The Current Research Landscape of the Application of Artificial Intelligence in Managing Cerebrovascular and Heart Diseases: A Bibliometric and Content Analysis.

The applications of artificial intelligence (AI) in aiding clinical decision-making and management of stroke and heart diseases have become increasingly common in recent years, thanks in part to technological advancements and the heightened interest of the research and medical community. This study aims to provide a comprehensive picture of global trends and developments of AI applications relating to stroke and heart diseases, identifying research gaps and suggesting future directions for research and policy-making. A novel analysis approach that combined bibliometrics analysis with a more complex analysis of abstract content using exploratory factor analysis and Latent Dirichlet allocation, which uncovered emerging research domains and topics, was adopted.

Name Norms: A Guide to Casting Your Next Experiment.

Psychologists often test hypotheses by constructing vignettes depicting people engaging in behavior and displaying characteristics designed to operationalize specific variables. People described in these vignettes are typically given names, but names have a variety of connotations that could lead to unwanted variance between conditions of an experiment and in other ways have implications for the results of a study. An up-to-date source of information to help guide the selection of names would be useful for researchers.

Work-family spillover and metabolic syndrome indicators: Findings from a national sample.

This study examines the link between negative work-family spillover and metabolic risk factors over a 9-year period. Data from two waves of the Midlife in the United States Survey were used to explore relationships between negative work-family spillover and four indicators of metabolic syndrome-blood pressure, triglycerides, body mass index, and glucose levels. In a sample of full-time working men and women ( = 630), increased negative spillover at baseline significantly predicted higher body mass index nearly a decade later, with a marginally significant effect for triglyceride levels.

Understanding how visual attention locks on to a location: Toward a computational model of the N2pc component.

Competing theories regarding the neural mechanism underlying the N2pc suggest that it reflects target enhancement or distractor suppression, but we suggest a different theory. In our study, participants viewed a bilateral dual-stream rapid serial visual presentation (RSVP). Each trial had either one or two targets (T1, T2) presented either in the same or different streams.