Institution
University of Alabama
Education•Tuscaloosa, Alabama, United States•
About: University of Alabama is a education organization based out in Tuscaloosa, Alabama, United States. It is known for research contribution in the topics: Population & Poison control. The organization has 27323 authors who have published 48609 publications receiving 1565337 citations. The organization is also known as: Alabama & Bama.
Topics: Population, Poison control, Large Hadron Collider, Galaxy, Health care
Papers published on a yearly basis
Papers
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01 Aug 2008TL;DR: In this paper, a finite element analysis (FEA) model was used to evaluate the distortions of a part and a parametric study, with three factors and three levels, was performed to evaluate effects of the deposition parameters on residual stresses and part distortions.
Abstract: A finite element analysis (FEA) model was previously developed by the current authors to simulate the fused deposition modelling (FDM) process. The model considered coupled thermal and mechanical phenomena and incorporated an element activation function to mimic the additive nature of FDM. Due to repetitive heating and cooling in the FDM process, residual stresses accumulate in a part during deposition. In this study, an FEA model is used to evaluate the distortions of a part. A parametric study, with three factors and three levels, is performed to evaluate the effects of the deposition parameters on residual stresses and part distortions. Prototype models with larger sizes are fabricated, measured, and compared with the simulations.The simulation results are summarized as follows. First, the scan speed is the most significant factor affecting part distortions, followed by the layer thickness. Second, the road width alone is insignificant. However, the interaction between the road width and the layer thic...
258 citations
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TL;DR: This paper proposes an adaption to the standard LSTM architecture, which it is called an Entity-Aware-L STM (EA-LSTM), that allows for learning catchment similarities as a feature layer in a deep learning model and shows that these learned caughtment similarities correspond well to what the authors would expect from prior hydrological understanding.
Abstract: . Regional rainfall–runoff modeling is an old but still mostly outstanding problem in the hydrological sciences. The problem currently is that traditional hydrological models degrade significantly in performance when calibrated for multiple basins together instead of for a single basin alone. In this paper, we propose a novel, data-driven approach using Long Short-Term Memory networks (LSTMs) and demonstrate that under a “big data” paradigm, this is not necessarily the case. By training a single LSTM model on 531 basins from the CAMELS dataset using meteorological time series data and static catchment attributes, we were able to significantly improve performance compared to a set of several different hydrological benchmark models. Our proposed approach not only significantly outperforms hydrological models that were calibrated regionally, but also achieves better performance than hydrological models that were calibrated for each basin individually. Furthermore, we propose an adaption to the standard LSTM architecture, which we call
an Entity-Aware-LSTM (EA-LSTM), that allows for learning catchment similarities as a
feature layer in a deep learning model. We show that these learned catchment
similarities correspond well to what we would expect from prior hydrological
understanding.
258 citations
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TL;DR: In this article, the concept of empathy is developed as an alternative to the popular notion of identification with characters in drama, and its usefulness in explaining emotional reactivity to drama is questioned.
258 citations
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TL;DR: In this article, the preferential treatment of customers holds the potential to contribute to important relational outcomes valued by firms, such as relationship commitment, increased purchases, share of customer, word of mouth, and customer feedback.
Abstract: Despite its often controversial and philosophically divisive nature, preferential treatment of customers holds the potential to contribute to important relational outcomes valued by firms. In this study, sampled customers (n = 2,461) of a national upscale department store chain representing recipients of three different levels of preferential treatment are tested. While controlling for individual customer characteristics, higher levels of preferential treatment are shown to positively influence relationship commitment, increased purchases, share of customer, word of mouth, and customer feedback. This study fills a major services marketing research gap by assessing the favorable effects of higher levels of preferential treatment as a relationship marketing strategy.
258 citations
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TL;DR: It is demonstrated that the underlying mechanism is the interplay of spin currents for the ferromagnetic (antiferromagnetic) configurations, which vary linearly (quadratically) with bias, respectively, due to the symmetric (asymmetric) nature of the barrier.
Abstract: We predict an anomalous bias dependence of the spin transfer torque parallel to the interface, Tparallel, in magnetic tunnel junctions, which can be selectively tuned by the exchange splitting. It may exhibit a sign reversal without a corresponding sign reversal of the bias or even a quadratic bias dependence. We demonstrate that the underlying mechanism is the interplay of spin currents for the ferromagnetic (antiferromagnetic) configurations, which vary linearly (quadratically) with bias, respectively, due to the symmetric (asymmetric) nature of the barrier. The spin transfer torque perpendicular to interface exhibits a quadratic bias dependence.
258 citations
Authors
Showing all 27508 results
Name | H-index | Papers | Citations |
---|---|---|---|
Jasvinder A. Singh | 176 | 2382 | 223370 |
Hongfang Liu | 166 | 2356 | 156290 |
Ian J. Deary | 166 | 1795 | 114161 |
Yongsun Kim | 156 | 2588 | 145619 |
Dong-Chul Son | 138 | 1370 | 98686 |
Simon C. Watkins | 135 | 950 | 68358 |
Kenichi Hatakeyama | 134 | 1731 | 102438 |
Conor Henderson | 133 | 1387 | 88725 |
Peter R Hobson | 133 | 1590 | 94257 |
Tulika Bose | 132 | 1285 | 88895 |
Helen F Heath | 132 | 1185 | 89466 |
James Rohlf | 131 | 1215 | 89436 |
Panos A Razis | 130 | 1287 | 90704 |
David B. Allison | 129 | 836 | 69697 |
Eduardo Marbán | 129 | 579 | 49586 |