Studies on plant diversity are usually based on the total number of species in a community.However,few studies have examined species richness(SR)of different plant life forms in a community along largescale environmen...Studies on plant diversity are usually based on the total number of species in a community.However,few studies have examined species richness(SR)of different plant life forms in a community along largescale environmental gradients.Particularly,the relative importance(RIV)of different plant life forms in a community and how they vary with environmental variables are still unclear.To fill these gaps,we determined plant diversity of ephemeral plants,annual herbs,perennial herbs,and woody plants from 187 sites across drylands in China.The SR patterns of herbaceous plants,especially perennial herbs,and their RIV in plant communities increased with increasing precipitation and soil nutrient content;however,the RIV of annual herbs was not altered along these gradients.The SR and RIV of ephemeral plants were affected mainly by precipitation seasonality.The SR of woody plants had a unimodal relationship with air temperature and exhibited the highest RIV and SR percentage in plant communities under the harshest environments.An obvious shift emerged in plant community composition,SR and their critical impact factors at 238.5 mm of mean annual precipitation(MAP).In mesic regions(>238.5 mm),herbs were the dominant species,and the SR displayed a relatively slow decreasing rate with increasing aridity,which was mediated mainly by MAP and soil nutrients.In arid regions(<238.5 mm),woody plants were the dominant species,and the SR displayed a relatively fast decreasing rate with increasing aridity,which was mediated mainly by climate variables,especially precipitation.Our findings highlight the importance of comparative life form studies in community structure and biodiversity,as their responses to gradients differed substantially on a large scale.展开更多
The traditional academic warning methods for students in higher vocational colleges are relatively backward,single,and have many influencing factors,which have a limited effect on improving their learning ability.A da...The traditional academic warning methods for students in higher vocational colleges are relatively backward,single,and have many influencing factors,which have a limited effect on improving their learning ability.A data set was established by collecting academic warning data of students in a certain university.The importance of the school,major,grade,and warning level for the students was analyzed using the Pearson correlation coefficient,random forest variable importance,and permutation importance.It was found that the characteristic of the major has a great impact on the academic warning level.Countermeasures such as dynamic adjustment of majors,reform of cognitive adaptation of courses,full-cycle academic support,and data-driven precise intervention were proposed to provide theoretical support and practical paths for universities to improve the efficiency of academic warning and enhance students’learning ability.展开更多
Rock slope along motorways in the Higher Himalayan terrains are prone to various types of failure.In order to effectively mitigate these failures,a thorough assessment of rock mass behavior is entailed.The present res...Rock slope along motorways in the Higher Himalayan terrains are prone to various types of failure.In order to effectively mitigate these failures,a thorough assessment of rock mass behavior is entailed.The present research employs and compares widely practiced geo-mechanical classification schemes viz.,RQD,RMR,SMR,Q-slope,and GSI.A 23 km road cut section,along Sangla to Chitkul route,in Higher Himalayan region(India)has been taken up for this work.Total of 18 locations were selected,and their slope and rockmass properties were examined.Afterwards,the most influencing parameters in RMR,SMR,and Q-Slope were evaluated through a machine learning algorithm,i.e.,Random Forest.For RMRbasic,about 83%of rock-slopes were designated in good condition and rest were of Fair quality.Evaluation of slope mass rating along all 18-locations highlighted eight-sites as partially unstable,six-sites as partially stable.Remaining four locations varied between,Very Bad to Bad slope-conditions,necessitating the installation of mechanical supports and redesign of slopes.For SMR classification,feature importance analysis revealed the predominance of F3 variable,RQD and intact rock strength.Q-Slope approach was incorporated to identify the most stable steepest angle of the examined locations.For Q-Slope rating,Jn and RQD were found to have the most influence in classification of the slopes.Three zones on the basis of GSI-scores have been identified in the study area,i.e.,A(6595),B(4555),and C(2535).This study highlights the application of multiple geomechanical classification schemes,demonstrating how each approach can complement the others.展开更多
The authors regret that the original publication of this paper did not include Jawad Fayaz as a co-author.After further discussions and a thorough review of the research contributions,it was agreed that his significan...The authors regret that the original publication of this paper did not include Jawad Fayaz as a co-author.After further discussions and a thorough review of the research contributions,it was agreed that his significant contributions to the foundational aspects of the research warranted recognition,and he has now been added as a co-author.展开更多
