Recent Year-to-year Variations in Seasonal Temperatures and Sea Ice Conditions in the Eastern Canadian Arctic

Mean summer and winter temperatures for the 1957-1978 period have been analyzed for four eastern Arctic stations. Standard deviations on the order of 3° C in winter and 10° C in summer indicate the magnitude of the interannual variations, and these departures are found to be synchronous over the region. Several indices of sea ice severity also show significant year-to-year variations, but these are not spatially coherent. Relationships between climatic parameters and sea ice are examined in order to explain these differences

Analysis of Effects of Inlet Pressure Losses on Performance of Axial-Flow Type Turbojet Engine

The experimentally determined performance characteristics of an axial-flow turbojet engine have been used to estimate the effects of inlet total-pressure losses on net thrust and specific fuel consumption at a constant engine speed. At low altitudes and flight Mach numbers, inlet pressure losses cause an increase in engine discharge temperature and it is possible that the maximum allowable turbine temperature maybe exceeded. An inlet absolute total-pressure loss of 10 percent will result in a thrust loss of 14 percent and a 15-percent increase in specific fuel consumption based on net thrust. At high altitudes and flight Mach numbers, choking conditions exist in the exhaust nozzle and the inlet pressure losses do not affect the discharge temperatures. Under these conditions, a 10-percent loss in inlet absolute total pressure produces a 22-percent loss in net thrust and a 16-percent increase in specific fuel consumption. If the exhaust-nozzle-outlet area is adjusted to compensate for the effect of inlet losses on discharge temperature in the nonchoking cases (low altitude and Mach numbers), the thrust and fuel consumption will be changed in a manner similar to the results obtained in the choking cases

Horizontal wavenumber spectra of winds, temperature, and trace gases during the Pacific Exploratory Missions: 2. Gravity waves, quasi-two-dimensional turbulence, and vortical modes

We examine the horizontal wavenumber spectra of horizontal velocity and potential temperature collected by aircraft above the Pacific Ocean to determine whether gravity waves, quasi-two-dimensional (Q-2-D) turbulence, or vortical modes dominate atmospheric fluctuations at scale sizes of 1–100 km and altitudes of 2–12 km. We conclude from the study of Doppler-shifting effects that Q-2-D turbulence and/or vortical modes are more prevalent than gravity waves over the ocean, except in the equatorial zone. The results are consistent with recent numerical simulations of Q-2-D turbulence, which show that the characteristic inverse cascade of energy is greatly facilitated by the presence of background rotation. Furthermore, a Stokes-parameter analysis reveals the general paucity of coherent wavelike motions, although specific cases of gravity-wave propagation are observed. Finally, a case study of a long flight segment displays a k⁻³ horizontal velocity variance spectrum at scales longer than about 100 km. A Stokes-parameter analysis indicates that these large-scale fluctuations were likely due to vortical modes rather than inertio-gravity waves.United States. National Aeronautics and Space Administration (Grant NAG1-1758)United States. National Aeronautics and Space Administration (Grant NAG1-1901

Bostonia. Volume 6

Founded in 1900, Bostonia magazine is Boston University's main alumni publication, which covers alumni and student life, as well as university activities, events, and programs

Characterizations of tropospheric turbulence and stability layers from aircraft observations

[1] Velocity, temperature, and specific humidity data collected by aircraft at 20-Hz resolution are analyzed for stability and turbulence parameters. Over 100 vertical profiles (mostly over the ocean) with a total of over 300 km in vertical airspace sampled are used. The compiled statistics show that anisotropy in the velocity fluctuations prevail down to the smallest spatial separations measured. A partitioning of convective versus dynamical instability indicates that in the free troposphere, the ratio of shear-produced turbulence to convectively produced turbulence increases from roughly 2:1 for weak turbulence (ϵ 10⁻⁴ m² s⁻³). For the boundary layer, this ratio is close to 1:1 for weak turbulence and roughly 2:1 for strong turbulence. There is also a correlation between the strength of the vertical shear in horizontal winds and the turbulence intensity. In the free troposphere the turbulence intensity is independent of the degree of static stability, whereas in the boundary layer the turbulence intensity increases with a fall in static stability. Vertical humidity gradients correlate with static stability for strong humidity gradients, which supports the basic notion that stable layers impede vertical mixing of trace gases and aerosols. Vertical shear correlates with vertical humidity gradient, so it appears that the effect of differential advection creating tracer gradients dominates the effect of differential advection destroying tracer gradients through shear-induced turbulence.United States. National Aeronautics and Space Administration (Grant NCC1-415)United States. National Aeronautics and Space Administration (Grant NAG1-2306

Quantitative analysis of dipyridamole-thallium images for the detection of coronary artery disease

