1 Introduction Co-salient object detection(CoSOD)aims to extract the salient object(s)that are common across a group of relevant images[1].Group-wise clue plays a crucial role in accurately predicting the co-salient r...1 Introduction Co-salient object detection(CoSOD)aims to extract the salient object(s)that are common across a group of relevant images[1].Group-wise clue plays a crucial role in accurately predicting the co-salient regions.Therefore,numerous groupwise deep models have been proposed by exploring consistency across images in unsupervised clustering manners[2-4]or the semantic connections guidance information[5].展开更多
A series of large pilot constructed wetland （CW） systems were constructed near the confluence of an urban stream to a larger fiver in Xi＇an, a northwestern megacity in China, for treating polluted stream water befo...A series of large pilot constructed wetland （CW） systems were constructed near the confluence of an urban stream to a larger fiver in Xi＇an, a northwestern megacity in China, for treating polluted stream water before it entered the receiving water body. Each CW system is a combination of surface- and subsurface-flow cells with local gravel, sand or slag as substrates and Phragmites australis and Typha orientalis as plants. During a one-year operation with an average surface loading of 0.053 m3/（m2.day）, the overall COD, BOD, NH3-N, total nitrogen （TN） and total phosphorus （TP） removals were 72.7% ~ 4.5%, 93.4% ＋ 2.1%, 54.0% ＋ 6.3%, 53.9% ~ 6.0% and 69.4% ：t： 4.6%, respectively, which brought about an effective improvement of the fiver water quality. Surface-flow cells showed better NH3-N removal than their TN removal while subsurface-flow cells showed better TN removal than their NH3-N removal. Using local slag as the substrate, the organic and phosphorus removal could be much improved. Seasonal variation was also found in the removal of all the pollutants and autumn seemed to be the best season for pollutant removal due to the moderate water temperature and well grown plants in the CWs.展开更多
基金supported in part by the National Natural Science Foundation of China (Grant No.62276141).
文摘1 Introduction Co-salient object detection(CoSOD)aims to extract the salient object(s)that are common across a group of relevant images[1].Group-wise clue plays a crucial role in accurately predicting the co-salient regions.Therefore,numerous groupwise deep models have been proposed by exploring consistency across images in unsupervised clustering manners[2-4]or the semantic connections guidance information[5].
基金supported by the National Natural Science Foundation of China(No.50838005,51021140002)the Program for Innovative Research Team in Shaanxi(No.2013KCT-13)
文摘A series of large pilot constructed wetland （CW） systems were constructed near the confluence of an urban stream to a larger fiver in Xi＇an, a northwestern megacity in China, for treating polluted stream water before it entered the receiving water body. Each CW system is a combination of surface- and subsurface-flow cells with local gravel, sand or slag as substrates and Phragmites australis and Typha orientalis as plants. During a one-year operation with an average surface loading of 0.053 m3/（m2.day）, the overall COD, BOD, NH3-N, total nitrogen （TN） and total phosphorus （TP） removals were 72.7% ~ 4.5%, 93.4% ＋ 2.1%, 54.0% ＋ 6.3%, 53.9% ~ 6.0% and 69.4% ：t： 4.6%, respectively, which brought about an effective improvement of the fiver water quality. Surface-flow cells showed better NH3-N removal than their TN removal while subsurface-flow cells showed better TN removal than their NH3-N removal. Using local slag as the substrate, the organic and phosphorus removal could be much improved. Seasonal variation was also found in the removal of all the pollutants and autumn seemed to be the best season for pollutant removal due to the moderate water temperature and well grown plants in the CWs.
