Browsing by Author "Rajamohan, Srijith"

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    Development of a Responsible Policy Index to Improve Statutory and Self-Regulatory Policies that Protect Children’s Diet and Health in the America’s Region
    Rincón-Gallardo Patiño, Sofía; Rajamohan, Srijith; Meaney, Kathleen; Coupey, Eloise; Serrano, Elena L.; Hedrick, Valisa E.; da Silva Gomes, Fabio; Polys, Nicholas F.; Kraak, Vivica (MDPI, 2020-01-13)
    In 2010, 193 Member States of the World Health Organization (WHO) endorsed World Health Assembly Resolution WHA63.14 to restrict the marketing of food and beverage products high in fat, sugar and salt (HFSS) to children to prevent obesity and non-communicable diseases (NCDs). No study has examined HFSS marketing policies across the WHO regional office countries in the Americas. Between 2018 and 2019, a transdisciplinary team examined policies to restrict HFSS food and beverage product marketing to children to develop a responsible policy index (RESPI) that provides a quality score based on policy characteristics and marketing techniques. After designing the RESPI, we conducted a comprehensive literature review through October 2019 to examine policies in 14 countries in the WHO Americans Region. We categorized policies (n = 38) as either self-regulatory or statutory and calculated the RESPI scores, ranked from 0 (lowest) to 10 (highest). Results showed Brazil, Canada, Chile, and Uruguay had the highest RESPI scores associated with statutory policies that restricted point of sale, cartoon, licensed media characters and celebrities; and HFSS products in schools and child care settings, and broadcast and print media. Policymakers can use the RESPI tool to evaluate marketing policies within and across geopolitical boundaries to protect children’s diet and health.
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    High-Dimensional Visual Analytics of Particle Kinematics
    Polys, Nicholas F.; Diefenthaler, Markus; Rajamohan, Srijith; Whang, JooYoung; Romanov, Dmitry; Dahshan, Mai (2020-03-31)
    The goal of this project was to explore the feasibility of Semantic Interaction (SI) methods [SI1, SI2] for Nuclear Femtography. Semantic Interaction is an approach to Human and Machine learning that enables the users to explore and refine their understanding of correlations and inter-relationships within large amounts of multidimensional data. Semantic Interaction combines statistical mathematics and machine learning with real-time scientific visualization. While a variety of visualization techniques can help scientists to gain a more comprehensive understandings of their data, Semantic Interaction uses the history of the user’s interaction to learn about what the user considers as relevant features and allows to map the n-dimensional correlations in a n-dimensional data set. Toward the exploration of high-dimensional nuclear physics data, we pursued two objectives: 1) adapt our Graphically-Linked Ensemble Explorer (GLEE) to load the results of nuclear physics experiments and 2) evaluate the results with Jefferson Lab scientists and the CNF community.
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    Mapping the Celebrity Endorsement of Branded Food and Beverage Products and Marketing Campaigns in the United States, 1990–2017
    Zhou, Mi; Rajamohan, Srijith; Hedrick, Valisa E.; Rincón-Gallardo Patiño, Sofía; Abidi, Faiz; Polys, Nicholas F.; Kraak, Vivica (MDPI, 2019-10-04)
    Celebrity endorsement used to promote energy-dense and nutrient-poor (EDNP) food and beverage products may contribute to poor dietary habits. This study examined celebrity endorsement of branded food and beverage products and marketing campaigns in the United States (US) from 1990 to 2017. Celebrity endorsement data were collected from peer-reviewed and grey literature. Interactive data visualizations were created for the endorsement relationships between celebrities, companies and products whose nutritional profiles were compared with the US Department of Agriculture’s (USDA’s) Smart Snacks Standards. Logistic regression was used to explore associations between celebrities’ demographic profiles and the nutritional profiles of products. Results showed 542 celebrities were associated with 732 endorsements representing 120 brands of 59 companies across 10 food and beverage categories. Two thirds (67.2%; n = 80) of the brands represented EDNP products that did not align with the USDA’s Smart Snacks Standards. Logistic regression analysis indicated that Millennial (p = 0.008) and male celebrities (p = 0.041) were more likely to endorse EDNP products than Generation Z teen and female celebrities, respectively. No statistical significance was observed for celebrities of other demographic profiles. This study may inform future policies and actions of the US government, industry, researchers and consumer advocacy organizations to use celebrity endorsement to promote healthy food environments for Americans.