两种含水炸药动压下减敏作用的实验研究

用实验方法对两种含水炸药—水胶炸药和珍珠岩敏化的乳化炸药动压下的减敏作用进行了研究。为了更全面深入地研究这问题,提出了临界压死距离和临界距离两个概念。研究表明,含水炸药的减敏作用与药包间距和延期时差关系很大。水胶炸药的临界压死距离大于乳化炸药,而它们的临界距离相当,这表明两种炸药发生减敏的难易程度相近,但是乳化炸药的抗压死能力比水胶炸药大。采用5段延期电雷管引爆时两种含水炸药的减敏作用比采用2段延期电雷管引爆严重些。     

TbFe磁性薄膜的磁力显微镜成像研究

利用磁力显微镜（MFM）对TbFe磁性薄膜进行了不同抬举距离（分别为60～780nm）的磁力成像研究．在实验中，比较了低抬举距离（100nm以下）磁力像中样品-针尖的互相干扰；同时发现在高抬举距离磁力像中，随着抬举距离的增大，出现了与高凸起形貌对应的图像衬度特征，并随抬举距离的变化也改变着位置与强度，一直到抬举距离为仪器的极限值780nm时，形貌干扰仍未消失，对其形成机制进行了分析．     

On the number of broadcast schemes in networks

Broadcasting is the process of spreading one piece of information among a group of individuals connected by an interconnection network. In this paper we give exact lower and upper bounds for the number of broadcast schemes in arbitrary networks. Also, we give the exact value for complete bipartite graphs and an upper bound for regular networks. Based on the counting method we describe a new random algorithm for broadcasting in networks.     

近距离下覆4#煤层综采工作面顶板管理经验

介绍了近距离深层综采工作面顶板和巷道顶板管理经验，及综采工作面在通过巷道冒项区域时所采取的办法和措施。     

Modern Teaching and Training in Metallurgical Engineering

This contribution deals with advanced educational technologies needed to equip customers from higher education institutions, research and industry with efficient tools supporting their work and operating new skills‐training methods. The challenges are reducing the training costs, improving quality and increasing the number of graduates in engineering departments. The concept of a Virtual Lab based on the combination of various teaching methods and tools is presented. Principles of mathematisation in metallurgical education and training are discussed. An example of online course designed in the form of Virtual Lab is demonstrated.     

Evaluation codes at singular points of algebraic differential equations

We use the special geometry of singular points of algebraic differential equations on the affine plane over finite fields to study the main features and parameters of error correcting codes giving by evaluating functions at sets of singular points. In particular, one gets new methods to construct codes with designed minimum distance. This work was partially supported by MCyT BFM2001-2251.     

Subspace distance analysis with application to adaptive Bayesian algorithm for face recognition

We propose subspace distance measures to analyze the similarity between intrapersonal face subspaces, which characterize the variations between face images of the same individual. We call the conventional intrapersonal subspace average intrapersonal subspace (AIS) because the image differences often come from a large number of persons. An intrapersonal subspace is referred to as specific intrapersonal subspace (SIS) if the image differences are from just one person. We demonstrate that SIS varies significantly from person to person, and most SISs are not similar to AIS. Based on these observations, we introduce the maximum a posteriori (MAP) adaptation to the problem of SIS estimation, and apply it to the Bayesian face recognition algorithm. Experimental results show that the adaptive Bayesian algorithm outperforms the non-adaptive Bayesian algorithm as well as Eigenface and Fisherface methods if a small number of adaptation images are available.     

D-side: A facility and workforce planning group multi-criteria decision support system for Johnson Space Center

“To understand and protect our home planet, to explore the universe and search for life, and to inspire the next generation of explorers” is NASA's mission. The Systems Management Office at Johnson Space Center (JSC) is searching for methods to effectively manage the Center's resources to meet NASA's mission. D-Side is a group multi-criteria decision support system (GMDSS) developed to support facility decisions at JSC. D-Side uses a series of sequential and structured processes to plot facilities in a three-dimensional (3-D) graph on the basis of each facility's alignment with NASA's mission and goals, the extent to which other facilities are dependent on the facility, and the dollar value of capital investments that have been postponed at the facility relative to the facility's replacement value. A similarity factor rank orders facilities based on their Euclidean distance from Ideal and Nadir points. These similarity factors are then used to allocate capital improvement resources across facilities. We also present a parallel model that can be used to support decisions concerning allocation of human resources investments across workforce units. Finally, we present results from a pilot study where 12 experienced facility managers from NASA used D-Side and the organization's current approach to rank order and allocate funds for capital improvement across 20 facilities. Users evaluated D-Side favorably in terms of ease of use, the quality of the decision-making process, decision quality, and overall value-added. Their evaluations of D-Side were significantly more favorable than their evaluations of the current approach.     

Automatic processing of psychological distance: Evidence from a Stroop task.

A picture-word version of the Stroop task was used to test the automatic activation of psychological distance by words carrying various senses of psychological distance: temporal (tomorrow, in a year), social (friend, enemy), and hypotheticality (sure, maybe). The pictures implied depth, with the words appearing relatively close to or distant from the observer. The participants classified the spatial distance of words faster when the word's implicit psychological distance matched its spatial distance (e.g., a geographically close word was classified faster when it was "friend" than when it was "enemy"). The findings are consistent with the idea that psychological distance is accessed automatically, even when it is not directly related to people's current goals, and suggest that psychological distance is an important dimension of meaning, common to spatial distance, temporal distance, social distance, and hypotheticality. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

On principal component analysis, cosine and Euclidean measures in information retrieval

Clustering groups document objects represented as vectors. An extensive vector space may cause obstacles to applying these methods. Therefore, the vector space was reduced with principal component analysis (PCA). The conventional cosine measure is not the only choice with PCA, which involves the mean-correction of data. Since mean-correction changes the location of the origin, the angles between the document vectors also change. To avoid this, we used a connection between the cosine measure and the Euclidean distance in association with PCA, and grounded searching on the latter. We applied the single and complete linkage and Ward clustering to Finnish documents utilizing their relevance assessment as a new feature. After the normalization of the data PCA was run and relevant documents were clustered.