Machine learning(ML)models are widely used for predicting undrained shear strength(USS),but interpretability has been a limitation in various studies.Therefore,this study introduced shapley additive explanations(SHAP)...Machine learning(ML)models are widely used for predicting undrained shear strength(USS),but interpretability has been a limitation in various studies.Therefore,this study introduced shapley additive explanations(SHAP)to clarify the contribution of each input feature in USS prediction.Three ML models,artificial neural network(ANN),extreme gradient boosting(XGBoost),and random forest(RF),were employed,with accuracy evaluated using mean squared error,mean absolute error,and coefficient of determination(R^(2)).The RF achieved the highest performance with an R^(2) of 0.82.SHAP analysis identified pre-consolidation stress as a key contributor to USS prediction.SHAP dependence plots reveal that the ANN captures smoother,linear feature-output relationships,while the RF handles complex,non-linear interactions more effectively.This suggests a non-linear relationship between USS and input features,with RF outperforming ANN.These findings highlight SHAP’s role in enhancing interpretability and promoting transparency and reliability in ML predictions for geotechnical applications.展开更多
To adapt to national climate change strategies,understanding the thermal vulnerability of urban functional zones(UFZs)is critical for enhancing the livability and sustainable development of cities.We address the limit...To adapt to national climate change strategies,understanding the thermal vulnerability of urban functional zones(UFZs)is critical for enhancing the livability and sustainable development of cities.We address the limitations of existing thermal vulnerability assessments by incorporating human perception into the analysis.Specifically,we introduce the sky openness index to reflect human perception of the thermal environment and examine variations in thermal vulnerability across different UFZs.Using the Extreme Gradient Boosting(XGBoost)-SHapley Additive exPlanations(SHAP)model,we analyze the contributions of 2D and 3D urban form indicators to thermal vulnerability and link these contributions to the unique needs and vulnerability characteristics of UFZs of Foshan City,China in 2023.The results reveal that:1)high-value heat-fragile areas(1.74–2.00]constitute 10.42%of Foshan City;2)traffic zone and publiccommercial zone exhibit the highest levels of thermal vulnerability;and 3)building height and the normalized building index are the most influential factors,with contributions of|0.06|and|0.03|,respectively.We provide a scientific foundation for developing governance strategies to promote urban resilience.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
A child is born to a father and a mother.This fact,however,is yet to be recognized by demography,in which fertility refers to women’s natural ability to give birth.The main reason for the absence of men is that data ...A child is born to a father and a mother.This fact,however,is yet to be recognized by demography,in which fertility refers to women’s natural ability to give birth.The main reason for the absence of men is that data on births are more often available for women than for men.But in the last few decades,data availability has greatly improved.Recent studies show that total fertility rates(TFRs)of men can be calculated for most countries in the world and that the difference between the TFRs of men and women can be quite large.For low-fertility countries,nonetheless,these studies show little difference between the TFRs of men and women,giving rise to the question:Is men’s fertility worth further investigation?To avoid ambiguity in describing a particular difference as small or big,this paper provides a formula for probabilistic TFRs.Using hypothesis test on probabilistic TFRs,we can say that the difference between the TFRs of men and women is statistically significant for all the G7 countries,except for France.To explain the differences between the TFRs of men and women,this study uses models of stable populations and concludes that the one-sex stable population models cannot explain the results whereas a two-sex joint stable population model can do so.By using the two-sex population model,we can explain why the TFR of men in France is almost the same as that of women,and why it is lower than that of women in the other six G7 countries.More specifically,by using the model,we can help explain 76%of the variance in the difference between the TFRs of men and women.Future studies may be able to show that men’s TFR is significantly lower than women’s in other countries too and explain why it is so.The above findings,however,require closer attention and further investigation,because low fertility could lead to socioeconomic problems.Beyond TFRs,extending fertility studies from women to men as well,that is,conducting fertility studies on both women and men,will fundamentally improve our knowledge about fertility age patterns,trends,determinants,policies and other related issues.展开更多
基金Ho Chi Minh City University of Technology (HCMUT), VNU-HCM for supporting this study
文摘Machine learning(ML)models are widely used for predicting undrained shear strength(USS),but interpretability has been a limitation in various studies.Therefore,this study introduced shapley additive explanations(SHAP)to clarify the contribution of each input feature in USS prediction.Three ML models,artificial neural network(ANN),extreme gradient boosting(XGBoost),and random forest(RF),were employed,with accuracy evaluated using mean squared error,mean absolute error,and coefficient of determination(R^(2)).The RF achieved the highest performance with an R^(2) of 0.82.SHAP analysis identified pre-consolidation stress as a key contributor to USS prediction.SHAP dependence plots reveal that the ANN captures smoother,linear feature-output relationships,while the RF handles complex,non-linear interactions more effectively.This suggests a non-linear relationship between USS and input features,with RF outperforming ANN.These findings highlight SHAP’s role in enhancing interpretability and promoting transparency and reliability in ML predictions for geotechnical applications.
