Discrete choice model acts as one of the most important tools for studies involving mode split in the context of transport demand forecast. As different types of discrete choice models display their merits and restric...Discrete choice model acts as one of the most important tools for studies involving mode split in the context of transport demand forecast. As different types of discrete choice models display their merits and restrictions diversely, how to properly select the specific type among discrete choice models for realistic application still remains to be a tough problem. In this article, five typical discrete choice models for transport mode split are, respectively, discussed, which includes multinomial logit model, nested logit model （NL）, heteroscedastic extreme value model, multinominal probit model and mixed multinomial logit model （MMNL）. The theoretical basis and application attributes of these five models are especially analysed with great attention, and they are also applied to a realistic intercity case of mode split forecast, which results indi- cating that NL model does well in accommodating similarity and heterogeneity across alternatives, while MMNL model serves as the most effective method for mode choice prediction since it shows the highest reliability with the least significant prediction errors and even outperforms the other four models in solving the heterogeneity and similarity problems. This study indicates that conclusions derived from a single discrete choice model are not reliable, and it is better to choose the proper model based on its characteristics.展开更多
The purpose of this study was to present a lock-in-amplifier model for analyzing the behavior of signal harmonics in magnetic particle imaging (MPI) and some simulation results based on this model. In the lock-in-ampl...The purpose of this study was to present a lock-in-amplifier model for analyzing the behavior of signal harmonics in magnetic particle imaging (MPI) and some simulation results based on this model. In the lock-in-amplifier model, the signal induced by magnetic nanoparticles (MNPs) in a receiving coil was multiplied with a reference signal, and was then fed through a low-pass filter to extract the DC component of the signal (output signal). The MPI signal was defined as the mean of the absolute value of the output signal. The magnetization and particle size distribution of MNPs were assumed to obey the Langevin theory of paramagnetism and a log-normal distribution, respectively, and the strength of the selection magnetic field (SMF) in MPI was assumed to be given by the product of the gradient strength of the SMF and the distance from the field-free region (x). In addition, Gaussian noise was added to the signal induced by MNPs using normally-distributed random numbers. The relationships between the MPI signal and x were calculated for the odd- and even-numbered harmonics and were investigated for various time constants of the low-pass filter used in the lock-in amplifier and particle sizes and their distributions of MNPs. We found that the behavior of the MPI signal largely depended on the time constant of the low-pass filter and the particle size of MNPs. This lock-in-amplifier model will be useful for better understanding, optimizing, and developing MPI, and for designing MNPs appropriate for MPI.展开更多
Objective To comparatively study the toxicity of four metal-containing nanoparticles(MNPs) and their chemical counterparts to the air-blood barrier(ABB) permeability using an in vitro model.Methods ABB model, which wa...Objective To comparatively study the toxicity of four metal-containing nanoparticles(MNPs) and their chemical counterparts to the air-blood barrier(ABB) permeability using an in vitro model.Methods ABB model, which was developed via the co-culturing of A549 and pulmonary capillary endothelium, was exposed to spherical CuO-NPs(divided into CuO-40, CuO-80, and CuO-100 based on particle size), nano-Al2O3(sheet and short-rod-shaped), nano-ZnO, nano-Pb S, CuSO4, Al2(SO4)3, Zn(CH3COO)2, and Pb(NO3)2 for 60 min.Every 10 min following exposure, the cumulative cleared volume(ΔTCL) of Lucifer yellow by the model was calculated.A clearance curve was established using linear regression analysis of ΔTCL versus time.Permeability coefficient(P) was calculated based on the slope of the curve to represent the degree of change in the ABB permeability.Results The results found the increased P values of CuO-40, CuO-80, sheet, and short-rod-shaped nano-Al2O3, Al2(SO4)3, and Pb(NO3)2.Among them, small CuO-40 and CuO-80 were stronger than CuO-100 and CuSO4;no difference was observed between Al2(SO4)3 and sheet and short-rod-shaped nano-Al2O3;and nano-Pb S was slightly weaker than Pb(NO3)2.So clearly the MNPs possess diverse toxicity.Conclusion ABB permeability abnormality means pulmonary toxicity potential.More studies are warranted to understand MNPs toxicity and ultimately control the health hazards.展开更多
基金supported by the Science&Technology pillar project(No.0556)of Guangzhou
文摘Discrete choice model acts as one of the most important tools for studies involving mode split in the context of transport demand forecast. As different types of discrete choice models display their merits and restrictions diversely, how to properly select the specific type among discrete choice models for realistic application still remains to be a tough problem. In this article, five typical discrete choice models for transport mode split are, respectively, discussed, which includes multinomial logit model, nested logit model （NL）, heteroscedastic extreme value model, multinominal probit model and mixed multinomial logit model （MMNL）. The theoretical basis and application attributes of these five models are especially analysed with great attention, and they are also applied to a realistic intercity case of mode split forecast, which results indi- cating that NL model does well in accommodating similarity and heterogeneity across alternatives, while MMNL model serves as the most effective method for mode choice prediction since it shows the highest reliability with the least significant prediction errors and even outperforms the other four models in solving the heterogeneity and similarity problems. This study indicates that conclusions derived from a single discrete choice model are not reliable, and it is better to choose the proper model based on its characteristics.
