Background: The hepatic hemodynamics is an essential parameter in surgical planning as well as in various disease processes. The transit time ultrasound(TTUS) perivascular flow probe technology is widely used in clini...Background: The hepatic hemodynamics is an essential parameter in surgical planning as well as in various disease processes. The transit time ultrasound(TTUS) perivascular flow probe technology is widely used in clinical practice to evaluate the hepatic inflow, yet invasive. The phase-contrast-MRI(PC-MRI) is not invasive and potentially applicable in assessing the hepatic blood flow. In the present study, we compared the hepatic inflow rates using the PC-MRI and the TTUS probe, and evaluated their predictive value of post-hepatectomy adverse events. Methods: Eighteen large white pigs were anaesthetized for PC-MRI and approximately 75% hepatic resection was performed under a unified protocol. The blood flow was measured in the hepatic artery(Qha), the portal vein(Qpv), and the aorta above the celiac trunk(Qca) using PC-MRI, and was compared to the TTUS probe. The Bland-Altman method was conducted and a partial least squares regression(PLS) model was implemented. Results: The mean Qpv measured in PC-MRI was 0.55 ± 0.12 L/min, and in the TTUS probe was 0.74 ± 0.17 L/min. Qca was 1.40 ± 0.47 L/min in the PC-MRI and 2.00 ± 0.60 L/min in the TTUS probe. Qha was 0.17 ± 0.10 L/min in the PC-MRI, and 0.13 ± 0.06 L/min in the TTUS probe. The Bland-Altman method revealed that the estimated bias of Qca in the PC-MRI was 32%(95% CI:-49% to 15%); Qha 17%(95% CI:-15% to 51%); and Qpv 40%(95% CI:-62% to 18%). The TTUS probe had a higher weight in predicting adverse outcomes after 75% resection compared to the PC-MRI( β= 0.35 and 0.43 vs β = 0.22 and 0.07, for tissue changes and premature death, respectively). Conclusions: There is a tendency of the PC-MRI to underestimate the flow measured by the TTUS probes. The TTUS probe measures are more predictive of relevant post-hepatectomy outcomes.展开更多
This paper mainly deals with the effects of transit stops on vehicle speeds and conversion lane numbers in a mixed traffic lane. Based on thorough research of traffic flow and cellular automata theory, it calibrates t...This paper mainly deals with the effects of transit stops on vehicle speeds and conversion lane numbers in a mixed traffic lane. Based on thorough research of traffic flow and cellular automata theory, it calibrates the cellular length and the running speed. Also, a cellular automata model for mixed traffic flow on a two-lane system under a periodic boundary condition is presented herewith, which also takes into consideration the harbour-shaped transit stop as well. By means of computer simulation, the article also studies the effects of bus parking time on the traffic volume, the transit speed and the fast lane speed at the same time. The results demonstrate that, with the increase of the bus parking time, the traffic volume of the transit stop and the transit speed decrease while the fast lane speed increases. This result could help calculate the transit delay correctly and make arrangements for transit routes reasonably and scientifically.展开更多
Transit-time flow technology is considered as a quality of care in bypass surgery especially in off pump revascularization. Transit time flow measurement is a real time, direct, easy and handy tool for assessment qual...Transit-time flow technology is considered as a quality of care in bypass surgery especially in off pump revascularization. Transit time flow measurement is a real time, direct, easy and handy tool for assessment quality of anastomosis and graft blood flow.展开更多
Accurate and real-time passenger flow prediction of rail transit is an important part of intelligent transportation systems(ITS).According to previous studies,it is found that the prediction effect of a single model i...Accurate and real-time passenger flow prediction of rail transit is an important part of intelligent transportation systems(ITS).According to previous studies,it is found that the prediction effect of a single model is not good for datasets with large changes in passenger flow characteristics and the deep learning model with added influencing factors has better prediction accuracy.In order to provide persuasive passenger flow forecast data for ITS,a deep learning model considering the influencing factors is proposed in this paper.In view of the lack of objective analysis on the selection of influencing factors by predecessors,this paper uses analytic hierarchy processes(AHP)and one-way ANOVA analysis to scientifically select the factor of time characteristics,which classifies and gives weight to the hourly passenger flow through Duncan test.Then,combining the time weight,BILSTM based model considering the hourly travel characteristics factors is proposed.The model performance is verified through the inbound passenger flow of Ningbo rail transit.The proposed model is compared with many current mainstream deep learning algorithms,the effectiveness of the BILSTM model considering influencing factors is validated.Through comparison and analysis with various evaluation indicators and other deep learning models,the results show that the R2 score of the BILSTM model considering influencing factors reaches 0.968,and the MAE value of the BILSTM model without adding influencing factors decreases by 45.61%.展开更多
