[Objectives]To explore the clinical nursing value of predictive nursing intervention in patients with deep venous thrombosis of lower extremities after cesarean section.[Methods]From December 2022 to April 2023,105 pr...[Objectives]To explore the clinical nursing value of predictive nursing intervention in patients with deep venous thrombosis of lower extremities after cesarean section.[Methods]From December 2022 to April 2023,105 pregnant and lying-in women who were hospitalized in the Gynecology Department of Pingquan Hospital and underwent cesarean section and met the inclusion criteria were included as the study objects.According to the medical records,they were divided into observation group(n=52 cases)and control group(n=53 cases).The clinical experimental subjects were divided into two groups.One group was the control group with routine nursing,and the other group was the observation group with predictive nursing intervention.The number of cases of deep venous thrombosis of lower extremities in the two groups was recorded to evaluate the clinical value.[Results]The incidence of deep venous thrombosis of lower extremities in the two groups after cesarean section was compared,and it was suggested that the incidence of the observation group was lower than that of the control group(P<0.05).[Conclusions]Special predictive nursing intervention can greatly reduce the incidence of deep venous thrombosis of lower extremities after cesarean section,improve nursing satisfaction,and improve clinical efficacy,which is worthy of recommendation.展开更多
Recently,a growing number of scientific applications have been migrated into the cloud.To deal with the problems brought by clouds,more and more researchers start to consider multiple optimization goals in workflow sc...Recently,a growing number of scientific applications have been migrated into the cloud.To deal with the problems brought by clouds,more and more researchers start to consider multiple optimization goals in workflow scheduling.However,the previous works ignore some details,which are challenging but essential.Most existing multi-objective work-flow scheduling algorithms overlook weight selection,which may result in the quality degradation of solutions.Besides,we find that the famous partial critical path(PCP)strategy,which has been widely used to meet the deadline constraint,can not accurately reflect the situation of each time step.Work-flow scheduling is an NP-hard problem,so self-optimizing algorithms are more suitable to solve it.In this paper,the aim is to solve a workflow scheduling problem with a deadline constraint.We design a deadline constrained scientific workflow scheduling algorithm based on multi-objective reinforcement learning(RL)called DCMORL.DCMORL uses the Chebyshev scalarization function to scalarize its Q-values.This method is good at choosing weights for objectives.We propose an improved version of the PCP strategy called MPCP.The sub-deadlines in MPCP regularly update during the scheduling phase,so they can accurately reflect the situation of each time step.The optimization objectives in this paper include minimizing the execution cost and energy consumption within a given deadline.Finally,we use four scientific workflows to compare DCMORL and several representa-tive scheduling algorithms.The results indicate that DCMORL outperforms the above algorithms.As far as we know,it is the first time to apply RL to a deadline constrained workflow scheduling problem.展开更多
Higher-order topological phases give rise to new bulk and boundary physics,as well as new classes of topological phase transitions.While the realization of higher-order topological phases has been confirmed in many pl...Higher-order topological phases give rise to new bulk and boundary physics,as well as new classes of topological phase transitions.While the realization of higher-order topological phases has been confirmed in many platforms by detecting the existence of gapless boundary modes,a direct determination of the higher-order topology and related topological phase transitions through the bulk in experiments has still been lacking.To bridge the gap,in this work we carry out the simulation of a twodimensional second-order topological phase in a superconducting qubit.Owing to the great flexibility and controllability of the quantum simulator,we observe the realization of higher-order topology directly through the measurement of the pseudo-spin texture in momentum space of the bulk for the first time,in sharp contrast to previous experiments based on the detection of gapless boundary modes in real space.Also through the measurement of the evolution of pseudo-spin texture with parameters,we further observe novel topological phase transitions from the second-order topological phase to the trivial phase,as well as to the first-order topological phase with nonzero Chern number.Our work sheds new light on the study of higher-order topological phases and topological phase transitions.展开更多
基金Chengde Science and Technology Plan Self-financing Project(202303A084).
