A new betweenness centrality measure based on an algorithm for ranking the nodes of a network

We propose and discuss a new centrality index for urban street patterns represented as networks in geographical space. This centrality measure, that we call ranking-betweenness centrality, combines the idea behind the random-walk betweenness centrality measure and the idea of ranking the nodes of a network produced by an adapted PageRank algorithm. We initially use a PageRank algorithm in which we are able to transform some information of the network that we want to analyze into numerical values. Numerical values summarizing the information are associated to each of the nodes by means of a data matrix. After running the adapted PageRank algorithm, a ranking of the nodes is obtained, according to their importance in the network. This classification is the starting point for applying an algorithm based on the random-walk betweenness centrality. A detailed example of a real urban street network is discussed in order to understand the process to evaluate the ranking-betweenness centrality proposed, performing some comparisons with other classical centrality measures.

A Model to Visualize Information in a Complex Streets’ Network

his paper discusses a process to graphically view and analyze information obtained from a network of urban streets, using an algorithm that establishes a ranking of importance of the nodes of the network itself. The basis of this process is to quantify the network information obtained by assigning numerical values to each node, representing numerically the information. These values are used to construct a data matrix that allows us to apply a classification algorithm of nodes in a network in order of importance. From this numerical ranking of the nodes, the process finish with the graphical visualization of the network. An example is shown to illustrate the whole process.

Growing Neural Gas approach for obtaining homogeneous maps by restricting the insertion of new nodes

The Growing Neural Gas model is used widely in artificial neural networks. However, its application is limited in some contexts by the proliferation of nodes in dense areas of the input space. In this study, we introduce some modifications to address this problem by imposing three restrictions on the insertion of new nodes. Each restriction aims to maintain the homogeneous values of selected criteria. One criterion is related to the square error of classification and an alternative approach is proposed for avoiding additional computational costs. Three parameters are added that allow the regulation of the restriction criteria. The resulting algorithm allows models to be obtained that suit specific needs by specifying meaningful parameters.

Behavior of different soot combustion catalysts under NOx/O2. Importance of the catalyst–soot contact

Four different catalysts (Pt/Al2O3, Ce0.8Zr0.2O2, PrO2−x and SrTiCuO3) have been investigated on a laboratory scale to evaluate their potential as diesel soot combustion catalysts under different experimental conditions, which simulate the situation found in a continuous regeneration technology trap (dual-bed configuration of catalyst and soot) or a catalyst-coated filter system (single-bed configuration, both catalyst and soot particles mixed under loose-contact mode). Under dual-bed configuration, the behavior of the catalysts towards soot combustion are very similar, despite the differences observed in the NO2 production profiles. However, under single-bed configuration, there are important differences in the soot combustion activities and in the NO2 slip profiles. The configurations chosen have an enormous impact on CO/(CO + CO2) ratios of combustion products as well. The most active catalyst under NOx + O2 is PrO2−x combining a high contribution of active oxygen-assisted soot combustion as well as high NO2 production activity along the catalytic bed.

Importance of the Support Properties for Immobilization or Purification of Enzymes

Immobilization and purification of enzymes are usual requirements for their industrial use. Both purification and immobilization have a common factor: they use a solid activated support. Using a support for enzyme purification means having mild conditions for enzyme release and a selective enzyme–support interaction is interesting. When using a support for immobilization, however, enzyme desorption is a problem. The improvement of enzyme features through immobilization is a usual objective (e.g., stability, selectivity). Thus, a support designed for enzyme purification and a support designed for enzyme immobilization may differ significantly. In this review, we will focus our attention on the requirements of a support surface to produce the desired objectives. The ideal physical properties of the matrix, the properties of the introduced reactive groups, the best surface activation degree to reach the desired objective, and the properties of the reactive groups will be discussed.