontainingZrO2nanoparticles.Ithasbeenfoundthatneuralnetwork(NN)andgeneexpressionprogramming(GEP)modelswillbevalidwithintherangesofvariables.Inneuralnetworksmodel,asthetrainingandtestingendedwhenminimumerrornormofnetworkgained,thebestresultswereobtainedandingeneticprogrammingmodel,when4geneswereselectedtoconstructthemodel,thebestresultswereacquired.Althoughneuralnetworkhavepredictedbetterresults,geneticprogrammingisabletopredictreasonablevalueswithasimplermethodratherthanneuralnetwork.