In this study,we developed a microfluidic paper analysis device(μPAD)for distance-based detection of Ag^(+)in water.TheμPAD was manufactured by wax printing method on filter paper.Then,a layer of gold nanoparticles(...In this study,we developed a microfluidic paper analysis device(μPAD)for distance-based detection of Ag^(+)in water.TheμPAD was manufactured by wax printing method on filter paper.Then,a layer of gold nanoparticles(AuNPs)was deposited and ascorbic acid was printed on the channel.In the detection,Ag^(+)was reduced by ascorbic acid and coated on the surface of the AuNPs on the channel,forming Au@Ag core/shell nanoparticles.Based on the capillary flow principle,diff erent concentrations of Ag^(+)formed diff erent distances of color ribbons.Thus,quantitative detection of Ag^(+)can be achieved by measuring the distance of the color ribbon.The detection limit of this method was as low as 1 mg·L^(-1)within 15 min and the interference of common metal ions in water can be eliminated.In conclusion,this method had successfully realized the leap from colorimetry to direct reading,realizing fast read and easy manipulation with low-cost.展开更多
Uncertain data are common due to the increasing usage of sensors, radio frequency identification(RFID), GPS and similar devices for data collection. The causes of uncertainty include limitations of measurements, inclu...Uncertain data are common due to the increasing usage of sensors, radio frequency identification(RFID), GPS and similar devices for data collection. The causes of uncertainty include limitations of measurements, inclusion of noise, inconsistent supply voltage and delay or loss of data in transfer. In order to manage, query or mine such data, data uncertainty needs to be considered. Hence,this paper studies the problem of top-k distance-based outlier detection from uncertain data objects. In this work, an uncertain object is modelled by a probability density function of a Gaussian distribution. The naive approach of distance-based outlier detection makes use of nested loop. This approach is very costly due to the expensive distance function between two uncertain objects. Therefore,a populated-cells list(PC-list) approach of outlier detection is proposed. Using the PC-list, the proposed top-k outlier detection algorithm needs to consider only a fraction of dataset objects and hence quickly identifies candidate objects for top-k outliers. Two approximate top-k outlier detection algorithms are presented to further increase the efficiency of the top-k outlier detection algorithm.An extensive empirical study on synthetic and real datasets is also presented to prove the accuracy, efficiency and scalability of the proposed algorithms.展开更多
基金supported by the Graduate Student Innovation Project of China University of Petroleum(East China)in 2020(No.YCX2020054)the financial support by the National Natural Science Foundation of China(No.21876206,21505157)+1 种基金the Key Fundamental Research Fund of Shandong Province(ZR2020ZD13)the Youth Innovation and Technology projects of Universities in Shandong Province(2020KJC007,ZR2020MB064)
文摘In this study,we developed a microfluidic paper analysis device(μPAD)for distance-based detection of Ag^(+)in water.TheμPAD was manufactured by wax printing method on filter paper.Then,a layer of gold nanoparticles(AuNPs)was deposited and ascorbic acid was printed on the channel.In the detection,Ag^(+)was reduced by ascorbic acid and coated on the surface of the AuNPs on the channel,forming Au@Ag core/shell nanoparticles.Based on the capillary flow principle,diff erent concentrations of Ag^(+)formed diff erent distances of color ribbons.Thus,quantitative detection of Ag^(+)can be achieved by measuring the distance of the color ribbon.The detection limit of this method was as low as 1 mg·L^(-1)within 15 min and the interference of common metal ions in water can be eliminated.In conclusion,this method had successfully realized the leap from colorimetry to direct reading,realizing fast read and easy manipulation with low-cost.
基金supported by Grant-in-Aid for Scientific Research(A)(#24240015A)
文摘Uncertain data are common due to the increasing usage of sensors, radio frequency identification(RFID), GPS and similar devices for data collection. The causes of uncertainty include limitations of measurements, inclusion of noise, inconsistent supply voltage and delay or loss of data in transfer. In order to manage, query or mine such data, data uncertainty needs to be considered. Hence,this paper studies the problem of top-k distance-based outlier detection from uncertain data objects. In this work, an uncertain object is modelled by a probability density function of a Gaussian distribution. The naive approach of distance-based outlier detection makes use of nested loop. This approach is very costly due to the expensive distance function between two uncertain objects. Therefore,a populated-cells list(PC-list) approach of outlier detection is proposed. Using the PC-list, the proposed top-k outlier detection algorithm needs to consider only a fraction of dataset objects and hence quickly identifies candidate objects for top-k outliers. Two approximate top-k outlier detection algorithms are presented to further increase the efficiency of the top-k outlier detection algorithm.An extensive empirical study on synthetic and real datasets is also presented to prove the accuracy, efficiency and scalability of the proposed algorithms.
