Modelling Plant Disease Epidemics

An epidemic is the progress of disease in time and space. Each epidemic has a structure whose temporal dynamics and spatial patterns are jointly determined by the pathosystem characteristics and environmental conditions. One of the important objectives in epidemiology is to understand such spatio-temporal dynamics via mathematical and statistical modelling. In this paper, we outline common methodologies that are used to quantify and model spatio-temporal dynamics of plant diseases, with emphasis on developing temporal forecast models and on quantifying spatial patterns. Several examples of epidemiological models in cereal crops are described, including one for Fusarium head blight.     

基于K-means动态聚类的鸢乌贼角质颚模式识别

本研究采用K-means动态聚类算法,对2014-2019年间采集于西北印度洋、热带东太平洋、中国南海的鸢乌贼(Sthenoteuthis oualaniensis)样本的角质颚进行识别.基于K-means动态聚类算法能够很好地区分来自3个海区的鸢乌贼,对数据进行z-score标准化后,任选2维角质颚形态学参数以曼哈顿...     

基于神经网络的树叶识别系统研究

在林业系统注册树木种类时,通常采用大量树叶上的可见外形特点来辨别数目繁多的树木种类。针对此问题,本文提出了基于神经网络的树叶识别方法,该方法建立并管理一个分等级的树叶图像体系,通过对不同种类树叶的边缘提取来识别每种树叶的特征点,从而得到树叶的外部轮廓来区分树木种类:给出了相应的改进型神经网络算法,并给出详细的论述过程:最后,通过Java语言给出了系统实现并做了大量的数值仿真。结果证明本文方法是可行有效的。图4表1参8。     

An aggregation index (AI) to quantify spatial patterns of landscapes

There is often need to measure aggregation levels of spatial patterns within a single map class in landscape ecological studies. The contagion index (CI), shape index (SI), and probability of adjacency of the same class (Qi), all have certain limits when measuring aggregation of spatial patterns. We have developed an aggregation index (AI) that is class specific and independent of landscape composition. AI assumes that a class with the highest level of aggregation (AI =1) is comprised of pixels sharing the most possible edges. A class whose pixels share no edges (completely disaggregated) has the lowest level of aggregation (AI =0). AI is similar to SI and Qi, but it calculates aggregation more precisely than the latter two. We have evaluated the performance of AI under varied levels of (1) aggregation, (2) number of patches, (3) spatial resolutions, and (4) real species distribution maps at various spatial scales. AI was able to produce reasonable results under all these circumstances. Since it is class specific, it is more precise than CI, which measures overall landscape aggregation. Thus, AI provides a quantitative basis to correlate the spatial pattern of a class with a specific process. Since AI is a ratio variable, map units do not affect the calculation. It can be compared between classes from the same or different landscapes, or even the same classes from the same landscape under different resolutions.     

Soil saturation effects on forest dynamics: scaling across a southern boreal/northern hardwood landscape

Patch modeling can be used to scale-up processes to portray landscape-level dynamics. Via direct extrapolation, a heterogeneous landscape is divided into its constituent patches; dynamics are simulated on each representative patch and are weighted and aggregated to formulate the higher level response. Further extrapolation may be attained by coarsening the resolution of or lumping environmental data (e.g., climatic, edaphic, hydrologic, topographic) used to delimit a patch.Forest patterns at the southern boreal/northern hardwood transition zone are often defined by soil heterogeneity, determined primarily by the extent and duration of soil saturation. To determine how landscape-level dynamics predicted from direct extrapolation compare when coarsening soil parameters, we simulated forest dynamics for soil series representing a range of drainage classes from east- central Maine. Responses were aggregated according to the distribution of soil associations comprising a 600 ha area based on local- (1:12,000), county- (1:120,000) and state- (1:250,000) scale soil maps. At the patch level, simulated aboveground biomass accumulated more slowly in poorer draining soils. Different soil series yielded different communities comprised of species with various tolerances for soil saturation. When aggregated, removal of waterlogging caused a 20–60% increase in biomass accumulation during the first 50 years of simulation. However, this early successional increase and the maximum level of biomass accumulation over a 200 year period varied by as much as 40% depending on the geospatial data. This marked discrepancy suggests caution when extrapolating with forest patch models by coarsening parameters and demonstrates how rules used to rescale environmental data need to be evaluated for consistency.     

