Stochastic unit commitment is one of the most powerful methods to address uncertainty. However, the existingscenario clustering technique for stochastic unit commitment cannot accurately select representative scenario...Stochastic unit commitment is one of the most powerful methods to address uncertainty. However, the existingscenario clustering technique for stochastic unit commitment cannot accurately select representative scenarios,which threatens the robustness of stochastic unit commitment and hinders its application. This paper providesa stochastic unit commitment with dynamic scenario clustering based on multi-parametric programming andBenders decomposition. The stochastic unit commitment is solved via the Benders decomposition, which decouplesthe primal problem into the master problem and two types of subproblems. In the master problem, the committedgenerator is determined, while the feasibility and optimality of generator output are checked in these twosubproblems. Scenarios are dynamically clustered during the subproblem solution process through the multiparametric programming with respect to the solution of the master problem. In other words, multiple scenariosare clustered into several representative scenarios after the subproblem is solved, and the Benders cut obtainedby the representative scenario is generated for the master problem. Different from the conventional stochasticunit commitment, the proposed approach integrates scenario clustering into the Benders decomposition solutionprocess. Such a clustering approach could accurately cluster representative scenarios that have impacts on theunit commitment. The proposed method is tested on a 6-bus system and the modified IEEE 118-bus system.Numerical results illustrate the effectiveness of the proposed method in clustering scenarios. Compared withthe conventional clustering method, the proposed method can accurately select representative scenarios whilemitigating computational burden, thus guaranteeing the robustness of unit commitment.展开更多
BACKGROUND The study focuses on the use of multi-parametric ultrasound[gray scale,color Doppler and shear wave elastography(SWE)]to differentiate stable renal allografts from acute graft dysfunction and to assess time...BACKGROUND The study focuses on the use of multi-parametric ultrasound[gray scale,color Doppler and shear wave elastography(SWE)]to differentiate stable renal allografts from acute graft dysfunction and to assess time-dependent changes in parenchymal stiffness,thereby assessing its use as an efficient monitoring tool for ongoing graft dysfunction.To date,biopsy is the gold standard for evaluation of acute graft dysfunction.However,because it is invasive,it carries certain risks and cannot be used for follow-up monitoring.SWE is a non-invasive imaging modality that identifies higher parenchymal stiffness values in cases of acute graft dysfunction compared to stable grafts.AIM To assess renal allograft parenchymal stiffness by SWE and to correlate its findings with functional status of the graft kidney.METHODS This prospective observational study included 71 renal allograft recipients.Multi-parametric ultrasound was performed on all patients,and biopsies were performed in cases of acute graft dysfunction.The study was performed for a period of 2 years at Sanjay Gandhi Postgraduate Institute of Medical Sciences,Lucknow,a tertiary care center in north India.Independent samples t-test was used to compare the means between two independent groups.Paired-samples t-test was used to test the change in mean value between baseline and follow-up obser-vations.RESULTS Thirty-one patients had experienced acute graft dysfunction at least once,followed by recovery,but none of them had a history of chronic renal allograft injury.Mean baseline parenchymal stiffness in stable grafts and acute graft dysfunction were 30.21+2.03 kPa(3.17+0.11 m/s)and 31.07+2.88 kPa(3.22+0.15 m/s),respectively;however,these differences were not statistically significant(P=0.305 and 0.252,respectively).There was a gradual decrease in SWE values during the first 3 postoperative months,followed by an increase in SWE values up to one-year post-transplantation.Patients with biopsy-confirmed graft dysfunction showed higher SWE values compared to those with a negative biopsy.However,receiver operating characteristic analysis failed to show statistically significant cut-off values to differentiate between the stable graft and acute graft dysfunction.CONCLUSION Acute graft dysfunction displays higher parenchymal stiffness values compared to stable grafts.Therefore,SWE may be useful in monitoring the functional status of allografts to predict any ongoing dysfunction.展开更多
Magnetic resonance (MR) imaging has been increasingly used in the evaluation of prostate cancer. As studies have suggested that the majority of cancers arise from the peripheral zone (PZ), MR imaging has focused on th...Magnetic resonance (MR) imaging has been increasingly used in the evaluation of prostate cancer. As studies have suggested that the majority of cancers arise from the peripheral zone (PZ), MR imaging has focused on the PZ of the prostate gland thus far. However, a considerable number of cancers (up to 30%) originate in the transition zone (TZ), substantially contributing to morbidity and mortality. Therefore, research is needed on the TZ of the prostate gland. Recently, MR imaging and advanced MR techniques have been gaining acceptance in evaluation of the TZ. In this article, the MR imaging features of TZ prostate cancers, the role of MR imaging in TZ cancer detection and staging, and recent advanced MR techniques will be discussed in light of the literature.展开更多
