Design knowledge capture for the space station

The benefits of design knowledge availability are identifiable and pervasive. The implementation of design knowledge capture and storage using current technology increases the probability for success, while providing for a degree of access compatibility with future applications. The space station design definition should be expanded to include design knowledge. Design knowledge should be captured. A critical timing relationship exists between the space station development program, and the implementation of this project

An approach to design knowledge capture for the space station

The design of NASA's space station has begun. During the design cycle, and after activation of the space station, the reoccurring need will exist to access not only designs, but also deeper knowledge about the designs, which is only hinted in the design definition. Areas benefiting from this knowledge include training, fault management, and onboard automation. NASA's Artificial Intelligence Office at Johnson Space Center and The MITRE Corporation have conceptualized an approach for capture and storage of design knowledge

Centrifuge liquefaction tests in a laminar box

The difficulties associated with instrumenting earthquake sites in order to record pore pressure changes in a future event led to the use of scaled model tests performed in a centrifuge. Both dry and saturated sands were employed, contained in a box constructed of aluminium laminae designed to move freely on each other. This would result in shearing distortions developing in the soil unimpeded by the container. Accelerometers, displacement transducers and pore pressure sensors were attached to the box and embedded in the soil at various elevations so as to record the response of the soil to an earthquake-like excitation supplied to the base of the container. A special apparatus was constructed to imitate earthquake motion. In some tests on saturated sand, the soil profile was liquefied. Test results of accelerations, lateral and vertical displacements and pore pressures against time for typical earthquake inputs are given. The data, obtained under controlled conditions, can be compared with the various calculation methods for dynamically generated pore pressures

Considerations for a design and operations knowledge support system for Space Station Freedom

Engineering and operations of modern engineered systems depend critically upon detailed design and operations knowledge that is accurate and authoritative. A design and operations knowledge support system (DOKSS) is a modern computer-based information system providing knowledge about the creation, evolution, and growth of an engineered system. The purpose of a DOKSS is to provide convenient and effective access to this multifaceted information. The complexity of Space Station Freedom's (SSF's) systems, elements, interfaces, and organizations makes convenient access to design knowledge especially important, when compared to simpler systems. The life cycle length, being 30 or more years, adds a new dimension to space operations, maintenance, and evolution. Provided here is a review and discussion of design knowledge support systems to be delivered and operated as a critical part of the engineered system. A concept of a DOKSS for Space Station Freedom (SSF) is presented. This is followed by a detailed discussion of a DOKSS for the Lyndon B. Johnson Space Center and Work Package-2 portions of SSF

Synthesis, characterization and bioactivity of mixed-ligand Cu(II) complexes containing Schiff bases derived from S-benzyldithiocarbazate and saccharinate ligand and the X-ray crystal structure of the copper-saccharinate complex containing S-benzyl-β-N-(acetylpyrid-2-yl)methylenedithiocarbazate

Mixed-ligand complexes of general formula, [Cu(NNS)(sac)] (NNS′ = S-benzyl-β-N-(2-acetylpyrid-2-yl)methylenedithiocarbazate, NNS″ = S-benzyl-β-N-(2-benzoylpyrid-2-yl)methylenedithiocarbazate and NNS = S-benzyl-β-N-(6-methylpyrid-2-yl)methylenedithio-carbazate, sac = the saccharinate anion) have been synthesized by reacting [Cu(sac)2(H2O)4] · 2H2O with the appropriate ligands in ethanol and characterized by various physico-chemical techniques. Magnetic and spectral evidence indicate that the complexes are four-coordinate in which the Schiff bases coordinate as NNS ligands and the sac- anion coordinates as a unidentate N-donor ligand. An X-ray crystallographic structural analysis of [Cu(NNS′)(sac)] shows that the complex has a distorted square-planar geometry with the Schiff base coordinated to the copper (II) ion as a uninegatively charged tridentate chelating agent via the pyridine nitrogen atom, the azomethine nitrogen atom and the thiolate sulphur atom while the fourth coordination position is occupied by the N-bonded saccharinate anion. The complexes have been evaluated for their biological activities against selected pathogens and cancer cell lines. They display weak activity against the pathogenic bacteria and fungi. The complexes were highly active against the leukemic cell line (HL-60) but only [Cu(NNS′)(sac)] was found to exhibit strong cytotoxicity against the ovarian cancer cell line (Caov-3). All complexes were inactive against the breast cancer cell line (MCF-7)

