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
This paper presents the use of a student model to improve the explanations provided by an intelligent tu- toring system,namely SimpleQuestl,in the domain of electronics.The method of overlay modelling is adopted to bu...This paper presents the use of a student model to improve the explanations provided by an intelligent tu- toring system,namely SimpleQuestl,in the domain of electronics.The method of overlay modelling is adopted to build the student model.The diagnosis is based on the comparison of the behavinurs of the student and the ex- pert.The student model is consulted by the “explainer” and “debugging” procedures in order to re-order the sequence of the explanation.展开更多
It focuses on that students must be developed the ability to solve the practical problem by building the mathematics models and the ability to combine the theory with the practice. It also states that students must be...It focuses on that students must be developed the ability to solve the practical problem by building the mathematics models and the ability to combine the theory with the practice. It also states that students must be improved the learning interests and practical experience.展开更多
In this study, the mathematical models of dynamics of student populations in the university departments are formulated. As a case study, we employ the data of registration section from Department of Mathematics, Facul...In this study, the mathematical models of dynamics of student populations in the university departments are formulated. As a case study, we employ the data of registration section from Department of Mathematics, Faculty of Applied Science, King Mongkut’s University of Technology North Bangkok (KMUTNB), Thailand, from calendar year 2006 to 2010. Using regression analysis, descriptive model and explanatory model are derived. The descriptive model is linear with R2 = 0.8864. Using log-transformation, the explanatory model gives the nonlinear approximation with R2 = 0.8293. The model predicts that the number of students of Department of Mathematics, KMUTNB has a tendency to linearly increase with slope of 20 with 95% CI (6.8417, 33.1583). The application of the models in educational management is discussed.展开更多
Objective To analyze the sensitivity of effect factors between the PCL-C and the SCL-90, to provide evidence for social psychological crisis screening and post-trauma interventions. Methods We administered the PCL-C a...Objective To analyze the sensitivity of effect factors between the PCL-C and the SCL-90, to provide evidence for social psychological crisis screening and post-trauma interventions. Methods We administered the PCL-C and SCL-90 to screen for PTSD and other psychological problems among students who survived the disaster and continued their school studies. The surveys were carried out 3, 6, 9, and 12 months after the earthquake. A bivariate 2-level logistic model was used to explore the different levels of sensitivity among students. The factors influencing the relationships between PTSD and depression, and between PTSD and anxiety were examined. Results We analyzed data from 1677 students, revealing that female students in higher grades were more likely to exhibit symptoms of depression, rather than PTSD, compared with the control group （males in lower grades）, and the difference was significant （P〈0.05）. In contrast, ethnic minorities were more likely to exhibit PTSD symptoms compared to the others. In addition, female students were more likely to exhibit symptoms of anxiety than PTSD. Other effects that did not reach statistical significance were suggested to have a similar influence on PTSD, depression, and anxiety. Conclusion After a natural disaster, specific aspects of depression and anxiety should be examined, avoiding an overemphasis on PTSD in social psychological crisis interventions.展开更多
In order to find an effective way to improve the quality of school management,finding valuable information from students' original data and providing feedback for student management are necessary. Firstly,some new...In order to find an effective way to improve the quality of school management,finding valuable information from students' original data and providing feedback for student management are necessary. Firstly,some new and successful educational data mining models were analyzed and compared. These models have better performance than traditional models( such as Knowledge Tracing Model) in efficiency,comprehensiveness,ease of use,stability and so on. Then,the neural network algorithm was conducted to explore the feasibility of the application of educational data mining in student management,and the results show that it has enough predictive accuracy and reliability to be put into practice. In the end,the possibility and prospect of the application of educational data mining in teaching management system for university students was assessed.展开更多
Objectives: To determine health promoting behaviors of university students in Jordan and factors influencing them. Design and Methods: A cross-sectional descriptive design was used to recruit convenience sample (n = 5...Objectives: To determine health promoting behaviors of university students in Jordan and factors influencing them. Design and Methods: A cross-sectional descriptive design was used to recruit convenience sample (n = 525) of university students receiving education from two governmental and one private universities in Jordan. Data were collected between September 2013 and January 2014 by using Health Promoting Lifestyle Profile II. Pender Model provided conceptual framework to guide the study. Results: The mean score of Health Promoting Lifestyle Profile of the student was at (127.87 ± 19.91). Significant differences were found between Health Promoting Lifestyle Profile mean score and the mean score of its subscales and student’s age, gender, employment status, family income, university type, and faculty type. Conclusions: These findings suggest that interventions are needed to enhance the practice of health promoting behaviours. These interventions should focus on demographic variations among university students.展开更多
Object: To explore the relationship between psychological suzhi and mental health among Chinese college students, and to gain psychological suzhi factors that are predictors for mental health. Method: By using stratif...Object: To explore the relationship between psychological suzhi and mental health among Chinese college students, and to gain psychological suzhi factors that are predictors for mental health. Method: By using stratified sampling method, an investigation was conducted among 734 subjects. They were assessed with the College Student Psychological Suzhi Scale (CSPS, including 3 subscales, 28 factors) and General Health Questionnaire-20 item (GHQ-20, including 3 subscales). Results: 1) Psychological suzhi score for Chinese college students had negative correlation with the score of GHQ-20, GHQ-depression and GHQ-anxiety (p < 0.001), and positive correlation with the score of GHQ-self-affirmation (p < 0.001);2) Psychological suzhi score for Chinese college students was predictor of the score for GHQ-20 and its subscales namely GHQ-self-affirmation, GHQ-depression, and GHQ-anxiety (β = ?0.448, 0.439, ?0.262, ?0.259, p < 0.001);the variance explained by the score of GHQ-20 and its subscales were 19.9%, 19.1%, 6.7%, 6.5%;3) There were 12 psychological suzhi factors that were predictors for GHQ-self-affirmation which was known as the positive indicator of mental health (p < 0.05);11 psychological suzhi factors were predictors for GHQ-depression and GHQ-anxiety which was known as the negative indicator of mental health (p < 0.05). Conclusion: There exists a correlation between psychological suzhi and mental health, particularly in positive mental health. Indeed, the psychological suzhi factors are able to enhance the pertinence of mental health 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.