Nirmal et al.presented a machine learning-based design of ternary organic solar cells,utilizing feature importance[1].This paper highlights the alarming potential biases in the use of feature importance in machine lea...Nirmal et al.presented a machine learning-based design of ternary organic solar cells,utilizing feature importance[1].This paper highlights the alarming potential biases in the use of feature importance in machine learning,which can lead to incorrect conclusions and outcomes.Many scientists and researchers including Nirmal et al.are unaware that feature importances in machine learning in general are model-specific and do not necessarily represent true associations between the target and features.展开更多
In this paper, we use sample average approximation with adaptive multiple importance sampling to explore moderate deviations for the optimal values. Utilizing the moderate deviation principle for martingale difference...In this paper, we use sample average approximation with adaptive multiple importance sampling to explore moderate deviations for the optimal values. Utilizing the moderate deviation principle for martingale differences and an appropriate Delta method, we establish a moderate deviation principle for the optimal value. Moreover, for a functional form of stochastic programming, we obtain a functional moderate deviation principle for its optimal value.展开更多
Protein aggregates,mitochondrial import stress and neurodegenerative disorders:A salient hallmark of several neurodegenerative diseases,including Parkinson’s disease,is the abundance of protein aggregates(Goiran et a...Protein aggregates,mitochondrial import stress and neurodegenerative disorders:A salient hallmark of several neurodegenerative diseases,including Parkinson’s disease,is the abundance of protein aggregates(Goiran et al.,2022).This molecular event is believed to lead to activation of stress pathways ultimately resulting in cellular dysfunction(Eldeeb et al.,2022).Accordingly,many lines of research investigations focused on dampening the formation of protein aggregates or augmenting the clearance of protein aggregates as a potential therapeutic strategy to counteract the progression of neurodegenerative diseases,albeit with little success(Costa-Mattioli and Walter,2020).Cell stress cues such as the accumulation of protein aggregates lead to the activation of stress response pathways that aid cells in responding to the damage.Despite the notion that the transient activation of these pathways helps cells cope with stressors,persistent activation can induce unwanted apoptosis of cells and reduce overall tissue strength as well as lead to an accumulation of aggregation-prone proteins(Hetz and Papa,2018).Mutations in proteins involved in stress signaling termination can cause conditions like ataxia and early-onset dementia(Conroy et al.,2014).Therefore,it is crucial for stress response signaling to be turned off once conditions have improved.Nevertheless,the mechanisms by which cells silence these signals are still elusive.展开更多
Global variance reduction is a bottleneck in Monte Carlo shielding calculations.The global variance reduction problem requires that the statistical error of the entire space is uniform.This study proposed a grid-AIS m...Global variance reduction is a bottleneck in Monte Carlo shielding calculations.The global variance reduction problem requires that the statistical error of the entire space is uniform.This study proposed a grid-AIS method for the global variance reduction problem based on the AIS method,which was implemented in the Monte Carlo program MCShield.The proposed method was validated using the VENUS-Ⅲ international benchmark problem and a self-shielding calculation example.The results from the VENUS-Ⅲ benchmark problem showed that the grid-AIS method achieved a significant reduction in the variance of the statistical errors of the MESH grids,decreasing from 1.08×10^(-2) to 3.84×10^(-3),representing a 64.00% reduction.This demonstrates that the grid-AIS method is effective in addressing global issues.The results of the selfshielding calculation demonstrate that the grid-AIS method produced accurate computational results.Moreover,the grid-AIS method exhibited a computational efficiency approximately one order of magnitude higher than that of the AIS method and approximately two orders of magnitude higher than that of the conventional Monte Carlo method.展开更多
为应对基于游戏的学习平台在知识追踪应用方面的不足,本研究利用Field Day Lab提供的教育游戏用户日志进行深入分析。采用方差法和Null Importance方法对数据集进行降维处理,并结合K折交叉验证与LightGBM算法,建立了一个高效的预测模型...为应对基于游戏的学习平台在知识追踪应用方面的不足,本研究利用Field Day Lab提供的教育游戏用户日志进行深入分析。采用方差法和Null Importance方法对数据集进行降维处理,并结合K折交叉验证与LightGBM算法,建立了一个高效的预测模型。此外,通过集成Logistic模型,构建起Stacking模型。研究表明,该模型在验证集上的Macro-F1值显著提升至0.699,同时也在测试集上显示出优异的泛化能力。本研究为教育游戏领域的知识追踪提供了创新方法,并为游戏开发与教育实践提供了宝贵参考,支持教育游戏的开发者为学生创造更有效的学习体验。展开更多
Importance measures can be used to identify the vulnerable components in an aviation system at the early design stage.However,due to lack of knowledge or less available information on the component or system,the epist...Importance measures can be used to identify the vulnerable components in an aviation system at the early design stage.However,due to lack of knowledge or less available information on the component or system,the epistemic uncertainties may be one of the challenging issues in importance evaluation.In addition,the properties of the aircraft system,which are the fundamentals of the component importance measure,including the hierarchy,dependency,randomness,and uncertainty,should be taken into consideration.To solve these problems,this paper proposes the component Uncertainty Integrated Importance Measure(component UIIM)which considers multiple epistemic uncertainties in the complex multi-state systems.The degradation process for the components is described by a Markov model,and the system reliability model is developed using the Markov hierarchal evidential network.The concept of integrated importance measure is then extended into component UIIM to evaluate the component criticality rather than the component state change criticality,from the perspective of system performance.A case study on displacement compensation hydraulic system is presented to show the effectiveness of the proposed uncertainty importance measure.The results show that the component UIIM can be an effective method for evaluating the component criticality from system performance perspective at the system early design.展开更多