We hypothesized that a relationship existed between the mechanical properties and the magnetic resonance imaging (MRI) parameters of muscles, as already demonstrated in cartilaginous tissues. The aim was to develop an...We hypothesized that a relationship existed between the mechanical properties and the magnetic resonance imaging (MRI) parameters of muscles, as already demonstrated in cartilaginous tissues. The aim was to develop an indirect evaluation tool of the mechanical properties of degenerated muscles. Leg and arm muscles of adult rabbits were dissected, and tested 12 hours post mortem, in a state of rigor mortis, or 72 hours post mortem, in a state of post-rigor mortis. The tests consisted of a multi-parametric MRI acquisition followed by a uniaxial tensile test until failure. The statistical analysis consisted of multiple linear regressions and principal component analysis. Significant differences existed between the rigor mortis and post-rigor mortis groups for E but not for the MRI parameters. 78%, 60% or 33% of the Young’s modulus could be explained by the MRI parameters in the post-rigor mortis group, rigor mortis group or both groups respectively. These relationships were confirmed by the principal component analysis. The proposed multi-parametric MRI protocol associated to principal component analysis is a promising tool for the indirect evaluation of muscle mechanical properties and should be useful to find biomarkers and predictive factors of the evolution of the pathologies.展开更多
A process represented by nonlinear multi-parametric binary dynamic system is investigated in this work. This process is characterized by the pseudo Boolean objective functional. Since the transfer functions on the pro...A process represented by nonlinear multi-parametric binary dynamic system is investigated in this work. This process is characterized by the pseudo Boolean objective functional. Since the transfer functions on the process are Boolean functions, the optimal control problem related to the process can be solved by relating between the transfer functions and the objective functional. An analogue of Bellman function for the optimal control problem mentioned is defined and consequently suitable Bellman equation is constructed.展开更多
This paper aims to develop an approach to investigating the effect of a particular parameter on the output accuracy of transformer thermal models,i.e.sensitivity analysis,which can not only reveal the most sensitive p...This paper aims to develop an approach to investigating the effect of a particular parameter on the output accuracy of transformer thermal models,i.e.sensitivity analysis,which can not only reveal the most sensitive parameter of a thermal model but also improve model output accuracies.For the first time,the nonlinear time constant(NTC)of transformer oil is proposed to reshape three practical top-oil temperature models based on an expression of nonlinear thermal conductance:the modified IEEE clause 7 model,Swift’s model,and Susa’s model.Then,the multi-parametric sensitivity analysis(MPSA)is undertaken to reveal the effect of each parameter on the model output accuracy.Through onsite data validation,the results show that the accuracy performance of the proposed NTC thermal models are improved significantly by considering the nonlinear effect of oil time constant.Moreover,the derived sensitivity performances can clearly reveal the most dominant parameter of the model,so as to simplify the model parameter identification process by reducing the number of insensitive parameters.Finally,the heat-run test data is used as a reference to validate parameters optimized through a genetic algorithm(GA),which demonstrates that the proposed NTC IEEE model has not only one sensitive parameter but also superior accuracy performance.展开更多
Due to continuous process scaling, process, voltage, and temperature （PVT） parameter variations have become one of the most problematic issues in circuit design. The resulting correlations among performance metrics ...Due to continuous process scaling, process, voltage, and temperature （PVT） parameter variations have become one of the most problematic issues in circuit design. The resulting correlations among performance metrics lead to a significant parametric yield loss. Previous algorithms on parametric yield prediction are limited to predicting a single-parametric yield or performing balanced optimization for several single-parametric yields. Consequently, these methods fail to predict the multiparametric yield that optimizes multiple performance metrics simultaneously, which may result in significant accuracy loss. In this paper we suggest an efficient multi-parametric yield prediction framework, in which multiple performance metrics are considered as simultaneous constraint conditions for