检索结果-南通市图书馆

Significant risk factors for intensive care unit-acquired weakness:A processing strategy based on repeated machine learning

引用

World Journal of Clinical Cases 2024年第7期12卷 1235-1242页

作者： ling wang Deng-Yan Long Intensive Care Unit People's Hospital of Qiandongnan Miao and Dong Autonomous PrefectureKaili 556000Guizhou ProvinceChina

BACKGROUND Intensive care unit-acquired weakness(ICU-AW)is a common complication that significantly impacts the patient's recovery process,even leading to adverse outcomes.Currently,there is a lack of effective preventive measures.AIM To identify significant risk factors for ICU-AW through iterative machine learning techniques and offer recommendations for its prevention and treatment.METHODS Patients were categorized into ICU-AW and non-ICU-AW groups on the 14th day post-ICU admission.Relevant data from the initial 14 d of ICU stay,such as age,comorbidities,sedative dosage,vasopressor dosage,duration of mechanical ventilation,length of ICU stay,and rehabilitation therapy,were gathered.The relationships between these variables and ICU-AW were examined.Utilizing iterative machine learning techniques,a multilayer perceptron neural network model was developed,and its predictive performance for ICU-AW was assessed using the receiver operating characteristic curve.RESULTS Within the ICU-AW group,age,duration of mechanical ventilation,lorazepam dosage,adrenaline dosage,and length of ICU stay were significantly higher than in the non-ICU-AW group.Additionally,sepsis,multiple organ dysfunction syndrome,hypoalbuminemia,acute heart failure,respiratory failure,acute kidney injury,anemia,stress-related gastrointestinal bleeding,shock,hypertension,coronary artery disease,malignant tumors,and rehabilitation therapy ratios were significantly higher in the ICU-AW group,demonstrating statistical significance.The most influential factors contributing to ICU-AW were identified as the length of ICU stay(100.0%)and the duration of mechanical ventilation(54.9%).The neural network model predicted ICU-AW with an area under the curve of 0.941,sensitivity of 92.2%,and specificity of 82.7%.CONCLUSION The main factors influencing ICU-AW are the length of ICU stay and the duration of mechanical ventilation.A primary preventive strategy,when feasible,involves minimizing both ICU stay and mechanical ventilation duration.

关键词： Intensive care unit-acquired weakness Risk factors Machine learning Prevention Strategies

来源：

维普期刊数据库评论

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Constrained Multi-Objective Optimization With Deep Reinforcement Learning Assisted Operator Selection

引用

IEEE/CAA Journal of Automatica Sinica 2024年第4期11卷 919-931页

作者： Fei Ming Wenyin Gong ling wang Yaochu Jin the School of Computer Science China University of GeosciencesWuhan 430074China the Department of Automation Tsinghua UniversityBeijing 100084China the Faculty of Technology Bielefeld UniversityNorth Rhine-Westphalia33619 BielefeldGermany

Solving constrained multi-objective optimization problems with evolutionary algorithms has attracted considerable attention.Various constrained multi-objective optimization evolutionary algorithms(CMOEAs)have been developed with the use of different algorithmic strategies,evolutionary operators,and constraint-handling techniques.The performance of CMOEAs may be heavily dependent on the operators used,however,it is usually difficult to select suitable operators for the problem at hand.Hence,improving operator selection is promising and necessary for CMOEAs.This work proposes an online operator selection framework assisted by Deep Reinforcement Learning.The dynamics of the population,including convergence,diversity,and feasibility,are regarded as the state;the candidate operators are considered as actions;and the improvement of the population state is treated as the reward.By using a Q-network to learn a policy to estimate the Q-values of all actions,the proposed approach can adaptively select an operator that maximizes the improvement of the population according to the current state and thereby improve the algorithmic performance.The framework is embedded into four popular CMOEAs and assessed on 42 benchmark problems.The experimental results reveal that the proposed Deep Reinforcement Learning-assisted operator selection significantly improves the performance of these CMOEAs and the resulting algorithm obtains better versatility compared to nine state-of-the-art CMOEAs.