The scientific community faces the challenge of measuring progress toward biodiversity targets and indices have been traditionally used.However,recent inventories in secondary tropical mountain forests using tradition...The scientific community faces the challenge of measuring progress toward biodiversity targets and indices have been traditionally used.However,recent inventories in secondary tropical mountain forests using traditional biodiversity indices have yielded results that are indistinct with primary ones.This shows the need to develop complementary indices that goes beyond species count but integrates the distribution and conservation status of the species.This study developed endemicity and conservation importance index for tropical forest that incorporated the distribution and conservation status of the species.These indices were applied to Mt.Natoo,a remnant primary mossy forest in Buguias,Benguet,Philippines,that resulted to endemicity index of 81.07 and conservation importance index of 42.90.Comparing these with secondary forest sites with comparable Shannon-Wiener,Simpson,Evenness and Margalef’s indices,our endemicity and conservation indices clearly differentiates primary forest(our study site)with higher values from secondary forests with much lower values.Thus,we are proposing these indices for a direct but scientifically-informed identification of specific sites for conservation and protection in tropical forests.Additionally,our study documented a total of 168 vascular plant species(79 endemic and 12 locally threatened species)in Mt.Nato-o.Majority are of tropical elements for both generic and species levels with some temperate elements that could be attributed to the site's high elevation and semi-temperate climate.These are important baseline information for conservation plans and monitoring of tropical mossy forests.展开更多
In the process of fault detection and classification,the operation mode usually drifts over time,which brings great challenges to the algorithms.Because traditional machine learning based fault classification cannot d...In the process of fault detection and classification,the operation mode usually drifts over time,which brings great challenges to the algorithms.Because traditional machine learning based fault classification cannot dynamically update the trained model according to the probability distribution of the testing dataset,the accuracy of these traditional methods usually drops significantly in the case of covariate shift.In this paper,an importance-weighted transfer learning method is proposed for fault classification in the nonlinear multi-mode industrial process.It effectively alters the drift between the training and testing dataset.Firstly,the mutual information method is utilized to perform feature selection on the original data,and a number of characteristic parameters associated with fault classification are selected according to their mutual information.Then,the importance-weighted least-squares probabilistic classifier(IWLSPC)is utilized for binary fault detection and multi-fault classification in covariate shift.Finally,the Tennessee Eastman(TE)benchmark is carried out to confirm the effectiveness of the proposed method.The experimental result shows that the covariate shift adaptation based on importance-weight sampling is superior to the traditional machine learning fault classification algorithms.Moreover,IWLSPC can not only be used for binary fault classification,but also can be applied to the multi-classification target in the process of fault diagnosis.展开更多
The importance of breathing training in dance teaching is reflected in the two aspects of enhancing the quality of dance movements and sublimating the connotation of dance movements.For example,high-quality breathing ...The importance of breathing training in dance teaching is reflected in the two aspects of enhancing the quality of dance movements and sublimating the connotation of dance movements.For example,high-quality breathing can help performers complete the dance movements and improve the coordination of the movements;at the same time,the unique body rhythm formed by breathing can strengthen the visual effect of the performance and convey its spirit and soul to the audience.This requires folk dance teachers to carry out relevant training and teaching activities based on the categories and skills of dance breathing,such as changing students’ideological cognition,developing periodic breathing training courses,providing personalized guidance to students,and allowing students to adjust their learning and practice methods in the evaluation.展开更多
基金supported by the National Key Research and Development Program of China(2023YFF0805602)National Natural Science Foundation of China(32225032,32001192,32271597)+1 种基金the Innovation Base Project of Gansu Province(2021YFF0703904)the Science and Technology Program of Gansu Province(24JRRA515,22JR5RA525,23JRRA1157).
