简介：Inmarineengineering,thestrengthofasubmarinesedimentisanindispensableparameterforassessmentofconstruction.Inthisstudy,afree-fallconepenetratornamedIPenwasdevelopedtorealizearapidandefficientmeasurementofsedimentstrength.Theequipmentischaracterizedbymodulardesignandself-containeddataacquisition.Itisequippedwithanaccelerationsensor,awaterpressuresensor,andapiezoconepenetrationtest(CPTu)probe.Itisdesignedtobereleasedfromnearseabedsurfacewithareleaserandthenfallfreelytoprovideahigherpenetrationvelocity.Itsmaximumworkingdepthisapproximately2500mandmaximumpenetrationdepthisapproximately3m.Toderivethecorrelationbetweenpenetrationresistanceandsedimentstrength,acalibratorwasdevisedtodeterminethepenetration-ratefactor.Inaddition,thefactorapplicabletoinsitutestpointswasdeterminedinlaboratoryexperiments.InJune2016,theIPenwastestedinsituintheSouthYellowSea,China,duringasharedvoyagefundedbytheNationalScienceFoundation.Meanwhile,undisturbedcolumnsampleswerecollectedforlaboratorytests.Basedontheinsitutestresults,itwasdemonstratedthattheIPencouldaccuratelyrecordtheworkingstatesofvarioussensorsduringthefreelyfallingcourse.IPentestresultsreliablyreflectedthesedimentstrengthatallthetestingpointswhencomparedwithlaboratorycalibrationtests,insituvanetestsandpenetrationtests,laboratorypenetrationtests,andunconsolidatedandundrainedtriaxialcompressiontests.

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.

简介：RiceisanimportantfoodcropinChina,andthedevelopmentofhybridriceisacrucialwaytoincreasegrainyield.Thecreationofdual-purposenuclear-sterilelinesfortwo-linehybridbreedinghasbecomevitalforcommercialricebreeding.WeconstructedthepC1300-2x35S::Cas9-sgRNAPTGMS2-1expressionvectorforeditingthemalefertilitygenePTGMS2-1intwowidelycompatiblericevarieties,93-11andHuazhan,byusingtheCRISPR/Cas9system.Weobtainedthemarker-freephotoperiod-/thermo-sensitivegenicmale-sterile(P/TGMS)linesinT1generation.Accordingtotheexperimentsinphytotronwithfourtemperatureandphotoperiodtreatments,wefoundthetemperatureisthemainfactorforrestoringthepollenfertilityofptgms2-1mutantsin93-11andHuazhan,andthephotoperiodalsohassomeeffectsonpollenfertilityintwodifferentricebackgrounds.Theapplicationofcultivatingnewmale-sterilelinesbygenomeeditingsystemwillsignificantlyacceleratethericebreedingprocess.