BACKGROUND Gallbladder cancer(GBC)is the most common malignant tumor of the biliary system,and is often undetected until advanced stages,making curative surgery unfeasible for many patients.Curative surgery remains th...BACKGROUND Gallbladder cancer(GBC)is the most common malignant tumor of the biliary system,and is often undetected until advanced stages,making curative surgery unfeasible for many patients.Curative surgery remains the only option for long-term survival.Accurate postsurgical prognosis is crucial for effective treatment planning.tumor-node-metastasis staging,which focuses on tumor infiltration,lymph node metastasis,and distant metastasis,limits the accuracy of prognosis.Nomograms offer a more comprehensive and personalized approach by visually analyzing a broader range of prognostic factors,enhancing the precision of treatment planning for patients with GBC.AIM A retrospective study analyzed the clinical and pathological data of 93 patients who underwent radical surgery for GBC at Peking University People's Hospital from January 2015 to December 2020.Kaplan-Meier analysis was used to calculate the 1-,2-and 3-year survival rates.The log-rank test was used to evaluate factors impacting prognosis,with survival curves plotted for significant variables.Single-factor analysis revealed statistically significant differences,and multivariate Cox regression identified independent prognostic factors.A nomogram was developed and validated with receiver operating characteristic curves and calibration curves.Among 93 patients who underwent radical surgery for GBC,30 patients survived,accounting for 32.26%of the sample,with a median survival time of 38 months.The 1-year,2-year,and 3-year survival rates were 83.87%,68.82%,and 53.57%,respectively.Univariate analysis revealed that carbohydrate antigen 19-9 expre-ssion,T stage,lymph node metastasis,histological differentiation,surgical margins,and invasion of the liver,ex-trahepatic bile duct,nerves,and vessels(P≤0.001)significantly impacted patient prognosis after curative surgery.Multivariate Cox regression identified lymph node metastasis(P=0.03),histological differentiation(P<0.05),nerve invasion(P=0.036),and extrahepatic bile duct invasion(P=0.014)as independent risk factors.A nomogram model with a concordance index of 0.838 was developed.Internal validation confirmed the model's consistency in predicting the 1-year,2-year,and 3-year survival rates.CONCLUSION Lymph node metastasis,tumor differentiation,extrahepatic bile duct invasion,and perineural invasion are independent risk factors.A nomogram based on these factors can be used to personalize and improve treatment strategies.展开更多
In this study, potential of Least Square-Support Vector Regression (LS-SVR) approach is utilized to model the daily variation of river flow. Inherent complexity, unavailability of reasonably long data set and heteroge...In this study, potential of Least Square-Support Vector Regression (LS-SVR) approach is utilized to model the daily variation of river flow. Inherent complexity, unavailability of reasonably long data set and heterogeneous catchment response are the couple of issues that hinder the generalization of relationship between previous and forthcoming river flow magnitudes. The problem complexity may get enhanced with the influence of upstream dam releases. These issues are investigated by exploiting the capability of LS-SVR–an approach that considers Structural Risk Minimization (SRM) against the Empirical Risk Minimization (ERM)–used by other learning approaches, such as, Artificial Neural Network (ANN). This study is conducted in upper Narmada river basin in India having Bargi dam in its catchment, constructed in 1989. The river gauging station–Sandia is located few hundred kilometer downstream of Bargi dam. The model development is carried out with pre-construction flow regime and its performance is checked for both pre- and post-construction of the dam for any perceivable difference. It is found that the performances are similar for both the flow regimes, which indicates that the releases from the dam at daily scale for this gauging site may be ignored. In order to investigate the temporal horizon over which the prediction performance may be relied upon, a multistep-ahead prediction is carried out and the model performance is found to be reasonably good up to 5-day-ahead predictions though the performance is decreasing with the increase in lead-time. Skills of both LS-SVR and ANN are reported and it is found that the former performs better than the latter for all the lead-times in general, and shorter lead times in particular.展开更多