文摘Studies on plant diversity are usually based on the total number of species in a community.However,few studies have examined species richness(SR)of different plant life forms in a community along largescale environmental gradients.Particularly,the relative importance(RIV)of different plant life forms in a community and how they vary with environmental variables are still unclear.To fill these gaps,we determined plant diversity of ephemeral plants,annual herbs,perennial herbs,and woody plants from 187 sites across drylands in China.The SR patterns of herbaceous plants,especially perennial herbs,and their RIV in plant communities increased with increasing precipitation and soil nutrient content;however,the RIV of annual herbs was not altered along these gradients.The SR and RIV of ephemeral plants were affected mainly by precipitation seasonality.The SR of woody plants had a unimodal relationship with air temperature and exhibited the highest RIV and SR percentage in plant communities under the harshest environments.An obvious shift emerged in plant community composition,SR and their critical impact factors at 238.5 mm of mean annual precipitation(MAP).In mesic regions(>238.5 mm),herbs were the dominant species,and the SR displayed a relatively slow decreasing rate with increasing aridity,which was mediated mainly by MAP and soil nutrients.In arid regions(<238.5 mm),woody plants were the dominant species,and the SR displayed a relatively fast decreasing rate with increasing aridity,which was mediated mainly by climate variables,especially precipitation.Our findings highlight the importance of comparative life form studies in community structure and biodiversity,as their responses to gradients differed substantially on a large scale.
基金supported by the Basic Ability Improvement Project of Young and Middle-Aged Teachers in Colleges and Universities of Guangxi(2022KY1922,2021KY1938).
文摘The traditional academic warning methods for students in higher vocational colleges are relatively backward,single,and have many influencing factors,which have a limited effect on improving their learning ability.A data set was established by collecting academic warning data of students in a certain university.The importance of the school,major,grade,and warning level for the students was analyzed using the Pearson correlation coefficient,random forest variable importance,and permutation importance.It was found that the characteristic of the major has a great impact on the academic warning level.Countermeasures such as dynamic adjustment of majors,reform of cognitive adaptation of courses,full-cycle academic support,and data-driven precise intervention were proposed to provide theoretical support and practical paths for universities to improve the efficiency of academic warning and enhance students’learning ability.
基金Anusandhan National Research Foundation(ANRF)(previously,Science and Engineering Research Board-SERB),India for the grant CRG/2022/002509.
文摘Rock slope along motorways in the Higher Himalayan terrains are prone to various types of failure.In order to effectively mitigate these failures,a thorough assessment of rock mass behavior is entailed.The present research employs and compares widely practiced geo-mechanical classification schemes viz.,RQD,RMR,SMR,Q-slope,and GSI.A 23 km road cut section,along Sangla to Chitkul route,in Higher Himalayan region(India)has been taken up for this work.Total of 18 locations were selected,and their slope and rockmass properties were examined.Afterwards,the most influencing parameters in RMR,SMR,and Q-Slope were evaluated through a machine learning algorithm,i.e.,Random Forest.For RMRbasic,about 83%of rock-slopes were designated in good condition and rest were of Fair quality.Evaluation of slope mass rating along all 18-locations highlighted eight-sites as partially unstable,six-sites as partially stable.Remaining four locations varied between,Very Bad to Bad slope-conditions,necessitating the installation of mechanical supports and redesign of slopes.For SMR classification,feature importance analysis revealed the predominance of F3 variable,RQD and intact rock strength.Q-Slope approach was incorporated to identify the most stable steepest angle of the examined locations.For Q-Slope rating,Jn and RQD were found to have the most influence in classification of the slopes.Three zones on the basis of GSI-scores have been identified in the study area,i.e.,A(6595),B(4555),and C(2535).This study highlights the application of multiple geomechanical classification schemes,demonstrating how each approach can complement the others.