parametric yield prediction, to maintain the correlations among metrics. First, the framework models the performance metrics in terms of PVT parameter variations by using the adaptive elastic net （AEN） method. Then the parametric yield for a single performance metric can be predicted through the computation of the cumulative distribution function （CDF） based on the multiplication theorem and the Markov chain Monte Carlo （MCMC） method. Finally, a copula-based parametric yield prediction procedure has been developed to solve the multi-parametric yield prediction problem, and to generate an accurate yield estimate. Experimental results demonstrate that the proposed multi-parametric yield prediction framework is able to provide the designer with either an accurate value for parametric yield under specific performance limits, or a multi-parametric yield surface under all ranges of performance limits.展开更多
Abstract-An analytic method is proposed to compute the price-reserve offer curve at the consumer level in hierarchical direct load control.The convexification of the consumer reserve provision is examined,and the anal...Abstract-An analytic method is proposed to compute the price-reserve offer curve at the consumer level in hierarchical direct load control.The convexification of the consumer reserve provision is examined,and the analytic expression of the optimal solution within each critical region is derived.Then,based on multi-parametric programming,a combinatorial enumeration method in conjunction with efficient reduction and pruning strategy is proposed to compute the optimal response of consumers in the whole price space.Numerical tests along with an application example in the bi-level aggregator pricing problem demonstrate the merit of this method.展开更多
BACKGROUND The nature of input data is an essential factor when training neural networks.Research concerning magnetic resonance imaging(MRI)-based diagnosis of liver tumors using deep learning has been rapidly advanci...BACKGROUND The nature of input data is an essential factor when training neural networks.Research concerning magnetic resonance imaging(MRI)-based diagnosis of liver tumors using deep learning has been rapidly advancing.Still,evidence to support the utilization of multi-dimensional and multi-parametric image data is lacking.Due to higher information content,three-dimensional input should presumably result in higher classification precision.Also,the differentiation between focal liver lesions(FLLs)can only be plausible with simultaneous analysis of multisequence MRI images.AIM To compare diagnostic efficiency of two-dimensional(2D)and three-dimensional(3D)-densely connected convolutional neural networks(DenseNet)for FLLs on multi-sequence MRI.METHODS We retrospectively collected T2-weighted,gadoxetate disodium-enhanced arterial phase,portal venous phase,and hepatobiliary phase MRI scans from patients with focal nodular hyperplasia(FNH),hepatocellular carcinomas(HCC)or liver metastases(MET).Our search identified 71 FNH,69 HCC and 76 MET.After volume registration,the same three most representative axial slices from all sequences were combined into four-channel images to train the 2D-DenseNet264 network.Identical bounding boxes were selected on all scans and stacked into 4D volumes to train the 3D-DenseNet264 model.The test set consisted of 10-10-10 tumors.The performance of the models was compared using area under the receiver operating characteristic curve(AUROC),specificity,sensitivity,positive predictive values(PPV),negative predictive values(NPV),and f1 scores.RESULTS The average AUC value of the 2D model(0.98)was slightly higher than that of the 3D model(0.94).Mean PPV,sensitivity,NPV,specificity and f1 scores(0.94,0.93,0.97,0.97,and 0.93)of the 2D model were also superior to metrics of the 3D model(0.84,0.83,0.92,0.92,and 0.83).The classification metrics of FNH were 0.91,1.00,1.00,0.95,and 0.95 using the 2D and 0.90,0.90,0.95,0.95,and 0.90 using the 3D models.The 2D and 3D networks'performance in the diagnosis of HCC were 1.00,0.80,0.91,1.00,and 0.89 and 0.88,0.70,0.86,0.95,and 0.78,respectively;while the evaluation of MET lesions resulted in 0.91,1.00,1.00,0.95,and 0.95 and 0.75,0.90,0.94,0.85,and 0.82 using the 2D and 3D networks,respectively.CONCLUSION Both 2D and 3D-DenseNets can differentiate FNH,HCC and MET with good accuracy when trained on hepatocyte-specific contrast-enhanced multi-sequence MRI volumes.展开更多
The instability of the rotor dynamic system supported by oil journal bearing is encountered frequently, such as the half speed whirl of the rotor, which is caused by oil film lubricant with nonlinearity. Currently, mo...The instability of the rotor dynamic system supported by oil journal bearing is encountered frequently, such as the half speed whirl of the rotor, which is caused by oil film lubricant with nonlinearity. Currently, more attention is paid to the physical characteristics of oil film due to an oil-lubricated journal bearing being the important supporting component of the bearing-rotor systems and its nonlinear nature. In order to analyze the lubrication characteristics of journal bearings efficiently and save computational c[~brts, an approximate solution of nonlinear oil film forces of a finite