Institution
University of Iowa
Education•Iowa City, Iowa, United States•
About: University of Iowa is a education organization based out in Iowa City, Iowa, United States. It is known for research contribution in the topics: Population & Poison control. The organization has 49229 authors who have published 109171 publications receiving 5021465 citations. The organization is also known as: UI & The University of Iowa.
Topics: Population, Poison control, Large Hadron Collider, Health care, Gene
Papers published on a yearly basis
Papers
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TL;DR: In this paper, the missing source of sulfate and particulate matter can be explained by reactive nitrogen chemistry in aerosol water, where the alkaline aerosol components trap SO 2, which is oxidized by NO 2 to form sulfate, whereby high reaction rates are sustained by the high neutralizing capacity of the atmosphere.
Abstract: Fine-particle pollution associated with winter haze threatens the health of more than 400 million people in the North China Plain. Sulfate is a major component of fine haze particles. Record sulfate concentrations of up to ~300 μg m −3 were observed during the January 2013 winter haze event in Beijing. State-of-the-art air quality models that rely on sulfate production mechanisms requiring photochemical oxidants cannot predict these high levels because of the weak photochemistry activity during haze events. We find that the missing source of sulfate and particulate matter can be explained by reactive nitrogen chemistry in aerosol water. The aerosol water serves as a reactor, where the alkaline aerosol components trap SO 2 , which is oxidized by NO 2 to form sulfate, whereby high reaction rates are sustained by the high neutralizing capacity of the atmosphere in northern China. This mechanism is self-amplifying because higher aerosol mass concentration corresponds to higher aerosol water content, leading to faster sulfate production and more severe haze pollution.
821 citations
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818 citations
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TL;DR: In this article, the authors used 91 sales representatives to test a process model that assessed the relationship of conscientiousness to job performance through mediating motivational (goal-setting) variables.
Abstract: The authors used 91 sales representatives to test a process model that assessed the relationship of conscientiousness to job performance through mediating motivational (goal-setting) variables. Linear structural equation modeling showed that sales representatives high in conscientiousness are more likely to set goals and are more likely to be committed to goals, which in turn is associated with greater sales volume and higher supervisory ratings of job performance. Results also showed that conscientiousness is directly related to supervisory ratings. Consistent with previous research, results showed that ability was also related to supervisory ratings of job performance and, to a lesser extent, sales volume
815 citations
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TL;DR: In this article, a convolution neural network (CNN)-based regularization prior is proposed for inverse problems with the arbitrary structure, where the forward model is explicitly accounted for and a smaller network with fewer parameters is sufficient to capture the image information compared to direct inversion.
Abstract: We introduce a model-based image reconstruction framework with a convolution neural network (CNN)-based regularization prior. The proposed formulation provides a systematic approach for deriving deep architectures for inverse problems with the arbitrary structure. Since the forward model is explicitly accounted for, a smaller network with fewer parameters is sufficient to capture the image information compared to direct inversion approaches. Thus, reducing the demand for training data and training time. Since we rely on end-to-end training with weight sharing across iterations, the CNN weights are customized to the forward model, thus offering improved performance over approaches that rely on pre-trained denoisers. Our experiments show that the decoupling of the number of iterations from the network complexity offered by this approach provides benefits, including lower demand for training data, reduced risk of overfitting, and implementations with significantly reduced memory footprint. We propose to enforce data-consistency by using numerical optimization blocks, such as conjugate gradients algorithm within the network. This approach offers faster convergence per iteration, compared to methods that rely on proximal gradients steps to enforce data consistency. Our experiments show that the faster convergence translates to improved performance, primarily when the available GPU memory restricts the number of iterations.
815 citations
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TL;DR: It is shown that vocal tract inertance reduces the oscillation threshold pressure, whereas vocal tract resistance increases it, and the treatment is harmonized with former treatments based on two-mass models and collapsible tubes.
Abstract: A theory of vocal fold oscillation is developed on the basis of the body‐cover hypothesis. The cover is represented by a distributed surface layer that can propagate a mucosal surface wave. Linearization of the surface‐wave displacement and velocity, and further small‐amplitude approximations, yields closed‐form expressions for conditions of oscillation. The theory predicts that the lung pressure required to sustain oscillation, i.e., the oscillation threshold pressure, is reduced by reducing the mucosal wave velocity, by bringing the vocal folds closer together and by reducing the convergence angle in the glottis. The effect of vocal tract acoustic loading is included. It is shown that vocal tract inertance reduces the oscillation threshold pressure, whereas vocal tract resistance increases it. The treatment, which is applicable to falsetto and breathy voice, as well as onset or release of phonation in the absence of vocal fold collision, is harmonized with former treatments based on two‐mass models and ...
815 citations
Authors
Showing all 49661 results
Name | H-index | Papers | Citations |
---|---|---|---|
Stephen V. Faraone | 188 | 1427 | 140298 |
Jie Zhang | 178 | 4857 | 221720 |
D. M. Strom | 176 | 3167 | 194314 |
Bradley T. Hyman | 169 | 765 | 136098 |
John H. Seinfeld | 165 | 921 | 114911 |
David Jonathan Hofman | 159 | 1407 | 140442 |
Stephen J. O'Brien | 153 | 1062 | 93025 |
John T. Cacioppo | 147 | 477 | 110223 |
Mark Raymond Adams | 147 | 1187 | 135038 |
E. L. Barberio | 143 | 1605 | 115709 |
Andrew Ivanov | 142 | 1812 | 97390 |
Stephen J. Lippard | 141 | 1201 | 89269 |
Russell Richard Betts | 140 | 1323 | 95678 |
Barry Blumenfeld | 140 | 1909 | 105694 |
Marcus Hohlmann | 140 | 1356 | 94739 |