关键词： Constrained multi-objective optimization deep Qlearning deep reinforcement learning(DRL) evolutionary algorithms evolutionary operator selection

来源：

维普期刊数据库

同方期刊数据库评论

在线全文

学校读者我要写书评

暂无评论

Reinforcement Learning-Based Dynamic Order Recommendation for On-Demand Food Delivery

引用

Tsinghua Science and Technology 2024年第2期29卷 356-367页

作者： Xing wang ling wang Chenxin Dong Hao Ren Ke Xing Department of Automation Tsinghua UniversityBeijing 100080China School of Mechanical and Automotive Engineering Qingdao Hengxing University of Science and TechnologyQingdao 266100China Meituan Beijing 100015China

On-demand food delivery(OFD)is gaining more and more popularity in modern society.As a kernel order assignment manner in OFD scenario,order recommendation directly influences the delivery efficiency of the platform and the delivery experience of riders.This paper addresses the dynamism of the order recommendation problem and proposes a reinforcement learning solution method.An actor-critic network based on long short term memory(LSTM)unit is designed to deal with the order-grabbing conflict between different riders.Besides,three rider sequencing rules are accordingly proposed to match different time steps of the LSTM unit with different riders.To test the performance of the proposed method,extensive experiments are conducted based on real data from Meituan delivery platform.The results demonstrate that the proposed reinforcement learning based order recommendation method can significantly increase the number of grabbed orders and reduce the number of order-grabbing conflicts,resulting in better delivery efficiency and experience for the platform and riders.

关键词： on-demand food delivery order recommendation reinforcement learning actor-critic network long short term memory

来源：

维普期刊数据库评论

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Promoting effect of FeOx addition on the mechanochemically prepared vanadium-based catalyst for real PCDD/Fs removal and mechanism insight

引用

Journal of Environmental Sciences 2024年第3期137卷 478-487页

作者： Minghui Tang ling wang Hongxian Li Xinlei Huang Cuicui Du Shengyong Lu State Key Laboratory for Clean Energy Utilization Institute for Thermal Power EngineeringZhejiang UniversityHangzhou 310027China

Industrial-use VOx-based catalysts usually have a higher active temperature window (>250-300℃),which becomes a“bottleneck”for the practical application of PCDD/Fs catalytic degradation technology.In this work,VO_(x)-FeO_(x)/TiO_(2) catalyst prepared via mechanochemically method was investigated for the catalytic removal of PCDD/Fs.The removal efficiency of 1,2-DCBz,pure PCDD/Fs gas generated in the lab,PCDD/Fs from actual fue gas,long-term were studied,and the degradation mechanism was explored using FTIR and TOFMS.The degradation efficiency of 1,2-DCBz and PCDD/Fs on VO_(x)-FeO_(x)/TiO_(2) were higher than that of VO_(x)/TiO_(2)catalyst,and the optimal FeOx addition ratio was 3 wt.%.The characterization results show that the addition of FeOx can effectively improve the pore structure,surface acidity,and VOx dispersion of the catalyst,thus contributing to increasing the V^(5+)content and surface-active oxygen,which is conducive to the improvement of adsorption and redox performance of the catalyst.Under the actual MSWI (municipal solid waste incineration)fue gas,the PCDD/Fs removal efficiency over VTi-3Fe-MC maintained long-term stability,higher than 85%for 240 min.This result was not significantly reduced compared with the data obtained in the laboratory.According to the analysis results of intermediate products by FTIR and GC-TOFMS,it can be inferred that the epoxidation fracture of benzene ring is the rate-limiting step of dioxin catalytic degradation reaction.This work gives an in-depth view into the PCDD/Fs removal over VO_(x)-FeO_(x)/TiO_(2) catalysts and could provide guidelines for the rational design of reliable catalysts for industrial applications.

关键词： PCDD/F removal MSWI fue gas VO_(x)-FeO_(x)/TiO_(2) Mechanochemical GC-TOFMS

来源：

维普期刊数据库

同方期刊数据库评论

在线全文

学校读者我要写书评

暂无评论

A Two-Layer Encoding Learning Swarm Optimizer Based on Frequent Itemsets for Sparse Large-Scale Multi-Objective Optimization

引用

IEEE/CAA Journal of Automatica Sinica 2024年第6期11卷 1342-1357页

作者： Sheng Qi Rui wang Tao Zhang Xu Yang Ruiqing Sun ling wang College of Systems Engineering National University of Defense TechnologyChangsha 410073China Xiangjiang Laboratory Changsha 410205China College of Computer National University of Defense TechnologyChangsha 410073China Department of Automation Tsinghua UniversityBeijing 100084China IEEE

