The Influence of Chronotype and Grit on Lifestyle and Physical Activity

Background:  The chronotype of a person refers to an individual's natural sleep-wake cycle and whether that individual prefers morning or evening activities, and grit is an individual's perseverance and passion for long-term goals.Aim: The purpose of this study was to investigate the relationship between grit, chronotype, physical activity, and leading a healthy lifestyle in college-age students.Methods:  Health and fitness data (i.e., chronotype, grit, lifestyle assessment score, and daily steps) from 431 first-semester university students at a private college were collected and analyzed. Results: This study found that grit and chronotype both have significant correlations with living a healthy lifestyle and with physical activity. Grit more accurately predicts a person's lifestyle (β = -13.712, r = 0.39, p < 0.0001) while chronotype more accurately predicts the physical activity, or steps, of a person (β = 66.48, r = .19, p = .0001). Chronotype can also accurately predict the grit of a person (r = .25, p < .0001), and it was found that morning people tend to have more grit.Conclusions:  This study concluded that grit, chronotype, steps, and a healthy lifestyle are all significantly correlated with each other. Knowing the relationship between endogenous chronotype, grit, and living a physically active and healthy lifestyle can help inform policy decisions related to the goal of strengthening an institution's inclusive and healthy academic community

The ChatGPT Artificial Intelligence Chatbot: How Well Does It Answer Accounting Assessment Questions?

ChatGPT, a language-learning model chatbot, has garnered considerable attention for its ability to respond to users’ questions. Using data from 14 countries and 186 institutions, we compare ChatGPT and student performance for 28,085 questions from accounting assessments and textbook test banks. As of January 2023, ChatGPT provides correct answers for 56.5 percent of questions and partially correct answers for an additional 9.4 percent of questions. When considering point values for questions, students significantly outperform ChatGPT with a 76.7 percent average on assessments compared to 47.5 percent for ChatGPT if no partial credit is awarded and 56.5 percent if partial credit is awarded. Still, ChatGPT performs better than the student average for 15.8 percent of assessments when we include partial credit. We provide evidence of how ChatGPT performs on different question types, accounting topics, class levels, open/closed assessments, and test bank questions. We also discuss implications for accounting education and research

The population dynamical implications of male-biased parasitism in different mating systems.

Although there is growing evidence that males tend to suffer higher levels of parasitism than females, the implications of this for the population dynamics of the host population are not yet understood. Here we build on an established ‘two-sex’ model and investigate how increased susceptibility to infection in males affects the dynamics, under different mating systems. We investigate the effect of pathogenic disease at different case mortalities, under both monogamous and polygynous mating systems. If the case mortality is low, then male-biased parasitism appears similar to unbiased parasitism in terms of its effect on the population dynamics. At higher case mortalities, we identified significant differences between male-biased and unbiased parasitism. A host population may therefore be differentially affected by male-biased and unbiased parasitism. The dynamical outcome is likely to depend on a complex interaction between the host's mating system and demography, and the parasite virulence

Assessment of yield gaps on global grazed-only permanent pasture using climate binning

To meet rising demands for agricultural products, existing agricultural lands must either produce more or expand in area. Yield gaps (YGs)-the difference between current and potential yield of agricultural systems-indicate the ability to increase output while holding land area constant. Here, we assess YGs in global grazed-only permanent pasture lands using a climate binning approach. We create a snapshot of circa 2000 empirical yields for meat and milk production from cattle, sheep, and goats by sorting pastures into climate bins defined by total annual precipitation and growing degree-days. We then estimate YGs from intra-bin yield comparisons. We evaluate YG patterns across three FAO definitions of grazed livestock agroecosystems (arid, humid, and temperate), and groups of animal production systems that vary in animal types and animal products. For all subcategories of grazed-only permanent pasture assessed, we find potential to increase productivity several-fold over current levels. However, because productivity of grazed pasture systems is generally low, even large relative increases in yield translated to small absolute gains in global protein production. In our dataset, milk-focused production systems were found to be seven times as productive as meat-focused production systems regardless of animal type, while cattle were four times as productive as sheep and goats regardless of animal output type. Sustainable intensification of pasture is most promising for local development, where large relative increases in production can substantially increase incomes or "spare" large amounts of land for other uses. Our results motivate the need for further studies to target agroecological and economic limitations on productivity to improve YG estimates and identify sustainable pathways toward intensification26318201832FUNDAÇÃO DE AMPARO À PESQUISA DO ESTADO DE SÃO PAULO - FAPESP2014/26767‐9; 2017/25023‐4; 2016/20307‐1; 2017/08970‐0; 2016/08741‐8; 2016/08742‐4; 2017/06037‐4; 2018/11052‐5We thank two anonymous reviewers for their valuable comments. We appreciate suggestions from Dr. Mario Herrero, Dr. Stephen Polasky, Dr. Marcelo Galdos, Dr. Jansle Rocha, W. Evan Sheehan, and Dr. Charles P. West in preliminary development of this work. We further thank Dr. Mario Herrero for providing permission and access to global livestock production data used in this study. Funding provided by FAPESP process nos 2014/26767‐9, 2017/25023‐4 and 2016/20307‐1, 2017/08970‐0, 2016/08741‐8, 2016/08742‐4, 2017/06037‐4, 2018/11052‐5, the Iola Hubbard Climate Change Endowment managed by the Earth Systems Research Center at the University of New Hampshire, and the National Science Foundation Graduate Research Fellowship under Grant No. DGE‐1313911. LL was supported by the Center for Bioenergy Innovation a U.S. Department of Energy Research Center supported by the Office of Biological and Environmental Research in the DOE Office of Science. AA was supported by the National Science Foundation Graduate Research Fellowship under Grant No. DGE‐1313911. DJ acknowledges support from the National Science Foundation: Innovations at the Nexus of Food, Energy, and Water Systems under Grant EAR‐163932