The main problems in seismic attribute technology are the redundancy of data and the uncertainty of attributes, and these problems become much more serious in multi-wave seismic exploration. Data redundancy will incre...The main problems in seismic attribute technology are the redundancy of data and the uncertainty of attributes, and these problems become much more serious in multi-wave seismic exploration. Data redundancy will increase the burden on interpreters, occupy large computer memory, take much more computing time, conceal the effective information, and especially cause the ＂curse of dimension＂. Uncertainty of attributes will reduce the accuracy of rebuilding the relationship between attributes and geological significance. In order to solve these problems, we study methods of principal component analysis （PCA）, independent component analysis （ICA） for attribute optimization and support vector machine （SVM） for reservoir prediction. We propose a flow chart of multi-wave seismic attribute process and further apply it to multi-wave seismic reservoir prediction. The processing results of real seismic data demonstrate that reservoir prediction based on combination of PP- and PS-wave attributes, compared with that based on traditional PP-wave attributes, can improve the prediction accuracy.展开更多
The most critical obstacle for four-wheel independently driven electric vehicles(4WID-EVs)is the driving range.Being the actuators of 4WID-EVs,motors account for its major power consumption.In this sense,by properly d...The most critical obstacle for four-wheel independently driven electric vehicles(4WID-EVs)is the driving range.Being the actuators of 4WID-EVs,motors account for its major power consumption.In this sense,by properly distributing torques to minimize the power consumption,the driving range of 4WID-EV can be effectively improved.This paper proposes a model predictive control(MPC)-based torque distribution scheme,which minimizes the power consumption of 4WID-EVs while guaranteeing its tracking performance of planar motions.By incorporating the motor model considering iron losses,the optimal torque distribution can be achieved without an additional torque controller.Also,for this reason,the proposed control scheme is computationally efficient,since the power consumption term to be optimized,which is expressed as the product of the motor voltages and currents,is much simpler than that derived from the efficiency map.With reasonable simplification and linearization,the MPC problem is converted to a quadratic programming problem,which can be solved efficiently.The simulation results in MATLAB and CarSim co-simulation environments demonstrate that the proposed scheme effectively reduces power consumption with guaranteed tracking performance.展开更多
Background and Purpose: To investigate target functional independence measure (FIM) items to achieve the prediction goal in terms of the causal relationships between prognostic prediction error and FIM among stroke pa...Background and Purpose: To investigate target functional independence measure (FIM) items to achieve the prediction goal in terms of the causal relationships between prognostic prediction error and FIM among stroke patients in the convalescent phase using the structural equation modeling (SEM) analysis. Methods: A total of 2992 stroke patients registered in the Japanese Rehabilitation Database were analyzed retrospectively. The prediction error was calculated