简介：Polystyrene(PS)fiberswithcore-shellstructureswerefabricatedbycoaxialelectrostaticspinning,[10_TD$IF]inwhichthereareliquidepoxyorcuringagentasthecoreandPSastheshell.Scanningelectronmicroscopy(SEM),Fouriertransforminfrared(FTIR)spectraandopticalmicroscopewereutilizedforcharacteringthemorphologyandcompositionofthefibers.Compositecoatingsembeddedwiththehealant-loadedcoreshellfibershavebeenpreparedandtheself-healingofthescratchonthecoatingshasbeenrevealed.

简介：Inthispaper,weinvestigatetheproblemofasize-constrainedk-coregroupquery(SCCGQ)insocialnetworks,takingbothuserclosenessandnetworktopologyintoconsideration.Morespecifically,SCCGQintendstofindagroupofhusersthathasthehighestsocialclosenesswhilebeingak-core.SCCGQcanbewidelyappliedtoeventplanning,taskassignment,socialanalysis,andmanyotherfields.Incontrasttoexistingworkonthek-coredetectionproblem,whichaimstofindak-coreinasocialnetwork,SCCGQnotonlyfocusesonk-coredetectionbutalsotakessizeconstraintsintoconsideration.Althoughtheconventionalk-coredetectionproblemcanbesolvedinlineartime,SCCGQhasahighercomplexity.TosolvetheproblemofSCCGQ,weproposeaBlastScatter(BS)algorithm,whichappointsthequerynodeasthecentertobeginoutwardexpansionsviabreadthsearch.Ineachoutwardexpansion,BSfindsanewcenterthroughagreedystrategyandthenselectsmultipleneighborsofthecenter.TospeeduptheBSalgorithm,weproposeanadvancedsearchalgorithm,calledBoundedExtension(BE).Specifically,BEcombinesaneffectivesocialdistancepruningstrategyandatightupperboundofsocialclosenesstoprunethesearchspaceconsiderably.Inaddition,weproposeanoffiinesocial-awareindextoacceleratethequeryprocessing.Finally,ourexperimentalresultsdemonstratetheefficiencyandeffectivenessofourproposedalgorithmsonlargereal-worldsocialnetworks.

简介：Thestructureandkineticsofthecomplexformedbyhyaluronicacid(HA)andpoly(L-lysine)(PLL)werestudiedbytimeresolvedlaserlightscattering,TEM,andAFM.BecauseHAhasahydrophilicbackbone,thecomplexesformedbyHAandPLLaredifferentfromthoseformedbyoppositelychargedpolyelectrolytesbothhavinghydrophobicbackbones.Insteadofformingstrongaggregatesandevenprecipitates,thecomplexinthepresenceofexcessHAisstableinthestudiedtimeperiod.Moreimportantly,thecomplexspontaneouslyformscore-coronastructurebytherearrangementofHAchains.ThecoreiscomposedofcomplexrichofPLLandthecoronaismainlyHA.FurtheranalysisshowsthatthehydrogenbondformedbyHAcreatesabarrierhinderingthefurtherrelaxationofHAchains.Theautomaticformationofcore-coronastructurebyPLL/HAishelpfulnotonlytounderstandtherelaxationofpolyelectrolyteincomplex,butalsotodevelopdrugcarrierswithdesirableproperties.

简介：Withtheadventofthebigdataera,theamountsofsamplingdataandthedimensionsofdatafeaturesarerapidlygrowing.Itishighlydesiredtoenablefastandefficientclusteringofunlabeledsamplesbasedonfeaturesimilarities.Asafundamentalprimitivefordataclustering,thek-meansoperationisreceivingincreasinglymoreattentionstoday.Toachievehighperformancek-meanscomputationsonmodernmulti-core/many-coresystems,weproposeamatrix-basedfusedframeworkthatcanachievehighperformancebyconductingcomputationsonadistancematrixandatthesametimecanimprovethememoryreusethroughthefusionofthedistance-matrixcomputationandthenearestcentroidsreduction.Weimplementandoptimizetheparallelk-meansalgorithmontheSW26010many-coreprocessor,whichisthemajorhorsepowerofSunwayTaihuLight.Inparticular,wedesignataskmappingstrategyforload-balancedtaskdistribution,adatasharingschemetoreducethememoryfootprintandaregisterblockingstrategytoincreasethedatalocality.Optimizationtechniquessuchasinstructionreorderinganddoublebufferingarefurtherappliedtoimprovethesustainedperformance.Discussionsonblock-sizetuningandperformancemodelingarealsopresented.Weshowbyexperimentsonbothrandomlygeneratedandreal-worlddatasetsthatourparallelimplementationofk-meansonSW26010cansustainadouble-precisionperformanceofover348.1Gflops,whichis46.9%ofthepeakperformanceand84%ofthetheoreticalperformanceupperboundonasinglecoregroup,andcanachieveanearlyidealscalabilitytothewholeSW26010processoroffourcoregroups.Performancecomparisonswiththepreviousstate-of-the-artonbothCPUandGPUarealsoprovidedtoshowthesuperiorityofouroptimizedk-meanskernel.