This study investigates the mechanism of digital linguistic landscapes in enabling engineering education for smart construction according to the educational dimensions of A (ability), S (skill), and K (knowledge). A questionnaire survey was conducted based on the core concepts of the informative dimension and symbolic dimension in digital language landscape as well as the ability dimension, knowledge dimension, and skill dimension in engineering education. Structural equation modeling (SEM) was used as the test method. The results of the research demonstrate that the informative dimension and symbolic dimension are two main aspects of DLL in education of engineering students for smart construction. Additionally, DLL has a significant positive impact on the ability, knowledge, and skill education of engineering students for smart construction. The research has theoretical and practical significance, as it not only enriches research on the relationship between DLL and engineering education for smart construction but also expands the theoretical understanding of engineering education from the perspective of linguistics. Furthermore, the study explores the path of the practical application of digital language landscape to engineering education for smart construction.
Download full-text PDF |
Source |
---|---|
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC9167075 | PMC |
http://dx.doi.org/10.1155/2022/4077516 | DOI Listing |
Plant Sci
January 2025
State Key Laboratory of Hybrid Rice, Key Laboratory for Research and Utilization of Heterosis in Indica Rice of the Ministry of Agriculture, Engineering Research Center for Plant Biotechology and Germplasm Utilization of the Ministry of Education, College of Life Science, Wuhan University, Wuhan 430072, China.
Sci Bull (Beijing)
December 2024
Department of Ocean Science and Engineering, Southern University of Science and Technology, Shenzhen 518055, China. Electronic address:
How tropical cyclone (TC) activity varies in response to a changing climate is widely debated. The accumulated cyclone energy (ACE) is one of the indicators of TC activity and has attracted considerable attention because of its close relationship with the damages caused by TCs. Previous studies have focused on detecting long-term trends in global ACE; however, the results are inconclusive.
View Article and Find Full Text PDFNutr Metab Cardiovasc Dis
December 2024
Department of Epidemiology and Statistics, Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences & School of Basic Medicine, Peking Union Medical College, Beijing, China; State Key Laboratory of Common Mechanism Research for Major Diseases, Beijing, China. Electronic address:
Background And Aim: The relationship between socio-economic inequalities (SEIs) and early life malnutrition with muscle health remains unclear. This study aims to examine the effects of SEIs and early life exposure to famine on relative hand grip strength (rHGS).
Methods And Results: We analyzed data of 37,008 individuals from the China National Health Survey.
Int J Biol Macromol
January 2025
The College of Forestry, Beijing Forestry University, 100083, Beijing, PR China. Electronic address:
This study aims to address the challenge of detoxifying ginkgolic acid and transform it from waste into a valuable resource. By using pseudo-template molecular imprinting technology to chemically modify polysaccharide materials, we developed a polysaccharide-based molecular imprinted material (MMCC-CD/CS-MIP) for the targeted separation and controlled release of ginkgolic acid. Under optimal conditions, MMCC-CD/CS-MIP demonstrated excellent adsorption performance (Q = 47.
View Article and Find Full Text PDFComput Biol Chem
January 2025
College of Artificial Intelligence, Tianjin University of Science and Technology, No. 9, 13th Street, Tianjin Economic-Technological Development Area, Tianjin, 300457, China. Electronic address:
The enzyme turnover number (k) is crucial for understanding enzyme kinetics and optimizing biotechnological processes. However, experimentally measured k values are limited due to the high cost and labor intensity of wet-lab measurements, necessitating robust computational methods. To address this issue, we propose PreTKcat, a framework that integrates pre-trained representation learning and machine learning to predict k values.
View Article and Find Full Text PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!