探讨危机管理渗透式风险预控急救护理对急性胸痛患者救治效果的影响。方法 本研究为随机对照研究,样本来自2022年1月至2022年12月玉林市红十字会医院收治的急性胸痛病例,共纳入90例患者,被分为常规护理组(常规急救护理)和危机管理组(危...探讨危机管理渗透式风险预控急救护理对急性胸痛患者救治效果的影响。方法 本研究为随机对照研究,样本来自2022年1月至2022年12月玉林市红十字会医院收治的急性胸痛病例,共纳入90例患者,被分为常规护理组(常规急救护理)和危机管理组(危机管理渗透式风险预控急救护理),每组各45例,分组方法为随机数表法。观察指标包括急救相关指标、并发症发生率、急救成功患者的生活质量、患者或其家属对护理工作的护理满意度。结果 与常规护理组82.22%的急救成功率比较,危机管理组95.56%的急救成功率明显较高(P<0.05)。危机管理组急救时间和住院时间短于常规护理组(18.22±5.23 VS 15.12±4.59、19.22±6.53 VS 15.70±4.62,P<0.05);在并发症方面,危机管理组与常规护理组比较无明显差异(P>0.05);危机管理组生活质量各维度得分、护理总满意度均高于常规护理组(P<0.05)。结论 危机管理渗透式风险预控急救护理能够显著提高急性胸痛患者的急救成功率、缩短其急救时间和住院时间,降低并发症发生率,提高患者的生活质量及护理满意度。展开更多
目的研究危机管理渗透式风险预控急救联合良肢位训练在重症脑卒中抢救及后期康复效果。方法①筛选宜兴市人民医院2016年1月-2019年1月间收治的90例临床资料完整的重症脑卒中患者纳为研究对象,按照患者就诊顺序,将其分为观察组(2017年6...目的研究危机管理渗透式风险预控急救联合良肢位训练在重症脑卒中抢救及后期康复效果。方法①筛选宜兴市人民医院2016年1月-2019年1月间收治的90例临床资料完整的重症脑卒中患者纳为研究对象,按照患者就诊顺序,将其分为观察组(2017年6月后就诊,危机管理渗透式风险预控急救护理,n=40)与对照组(2017年6月之前就诊,常规抢救,n=50),比较两组抢救效果。②急救完成后,采用随机数字表法将观察组患者分为第一组(n=20)与第二组(n=20),其中第一组病情稳定后接受良肢位训练,第二组接受常规术后康复训练,连续干预3周后,比较两组干预效果。结果①观察组发病至到院时间、转入专科治疗时间均显著短于对照组(P<0.05),观察组院外气管插管率、院外开通静脉通道率及院外吸氧率均显著高于对照组(P<0.05);观察组抢救过程中危机事件发生率显著低于对照组(P<0.05);两组入院3 d后,急性生理学及慢性健康状况评分系统(acute physiology and chronic health evaluationⅡ,APACHEⅡ)评分均较同组出院时显著下降(P<0.05),格拉斯哥昏迷量表评分(Glasgow Coma Scale,GCS)均较同组入院时显著上升(P<0.05),且观察组入院3 d后,APACHEⅡ评分显著低于对照组,GCS评分显著高于对照组(P<0.05);②康复训练干预3周后,两组运动功能得分均较同组干预前显著上升(P<0.05),且第一组干预3周后,运动功能得分均显著高于第二组(P<0.05);第一组肩关节脱位、患足跖屈内翻、肢体痉挛及异常运动模式发生率均低于第二组,其中两组肩关节脱位及肢体痉挛发生率差异显著(P<0.05)。结论危机管理渗透式风险预控急救护理能有效优化急救护理质量,提高重症脑卒中救治效果,而联合良肢位训练,可有效减少患者肢体痉挛、异常运动模式等并发症,提高患者运动功能。展开更多
A new multi-step adaptive predictive control algorithm for a class of bilinear systems is presented. The structure of the bilinear system is converted into a simple linear model by using nonlinear support vector machi...A new multi-step adaptive predictive control algorithm for a class of bilinear systems is presented. The structure of the bilinear system is converted into a simple linear model by using nonlinear support vector machine (SVM) dynamic approximation with analytical control law derived. The method does not need on-line parameters estimation because the system’s internal model has been transformed into an off-line global model. Compared with other traditional methods, this control law reduces on-line parameter estimating burden. In addition, its overall linear behavior treating method allows an analytical control law available and avoids on-line nonlinear optimization. Simulation results are presented in the article to illustrate the efficiency of the method.展开更多
Tube furnaces are essential and primary energy intensive facilities in petrochemical plants. Operational optimization of furnaces could not only help to improve product quality but also benefit to reduce energy consum...Tube furnaces are essential and primary energy intensive facilities in petrochemical plants. Operational optimization of furnaces could not only help to improve product quality but also benefit to reduce energy consumption and exhaust emission. Inspired by this idea, this paper presents a composite model predictive control(CMPC)strategy, which, taking advantage of distributed model predictive control architectures, combines tracking nonlinear model predictive control and economic nonlinear model predictive control metrics to keep process running smoothly and optimize operational conditions. The controllers connected with two kinds of communication networks are easy to organize and maintain, and stable to process interferences. A fast solution algorithm combining interior point solvers and Newton's method is accommodated to the CMPC realization, with reasonable CPU computing time and suitable online applications. Simulation for industrial case demonstrates that the proposed approach can ensure stable operations of furnaces, improve heat efficiency, and reduce the emission effectively.展开更多