Traditional large-scale multi-objective optimization algorithms(LSMOEAs)encounter difficulties when dealing with sparse large-scale multi-objective optimization problems(SLM-OPs)where most decision variables are zero.As a result,many algorithms use a two-layer encoding approach to optimize binary variable Mask and real variable Dec separately.Nevertheless,existing optimizers often focus on locating non-zero variable posi-tions to optimize the binary variables Mask.However,approxi-mating the sparse distribution of real Pareto optimal solutions does not necessarily mean that the objective function is optimized.In data mining,it is common to mine frequent itemsets appear-ing together in a dataset to reveal the correlation between data.Inspired by this,we propose a novel two-layer encoding learning swarm optimizer based on frequent itemsets(TELSO)to address these SLMOPs.TELSO mined the frequent terms of multiple particles with better target values to find mask combinations that can obtain better objective values for fast convergence.Experi-mental results on five real-world problems and eight benchmark sets demonstrate that TELSO outperforms existing state-of-the-art sparse large-scale multi-objective evolutionary algorithms(SLMOEAs)in terms of performance and convergence speed.

关键词： Evolutionary algorithms learning swarm optimiza-tion sparse large-scale optimization sparse large-scale multi-objec-tive problems two-layer encoding.

来源：

维普期刊数据库

同方期刊数据库博看期刊评论

在线全文

学校读者我要写书评

暂无评论

Learning to Branch in Combinatorial Optimization With Graph Pointer Networks

引用

IEEE/CAA Journal of Automatica Sinica 2024年第1期11卷 157-169页

作者： Rui wang Zhiming Zhou Kaiwen Li Tao Zhang ling wang Xin Xu Xiangke Liao IEEE the Xiangjiang Laboratory and the College of Systems Engineering National University of Defense TechnologyChangsha 410073China the Institute of Automation Chinese Academy of SciencesBeijing 100190China the College of Systems Engineering National University of Defense TechnologyChangsha 410073 the Hunan Key Laboratory of Multi-Energy System Intelligent Interconnection Technology Changsha 410073China the Department of Automation Tsinghua UniversityBeijing 100084China the College of Intelligence Science and Technology National University of Defense TechnologyChangsha 410073China the College of Computer Science and Technology National University of Defense TechnologyChangsha 410073China

Traditional expert-designed branching rules in branch-and-bound(B&B) are static, often failing to adapt to diverse and evolving problem instances. Crafting these rules is labor-intensive, and may not scale well with complex problems.Given the frequent need to solve varied combinatorial optimization problems, leveraging statistical learning to auto-tune B&B algorithms for specific problem classes becomes attractive. This paper proposes a graph pointer network model to learn the branch rules. Graph features, global features and historical features are designated to represent the solver state. The graph neural network processes graph features, while the pointer mechanism assimilates the global and historical features to finally determine the variable on which to branch. The model is trained to imitate the expert strong branching rule by a tailored top-k Kullback-Leibler divergence loss function. Experiments on a series of benchmark problems demonstrate that the proposed approach significantly outperforms the widely used expert-designed branching rules. It also outperforms state-of-the-art machine-learning-based branch-and-bound methods in terms of solving speed and search tree size on all the test instances. In addition, the model can generalize to unseen instances and scale to larger instances.

关键词： Branch-and-bound(B&B) combinatorial optimization deep learning graph neural network imitation learning

来源：

维普期刊数据库

同方期刊数据库评论

在线全文

学校读者我要写书评

暂无评论

Enhancing mechanisms of N-doped biomass carbon on the vanadium-based catalyst for furan degradation at low temperature

引用

Waste Disposal and Sustainable Energy 2024年第1期6卷 1-9页

作者： ling wang Minghui Tang Hongxian Li Jiamin Ding Juan Qiu Shengyong Lu State Key Laboratory of Clean Energy Utilization Zhejiang University38#Zheda RoadHangzhou310027China Taizhou Institute of Zhejiang University Taizhou318012China Qingshanhu Energy Research Center Zhejiang University1699 Dayuan RoadQingshanhu Science and Technology CityHangzhou311300China