文摘The purpose of this study was to present a lock-in-amplifier model for analyzing the behavior of signal harmonics in magnetic particle imaging (MPI) and some simulation results based on this model. In the lock-in-amplifier model, the signal induced by magnetic nanoparticles (MNPs) in a receiving coil was multiplied with a reference signal, and was then fed through a low-pass filter to extract the DC component of the signal (output signal). The MPI signal was defined as the mean of the absolute value of the output signal. The magnetization and particle size distribution of MNPs were assumed to obey the Langevin theory of paramagnetism and a log-normal distribution, respectively, and the strength of the selection magnetic field (SMF) in MPI was assumed to be given by the product of the gradient strength of the SMF and the distance from the field-free region (x). In addition, Gaussian noise was added to the signal induced by MNPs using normally-distributed random numbers. The relationships between the MPI signal and x were calculated for the odd- and even-numbered harmonics and were investigated for various time constants of the low-pass filter used in the lock-in amplifier and particle sizes and their distributions of MNPs. We found that the behavior of the MPI signal largely depended on the time constant of the low-pass filter and the particle size of MNPs. This lock-in-amplifier model will be useful for better understanding, optimizing, and developing MPI, and for designing MNPs appropriate for MPI.
基金sponsored by the National Natural Science Foundation of China [No.81372949]the Young Scholar Scientific Research Foundation of China CDC [No.2016A206]
文摘Objective To comparatively study the toxicity of four metal-containing nanoparticles(MNPs) and their chemical counterparts to the air-blood barrier(ABB) permeability using an in vitro model.Methods ABB model, which was developed via the co-culturing of A549 and pulmonary capillary endothelium, was exposed to spherical CuO-NPs(divided into CuO-40, CuO-80, and CuO-100 based on particle size), nano-Al2O3(sheet and short-rod-shaped), nano-ZnO, nano-Pb S, CuSO4, Al2(SO4)3, Zn(CH3COO)2, and Pb(NO3)2 for 60 min.Every 10 min following exposure, the cumulative cleared volume(ΔTCL) of Lucifer yellow by the model was calculated.A clearance curve was established using linear regression analysis of ΔTCL versus time.Permeability coefficient(P) was calculated based on the slope of the curve to represent the degree of change in the ABB permeability.Results The results found the increased P values of CuO-40, CuO-80, sheet, and short-rod-shaped nano-Al2O3, Al2(SO4)3, and Pb(NO3)2.Among them, small CuO-40 and CuO-80 were stronger than CuO-100 and CuSO4;no difference was observed between Al2(SO4)3 and sheet and short-rod-shaped nano-Al2O3;and nano-Pb S was slightly weaker than Pb(NO3)2.So clearly the MNPs possess diverse toxicity.Conclusion ABB permeability abnormality means pulmonary toxicity potential.More studies are warranted to understand MNPs toxicity and ultimately control the health hazards.
