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This study aims to identify the elements that contribute to the digital competencies of primary school teachers working in the mountainous region of Northern Vietnam and assess the significance of these factors. The research sample consisted of 260 primary school teachers from this region. Exploratory factor analysis (EFA) and multivariate regression methods are used. The results of this research indicate that seven characteristics have a substantial impact on the digital abilities of teachers: ability to incorporate digital technology into teaching, conditions for digital transformation in teaching and learning activities are found to be influential, ability to create digital resources is highlighted as an important factor, ability to select teaching content with opportunities for technology usage, professional development needs of teachers, school policy and ability to select digital resources. These results offer education administrators a comprehensive understanding of the factors influencing primary teachers' digital competencies and enable them to implement strategic changes and prioritize each element. Moreover, this study provides a valuable reference for future researchers seeking to explore new research directions in this field and analyze the correlation between factors to propose solutions to develop digital competencies of primary school teachers suitable to specific contexts and conditions.

Density functional theory and a semi-empirical quantum chemical approach were used to evaluate the photocatalytic efficiency of ceria (CeO2) combined with reduced graphene oxide (rGO) and graphene (GP) for degrading methylene blue (MB). Two main aspects were examined: the adsorption ability of rGO and GP for MB, and the separation of photogenerated electrons and holes in CeO2/rGO and CeO2/GP. Our results, based on calculations of the adsorption energy, population analysis, bond strength index, and reduced density gradient, show favorable energetics for MB adsorption on both rGO and GP surfaces. The process is driven by weak, non-covalent interactions, with rGO showing better MB adsorption. A detailed analysis involving parameters like fractional occupation density, the centroid distance between molecular orbitals, and the Lewis acid index of the catalysts highlights the effective charge separation in CeO2/rGO compared to CeO2/GP. These findings are crucial for understanding photocatalytic degradation mechanisms of organic dyes and developing efficient photocatalysts. [ABSTRACT FROM AUTHOR]

The construction process generates a huge amount of waste. If not well managed, this amount of waste will cause unnecessary risks for the project construction process in particular and for the environment in general. Therefore, many studies on waste risk management from the construction industry have been carried out. In recent years, BIM technology has been developed and applied a lot in the field of project management. In this study, the authors give a new perspective on BIM application in the field of project management - BIM application in construction waste management in the construction stage. The first part of the paper will present the research method, the literature review of the research. The final result is the result after applying the proposed research process. The final part of the paper will present the results of the study.

Abstract This study represents an efficient method for extracting product aspects from customer reviews and give solutions for inferring aspect ratings and aspect weights. Aspect ratings often reflect the user’s satisfaction on aspects of a product and aspect weights reflect the degree of importance of the aspects posed by the user. These tasks therefore play a very important role for manufacturers to better understand their customers’ opinion on their products and services. The study addresses the problem of aspect extraction by using aspect words based on conditional probability combined with the bootstrap technique. To infer the user’s rating for aspects, a supervised approach called the Naïve Bayes classification method is proposed to learn the aspect ratings in which sentiment words are considered as features. The weight of an aspect is estimated by leveraging the frequencies of aspect words within each review and the aspect consistency across all reviews. Experimental results show that the proposed method obtains very good performance on real world datasets in comparison with other state-of-the-art methods.