based on a prognostic prediction formula proposed in a previous study. An exploratory factor analysis (EFA) then the factor was determined using confirmatory factorial analysis (CFA). Finally, multivariate analyses were performed using SEM analysis. Results: The fitted indices of the hypothesized model estimated based on EFA were confirmed by CFA. The factors estimated by EFA were applied, and interpreted as follows: “Transferring (T-factor),” “Dressing (D-factor),” and “Cognitive function (C-factor).” The fit of the structural model based on the three factors and prediction errors was supported by the SEM analysis. The effects of the D- and C-factors yielded similar causal relationships on prediction error. Meanwhile, the effects between the prediction error and the T-factor were low. Observed FIM items were related to their domains in the structural model, except for the dressing of the upper body and memory (p < 0.01). Conclusions: Transfer, which was not heavily considered in the previous prediction formula, was found in causal relationships with prediction error. It is suggested to intervene to transfer together with positive factors to recovery for achieving the prediction goal.展开更多
By means of analysing the mechanism of blending materials,a general blending efficiency model was proposed.Applying this general model to an example 9 a suitable formula of blending efficiency which is more accurate t...By means of analysing the mechanism of blending materials,a general blending efficiency model was proposed.Applying this general model to an example 9 a suitable formula of blending efficiency which is more accurate than those in papers[2-3]was obtained.Finally,a high-precision optimal combining prediction formula for calculating blending efficiency was proposed.展开更多
目的:探讨机器学习模型与逐步线性回归(Stepwise linear regression,SLR)模型在亚急性期脑卒中患者康复后功能结局预测中的价值。方法:选取中国人民解放军联勤保障部队第九四五医院2013年1月~2023年12月收治的亚急性期脑卒中患者1046例...目的:探讨机器学习模型与逐步线性回归(Stepwise linear regression,SLR)模型在亚急性期脑卒中患者康复后功能结局预测中的价值。方法:选取中国人民解放军联勤保障部队第九四五医院2013年1月~2023年12月收治的亚急性期脑卒中患者1046例为研究对象,取患者一般资料以及入院时功能独立性量表(Functional Independence Measure,FIM)评分构建SLR、回归树(Regression trees.RT)、集成学习(Ensemble learning,EL)、人工神经网络(Artificial neural network,ANN)、支持向量回归(Support vector regression,SVR)以及高斯过程回归(Gaussian process regression,GPR)预测模型,并采用10折交叉验证,比较各模型实际与预测出院FIM评分以及FIM增益的决定系数(R^(2))、均方根误差(Root Mean Squared Error,RMSE)。结果:机器学习模型(R^(2):RT=0.75,EL=0.78,ANN=0.81,SVR=0.80,GPR=0.81)在预测FIM运动评分方面优于SLR(0.70)。机器学习模型对FIM增益总分的预测准确性(R^(2):RT=0.48,EL=0.51,ANN=0.50,SVR=0.51,GPR=0.54)也优于SLR(0.22)。结论:机器学习模型在预测FIM预后方面优于SLR:仅包含患者一般信息和入院FIM评分的机器学习模型的预测准确性优于既往研究,同时GPR对FIM预后的预测准确性最高。展开更多
Four-wheel independently driven electric vehicles(FWID-EV)endow a flexible and scalable control framework to improve vehicle performance.This paper integrates the torque vectoring and active suspension system(ASS)to e...Four-wheel independently driven electric vehicles(FWID-EV)endow a flexible and scalable control framework to improve vehicle performance.This paper integrates the torque vectoring and active suspension system(ASS)to enhance the vehicle’s longitudinal and vertical motion control performance.While the nonlinear characteristic of the tire model leads to a relatively heavier computational burden.To facilitate the controller design and ease the load,a half-vehicle dynamics system is built and simplified to the linear-time-varying(LTV)model.Then a model predictive controller is developed by formulating the objective function by comprehensively considering the safety,energy-saving and comfort requirements.The in-wheel motor efficiency and the power loss of tire slip are treated as optimization indices in this work to reduce energy consumption.Finally,the effectiveness of the proposed controller is verified through the rapid-control-prototype(RCP)test.The results demonstrate the enhancement of the energy-saving as well as comfort on the basis of vehicle stability.展开更多
基金Supported by Xiao-Ping Chen Foundation for The Development of Science and Technology of Hubei Province,No.CXPJJH122002-061.
