三阶时滞泛函微分方程解的渐近性

本文讨论一类三阶时滞泛函微分方程解的渐近性质,给出了若干解的有界性及解趋于零的判定准则.     

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     

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。