length turbulent journal bearing with couple stress flow is proposed based on Sommerfeld and Ocvirk numbers. Reynolds equation in lubrication of a finite length turbulent .journal bearing is solved based on multi-parametric principle. Load-carrying capacity of nonlinear oil film is obtained, and the results obtained by different methods are compared. The validation of the proposed method is verified, meanwhile, the relationships of load-carrying capacity versus eccentricity ratio and width-to-diameter ratio under turbulent and couple stress working conditions are analyzed. The numerical results show that both couple stress flow and eccentricity ratio have obvious influence on oil film pressure distribution, and the proposed method approximates the load-carrying capacity of turbulent journal bearings efficiently with various width-to-diameter ratios. This research proposes an approximate solution of oil film load-carrying capacity of turbulent journal bearings with different width-to-diameter ratios, whicb are suitable for high eccentricity ratios and heavy loads.展开更多
An efficient algorithm is proposed for computing the solution to the constrained finite time optimal control （CFTOC） problem for discrete-time piecewise affine （PWA） systems with a quadratic performance index. The...An efficient algorithm is proposed for computing the solution to the constrained finite time optimal control （CFTOC） problem for discrete-time piecewise affine （PWA） systems with a quadratic performance index. The maximal positively invariant terminal set, which is feasible and invariant with respect to a feedback control law, is computed as terminal target set and an associated Lyapunov function is chosen as terminal cost. The combination of these two components guarantees constraint satisfaction and closed-loop stability for all time. The proposed algorithm combines a dynamic programming strategy with a multi-parametric quadratic programming solver and basic polyhedral manipulation. A numerical example shows that a larger stabilizable set of states can be obtained by the proposed algorithm than precious work.展开更多
Immunophenotyping is proving crucial to understanding the role of the immune system in health and disease.High-through-put flow cytometry has been used extensively to reveal changes in immune cell composition and func...Immunophenotyping is proving crucial to understanding the role of the immune system in health and disease.High-through-put flow cytometry has been used extensively to reveal changes in immune cell composition and function at the single-cell level.Here,we describe six optimized 11-color flow cytometry panels for deep immunophenotyping of human whole blood.A total of 51 surface antibodies,which are readily available and validated,were selected to identify the key immune cell populations and evaluate their functional state in a single assay.The gating strategies for effective flow cytometry data analysis are included in the protocol.To ensure data reproducibility,we provide detailed procedures in three parts,including(1)instrument characterization and detector gain optimization,(2)antibody titration and sample staining,and(3)data acquisition and quality checks.This standardized approach has been applied to a variety of donors for a better understanding of the complexity of the human immune system.展开更多
基金the Science and Technology Project of State Grid Corporation of China,Grant Number 5108-202304065A-1-1-ZN.
文摘Stochastic unit commitment is one of the most powerful methods to address uncertainty. However, the existingscenario clustering technique for stochastic unit commitment cannot accurately select representative scenarios,which threatens the robustness of stochastic unit commitment and hinders its application. This paper providesa stochastic unit commitment with dynamic scenario clustering based on multi-parametric programming andBenders decomposition. The stochastic unit commitment is solved via the Benders decomposition, which decouplesthe primal problem into the master problem and two types of subproblems. In the master problem, the committedgenerator is determined, while the feasibility and optimality of generator output are checked in these twosubproblems. Scenarios are dynamically clustered during the subproblem solution process through the multiparametric programming with respect to the solution of the master problem. In other words, multiple scenariosare clustered into several representative scenarios after the subproblem is solved, and the Benders cut obtainedby the representative scenario is generated for the master problem. Different from the conventional stochasticunit commitment, the proposed approach integrates scenario clustering into the Benders decomposition solutionprocess. Such a clustering approach could accurately cluster representative scenarios that have impacts on theunit commitment. The proposed method is tested on a 6-bus system and the modified IEEE 118-bus system.Numerical results illustrate the effectiveness of the proposed method in clustering scenarios. Compared withthe conventional clustering method, the proposed method can accurately select representative scenarios whilemitigating computational burden, thus guaranteeing the robustness of unit commitment.
