6,539 research outputs found

    OFT ascent/descent ancillary data requirements document

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    Requirements are presented for the ascent/descent (A/D) navigation and attitude-dependent ancillary data products to be generated for the space shuttle orbiter in support of orbital flight test requirements, MPAD guidance and navigation performance assessment, and the mission evaluation team. It was intended that this document serve as the sole requirements control instrument between MPB/MPAD and the A/D ancillary data users. The requirements are primarily functional in nature, but some detail level requirements are also included

    ELECTROKINETIC PHENOMENA : VI. RELATIONSHIP BETWEEN ELECTRIC MOBILITY, CHARGE, AND TITRATION OF PROTEINS

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    1. By combining the theories of Smoluchowski, Debye and Hückel, and Henry it is possible to state explicitly (making necessary assumptions) under what conditions the following simple rule should be valid for proteins: In solutions of the same ionic strength, the electric mobilities of the same protein at different hydrogen ion activities should be proportional to the number of hydrogen (hydroxyl) ions bound. 2. Data of Tiselius and of the writer confirm this rule for (a) egg albumin, (b) serum albumin, (c) deaminized gelatin and gelatin, and (d) casein. 3. On the basis of the confirmed theory the titration curves of certain proteins are predicted from their mobilities. 4. It is shown that when certain proteins are adsorbed by quartz the apparent dissociation constant of the adsorbed protein is practically unchanged. The mass law must also be valid at the phase boundary. 5. The facts of paragraphs (1) to (4) are discussed in connection with the mechanism of (a) protein adsorption, (b) enzyme activity, (c) immune reactions, (d) the calculation of the electric charge of cells, and (e) criteria of surface similarity

    Regression of ranked responses when raw responses are censored

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    We discuss semiparametric regression when only the ranks of responses are observed. The model is Yi=F(xi′β0+εi)Y_i = F (\mathbf{x}_i'{\boldsymbol\beta}_0 + \varepsilon_i), where YiY_i is the unobserved response, FF is a monotone increasing function, xi\mathbf{x}_i is a known p−p-vector of covariates, β0{\boldsymbol\beta}_0 is an unknown pp-vector of interest, and εi\varepsilon_i is an error term independent of xi\mathbf{x}_i. We observe {(xi,Rn(Yi)):i=1,…,n}\{(\mathbf{x}_i,R_n(Y_i)) : i = 1,\ldots ,n\}, where RnR_n is the ordinal rank function. We explore a novel estimator under Gaussian assumptions. We discuss the literature, apply the method to an Alzheimer's disease biomarker, conduct simulation studies, and prove consistency and asymptotic normality.Comment: 33 pages, 6 figure

    Application of Sequential Quadratic Programming to Large-Scale Structural Design Problems

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    Large-scale structural optimization problems are often difficult to solve with reasonable efficiency and accuracy. Such problems are often characterized by constraint functions which are not explicitly defined. Constraint and gradient functions are usually expensive to evaluate. An optimization approach which uses the NLPQL sequential quadratic programming algorithm of Schittkowski, integrated with the Automated Structural Optimization System ASTROS is tested. The traditional solution approach involves the formulation and solution of an explicitly defined approximate problem during each iteration. This approach is replaced by a simpler approach in which the approximate problem is eliminated. In the simpler approach, each finite element analysis is followed by one iteration of the optimizer. To compensate for the cost of additional analyses incurred by the elimination of the approximate problem, a much more restrictive active set strategy is used. The approach is applied to three large structures problems, including one with constraints from multiple disciplines. Results and algorithm performance comparisons are given. Although not much computational efficiency is gained, the alternative approach gives accurate solutions. The largest of the three problems, which had 1527 design variables and 6124 constraints was solved with ASTROS for the first time using a direct method. The resulting design represents the lowest weight feasible design recorded to date
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