Capturing variability in Model Based Systems Engineering

International audienceAutomotive model-based systems engineering needs to be dapted to the industry specific needs, in particular by implementing appropriate means of representing and operating with variability. We rely on existing modeling techniques as an opportunity to provide a description of variability adapted to a systems en- gineering model. However, we also need to take into account requirements related to backwards compatibility with current practices, given the industry experience in mass customization. We propose to adopt the product line paradigm in model-based systems engineering by extending the orthogonal variability model, and adapting it to our specific needs. This brings us to an expression closer to a description of constraints, related to both orthogonal variability, and to SysML system models. We introduce our approach through a discussion on the different aspects that need to be covered for expressing variability in systems engineering. We explore these aspects by observing an automotive case study, and relate them to a list of contextual requirements for variability management

A Large, Uniform Sample of X-ray Emitting AGN: Selection Approach and an Initial Catalog from the ROSAT All-Sky and Sloan Digital Sky Surveys

Many open questions in X-ray astronomy are limited by the relatively small number of objects in uniform optically-identified samples, especially when rare subclasses are considered, or subsets isolated to search for evolution or correlations between wavebands. We describe initial results of a program aimed to ultimately yield 10^4 X-ray source identifications--a sample about an order of magnitude larger than earlier efforts. The technique employs X-ray data from the ROSAT All-Sky Survey (RASS), and optical imaging and spectroscopic followup from the Sloan Digital Sky Survey (SDSS). Optical objects in the SDSS catalogs are automatically cross-correlated with RASS X-ray source positions; then priorities for follow-on SDSS optical spectra of candidate counterparts are automatically assigned using an algorithm based on the known fx/fopt ratios for various classes of X-ray emitters. SDSS parameters for optical morphology, magnitude, colors, plus FIRST radio data, serve as proxies for object class. Initial application of this approach to 1400 deg^2 of sky provides a catalog of 1200 spectroscopically confirmed quasars/AGN that are probable RASS identifications. Most of the IDs are new, and only a few percent of the AGN are likely to be random superpositions. The magnitude and redshift ranges of the counterparts extend over 15<m<21 and 0.03<z<3.6. Although most IDs are quasars and Sy 1s, a variety of other AGN subclasses are also sampled. Substantial numbers of rare AGN are found, including more than 130 narrow-line Seyfert 1s and 45 BL Lac candidates. These results already provide a sizeable set of new IDs, show utility of the sample in multi-waveband studies, and demonstrate the capability of the RASS/SDSS approach to efficiently proceed towards the largest homogeneously selected/observed sample of X-ray emitting AGN. Abridged AbstractComment: 39 pages, 11 bitmapped figs (PDF view or print OK). Version accepted by AJ: slightly expanded sample, 1 new fig, minor modification

Multi-Method Analysis of MRI Images in Early Diagnostics of Alzheimer's Disease

The role of structural brain magnetic resonance imaging (MRI) is becoming more and more emphasized in the early diagnostics of Alzheimer's disease (AD). This study aimed to assess the improvement in classification accuracy that can be achieved by combining features from different structural MRI analysis techniques. Automatically estimated MR features used are hippocampal volume, tensor-based morphometry, cortical thickness and a novel technique based on manifold learning. Baseline MRIs acquired from all 834 subjects (231 healthy controls (HC), 238 stable mild cognitive impairment (S-MCI), 167 MCI to AD progressors (P-MCI), 198 AD) from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database were used for evaluation. We compared the classification accuracy achieved with linear discriminant analysis (LDA) and support vector machines (SVM). The best results achieved with individual features are 90% sensitivity and 84% specificity (HC/AD classification), 64%/66% (S-MCI/P-MCI) and 82%/76% (HC/P-MCI) with the LDA classifier. The combination of all features improved these results to 93% sensitivity and 85% specificity (HC/AD), 67%/69% (S-MCI/P-MCI) and 86%/82% (HC/P-MCI). Compared with previously published results in the ADNI database using individual MR-based features, the presented results show that a comprehensive analysis of MRI images combining multiple features improves classification accuracy and predictive power in detecting early AD. The most stable and reliable classification was achieved when combining all available features

Prostaglandin F-2 alpha-induced Prostate Transmembrane Protein, Androgen Induced 1 mediates ovarian cancer progression increasing epithelial plasticity

Cellular mechanisms in basic and clinical gastroenterology and hepatolog