Polychlorinated dibenzo-p-dioxins and polychlorinated dibenzofurans(PCDD/Fs)have attracted widespread concern due to their high toxicity,and their difficult manipulation in laboratories has made the research process tough.Thus,in our work,furan is selected as the model compound owing to the same structure of a central oxygenate ring.Although catalytic oxidation is regarded as an effective and applicable method for the abatement of PCDD/Fs,the synthesis of low-temperature catalysts is still a challenging problem in practical applications.Considering this situation,we prepared a novel V_(2)O_(5)/TiO_(2)catalyst modified with N-doped hierarchical porous carbon(NHPC)via a wet impregnation method.The V/T-1%NHPC catalyst could achieve expectant low-temperature performances with 50%furan conversion at 150℃and a complete conversion at 200℃,which decreased 23℃and 40℃compared to the V/T catalyst respectively.Moreover,the addition of NHPC presented lifting chemical stability during long-time test.The addition of NHPC in V/T catalysts decreased the formation of crystalline V_(2)O_(5) and increased the percentages of V^(5+)and O_(lat),which improved the utilization of vanadium ions and the catalytic activity.Simultaneously,the higher binding energy shift of O_(lat) implied more reaction possibility with other oxidise reactants.Importantly,this work proved the lifting catalytic activity by the interaction between catalysts and NHPC,and proposed the promoting effects of the N element.The results showed that the content of the pyridinic N and graphitic N in NHPC changed after combining with V/T catalyst,which played crucial roles in the excellent catalytic performance.Overall,this work provides comprehensive research of the V/T-1%NHPC catalyst toward furan oxidation at low temperature and explain the effects of N-doped biomass carbon in catalytic activity clearly,which gave a new thought to design low-temperature catalysts in PCDD/Fs degradation.Besides,the internal functional mechanisms of N species are worth further exploratio

关键词： Polychlorinated dibenzo-p-dioxins and polychlorinated dibenzofurans Furan Low-temperature catalyst V_(2)O_(5)/TiO_(2)catalyst N-doped

来源：

维普期刊数据库评论

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

A Matching Algorithm with Reinforcement Learning and Decoupling Strategy for Order Dispatching in On-Demand Food Delivery

引用

Tsinghua Science and Technology 2024年第2期29卷 386-399页

作者： Jingfang Chen ling wang Zixiao Pan Yuting Wu Jie Zheng Xuetao Ding Department of Automation Tsinghua UniversityBeijing 100084China Department of Delivery Technology MeituanBeijing 100102China

The on-demand food delivery(OFD)service has gained rapid development in the past decades but meanwhile encounters challenges for further improving operation quality.The order dispatching problem is one of the most concerning issues for the OFD platforms,which refer to dynamically dispatching a large number of orders to riders reasonably in very limited decision time.To solve such a challenging combinatorial optimization problem,an effective matching algorithm is proposed by fusing the reinforcement learning technique and the optimization method.First,to deal with the large-scale complexity,a decoupling method is designed by reducing the matching space between new orders and riders.Second,to overcome the high dynamism and satisfy the stringent requirements on decision time,a reinforcement learning based dispatching heuristic is presented.To be specific,a sequence-to-sequence neural network is constructed based on the problem characteristic to generate an order priority sequence.Besides,a training approach is specially designed to improve learning performance.Furthermore,a greedy heuristic is employed to effectively dispatch new orders according to the order priority sequence.On real-world datasets,numerical experiments are conducted to validate the effectiveness of the proposed algorithm.Statistical results show that the proposed algorithm can effectively solve the problem by improving delivery efficiency and maintaining customer satisfaction.

关键词： order dispatching on-demand delivery reinforcement learning decoupling strategy sequence-to-sequence neural network

来源：

维普期刊数据库评论

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Stabilizing zinc anode using zeolite imidazole framework functionalized separator for durable aqueous zinc-ion batteries

引用

Journal of Energy Chemistry 2024年第3期90卷 23-31,I0003页

作者： Weisong Zhang Xinyan Zhu ling Kang Ziyu Peng Jing Zhu Liang Pan Lei Dai Shude Liu ling wang Yongguang Liu Zhangxing He School of Chemical Engineering North China University of Science and TechnologyTangshan 063009HebeiChina Shanghai Key Laboratory of Multidimensional Information Processing East China Normal UniversityShanghai 201620China College of Textiles Donghua UniversityShanghai 201620China

