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Due to globalization and the formation of the global village, people worldwide interact in multiple languages frequently. Concurrently, there has been an increase in motivation to learn different languages for individual purposes. As well, we see that the importance of language education is also being promoted. Past language methods have merit, such as visual aids, but it is not a good idea nor appropriate for teachers to just follow suit without considering the language system of the learners, the country of origin of the learners, and the various background and culture of the learners. In view of these characteristics of immigrants as adult learners participating in Chinese language classes, which are different from adult learners of other languages, this paper tries to develop multi-integrated instructional strategies, which include supportive strategy, auxiliary strategy and core strategy. These strategies are based on theories of accelerated learning, whole brain learning, and situated learning. I propose that Chinese language teachers use these strategies in classes for immigrants. The strategies developed will have implications for immigrants, teacher educators, language program administrators, and other stakeholders in similar contexts.

Rice blast is one of the most devastating diseases that threatens rice production in Taiwan. A rice blast forecasting model is required to guide the precise application of fungicide. Therefore, BlastGRU-TW model based on deep learning algorithms was developed in this study. The input data comprised approximately 1000 rice blast surveys, collected from 50 fields throughout Taiwan between 2014 and 2021, and the corresponding weather data retrieved from weather observation network in Taiwan. Common and easily accessible meteorological factors, i.e. temperature, humidity, precipitation, and wind, were converted into 20 daily meteorological features which were coupled with different time intervals between 1 and 30 days before survey (DBS) to train the model. The results showed that seven meteorological features (daily maximum temperature, daily mean temperature, daily minimum temperature, daily mean humidity, daily mean WV and daily mean Wu) and the interval from −4 to −24 DBS were informative in disease prediction, thereby indicating that the proposed model could predict the risk of rice blast by using meteorological data 4–24 days before new disease symptoms appeared. The proposed BlastGRU-TW model achieved an accuracy of 87.3%. Furthermore, on adding the 3 day forecast weather data from Weather Research and Forecasting (WRF) model in the proposed model, the forecast extended to 7 days ahead of the appearance of new symptoms. Moreover, the BLASTAM model developed in Japan was implemented and validated in Taiwan to evaluate its applicability in different geographical areas. Finally, a rice blast early warning system (https://mycolab.pp.nchu.edu.tw/blast%5fforecast/index%5fen.php) equipped with an interactive web-based map is now available for real-time forecasting of the risk of rice blast in paddy field across Taiwan. • Input data from 1000 rice blast surveys in Taiwan for 2014–2021. • BlastGRU-TW model for deep learning-based rice blast prediction • The model achieves an accuracy of 87.3% • The BLASTAM model developed in Japan was the first to be implemented and validated outside Japan. • Forecast results can be visualized on a user-friendly, interactive web-based map. [ABSTRACT FROM AUTHOR]

Credit scoring is playing an increasingly critical role with the rising number of lending operations for micro and small enterprises as well as individuals. A large number of research is primarily based on the combination and construction methods of credit scoring models by the experts. However, different credit data have distinct requirements for the models, and how to automatically search and construct credit scoring models according to credit data has become essential, that is the main concern of this paper. In response to the current challenges for credit scoring research, we proposed an Automatic Credit Scoring Strategy (ACSS), designed a credit assessment platform which includes data import, classification model automatic search, feature selection, hyperparameter optimization, data mining, classification output and other modules. Aiming at the problem of substantial imbalance in credit data, we propose an improved SMOTE algorithm that is capable of generating supplementary data for the lack of minority in credit data, thereby making the credit data distribution well balanced. As for classification model selection, features engineering, parameter optimization and other parts, we further incorporate automatic search ways to reduce manual interaction. We utilize public and self-owned credit data sets to conduct experiments and compare them with the latest credit assessment methods. Extensive experiments have demonstrated that our ACSS method achieves relatively noticeable performance improvements using the German credit dataset, the Taiwan credit dataset and the personal credit dataset for credit scoring. The best results achieved by our proposed method are 0.98, 0.99, 0.895 and 0.901 for MAE, RMSE, accuracy and precision respectively. In addition, the experimental results also show that our proposed improved SMOTE algorithm contributes to the credit scoring performance enhancement. The experimental findings suggest that our proposed ACSS balances automation and accuracy, which can be implemented to the consumption industry to enhance the reliability of credit assessments. • We utilize an improved SMOTE that could decrease the impact of data imbalance that exists in the credit data. • We propose to leverage the credit feature pruning algorithm and memetic optimization algorithm. • We propose an automated credit scoring strategy (ACSS) method with a cost-effective neural architecture search (C-NAS) scheme. [ABSTRACT FROM AUTHOR]