Nonlinear model predictive control(NMPC) is an appealing control technique for improving the performance of batch processes, but its implementation in industry is not always possible due to its heavy on-line computati...Nonlinear model predictive control(NMPC) is an appealing control technique for improving the performance of batch processes, but its implementation in industry is not always possible due to its heavy on-line computation. To facilitate the implementation of NMPC in batch processes, we propose a real-time updated model predictive control method based on state estimation. The method includes two strategies: a multiple model building strategy and a real-time model updated strategy. The multiple model building strategy is to produce a series of sim-plified models to reduce the on-line computational complexity of NMPC. The real-time model updated strategy is to update the simplified models to keep the accuracy of the models describing dynamic process behavior. The me-thod is validated with a typical batch reactor. Simulation studies show that the new method is efficient and robust with respect to model mismatch and changes in process parameters.展开更多
A predictive current control algorithm for the Buck-Boost DC-DC converter is presented in this paper. The continuous time model of the system is properly introduced, then, by imposing a proper PWM modulation pattern, ...A predictive current control algorithm for the Buck-Boost DC-DC converter is presented in this paper. The continuous time model of the system is properly introduced, then, by imposing a proper PWM modulation pattern, its discrete time model is achieved. This last one is successfully employed in determining the steady state locus of the Buck-Boost converter, both in CCM （continuous conduction mode） and DCM （discontinuous conduction mode）. A novel continuous time equivalent circuit of the converter is introduced too, with the aim of determining a ripple free representation of the state variables of the system, over both transient and steady state operation. Then, a predictive current control algorithm, suitable in both CCM and DCM, is developed and properly checked by means of computer simulations. The corresponding results have highlighted the effectiveness of the proposed modelling and of the predictive control algorithm, both in CCM and DCM.展开更多
文摘探讨危机管理渗透式风险预控急救护理对急性胸痛患者救治效果的影响。方法 本研究为随机对照研究,样本来自2022年1月至2022年12月玉林市红十字会医院收治的急性胸痛病例,共纳入90例患者,被分为常规护理组(常规急救护理)和危机管理组(危机管理渗透式风险预控急救护理),每组各45例,分组方法为随机数表法。观察指标包括急救相关指标、并发症发生率、急救成功患者的生活质量、患者或其家属对护理工作的护理满意度。结果 与常规护理组82.22%的急救成功率比较,危机管理组95.56%的急救成功率明显较高(P<0.05)。危机管理组急救时间和住院时间短于常规护理组(18.22±5.23 VS 15.12±4.59、19.22±6.53 VS 15.70±4.62,P<0.05);在并发症方面,危机管理组与常规护理组比较无明显差异(P>0.05);危机管理组生活质量各维度得分、护理总满意度均高于常规护理组(P<0.05)。结论 危机管理渗透式风险预控急救护理能够显著提高急性胸痛患者的急救成功率、缩短其急救时间和住院时间,降低并发症发生率,提高患者的生活质量及护理满意度。
文摘目的研究危机管理渗透式风险预控急救联合良肢位训练在重症脑卒中抢救及后期康复效果。方法①筛选宜兴市人民医院2016年1月-2019年1月间收治的90例临床资料完整的重症脑卒中患者纳为研究对象,按照患者就诊顺序,将其分为观察组(2017年6月后就诊,危机管理渗透式风险预控急救护理,n=40)与对照组(2017年6月之前就诊,常规抢救,n=50),比较两组抢救效果。②急救完成后,采用随机数字表法将观察组患者分为第一组(n=20)与第二组(n=20),其中第一组病情稳定后接受良肢位训练,第二组接受常规术后康复训练,连续干预3周后,比较两组干预效果。结果①观察组发病至到院时间、转入专科治疗时间均显著短于对照组(P<0.05),观察组院外气管插管率、院外开通静脉通道率及院外吸氧率均显著高于对照组(P<0.05);观察组抢救过程中危机事件发生率显著低于对照组(P<0.05);两组入院3 d后,急性生理学及慢性健康状况评分系统(acute physiology and chronic health evaluationⅡ,APACHEⅡ)评分均较同组出院时显著下降(P<0.05),格拉斯哥昏迷量表评分(Glasgow Coma Scale,GCS)均较同组入院时显著上升(P<0.05),且观察组入院3 d后,APACHEⅡ评分显著低于对照组,GCS评分显著高于对照组(P<0.05);②康复训练干预3周后,两组运动功能得分均较同组干预前显著上升(P<0.05),且第一组干预3周后,运动功能得分均显著高于第二组(P<0.05);第一组肩关节脱位、患足跖屈内翻、肢体痉挛及异常运动模式发生率均低于第二组,其中两组肩关节脱位及肢体痉挛发生率差异显著(P<0.05)。结论危机管理渗透式风险预控急救护理能有效优化急救护理质量,提高重症脑卒中救治效果,而联合良肢位训练,可有效减少患者肢体痉挛、异常运动模式等并发症,提高患者运动功能。
基金Project (No. 60421002) supported by the National Natural ScienceFoundation of China
