Substructure: Clues to the Formation of Clusters of Galaxies

We have examined the spatial distribution of substructure in clusters of galaxies using Einstein X-ray observations. Subclusters are found to have a markedly anisotropic distribution that reflects the surrounding matter distribution on supercluster scales. Our results suggest a picture in which cluster formation proceeds by mergers of subclusters along large-scale filaments. The implications of such an anisotropic formation process for the shapes, orientations and kinematics of clusters are discussed briefly.Comment: 7 pages, uuencoded compressed postscript. To appear in ApJ Letters (September 20, 1995 issue

Do Stock Prices Really Reflect Fundamental Values? The Case of REITs

Real estate investment trust (REIT) stock prices deviate substantially from net asset values (NAV). Using REIT data since 1990, we find large positive excess returns to a strategy of buying stocks that trade at a discount to NAV, and shorting stocks trading at a premium to NAV. Estimated alphas from this strategy are between 0.9% and 1.8% per month, with little risk. Trading costs and short-sale constraints are not prohibitive and the results strengthen when we control for differences in liquidity or the extent of institutional ownership. We find that some variation in P/NAV makes sense, as premiums are positively related to recent and future NAV growth. However, there appears to be too much volatility in P/NAV, giving rise to potential profits from short-term mean reversion. The closed-end fund literature has some similar findings on stock price deviations from fundamental value, but compared to closed-end funds REITs are much larger and have much higher insider and institutional ownership. These differences suggest that REIT premiums and discounts reflect more than just small investor sentiment, which is a common explanation of why closed-end fund prices deviate from their fundamental value.

Stagnation-point heat-transfer rate predictions at aeroassist flight conditions

The results are presented for the stagnation-point heat-transfer rates used in the design process of the Aeroassist Flight Experiment (AFE) vehicle over its entire aeropass trajectory. The prediction methods used in this investigation demonstrate the application of computational fluid dynamics (CFD) techniques to a wide range of flight conditions and their usefulness in a design process. The heating rates were computed by a viscous-shock-layer (VSL) code at the lower altitudes and by a Navier-Stokes (N-S) code for the higher altitude cases. For both methods, finite-rate chemically reacting gas was considered, and a temperature-dependent wall-catalysis model was used. The wall temperature for each case was assumed to be radiative equilibrium temperature, based on total heating. The radiative heating was estimated by using a correlation equation. Wall slip was included in the N-S calculation method, and this method implicitly accounts for shock slip. The N-S/VSL combination of projection methods was established by comparison with the published benchmark flow-field code LAURA results at lower altitudes, and the direct simulation Monte Carlo results at higher altitude cases. To obtain the design heating rate over the entire forward face of the vehicle, a boundary-layer method (BLIMP code) that employs reacting chemistry and surface catalysis was used. The ratio of the VSL or N-S method prediction to that obtained from the boundary-layer method code at the stagnation point is used to define an adjustment factor, which accounts for the errors involved in using the boundary-layer method

Intracluster Globular Clusters

Globular cluster populations of supergiant elliptical galaxies are known to vary widely, from extremely populous systems like that of UGC 9799, the centrally dominant galaxy in Abell 2052, to globular-cluster-poor galaxies such as NGC 5629 in Abell 2666. Here we propose that these variations point strongly to the existence of a population of globular clusters that are not bound to individual galaxies, but rather move freely throughout the cores of clusters of galaxies. Such intracluster globular clusters may have originated as tidally stripped debris from galaxy interactions and mergers, or alternatively they may have formed in situ in some scenarios of globular cluster formation.Comment: 9 pages, uuencoded compressed postscript. Accepted for publication in the Astrophysical Journal Letter

Alternative electrical energy sources for Maine

Prepared for the Central Maine Power Company Summary report.--Appendix A. Glaser, C. and Ruane, M. Conversion of biomass.--Appendix B. Jones, W. J. [et al.]. Conservation.--Appendix C. Waterflow, A. Geothermal energy conversion.--Appendix D. Ruane, M. Ocean thermal energy conversion.--Appendix E. Jones, W. J. Fuel cells.--Appendix F. Geary, J. and Jones, W. J. Solar energy conversion.--Appendix G. Ruane, M. Conversion of solid wastes.--Appendix H. Ruane, M. Storage of energy.--Appendix I. Mays, J. Wave energy conversion.--Appendix J. Mays, J. Ocean and riverine current energy conversion.--Appendix K. Labuszewski, T. Wind energy conversion.--Appendix L. Gruhl, J. Environmental impacts

U.S. Demand for Source–Differentiated Shrimp: A Differential Approach

Estimates of price and scale elasticities for U.S. consumed shrimp are derived using aggregate shrimp data differentiated by source country. Own-price elasticities for all countries had the expected negative signs, were statistically significant, and inelastic. The scale elasticities for all countries were positive and statistically significant at the 1% level with only the United States and Ecuador having scale elasticities of less than one. For the most part, the compensated demand effects showed that most of the cross-price effects were positive. Our results also suggest that despite the countervailing duties imposed by the United States, shrimp demand was fairly stable.CBS, conditional demand, countervailing duty, imports, scale elasticity, shrimp, Agribusiness, Farm Management, Food Consumption/Nutrition/Food Safety, Production Economics, C32, D12, Q13, Q22,