基金Under the auspices of Liaoning Revitalization Talents Program(No.XLYC2202024)Basic Scientific Research Project(Key Project)of the Education Department of Liaoning Province(No.LJ212410165084)the Fundamental Research Funds for the Central Universities(No.N2111003,N2411001)。
文摘To adapt to national climate change strategies,understanding the thermal vulnerability of urban functional zones(UFZs)is critical for enhancing the livability and sustainable development of cities.We address the limitations of existing thermal vulnerability assessments by incorporating human perception into the analysis.Specifically,we introduce the sky openness index to reflect human perception of the thermal environment and examine variations in thermal vulnerability across different UFZs.Using the Extreme Gradient Boosting(XGBoost)-SHapley Additive exPlanations(SHAP)model,we analyze the contributions of 2D and 3D urban form indicators to thermal vulnerability and link these contributions to the unique needs and vulnerability characteristics of UFZs of Foshan City,China in 2023.The results reveal that:1)high-value heat-fragile areas(1.74–2.00]constitute 10.42%of Foshan City;2)traffic zone and publiccommercial zone exhibit the highest levels of thermal vulnerability;and 3)building height and the normalized building index are the most influential factors,with contributions of|0.06|and|0.03|,respectively.We provide a scientific foundation for developing governance strategies to promote urban resilience.
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A child is born to a father and a mother.This fact,however,is yet to be recognized by demography,in which fertility refers to women’s natural ability to give birth.The main reason for the absence of men is that data on births are more often available for women than for men.But in the last few decades,data availability has greatly improved.Recent studies show that total fertility rates(TFRs)of men can be calculated for most countries in the world and that the difference between the TFRs of men and women can be quite large.For low-fertility countries,nonetheless,these studies show little difference between the TFRs of men and women,giving rise to the question:Is men’s fertility worth further investigation?To avoid ambiguity in describing a particular difference as small or big,this paper provides a formula for probabilistic TFRs.Using hypothesis test on probabilistic TFRs,we can say that the difference between the TFRs of men and women is statistically significant for all the G7 countries,except for France.To explain the differences between the TFRs of men and women,this study uses models of stable populations and concludes that the one-sex stable population models cannot explain the results whereas a two-sex joint stable population model can do so.By using the two-sex population model,we can explain why the TFR of men in France is almost the same as that of women,and why it is lower than that of women in the other six G7 countries.More specifically,by using the model,we can help explain 76%of the variance in the difference between the TFRs of men and women.Future studies may be able to show that men’s TFR is significantly lower than women’s in other countries too and explain why it is so.The above findings,however,require closer attention and further investigation,because low fertility could lead to socioeconomic problems.Beyond TFRs,extending fertility studies from women to men as well,that is,conducting fertility studies on both women and men,will fundamentally improve our knowledge about fertility age patterns,trends,determinants,policies and other related issues.
