基于一种树型方法的自动推理

根据表方法建立了一种树型证明方法,在机器实现方面比表方法优越,而且还可以证明这一方法是可靠的和完备的.以PROLOG语言为背景,实现了由此方法而建立的命题逻辑中命题演绎的自动推理.

基于树型方法计算双基链的研究与实现

标量乘法是影响椭圆曲线加密算法执行效率的一个重要因素,椭圆曲线的标量乘法的方法很多,基于双基链的标量乘法是一种效率较好的算法,文中讨论基于树型方法计算双基链表示的基本原理和在Delphi环境下的实现。

减小MPU运算中舍入误差的树型分析方法

采用ＭＰＵ进行浮点运算时的舍入误差现象的分析，提出了一种减小舍入误差的树型分析法。利用此分析方法可以确定运算顺序，这种顺序能使表达式运算过程中的舍入误差减至最小程度，不必引入多倍字长运算，从而达到减少数据存储容量和提高执行速度的目的．

Method and standard for selecting elite nut trees of Korean pine

The fruit-bearing quantities of nut Korean pines (Pinus Koraiensis) of natural stands in Changbai Mountain, Xiaoxing'an Mountain, and Wanda Mountain and of artificial forest in Hegang area of Heilongjiang Province were investigated and measured by seed collection of singletree during 1988–1998. In order to evaluate the elite nut tree of fructification, the characteristics of fructification of Korena pine, including, the fruit-bearing quantity, output of seed, quantity of cone, cone size, seed size, the ratio of null seed by solid seed, seed percentage of cone, rate of the cones infested with pest, and fruit-bearing index, etc., were analyzed with the variance analysis, multiple comparison and stepwise regression to obtain the contribution ratio of each fruit-bearing factor to fruit-bearing quantity. The multiple correlation factors and the partial correlation factors for fruit-bearing quantities of Korean pine were determined for different geographical areas, and the cone length, thousand-grain-weight, and the seed percentage of cone were considered as important indices for selection of elite trees. The method of modified weighted coefficients was adopted to select the elite nut trees of Korean pine. Standards for selecting elite nut trees from the natural stands and artificial forest of Korean pine were established. This study could provde selection method and standard of elite nut trees for setting up seed orchard of Korean Pine.

Relationship between Microcellular Foaming Injection Molding Process Parameters and Cell Size

In order to study the relationship between the main process parameters and the cell size, the mathematical model of cell growth of microcellular foaming injection process is built. Then numeric simulation is employed as experimental method, and the Taguchi method is used to analyze significance of effect of process parameters on the cell size. At last the process parameters are focused on melt temperature, injection time, mold temperature and pretidied volume. The significance order from big to small of the effect of each process parameters on cell size is melt temperature, pre-filled volume, injection time, and mold temperature. On the basis of above research, the effect of each process parameter on cell size is further researched. Appropriate reduction of the melt temperature and increase of the pre-filled volume can optimize the cell size effectively, while the effects of injection time and mold temperature on cell size are less significant.

Bayesian texture segmentation based on wavelet domain hidden markov tree and the SMAP rule

According to the sequential maximum a posteriori probability (SMAP) rule, this paper proposes a novel multi-scale Bayesian texture segmentation algorithm based on the wavelet domain Hidden Markov Tree (HMT) model. In the proposed scheme, interscale label transition probability is directly defined and resoled by an EM algorithm. In order to smooth out the variations in the homogeneous regions, intrascale context information is considered. A Gaussian mixture model (GMM) in the redundant wavelet domain is also exploited to formulate the pixel-level statistical features of texture pattern so as to avoid the influence of the variance of pixel brightness. The performance of the proposed method is compared with the state-of-the-art HMTSeg method and evaluated by the experiment results.

Hierarchical topic modeling with nested hierarchical Dirichlet process

This paper deals with the statistical modeling of latent topic hierarchies in text corpora. The height of the topic tree is assumed as fixed, while the number of topics on each level as unknown a priori and to be inferred from data. Taking a nonpara-metric Bayesian approach to this problem, we propose a new probabilistic generative model based on the nested hierarchical Dirichlet process (nHDP) and present a Markov chain Monte Carlo sampling algorithm for the inference of the topic tree structure as well as the word distribution of each topic and topic distribution of each document. Our theoretical analysis and experiment results show that this model can produce a more compact hierarchical topic structure and captures more fine-grained topic rela-tionships compared to the hierarchical latent Dirichlet allocation model.

Dendrimer-based magnetic resonance imaging agents for brain cancer

Brain cancer is one of the most lethal and difficult-to-treat cancers because of its physical location and biological barriers. The mainstay of brain cancer treatment is surgical resection, which demands precise imaging for tumor localization and delineation. Thanks to advances in bioimaging, brain cancer can be detected earlier and resected more reliably. Magnetic resonance imaging（MRI） is the most common and preferred method to delineate brain cancer, and a contrast agent is often required to enhance imaging contrast.Dendrimers, a special family of synthetic macromolecules,constitute a particularly appealing platform for constructing MRI contrast agents by virtue of their well-defined three-dimensional structure, tunable nanosize and abundant surface terminals, which allow the accommodation of high payloads and numerous functionalities. Tuning the dendrimer size,branching and surface composition in conjunction with conjugation of MRI functionalities and targeting moieties can alter the relaxivity for MRI, overcome the blood-brain barrier and enhance tumor-specific targeting, hence improving the imaging quality and safety profile for precise and accurate imaging of brain tumors. This short review highlights the recent progress, opportunities and challenges in developing dendrimer-based MRI contrast agents for brain tumor imaging.