Institution
University of Nebraska–Lincoln
Education•Lincoln, Nebraska, United States•
About: University of Nebraska–Lincoln is a education organization based out in Lincoln, Nebraska, United States. It is known for research contribution in the topics: Population & Poison control. The organization has 28059 authors who have published 61544 publications receiving 2139104 citations. The organization is also known as: Nebraska & UNL.
Papers published on a yearly basis
Papers
More filters
••
TL;DR: The intrinsic coercive field is realized for the first time, in two-dimensional Langmuir-Blodgett polymer films as thin as 1 nm, in good agreement with the theoretical intrinsic value, exhibits the expected dependence on temperature, and does not depend on thickness below 15 nm.
Abstract: The Landau-Ginzburg theory of ferroelectricity predicts the intrinsic coercive field for polarization reversal, but the observed extrinsic coercive field is always much smaller as a result of nucleation, dynamic processes not covered by the static theory. We have realized the intrinsic coercive field for the first time, in two-dimensional Langmuir-Blodgett polymer films as thin as 1 nm. The measured coercive field is in good agreement with the theoretical intrinsic value, exhibits the expected dependence on temperature, and does not depend on thickness below 15 nm.
317 citations
••
TL;DR: O observation of the electroweak production of single top quarks in pp[over ] collisions at sqrt[s]=1.96 TeV based on 2.3 fb(-1) of data collected by the D0 detector at the Fermilab Tevatron Collider is reported.
Abstract: We report observation of the electroweak production of single top quarks in pp collisions at s=1.96 TeV based on 2.3 fb(-1) of data collected by the D0 detector at the Fermilab Tevatron Collider. Using events containing an isolated electron or muon and missing transverse energy, together with jets originating from the fragmentation of b quarks, we measure a cross section of sigma(pp -> tb+X,tqb+X)=3.94 +/- 0.88 pb. The probability to measure a cross section at this value or higher in the absence of signal is 2.5x10(-7), corresponding to a 5.0 standard deviation significance for the observation.
316 citations
•
University of Wisconsin-Madison1, University of Pennsylvania2, New York University3, University of Houston4, University of Maryland, College Park5, Georgia State University6, University of Connecticut7, Dartmouth College8, University of Nebraska–Lincoln9, Peking University10, Northwestern University11
TL;DR: One-to-one marketing advocates tailoring of one or more aspects of the firm's marketing mix to the individual customer (Peppers and Rogers 1997; Peppers, Rogers and Dorf 1999; Shaffer and Zhang 2002).
Abstract: One-to-one marketing advocates tailoring of one or more aspects of the firm's marketing mix to the individual customer (Peppers and Rogers 1997; Peppers, Rogers and Dorf 1999; Shaffer and Zhang 2002). One-to-one marketing represents an extreme form of segmentation, with a target segment of size one. There are two forms of one-to-one marketing: personalization and customization. Personalization is when the firm decides, usually based on previously collected customer data, what marketing mix is suitable for the individual. A good example is Amazon.com's personalized book and music recommendations (Nunes and Kambil 2001). The e-commerce arena is replete with other instances of personalization. Nytimes.com allows readers to get personalized news articles of interest, MLS.ca in Canada screens houses for buyers depending on their preferences for location, size and features. Customization is when the customer proactively specifies one or more elements of his or her marketing mix. Dell computer allows customers to customize the computer they order. The MyYahoo feature at Yahoo.com allows users to specify elements of their home page such as the weather forecast, reports on their favorite stocks, or priorities given to local sports news. The purpose of this paper is to summarize key challenges and knowledge gaps in understanding the choices that both firms and customers make in a personalization/customization environment. We start with a summary of personalization and customization in practice, and then draw on research in economics, statistical, and consumer behavior to identify what we know and do not know. We conclude with a summary of key research opportunities.
316 citations
••
TL;DR: In this article, the authors suggest that the non-normality rates are closely related to local precipitation climates and recommend that the user should focus on the duration of the drought rather than on just its severity.
Abstract: The Standardized Precipitation Index (SPI) is now widely used throughout the world in both a research and an operational mode. For arid climates, or those with a distinct dry season where zero values are common, the SPI at short time scales is lower bounded, referring to non-normally distributed in this study. In these cases, the SPI is always greater than a certain value and fails to indicate a drought occurrence. The nationwide statistics based on our study suggest that the non-normality rates are closely related to local precipitation climates. In the eastern United States, SPI values at short time scales can be used in drought/flood monitoring and research in any season, while in the western United States, because of its distinct seasonal precipitation distribution, the appropriate usage and interpretation of this index becomes complicated. This would also be the case for all arid climates. From a mathematical point of view, the non-normally distributed SPI is caused by a high probability of no-rain cases represented in the mixed distribution that is employed in the SPI construction. From a statistical point of view, the 2-parameter gamma model used to estimate the precipitation probability density function and the limited sample size in dry areas and times would also reduce the confidence of the SPI values.
On the basis of the results identified within this study, we recommend that the SPI user be cautious when applying short-time-scale SPIs in arid climatic regimes, and interpret the SPI values appropriately. In dry climates, the user should focus on the duration of the drought rather than on just its severity. It is also worth noting that the SPI results from a statistical product of the input data. This character makes it difficult to link the SPI data to the physical functioning of the Earth system. Copyright © 2006 Royal Meteorological Society.
316 citations
••
TL;DR: In this paper, a search for the standard model Higgs boson decaying to bb¯ when produced in association with a weak vector boson (V) is reported for the following channels: W(μν)H, W(eν), W(τν), H, Z(μμ), Z(ee, H, and Z(νν), where the search is performed in data samples corresponding to integrated luminosities of up to 5.1 inverse femtobarns at s√=7
Abstract: A search for the standard model Higgs boson (H) decaying to bb¯ when produced in association with a weak vector boson (V) is reported for the following channels: W(μν)H, W(eν)H, W(τν)H, Z(μμ)H, Z(ee)H, and Z(νν)H. The search is performed in data samples corresponding to integrated luminosities of up to 5.1 inverse femtobarns at s√=7 TeV and up to 18.9 fb−1 at s√=8 TeV, recorded by the CMS experiment at the LHC. An excess of events is observed above the expected background with a local significance of 2.1 standard deviations for a Higgs boson mass of 125 GeV, consistent with the expectation from the production of the standard model Higgs boson. The signal strength corresponding to this excess, relative to that of the standard model Higgs boson, is 1.0±0.5.
316 citations
Authors
Showing all 28272 results
Name | H-index | Papers | Citations |
---|---|---|---|
Donald P. Schneider | 242 | 1622 | 263641 |
Suvadeep Bose | 154 | 960 | 129071 |
David D'Enterria | 150 | 1592 | 116210 |
Aaron Dominguez | 147 | 1968 | 113224 |
Gregory R Snow | 147 | 1704 | 115677 |
J. S. Keller | 144 | 981 | 98249 |
Andrew Askew | 140 | 1496 | 99635 |
Mitchell Wayne | 139 | 1810 | 108776 |
Kenneth Bloom | 138 | 1958 | 110129 |
P. de Barbaro | 137 | 1657 | 102360 |
Randy Ruchti | 137 | 1832 | 107846 |
Ia Iashvili | 135 | 1676 | 99461 |
Yuichi Kubota | 133 | 1695 | 98570 |
Ilya Kravchenko | 132 | 1366 | 93639 |
Andrea Perrotta | 131 | 1380 | 85669 |