Sinatra Living: Elevation South

An architectural drawing plan of the Sinatra Living house

Sinatra Living: Elevation West

An architectural drawing plan of the Sinatra Living house

Sinatra Living: Elevation North

An architectural drawing plan of the Sinatra Living house

Elevation and cholera: an epidemiological spatial analysis of the cholera epidemic in Harare, Zimbabwe, 2008-2009

BACKGROUND: In highly populated African urban areas where access to clean water is a challenge, water source contamination is one of the most cited risk factors in a cholera epidemic. During the rainy season, where there is either no sewage disposal or working sewer system, runoff of rains follows the slopes and gets into the lower parts of towns where shallow wells could easily become contaminated by excretes. In cholera endemic areas, spatial information about topographical elevation could help to guide preventive interventions. This study aims to analyze the association between topographic elevation and the distribution of cholera cases in Harare during the cholera epidemic in 2008 and 2009. METHODS: We developed an ecological study using secondary data. First, we described attack rates by suburb and then calculated rate ratios using whole Harare as reference. We illustrated the average elevation and cholera cases by suburbs using geographical information. Finally, we estimated a generalized linear mixed model (under the assumption of a Poisson distribution) with an Empirical Bayesian approach to model the relation between the risk of cholera and the elevation in meters in Harare. We used a random intercept to allow for spatial correlation of neighboring suburbs. RESULTS: This study identifies a spatial pattern of the distribution of cholera cases in the Harare epidemic, characterized by a lower cholera risk in the highest elevation suburbs of Harare. The generalized linear mixed model showed that for each 100 meters of increase in the topographical elevation, the cholera risk was 30% lower with a rate ratio of 0.70 (95% confidence interval=0.66-0.76). Sensitivity analysis confirmed the risk reduction with an overall estimate of the rate ratio between 20% and 40%. CONCLUSION: This study highlights the importance of considering topographical elevation as a geographical and environmental risk factor in order to plan cholera preventive activities linked with water and sanitation in endemic areas. Furthermore, elevation information, among other risk factors, could help to spatially orientate cholera control interventions during an epidemic

Location of the elevation axis in a large optical telescope

Proposed designs for the next generation of large optical telescopes favor a tripod or quadrupod secondary support, and a primary supported from the back, but it is not yet clear whether the elevation axis should be in front of the primary or behind it. A study is described of the effect of elevation-axis location on key performance parameters (fundamental frequency, blockage, and wind-induced secondary decenter) for a 30-m Cassegrain telescope with a mount configuration that is typical of the new designs. For a fast (e.g., f/1) primary, the best location for the elevation axis is behind the primary. The penalty for moving the elevation axis in front of the primary is roughly a 40% decrease in fundamental frequency and a corresponding reduction in the control bandwidth for pointing and optical alignment

Mars elevation distribution

A Digital Terrain Model (DTM) of Mars was derived with both Mercator and Sinusoidal Equal-Area projections from the global topographic map of Mars (scale 1:15 million, contour interval 1 km). Elevations on the map are referred to Mars' topographic datum that is defined by the gravity field at a 6.1-millibar pressure surface with respect to the center of mass of Mars. The DTM has a resolution at the equator of 1/59.226 degrees (exactly 1 km) per pixel. By using the DTM, the volumetric distribution of Mars topography above and below the datum has previously been calculated. Three types of elevation distributions of Mars' topography were calculated from the same DTM: (1) the frequency distribution of elevations at the pixel resolution; (2) average elevations in increments of 6 degrees in both longitude and latitude; and (3) average elevations in 36 separate blocks, each covering 30 degrees of latitude and 60 degrees of longitude

A seakeeping analysis method for an air-lifted vessel

A seakeeping analysis in the frequency domain is presented to predict the motion response of an airlifted vessel (ALV) in waves. The ALV is supported by pressurised air in two separate cushion chambers; the pressure variation in the cushions has a significant effect on the motions of the vessel. The adiabatic gas law is used to couple cushion pressure and the free-surface elevation of water inside the chamber. Attention is focused on the waves generated by the pressure, and a method is presented to compute the corresponding free-surface elevation. New numerical schemes are proposed for calculating the threedimensional free-surface elevation for the four wave numbers. Numerical results of the free-surface elevation, escape area, escape volume and motion responses of the ALV are provided. & 2008 Elsevier Ltd. All rights reserved

On modeling the variability of bedform dimensions

ABSTRACT: Bedforms are irregular features that cannot easily be described by mean values. The variations in the geometric dimensions affect the bed roughness, and they are important in the modeling of vertical sorting and in modeling the thickness of cross-strata sets. The authors analyze the variability of bedform dimensions for three sets of flume experiments, considering PDFs of bedform height, trough elevation and crest elevation divided by its mean value. It appears that the dimensionless standard deviation of the bedform height is within a narrow range for nearly all experiments. This appears to be valid for the trough elevation and crest elevation, as well. For some modeling purposes, it seems sufficient to assume that the standard deviation is a constant, so that the variation in bedform dimension can be modeled by only predicting the mean bedform dimension.