Mapping Invasive Species Risks with Stochastic Models: A Cross-Border United States-Canada Application for Sirex noctilio Fabricius

Nonindigenous species have caused significant impacts to North American forests despite past and present international phytosanitary efforts. Though broadly acknowledged, the risks of pest invasions are difficult to quantify as they involve interactions between many factors that operate across a range of spatial and temporal scales: the transmission of invading organisms via various pathways, their spread and establishment in new environments. Our study presents a stochastic simulation approach to quantify these risks and associated uncertainties through time in a unified fashion. We outline this approach with an example of a forest pest recently detected in North America, Sirex noctilio Fabricius. We simulate new potential entries of S. noctilio as a stochastic process, based on recent volumes of marine shipments of commodities from countries where S. noctilio is established, as well as the broad dynamics of foreign marine imports. The results are then linked with a spatial model that simulates the spread of S. noctilio within the geographical distribution of its hosts (pines) while incorporating existing knowledge about its behavior in North American landscapes. Through replications, this approach yields a spatial representation of S. noctilio risks and uncertainties in a single integrated product. The approach should also be appealing to decisionmakers, since it accounts for projected flows of commodities that may serve as conduits for pest entry. Our 30-year forecasts indicate high establishment probability in Ontario, Quebec, and the northeastern United States, but further southward expansion of S. noctilio is uncertain, ultimately depending on the impact of recent international treatment standards for wood packing materials.     

Estimating utility functions using generalized maximum entropy

This paper estimates von Neumann and Morgenstern utility functions using the generalized maximum entropy (GME), applied to data obtained by utility elicitation methods. Given the statistical advantages of this approach, we provide a comparison of the performance of the GME estimator with ordinary least square (OLS) in a real data small sample setup. The results confirm the ones obtained for small samples through Monte Carlo simulations. The difference between the two estimators is small and it decreases as the width of the parameter support vector increases. Moreover, the GME estimator is more precise than the OLS one. Overall, the results suggest that GME is an interesting alternative to OLS in the estimation of utility functions when data are generated by utility elicitation methods.     

△-correct decision for location and scale parameters

In selecting t out of k populations, a △-correct decision is said to be made if the smallest location parameter for the selected populations is not more than △ below the largest location parameter for the non-selected populations. (For seal? parameters there is a similar definition in terms of ratio3.) The minimum probability of △-correct decision over the entire parameter pace is shown to be equal to the minimum probability of correct selection over a preference zone determined by △.     

冲旋钻头破岩机理仿真研究

介绍了冲旋钻井中冲旋钻头与岩石互作用有限元模型的建立过程，并对冲旋钻头在冲击旋转和单纯冲击载荷作用下的破岩过程进行了计算机仿真研究，描述了冲旋钻头破岩过程，求出了相应的钻头破碎岩石的深度、体积时程关系对比曲线，揭示了冲旋钻头的破岩机理——钻头在冲旋作用下破碎岩石的过程可分为4个阶段、钻头破碎岩石深度主要取决于冲击作用而与旋转作用关系不大、破岩体积在冲旋作用下比单纯冲击载荷作用下要大一倍左右，为冲旋钻头在石油工业中的进一步推广应用提供了理论依据。     

Optimal Information Processing and Bayes's Theorem

In this article statistical inference is viewed as information processing involving input information and output information. After introducing information measures for the input and output information, an information criterion functional is formulated and optimized to obtain an optimal information processing rule (IPR). For the particular information measures and criterion functional adopted, it is shown that Bayes's theorem is the optimal IPR. This optimal IPR is shown to be 100% efficient in the sense that its use leads to the output information being exactly equal to the given input information. Also, the analysis links Bayes's theorem to maximum-entropy considerations.     

On Comparison Of Estimates Of Dispersion Using Generalized Pitman Nearness Criterion

In this paper, estimates QP dispersion matrix and its functions are compared based on generalized Pitman nearness criterion, Various Iosa functions are considered for the purpose. Locally superior estimates are defined and obtained. Comparison of these estimates are made with other standard ones. It is snown that within certain classes, defined in the paper, these are the best estimatcrs ia the generalized Fitman nearness sense     

西安经济技术开发区二次创业发展阶段主导产业的选择及对策

该研究报告立足西安经济技术开发区的区情,在“三为主一致力”方针和主导产业选择基准的基础上,论证将重加工组装工业中的装备制造业和高新技术产业中的新兴产业提升为开发区二次创业阶段的两大主导产业,并细化到具体小行业,从而突出开发区有别于其它开发区的产业特色,同时分析预测开发区产业结构中长期演变趋势,最后针对性地提出扶持开发区二次创业阶段主导产业成长的九项对策建议。     

对马克思货币理论的思考

商品第一相对价值表现和商品的第一形态变化之间、商品第二相对价值表现和商品的第二形态变化之间,分别具有对应关系.这是在"货币理论带"或"货币理论三边形"的基础上,通过把价值方程式和商品的总形态变化公式的有关顶端项联系起来进行分析之后得出的逻辑的结论.理解和把握上述两种性质不同的价值表现和两种相反的形态变化之间分别具有的对应关系,对于从整体上理解和把握马克思的货币理论,具有重要意义.     

采用熵的多维K-匿名划分方法

K-匿名是数据发布应用场景下重要的隐私保护模型。近年来数据集K-匿名化的算法得到广泛的研究,Median Mondrian算法是目前唯一的多维K-匿名划分方法。文中研究了Median Mondrian算法,指出其不能有效地平衡数据划分精度与数据隐私安全性之间的矛盾,由此提出基于熵测度机制的多维K-匿名划分方法以及评估K-匿名化结果安全性的测量标准。实验表明该算法是可行的,能有效地提高数据安全性。     

Censored Kullback-Leibler Information and Goodness-of-Fit Test with Type II Censored Data

The Kulback-Leibler information has been considered for establishing goodness-of-fit test statistics, which have been shown to perform very well (Arizono & Ohta, 1989; Ebrahimi et al., 1992, etc). In this paper, we propose censored Kullback-Leibler information to generalize the discussion of the Kullback-Leibler information to the censored case. Then we establish a goodness-of-fit test statistic based on the censored Kullback-Leibler information with the type 2 censored data, and compare the test statistics with some existing test statistics for the exponential and normal distributions.