叶顶间隙对多相混输泵内流动特性的影响

为了揭示叶顶间隙对多相混输泵内流动特性的影响规律,在进口含气率为10%时,利用ANSYS CFX软件对不同叶顶间隙下泵内气液两相流态进行模拟,同时分析和总结了叶顶间隙对流道内流动特性的影响.研究表明,当叶顶间隙增加时,叶轮叶片进口附近低压区占比明显减少,并且叶轮进出口的压差也在逐渐减小.另外,在靠近叶片顶端时,压力等值线存在偏折现象.随着叶顶间隙逐渐增加,偏折加剧.存在叶顶间隙时,叶轮轮毂附近气相聚集得到了明显的改善,并且随着叶顶间隙的增加,气相在叶顶间隙内靠近叶顶处的聚集现象更加明显,导叶轮毂气相聚集位置沿着流动方向向下游移动.研究结果可为多相混输泵水力优化、性能改善和结构设计等提供依据.同时,研究结果进一步揭示了多相混输泵叶顶间隙流对混输泵性能的影响规律.     

开垦对土壤团聚体含量及微生物群落代谢多样性的影响

以新疆玛纳斯河流域盐渍化弃耕地为研究对象,基于Biolog生态平板法,分析开垦前后土壤团聚体组成分布及团聚体微生物代谢多样性的变化,从而评价人为开垦对弃耕地土壤质量的影响。结果表明:弃耕地开垦增加了各土层0.25 mm团聚体的含量,增加范围在3%～18%。弃耕地开垦前后土壤均以0.25～0.053 mm团聚体比例最高,与其它各粒径团聚体差异显著(P0.05)。开垦后土壤团聚体微生物平均颜色变化率显著高于弃耕地。弃耕地0.053 mm团聚体微生物群落AWCD显著高于0.25 mm各粒径团聚体,开垦后各粒径团聚体微生物群落AWCD关系为:2～1 mm0.25～0.053 mm1～0.25 mm(0.053 mm)(5 mm)5～2 mm。开垦增加了0.25 mm团聚体微生物物种丰富度指数,而0.25 mm团聚体却相反;开垦后土壤各粒径团聚体微生物均匀度指数均显著高于弃耕地,而土壤微生物优势度指数均低于弃耕地,其中在5～2、2～1、1～0.25 mm和0.25～0.053 mm四个粒径中更为显著。开垦前后土壤团聚体微生物对6大碳源利用强度存在着差异,弃耕地土壤团聚体微生物对多聚物的利用率最高,开垦后其优势碳源转变为碳水化合物。主成分分析表明,开垦前后土壤团聚体微生物群落碳源利用在PC1上出现差异。碳水化合物类、羧酸类和氨基酸类碳源是影响土壤团聚体微生物代谢多样性的主要碳源。总体来说,开垦对土壤团聚体微生物群落代谢多样性具有重要影响。     

蝗虫的聚集行为机制研究进展

蝗虫具有聚集为害的特征。群居型蝗虫形态、生理、生化、行为等与散居型明显不同。蝗虫的密度、资源的空间分布方式、化学信息素和特定行为均能够引起蝗虫聚集行为反应。蝗群聚集后,通过遵守简单的行为规则,形成了一个高度有序的复杂系统,在这个有序的复杂系统里群体蝗虫能够取得散居个体不能获取的生态效益。     

新西兰实验兔体重及体尺增长模式

对自繁培育的普通级新西兰兔不同时期体重、体长、胸围、耳长及尾长的增长模式进行观察，结果表明：哺乳期（０～６周龄）体重及体尺增长比较缓慢，断奶后迅速增长；其中体重日均增长，０～２周龄最低，为９．２１ｇ，１２～１６周龄最高，为３７．２４ｇ；体长与耳长的日均增长，２～４周龄最高，１２～１６周龄最低；胸围及尾长的日均增长则是４～８周龄最高，１２～１６周龄最低。