scispace - formally typeset
Search or ask a question
Institution

University of Texas at Arlington

EducationArlington, Texas, United States
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.


Papers
More filters
Journal ArticleDOI
TL;DR: Understanding and preventing delayed RTW will require application of new concepts and study designs, better measures of determinants and outcomes, and more translational research.
Abstract: Background: Despite considerable multidisciplinary research on return to work (RTW), there has been only modest progress in implementation of study results, and little change in overall rates of work disability in developed countries. Methods: Thirty RTW researchers, representing over 20 institutions, assembled to review the current state of the art in RTW research, to identify promising areas for further development, and to provide direction for future investigations. Results and Conclusion: Six major themes were selected as priority areas: early risk prediction; psychosocial, behavioral and cognitive interventions; physical treatments; the challenge of implementing evidence in the workplace context; effective methods to engage multiple stakeholders; and identification of outcomes that are relevant to both RTW stakeholders and different phases of the RTW process. Understanding and preventing delayed RTW will require application of new concepts and study designs, better measures of determinants and outcomes, and more translational research. Greater stakeholder involvement and commitment, and methods to address the unique challenges of each situation are required.

170 citations

Journal ArticleDOI
TL;DR: The problem of finding the optimal parameters of the prescribed robot impedance model is transformed into a linear quadratic regulator (LQR) problem which minimizes the human effort and optimizes the closed-loop behavior of the HRI system for a given task.
Abstract: An intelligent human–robot interaction (HRI) system with adjustable robot behavior is presented. The proposed HRI system assists the human operator to perform a given task with minimum workload demands and optimizes the overall human–robot system performance. Motivated by human factor studies, the presented control structure consists of two control loops. First, a robot-specific neuro-adaptive controller is designed in the inner loop to make the unknown nonlinear robot behave like a prescribed robot impedance model as perceived by a human operator. In contrast to existing neural network and adaptive impedance-based control methods, no information of the task performance or the prescribed robot impedance model parameters is required in the inner loop. Then, a task-specific outer-loop controller is designed to find the optimal parameters of the prescribed robot impedance model to adjust the robot’s dynamics to the operator skills and minimize the tracking error. The outer loop includes the human operator, the robot, and the task performance details. The problem of finding the optimal parameters of the prescribed robot impedance model is transformed into a linear quadratic regulator (LQR) problem which minimizes the human effort and optimizes the closed-loop behavior of the HRI system for a given task. To obviate the requirement of the knowledge of the human model, integral reinforcement learning is used to solve the given LQR problem. Simulation results on an ${x}$ - ${y}$ table and a robot arm, and experimental implementation results on a PR2 robot confirm the suitability of the proposed method.

170 citations

Journal ArticleDOI
TL;DR: This study examines how users' attention to “visual triggers” and “phishing deception indicators” influence their decision-making processes and consequently their decisions, and suggests that overall cognitive effort expended in email processing decreases with attention to visual triggers and phishing deceived indicators.
Abstract: Research problem: Phishing is an email-based scam where a perpetrator camouflages emails to appear as a legitimate request for personal and sensitive information. Research question: How do individuals process a phishing email, and determine whether to respond to it? Specifically, this study examines how users' attention to “visual triggers” and “phishing deception indicators” influence their decision-making processes and consequently their decisions. Literature review: This paper draws upon the theory of deception and the literature on mediated cognition and learning, including the critical role of attention and elaboration in deception detection. From this literature, we developed a research model to suggest that overall cognitive effort expended in email processing decreases with attention to visual triggers and phishing deception indicators. The likelihood to respond to phishing emails increases with attention to visceral cues, but decreases with attention to phishing deception indicators and cognitive effort. Knowledge of email-based scams increases attention to phishing deception indicators, and directly decreases response likelihood. It also moderates the impact of attention to visceral triggers and that of phishing deception indicators on likelihood to respond. Methodology: Using a real phishing email as a stimulus, a survey of 321 members of a public university community in the Northeast US, who were intended victims of a spear phishing attack that took place, was conducted. The survey used validated measures developed in prior literature for the most part and tested results using the partial least-squares regression. Results and discussion: Our research model and hypotheses were supported by the data except that we did not find that cognitive effort significantly affects response likelihood. The implication of the study is that attention to visceral triggers, attention to phishing deception indicators, and phishing knowledge play critical roles in phishing detection. The limitations of the study were that the data were drawn from students, and the study explored one phishing attack, relied on some single-item measures, cognitive effort measure, and a one-round survey. Future research would examine the impact of a varying degree of urgency and a varying level of phishing deception indicators, and actual victims of phishing attacks.

170 citations

Journal ArticleDOI
TL;DR: Results show that the small differences in the solvent properties of the phosphonium ILs compared with ammonium-based ILs will allow for different and unique separation selectivities, and the phosphorus-based stationary phases tend to be more thermally stable than nitrogen- based ILs, which is an advantage in many GC applications.
Abstract: In recent years, room temperature ionic liquids (RTILs) have proven to be of great interest to analytical chemists. One important development is the use of RTILs as highly thermally stable GLC stationary phases. To date, nearly all of the RTIL stationary phases have been nitrogen-based (ammonium, pyrrolidinium, imidazolium, etc.). In this work, eight new monocationic and three new dicationic phosphonium-based RTILs are used as gas-liquid chromatography (GLC) stationary phases. Inverse gas chromatography (GC) analyses are used to study the solvation properties of the phosphonium RTILs through a linear solvation energy model. This model describes the multiple solvation interactions that the phosphonium RTILs can undergo and is useful in understanding their properties. In addition, the phosphonium-based stationary phases are used to separate complex analyte mixtures by GLC. Results show that the small differences in the solvent properties of the phosphonium ILs compared with ammonium-based ILs will allow for different and unique separation selectivities. Also, the phosphonium-based stationary phases tend to be more thermally stable than nitrogen-based ILs, which is an advantage in many GC applications.

169 citations


Authors

Showing all 11918 results

NameH-indexPapersCitations
Zhong Lin Wang2452529259003
Hyun-Chul Kim1764076183227
David H. Adams1551613117783
Andrew White1491494113874
Kaushik De1391625102058
Steven F. Maier13458860382
Andrew Brandt132124694676
Amir Farbin131112583388
Evangelos Gazis131114784159
Lee Sawyer130134088419
Fernando Barreiro130108283413
Stavros Maltezos12994379654
Elizabeth Gallas129115785027
Francois Vazeille12995279800
Sotirios Vlachos12878977317
Network Information
Related Institutions (5)
Georgia Institute of Technology
119K papers, 4.6M citations

95% related

University of Maryland, College Park
155.9K papers, 7.2M citations

95% related

Pennsylvania State University
196.8K papers, 8.3M citations

95% related

University of Illinois at Urbana–Champaign
225.1K papers, 10.1M citations

94% related

University of Texas at Austin
206.2K papers, 9M citations

94% related

Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
202353
2022243
20211,721
20201,664
20191,493
20181,462