文摘BACKGROUND The study focuses on the use of multi-parametric ultrasound[gray scale,color Doppler and shear wave elastography(SWE)]to differentiate stable renal allografts from acute graft dysfunction and to assess time-dependent changes in parenchymal stiffness,thereby assessing its use as an efficient monitoring tool for ongoing graft dysfunction.To date,biopsy is the gold standard for evaluation of acute graft dysfunction.However,because it is invasive,it carries certain risks and cannot be used for follow-up monitoring.SWE is a non-invasive imaging modality that identifies higher parenchymal stiffness values in cases of acute graft dysfunction compared to stable grafts.AIM To assess renal allograft parenchymal stiffness by SWE and to correlate its findings with functional status of the graft kidney.METHODS This prospective observational study included 71 renal allograft recipients.Multi-parametric ultrasound was performed on all patients,and biopsies were performed in cases of acute graft dysfunction.The study was performed for a period of 2 years at Sanjay Gandhi Postgraduate Institute of Medical Sciences,Lucknow,a tertiary care center in north India.Independent samples t-test was used to compare the means between two independent groups.Paired-samples t-test was used to test the change in mean value between baseline and follow-up obser-vations.RESULTS Thirty-one patients had experienced acute graft dysfunction at least once,followed by recovery,but none of them had a history of chronic renal allograft injury.Mean baseline parenchymal stiffness in stable grafts and acute graft dysfunction were 30.21+2.03 kPa(3.17+0.11 m/s)and 31.07+2.88 kPa(3.22+0.15 m/s),respectively;however,these differences were not statistically significant(P=0.305 and 0.252,respectively).There was a gradual decrease in SWE values during the first 3 postoperative months,followed by an increase in SWE values up to one-year post-transplantation.Patients with biopsy-confirmed graft dysfunction showed higher SWE values compared to those with a negative biopsy.However,receiver operating characteristic analysis failed to show statistically significant cut-off values to differentiate between the stable graft and acute graft dysfunction.CONCLUSION Acute graft dysfunction displays higher parenchymal stiffness values compared to stable grafts.Therefore,SWE may be useful in monitoring the functional status of allografts to predict any ongoing dysfunction.
文摘Magnetic resonance (MR) imaging has been increasingly used in the evaluation of prostate cancer. As studies have suggested that the majority of cancers arise from the peripheral zone (PZ), MR imaging has focused on the PZ of the prostate gland thus far. However, a considerable number of cancers (up to 30%) originate in the transition zone (TZ), substantially contributing to morbidity and mortality. Therefore, research is needed on the TZ of the prostate gland. Recently, MR imaging and advanced MR techniques have been gaining acceptance in evaluation of the TZ. In this article, the MR imaging features of TZ prostate cancers, the role of MR imaging in TZ cancer detection and staging, and recent advanced MR techniques will be discussed in light of the literature.
文摘We hypothesized that a relationship existed between the mechanical properties and the magnetic resonance imaging (MRI) parameters of muscles, as already demonstrated in cartilaginous tissues. The aim was to develop an indirect evaluation tool of the mechanical properties of degenerated muscles. Leg and arm muscles of adult rabbits were dissected, and tested 12 hours post mortem, in a state of rigor mortis, or 72 hours post mortem, in a state of post-rigor mortis. The tests consisted of a multi-parametric MRI acquisition followed by a uniaxial tensile test until failure. The statistical analysis consisted of multiple linear regressions and principal component analysis. Significant differences existed between the rigor mortis and post-rigor mortis groups for E but not for the MRI parameters. 78%, 60% or 33% of the Young’s modulus could be explained by the MRI parameters in the post-rigor mortis group, rigor mortis group or both groups respectively. These relationships were confirmed by the principal component analysis. The proposed multi-parametric MRI protocol associated to principal component analysis is a promising tool for the indirect evaluation of muscle mechanical properties and should be useful to find biomarkers and predictive factors of the evolution of the pathologies.
文摘A process represented by nonlinear multi-parametric binary dynamic system is investigated in this work. This process is characterized by the pseudo Boolean objective functional. Since the transfer functions on the process are Boolean functions, the optimal control problem related to the process can be solved by relating between the transfer functions and the objective functional. An analogue of Bellman function for the optimal control problem mentioned is defined and consequently suitable Bellman equation is constructed.
基金supported in part by the National Key R&D Program of China under Grant No.2018YFE0208400in part by Science and Technology Project of Guangdong Power Grid Company under Grant No.031900KK52180153.
