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The purpose of this study is to evaluate the relationship between weight status and food environments, peer influence and dietary intake among high-school students in Shiraz, Iran.

Applying a case-control design, 406 adolescents (n = 203 overweight or obese and n = 203 normal weight) aged 14–18 years were selected using a multistage cluster random sampling method. Demographic information, physical activity level and anthropometric indices were collected. Dietary intake was determined using a 147-item food frequency questionnaire. Food environment (home and out of home) and peer influence were determined by a validated questionnaire.

The type of food purchased using pocket money was different between adolescents with overweight or obesity and normal weight adolescents (p < 0.001). The out-of-home food environment score was not different between groups, but the home food environment score (p = 0.004) and the peer influence score (p = 0.001) were higher in normal weight adolescents. Adolescents with overweight or obesity consumed higher amounts of carbohydrate (p = 0.006) and lower amounts of protein (p = 0.01) and more sweet junk foods (p = 0.01), nonstarchy vegetables (p = 0.03) and fruits (p = 0.01) compared to the normal weight group.

Home food environment, peer influence, differences in macronutrient intake and dietary patterns may be contributing factors to adolescent weight status.

The study aims to analyze the synergy of artificial intelligence (AI), with scientometrics, webometrics and bibliometrics to unlock and to emphasize the potential of the applications and benefits of AI algorithms in these fields.

By conducting a systematic literature review, our aim is to explore the potential of AI in revolutionizing the methods used to measure and analyze scholarly communication, identify emerging research trends and evaluate the impact of scientific publications. To achieve this, we implemented a comprehensive search strategy across reputable databases such as ProQuest, IEEE Explore, EBSCO, Web of Science and Scopus. Our search encompassed articles published from January 1, 2000, to September 2022, resulting in a thorough review of 61 relevant articles.

(1) Regarding scientometrics, the application of AI yields various distinct advantages, such as conducting analyses of publications, citations, research impact prediction, collaboration, research trend analysis and knowledge mapping, in a more objective and reliable framework. (2) In terms of webometrics, AI algorithms are able to enhance web crawling and data collection, web link analysis, web content analysis, social media analysis, web impact analysis and recommender systems. (3) Moreover, automation of data collection, analysis of citations, disambiguation of authors, analysis of co-authorship networks, assessment of research impact, text mining and recommender systems are considered as the potential of AI integration in the field of bibliometrics.

This study covers the particularly new benefits and potential of AI-enhanced scientometrics, webometrics and bibliometrics to highlight the significant prospects of the synergy of this integration through AI.