It is generally believed that intelligent management for sewage treatment plants(STPs) is essential to the sustainable engineering of future smart cities.The core of management lies in the precise prediction of daily ...It is generally believed that intelligent management for sewage treatment plants(STPs) is essential to the sustainable engineering of future smart cities.The core of management lies in the precise prediction of daily volumes of sewage.The generation of sewage is the result of multiple factors from the whole social system.Characterized by strong process abstraction ability,data mining techniques have been viewed as promising prediction methods to realize intelligent STP management.However,existing data mining-based methods for this purpose just focus on a single factor such as an economical or meteorological factor and ignore their collaborative effects.To address this challenge,a deep learning-based intelligent management mechanism for STPs is proposed,to predict business volume.Specifically,the grey relation algorithm(GRA) and gated recursive unit network(GRU) are combined into a prediction model(GRAGRU).The GRA is utilized to select the factors that have a significant impact on the sewage business volume,and the GRU is set up to output the prediction results.We conducted a large number of experiments to verify the efficiency of the proposed GRA-GRU model.展开更多
目的:构建A型主动脉夹层患者围手术期数智平台管理方案并评价其应用效果,以期改善患者结局。方法:课题小组根据文献检索及专家咨询结果,形成A型主动脉夹层患者围手术期数智平台管理方案。采用便利抽样法,选取2022年1月至2024年1月于浙...目的:构建A型主动脉夹层患者围手术期数智平台管理方案并评价其应用效果,以期改善患者结局。方法:课题小组根据文献检索及专家咨询结果,形成A型主动脉夹层患者围手术期数智平台管理方案。采用便利抽样法,选取2022年1月至2024年1月于浙江省某三级甲等医院行A型主动脉夹层手术患者为研究对象。干预组(n=56)于2023年2月至2024年1月采用数智平台方案管理,对照组(n=53)于2022年1月—11月采用常规方案管理。比较两组患者术前准备时间、术后机械通气时长及ICU停留时长,比较两组手术中巡回护士外出次数,并采用有效性、满意度、易用性(Usefulness,Satisfaction and Ease of Use,USE)问卷评估平台可用性。结果 :与对照组相比,干预组患者术前准备时间、术后机械通气时长及ICU停留时长均缩短,手术中巡回护士外出次数减少,差异有统计意义(P<0.001),医护人员关于平台使用的USE问卷得分为(6.34±0.29)分。结论:A型主动脉夹层患者围手术期数智平台管理方案有利于缩短该类患者术前准备时间、术后机械通气时长及ICU停留时长,减少手术中巡回护士外出次数,且该平台可用性良好。展开更多
基金Project(KJZD-M202000801) supported by the Major Project of Chongqing Municipal Education Commission,ChinaProject(2016YFE0205600) supported by the National Key Research&Development Program of China+1 种基金Project(CXQT19023) supported by the Chongqing University Innovation Group Project,ChinaProjects(KFJJ2018069,1853061,1856033) supported by the Key Platform Opening Project of Chongqing Technology and Business University,China。
文摘It is generally believed that intelligent management for sewage treatment plants(STPs) is essential to the sustainable engineering of future smart cities.The core of management lies in the precise prediction of daily volumes of sewage.The generation of sewage is the result of multiple factors from the whole social system.Characterized by strong process abstraction ability,data mining techniques have been viewed as promising prediction methods to realize intelligent STP management.However,existing data mining-based methods for this purpose just focus on a single factor such as an economical or meteorological factor and ignore their collaborative effects.To address this challenge,a deep learning-based intelligent management mechanism for STPs is proposed,to predict business volume.Specifically,the grey relation algorithm(GRA) and gated recursive unit network(GRU) are combined into a prediction model(GRAGRU).The GRA is utilized to select the factors that have a significant impact on the sewage business volume,and the GRU is set up to output the prediction results.We conducted a large number of experiments to verify the efficiency of the proposed GRA-GRU model.
文摘目的:构建A型主动脉夹层患者围手术期数智平台管理方案并评价其应用效果,以期改善患者结局。方法:课题小组根据文献检索及专家咨询结果,形成A型主动脉夹层患者围手术期数智平台管理方案。采用便利抽样法,选取2022年1月至2024年1月于浙江省某三级甲等医院行A型主动脉夹层手术患者为研究对象。干预组(n=56)于2023年2月至2024年1月采用数智平台方案管理,对照组(n=53)于2022年1月—11月采用常规方案管理。比较两组患者术前准备时间、术后机械通气时长及ICU停留时长,比较两组手术中巡回护士外出次数,并采用有效性、满意度、易用性(Usefulness,Satisfaction and Ease of Use,USE)问卷评估平台可用性。结果 :与对照组相比,干预组患者术前准备时间、术后机械通气时长及ICU停留时长均缩短,手术中巡回护士外出次数减少,差异有统计意义(P<0.001),医护人员关于平台使用的USE问卷得分为(6.34±0.29)分。结论:A型主动脉夹层患者围手术期数智平台管理方案有利于缩短该类患者术前准备时间、术后机械通气时长及ICU停留时长,减少手术中巡回护士外出次数,且该平台可用性良好。