文摘This paper aims to develop an approach to investigating the effect of a particular parameter on the output accuracy of transformer thermal models,i.e.sensitivity analysis,which can not only reveal the most sensitive parameter of a thermal model but also improve model output accuracies.For the first time,the nonlinear time constant(NTC)of transformer oil is proposed to reshape three practical top-oil temperature models based on an expression of nonlinear thermal conductance:the modified IEEE clause 7 model,Swift’s model,and Susa’s model.Then,the multi-parametric sensitivity analysis(MPSA)is undertaken to reveal the effect of each parameter on the model output accuracy.Through onsite data validation,the results show that the accuracy performance of the proposed NTC thermal models are improved significantly by considering the nonlinear effect of oil time constant.Moreover,the derived sensitivity performances can clearly reveal the most dominant parameter of the model,so as to simplify the model parameter identification process by reducing the number of insensitive parameters.Finally,the heat-run test data is used as a reference to validate parameters optimized through a genetic algorithm(GA),which demonstrates that the proposed NTC IEEE model has not only one sensitive parameter but also superior accuracy performance.
基金Project supposed by the Natural Science Foundation of Jiangsu Province （Nos. BK20161072, BK20150785, and BK20130877） and the National Natural Science Foundation of China （Nos. 61502234 and 71301081）
文摘Due to continuous process scaling, process, voltage, and temperature （PVT） parameter variations have become one of the most problematic issues in circuit design. The resulting correlations among performance metrics lead to a significant parametric yield loss. Previous algorithms on parametric yield prediction are limited to predicting a single-parametric yield or performing balanced optimization for several single-parametric yields. Consequently, these methods fail to predict the multiparametric yield that optimizes multiple performance metrics simultaneously, which may result in significant accuracy loss. In this paper we suggest an efficient multi-parametric yield prediction framework, in which multiple performance metrics are considered as simultaneous constraint conditions for parametric yield prediction, to maintain the correlations among metrics. First, the framework models the performance metrics in terms of PVT parameter variations by using the adaptive elastic net （AEN） method. Then the parametric yield for a single performance metric can be predicted through the computation of the cumulative distribution function （CDF） based on the multiplication theorem and the Markov chain Monte Carlo （MCMC） method. Finally, a copula-based parametric yield prediction procedure has been developed to solve the multi-parametric yield prediction problem, and to generate an accurate yield estimate. Experimental results demonstrate that the proposed multi-parametric yield prediction framework is able to provide the designer with either an accurate value for parametric yield under specific performance limits, or a multi-parametric yield surface under all ranges of performance limits.
基金General Research Fund(No.17209419)the National Science Foundation of China(No.51725703)State Key Laboratory of Power System and Generation Equipment(No.SK1D20M06).
文摘Abstract-An analytic method is proposed to compute the price-reserve offer curve at the consumer level in hierarchical direct load control.The convexification of the consumer reserve provision is examined,and the analytic expression of the optimal solution within each critical region is derived.Then,based on multi-parametric programming,a combinatorial enumeration method in conjunction with efficient reduction and pruning strategy is proposed to compute the optimal response of consumers in the whole price space.Numerical tests along with an application example in the bi-level aggregator pricing problem demonstrate the merit of this method.
文摘BACKGROUND The nature of input data is an essential factor when training neural networks.Research concerning magnetic resonance imaging(MRI)-based diagnosis of liver tumors using deep learning has been rapidly advancing.Still,evidence to support the utilization of multi-dimensional and multi-parametric image data is lacking.Due to higher information content,three-dimensional input should presumably result in higher classification precision.Also,the differentiation between focal liver lesions(FLLs)can only be plausible with simultaneous analysis of multisequence MRI images.AIM To compare diagnostic efficiency of two-dimensional(2D)and three-dimensional(3D)-densely connected convolutional neural networks(DenseNet)for FLLs on multi-sequence MRI.METHODS We retrospectively collected T2-weighted,gadoxetate disodium-enhanced arterial phase,portal venous phase,and hepatobiliary phase MRI scans from patients with focal nodular hyperplasia(FNH),hepatocellular carcinomas(HCC)or liver metastases(MET).Our search identified 71 FNH,69 HCC and 76 MET.After volume registration,the same three most representative axial slices from all sequences were combined into four-channel images to train the 2D-DenseNet264 network.Identical bounding boxes were selected on all scans and stacked into 4D volumes to train the 3D-DenseNet264 model.The test set consisted of 10-10-10 tumors.The performance of the models was compared using area under the receiver operating characteristic curve(AUROC),specificity,sensitivity,positive predictive values(PPV),negative predictive values(NPV),and f1 scores.RESULTS The average AUC value of the 2D model(0.98)was slightly higher than that of the 3D model(0.94).Mean PPV,sensitivity,NPV,specificity and f1 scores(0.94,0.93,0.97,0.97,and 0.93)of the 2D model were also superior to metrics of the 3D model(0.84,0.83,0.92,0.92,and 0.83).The classification metrics of FNH were 0.91,1.00,1.00,0.95,and 0.95 using the 2D and 0.90,0.90,0.95,0.95,and 0.90 using the 3D models.The 2D and 3D networks'performance in the diagnosis of HCC were 1.00,0.80,0.91,1.00,and 0.89 and 0.88,0.70,0.86,0.95,and 0.78,respectively;while the evaluation of MET lesions resulted in 0.91,1.00,1.00,0.95,and 0.95 and 0.75,0.90,0.94,0.85,and 0.82 using the 2D and 3D networks,respectively.CONCLUSION Both 2D and 3D-DenseNets can differentiate FNH,HCC and MET with good accuracy when trained on hepatocyte-specific contrast-enhanced multi-sequence MRI volumes.
