Many teaching strategies have been used to promote the development of critical thinking skills, among which the most frequently used are group discussion, concept mapping, and analytical questioning. The study aims to explore learners' voice and learning experience in the pedagogical contributions of these strategies to the development of critical thinking skills in Chinese EFL (English as a Foreign Language) learners. One full university class was chosen as the sample in the instruction of critical thinking skills and to take part in a learner voice survey, among which 15 participants were chosen by purposeful sampling for semistructured interviews. The instruction of critical thinking was embedded in an English reading class by using the three teaching strategies, during which four interviews were conducted for each critical thinking skill. After the instruction of critical thinking skills was completed, all participants were surveyed with the learner voice questionnaire. The results show that participants thought the three teaching strategies could improve critical thinking skills. Each teaching strategy made unique and specific contributions to the development of critical thinking skills. These findings have pedagogical implications for using these teaching strategies in the instruction of critical thinking skills in Chinese EFL learners.

Traditional English teaching model neglects student emotions, making many tired of learning. Machine learning supports end-to-end recognition of learning emotions, such that the recognition system can adaptively adjust the learning difficulty in English classroom. With the help of machine learning, this paper presents a method to extract the facial expression features of students in business English class, and establishes a student emotion recognition model, which consists of such modules as emotion mechanism, signal acquisition, analysis and recognition, emotion understanding, emotion expression, and wearable equipment. The results show that the proposed emotion recognition model monitors the real-time emotional states of each student during English learning; upon detecting frustration or boredom, machine learning will timely switch to the contents that interest the student or easier to learn, keeping the student active in learning. The research provides an end-to-end student emotion recognition system to assist with classroom teaching, and enhance the positive emotions of students in English learning.

Motivation Rapidly generated scRNA-seq datasets enable us to understand cellular differences and the function of each individual cell at single-cell resolution. Cell type classification, which aims at characterizing and labeling groups of cells according to their gene expression, is one of the most important steps for single-cell analysis. To facilitate the manual curation process, supervised learning methods have been used to automatically classify cells. Most of the existing supervised learning approaches only utilize annotated cells in the training step while ignoring the more abundant unannotated cells. In this paper, we proposed scPretrain, a multi-task self-supervised learning approach that jointly considers annotated and unannotated cells for cell type classification. scPretrain consists of a pre-training step and a fine-tuning step. In the pre-training step, scPretrain uses a multi-task learning framework to train a feature extraction encoder based on each dataset’s pseudo-labels, where only unannotated cells are used. In the fine-tuning step, scPretrain fine-tunes this feature extraction encoder using the limited annotated cells in a new dataset. Results We evaluated scPretrain on 60 diverse datasets from different technologies, species and organs, and obtained a significant improvement on both cell type classification and cell clustering. Moreover, the representations obtained by scPretrain in the pre-training step also enhanced the performance of conventional classifiers such as random forest, logistic regression and support vector machines. scPretrain is able to effectively utilize the massive amount of unlabelled data and be applied to annotating increasingly generated scRNA-seq datasets. Availability https://github.com/ruiyi-zhang/scPretrain and https://zenodo.org/record/5802306

Latent heat flux (LHF) plays an important role in the global hydrological cycle and is therefore necessary to understand global climate variability. It has been reported that the near-surface specific humidity is a major source of error for satellite-derived LHF. Here, a new empirical model relating multichannel brightness temperatures ( T B ) obtained from the Fengyun-3 (FY-3C) microwave radiometer and sea surface air specific humidity ( Q a ) is proposed. It is based on the relationship between T B , Q a , sea surface temperature (SST), and water vapor scale height. Compared with in situ data, the new satellite-derived Q a and LHF both exhibit better statistical results than previous estimates. For Q a , the bias, root mean square difference (RMSD), and the correlation coefficient (R2) between satellite and buoy in the mid-latitude region are 0.08 g/kg, 1.76 g/kg, and 0.92, respectively. For LHF, the bias, RMSD, and R2 are 2.40 W/m2, 34.24 W/m2, and 0.87, respectively. The satellite-derived Q a are also compared with National Oceanic and Atmospheric Administration (NOAA) Cooperative Institute for Research in Environmental Sciences (CIRES) humidity datasets, with a bias, RMSD, and R2 of 0.02 g/kg, 1.02 g/kg, and 0.98, respectively. The proposed method can also be extended in the future to observations from other space-borne microwave radiometers.

Land use/cover change is the main reason for the variation of ecosystem carbon storage. The study of the impact of land use on carbon storage has certain reference values for realizing high-quality development in the Yellow River Basin. In this paper, the InVEST model was used to simulate the variation of carbon storage in the Yellow River Basin in 2000, 2005, 2010, 2015, and 2020, and to predict the carbon storage in 2030 in combination with the CA-Markov model, as well as to discuss the impact of land use on carbon storage. The results showed that: (1) The variation trend of carbon storage for different land use types in the Yellow River Basin was different and was mainly manifested as a decrease of cultivated land and unused land, and an increase of forest land, grassland, water, and construction land. The carbon storage in the provincial key development prioritized zone, national development optimized zone, and provincial development optimized zone showed decreasing trends, while the national key development prioritized zone and national major grain producing zone presented a fluctuating downward trend. (2) The ecosystem carbon storage function weakened after 2000, and part of the carbon sink area transformed into a carbon source area. The area with low carbon storage was distributed in the west of the provincial key ecological function zone, and the area with high carbon storage was concentrated in the south and middle of national key ecological function zone and the east of the provincial key ecological function zone. (3) The carbon loss was largest in the urban expansion scenario (UES), followed by the natural development scenario (NDS) and ecological protection scenario (EPS). The carbon storage of different scenarios presented significant positive correlations with land use intensity.