加速广义Laguerre多项式级数的Laplace变换的数值反演方法

本文利用Levin t-变换迭代法，对加速广义Laguerre多项式级数，提出了一种新的Laplace变换的数值反演方法，这种方法在精度上和数值稳定性上的效果都较好。     

两种一年生植物在替代试验中的相互竞争关系

（1）用替代试验研究了燕麦（Aneva sativa）和箭舌豌豆（Vicia sativa）在混播中的竞争相互作用。为了攻混播比例对两种植物在混播条件下的行为影响，田间试验设为5个混播总密度和5种混播比例，共了5次。（2）方差分析表明，相对总生物量（RYT）和竞争平衡指数（CBI）对总密度，混播比例和生长时间的反应敏感。总密度对RYT和CBI的影响高度显著，在大多数情况下，混播比例对RYT和CBI的影响不显著。（3）本文研究的结果表明，持续时间对RYT和CBI很强的影响，在第1次测定时，RYT值接近1而BI值接近0，这一事实表明，种间无竞争或互惠作用。而在生长旺盛期，种间竞争较为剧烈。（4）本研究结果表明，箭舌豌豆的生长严惩受到燕麦的抑制作用，这说明箭舌豌豆在与燕麦混播时表现为竞争上的弱者。     

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     

小麦矮秆育种中性状间关系的多元分析

以7个株高梯度系列的35个系统及5个亲本品种为材料,采用典范分析和逐步回归分析研究了株高及其构成因子与产量因子、产量生理性状间的关系。结果表明:①株高因子通过产量生理性状影响产量因子,一定的生物学产量是获得高产的基础。②矮秆品种的高化对提高产量、千粒重和收获指数有利,对蛋白质含量的影响不明显;高秆品种矮化能显著提高收获指数,对籽粒的蛋白质含量影响不大。③在主要性状符合育种目标的前提下,选择倒二节间较长、第一和第二叶间距略长、穗颈和穗部较长的类型可能对提高产量有利。     

Trends in NE Atlantic landings (southern Portugal): identifying the relative importance of fisheries and environmental variables

Time series of commercial landings from the Algarve (southern Portugal) from 1982 to 1999 were analyzed using min/max autocorrelation factor analysis (MAFA) and dynamic factor analysis (DFA). These techniques were used to identify trends and explore the relationships between the response variables (annual landings of 12 species) and explanatory variables [sea surface temperature, rainfall, an upwelling index, Guadiana river (south‐east Portugal) flow, the North Atlantic oscillation, the number of licensed fishing vessels and the number of commercial fishermen]. Landings were more highly correlated with non‐lagged environmental variables and in particular with Guadiana river flow. Both techniques gave coherent results, with the most important trend being a steady decline over time. A DFA model with two explanatory variables (Guadiana river flow and number of fishermen) and three common trends (smoothing functions over time) gave good fits to 10 of the 12 species. Results of other models indicated that river flow is the more important explanatory variable in this model. Changes in the mean flow and discharge regime of the Guadiana river resulting from the construction of the Alqueva dam, completed in 2002, are therefore likely to have a significant and deleterious impact on Algarve fisheries landings.     

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。