An environmental input output model (EIOM) was introduced to the regional solid waste management sectors, which can reflect the direct and indirect relations between the environment and the regional economy developme...An environmental input output model (EIOM) was introduced to the regional solid waste management sectors, which can reflect the direct and indirect relations between the environment and the regional economy development. Some details about how to use the EIOM was discussed. The EIOM was applied to the Changsha City in China. The example results indicate that much useful information related to the environment and the regional economy development can be gained from the solution of the EIOM. Thus, the EIOM can be used as a useful tool for the sustainable development planning including the solid waste management sectors.展开更多
Water quality analysis is essential to understand the ecological status of aquatic life.Conventional water quality index(WQI)assessment methods are limited to features such as water acidic or basicity(pH),dissolved ox...Water quality analysis is essential to understand the ecological status of aquatic life.Conventional water quality index(WQI)assessment methods are limited to features such as water acidic or basicity(pH),dissolved oxygen(DO),biological oxygen demand(BOD),chemical oxygen demand(COD),ammoniacal nitrogen(NH3-N),and suspended solids(SS).These features are often insufficient to represent the water quality of a heavy metal–polluted river.Therefore,this paper aims to explore and analyze novel input features in order to formulate an improved WQI.In this work,prospective insights on the feasibility of alternative water quality input variables as new discriminant features are discussed.The new discriminant features are a step toward formulating adaptive water quality parameters according to the land use activities surrounding the river.The results and analysis obtained from this study have proven the possibility of predicting WQI using new input features.This work analyzes 17 new input features,namely conductivity(COND),salinity(SAL),turbidity(TUR),dissolved solids(DS),nitrate(NO3),chloride(Cl),phosphate(PO4),arsenic(As),chromium(Cr),zinc(Zn),calcium(Ca),iron(Fe),potassium(K),magnesium(Mg),sodium(Na),E.coli,and total coliform,in predicting WQI using machine learning techniques.Five regression algorithms-random forest(RF),AdaBoost,support vector regression(SVR),decision tree regression(DTR),and multilayer perception(MLP)-are applied for preliminary model selection.The results show that the RF algorithm exhibits better prediction performance,with R2 of 0.974.Then,this work proposes a modified RF by incorporating the synthetic minority oversampling technique(SMOTE)into the conventional RF method.The proposed modified RF method is shown to achieve 77.68%,74%,69%,and 71%accuracy,precision,recall,and F1-score,respectively.In addition,the sensitivity analysis is included to highlight the importance of the turbidity variable in WQI prediction.The results of sensitivity analysis highlight the importance of certain water quality variables that are not present in the conventional WQI formulation.展开更多
This study takes the virtual business society environment(VBSE)practical training course as a case study and applies the theoretical framework of the context,input,process,product(CIPP)model to construct an evaluation...This study takes the virtual business society environment(VBSE)practical training course as a case study and applies the theoretical framework of the context,input,process,product(CIPP)model to construct an evaluation indicator system for the application of civic and politics in professional practice courses.The context evaluation is measured from the support of the VBSE practical training course into course civic and politics,teachers’cognition,and the integration of course objectives;the input evaluation is measured from the matching degree of teachers’civic and political competence,and the matching degree of teaching resources;the process evaluation is measured from the degree of implementation of civic and politics teaching and the degree of students’acceptance;and the product evaluation is measured from the degree of impact of civic and politics teaching.展开更多
文摘An environmental input output model (EIOM) was introduced to the regional solid waste management sectors, which can reflect the direct and indirect relations between the environment and the regional economy development. Some details about how to use the EIOM was discussed. The EIOM was applied to the Changsha City in China. The example results indicate that much useful information related to the environment and the regional economy development can be gained from the solution of the EIOM. Thus, the EIOM can be used as a useful tool for the sustainable development planning including the solid waste management sectors.
基金supported by the Ministry of Higher Education through MRUN Young Researchers Grant Scheme(MY-RGS),MR001-2019,entitled“Climate Change Mitigation:Artificial Intelligence-Based Integrated Environmental System for Mangrove Forest Conservation”and UM-RU Grant,ST065-2021,entitled“Climate-Smart Mitigation and Adaptation:Integrated Climate Resilience Strategy for Tropical Marine Ecosystem.”。
文摘Water quality analysis is essential to understand the ecological status of aquatic life.Conventional water quality index(WQI)assessment methods are limited to features such as water acidic or basicity(pH),dissolved oxygen(DO),biological oxygen demand(BOD),chemical oxygen demand(COD),ammoniacal nitrogen(NH3-N),and suspended solids(SS).These features are often insufficient to represent the water quality of a heavy metal–polluted river.Therefore,this paper aims to explore and analyze novel input features in order to formulate an improved WQI.In this work,prospective insights on the feasibility of alternative water quality input variables as new discriminant features are discussed.The new discriminant features are a step toward formulating adaptive water quality parameters according to the land use activities surrounding the river.The results and analysis obtained from this study have proven the possibility of predicting WQI using new input features.This work analyzes 17 new input features,namely conductivity(COND),salinity(SAL),turbidity(TUR),dissolved solids(DS),nitrate(NO3),chloride(Cl),phosphate(PO4),arsenic(As),chromium(Cr),zinc(Zn),calcium(Ca),iron(Fe),potassium(K),magnesium(Mg),sodium(Na),E.coli,and total coliform,in predicting WQI using machine learning techniques.Five regression algorithms-random forest(RF),AdaBoost,support vector regression(SVR),decision tree regression(DTR),and multilayer perception(MLP)-are applied for preliminary model selection.The results show that the RF algorithm exhibits better prediction performance,with R2 of 0.974.Then,this work proposes a modified RF by incorporating the synthetic minority oversampling technique(SMOTE)into the conventional RF method.The proposed modified RF method is shown to achieve 77.68%,74%,69%,and 71%accuracy,precision,recall,and F1-score,respectively.In addition,the sensitivity analysis is included to highlight the importance of the turbidity variable in WQI prediction.The results of sensitivity analysis highlight the importance of certain water quality variables that are not present in the conventional WQI formulation.
基金2022 Southwest Forestry University Educational Science Research Project:Surface Project Grant(Project number:YB202227)Grant No.42 of 2024 Curriculum Civics Construction(Teaching Research Project)of Southwest Forestry University。
文摘This study takes the virtual business society environment(VBSE)practical training course as a case study and applies the theoretical framework of the context,input,process,product(CIPP)model to construct an evaluation indicator system for the application of civic and politics in professional practice courses.The context evaluation is measured from the support of the VBSE practical training course into course civic and politics,teachers’cognition,and the integration of course objectives;the input evaluation is measured from the matching degree of teachers’civic and political competence,and the matching degree of teaching resources;the process evaluation is measured from the degree of implementation of civic and politics teaching and the degree of students’acceptance;and the product evaluation is measured from the degree of impact of civic and politics teaching.
