Research and development study related to the synthesis of formaldehyde from CO2 and H2 Quarterly progress report, Aug. - Oct. 1966

Formaldehyde synthesis procedures under space conditions, with bibliograph

Research and Development Study Related to the Synthesis of Formaldehyde from CO2 and H2 Quarterly Progress Report, Feb. - Apr. 1967

Oxidation of methane to formaldehyde in reactors coated with potassium tetraborat

Research and development study related to the synthesis of formaldehyde from CO2 and H2 Quarterly progress report, May - Jul. 1967

Synthesis of formaldehyde by oxidation of methan

Research and development study related to synthesis of formaldehyde from CO2 and H2 Quarterly progress report, Nov. 1966 - Jan. 1967

Solid catalyst selection for formaldehyde synthesis by methane oxidatio

Design and fabrication of a prototype for an automatic transport system for transferring human and other wastes to an incinerator unit onboard spacecraft, phase A

Three transport system concepts were experimentally evaluated for transferring human and nonhuman wastes from a collection site to an incineration unit onboard spacecraft. The operating parameters, merits, and shortcomings of a porous-pneumatic, nozzle-pneumatic, and a mechanical screw-feed system were determined. An analysis of the test data was made and a preliminary design of two prototype systems was prepared

NEURAL NETWORKS IN FORECASTING AND DECISION MAKING

Neural networks (NN) have been widely touted as solving many forecasting and decision modeling problems. For example, they are argued to be able to model easily any type of parametric or non-parametric process and also automatically and optimally transform the input data. Also, they are easy to embed in information systems and they can learn how to perform simple forecasting and decision making tasks without human input. Our research-in-progress evaluates these claims. We will spend the first half of the session reviewing our work comparing neural networks to classical techniques in time series forecasting, regression-based causal forecasting, and regression-based decision models. In tile second half of the session, we will discuss the art and science of building these models. In Hill, O\u27Connor and Remus (1992), time series forecasts based on neural networks were compared with forecasts from six statistical time series methods (including exponential smoothing and Box-Jenkins) and two judgment-based methods; we did this for 111 real financial time series. The classical methods were all estimated by experts. Across all series, the neural networks did better than or as good as statistical and judgment methods. In Marquez et al. (forthcoming), data representing three common bivariate functional forms used in causal forecasting (linear, log-linear, and reciprocal) were generated and the performance of the neural network models was compared against the true regression model across differing functional forms, sample sizes, and noise levels. The results showed that neural network models perform within 2% of the mean absolute percentage error (MAPE); this is very good performance in the real world. This work is continuing as Marquez studies issues such as the vulnerability of neural networks and regression to multicolinearity, outliers, and other data problems. In Remus and Hill (forthcoming), tile production scheduling decisions as modeled by neural networks and regression-based decision rules for sixty-two decision makers were compared. Neural network models performed as well as but not better than those using the linear regression models. In Hill and Remus (forthcoming), the above research was continued and composite neural network models were estimated. The neural networks performed better than both the classical models and neural networks from the earlier study. The coinposite neural network also performed at least as well as classical composite models

Vacuum distillation/vapor filtration water recovery

The development and evaluation of a vacuum distillation/vapor filtration (VD/VF) water recovery system are considered. As a functional model, the system converts urine and condensates waste water from six men to potable water on a steady-state basis. The system is designed for 180-day operating durations and for function on the ground, on zero-g aircraft, and in orbit. Preparatory tasks are summarized for conducting low gravity tests of a vacuum distillation/vapor filtration system for recovering water from urine

Connecting Angular Momentum and Galactic Dynamics: The complex Interplay between Spin, Mass, and Morphology

The evolution and distribution of the angular momentum of dark matter (DM) halos have been discussed in several studies over the past decades. In particular, the idea arose that angular momentum conservation should allow to infer the total angular momentum of the entire DM halo from measuring the angular momentum of the baryonic component, which is populating the center of the halo, especially for disk galaxies. To test this idea and to understand the connection between the angular momentum of the DM halo and its galaxy, we use the Magneticum simulations. We successfully produce populations of spheroidal and disk galaxies self-consistently. Thus, we are able to study the dependence of galactic properties on their morphology. We find that (1) the specific angular momentum of stars in disk and spheroidal galaxies as a function of their stellar mass compares well with observational results; (2) the specific angular momentum of the stars in disk galaxies is slightly smaller compared to the specific angular momentum of the cold gas, in good agreement with observations; (3) simulations including the baryonic component show a dichotomy in the specific stellar angular momentum distribution when splitting the galaxies according to their morphological type (this dichotomy can also be seen in the spin parameter, where disk galaxies populate halos with slightly larger spin compared to spheroidal galaxies); (4) disk galaxies preferentially populate halos in which the angular momentum vector of the DM component in the central part shows a better alignment to the angular momentum vector of the entire halo; and (5) the specific angular momentum of the cold gas in disk galaxies is approximately 40 percent smaller than the specific angular momentum of the total DM halo and shows a significant scatter.Comment: 25 pages, accepted by ApJ, www.magneticum.or

Systematic variation of central mass density slope in early-type galaxies

We study the total density distribution in the central regions ($<\, 1$ effective radius, $R_{\rm e}$) of early-type galaxies (ETGs), using data from the SPIDER survey. We model each galaxy with two components (dark matter halo + stars), exploring different assumptions for the dark matter (DM) halo profile, and leaving stellar mass-to-light ($M_{\rm \star}/L$) ratios as free fitting parameters to the data. For a Navarro et al. (1996) profile, the slope of the total mass profile is non-universal. For the most massive and largest ETGs, the profile is isothermal in the central regions ($\sim R_{\rm e}/2$), while for the low-mass and smallest systems, the profile is steeper than isothermal, with slopes similar to those for a constant-M/L profile. For a concentration-mass relation steeper than that expected from simulations, the correlation of density slope with mass tends to flatten. Our results clearly point to a "non-homology" in the total mass distribution of ETGs, which simulations of galaxy formation suggest may be related to a varying role of dissipation with galaxy mass.Comment: 3 pages, 1 figure, to appear on the refereed Proceeding of the "The Universe of Digital Sky Surveys" conference held at the INAF--OAC, Naples, on 25th-28th november 2014, to be published on Astrophysics and Space Science Proceedings, edited by Longo, Napolitano, Marconi, Paolillo, Iodic