University of Nevada, Reno

About: University of Nevada, Reno is a education organization based out in Reno, Nevada, United States. It is known for research contribution in the topics: Population & Poison control. The organization has 13561 authors who have published 28217 publications receiving 882002 citations. The organization is also known as: University of Nevada & Nevada State University.

The Moderating Effects of Employee Tenure on the Relation Between Organizational Commitment and Job Performance: A Meta-Analysis

Abstract: This meta-analysis investigated the correlation between attitudinal commitment and job performance for 3,630 employees obtained from 27 independent studies across various levels of employee tenure. Controlling for employee age and other nuisance variables, the authors found that tenure had a very strong nonlinear moderating effect on the commitment-performance correlation, with correlations tending to decrease exponentially with increasing tenure. These findings do not appear to be the result of differences across studies in terms of the type of performance measure (supervisory vs. self), type of tenure (job vs. organizational), or commitment measure (Organizational Commitment Questionnaire [L. W. Porter, R. M. Steers, R. T. Mowday, & P. V. Boulian, 1974] vs. other). The implications and future research directions of these results are discussed.

Differences between forward and reverse logistics in a retail environment

Abstract: This paper compares and contrasts forward and reverse logistics in a retail environment, with the focus on the reverse flow of product. Many differences between forward and reverse flows of logistics systems are presented. The impact of these factors depends to some extent on the supply chain position of a firm. Unlike much reverse logistics research, which is written from the perspective of the firm which will remanufacture or refurbish the product in the reverse flow, we consider the issues from the perspective of the firm generating the reverse flow.

Transfer of a conditional ordering response through conditional equivalence classes.

Abstract: Eight adult humans were taught conditional discriminations in a matching-to-sample format that led to the formation of two four-member equivalence classes. When subjects were taught to select one comparison stimulus from each class in a set order, they then ordered all other members of the equivalence classes without explicit training. When the ordering response itself was brought under conditional control, conditional sequencing also transferred to all other members of the two equivalence classes. When the conditional discriminations in the matching-to-sample task were brought under higher order conditional control, the eight stimulus members were arranged into four conditional equivalence classes. Both ordering and conditional ordering transferred to all members of the four conditional equivalence classes; for some subjects this occurred without a typical test for equivalence. One hundred twenty untrained sequences emerged from eight trained sequences for all subjects. Transfer of functions through equivalence classes may contribute to a behavior-analytic approach to semantics and generative grammar.

Estimating PM2.5 Concentrations in the Conterminous United States Using the Random Forest Approach.

Abstract: To estimate PM25 concentrations, many parametric regression models have been developed, while nonparametric machine learning algorithms are used less often and national-scale models are rare In this paper, we develop a random forest model incorporating aerosol optical depth (AOD) data, meteorological fields, and land use variables to estimate daily 24 h averaged ground-level PM25 concentrations over the conterminous United States in 2011 Random forests are an ensemble learning method that provides predictions with high accuracy and interpretability Our results achieve an overall cross-validation (CV) R2 value of 080 Mean prediction error (MPE) and root mean squared prediction error (RMSPE) for daily predictions are 178 and 283 μg/m3, respectively, indicating a good agreement between CV predictions and observations The prediction accuracy of our model is similar to those reported in previous studies using neural networks or regression models on both national and regional scales In addition, the