Use of Taguchi Design of Experiments to Determine ALPLS Ascent Delta-5 Sensitivities and Total Mass Sensitivities to Release Conditions and Vehicle Parameters

The objective of this study is to evaluate the use of Taguchi's Design of Experiment Methods to improve the effectiveness of this and future parametric studies. Taguchi Methods will be applied in addition to the typical approach to provide a mechanism for comparing the results and the cost or effort necessary to complete the studies. It is anticipated that results of this study should include an improved systematic analysis process, an increase in information obtained at a lower cost, and a more robust, cost effective vehicle design

Characterization and Defect Analysis of Machined Regions in Al-SiC Metal Matrix Composites Using an Abrasive Water Jet Machining Process

Metal matrix composite (MMC) materials are increasingly used in industrial sectors such as energy, structural, aerospace, and automotive. This is due to the improvement of properties by the addition of reinforcements. Thus, it is possible to obtain properties of higher strength, better rigidity, controlled thermal expansion, and elevated wear resistance. However, due to the extreme hardness achieved during their manufacture, these composites pose a challenge to the conventional machining industry due to the rapid deterioration experienced by cutting tools. This article therefore proposes the use of an unconventional machining method that is becoming increasingly widely used: abrasive water jet cutting. This process is characterized by high production rates, absence of wear, and environmental friendliness, among other advantages. Experimental tests were carried out in order to analyze results that minimize the formation of defects in the machining of metal matrix composite consisting of aluminium matrix with silicon carbide (Al-SiC MMC). To this end, results were analyzed using Scanning Optical and Electron Microscope (SOM/SEM) techniques, the taper angle was calculated, and areas with different surface quality were detected by measuring the roughness

Multi-level algorithms for modularity clustering

Modularity is one of the most widely used quality measures for graph clusterings. Maximizing modularity is NP-hard, and the runtime of exact algorithms is prohibitive for large graphs. A simple and effective class of heuristics coarsens the graph by iteratively merging clusters (starting from singletons), and optionally refines the resulting clustering by iteratively moving individual vertices between clusters. Several heuristics of this type have been proposed in the literature, but little is known about their relative performance. This paper experimentally compares existing and new coarsening- and refinement-based heuristics with respect to their effectiveness (achieved modularity) and efficiency (runtime). Concerning coarsening, it turns out that the most widely used criterion for merging clusters (modularity increase) is outperformed by other simple criteria, and that a recent algorithm by Schuetz and Caflisch is no improvement over simple greedy coarsening for these criteria. Concerning refinement, a new multi-level algorithm is shown to produce significantly better clusterings than conventional single-level algorithms. A comparison with published benchmark results and algorithm implementations shows that combinations of coarsening and multi-level refinement are competitive with the best algorithms in the literature.Comment: 12 pages, 10 figures, see http://www.informatik.tu-cottbus.de/~rrotta/ for downloading the graph clustering softwar

Application of new probabilistic graphical models in the genetic regulatory networks studies

This paper introduces two new probabilistic graphical models for reconstruction of genetic regulatory networks using DNA microarray data. One is an Independence Graph (IG) model with either a forward or a backward search algorithm and the other one is a Gaussian Network (GN) model with a novel greedy search method. The performances of both models were evaluated on four MAPK pathways in yeast and three simulated data sets. Generally, an IG model provides a sparse graph but a GN model produces a dense graph where more information about gene-gene interactions is preserved. Additionally, we found two key limitations in the prediction of genetic regulatory networks using DNA microarray data, the first is the sufficiency of sample size and the second is the complexity of network structures may not be captured without additional data at the protein level. Those limitations are present in all prediction methods which used only DNA microarray data.Comment: 38 pages, 3 figure

Edge-weighting of gene expression graphs

In recent years, considerable research efforts have been directed to micro-array technologies and their role in providing simultaneous information on expression profiles for thousands of genes. These data, when subjected to clustering and classification procedures, can assist in identifying patterns and providing insight on biological processes. To understand the properties of complex gene expression datasets, graphical representations can be used. Intuitively, the data can be represented in terms of a bipartite graph, with weighted edges corresponding to gene-sample node couples in the dataset. Biologically meaningful subgraphs can be sought, but performance can be influenced both by the search algorithm, and, by the graph-weighting scheme and both merit rigorous investigation. In this paper, we focus on edge-weighting schemes for bipartite graphical representation of gene expression. Two novel methods are presented: the first is based on empirical evidence; the second on a geometric distribution. The schemes are compared for several real datasets, assessing efficiency of performance based on four essential properties: robustness to noise and missing values, discrimination, parameter influence on scheme efficiency and reusability. Recommendations and limitations are briefly discussed