To determine if the detection of coronary artery disease by dipyridamole-thallium imaging is improved by 1) quantitative versus qualitative analysis, and 2) combining quantitative variables, 80 patients with chest pain (53 with and 27 without coronary artery disease) who underwent cardiac catheterization were studied. Segmental thallium initial uptake, linear clearance, mono-exponential clearance and redistribution were measured from early, intermediate and delayed images acquired in three projections. Normal values were determined from 13 other clinically normal subjects.When five segments per view were used for quantitative analysis, sensitivity and specificity were 87 and 63%, respectively, for uptake, 77 and 67% for linear clearance, 60 and 60% for monoexponential clearance and 62 and 56% for redistribution. Of the four variables, uptake and linear clearance were the most sensitive (p < 0.01) and specificity did not differ significantly. Using three segments per view, the specificity of uptake increased (p < 0.05) to 78% without a significant change in sensitivity (85%). With this approach, sensitivity and specificity did not differ from those of qualitative analysis (85 and 78%, respectively).Stepwise logistic regression analysis demonstrated that the best quantitative thallium correlate of the presence of coronary artery disease was a combination variable of “either abnormal uptake or abnormal linear clearance, or both.” Using five segments per view, the model's specificity (85%) was greater than that of uptake alone (p < 0.02), with similar sensitivity (92%). Using three segments per view, the model's specificity (93%) was greater than that of uptake alone (p < 0.05) and of qualitative analysis (p < 0.05), with similar sensitivity (85%). Compared with qualitative analysis, the diagnostic accuracy of the model was greater using either five segments (90 versus 82%, p < 0.01) or three segments (88 versus 82%, p < 0.05) per view.Quantitative analysis of dipyridamole-thallium images using single individual variables provides results comparable with those of qualitative analysis and this can be further optimized when a combination of quantitative variables is used

Statistical and machine learning methods evaluated for incorporating soil and weather into corn nitrogen recommendations

Nitrogen (N) fertilizer recommendation tools could be improved for estimating corn (Zea mays L.) N needs by incorporating site-specific soil and weather information. However, an evaluation of analytical methods is needed to determine the success of incorporating this information. The objectives of this research were to evaluate statistical and machine learning (ML) algorithms for utilizing soil and weather information for improving corn N recommendation tools. Eight algorithms [stepwise, ridge regression, least absolute shrinkage and selection operator (Lasso), elastic net regression, principal component regression (PCR), partial least squares regression (PLSR), decision tree, and random forest] were evaluated using a dataset containing measured soil and weather variables from a regional database. The performance was evaluated based on how well these algorithms predicted corn economically optimal N rates (EONR) from 49 sites in the U.S. Midwest. Multiple algorithm modeling scenarios were examined with and without adjustment for multicollinearity and inclusion of two-way interaction terms to identify the soil and weather variables that could improve three dissimilar N recommendation tools. Results showed the out-of-sample root-mean-square error (RMSE) for the decision tree and some random forest modeling scenarios were better than the stepwise or ridge regression, but not significantly different than any other algorithm. The best ML algorithm for adjusting N recommendation tools was the random forest approach (r2 increased between 0.72 and 0.84 and the RMSE decreased between 41 and 94 kg N ha−1). However, the ML algorithm that best adjusted tools while using a minimal amount of variables was the decision tree. This method was simple, needing only one or two variables (regardless of modeling scenario) and provided moderate improvement as r2 values increased between 0.15 and 0.51 and RMSE decreased between 16 and 66 kg N ha−1. Using ML algorithms to adjust N recommendation tools with soil and weather information shows promising results for better N management in the U.S. Midwest

Echocardiographic assessment of patients with infectious endocarditis: Prediction of risk for complications

AbstractTo enhance the echocardiographic identification of high risk lesions in patients with infectious endocarditis, the medical records and two-dimensional echocardiograms of 204 patients with this condition were analyzed. The occurrence of specific clinical complications was recorded and vegetations were assessed with respect to predetermined morphologic characteristics.The overall complication rates were roughly equivalent for patients with mitral (53%), aortic (62%), tricuspid (77%) and prosthetic valve (61%) vegetations, as well as for those with nonspecific valvular changes but no discrete vegetations (57%), although the distribution of specific complications varied considerably among these groups. There were significantly fewer complications in patients without discernible valvular abnormalities (27%).In native left-sided valve endocarditis, vegetation size, extent, mobility and consistency were all found to be significant univariate predictors of complications. In multivariate analysis, vegetation size, extent and mobility emerged as optimal predictors and an echocardiographic score based on these factors predicted the occurrence of complications with 70% sensitivity and 92% specificity in mitral valve endocarditis and with 76% sensitivity and 62% specificity in aortic valve endocarditis

Increased parental effort fails to buffer the cascading effects of warmer seas on common guillemot demographic rates

Research Funding Natural Environment Research Council Award. Grant Number: NE/R016429/1 UK-SCAPE Programme Delivering National Capability Joint Nature Conservation Committee EU ‘The Effect of Large-scale Industrial Fisheries On Non-Target Species’ FP5 Project ‘Interactions between the Marine environment, PREdators and Prey: Implications for Sustainable Sandeel Fisheries’. Grant Numbers: MS21-013, Q5RS-2000-30864 Ministry of Universities-University of ValenciaPeer reviewedPublisher PD