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Advanced nuclear fuels are designed to offer improved performance and accident tolerance, with an emphasis on achieving higher thermal conductivity. While promising fuel candidates like uranium nitrides, carbides, and silicides have been widely studied, the majority of uranium compounds remain unexplored. To search for potential candidates among these unexplored uranium compounds, we incorporated machine learning to accelerate the material discovery process. In this study, we trained a multiclass classification model to predict a compound's thermal conductivity based on 133 input features derived from element properties and temperature. The initial training data consist of over 160,000 processed thermal conductivity records from the Starrydata2 database, but a skewed data class distribution led the trained model to underestimate compound's thermal conductivity. Consequently, we addressed the issue of class imbalance by applying Synthetic Minority Oversampling TEchnique and Random UnderSampling, improving the recall for materials with thermal conductivity higher than 15 W/mK from 0.64 to 0.71. Finally, our best model is used to identify 119 potential advanced fuel candidates with high thermal conductivity among 774 stable uranium compounds. Our results underscore the potential of machine learning in the field of nuclear science, accelerating the discovery of advanced nuclear materials. [ABSTRACT FROM AUTHOR]

As the objective of language teaching is shifting from producing so‐called "native‐like" speakers to fostering speakers competent in intercultural communication, it has become necessary to identify the kinds of learning resources that may be related to the learner's development of intercultural communicative competence (ICC) in today's context of globalization and technological innovation. Employing a mixed‐method approach with focus group interviews (n = 46) and a survey (n = 342), this study examines Chinese, Japanese, and Korean language learners' out‐of‐class intercultural experience and examines how interpersonal engagement and media usage are associated with the three dimensions of ICC: approach, analyze, and act. The study found that while both interpersonal interactions and media usage related to target language were positively associated with self‐reported gains in all three dimensions of ICC, media use consistently had a greater effect on ICC than interpersonal interactions. The implications of these findings for language educators are discussed. The Challenge: With internationalized campuses and global presence of East Asian media, learners of Chinese, Japanese, or Korean have various out‐of‐class intercultural engagement opportunities. How and to what extent do learners interact with individuals and media from the target language culture? What impact does this have on their intercultural communicative competence? [ABSTRACT FROM AUTHOR]