文摘[Objectives]To explore the clinical nursing value of predictive nursing intervention in patients with deep venous thrombosis of lower extremities after cesarean section.[Methods]From December 2022 to April 2023,105 pregnant and lying-in women who were hospitalized in the Gynecology Department of Pingquan Hospital and underwent cesarean section and met the inclusion criteria were included as the study objects.According to the medical records,they were divided into observation group(n=52 cases)and control group(n=53 cases).The clinical experimental subjects were divided into two groups.One group was the control group with routine nursing,and the other group was the observation group with predictive nursing intervention.The number of cases of deep venous thrombosis of lower extremities in the two groups was recorded to evaluate the clinical value.[Results]The incidence of deep venous thrombosis of lower extremities in the two groups after cesarean section was compared,and it was suggested that the incidence of the observation group was lower than that of the control group(P<0.05).[Conclusions]Special predictive nursing intervention can greatly reduce the incidence of deep venous thrombosis of lower extremities after cesarean section,improve nursing satisfaction,and improve clinical efficacy,which is worthy of recommendation.
基金the National Natural Science Foundation of China(Grant No.61672323)the Fundamental Research Funds of Shandong University(2017JC043)+1 种基金the Key Research and Development Program of Shandong Province(2017GGX10122,2017GGX10142,and 2019JZZY010134)the Natural Science Foundation of Shandong Province(ZR2019MF072).
文摘Recently,a growing number of scientific applications have been migrated into the cloud.To deal with the problems brought by clouds,more and more researchers start to consider multiple optimization goals in workflow scheduling.However,the previous works ignore some details,which are challenging but essential.Most existing multi-objective work-flow scheduling algorithms overlook weight selection,which may result in the quality degradation of solutions.Besides,we find that the famous partial critical path(PCP)strategy,which has been widely used to meet the deadline constraint,can not accurately reflect the situation of each time step.Work-flow scheduling is an NP-hard problem,so self-optimizing algorithms are more suitable to solve it.In this paper,the aim is to solve a workflow scheduling problem with a deadline constraint.We design a deadline constrained scientific workflow scheduling algorithm based on multi-objective reinforcement learning(RL)called DCMORL.DCMORL uses the Chebyshev scalarization function to scalarize its Q-values.This method is good at choosing weights for objectives.We propose an improved version of the PCP strategy called MPCP.The sub-deadlines in MPCP regularly update during the scheduling phase,so they can accurately reflect the situation of each time step.The optimization objectives in this paper include minimizing the execution cost and energy consumption within a given deadline.Finally,we use four scientific workflows to compare DCMORL and several representa-tive scheduling algorithms.The results indicate that DCMORL outperforms the above algorithms.As far as we know,it is the first time to apply RL to a deadline constrained workflow scheduling problem.
基金supported by the Key-Area Research and Development Program of Guangdong Province(2018B030326001)the National Natural Science Foundation of China(U1801661 and 11904417)+4 种基金the Guangdong Innovative and Entrepreneurial Research Team Program(2016ZT06D348)the Guangdong Provincial Key Laboratory(2019B121203002)the Natural Science Foundation of Guangdong Province(2017B030308003)the Science,Technology and Innovation Commission of Shenzhen Municipality(JCYJ20170412152620376,and KYTDPT20181011104202253)the NSF of Beijing(Z190012)。
文摘Higher-order topological phases give rise to new bulk and boundary physics,as well as new classes of topological phase transitions.While the realization of higher-order topological phases has been confirmed in many platforms by detecting the existence of gapless boundary modes,a direct determination of the higher-order topology and related topological phase transitions through the bulk in experiments has still been lacking.To bridge the gap,in this work we carry out the simulation of a twodimensional second-order topological phase in a superconducting qubit.Owing to the great flexibility and controllability of the quantum simulator,we observe the realization of higher-order topology directly through the measurement of the pseudo-spin texture in momentum space of the bulk for the first time,in sharp contrast to previous experiments based on the detection of gapless boundary modes in real space.Also through the measurement of the evolution of pseudo-spin texture with parameters,we further observe novel topological phase transitions from the second-order topological phase to the trivial phase,as well as to the first-order topological phase with nonzero Chern number.Our work sheds new light on the study of higher-order topological phases and topological phase transitions.
