目的分析概念分析在护理领域的研究热点及前沿进展,以期为相关研究提供参考。方法以中国知网、万方、维普网、中国生物医学文献服务系统、Web of Science核心合集为数据来源,通过CiteSpace 6.2.R2软件进行分析。结果共纳入中文文献440篇...目的分析概念分析在护理领域的研究热点及前沿进展,以期为相关研究提供参考。方法以中国知网、万方、维普网、中国生物医学文献服务系统、Web of Science核心合集为数据来源,通过CiteSpace 6.2.R2软件进行分析。结果共纳入中文文献440篇,英文文献5216篇,年发文量总体呈上升趋势,但国内发文较国际仍有较大差距;研究热点主要聚焦于健康教育、护理实践方面;癌症相关概念、积极应对老龄化是重点研究方向;心理健康,提高生活质量将是未来的发展趋势。结论与国际护理领域概念分析相比,国内研究尚属于探索阶段,目前发展较为缓慢。我国可借助国际研究热点及前沿扩充研究思路,加大对该领域的关注,促进护理学科基础概念的发展。展开更多
目的探讨有氧运动联合营养干预对妊娠糖尿病(GDM)患者血糖血脂水平的影响。方法选取2022年1月至2023年1月宜春市妇幼保健院妇产科收治的66例GDM患者作为研究对象,采用随机数字表法将其分为对照组和观察组,各33例。对照组采用常规门诊健...目的探讨有氧运动联合营养干预对妊娠糖尿病(GDM)患者血糖血脂水平的影响。方法选取2022年1月至2023年1月宜春市妇幼保健院妇产科收治的66例GDM患者作为研究对象,采用随机数字表法将其分为对照组和观察组,各33例。对照组采用常规门诊健康教育,观察组采用有氧运动联合营养干预。比较两组的血糖、血脂水平、围生期并发症及不良妊娠结局。结果观察组干预后的空腹血糖(FBG)、餐后2 h血糖(2 h PG)、糖化血红蛋白(HbA1c)水平低于对照组,差异有统计学意义(P<0.05);观察组干预后的血清总胆固醇(TC)、三酰甘油(TG)、低密度脂蛋白(LDL)水平低于对照组,高密度脂蛋白(HDL)水平高于对照组,差异有统计学意义(P<0.05);观察组的围生期并发症总发生率低于对照组,差异有统计学意义(P<0.05);观察组的不良妊娠结局总发生率低于对照组,差异有统计学意义(P<0.05)。结论对GDM患者实施有氧运动联合营养干预效果理想,能够降低GDM患者血糖及血脂水平,减少围生期并发症及不良妊娠结局的发生。展开更多
We introduce Quafu-Qcover,an open-source cloud-based software package developed for solving combinatorial optimization problems using quantum simulators and hardware backends.Quafu-Qcover provides a standardized and c...We introduce Quafu-Qcover,an open-source cloud-based software package developed for solving combinatorial optimization problems using quantum simulators and hardware backends.Quafu-Qcover provides a standardized and comprehensive workflow that utilizes the quantum approximate optimization algorithm(QAOA).It facilitates the automatic conversion of the original problem into a quadratic unconstrained binary optimization(QUBO)model and its corresponding Ising model,which can be subsequently transformed into a weight graph.The core of Qcover relies on a graph decomposition-based classical algorithm,which efficiently derives the optimal parameters for the shallow QAOA circuit.Quafu-Qcover incorporates a dedicated compiler capable of translating QAOA circuits into physical quantum circuits that can be executed on Quafu cloud quantum computers.Compared to a general-purpose compiler,our compiler demonstrates the ability to generate shorter circuit depths,while also exhibiting superior speed performance.Additionally,the Qcover compiler has the capability to dynamically create a library of qubits coupling substructures in real-time,utilizing the most recent calibration data from the superconducting quantum devices.This ensures that computational tasks can be assigned to connected physical qubits with the highest fidelity.The Quafu-Qcover allows us to retrieve quantum computing sampling results using a task ID at any time,enabling asynchronous processing.Moreover,it incorporates modules for results preprocessing and visualization,facilitating an intuitive display of solutions for combinatorial optimization problems.We hope that Quafu-Qcover can serve as an instructive illustration for how to explore application problems on the Quafu cloud quantum computers.展开更多
With the rapid advancement of quantum computing,hybrid quantum–classical machine learning has shown numerous potential applications at the current stage,with expectations of being achievable in the noisy intermediate...With the rapid advancement of quantum computing,hybrid quantum–classical machine learning has shown numerous potential applications at the current stage,with expectations of being achievable in the noisy intermediate-scale quantum(NISQ)era.Quantum reinforcement learning,as an indispensable study,has recently demonstrated its ability to solve standard benchmark environments with formally provable theoretical advantages over classical counterparts.However,despite the progress of quantum processors and the emergence of quantum computing clouds,implementing quantum reinforcement learning algorithms utilizing parameterized quantum circuits(PQCs)on NISQ devices remains infrequent.In this work,we take the first step towards executing benchmark quantum reinforcement problems on real devices equipped with at most 136 qubits on the BAQIS Quafu quantum computing cloud.The experimental results demonstrate that the policy agents can successfully accomplish objectives under modified conditions in both the training and inference phases.Moreover,we design hardware-efficient PQC architectures in the quantum model using a multi-objective evolutionary algorithm and develop a learning algorithm that is adaptable to quantum devices.We hope that the Quafu-RL can be a guiding example to show how to realize machine learning tasks by taking advantage of quantum computers on the quantum cloud platform.展开更多
