Predicting the displacement of landslide is of utmost practical importance as the landslide can pose serious threats to both human life and property.However,traditional methods have the limitation of random selection ...Predicting the displacement of landslide is of utmost practical importance as the landslide can pose serious threats to both human life and property.However,traditional methods have the limitation of random selection in sliding window selection and seldom incorporate weather forecast data for displacement prediction,while a single structural model cannot handle input sequences of different lengths at the same time.In order to solve these limitations,in this study,a new approach is proposed that utilizes weather forecast data and incorporates the maximum information coefficient(MIC),long short-term memory network(LSTM),and attention mechanism to establish a teacher-student coupling model with parallel structure for short-term landslide displacement prediction.Through MIC,a suitable input sequence length is selected for the LSTM model.To investigate the influence of rainfall on landslides during different seasons,a parallel teacher-student coupling model is developed that is able to learn sequential information from various time series of different lengths.The teacher model learns sequence information from rainfall intensity time series while incorporating reliable short-term weather forecast data from platforms such as China Meteorological Administration(CMA)and Reliable Prognosis(https://rp5.ru)to improve the model’s expression capability,and the student model learns sequence information from other time series.An attention module is then designed to integrate different sequence information to derive a context vector,representing seasonal temporal attention mode.Finally,the predicted displacement is obtained through a linear layer.The proposed method demonstrates superior prediction accuracies,surpassing those of the support vector machine(SVM),LSTM,recurrent neural network(RNN),temporal convolutional network(TCN),and LSTM-Attention models.It achieves a mean absolute error(MAE)of 0.072 mm,root mean square error(RMSE)of 0.096 mm,and pearson correlation coefficients(PCCS)of 0.85.Additionally,it exhibits enhanced prediction stability and interpretability,rendering it an indispensable tool for landslide disaster prevention and mitigation.展开更多
Teachers’language modeling behaviors,including frequent conversation,open-ended questions,repetition and extension,self-and parallel talks,and advanced language,have significantly impacted young children’s language ...Teachers’language modeling behaviors,including frequent conversation,open-ended questions,repetition and extension,self-and parallel talks,and advanced language,have significantly impacted young children’s language learning and development.This study examined 60 classrooms from 20 kindergartens in Guangzhou,China,and analyzed 62 films of daily activities and 57 videos of free play.It aims to address the research gap in existing research that pays little attention to teachers’language modeling behaviors in daily activities and free play.The results indicate that the more frequent teachers’language modeling behaviors,the larger the vocabulary young children use and the better their performance in lexical richness.However,such behaviors in daily activities and free play are infrequent and superficial,failing to guide young children’s language development effectively.To optimize teachers’language modeling behaviors in daily activities and free play,they are expected to establish positive emotional bonds with young children in a kind and respectful manner and receive training.Teachers are also encouraged to frequently communicate and engage in dialogues with young children,create contexts that facilitate the use of language,increase the frequency of stimuli for vocabulary learning,and guide and encourage young children’s advanced language.展开更多
The establishment of normal colleges and universities is an important component of building a modern country,which possesses different value ethos with the universities.The emergence of theÉcole Normale Supé...The establishment of normal colleges and universities is an important component of building a modern country,which possesses different value ethos with the universities.The emergence of theÉcole Normale Supérieure in Paris and the local normal schools has set a new model for teacher education around the world and promoted values and knowledge patterns promoted by them quite distinctive from those of the traditional European university.In order to improve the quality of teacher education,the models of teacher education in the U.S.,Japan,U.K.,etc.,are being continually innovated.China has created the teacher education model different with the U.S.,Japan,U.K.and France,which is a contribution to the development of international teacher education.展开更多
基金This research work is supported by Sichuan Science and Technology Program(Grant No.2022YFS0586)the National Key R&D Program of China(Grant No.2019YFC1509301)the National Natural Science Foundation of China(Grant No.61976046).
文摘Predicting the displacement of landslide is of utmost practical importance as the landslide can pose serious threats to both human life and property.However,traditional methods have the limitation of random selection in sliding window selection and seldom incorporate weather forecast data for displacement prediction,while a single structural model cannot handle input sequences of different lengths at the same time.In order to solve these limitations,in this study,a new approach is proposed that utilizes weather forecast data and incorporates the maximum information coefficient(MIC),long short-term memory network(LSTM),and attention mechanism to establish a teacher-student coupling model with parallel structure for short-term landslide displacement prediction.Through MIC,a suitable input sequence length is selected for the LSTM model.To investigate the influence of rainfall on landslides during different seasons,a parallel teacher-student coupling model is developed that is able to learn sequential information from various time series of different lengths.The teacher model learns sequence information from rainfall intensity time series while incorporating reliable short-term weather forecast data from platforms such as China Meteorological Administration(CMA)and Reliable Prognosis(https://rp5.ru)to improve the model’s expression capability,and the student model learns sequence information from other time series.An attention module is then designed to integrate different sequence information to derive a context vector,representing seasonal temporal attention mode.Finally,the predicted displacement is obtained through a linear layer.The proposed method demonstrates superior prediction accuracies,surpassing those of the support vector machine(SVM),LSTM,recurrent neural network(RNN),temporal convolutional network(TCN),and LSTM-Attention models.It achieves a mean absolute error(MAE)of 0.072 mm,root mean square error(RMSE)of 0.096 mm,and pearson correlation coefficients(PCCS)of 0.85.Additionally,it exhibits enhanced prediction stability and interpretability,rendering it an indispensable tool for landslide disaster prevention and mitigation.
基金funded by the Ministry of Education of China Humanities and Social Sciences Project“Research on Strategies for Improving the Quality of Kindergarten Teacher-Child Interaction”(No.19YJA880065).
文摘Teachers’language modeling behaviors,including frequent conversation,open-ended questions,repetition and extension,self-and parallel talks,and advanced language,have significantly impacted young children’s language learning and development.This study examined 60 classrooms from 20 kindergartens in Guangzhou,China,and analyzed 62 films of daily activities and 57 videos of free play.It aims to address the research gap in existing research that pays little attention to teachers’language modeling behaviors in daily activities and free play.The results indicate that the more frequent teachers’language modeling behaviors,the larger the vocabulary young children use and the better their performance in lexical richness.However,such behaviors in daily activities and free play are infrequent and superficial,failing to guide young children’s language development effectively.To optimize teachers’language modeling behaviors in daily activities and free play,they are expected to establish positive emotional bonds with young children in a kind and respectful manner and receive training.Teachers are also encouraged to frequently communicate and engage in dialogues with young children,create contexts that facilitate the use of language,increase the frequency of stimuli for vocabulary learning,and guide and encourage young children’s advanced language.
文摘The establishment of normal colleges and universities is an important component of building a modern country,which possesses different value ethos with the universities.The emergence of theÉcole Normale Supérieure in Paris and the local normal schools has set a new model for teacher education around the world and promoted values and knowledge patterns promoted by them quite distinctive from those of the traditional European university.In order to improve the quality of teacher education,the models of teacher education in the U.S.,Japan,U.K.,etc.,are being continually innovated.China has created the teacher education model different with the U.S.,Japan,U.K.and France,which is a contribution to the development of international teacher education.