Prediction models for the impact of the COVID-19 pandemic on research activities of Japanese nursing researchers using deep learning.

AIM: This study aimed to construct and evaluate prediction models using deep learning to explore the impact of attributes and lifestyle factors on research activities of nursing researchers during the COVID-19 pandemic. METHODS: A secondary data analysis was conducted from a cross-sectional online survey by the Japanese Society of Nursing Science at the inception of the COVID-19 pandemic. A total of 1089 respondents from nursing faculties were divided into a training dataset and a test dataset. We constructed two prediction models with the training dataset using artificial intelligence (AI) predictive analysis tools; motivation and time were used as predictor items for negative impact on research activities. Predictive factors were attributes, lifestyle, and predictor items for each other. The models' accuracy and internal validity were evaluated using an ordinal logistic regression analysis to assess goodness-of-fit; the test dataset was used to assess external validity. Predicted contributions by each factor were also calculated. RESULTS: The models' accuracy and goodness-of-fit were good. The prediction contribution analysis showed that no increase in research motivation and lack of increase in research time strongly influenced each other. Other factors that negatively influenced research motivation and research time were residing outside the special alert area and lecturer position and living with partner/spouse and associate professor position, respectively. CONCLUSIONS: Deep learning is a research method enabling early prediction of unexpected events, suggesting new applicability in nursing science. To continue research activities during the COVID-19 pandemic and future contingencies, the research environment needs to be improved, workload corrected by position, and considered in terms of work-life balance.

Indium-Mediated Allylation of Carbonyl Compounds in Ionic Liquids: Effect of Salts in Ionic Liquids.

The In-mediated allylation of carbonyl compounds can be performed in various types of solvents including ionic liquids. However, we have found that in [bmim][BF4] (where bmim = 1-butyl-3-methylimidazolium), the In-mediated coupling of crotyl bromide with benzaldehyde gives a complex mixture, and some additives, such as halides and amines, are crucial for the successful conversion to the corresponding γ-adduct. Instead, the addition of alcohols or water promotes the formation of the α-adduct. An asymmetric induction with up to 62% enantiomeric excess (ee) was observed employing cinchonidine as an additive in a binary solvent consisting of an ionic liquid and dichloromethane.