文摘BACKGROUND Gallbladder cancer(GBC)is the most common malignant tumor of the biliary system,and is often undetected until advanced stages,making curative surgery unfeasible for many patients.Curative surgery remains the only option for long-term survival.Accurate postsurgical prognosis is crucial for effective treatment planning.tumor-node-metastasis staging,which focuses on tumor infiltration,lymph node metastasis,and distant metastasis,limits the accuracy of prognosis.Nomograms offer a more comprehensive and personalized approach by visually analyzing a broader range of prognostic factors,enhancing the precision of treatment planning for patients with GBC.AIM A retrospective study analyzed the clinical and pathological data of 93 patients who underwent radical surgery for GBC at Peking University People's Hospital from January 2015 to December 2020.Kaplan-Meier analysis was used to calculate the 1-,2-and 3-year survival rates.The log-rank test was used to evaluate factors impacting prognosis,with survival curves plotted for significant variables.Single-factor analysis revealed statistically significant differences,and multivariate Cox regression identified independent prognostic factors.A nomogram was developed and validated with receiver operating characteristic curves and calibration curves.Among 93 patients who underwent radical surgery for GBC,30 patients survived,accounting for 32.26%of the sample,with a median survival time of 38 months.The 1-year,2-year,and 3-year survival rates were 83.87%,68.82%,and 53.57%,respectively.Univariate analysis revealed that carbohydrate antigen 19-9 expre-ssion,T stage,lymph node metastasis,histological differentiation,surgical margins,and invasion of the liver,ex-trahepatic bile duct,nerves,and vessels(P≤0.001)significantly impacted patient prognosis after curative surgery.Multivariate Cox regression identified lymph node metastasis(P=0.03),histological differentiation(P<0.05),nerve invasion(P=0.036),and extrahepatic bile duct invasion(P=0.014)as independent risk factors.A nomogram model with a concordance index of 0.838 was developed.Internal validation confirmed the model's consistency in predicting the 1-year,2-year,and 3-year survival rates.CONCLUSION Lymph node metastasis,tumor differentiation,extrahepatic bile duct invasion,and perineural invasion are independent risk factors.A nomogram based on these factors can be used to personalize and improve treatment strategies.
文摘In this study, potential of Least Square-Support Vector Regression (LS-SVR) approach is utilized to model the daily variation of river flow. Inherent complexity, unavailability of reasonably long data set and heterogeneous catchment response are the couple of issues that hinder the generalization of relationship between previous and forthcoming river flow magnitudes. The problem complexity may get enhanced with the influence of upstream dam releases. These issues are investigated by exploiting the capability of LS-SVR–an approach that considers Structural Risk Minimization (SRM) against the Empirical Risk Minimization (ERM)–used by other learning approaches, such as, Artificial Neural Network (ANN). This study is conducted in upper Narmada river basin in India having Bargi dam in its catchment, constructed in 1989. The river gauging station–Sandia is located few hundred kilometer downstream of Bargi dam. The model development is carried out with pre-construction flow regime and its performance is checked for both pre- and post-construction of the dam for any perceivable difference. It is found that the performances are similar for both the flow regimes, which indicates that the releases from the dam at daily scale for this gauging site may be ignored. In order to investigate the temporal horizon over which the prediction performance may be relied upon, a multistep-ahead prediction is carried out and the model performance is found to be reasonably good up to 5-day-ahead predictions though the performance is decreasing with the increase in lead-time. Skills of both LS-SVR and ANN are reported and it is found that the former performs better than the latter for all the lead-times in general, and shorter lead times in particular.
基金supported by China Important National Science & Technology Specific Projects (No.2011ZX05019-008)National Natural Science Foundation of China (No.40839901)
文摘The main problems in seismic attribute technology are the redundancy of data and the uncertainty of attributes, and these problems become much more serious in multi-wave seismic exploration. Data redundancy will increase the burden on interpreters, occupy large computer memory, take much more computing time, conceal the effective information, and especially cause the ＂curse of dimension＂. Uncertainty of attributes will reduce the accuracy of rebuilding the relationship between attributes and geological significance. In order to solve these problems, we study methods of principal component analysis （PCA）, independent component analysis （ICA） for attribute optimization and support vector machine （SVM） for reservoir prediction. We propose a flow chart of multi-wave seismic attribute process and further apply it to multi-wave seismic reservoir prediction. The processing results of real seismic data demonstrate that reservoir prediction based on combination of PP- and PS-wave attributes, compared with that based on traditional PP-wave attributes, can improve the prediction accuracy.
基金supported in part by National Natural Science Foundation of China(NSFC)under Project No.51737010.
