University of Arkansas

About: University of Arkansas is a education organization based out in Fayetteville, Arkansas, United States. It is known for research contribution in the topics: Population & Poison control. The organization has 17225 authors who have published 33329 publications receiving 941102 citations. The organization is also known as: Arkansas & UA.

Linking water quality and well-being for improved assessment and valuation of ecosystem services

Abstract: Despite broad recognition of the value of the goods and services provided by nature, existing tools for assessing and valuing ecosystem services often fall short of the needs and expectations of decision makers. Here we address one of the most important missing components in the current ecosystem services toolbox: a comprehensive and generalizable framework for describing and valuing water quality-related services. Water quality is often misrepresented as a final ecosystem service. We argue that it is actually an important contributor to many different services, from recreation to human health. We present a valuation approach for water quality-related services that is sensitive to different actions that affect water quality, identifies aquatic endpoints where the consequences of changing water quality on human well-being are realized, and recognizes the unique groups of beneficiaries affected by those changes. We describe the multiple biophysical and economic pathways that link actions to changes in water quality-related ecosystem goods and services and provide guidance to researchers interested in valuing these changes. Finally, we present a valuation template that integrates biophysical and economic models, links actions to changes in service provision and value estimates, and considers multiple sources of water quality-related ecosystem service values without double counting.

Achieving k-anonymity in privacy-aware location-based services

Abstract: Location-Based Service (LBS) has become a vital part of our daily life. While enjoying the convenience provided by LBS, users may lose privacy since the untrusted LBS server has all the information about users in LBS and it may track them in various ways or release their personal data to third parties. To address the privacy issue, we propose a Dummy- Location Selection (DLS) algorithm to achieve k-anonymity for users in LBS. Different from existing approaches, the DLS algorithm carefully selects dummy locations considering that side information may be exploited by adversaries. We first choose these dummy locations based on the entropy metric, and then propose an enhanced-DLS algorithm, to make sure that the selected dummy locations are spread as far as possible. Evaluation results show that the proposed DLS algorithm can significantly improve the privacy level in terms of entropy. The enhanced-DLS algorithm can enlarge the cloaking region while keeping similar privacy level as the DLS algorithm. I. INTRODUCTION With the rapid development of mobile devices and social networks, Location-Based Service (LBS) has become a vital part in our daily activities in recent years. With smartphones or tablets, users can download location-based applications from Apple Store or Google Play Store. With the help of these

Fear of COVID-19 and the mental health consequences in America.

Abstract: The intent of this work was to examine the intersection of COVID-19 fear with social vulnerabilities and mental health consequences among adults living in the United States. Data are from a nationally representative sample (n = 10,368) of U.S. adults surveyed online during demographic subgroups (gender, age, income, race and ethnicity, geography). The sample week of March 23, 2020. The sample was poststratification weighted to ensure a balanced representation across social and demographic subgroups (gender, age, income, race or ethnicity, geography). The sample comprised 51% female; 23% non-White; 18% Hispanic; 25% of households with children under 18 years of age; 55% unmarried; and nearly 20% unemployed, laid off, or furloughed at the time of the interview. Respondents were fearful, averaging a score of nearly 7 on a scale of 10 when asked how fearful they were of COVID-19. Preliminary analysis suggests clear spatial diffusion of COVID-19 fear. Fear appears to be concentrated in regions with the highest reported COVID-19 cases. Significant differences across several U.S. census regions are noted (p < .01). Additionally, significant bivariate relationships were found between socially vulnerable respondents (female, Asians, Hispanic, foreign-born, families with children) and fear, as well as with mental health consequences (anxiety and depressive symptoms). Depressive symptoms, on average, were high (16+ on the Center for Epidemiologic Studies Depression scale), and more than 25% of the sample reported moderate to severe anxiety symptoms. More in-depth psychosocial research is needed using nationally representative samples that can help to inform potential mental health risks, as well as by targeting specific mental health interventions. (PsycInfo Database Record (c) 2020 APA, all rights reserved).