简介：Light-weightcompositepanelsweremanufacturedusingkenafcoreparticlesascorematerialandkenafbastfiber-wovensheetsastopandbottomsurfaces.Methylenediphenyldiisocyanate(MDI)resinwasusedastheadhesivewiththeresincontentof4%forcoreparticlesand50g/m2forbastfiber-wovensheets.Thetargetboarddensitiesweresetat0.35,0.45and0.55g/cm3.ThecompositepanelswereevaluatedwithJapaneseIndustrialStandardforParticleboards(JISA5908-2003).Theresultsshowthatthecompositepanelhashighmodulusofruptureandinternalbondingstrength.Thepropertiesof0.45g/cm3densitycompositepanelare:MOR20.4MPa,MOE1.94MPa,IB0.36MPa,WA142%,TS21%.Kenafisagoodrawmaterialformakinglight-weightcompositepanels.

简介：当为林地生产提起机械化层次时，林地机械的合适的匹配是重要的。在匹配的过程，林地机械不必仅仅为解决问题的专家知识，而且改进方法。我由在一个分析、层次的过程计算各种各样的林地机器的量的参数的类似建议基于盒子的推理的联合(CBR)(RBR)并且基于规则的推理。我计算了在森林工业过去常启用更好的选择的机械的类似并且设备匹配。我建议基于单个参数重量在申请的进程在被修改的偏差的广场的和调整方法的重量值。在系统设计的过程期间，我提出了一个设计方法知识库并且产生了推理的一张动态网集成森林工业的过程的框架机械选择和重量值调整。这启用推理效率的完全的系统和改进的范围的扩大。我用一个实际例子表明这个方法的有效性和有实行可能。

简介：Moleculardesignbreedingisoneofstraightforwardapproachestobreakyieldbarriersinrice.Inthisstudy,GW6geneforgrainlengthandwidthfromBaodaliwastransferredintoanindicarecurrentparent9311andajaponicavarietyZhonghua11(ZH11)usingmarker-assistedbackcross(MAB).Oneandthreeintrogressionlineswereselectedforphenotypicanalysisfrom9311andZH11geneticbackgrounds,respectively.SSL-1,animproved9311nearisogeniclinewithGW6performed11%,19%and6.7%higherofgrainlength,1000-grainweightandsingleplantyield,respectively,ascomparedwith9311.AllthethreeimprovedZH11-GW6lines,R1,R2andR3,hadmorethan30%increaseingrainweightandabout7%higheringrainyield.SeedplumpnessofR1,R2andR3wasimprovedsynchronouslybecausethethreeZH11-GW6linescontainedGIF1(GrainIncompleteFilling1),adominantgrainfillinggene.Thus,GW6hashighpotentialinincreasingtheyieldofinbredlinesthroughMAB,makingitanimportantgeneticresourceinsuperhybridricebreeding.ThisstudyprovidesinsightsintheutilizationofGW6forlargegrainandhighyieldricebreedingviamoleculardesignbreeding.

简介：BPandRBFneuralnetworktopredictforeststockvolumewerestudied,butthestudyinevaluatingbothnetworks’applicationeffectswasnotconducted.Inordertofindahigherforecastprecision,morestrongapplicativemethod,thecomprehensiveanalysisandevaluationonthetwomethodswerecarriedoutinthepracticalapplication.Bythecorrelationanalysis,crowndensity,shady-slopeandsunny-slope,TM1,TM2,TM3,TM5,TM7,NDVI,TM,(4-3),TM4/3wereselectedasinputvariables,andtheforestvolumeofMiyunCountyasoutputvariables,RBFandBPneuralnetworkmodelsforforecastingtheforestvolumewereestablished.Andtheneuralnetworktrainingsteplength,trainingtime,predictionaccuracyandtheapplicabilitymodelofthetwomethodswerecomprehensivelyanalyzed.TheresultsshowthattheRBFneuralnetworkmodelissuperiortotheBPneuralnetworkmodel.

简介：Low-molecular-weight(LMW)organicacidsexistwidelyinsoilsandhavebeenimplicatedinmanysoilprocesses.TheobjectiveofthepresentpaperwastoevaluateeffectoftwoLMWorganicacids,citricacidandoxalicacid,onCl-adsorptionbythreevariablechargesoils,alatosol,alateriticredsoilandaredsoil,usingabatchmethod.TheresultsshowedthatthepresenceofcitricacidandoxalicacidledtoadecreaseinCl-adsorptionwithlargerdecreasesforcitricacid.AmongthedifferentsoilsGl-adsorptioninthelateriticredsoilandtheredsoilwasmoreaffectedbyboththeLMWorganicacidsthanthatinthelatosol.

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简介：Informationaboutthespatialdistributionofsoilattributesisindispensableformanylandresourcemanagementapplications;however,theabilityofsoilmapstosupplysuchinformationformodernmodelingtoolsisquestionable.Theobjectivesofthisstudyweretoinvestigatethepossibilityofpredictingsoildepthusingsometerrainattributesderivedfromdigitalelevationmodels(DEMs)withgeographicinformationsystems(GIS)andtosuggestanapproachtopredictothersoilattributes.Soildepthwasdeterminedat652fieldobservationsovertheAl-MuwaqqarWatershed(70km2)inJordan.Terrainattributesderivedfrom30-mresolutionDEMswereutilizedtopredictsoildepth.Theresultsindicatedthattheuseofmultiplelinearregressionmodelswithinsmallwatershedsubdivisionsenabledthepredictionofsoildepthwithadifferenceof50cmfor77%ofthefieldobservations.Thespatialdistributionofthepredictedsoildepthwasvisuallycoincidedandhadgoodcorrelationswiththespatialdistributionoftheclassesamalgamatingthreeterrainattributes,slopesteepness,slopeshape,andcompoundtopographicindex.Thesesuggestedthatthemodelingofsoil-landscaperelationshipswithinsmallwatershedsubdivisionsusingthethreeterrainattributeswasapromisingapproachtopredictothersoilattributes.

简介：高度直径关系是森林评价和当模特儿的努力的必要元素。在这个工作，二线性并且十八个非线性的高度直径方程被评估在伊朗在赫卡纳恩·福雷斯特为东方山毛榉(FagusorientalisLipsky)发现一个本地模型。这些模型的预兆的表演被不同评估标准首先估计：调整R2(R形容词2)，根均方差(RMSE)，相对RMSE(%RMSE)，偏爱，和亲戚偏导(%偏爱)标准。最好的模型用作基础混合效果模型被选择。为测试阴谋的随机的参数与不同的树选择选择被估计。结果证明Chapman-Richards当模特儿最好预兆的能力以形容词R2(0.81)，RMSE(3.7?m)，%RMSE(12.9)，偏爱(0.8)，%偏爱(2.79)比另外的模型。而且，基于从样品阴谋的四棵树的一种选择，刻度反应为大约1.6-2.7%的偏爱和RMSE导致了一个减小百分比。我们的结果显示校准的模型生产了最精确的结果。

简介：PhysiographyandsoilinMaeRimwatershed,ChiangMaiProvince,Thailandwereinvestigatedbyusingaerialphotographsandsatelliteimageinconjunctionwithfieldwork,andsoilinfiltrationrateandsoilshearresistanceweremeasuredinfield.ManyfactorsaffectingrunoffwereanalyzedusigtheIntegratedLandandWaterInformaitonSystem(ILWIS).Asaresult,amodeldeterminingfloodhazarwassetup.Threempsincludingrunoffcurvenumbermap,runoffcoefficentmap,andfloodinumdationmapwerecreated,Inaddition,thetimeofconcentrationwaspredicted.