文摘The authors regret that the original publication of this paper did not include Jawad Fayaz as a co-author.After further discussions and a thorough review of the research contributions,it was agreed that his significant contributions to the foundational aspects of the research warranted recognition,and he has now been added as a co-author.
文摘Nirmal et al.presented a machine learning-based design of ternary organic solar cells,utilizing feature importance[1].This paper highlights the alarming potential biases in the use of feature importance in machine learning,which can lead to incorrect conclusions and outcomes.Many scientists and researchers including Nirmal et al.are unaware that feature importances in machine learning in general are model-specific and do not necessarily represent true associations between the target and features.
基金Supported by the National Natural Science Foundation of China(Grant No.12071175)。
文摘In this paper, we use sample average approximation with adaptive multiple importance sampling to explore moderate deviations for the optimal values. Utilizing the moderate deviation principle for martingale differences and an appropriate Delta method, we establish a moderate deviation principle for the optimal value. Moreover, for a functional form of stochastic programming, we obtain a functional moderate deviation principle for its optimal value.
文摘Protein aggregates,mitochondrial import stress and neurodegenerative disorders:A salient hallmark of several neurodegenerative diseases,including Parkinson’s disease,is the abundance of protein aggregates(Goiran et al.,2022).This molecular event is believed to lead to activation of stress pathways ultimately resulting in cellular dysfunction(Eldeeb et al.,2022).Accordingly,many lines of research investigations focused on dampening the formation of protein aggregates or augmenting the clearance of protein aggregates as a potential therapeutic strategy to counteract the progression of neurodegenerative diseases,albeit with little success(Costa-Mattioli and Walter,2020).Cell stress cues such as the accumulation of protein aggregates lead to the activation of stress response pathways that aid cells in responding to the damage.Despite the notion that the transient activation of these pathways helps cells cope with stressors,persistent activation can induce unwanted apoptosis of cells and reduce overall tissue strength as well as lead to an accumulation of aggregation-prone proteins(Hetz and Papa,2018).Mutations in proteins involved in stress signaling termination can cause conditions like ataxia and early-onset dementia(Conroy et al.,2014).Therefore,it is crucial for stress response signaling to be turned off once conditions have improved.Nevertheless,the mechanisms by which cells silence these signals are still elusive.
基金supported by the Platform Development Foundation of the China Institute for Radiation Protection(No.YP21030101)the National Natural Science Foundation of China(General Program)(Nos.12175114,U2167209)+1 种基金the National Key R&D Program of China(No.2021YFF0603600)the Tsinghua University Initiative Scientific Research Program(No.20211080081).
文摘Global variance reduction is a bottleneck in Monte Carlo shielding calculations.The global variance reduction problem requires that the statistical error of the entire space is uniform.This study proposed a grid-AIS method for the global variance reduction problem based on the AIS method,which was implemented in the Monte Carlo program MCShield.The proposed method was validated using the VENUS-Ⅲ international benchmark problem and a self-shielding calculation example.The results from the VENUS-Ⅲ benchmark problem showed that the grid-AIS method achieved a significant reduction in the variance of the statistical errors of the MESH grids,decreasing from 1.08×10^(-2) to 3.84×10^(-3),representing a 64.00% reduction.This demonstrates that the grid-AIS method is effective in addressing global issues.The results of the selfshielding calculation demonstrate that the grid-AIS method produced accurate computational results.Moreover,the grid-AIS method exhibited a computational efficiency approximately one order of magnitude higher than that of the AIS method and approximately two orders of magnitude higher than that of the conventional Monte Carlo method.
文摘为应对基于游戏的学习平台在知识追踪应用方面的不足,本研究利用Field Day Lab提供的教育游戏用户日志进行深入分析。采用方差法和Null Importance方法对数据集进行降维处理,并结合K折交叉验证与LightGBM算法,建立了一个高效的预测模型。此外,通过集成Logistic模型,构建起Stacking模型。研究表明,该模型在验证集上的Macro-F1值显著提升至0.699,同时也在测试集上显示出优异的泛化能力。本研究为教育游戏领域的知识追踪提供了创新方法,并为游戏开发与教育实践提供了宝贵参考,支持教育游戏的开发者为学生创造更有效的学习体验。
基金the National Natural Science Foundation of China(Nos.52375036,U2233212,52272409,62303030)Beijing Municipal Natural Science Foundation-Fengtai Rail Transit Frontier Research Joint Foundation,China(No.L221008)+1 种基金the fellowship of China Postdoctoral Science Foundation(No.2022M710305)the program of China Scholarship Council(Nos.202106020106,202306020133).