基金Supported by National Natural Science Foundation of China(Grant No.51375380)Open Project of State Key Laboratory for Strength and Vibration of Mechanical Structures of China(Grant No.SV2014-KF-08)Shaanxi Provincial Natural Science Foundation of China(Grant No.2013JQ7008)
文摘The instability of the rotor dynamic system supported by oil journal bearing is encountered frequently, such as the half speed whirl of the rotor, which is caused by oil film lubricant with nonlinearity. Currently, more attention is paid to the physical characteristics of oil film due to an oil-lubricated journal bearing being the important supporting component of the bearing-rotor systems and its nonlinear nature. In order to analyze the lubrication characteristics of journal bearings efficiently and save computational c[~brts, an approximate solution of nonlinear oil film forces of a finite length turbulent journal bearing with couple stress flow is proposed based on Sommerfeld and Ocvirk numbers. Reynolds equation in lubrication of a finite length turbulent .journal bearing is solved based on multi-parametric principle. Load-carrying capacity of nonlinear oil film is obtained, and the results obtained by different methods are compared. The validation of the proposed method is verified, meanwhile, the relationships of load-carrying capacity versus eccentricity ratio and width-to-diameter ratio under turbulent and couple stress working conditions are analyzed. The numerical results show that both couple stress flow and eccentricity ratio have obvious influence on oil film pressure distribution, and the proposed method approximates the load-carrying capacity of turbulent journal bearings efficiently with various width-to-diameter ratios. This research proposes an approximate solution of oil film load-carrying capacity of turbulent journal bearings with different width-to-diameter ratios, whicb are suitable for high eccentricity ratios and heavy loads.
基金supported by the National Natural Science Foundation of China (60702033)Natural Science Foundation of Zhe-jiang Province (Y107440)
文摘An efficient algorithm is proposed for computing the solution to the constrained finite time optimal control （CFTOC） problem for discrete-time piecewise affine （PWA） systems with a quadratic performance index. The maximal positively invariant terminal set, which is feasible and invariant with respect to a feedback control law, is computed as terminal target set and an associated Lyapunov function is chosen as terminal cost. The combination of these two components guarantees constraint satisfaction and closed-loop stability for all time. The proposed algorithm combines a dynamic programming strategy with a multi-parametric quadratic programming solver and basic polyhedral manipulation. A numerical example shows that a larger stabilizable set of states can be obtained by the proposed algorithm than precious work.
基金supported by the National Key Research and Development Program of China(2021YFA1301000)Shanghai Municipal Science and Technology Major Project(Grant No.2017SHZDZX01).
文摘Immunophenotyping is proving crucial to understanding the role of the immune system in health and disease.High-through-put flow cytometry has been used extensively to reveal changes in immune cell composition and function at the single-cell level.Here,we describe six optimized 11-color flow cytometry panels for deep immunophenotyping of human whole blood.A total of 51 surface antibodies,which are readily available and validated,were selected to identify the key immune cell populations and evaluate their functional state in a single assay.The gating strategies for effective flow cytometry data analysis are included in the protocol.To ensure data reproducibility,we provide detailed procedures in three parts,including(1)instrument characterization and detector gain optimization,(2)antibody titration and sample staining,and(3)data acquisition and quality checks.This standardized approach has been applied to a variety of donors for a better understanding of the complexity of the human immune system.