文摘This paper presents the use of a student model to improve the explanations provided by an intelligent tu- toring system,namely SimpleQuestl,in the domain of electronics.The method of overlay modelling is adopted to build the student model.The diagnosis is based on the comparison of the behavinurs of the student and the ex- pert.The student model is consulted by the “explainer” and “debugging” procedures in order to re-order the sequence of the explanation.
文摘It focuses on that students must be developed the ability to solve the practical problem by building the mathematics models and the ability to combine the theory with the practice. It also states that students must be improved the learning interests and practical experience.
文摘In this study, the mathematical models of dynamics of student populations in the university departments are formulated. As a case study, we employ the data of registration section from Department of Mathematics, Faculty of Applied Science, King Mongkut’s University of Technology North Bangkok (KMUTNB), Thailand, from calendar year 2006 to 2010. Using regression analysis, descriptive model and explanatory model are derived. The descriptive model is linear with R2 = 0.8864. Using log-transformation, the explanatory model gives the nonlinear approximation with R2 = 0.8293. The model predicts that the number of students of Department of Mathematics, KMUTNB has a tendency to linearly increase with slope of 20 with 95% CI (6.8417, 33.1583). The application of the models in educational management is discussed.
基金supported by the Research Fund from the Department of Education of Sichuan Province (NO:08SA093)
文摘Objective To analyze the sensitivity of effect factors between the PCL-C and the SCL-90, to provide evidence for social psychological crisis screening and post-trauma interventions. Methods We administered the PCL-C and SCL-90 to screen for PTSD and other psychological problems among students who survived the disaster and continued their school studies. The surveys were carried out 3, 6, 9, and 12 months after the earthquake. A bivariate 2-level logistic model was used to explore the different levels of sensitivity among students. The factors influencing the relationships between PTSD and depression, and between PTSD and anxiety were examined. Results We analyzed data from 1677 students, revealing that female students in higher grades were more likely to exhibit symptoms of depression, rather than PTSD, compared with the control group （males in lower grades）, and the difference was significant （P〈0.05）. In contrast, ethnic minorities were more likely to exhibit PTSD symptoms compared to the others. In addition, female students were more likely to exhibit symptoms of anxiety than PTSD. Other effects that did not reach statistical significance were suggested to have a similar influence on PTSD, depression, and anxiety. Conclusion After a natural disaster, specific aspects of depression and anxiety should be examined, avoiding an overemphasis on PTSD in social psychological crisis interventions.
基金Sponsored by the Ability Enhancement Project of Teaching Staff in Harbin Institute of Technology(Grant No.06)
文摘In order to find an effective way to improve the quality of school management,finding valuable information from students' original data and providing feedback for student management are necessary. Firstly,some new and successful educational data mining models were analyzed and compared. These models have better performance than traditional models( such as Knowledge Tracing Model) in efficiency,comprehensiveness,ease of use,stability and so on. Then,the neural network algorithm was conducted to explore the feasibility of the application of educational data mining in student management,and the results show that it has enough predictive accuracy and reliability to be put into practice. In the end,the possibility and prospect of the application of educational data mining in teaching management system for university students was assessed.
文摘Objectives: To determine health promoting behaviors of university students in Jordan and factors influencing them. Design and Methods: A cross-sectional descriptive design was used to recruit convenience sample (n = 525) of university students receiving education from two governmental and one private universities in Jordan. Data were collected between September 2013 and January 2014 by using Health Promoting Lifestyle Profile II. Pender Model provided conceptual framework to guide the study. Results: The mean score of Health Promoting Lifestyle Profile of the student was at (127.87 ± 19.91). Significant differences were found between Health Promoting Lifestyle Profile mean score and the mean score of its subscales and student’s age, gender, employment status, family income, university type, and faculty type. Conclusions: These findings suggest that interventions are needed to enhance the practice of health promoting behaviours. These interventions should focus on demographic variations among university students.
文摘Object: To explore the relationship between psychological suzhi and mental health among Chinese college students, and to gain psychological suzhi factors that are predictors for mental health. Method: By using stratified sampling method, an investigation was conducted among 734 subjects. They were assessed with the College Student Psychological Suzhi Scale (CSPS, including 3 subscales, 28 factors) and General Health Questionnaire-20 item (GHQ-20, including 3 subscales). Results: 1) Psychological suzhi score for Chinese college students had negative correlation with the score of GHQ-20, GHQ-depression and GHQ-anxiety (p < 0.001), and positive correlation with the score of GHQ-self-affirmation (p < 0.001);2) Psychological suzhi score for Chinese college students was predictor of the score for GHQ-20 and its subscales namely GHQ-self-affirmation, GHQ-depression, and GHQ-anxiety (β = ?0.448, 0.439, ?0.262, ?0.259, p < 0.001);the variance explained by the score of GHQ-20 and its subscales were 19.9%, 19.1%, 6.7%, 6.5%;3) There were 12 psychological suzhi factors that were predictors for GHQ-self-affirmation which was known as the positive indicator of mental health (p < 0.05);11 psychological suzhi factors were predictors for GHQ-depression and GHQ-anxiety which was known as the negative indicator of mental health (p < 0.05). Conclusion: There exists a correlation between psychological suzhi and mental health, particularly in positive mental health. Indeed, the psychological suzhi factors are able to enhance the pertinence of mental health education.
