A research program to reduce interior noise in general aviation airplanes: Investigation of the characteristics of an acoustic panel test facility

Sound pressure levels in the test facility were studied that are caused by varying: (1) microphone positions; (2) equalizer setting; and (3) panel clamping forces. Measurements were done by using a Beranek tube or this Beranek tube in combinations with an extension tube and a special test section. In all configurations tests were executed with and without a test panel installed. The influence of the speaker back panel and the back panel of the Beranek tube on the sound pressure levels inside the test tube were also investigated. It is shown that the definition of noise reduction is more useful in relation to this test facility than transmission loss

Experimental study of flow deflectors designed to alleviate ground winds induced by exhaust of 80-by 120-foot wind tunnel

An experimental study directed at finding a deflector ramp that will reduce to an acceptable level the ground winds under the exhaust jet of the 80 by 120 Foot Wind Tunnel at NASA Ames Center is described. A one-fifieth scale model of the full-scale facility was used to investigate how the jet flow field was modified by the various design parameters of the ramp. It was concluded that the ground winds were alleviated sufficiently by a ramp with end plates located next to the wind tunnel building along the ground edge of the exhaust opening. At full scale, the ramp should have a slant length of 7.62 m (25 ft) or more, and would be elevated at about 45 degrees to the ground plane. The material should have holes less than 15.2 (6 in) in diameter distributed uniformly over its surface to produce a porosity of about 30%

One-fiftieth scale model studies of 40-by 80-foot and 80-by 120-foot wind tunnel complex at NASA Ames Research Center

The features of a 1/50-scale model of the National Full-Scale Aerodynamics Complex are first described. An overview is then given of some results from the various tests conducted with the model to aid in the design of the full-scale facility. It was found that the model tunnel simulated accurately many of the operational characteristics of the full-scale circuits. Some characteristics predicted by the model were, however, noted to differ from previous full-scale results by about 10%

Improved diagnostic prediction of the pathogenicity of bloodstream isolates of Staphylococcus epidermidis.

With an estimated 440,000 active cases occurring each year, medical device associated infections pose a significant burden on the US healthcare system, costing about $9.8 billion in 2013. Staphylococcus epidermidis is the most common cause of these device-associated infections, which typically involve isolates that are multi-drug resistant and possess multiple virulence factors. S. epidermidis is also frequently a benign contaminant of otherwise sterile blood cultures. Therefore, tests that distinguish pathogenic from non-pathogenic isolates would improve the accuracy of diagnosis and prevent overuse/misuse of antibiotics. Attempts to use multi-locus sequence typing (MLST) with machine learning for this purpose had poor accuracy (~73%). In this study we sought to improve the diagnostic accuracy of predicting pathogenicity by focusing on phenotypic markers (i.e., antibiotic resistance, growth fitness in human plasma, and biofilm forming capacity) and the presence of specific virulence genes (i.e., mecA, ses1, and sdrF). Commensal isolates from healthy individuals (n = 23), blood culture contaminants (n = 21), and pathogenic isolates considered true bacteremia (n = 54) were used. Multiple machine learning approaches were applied to characterize strains as pathogenic vs non-pathogenic. The combination of phenotypic markers and virulence genes improved the diagnostic accuracy to 82.4% (sensitivity: 84.9% and specificity: 80.9%). Oxacillin resistance was the most important variable followed by growth rate in plasma. This work shows promise for the addition of phenotypic testing in clinical diagnostic applications

IMMUNOHISTOCHEMICAL ANALYSIS OF E-CADHERIN EXPRESSION IN HUMAN COLORECTAL TUMORS.

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