Aqueous zinc-ion batteries(AZIBs) hold great promise as a viable alternative to lithium-ion batteries owing to their high energy density and environmental friendliness.However,AZIBs are consistently plagued by the formation of zinc dendrites and concurrent side reactions,which significantly diminish their overall service life,In this study,the glass fiber separator(GF) is modified using zeolite imidazole salt framework-8(ZIF-8),enabling the development of efficient AZIBs.ZIF-8,which is abundant in nitrogen content,efficiently regulates the desolvation of [Zn(H_(2)O)_(6)]^(2+) to inhibit hydrogen production.Moreover,it possesses abundant nanochannels that facilitate the uniform deposition of Zn~(2+) via a localized action,thereby hindering the formation of dendrites.The insulating properties of ZIF-8 help prevent Zn^(2+) and water from trapping electron reduction at the layer surface,which reduces corrosion of the zinc anode.Consequently,ZIF-8-GF achieves the even transport of Zn^(2+) and regulates the homogeneous deposition along the Zn(002) crystal surface,thus significantly enhancing the electrochemical performance of the AZIBs,In particular,the Zn|Zn symmetric cell with the ZIF-8-GF separator delivers a stable cycle life at0.5 mA cm^(-2) of 2300 h.The Zn|ZIF-8-GF|MnO_(2) cell exhibits reduced voltage polarization while maintaining a capacity retention rate(93.4%) after 1200 cycles at 1.2 A g^(-1) The unique design of the modified diaphragm provides a new approach to realizing high-performance AZIBs.

关键词： Aqueous zinc-ion batteries Separators modifications ZIF-8 Zn deposition Dendrite-free

来源：

维普期刊数据库

同方期刊数据库评论

在线全文

学校读者我要写书评

暂无评论

Impact of propofol and sevoflurane anesthesia on cognition and emotion in gastric cancer patients undergoing radical resection

引用

World Journal of Gastrointestinal Oncology 2024年第1期16卷 79-89页

作者： Ao-Han Li Su Bu ling wang Ai-Min Liang Hui-Yu Luo Department of Anesthesiology Xiangyang No.1 People's HospitalHubei University of MedicineXiangyang 441000Hubei ProvinceChina Department of Cardiothoracic Surgery Xiangyang No.1 People's HospitalHubei University of MedicineXiangyang 441000Hubei ProvinceChina Department of Rehabilitation Xiangyang No.1 People's HospitalHubei University of MedicineXiangyang 441000Hubei ProvinceChina Department of Internal Medicine-Cardiovascular Xiangyang No.1 People's HospitalHubei University of MedicineXiangyang 441000Hubei ProvinceChina

BACKGROUND Propofol and sevoflurane are commonly used anesthetic agents for maintenance anesthesia during radical resection of gastric cancer.However,there is a debate concerning their differential effects on cognitive function,anxiety,and depression in patients undergoing this procedure.AIM To compare the effects of propofol and sevoflurane anesthesia on postoperative cognitive function,anxiety,depression,and organ function in patients undergoing radical resection of gastric cancer.METHODS A total of 80 patients were involved in this research.The subjects were divided into two groups:Propofol group and sevoflurane group.The evaluation scale for cognitive function was the Loewenstein occupational therapy cognitive assessment(LOTCA),and anxiety and depression were assessed with the aid of the self-rating anxiety scale(SAS)and self-rating depression scale(SDS).Hemodynamic indicators,oxidative stress levels,and pulmonary function were also measured.RESULTS The LOTCA score at 1 d after surgery was significantly lower in the propofol group than in the sevoflurane group.Additionally,the SAS and SDS scores of the sevoflurane group were significantly lower than those of the propofol group.The sevoflurane group showed greater stability in heart rate as well as the mean arterial pressure compared to the propofol group.Moreover,the sevoflurane group displayed better pulmonary function and less lung injury than the propofol group.CONCLUSION Both propofol and sevoflurane could be utilized as maintenance anesthesia during radical resection of gastric cancer.Propofol anesthesia has a minimal effect on patients'pulmonary function,consequently enhancing their postoperative recovery.Sevoflurane anesthesia causes less impairment on patients'cognitive function and mitigates negative emotions,leading to an improved postoperative mental state.Therefore,the selection of anesthetic agents should be based on the individual patient's specific circumstances.

关键词： Propofol Sevoflurane Radical resection of gastric cancer Anesthetic effect Cognitive function Negative emotion

来源：

维普期刊数据库评论

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

欢迎您,

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

在线全文

在线全文

在线全文

在线全文

在线全文

在线全文

在线全文

在线全文

在线全文

在线全文

请选择保存的检索档案：

请选择收藏分类：

通借通还

欢迎您,

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

在线全文

在线全文

在线全文

在线全文

在线全文

在线全文

在线全文

在线全文

在线全文

在线全文

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：