文摘The most critical obstacle for four-wheel independently driven electric vehicles(4WID-EVs)is the driving range.Being the actuators of 4WID-EVs,motors account for its major power consumption.In this sense,by properly distributing torques to minimize the power consumption,the driving range of 4WID-EV can be effectively improved.This paper proposes a model predictive control(MPC)-based torque distribution scheme,which minimizes the power consumption of 4WID-EVs while guaranteeing its tracking performance of planar motions.By incorporating the motor model considering iron losses,the optimal torque distribution can be achieved without an additional torque controller.Also,for this reason,the proposed control scheme is computationally efficient,since the power consumption term to be optimized,which is expressed as the product of the motor voltages and currents,is much simpler than that derived from the efficiency map.With reasonable simplification and linearization,the MPC problem is converted to a quadratic programming problem,which can be solved efficiently.The simulation results in MATLAB and CarSim co-simulation environments demonstrate that the proposed scheme effectively reduces power consumption with guaranteed tracking performance.
文摘Background and Purpose: To investigate target functional independence measure (FIM) items to achieve the prediction goal in terms of the causal relationships between prognostic prediction error and FIM among stroke patients in the convalescent phase using the structural equation modeling (SEM) analysis. Methods: A total of 2992 stroke patients registered in the Japanese Rehabilitation Database were analyzed retrospectively. The prediction error was calculated based on a prognostic prediction formula proposed in a previous study. An exploratory factor analysis (EFA) then the factor was determined using confirmatory factorial analysis (CFA). Finally, multivariate analyses were performed using SEM analysis. Results: The fitted indices of the hypothesized model estimated based on EFA were confirmed by CFA. The factors estimated by EFA were applied, and interpreted as follows: “Transferring (T-factor),” “Dressing (D-factor),” and “Cognitive function (C-factor).” The fit of the structural model based on the three factors and prediction errors was supported by the SEM analysis. The effects of the D- and C-factors yielded similar causal relationships on prediction error. Meanwhile, the effects between the prediction error and the T-factor were low. Observed FIM items were related to their domains in the structural model, except for the dressing of the upper body and memory (p < 0.01). Conclusions: Transfer, which was not heavily considered in the previous prediction formula, was found in causal relationships with prediction error. It is suggested to intervene to transfer together with positive factors to recovery for achieving the prediction goal.
文摘By means of analysing the mechanism of blending materials,a general blending efficiency model was proposed.Applying this general model to an example 9 a suitable formula of blending efficiency which is more accurate than those in papers[2-3]was obtained.Finally,a high-precision optimal combining prediction formula for calculating blending efficiency was proposed.
基金Supported by National Natural Science Foundation of China(Grant Nos.51975118,52025121)Foundation of State Key Laboratory of Automotive Simulation and Control of China(Grant No.20210104)+1 种基金Foundation of State Key Laboratory of Automobile Safety and Energy Saving of China(Grant No.KFZ2201)Special Fund of Jiangsu Province for the Transformation of Scientific and Technological Achievements of China(Grant No.BA2021023).
文摘Four-wheel independently driven electric vehicles(FWID-EV)endow a flexible and scalable control framework to improve vehicle performance.This paper integrates the torque vectoring and active suspension system(ASS)to enhance the vehicle’s longitudinal and vertical motion control performance.While the nonlinear characteristic of the tire model leads to a relatively heavier computational burden.To facilitate the controller design and ease the load,a half-vehicle dynamics system is built and simplified to the linear-time-varying(LTV)model.Then a model predictive controller is developed by formulating the objective function by comprehensively considering the safety,energy-saving and comfort requirements.The in-wheel motor efficiency and the power loss of tire slip are treated as optimization indices in this work to reduce energy consumption.Finally,the effectiveness of the proposed controller is verified through the rapid-control-prototype(RCP)test.The results demonstrate the enhancement of the energy-saving as well as comfort on the basis of vehicle stability.
