磁场处理种子对玉米籽粒灌浆过程和产量性状的影响

玉米种子经0.003mT,50mT和100mT的磁场处理后，灌浆速度增大，籽粒中营养物质含量增高；穗粒数、粒重等性状得以改善，双穗率增加，空秆和倒折率降低，产量提高2.06%-9.46%；与对照相比丹玉13号0.003mT、掖单19号100mT处理的增产效果达到了5％显水平，掖单19号0.003mT达到1％显水平。     

北方粳稻籽粒多胺合成与灌浆的关系

以不同水稻品种为试材,研究了籽粒多胺含量与灌浆进程的关系,结果表明:在灌浆期 ,籽粒多胺含量依次为:腐胺穴PUT雪>亚精胺SPD>精胺SPM;随花后时间的推移,各种胺 含量逐渐减小。势粒中,SPD与PUT变化趋势大致相同,在雪灌浆期呈下降趋势;在灌浆 后期,各品种籽粒SPD和PUT含量差别不大,SPM含量下降幅度较PUT和SPD小。常规稻沈 农8714与沈农 8801在不同灌浆阶段SPD含量均高于杂交稻辽优18与辽优3225。弱势粒 中, 不同品种多胺含量在整个灌浆期间差别均较大, PUT、SPD和SPM含量降低速度均 较快, 而且峰谷出现的时间也提前。PUT含量最高时灌浆速率最大沈农8801, SPM 对 灌浆后期籽粒充实起主要作用。     

不同水稻品种播期与品质的关系

通过对6个品种的分期播种试验,研究了稻米品质性状在播期间和年季间的变异特征。通过对各播期米质性状的综合评定得出,沈农315、奥羽316和沈农9457适于推迟播期到第2期或第3期;垩白是辽宁省地区推迟播期、品质改良的关键因素,垩白度较高的品种随着播期延迟垩白度显著降低,相反则升高;温度因子中灌浆结实期的日较差和齐穗后的有效积温对品质性状影响较大;品种的灌浆速率与垩白度有较大关系,而播期对灌浆速率影响很大。     

湖南烤烟总糖、还原糖含量与几种土壤养分的关系分析

研究了湖南烤烟177份烟叶样品糖含量与土壤养分的关系。结果表明:(1)烟叶糖含量在样品间存在着广泛的变异,B2F、C3F和X2F等级的总糖含量分别为22.28±3.32%、24.84±2.91%和24.18±3.49%,还原糖含量分别为19.69±2.47%、22.10±2.55%和21.33±3.08%;(2)不同等级烟叶中糖含量与土壤全氮和碱解氮含量呈负相关,而与土壤全钾和速效钾含量呈正相关,但不同等级烟叶中影响糖含量的主导因子不尽一致;(3)对土壤中全氮、全钾、碱解氮和速效钾含量进行分组,研究了烟叶总糖和还原糖含量在不同组间的变化,表明烟叶糖含量一般随土壤钾素含量增加而增加,随土壤氮素含量增加而降低。     

土壤有机质含量与玉米生产力的关系

土壤有机质是土壤肥力的重要指标。本文通过空间移位的方法,将东北黑土带由南向北5个点有机质含量为18.1g kg-1、31.1g kg-1、54.6g kg-1、103.9g kg-1、53.6g kg-1的农田黑土,分别移至黑龙江省的海伦市和吉林省的德惠市的两种气候下,通过田间试验的方法,研究了黑土有机质含量与玉米生产力的关系。结果表明,在相同的施肥条件下,土壤有机质含量与玉米产量间不存在显著相关关系,产量差异不显著;施肥对各种有机质含量的黑土均有显著增产作用,增产幅度在12.3%～64.1%,黑土带的南部区域德惠市的施肥增产作用要明显高于北部区域海伦市的增产作用。     

Relationship Between Support Vector Set and Kernel Functions in SVM

Based on a constructive learning approach,covering algorithms,we investigate the relationship between support vector sets and kernel functions in support vector machines (SVM).An interesting result is obtained.That is,in the linearly non-separable case,any sample of a given sample set K can become a support vector under a certain kernel function.The result shows that when the sample set K is linearly non-separable,although the chosen kernel function satisfies Mercer‘s condition its corresponding support vector set is not necessarily the subset of K that plays a crucial role in classifying K.For a given sample set,what is the subset that plays the crucial role in classification?In order to explore the problem,a new concept,boundary or boundary points,is defined and its properties are discussed.Given a sample set K,we show that the decision functions for classifying the boundary points of K are the same as that for classifying the K itself.And the boundary points of K only depend on K and the structure of the space at which k is located and independent of the chosen approach for finding the boundary.Therefore,the boundary point set may become the subset of K that plays a crucial role in classification.These results are of importance to understand the principle of the support vector machine(SVM) and to develop new learning algorithms.