Modelling of big game populations when hunting is age and sex selective

The main objective of this work is to model the dynamics of cynegetic populations by means of a linear control system. We want to estimate the annual corrective measures, which may be improvements or hunting, that must be implemented to achieve and maintain a study population around the carrying capacity of a certain area. Moreover, the model must be able to distinguish between different age-sex classes and lead the population to a desired distribution of individuals among these stage groups.Supported by the Spanish DGI grant MTM2010-18228 and by the UPV under its research program PAID-06-10.Cantó Colomina, R.; Ricarte Benedito, B.; Urbano Salvador, AM. (2011). Modelling of big game populations when hunting is age and sex selective. Mathematical and Computer Modelling. 57(7-8):1744-1750. https://doi.org/10.1016/j.mcm.2011.11.027S17441750577-

High Fat Relative to Low Fat Ground Beef Consumption Lowers Blood Pressure and Does Not Negatively Alter Arterial Stiffness

Beef consumption has been stigmatized as an unhealthy dietary choice. However, randomized control trials to support this claim are lacking. PURPOSE: To examine the effect of low-fat (5%) and high-fat (25%) ground beef consumption on blood pressure (BP) and carotid-femoral pulse wave velocity (PWV).METHODS: Twenty-three male subjects (age 40±11 yrs, height 177.4±6.7 cm, weight 97.3±25.0 kg, lean mass 64.5±9.5 kg, fat mass 30.6±19.1 kg) volunteered to participate in this cross-over design study. Each participant completed two, 5-week ground beef interventions in a randomized order with a 4-week washout period in-between. All participants visited the lab four times after an overnight fast. Each visit to the lab consisted of supine BP, dual energy x-ray absorptiometry (DXA) scan to assess body composition, and PWV analysis. The PWV recording was assessed on the right carotid and femoral arteries. The distance used for the PWV calculation was 80% of the actual distance between carotid and femoral sites. All PWV measures were completed according to previously published procedures (Van Bortel, 2011). BP and PWV results were analyzed separately via 2x2 repeated measures ANOVA. RESULTS: Our results indicate there was a significant decrease in systolic BP (p=0.01) following the high-fat ground beef intervention compared to the low-fat. The BP values for low-fat beef and high-fat beef are 120/74 and 116/73 mmHg, respectively. Further, there were no significant differences between the PWV measures. CONCLUSION: Based on our results, high fat ground beef favorably alters systolic BP and does not negatively affect PWV measures

Bis{N′-[3-(4-nitrophenyl)-1-phenylprop-2-en-1-ylidene]-N-phenylcarbamimidothioato}zinc(II): crystal structure, Hirshfeld surface analysis and computational study

The title zinc bis­(thio­semicarbazone) complex, [Zn(C22H17N4O2S)2], comprises two N,S-donor anions, leading to a distorted tetra­hedral N2S2 donor set. The resultant five-membered chelate rings are nearly planar and form a dihedral angle of 73.28 (3)°. The configurations about the endocyclic- and exocyclic-imine bonds are Z and E, respectively, and that about the ethyl­ene bond is E. The major differences in the conformations of the ligands are seen in the dihedral angles between the chelate ring and nitro­benzene rings [40.48 (6) cf. 13.18 (4)°] and the N-bound phenyl and nitro­benzene ring [43.23 (8) and 22.64 (4)°]. In the crystal, a linear supra­molecular chain along the b-axis direction features amine-N—H...O(nitro) hydrogen bonding. The chains assemble along the 21-screw axis through a combination of phenyl-C—H...O(nitro) and π(chelate ring)–π(phen­yl) contacts. The double chains are linked into a three-dimensional architecture through phenyl-C—H...O(nitro) and nitro-O...π(phen­yl) inter­actions

Structured Sparsity: Discrete and Convex approaches

Compressive sensing (CS) exploits sparsity to recover sparse or compressible signals from dimensionality reducing, non-adaptive sensing mechanisms. Sparsity is also used to enhance interpretability in machine learning and statistics applications: While the ambient dimension is vast in modern data analysis problems, the relevant information therein typically resides in a much lower dimensional space. However, many solutions proposed nowadays do not leverage the true underlying structure. Recent results in CS extend the simple sparsity idea to more sophisticated {\em structured} sparsity models, which describe the interdependency between the nonzero components of a signal, allowing to increase the interpretability of the results and lead to better recovery performance. In order to better understand the impact of structured sparsity, in this chapter we analyze the connections between the discrete models and their convex relaxations, highlighting their relative advantages. We start with the general group sparse model and then elaborate on two important special cases: the dispersive and the hierarchical models. For each, we present the models in their discrete nature, discuss how to solve the ensuing discrete problems and then describe convex relaxations. We also consider more general structures as defined by set functions and present their convex proxies. Further, we discuss efficient optimization solutions for structured sparsity problems and illustrate structured sparsity in action via three applications.Comment: 30 pages, 18 figure