文摘Importance measures can be used to identify the vulnerable components in an aviation system at the early design stage.However,due to lack of knowledge or less available information on the component or system,the epistemic uncertainties may be one of the challenging issues in importance evaluation.In addition,the properties of the aircraft system,which are the fundamentals of the component importance measure,including the hierarchy,dependency,randomness,and uncertainty,should be taken into consideration.To solve these problems,this paper proposes the component Uncertainty Integrated Importance Measure(component UIIM)which considers multiple epistemic uncertainties in the complex multi-state systems.The degradation process for the components is described by a Markov model,and the system reliability model is developed using the Markov hierarchal evidential network.The concept of integrated importance measure is then extended into component UIIM to evaluate the component criticality rather than the component state change criticality,from the perspective of system performance.A case study on displacement compensation hydraulic system is presented to show the effectiveness of the proposed uncertainty importance measure.The results show that the component UIIM can be an effective method for evaluating the component criticality from system performance perspective at the system early design.
文摘The scientific community faces the challenge of measuring progress toward biodiversity targets and indices have been traditionally used.However,recent inventories in secondary tropical mountain forests using traditional biodiversity indices have yielded results that are indistinct with primary ones.This shows the need to develop complementary indices that goes beyond species count but integrates the distribution and conservation status of the species.This study developed endemicity and conservation importance index for tropical forest that incorporated the distribution and conservation status of the species.These indices were applied to Mt.Natoo,a remnant primary mossy forest in Buguias,Benguet,Philippines,that resulted to endemicity index of 81.07 and conservation importance index of 42.90.Comparing these with secondary forest sites with comparable Shannon-Wiener,Simpson,Evenness and Margalef’s indices,our endemicity and conservation indices clearly differentiates primary forest(our study site)with higher values from secondary forests with much lower values.Thus,we are proposing these indices for a direct but scientifically-informed identification of specific sites for conservation and protection in tropical forests.Additionally,our study documented a total of 168 vascular plant species(79 endemic and 12 locally threatened species)in Mt.Nato-o.Majority are of tropical elements for both generic and species levels with some temperate elements that could be attributed to the site's high elevation and semi-temperate climate.These are important baseline information for conservation plans and monitoring of tropical mossy forests.
文摘In the process of fault detection and classification,the operation mode usually drifts over time,which brings great challenges to the algorithms.Because traditional machine learning based fault classification cannot dynamically update the trained model according to the probability distribution of the testing dataset,the accuracy of these traditional methods usually drops significantly in the case of covariate shift.In this paper,an importance-weighted transfer learning method is proposed for fault classification in the nonlinear multi-mode industrial process.It effectively alters the drift between the training and testing dataset.Firstly,the mutual information method is utilized to perform feature selection on the original data,and a number of characteristic parameters associated with fault classification are selected according to their mutual information.Then,the importance-weighted least-squares probabilistic classifier(IWLSPC)is utilized for binary fault detection and multi-fault classification in covariate shift.Finally,the Tennessee Eastman(TE)benchmark is carried out to confirm the effectiveness of the proposed method.The experimental result shows that the covariate shift adaptation based on importance-weight sampling is superior to the traditional machine learning fault classification algorithms.Moreover,IWLSPC can not only be used for binary fault classification,but also can be applied to the multi-classification target in the process of fault diagnosis.
文摘The importance of breathing training in dance teaching is reflected in the two aspects of enhancing the quality of dance movements and sublimating the connotation of dance movements.For example,high-quality breathing can help performers complete the dance movements and improve the coordination of the movements;at the same time,the unique body rhythm formed by breathing can strengthen the visual effect of the performance and convey its spirit and soul to the audience.This requires folk dance teachers to carry out relevant training and teaching activities based on the categories and skills of dance breathing,such as changing students’ideological cognition,developing periodic breathing training courses,providing personalized guidance to students,and allowing students to adjust their learning and practice methods in the evaluation.