文摘目的分析概念分析在护理领域的研究热点及前沿进展,以期为相关研究提供参考。方法以中国知网、万方、维普网、中国生物医学文献服务系统、Web of Science核心合集为数据来源,通过CiteSpace 6.2.R2软件进行分析。结果共纳入中文文献440篇,英文文献5216篇,年发文量总体呈上升趋势,但国内发文较国际仍有较大差距;研究热点主要聚焦于健康教育、护理实践方面;癌症相关概念、积极应对老龄化是重点研究方向;心理健康,提高生活质量将是未来的发展趋势。结论与国际护理领域概念分析相比,国内研究尚属于探索阶段,目前发展较为缓慢。我国可借助国际研究热点及前沿扩充研究思路,加大对该领域的关注,促进护理学科基础概念的发展。
文摘目的探讨有氧运动联合营养干预对妊娠糖尿病(GDM)患者血糖血脂水平的影响。方法选取2022年1月至2023年1月宜春市妇幼保健院妇产科收治的66例GDM患者作为研究对象,采用随机数字表法将其分为对照组和观察组,各33例。对照组采用常规门诊健康教育,观察组采用有氧运动联合营养干预。比较两组的血糖、血脂水平、围生期并发症及不良妊娠结局。结果观察组干预后的空腹血糖(FBG)、餐后2 h血糖(2 h PG)、糖化血红蛋白(HbA1c)水平低于对照组,差异有统计学意义(P<0.05);观察组干预后的血清总胆固醇(TC)、三酰甘油(TG)、低密度脂蛋白(LDL)水平低于对照组,高密度脂蛋白(HDL)水平高于对照组,差异有统计学意义(P<0.05);观察组的围生期并发症总发生率低于对照组,差异有统计学意义(P<0.05);观察组的不良妊娠结局总发生率低于对照组,差异有统计学意义(P<0.05)。结论对GDM患者实施有氧运动联合营养干预效果理想,能够降低GDM患者血糖及血脂水平,减少围生期并发症及不良妊娠结局的发生。
基金supported by the National Natural Science Foundation of China(Grant No.92365206)the support of the China Postdoctoral Science Foundation(Certificate Number:2023M740272)+1 种基金supported by the National Natural Science Foundation of China(Grant No.12247168)China Postdoctoral Science Foundation(Certificate Number:2022TQ0036)。
文摘We introduce Quafu-Qcover,an open-source cloud-based software package developed for solving combinatorial optimization problems using quantum simulators and hardware backends.Quafu-Qcover provides a standardized and comprehensive workflow that utilizes the quantum approximate optimization algorithm(QAOA).It facilitates the automatic conversion of the original problem into a quadratic unconstrained binary optimization(QUBO)model and its corresponding Ising model,which can be subsequently transformed into a weight graph.The core of Qcover relies on a graph decomposition-based classical algorithm,which efficiently derives the optimal parameters for the shallow QAOA circuit.Quafu-Qcover incorporates a dedicated compiler capable of translating QAOA circuits into physical quantum circuits that can be executed on Quafu cloud quantum computers.Compared to a general-purpose compiler,our compiler demonstrates the ability to generate shorter circuit depths,while also exhibiting superior speed performance.Additionally,the Qcover compiler has the capability to dynamically create a library of qubits coupling substructures in real-time,utilizing the most recent calibration data from the superconducting quantum devices.This ensures that computational tasks can be assigned to connected physical qubits with the highest fidelity.The Quafu-Qcover allows us to retrieve quantum computing sampling results using a task ID at any time,enabling asynchronous processing.Moreover,it incorporates modules for results preprocessing and visualization,facilitating an intuitive display of solutions for combinatorial optimization problems.We hope that Quafu-Qcover can serve as an instructive illustration for how to explore application problems on the Quafu cloud quantum computers.
基金supported by the Beijing Academy of Quantum Information Sciencessupported by the National Natural Science Foundation of China(Grant No.92365206)+2 种基金the support of the China Postdoctoral Science Foundation(Certificate Number:2023M740272)supported by the National Natural Science Foundation of China(Grant No.12247168)China Postdoctoral Science Foundation(Certificate Number:2022TQ0036)。
文摘With the rapid advancement of quantum computing,hybrid quantum–classical machine learning has shown numerous potential applications at the current stage,with expectations of being achievable in the noisy intermediate-scale quantum(NISQ)era.Quantum reinforcement learning,as an indispensable study,has recently demonstrated its ability to solve standard benchmark environments with formally provable theoretical advantages over classical counterparts.However,despite the progress of quantum processors and the emergence of quantum computing clouds,implementing quantum reinforcement learning algorithms utilizing parameterized quantum circuits(PQCs)on NISQ devices remains infrequent.In this work,we take the first step towards executing benchmark quantum reinforcement problems on real devices equipped with at most 136 qubits on the BAQIS Quafu quantum computing cloud.The experimental results demonstrate that the policy agents can successfully accomplish objectives under modified conditions in both the training and inference phases.Moreover,we design hardware-efficient PQC architectures in the quantum model using a multi-objective evolutionary algorithm and develop a learning algorithm that is adaptable to quantum devices.We hope that the Quafu-RL can be a guiding example to show how to realize machine learning tasks by taking advantage of quantum computers on the quantum cloud platform.
