Multiple sclerosis(MS) is a chronic inflammatory condition of the central nervous system determined by a presumed autoimmune process mainly directed against myelin components but also involving axons and neurons. Acut...Multiple sclerosis(MS) is a chronic inflammatory condition of the central nervous system determined by a presumed autoimmune process mainly directed against myelin components but also involving axons and neurons. Acute demyelination shows as clinical relapses that may fully or partially resolve, while chronic demyelination and neuroaxonal injury lead to persistent and irreversible neurological symptoms, often progressing over time. Currently approved disease-modifying therapies are immunomodulatory or immunosuppressive drugs that significantly although variably reduce the frequency of attacks of the relapsing forms of the disease. However, they have limited efficacy in preventing the transition to the progressive phase of MS and are of no benefit after it has started. It is therefore likely that the potential advantage of a given treatment is condensed in a relatively limited window of opportunity for each patient, depending on individual characteristics and disease stage, most frequently but not necessarily in the early phase of the disease. In addition, a sizable proportion of patients with MS may have a very mild clinical course not requiring a disease-modifying therapy. Finally, individual response to existing therapies for MS varies significantly across subjects and the risk of serious adverse events remains an issue, particularly for the newest agents. The present review is aimed at critically describing current treatment strategies for MS with a particular focus on the decision of starting, switching and stopping commercially available immunomodulatory and immunosuppressive therapies.展开更多
This paper investigates the start-up and shutdown phases of a five-bladed closed-impeller centrifugal pump through experimental analysis,capturing the temporal evolution of its hydraulic performances.The study also pr...This paper investigates the start-up and shutdown phases of a five-bladed closed-impeller centrifugal pump through experimental analysis,capturing the temporal evolution of its hydraulic performances.The study also predicts the transient characteristics of the pump under non-rated operating conditions to assess the accuracy of various machine learning methods in forecasting its instantaneous performance.Results indicate that the pump’s transient behavior in power-frequency mode markedly differs from that in frequency-conversion mode.Specifically,the power-frequency mode achieves steady-state values faster and exhibits smaller fluctuations before stabilization compared to the other mode.During the start-up phase,as the steady-state flow rate increases,inlet and outlet pressures and head also rise,while torque and shaft power decrease,with rotational speed remaining largely unchanged.Conversely,during the shutdown phase,no significant changes were observed in torque,shaft power,or rotational speed.Six machine learning models,including Gaussian Process Regression(GPR),Decision Tree Regression(DTR),and Deep Learning Networks(DLN),demonstrated high accuracy in predicting the hydraulic performance of the centrifugal pump during the start-up and shutdown phases in both power-frequency and frequency-conversion conditions.The findings provide a theoretical foundation for improved prediction of pump hydraulic performance.For instance,when predicting head and flow rate during power-frequency start-up,GPR achieved absolute and relative errors of 0.54 m(7.84%)and 0.21 m3/h(13.57%),respectively,while the Feedforward Neural Network(FNN)reported errors of 0.98 m(8.24%)and 0.10 m3/h(16.71%).By contrast,the Support Vector Machine Regression(SVMR)and Generalized Additive Model(GAM)generally yielded less satisfactory prediction accuracy compared to the other methods.展开更多
In this paper we present a regularized Newton-type method for ill-posed problems, by using the A-smooth regularization to solve the linearized ill-posed equations. For noisy data a proper a posteriori stopping rule is...In this paper we present a regularized Newton-type method for ill-posed problems, by using the A-smooth regularization to solve the linearized ill-posed equations. For noisy data a proper a posteriori stopping rule is used that yields convergence of the Newton iteration to a solution, as the noise level goes to zero, under certain smoothness conditions on the nonlinear operator. Some appropriate assumptions on the closedness and smoothness of the starting value and the solution are shown to lead to optimal convergence rates.展开更多
文摘Multiple sclerosis(MS) is a chronic inflammatory condition of the central nervous system determined by a presumed autoimmune process mainly directed against myelin components but also involving axons and neurons. Acute demyelination shows as clinical relapses that may fully or partially resolve, while chronic demyelination and neuroaxonal injury lead to persistent and irreversible neurological symptoms, often progressing over time. Currently approved disease-modifying therapies are immunomodulatory or immunosuppressive drugs that significantly although variably reduce the frequency of attacks of the relapsing forms of the disease. However, they have limited efficacy in preventing the transition to the progressive phase of MS and are of no benefit after it has started. It is therefore likely that the potential advantage of a given treatment is condensed in a relatively limited window of opportunity for each patient, depending on individual characteristics and disease stage, most frequently but not necessarily in the early phase of the disease. In addition, a sizable proportion of patients with MS may have a very mild clinical course not requiring a disease-modifying therapy. Finally, individual response to existing therapies for MS varies significantly across subjects and the risk of serious adverse events remains an issue, particularly for the newest agents. The present review is aimed at critically describing current treatment strategies for MS with a particular focus on the decision of starting, switching and stopping commercially available immunomodulatory and immunosuppressive therapies.
基金financially supported by Science and Technology Project of Quzhou(Grant Nos.2023K256,2023NC08)Research Grants Program of Department of Education of Zhejiang Province(No.Y202455709)+1 种基金Zhejiang Provincial Natural Science Foundation of China(Grant No.LZY21E050001)University-Enterprise Cooperation Program for Visiting Engineers in Higher Education Institutions in Zhejiang Province(No.FG2020215).
文摘This paper investigates the start-up and shutdown phases of a five-bladed closed-impeller centrifugal pump through experimental analysis,capturing the temporal evolution of its hydraulic performances.The study also predicts the transient characteristics of the pump under non-rated operating conditions to assess the accuracy of various machine learning methods in forecasting its instantaneous performance.Results indicate that the pump’s transient behavior in power-frequency mode markedly differs from that in frequency-conversion mode.Specifically,the power-frequency mode achieves steady-state values faster and exhibits smaller fluctuations before stabilization compared to the other mode.During the start-up phase,as the steady-state flow rate increases,inlet and outlet pressures and head also rise,while torque and shaft power decrease,with rotational speed remaining largely unchanged.Conversely,during the shutdown phase,no significant changes were observed in torque,shaft power,or rotational speed.Six machine learning models,including Gaussian Process Regression(GPR),Decision Tree Regression(DTR),and Deep Learning Networks(DLN),demonstrated high accuracy in predicting the hydraulic performance of the centrifugal pump during the start-up and shutdown phases in both power-frequency and frequency-conversion conditions.The findings provide a theoretical foundation for improved prediction of pump hydraulic performance.For instance,when predicting head and flow rate during power-frequency start-up,GPR achieved absolute and relative errors of 0.54 m(7.84%)and 0.21 m3/h(13.57%),respectively,while the Feedforward Neural Network(FNN)reported errors of 0.98 m(8.24%)and 0.10 m3/h(16.71%).By contrast,the Support Vector Machine Regression(SVMR)and Generalized Additive Model(GAM)generally yielded less satisfactory prediction accuracy compared to the other methods.
文摘In this paper we present a regularized Newton-type method for ill-posed problems, by using the A-smooth regularization to solve the linearized ill-posed equations. For noisy data a proper a posteriori stopping rule is used that yields convergence of the Newton iteration to a solution, as the noise level goes to zero, under certain smoothness conditions on the nonlinear operator. Some appropriate assumptions on the closedness and smoothness of the starting value and the solution are shown to lead to optimal convergence rates.