Previous studies suggest that there are three different jam phases in the cellular automata automaton model with a slow-to-start rule under open boundaries.In the present paper,the dynamics of each free-flow-jam phase...Previous studies suggest that there are three different jam phases in the cellular automata automaton model with a slow-to-start rule under open boundaries.In the present paper,the dynamics of each free-flow-jam phase transition is studied.By analysing the microscopic behaviour of the traffic flow,we obtain analytical results on the phase transition dynamics.Our results can describe the detailed time evolution of the system during phase transition,while they provide good approximation for the numerical simulation data.These findings can perfectly explain the microscopic mechanism and details of the boundary-triggered phase transition dynamics.展开更多
We study the characteristics of phase transition to take the top-priority of randomization in the rules of NaSch model （i.e. noise-first model） into account via computing the relaxation time and the order parameter...We study the characteristics of phase transition to take the top-priority of randomization in the rules of NaSch model （i.e. noise-first model） into account via computing the relaxation time and the order parameter. The scaling exponents of the relaxation time and the scaling relation of order parameter, respectively, are obtained.展开更多
Accurate flood prediction is an important tool for risk management and hydraulic works design on a watershed scale. The objective of this study was to calibrate and validate 24 linear and non-linear regression models,...Accurate flood prediction is an important tool for risk management and hydraulic works design on a watershed scale. The objective of this study was to calibrate and validate 24 linear and non-linear regression models, using only upstream data to estimate real-time downstream flooding. Four critical downstream estimation points in the Mataquito and Maule river basins located in central Chile were selected to estimate peak flows using data from one, two, or three upstream stations. More than one thousand paper-based storm hydrographs were manually analyzed for rainfall events that occurred between 1999 and 2006, in order to determine the best models for predicting downstream peak flow. The Peak Flow Index (IQP) (defined as the quotient between upstream and downstream data) and the Transit Times (TT) between upstream and downstream points were also obtained and analyzed for each river basin. The Coefficients of Determination (R2), the Standard Error of the Estimate (SEE), and the Bland-Altman test (ACBA) were used to calibrate and validate the best selected model at each basin. Despite the high variability observed in peak flow data, the developed models were able to accurately estimate downstream peak flows using only upstream flow data.展开更多
基金supported mainly by the “Agence de la Biomedecine” through its program of Research(AOR 2009)BM,AC,BP,WM,VCI and VE acknowledged funding of project ANR-13-TECS-0006 by the Agence Nationale de la Recherche
文摘Background: The hepatic hemodynamics is an essential parameter in surgical planning as well as in various disease processes. The transit time ultrasound(TTUS) perivascular flow probe technology is widely used in clinical practice to evaluate the hepatic inflow, yet invasive. The phase-contrast-MRI(PC-MRI) is not invasive and potentially applicable in assessing the hepatic blood flow. In the present study, we compared the hepatic inflow rates using the PC-MRI and the TTUS probe, and evaluated their predictive value of post-hepatectomy adverse events. Methods: Eighteen large white pigs were anaesthetized for PC-MRI and approximately 75% hepatic resection was performed under a unified protocol. The blood flow was measured in the hepatic artery(Qha), the portal vein(Qpv), and the aorta above the celiac trunk(Qca) using PC-MRI, and was compared to the TTUS probe. The Bland-Altman method was conducted and a partial least squares regression(PLS) model was implemented. Results: The mean Qpv measured in PC-MRI was 0.55 ± 0.12 L/min, and in the TTUS probe was 0.74 ± 0.17 L/min. Qca was 1.40 ± 0.47 L/min in the PC-MRI and 2.00 ± 0.60 L/min in the TTUS probe. Qha was 0.17 ± 0.10 L/min in the PC-MRI, and 0.13 ± 0.06 L/min in the TTUS probe. The Bland-Altman method revealed that the estimated bias of Qca in the PC-MRI was 32%(95% CI:-49% to 15%); Qha 17%(95% CI:-15% to 51%); and Qpv 40%(95% CI:-62% to 18%). The TTUS probe had a higher weight in predicting adverse outcomes after 75% resection compared to the PC-MRI( β= 0.35 and 0.43 vs β = 0.22 and 0.07, for tissue changes and premature death, respectively). Conclusions: There is a tendency of the PC-MRI to underestimate the flow measured by the TTUS probes. The TTUS probe measures are more predictive of relevant post-hepatectomy outcomes.
