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Background: Previous research in the field of content and language integrated learning (CLIL) has not yet comprehensively investigated the interaction between learners' expertise and the instructional effectiveness. Aims: Taking cognitive load theory as the theoretical framework, a study was conducted to investigate the expertise reversal effect on learning English and mathematics simultaneously: whether an integrated approach (i.e. learning both English and mathematics simultaneously) could facilitate the acquisition of mathematic skills and English linguistic skills as a foreign language more effectively and efficiently than a separated learning approach (i.e. learning Mathematics and English separately). Materials: The materials for the integrated learning approach were in English-only, and the materials for the separated learning approach were in English-and-Chinese. Both sets of materials were given as reading content for teaching mathematic skills and English as a foreign language. Methods: The study adopted a 2 (language expertise: low vs. high) × 2 (instruction: integrated vs. separated) between-subject factorial design with instructional approaches and learners' expertise in English as independent variables, the learning performance in Mathematics and English with the cognitive load ratings as the dependent variables. Sixty-five Year-10 students with lower expertise in English and 56 Year-2 college students with higher expertise in English in China were recruited and allocated to two instructional conditions respectively. Results: An expertise reversal effect was confirmed: the English and mathematics integrated learning approach was more effective for higher expertise learners while the English and mathematics separated learning condition was more beneficial for lower expertise learners.

Objective: With the improvement of living standards, consumers are paying more and more attention to the quality of rice. Traditional rice quality detection relies on human sensory judgment, which is inaccurate and inefficient. With the continuous development of molecular imaging technology, more and more scholars at home and abroad have begun to pay attention to its application in the nondestructive testing of agricultural products. Molecular imaging technology combines the advantages of spectral technology and image technology, which can achieve rapid, nondestructive and accurate detection of rice quality. In this paper, taking rice as the research object, we carried out nondestructive detection research on rice varieties, moisture and starch content using molecular imaging technology. We proposed a rapid detection method based on molecular imaging technology for rice variety identification, moisture content and starch content. Molecular images of the rice samples from four origins were obtained using a molecular imaging system, the regions of interest of the rice were identified and, spectral data, textural features and morphological features of the rice were extracted. Spectral, textural and morphological features were selected by principal component analysis (PCA), and nine feature wavelengths were obtained and an optimal model was established with an accuracy of 91.67%, which demonstrated the feasibility of molecular imaging. By comparing the models, the BCC-LS-SVR model based on the RB function had the highest accuracy with R2 of 0.989, RMSEP of 0.767%, R2 of 0.985, and RMSEC of 0.591%. Moreover, starchy rice was detected using molecular imaging. The PCA-SVR model based on the RBF kernel function had the highest accuracy with R2 of 0.989, RMSEC of 0.445%, R2 of 0.991, and RMSEP of 0.669%. Our models demonstrated high accuracy in identifying rice varieties, as well as quantifying moisture and starch content, showcasing the feasibility of molecular imaging technology in rice quality assessment. This research offers a rapid, nondestructive, and accurate method for rice quality assessment, promising significant benefits for agricultural producers and consumers.