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.

Environmental and plant community determinants of species loss following nitrogen enrichment.

Abstract: Global energy use and food production have increased nitrogen inputs to ecosystems worldwide, impacting plant community diversity, composition, and function. Previous studies show considerable variation across terrestrial herbaceous ecosystems in the magnitude of species loss following nitrogen (N) enrichment. What controls this variation remains unknown. We present results from 23 N-addition experiments across North America, representing a range of climatic, soil and plant community properties, to determine conditions that lead to greater diversity decline. Species loss in these communities ranged from 0 to 65% of control richness. Using hierarchical structural equation modelling, we found greater species loss in communities with a lower soil cation exchange capacity, colder regional temperature, and larger production increase following N addition, independent of initial species richness, plant productivity, and the relative abundance of most plant functional groups. Our results indicate sensitivity to N addition is co-determined by environmental conditions and production responsiveness, which overwhelm the effects of initial community structure and composition.

A review on design, modeling and applications of computer experiments

Abstract: In this paper, we provide a review of statistical methods that are useful in conducting computer experiments. Our focus is on the task of metamodeling, which is driven by the goal of optimizing a complex system via a deterministic simulation model. However, we also mention the case of a stochastic simulation, and examples of both cases are discussed. The organization of our review first presents several engineering applications, it then describes approaches for the two primary tasks of metamodeling: (i) selecting an experimental design; and (ii) fitting a statistical model. Seven statistical modeling methods are included. Both classical and newer experimental designs are discussed. Finally, our own computational study tests the various metamodeling options on two two-dimensional response surfaces and one ten-dimensional surface.

Size‐Dependent Chemical and Magnetic Ordering in L10‐FePt Nanoparticles

Abstract: FePt nanoparticles have great application potential in advanced magnetic materials such as ultrahigh-density recording media and high-performance permanent magnets. The key for applications is the very high uniaxial magnetocrystalline anisotropy of the L10-FePt phase, which is based on crystalline ordering of the face-centered tetragonal (fct) structure, described by the chemical-ordering parameter S. Higher chemical ordering results in higher magnetocrystalline anisotropy. Unfortunately, as-synthesized FePt nanoparticles take a disordered face-centered cubic (fcc) structure that has low magnetocrystalline anisotropy. Heat-treatment is necessary to convert the fcc structure to the ordered fct structure. Several previous theoretical and experimental investigations have been reported on the size-dependent chemical ordering of FePt nanoparticles. It has been observed that the degree of ordering decreases with decreasing particle size of the sputtered FePt nanoparticles. Theoretical simulation predicted that the ordering would not take place when the particle size is below a critical value. However, there have not been systematic experimental studies on quantitative size dependence of chemical ordering of FePt nanoparticles due to the lack of monodisperse L10-FePt nanoparticles with controllable sizes. There are also few studies reported to date on the quantitative particle size dependence of magnetic properties, including the Curie temperature, coercivity, and magnetization of the L10-FePt phase, although it has been well accepted that there is a size effect on the ferromagnetism of any low-dimensional magnets. Additionally, the magnetic properties of FePt ferromagnets, as observed in thin-film samples, are affected by the degree of chemical ordering, which is in turn size dependent. It is therefore highly desirable to understand the size and chemical-ordering effects, and their influence on the magnetic properties of the nanoparticles. A major hurdle in obtaining the particle size dependence of structural and magnetic properties of the L10 phase is particle sintering during heat-treatments that convert the fcc phase to the fct phase. This long-pending problem has been solved recently by adopting the salt-matrix annealing technique. With this technique, particle aggregation during the phase transformation has been avoided so that the true size-dependent properties of the fct phase can be measured. In this paper, we report results on quantitative particle size dependence of the chemical-ordering parameter S and selected magnetic properties, including the Curie temperature, Tc, magnetization, Ms, and coercivity, Hc, with the particle size varying from 2 to 15 nm. Figure 1 shows the transmission electron microscopy (TEM) images of the FePt nanoparticles with different sizes before and after annealing in a salt matrix at 973 K for 4 h. The images, from left to right, show nanoparticles with nominal diameters of 2, 4, 6, 8, and 15 nm, respectively. The upper and lower rows are images of as-synthesized and salt-matrixannealed nanoparticles, respectively. As shown in Figure 1, the particle size is retained well upon annealing. Both the assynthesized and annealed nanoparticles are monodisperse with a standard deviation of 5–10 % in diameter. TEM observations also revealed that when the particle size is smaller than or equal to 8 nm, the fct nanoparticles are monocrystalline, whereas the 15 nm fct particles are polycrystalline. It is interesting to see that the L10 nanoparticles, tiny ferromagnets at room temperature, are dispersed very well without agglomeration despite the dipolar interaction between the particles, if a solvent with high viscosity is chosen and if the solution is diluted. Extensive TEM and X-ray diffraction (XRD) analyses have proved that the technique of salt-matrix annealing can be applied to heat-treatments of the FePt nanoparticles without leading to particle agglomeration and sintering, if a suitable salt-to-particle ratio and proper annealing conditions are chosen. Figure 2 shows the XRD patterns of the 4 nm, as-synthesized, fcc-structured nanoparticles and the particles annealed in a salt matrix at 873 K for 2 h, 973 K for 2 h, and 973 K for 4 h (from bottom to top), respectively. As shown in the figure, the positions of the (111) peaks shift in the higher-anC O M M U N IC A TI O N

Dynamic analysis and control of a Stewart platform manipulator

Abstract: The Stewart platform is a six-axis parallel robot manipulator with a force-to-weight ratio and positioning accuracy far exceeding that of a conventional serial-link arm. Its stiffness and accuracy approach that of a machine tool yet its workspace dexterity approaches that of a conventional manipulator. In this article, we study the dynamic equations of the Stewart platform manipulator. Our derivation is closed to that of Nguyen and Pooran because the dynamics are not explicitly given but are in a step-bystep algorithm. However, we give some insight into the structure and properties of these equations: We obtain compact expressions of some coefficients. These expressions should be interesting from a control point of view. A stiffness control scheme is designed for milling application. Some path-planning notions are discussed that take into account singularity positions and the required task. The objective is to make the milling station into a semiautonomous robotic tool needing some operator interaction but having some intelligence of its own. It should interface naturally with part delivery and other higher-level tasks. 0 1993 John Wiley & Sons, Inc.

Current state and future trends: a citation network analysis of the learning analytics field

Abstract: This paper provides an evaluation of the current state of the field of learning analytics through analysis of articles and citations occurring in the LAK conferences and identified special issue journals. The emerging field of learning analytics is at the intersection of numerous academic disciplines, and therefore draws on a diversity of methodologies, theories and underpinning scientific assumptions. Through citation analysis and structured mapping we aimed to identify the emergence of trends and disciplinary hierarchies that are influencing the development of the field to date. The results suggest that there is some fragmentation in the major disciplines (computer science and education) regarding conference and journal representation. The analyses also indicate that the commonly cited papers are of a more conceptual nature than empirical research reflecting the need for authors to define the learning analytics space. An evaluation of the current state of learning analytics provides numerous benefits for the development of the field, such as a guide for under-represented areas of research and to identify the disciplines that may require more strategic and targeted support and funding opportunities.