University of Texas at Arlington

About: University of Texas at Arlington is a education organization based out in Arlington, Texas, United States. It is known for research contribution in the topics: Population & Large Hadron Collider. The organization has 11758 authors who have published 28598 publications receiving 801626 citations. The organization is also known as: UT Arlington & University of Texas-Arlington.

Voice, silence, and diversity in 21st century organizations: Strategies for inclusion of gay, lesbian, bisexual, and transgender employees

Abstract: Employee voice has been largely examined as a universal concept in unionized and non-unionized settings, with insuffi cient attention to diversity of workers (Rank, 2009). As invisible minorities, gay, lesbian, bisexual, and transgender (GLBT) employees provide a valuable focal point from which to examine employee voice mechanisms. Positing that GLBT employees are often silenced by what is perceived as “normal” in work organizations, this paper identifi es some of the negative consequences of this silencing and proposes ways in which the voices of GLBT employees and other invisible minorities can be heard. With its relevance to policies and practices in other organizations, the “Don’t ask; don’t tell” policy of the U.S. military is used as a lens through which to analyze voice, silence, and GLBT employees in other organizations. Heterosexist environments can foster organizational climates of silence, where the feeling that speaking up is futile or dangerous is widespread among employees. Specifi c recommendations are provided for HR managers to facilitate the expression of voice for GLBT employees in today’s increasingly diverse organizations.

Off-Policy Actor-Critic Structure for Optimal Control of Unknown Systems With Disturbances

Abstract: An optimal control method is developed for unknown continuous-time systems with unknown disturbances in this paper. The integral reinforcement learning (IRL) algorithm is presented to obtain the iterative control. Off-policy learning is used to allow the dynamics to be completely unknown. Neural networks are used to construct critic and action networks. It is shown that if there are unknown disturbances, off-policy IRL may not converge or may be biased. For reducing the influence of unknown disturbances, a disturbances compensation controller is added. It is proven that the weight errors are uniformly ultimately bounded based on Lyapunov techniques. Convergence of the Hamiltonian function is also proven. The simulation study demonstrates the effectiveness of the proposed optimal control method for unknown systems with disturbances.

Research on outsourcing results: current literature and future opportunities

Abstract: Purpose – Outsourcing emerged as a popular operational strategy in the 1990s and most of current literature was established in the same time. However, the result of outsourcing is still vague. The purpose of this article is to point out gaps in the current literature and examine the link between outsourcing implementation and firms' performance metrics by analysing hard data.Design/methodology/approach – In this research, current outsourcing research (from 1990 to 2003) methodologies are grouped by five categories: case study, survey, conceptual framework, mathematical modeling, and financial data analyses; research scope is identified by three areas: outsourcing determinant, outsourcing process, and outsourcing result.Findings – This article figures out three main gaps in the current literature: lack of objective metrics for outsourcing results evaluation, lack of research on the relationship between outsourcing implementation and firms' value, and lack of research on the outsourcing contract itself.Rese...

Magnetic nanoparticles produced by surfactant-assisted ball milling

Abstract: Nanoparticles of Fe, Co, FeCo, SmCo, and NdFeB systems with sizes smaller than 30nm and narrow size distribution have been successfully prepared by ball milling in the presence of surfactants and organic carrier liquid. It has been observed that the nanoparticles prepared by milling Fe and FeCo powders were close to spherical in their shapes, whereas those of Co, SmCo, and Nd–Fe–B showed elongated rod shapes. The nanoparticles showed superparamagnetic behavior at room temperature, except for the SmCo nanoparticles that were ferromagnetic. Nanoparticles of all types showed ferromagnetic behavior at low temperatures. The compositions of nanoparticles prepared by milling the SmCo, NdFeB, and FeCo powders were found to be deviated from the starting powders.