The complex relationships between indicators and water conditions cause fuzzy and gray uncertainties in evaluation of water quality. Compared to conventional single-factor evaluation methods, the combination evaluatio...The complex relationships between indicators and water conditions cause fuzzy and gray uncertainties in evaluation of water quality. Compared to conventional single-factor evaluation methods, the combination evaluation method can consider these two uncertainties to produce more objective and reasonable evaluation results. In this paper, we propose a combination evaluation method with two main parts:(1) the use of fuzzy comprehensive evaluation and gray correlation analysis as submodels with which to consider the fuzzy and gray uncertainties and(2) the establishment of a combination model based on minimum bias squares. In addition, using this method, we evaluate the water quality of a ditch in a typical rice–wheat system of Yixing city in the Taihu Lake Basin during three rainfall events. The results show that the ditch water quality is not good and we found the chemical oxygen demand to be the key indicator that affects water quality most significantly. The proposed combination evaluation method is more accurate and practical than single-factor evaluation methods in that it considers the uncertainties of fuzziness and grayness.展开更多
This paper is concerned with the oscillatory properties of the third-order nonlinear delay dynamic equations of the form??on time scales , where ?is a quotient of odd positive integers. Applying the inequality techniq...This paper is concerned with the oscillatory properties of the third-order nonlinear delay dynamic equations of the form??on time scales , where ?is a quotient of odd positive integers. Applying the inequality technique we present two new sufficient conditions which ensure that every solution of equations is oscillatory or converges to zero. The results obtained improve and complement some known results in the literature.展开更多
基金supported by the National Key Research and Development Program of China (No. 2017YFC0405006)the Innovative Research Groups of the National Natural Science Foundation of China (No. 51621092)the Natural Science Foundation of Tianjin (No. 16JCYBJC23100)
文摘The complex relationships between indicators and water conditions cause fuzzy and gray uncertainties in evaluation of water quality. Compared to conventional single-factor evaluation methods, the combination evaluation method can consider these two uncertainties to produce more objective and reasonable evaluation results. In this paper, we propose a combination evaluation method with two main parts:(1) the use of fuzzy comprehensive evaluation and gray correlation analysis as submodels with which to consider the fuzzy and gray uncertainties and(2) the establishment of a combination model based on minimum bias squares. In addition, using this method, we evaluate the water quality of a ditch in a typical rice–wheat system of Yixing city in the Taihu Lake Basin during three rainfall events. The results show that the ditch water quality is not good and we found the chemical oxygen demand to be the key indicator that affects water quality most significantly. The proposed combination evaluation method is more accurate and practical than single-factor evaluation methods in that it considers the uncertainties of fuzziness and grayness.
文摘This paper is concerned with the oscillatory properties of the third-order nonlinear delay dynamic equations of the form??on time scales , where ?is a quotient of odd positive integers. Applying the inequality technique we present two new sufficient conditions which ensure that every solution of equations is oscillatory or converges to zero. The results obtained improve and complement some known results in the literature.
基金supported by the National Key Research and Development Program of China (2018YFA0605700)supported by the Joint Research Centre for Southern Hemisphere Oceans Research (CSHOR)between the Qingdao National Laboratory for Marine Science and Technology (QNLM)and the Commonwealth Scientific and Industrial Research Organisation (CSIRO)+10 种基金supported by the Australian Research Council Special Research Initiative for Securing Antarctica’s Environmental Future (SR200100005)supported by the National Natural Science Foundation of China (41876231)supported by the National Natural Science Foundation of China (42230405 and 41976006)the National Natural Science Foundation of China (41876008 and 41730534)supported by the National Natural Science Foundation of China (41830538)the Program of Impact and Response of Antarctic Seas to Climate Change (IRASCC 01-01-01A)supported by the National Key Research and Development Program of China (2020YFA0608801)the Youth Innovation Promotion Association of Chinese Academy of Sciences (2021205)supported by the National Science Foundation (AGS-1934392)supported by the National Science Foundation (OCE-2048336)the International Partnership Program of Chinese Academy of Sciences (183311KYSB20200015)。