基金Project supported by the Science and Technology Support Program of Gansu Province,China (Grant No. 0804GKCA038)
文摘This paper mainly deals with the effects of transit stops on vehicle speeds and conversion lane numbers in a mixed traffic lane. Based on thorough research of traffic flow and cellular automata theory, it calibrates the cellular length and the running speed. Also, a cellular automata model for mixed traffic flow on a two-lane system under a periodic boundary condition is presented herewith, which also takes into consideration the harbour-shaped transit stop as well. By means of computer simulation, the article also studies the effects of bus parking time on the traffic volume, the transit speed and the fast lane speed at the same time. The results demonstrate that, with the increase of the bus parking time, the traffic volume of the transit stop and the transit speed decrease while the fast lane speed increases. This result could help calculate the transit delay correctly and make arrangements for transit routes reasonably and scientifically.
文摘Transit-time flow technology is considered as a quality of care in bypass surgery especially in off pump revascularization. Transit time flow measurement is a real time, direct, easy and handy tool for assessment quality of anastomosis and graft blood flow.
基金supported by the Program of Humanities and Social Science of Education Ministry of China(Grant No.20YJA630008)the Ningbo Natural Science Foundation of China(Grant No.202003N4142)+1 种基金the Natural Science Foundation of Zhejiang Province,China(Grant No.LY20G010004)the K.C.Wong Magna Fund in Ningbo University,China.
文摘Accurate and real-time passenger flow prediction of rail transit is an important part of intelligent transportation systems(ITS).According to previous studies,it is found that the prediction effect of a single model is not good for datasets with large changes in passenger flow characteristics and the deep learning model with added influencing factors has better prediction accuracy.In order to provide persuasive passenger flow forecast data for ITS,a deep learning model considering the influencing factors is proposed in this paper.In view of the lack of objective analysis on the selection of influencing factors by predecessors,this paper uses analytic hierarchy processes(AHP)and one-way ANOVA analysis to scientifically select the factor of time characteristics,which classifies and gives weight to the hourly passenger flow through Duncan test.Then,combining the time weight,BILSTM based model considering the hourly travel characteristics factors is proposed.The model performance is verified through the inbound passenger flow of Ningbo rail transit.The proposed model is compared with many current mainstream deep learning algorithms,the effectiveness of the BILSTM model considering influencing factors is validated.Through comparison and analysis with various evaluation indicators and other deep learning models,the results show that the R2 score of the BILSTM model considering influencing factors reaches 0.968,and the MAE value of the BILSTM model without adding influencing factors decreases by 45.61%.
基金Project supported by the National Natural Science Foundation of China (Grant Nos. 70971094 and 50908155)the Program for Changjiang Scholars and Innovative Research Team in University (PCSIRT)
文摘Previous studies suggest that there are three different jam phases in the cellular automata automaton model with a slow-to-start rule under open boundaries.In the present paper,the dynamics of each free-flow-jam phase transition is studied.By analysing the microscopic behaviour of the traffic flow,we obtain analytical results on the phase transition dynamics.Our results can describe the detailed time evolution of the system during phase transition,while they provide good approximation for the numerical simulation data.These findings can perfectly explain the microscopic mechanism and details of the boundary-triggered phase transition dynamics.
基金The project supported by National Natural Science Foundation of China under Grant Nos. 10362001 and 10532060 and the Natural Science Foundation of Guangxi Zhuang Autonomous Region under Grant Nos. 0342012 and 0640003
文摘We study the characteristics of phase transition to take the top-priority of randomization in the rules of NaSch model （i.e. noise-first model） into account via computing the relaxation time and the order parameter. The scaling exponents of the relaxation time and the scaling relation of order parameter, respectively, are obtained.
文摘Accurate flood prediction is an important tool for risk management and hydraulic works design on a watershed scale. The objective of this study was to calibrate and validate 24 linear and non-linear regression models, using only upstream data to estimate real-time downstream flooding. Four critical downstream estimation points in the Mataquito and Maule river basins located in central Chile were selected to estimate peak flows using data from one, two, or three upstream stations. More than one thousand paper-based storm hydrographs were manually analyzed for rainfall events that occurred between 1999 and 2006, in order to determine the best models for predicting downstream peak flow. The Peak Flow Index (IQP) (defined as the quotient between upstream and downstream data) and the Transit Times (TT) between upstream and downstream points were also obtained and analyzed for each river basin. The Coefficients of Determination (R2), the Standard Error of the Estimate (SEE), and the Bland-Altman test (ACBA) were used to calibrate and validate the best selected model at each basin. Despite the high variability observed in peak flow data, the developed models were able to accurately estimate downstream peak flows using only upstream flow data.
