Fluid mechanics approach to acoustic liner design

Fluid mechanics approach to acoustic liner desig

Combustion instability research Summary report, 1970

Combustion instability in liquid rocket engine

Summary of combustion instability research at Princeton University, 1969

Control and causes of combustion instability in rocket engine

Nitrogen deposition outweighs climatic variability in driving annual growth rate of canopy beech trees: Evidence from long-term growth reconstruction across a geographic gradient

In this study, we investigated the role of climatic variability and atmospheric nitrogen deposition in driving long-term tree growth in canopy beech trees along a geographic gradient in the montane belt of the Italian peninsula, from the Alps to the southern Apennines. We sampled dominant trees at different developmental stages (from young to mature tree cohorts, with tree ages spanning from 35 to 160 years) and used stem analysis to infer historic reconstruction of tree volume and dominant height. Annual growth volume (G V ) and height (G H ) variability were related to annual variability in model simulated atmospheric nitrogen deposition and site-specific climatic variables, (i.e. mean annual temperature, total annual precipitation, mean growing period temperature, total growing period precipitation, and standard precipitation evapotranspiration index) and atmospheric CO 2 concentration, including tree cambial age among growth predictors. Generalized additive models (GAM), linear mixed-effects models (LMM), and Bayesian regression models (BRM) were independently employed to assess explanatory variables. The main results from our study were as follows: (i) tree age was the main explanatory variable for long-term growth variability; (ii) GAM, LMM, and BRM results consistently indicated climatic variables and CO 2 effects on G V and G H were weak, therefore evidence of recent climatic variability influence on beech annual growth rates was limited in the montane belt of the Italian peninsula; (iii) instead, significant positive nitrogen deposition (N dep ) effects were repeatedly observed in G V and G H ; the positive effects of N dep on canopy height growth rates, which tended to level off at N dep values greater than approximately 1.0 g m −2  y −1 , were interpreted as positive impacts on forest stand above-ground net productivity at the selected study sites

Earmarked: The political economy of agricultural research appropriations

Since 1965 a significant portion of the US Department of Agriculture's extramural research budget has been earmarked by Congress for particular research projects. We analyze the process by which a minority of Congress induces the USDA to carry out its budgetary suggestions. We present evidence demonstrating the influence that appropriators possess over the allocation of earmarked grants. Finally, we argue that this program provides an excellent illustration of path-dependence in government policy, and that an understanding of the special grants program may shed light on the decline of science at the USDA and Congress's reluctance to increase agricultural research funding. A t the National Institutes of Health (NIH) and the National Science Foundation (NSF), decisions about which scientific research projects to fund are largely made by other scientists through the competitive peer-review process. Decisions about the allocation of research projects at other agencies, especially those at the Department of Defense, National Aeronautics and Space Administration, and the Department of Agriculture (USDA) are not. For instance, less than 20% of USDA extramural research dollars were allocated through the competitive peer-review process. 1 At these agencies, research projects are often "earmarked" by members of Congress. 2 In the context of agriculture, the House and Senate Agricultural Appropriations Subcommittees let the USDA know which research projects should be funded through a system of "special grants." Since 1965, an increasing amount of federal agricultural research dollars has been spent on earmarked special grants. How did agricultural appropriators acquire this power over the allocation of agricultural research funds

Avaliação de genótipos de soja de diferentes grupos de maturação e resistência aos percevejos.

bitstream/item/71814/1/ID-30968.pd

Current State-of-the-Art of AI Methods Applied to MRI

Di Noia, C., Grist, J. T., Riemer, F., Lyasheva, M., Fabozzi, M., Castelli, M., Lodi, R., Tonon, C., Rundo, L., & Zaccagna, F. (2022). Predicting Survival in Patients with Brain Tumors: Current State-of-the-Art of AI Methods Applied to MRI. Diagnostics, 12(9), 1-16. [2125]. https://doi.org/10.3390/diagnostics12092125Given growing clinical needs, in recent years Artificial Intelligence (AI) techniques have increasingly been used to define the best approaches for survival assessment and prediction in patients with brain tumors. Advances in computational resources, and the collection of (mainly) public databases, have promoted this rapid development. This narrative review of the current state-of-the-art aimed to survey current applications of AI in predicting survival in patients with brain tumors, with a focus on Magnetic Resonance Imaging (MRI). An extensive search was performed on PubMed and Google Scholar using a Boolean research query based on MeSH terms and restricting the search to the period between 2012 and 2022. Fifty studies were selected, mainly based on Machine Learning (ML), Deep Learning (DL), radiomics-based methods, and methods that exploit traditional imaging techniques for survival assessment. In addition, we focused on two distinct tasks related to survival assessment: the first on the classification of subjects into survival classes (short and long-term or eventually short, mid and long-term) to stratify patients in distinct groups. The second focused on quantification, in days or months, of the individual survival interval. Our survey showed excellent state-of-the-art methods for the first, with accuracy up to ∼98%. The latter task appears to be the most challenging, but state-of-the-art techniques showed promising results, albeit with limitations, with C-Index up to ∼0.91. In conclusion, according to the specific task, the available computational methods perform differently, and the choice of the best one to use is non-univocal and dependent on many aspects. Unequivocally, the use of features derived from quantitative imaging has been shown to be advantageous for AI applications, including survival prediction. This evidence from the literature motivates further research in the field of AI-powered methods for survival prediction in patients with brain tumors, in particular, using the wealth of information provided by quantitative MRI techniques.publishersversionpublishe