Location based social networks（ LBSNs） provide location specific data generated from smart phone into online social networks thus people can share their points of interest（ POIs）. POI collections are complex and c...Location based social networks（ LBSNs） provide location specific data generated from smart phone into online social networks thus people can share their points of interest（ POIs）. POI collections are complex and can be influenced by various factors,such as user preferences,social relationships and geographical influence. Therefore,recommending new locations in LBSNs requires to take all these factors into consideration. However,one problem is how to determine optimal weights of influencing factors in an algorithm in which these factors are combined. The user similarity can be obtained from the user check-in data,or from the user friend information,or based on the different geographical influences on each user＇s check-in activities. In this paper,we propose an algorithm that calculates the user similarity based on check-in records and social relationships,using a proposed weighting function to adjust the weights of these two kinds of similarities based on the geographical distance between users. In addition,a non-parametric density estimation method is applied to predict the unique geographical influence on each user by getting the density probability plot of the distance between every pair of user＇s check-in locations. Experimental results,using foursquare datasets,have shown that comparisons between the proposed algorithm and the other five baseline recommendation algorithms in LBSNs demonstrate that our proposed algorithm is superior in accuracy and recall,furthermore solving the sparsity problem.展开更多
It is necessary to identify a gaseous pollutant source rapidly so that prompt actions can be taken, but this is one of the difficulties in the inverse problem areas. In this paper, an approach to identifying a sudden ...It is necessary to identify a gaseous pollutant source rapidly so that prompt actions can be taken, but this is one of the difficulties in the inverse problem areas. In this paper, an approach to identifying a sudden continuous emission pollutant source based on single sensor information is developed to locate a source in an enclosed space with a steady velocity field. Because the gravity has a very important influence on the gaseous pollutant transport and the source identification, its influence is analyzed theoretically and a conclusion is drawn that the velocity of fluid is a key factor to effectively help weaken the gravitational influence. Further studies for a given 2-D case by using the computational fluid dynamics （CFD） method show that when the velocity of inlet is less than one certain value, the influence of gravity on the pollutant transport is very significant, which will change the velocity field obviously. In order to quantitatively judge the practical applicability of identification approach, a synergy degree of the velocity fields before and after a source appearing is proposed as a condition for considering the influence of gravity. An experimental device simulating pollutant transmission was set up and some experiments were conducted to verify the practical application of the above studies in the actual gravitational environment. The results show that the proposed approach can successfully locate the sudden constant source when the experimental situations meet the identified conditions.展开更多
Biomimetic scaffolds provide a suitable growth environment for tissue engineering and demonstrate good potential for application in biomedical fields.Different-sized copolymerized biomimetic scaffolds degrade differen...Biomimetic scaffolds provide a suitable growth environment for tissue engineering and demonstrate good potential for application in biomedical fields.Different-sized copolymerized biomimetic scaffolds degrade differently,and the degradation rate is affected by the copolymerization ratio.The study of the degradation property is the foundational research necessary for realizing individualized biomimetic scaffold design.The degradation performance of polyesters with different copolymerization ratios has been widely reported;however,the modeling of this performance has been rarely reported.In this research,the degradation of copolymers was studied with multi-scale modeling,in which the copolymers were dispersed in a cellular manner,the chain break time was simulated,and the chain selection was based on the Monte Carlo(MC)algorithm.The probability model of the copolymer's chain break position was established as a//roulette,/model,whose probability values were estimated by the calculation of the potential energy difference at different chain break positions by molecular dynamics that determined the position of chain shear,thereby fully realizing the simulation of the chain micro-break process.The diffusion of the oligomers was then calculated using the macro diffusion equation,and the degradation process of the copolymer was simulated by three-scale coupling calculations.The calculation results were in good agreement with the experimental data,demonstrating the effectiveness of the proposed method.展开更多
文摘Location based social networks（ LBSNs） provide location specific data generated from smart phone into online social networks thus people can share their points of interest（ POIs）. POI collections are complex and can be influenced by various factors,such as user preferences,social relationships and geographical influence. Therefore,recommending new locations in LBSNs requires to take all these factors into consideration. However,one problem is how to determine optimal weights of influencing factors in an algorithm in which these factors are combined. The user similarity can be obtained from the user check-in data,or from the user friend information,or based on the different geographical influences on each user＇s check-in activities. In this paper,we propose an algorithm that calculates the user similarity based on check-in records and social relationships,using a proposed weighting function to adjust the weights of these two kinds of similarities based on the geographical distance between users. In addition,a non-parametric density estimation method is applied to predict the unique geographical influence on each user by getting the density probability plot of the distance between every pair of user＇s check-in locations. Experimental results,using foursquare datasets,have shown that comparisons between the proposed algorithm and the other five baseline recommendation algorithms in LBSNs demonstrate that our proposed algorithm is superior in accuracy and recall,furthermore solving the sparsity problem.
基金supported by the National Natural Science Foundation of China (No. 50808007)
文摘It is necessary to identify a gaseous pollutant source rapidly so that prompt actions can be taken, but this is one of the difficulties in the inverse problem areas. In this paper, an approach to identifying a sudden continuous emission pollutant source based on single sensor information is developed to locate a source in an enclosed space with a steady velocity field. Because the gravity has a very important influence on the gaseous pollutant transport and the source identification, its influence is analyzed theoretically and a conclusion is drawn that the velocity of fluid is a key factor to effectively help weaken the gravitational influence. Further studies for a given 2-D case by using the computational fluid dynamics （CFD） method show that when the velocity of inlet is less than one certain value, the influence of gravity on the pollutant transport is very significant, which will change the velocity field obviously. In order to quantitatively judge the practical applicability of identification approach, a synergy degree of the velocity fields before and after a source appearing is proposed as a condition for considering the influence of gravity. An experimental device simulating pollutant transmission was set up and some experiments were conducted to verify the practical application of the above studies in the actual gravitational environment. The results show that the proposed approach can successfully locate the sudden constant source when the experimental situations meet the identified conditions.
基金This paper is sponsored by the National Study Abroad Fund of China and supported by The National Key Research and Development Program of China(2017YFB1002304).
文摘Biomimetic scaffolds provide a suitable growth environment for tissue engineering and demonstrate good potential for application in biomedical fields.Different-sized copolymerized biomimetic scaffolds degrade differently,and the degradation rate is affected by the copolymerization ratio.The study of the degradation property is the foundational research necessary for realizing individualized biomimetic scaffold design.The degradation performance of polyesters with different copolymerization ratios has been widely reported;however,the modeling of this performance has been rarely reported.In this research,the degradation of copolymers was studied with multi-scale modeling,in which the copolymers were dispersed in a cellular manner,the chain break time was simulated,and the chain selection was based on the Monte Carlo(MC)algorithm.The probability model of the copolymer's chain break position was established as a//roulette,/model,whose probability values were estimated by the calculation of the potential energy difference at different chain break positions by molecular dynamics that determined the position of chain shear,thereby fully realizing the simulation of the chain micro-break process.The diffusion of the oligomers was then calculated using the macro diffusion equation,and the degradation process of the copolymer was simulated by three-scale coupling calculations.The calculation results were in good agreement with the experimental data,demonstrating the effectiveness of the proposed method.
