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.

Metal Hexacyanoferrates: Electrosynthesis, in Situ Characterization, and Applications

Abstract: This review focuses on the electrosynthesis, in situ characterization, and technological applications of Prussian blue analogues with the generic formula AhMk[Fe(CN)6]l·mH2O, where h, k, l, and m are stoichiometric numbers, A = alkali metal cation, and M is a transition metal. Six such metal hexacyanoferrate (MHCF) compounds derived from Cu, Pd, In, V, Co, and Ni are featured in this article against the backdrop of the Prussian blue parent compound itself and other related compounds. The use of cyclic voltammetry and complementary techniques including scanning probe microscopies, quartz crystal microgravimetry, and ac impedance spectroscopy, for studying the growth of MHCF films on targeted substrates, is discussed. Spectroelectrochemical in situ characterization of these films in the UV−visible and IR regions is then reviewed. Finally, the use of Prussian blue and its analogues in devices for displays and “smart” windows, photoimaging, environmental remediation, chemical/biological sensing, energy conver...

Exploring human capital: putting ‘human’ back into strategic human resource management

Abstract: The field of strategic human resource management has seemingly rediscovered human capital with increasing research focused on human capital as a mediator in the relationship between HR practices and performance. In this paper we review human capital definitions and measurement approaches within this literature. We then identify some of the issues emerging with human capital research. Finally, we propose some future directions for research on human capital in organisations.

Advancing data assimilation in operational hydrologic forecasting: progresses, challenges, and emerging opportunities

Abstract: Data assimilation (DA) holds considerable potential for improving hydrologic predictions as demonstrated in numerous research studies. However, advances in hydrologic DA research have not been adequately or timely implemented in operational forecast systems to improve the skill of forecasts for better informed real-world decision making. This is due in part to a lack of mechanisms to properly quantify the uncertainty in observations and forecast models in real-time forecasting situations and to conduct the merging of data and models in a way that is adequately efficient and transparent to operational forecasters. The need for effective DA of useful hydrologic data into the forecast process has become increasingly recognized in recent years. This motivated a hydrologic DA workshop in Delft, the Netherlands in November 2010, which focused on advancing DA in operational hydrologic forecasting and water resources management. As an outcome of the workshop, this paper reviews, in relevant detail, the current status of DA applications in both hydrologic research and operational practices, and discusses the existing or potential hurdles and challenges in transitioning hydrologic DA research into cost-effective operational forecasting tools, as well as the potential pathways and newly emerging opportunities for overcoming these challenges. Several related aspects are discussed, including (1) theoretical or mathematical aspects in DA algorithms, (2) the estimation of different types of uncertainty, (3) new observations and their objective use in hydrologic DA, (4) the use of DA for real-time control of water resources systems, and (5) the development of community-based, generic DA tools for hydrologic applications. It is recommended that cost-effective transition of hydrologic DA from research to operations should be helped by developing community-based, generic modeling and DA tools or frameworks, and through fostering collaborative efforts among hydrologic modellers, DA developers, and operational forecasters.

A Unified Framework for Gesture Recognition and Spatiotemporal Gesture Segmentation

Abstract: Within the context of hand gesture recognition, spatiotemporal gesture segmentation is the task of determining, in a video sequence, where the gesturing hand is located and when the gesture starts and ends. Existing gesture recognition methods typically assume either known spatial segmentation or known temporal segmentation, or both. This paper introduces a unified framework for simultaneously performing spatial segmentation, temporal segmentation, and recognition. In the proposed framework, information flows both bottom-up and top-down. A gesture can be recognized even when the hand location is highly ambiguous and when information about when the gesture begins and ends is unavailable. Thus, the method can be applied to continuous image streams where gestures are performed in front of moving, cluttered backgrounds. The proposed method consists of three novel contributions: a spatiotemporal matching algorithm that can accommodate multiple candidate hand detections in every frame, a classifier-based pruning framework that enables accurate and early rejection of poor matches to gesture models, and a subgesture reasoning algorithm that learns which gesture models can falsely match parts of other longer gestures. The performance of the approach is evaluated on two challenging applications: recognition of hand-signed digits gestured by users wearing short-sleeved shirts, in front of a cluttered background, and retrieval of occurrences of signs of interest in a video database containing continuous, unsegmented signing in American sign language (ASL).