小麦条锈病流行程度趋势预测专家系统雏型

本文运用知识工程语言 M·1构造了一个用于小麦条锈病流行程度趋势预测的专家系统雏型。专家的知识用产生式规则来表示。知识库中的知识规则可相对独立,便于知识库的扩充和完善。     

GI PiCO2: Tissue Specific Monitoring For Improving Patient Outcomes

Improvements in human patient monitoring despite their development in animals, do not always find their way into veterinary clinical use due to financial constraints. Gastrointestinal intraluminal CO₂ partial pressure (Gip₁CO₂) monitoring, however, is not only proving very beneficial in human trauma and critical patient care but is also very likely to become relatively inexpensive. By providing information on the perfusion adequacy of a high risk, critically important tissue, the GI mucosa, GI P₁CO₂ monitoring offers an easily accesible indicator of the efficacy and adequacy of resuscitative interventions. The potential for decreasing morbidity and mortality is enormous. Therefore, the practicing veterinarian should become familiar with GI P₁CO₂ monitoring theory and technology so he or she can be better prepared to incorporate it into practice when in becomes available.     

应用积分GM(1,1)模型进行杉木炭疽病预测预报的研究

以积分生成代替传统的累加生成方法 ,并用双向差分建立积分GM (1,1)灰色预测模型进行杉木炭疽病预测预报的研究 ,结果表明积分GM (1,1)模型能较好地预测杉木炭疽病在三明市的发生     

酥梨采后灰霉病生防菌株的鉴定及抑菌作用

以微生物拮抗病原菌是防治病害安全高效的可行手段。通过对果实表面微生物进行筛选鉴定,以期得到一株可以有效抑制灰霉病的生物防治菌株。对拮抗菌株H-1进行形态学特征、生理生化特性分析,结合16S rDNA序列分析和gyrB 基因序列分析构建系统发育树;采用平板对峙法结合果实伤口试验考察拮抗菌H-1在体外及梨果表面的抑菌作用。结果表明：经鉴定H-I菌株为贝莱斯芽孢杆菌（Bacillus velezensis）;B. velezensis H-1在体外条件下可显著抑制灰葡萄孢霉（Botrytis cinerea）、细极链格孢（Alternaria tenuissima）和串珠镰孢菌（Fusarium verticillioides）的菌丝生长,其中对灰葡萄孢霉菌丝的抑制作用最强;果实伤口试验表明,1×1011 CFU/mL的B. velezensis H-1可完全抑制梨果表面灰霉菌生长。由此可见,B. velezensis H-1可以有效抑制梨果采后灰霉病的发生。     

Predicting the spatial distribution of ground flora on large domains using a hierarchical Bayesian model

Accomodation of important sources of uncertainty in ecological models is essential to realistically predicting ecological processes. The purpose of this project is to develop a robust methodology for modeling natural processes on a landscape while accounting for the variability in a process by utilizing environmental and spatial random effects. A hierarchical Bayesian framework has allowed the simultaneous integration of these effects. This framework naturally assumes variables to be random and the posterior distribution of the model provides probabilistic information about the process. Two species in the genus Desmodium were used as examples to illustrate the utility of the model in Southeast Missouri, USA. In addition, two validation techniques were applied to evaluate the qualitative and quantitative characteristics of the predictions.This revised version was published online in May 2005 with corrections to the Cover Date.     

新亚欧大陆桥新疆段受损区段的灰色预测

在对新亚欧大陆桥新疆段受损区段、受损长度及受损频率和受损密度详尽分析的基础上，揭示了陆桥新疆段受损区段的时空分布规律。并依据灾害的时序分布特征，运用灰色原理和方法，根据受损区段数确立阈值和相应的年份序列，分别建立GM（1，1）模型，求出年份序列的序号响应式，并对实际值和理论预测结果进行了拟合检验，模型群的原点误差范围为0.198-1.314之间，平均误差为0.823-1.917，并同时预测了陆桥新疆段2001－2005年间可能的受损区段数。     

Further suppression of<Emphasis Type="Italic">Botrytis cinerea</Emphasis> disease in cucumber seedlings by chitosan-copper complex as compared with chitosan alone

Chitosan-copper complex compared with chitosan alone enhanced suppression ofBotrytis cinerea rot development on four-true-leaf cucumber seedlings in controlled growth chambers. This paper constitutes the first report of such enhancement. The optimal concentrations for the most effective suppression ofBotrytis development were 0.2 gl ⁻¹ chitosan and 1.6 mmole copper. After 12 days’ incubation, marked and significantly better disease suppression was obtained with chitosan-copper complex (75% suppression) than with chitosan alone. The chitosan-copper complex could be a very promising decay control agent for use in both conventional and organic agriculture. http://www.phytoparasitica.org posting Dec. 18, 2002.     

闭锁群体BLUP选择下遗传方差和近交系数的变化

采用计算机随机模拟方法模拟了在一个闭锁群体内连续对单个性状进行 1 5个世代选择的情况。选择过程中世代不重叠 ,每个世代的种畜根据动物模型最佳线性无偏预测 (BLUP)法估计的育种值进行选留 ,并在此基础上系统地比较了不同群体规模、公母比例和性状遗传力对群体遗传方差和近交系数变化的影响。结果表明 ,扩大育种群规模、增加公畜比例以及对低遗传力性状进行选择时 ,群体遗传方差降低的速度和近交系数上升的速度会更慢 ,在长期选择时可望获得更大的持续进展和适宜的近交增量     

ANN在森林资源预测中的应用研究

人工神经网络(ANN)方法是基于实例的方法,不需要考虑数学模型的内部结构,不需要假设前提条件,不需要人为地确定因子权重,作为一个黑箱综合地映射研究对象的整体性。应用人工神经网络多步预测方法对甘肃连城林场吐鲁沟营林区有林地面积进行预测,网络模型的最大相对误差为0 080 8％,最小相对误差达到0 0089％,平均为0.038 6％,表明预测值与实际值吻合程度很好,因此模型的精度较高,并且建模简单经预测,林场2000-2004年有林地面积稍有下降趋势,分别为2 873.2 hm2,2 618 7 hm2,2 484.5 hm2,2 346 hm2,2 171 6hm2。