High-entropy alloys (HEAs) exhibit complex phase formation behavior, challenging conventional predictive methods. This study presents a machine learning (ML) framework for phase prediction in HEAs, using a curated dataset of 648 experimentally characterized compositions and features derived from thermodynamic and electronic descriptors. Three classifiers—random forest, gradient boosting, and CatBoost—were trained and validated through cross-validation and testing. Gradient boosting achieved the highest accuracy, and valence electron concentration (VEC), atomic size mismatch (δ), and enthalpy of mixing (ΔHmix) were identified as the most influential features. The model predictions were experimentally verified using a non-equiatomic Al30Cu17.5Fe17.5Cr17.5Mn17.5 alloy and the equiatomic Cantor alloy (CoCrFeMnNi), both of which showed strong agreement with predicted phase structures. The results demonstrate that combining physically informed feature engineering with ML enables accurate and generalizable phase prediction, supporting accelerated HEA design.

The purpose of the research is to identify antecedents of mobile wallet continuance intention in Vietnam. A self-administered questionnaire was distributed to collect data from a total of 276 respondents. Partial least squares structural equation modeling was employed for analyzing the data. Five mobile wallet features – mobile application quality, mobile wallet familiarity, situational normality, payment security, and feedback mechanism – are introduced as fundamental elements, which influence customer’ continuance intention to use mobile wallet in Vietnam. The results indicate that mobile quality application and familiarity can significantly influence perceived ease-of-use (PEOU) and perceived usefulness (PU), but situational normality has an impact only on PEOU. PEOU and PU are positively related to satisfaction. On the other hand, payment security and feedback mechanism affect positively customer’ trust. As a result, the positive effects that satisfaction and trust have on electronic wallet continuance intention are confirmed. The findings can be used to advise mobile wallet providers to improve their platform design and services to retain users. As a theoretical contribution, this study combines the Technology Acceptance Model, Unified Theory of Acceptance and Use of Technology to investigate the key determinants on continuance intention in the context of electronic wallet in Vietnam.