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.

Consumption as Voting: An Exploration of Consumer Empowerment

Abstract: Purpose – Increasing numbers of consumers are expressing concerns about reports of questionable corporate practices and are responding through boycotts and buycotts. This paper compares competing theories of consumer empowerment and details findings that examine the applicability of the theory to “ethical consumer” narratives. The nature and impact of consumer empowerment in consumer decision making is then discussed.Design/methodology/approach – The study takes an exploratory approach by conducting semi‐structured in‐depth interviews with a purposive sample of ten consumers. These were recruited from an “ethical product” fair in Scotland.Findings – Results indicate that the participating consumers embraced a voting metaphor, either explicitly or implicitly, to view consumption as an ethical/political domain. Setting their choices within perceived collective consumer behaviour, they characterised their consumption as empowering. This results in an ethical consumer project that can be seen as operating wit...

Evolutionary origins of the SARS-CoV-2 sarbecovirus lineage responsible for the COVID-19 pandemic

Abstract: There are outstanding evolutionary questions on the recent emergence of coronavirus SARS-CoV-2/hCoV-19 in Hubei province that caused the COVID-19 pandemic, including (1) the relationship of the new virus to the SARS-related coronaviruses, (2) the role of bats as a reservoir species, (3) the potential role of other mammals in the emergence event, and (4) the role of recombination in viral emergence. Here, we address these questions and find that the sarbecoviruses -- the viral subgenus responsible for the emergence of SARS-CoV and SARS-CoV-2 -- exhibit frequent recombination, but the SARS-CoV-2 lineage itself is not a recombinant of any viruses detected to date. In order to employ phylogenetic methods to date the divergence events between SARS-CoV-2 and the bat sarbecovirus reservoir, recombinant regions of a 68-genome sarbecovirus alignment were removed with three independent methods. Bayesian evolutionary rate and divergence date estimates were consistent for all three recombination-free alignments and robust to two different prior specifications based on HCoV-OC43 and MERS-CoV evolutionary rates. Divergence dates between SARS-CoV-2 and the bat sarbecovirus reservoir were estimated as 1948 (95% HPD: 1879-1999), 1969 (95% HPD: 1930-2000), and 1982 (95% HPD: 1948-2009). Despite intensified characterization of sarbecoviruses since SARS, the lineage giving rise to SARS-CoV-2 has been circulating unnoticed for decades in bats and been transmitted to other hosts such as pangolins. The occurrence of a third significant coronavirus emergence in 17 years together with the high prevalence and virus diversity in bats implies that these viruses are likely to cross species boundaries again.

Reduced-Reference Image Quality Assessment Using Divisive Normalization-Based Image Representation

Abstract: Reduced-reference image quality assessment (RRIQA) methods estimate image quality degradations with partial information about the ldquoperfect-qualityrdquo reference image. In this paper, we propose an RRIQA algorithm based on a divisive normalization image representation. Divisive normalization has been recognized as a successful approach to model the perceptual sensitivity of biological vision. It also provides a useful image representation that significantly improves statistical independence for natural images. By using a Gaussian scale mixture statistical model of image wavelet coefficients, we compute a divisive normalization transformation (DNT) for images and evaluate the quality of a distorted image by comparing a set of reduced-reference statistical features extracted from DNT-domain representations of the reference and distorted images, respectively. This leads to a generic or general-purpose RRIQA method, in which no assumption is made about the types of distortions occurring in the image being evaluated. The proposed algorithm is cross-validated using two publicly-accessible subject-rated image databases (the UT-Austin LIVE database and the Cornell-VCL A57 database) and demonstrates good performance across a wide range of image distortions.

The effects of new science curricula on student performance

Abstract: This study summarizes the results of a quantitative synthesis of the retrievable primary research dealing with the effects of new science curricula on student performance. This study synthesizes the results of 105 experimental studies involving more than 45,000 students and utilizes the quantitative synthesis perspective to research integration known as meta-analysis (Glass, 1976). A total of 27 different new science curricula involving one or more measures of student performance are included in this meta-analysis. Data were collected for 18 a priori selected student performance measures. The results of this meta-analysis reveal definite positive patterns of student performance in new science curricula. Across all new science curricula analyzed, students exposed to new science curricula performed better than students in traditional courses in general achievement, analytic skills, process skills, and related skills (reading, mathematics, social studies and communication), as well as developing a more positive attitude toward science. On a composite basis, the average student in new science curricula exceeded the performance of 63% of the students in traditional science courses.