文摘A new multi-step adaptive predictive control algorithm for a class of bilinear systems is presented. The structure of the bilinear system is converted into a simple linear model by using nonlinear support vector machine (SVM) dynamic approximation with analytical control law derived. The method does not need on-line parameters estimation because the system’s internal model has been transformed into an off-line global model. Compared with other traditional methods, this control law reduces on-line parameter estimating burden. In addition, its overall linear behavior treating method allows an analytical control law available and avoids on-line nonlinear optimization. Simulation results are presented in the article to illustrate the efficiency of the method.
文摘Tube furnaces are essential and primary energy intensive facilities in petrochemical plants. Operational optimization of furnaces could not only help to improve product quality but also benefit to reduce energy consumption and exhaust emission. Inspired by this idea, this paper presents a composite model predictive control(CMPC)strategy, which, taking advantage of distributed model predictive control architectures, combines tracking nonlinear model predictive control and economic nonlinear model predictive control metrics to keep process running smoothly and optimize operational conditions. The controllers connected with two kinds of communication networks are easy to organize and maintain, and stable to process interferences. A fast solution algorithm combining interior point solvers and Newton's method is accommodated to the CMPC realization, with reasonable CPU computing time and suitable online applications. Simulation for industrial case demonstrates that the proposed approach can ensure stable operations of furnaces, improve heat efficiency, and reduce the emission effectively.
基金Supported by the National Natural Science Foundation of China(21136003,21176089)the National Science&Technology Support Plan(2012BAK13B02)+2 种基金the National Major Basic Research Program(2014CB744306)the Natural Science Foundation Team Project of Guangdong Province(S2011030001366)the Fundamental Research Funds for Central Universities(2013ZP0010)
文摘Nonlinear model predictive control(NMPC) is an appealing control technique for improving the performance of batch processes, but its implementation in industry is not always possible due to its heavy on-line computation. To facilitate the implementation of NMPC in batch processes, we propose a real-time updated model predictive control method based on state estimation. The method includes two strategies: a multiple model building strategy and a real-time model updated strategy. The multiple model building strategy is to produce a series of sim-plified models to reduce the on-line computational complexity of NMPC. The real-time model updated strategy is to update the simplified models to keep the accuracy of the models describing dynamic process behavior. The me-thod is validated with a typical batch reactor. Simulation studies show that the new method is efficient and robust with respect to model mismatch and changes in process parameters.
文摘A predictive current control algorithm for the Buck-Boost DC-DC converter is presented in this paper. The continuous time model of the system is properly introduced, then, by imposing a proper PWM modulation pattern, its discrete time model is achieved. This last one is successfully employed in determining the steady state locus of the Buck-Boost converter, both in CCM （continuous conduction mode） and DCM （discontinuous conduction mode）. A novel continuous time equivalent circuit of the converter is introduced too, with the aim of determining a ripple free representation of the state variables of the system, over both transient and steady state operation. Then, a predictive current control algorithm, suitable in both CCM and DCM, is developed and properly checked by means of computer simulations. The corresponding results have highlighted the effectiveness of the proposed modelling and of the predictive control algorithm, both in CCM and DCM.
