明确城市森林环境对正负情绪状态的影响程度及影响因素,为城市森林建设提供科学依据。对Web of Science和CNKI数据库进行检索,对最终纳入的文献采用Meta分析的方法,研究城市森林环境对正负情绪状态的影响。结果表明,纳入的17篇文献中有1...明确城市森林环境对正负情绪状态的影响程度及影响因素,为城市森林建设提供科学依据。对Web of Science和CNKI数据库进行检索,对最终纳入的文献采用Meta分析的方法,研究城市森林环境对正负情绪状态的影响。结果表明,纳入的17篇文献中有13项随机对照试验,21项自身前后对照试验,文献质量一般;进入森林对积极情绪影响的总效应量SMD=0.33,I^(2)=33%,95%置信区间Cl[0.23,0.44];进入森林环境对消极情绪影响总效应量SMD=-0.35,I^(2)=0,95%置信区间Cl[-0H46,-0.25];与城市环境相比,处于城市森林对积极情绪状态总效应量SMD=0.59,I^(2)=42%,95%置信区间Cl[0.44,0.75];对消极情绪的影响总效应量SMD=-0.45,I^(2)=0,95%置信区间Cl[-0.61,-0.30],城市森林对积极情绪影响效果的异质性来源主要为心理健康程度和城市森林自然环境。综上所述,城市森林环境对人的心理健康有积极促进效果,进入森林环境后可降低人的消极情绪,心理健康状态和城市森林自然环境影响城市森林对积极情绪的提升效果。展开更多
In a rechargeable wireless sensor network,utilizing the unmanned aerial vehicle(UAV)as a mobile base station(BS)to charge sensors and collect data effectively prolongs the network’s lifetime.In this paper,we jointly ...In a rechargeable wireless sensor network,utilizing the unmanned aerial vehicle(UAV)as a mobile base station(BS)to charge sensors and collect data effectively prolongs the network’s lifetime.In this paper,we jointly optimize the UAV’s flight trajectory and the sensor selection and operation modes to maximize the average data traffic of all sensors within a wireless sensor network(WSN)during finite UAV’s flight time,while ensuring the energy required for each sensor by wireless power transfer(WPT).We consider a practical scenario,where the UAV has no prior knowledge of sensor locations.The UAV performs autonomous navigation based on the status information obtained within the coverage area,which is modeled as a Markov decision process(MDP).The deep Q-network(DQN)is employed to execute the navigation based on the UAV position,the battery level state,channel conditions and current data traffic of sensors within the UAV’s coverage area.Our simulation results demonstrate that the DQN algorithm significantly improves the network performance in terms of the average data traffic and trajectory design.展开更多
文摘明确城市森林环境对正负情绪状态的影响程度及影响因素,为城市森林建设提供科学依据。对Web of Science和CNKI数据库进行检索,对最终纳入的文献采用Meta分析的方法,研究城市森林环境对正负情绪状态的影响。结果表明,纳入的17篇文献中有13项随机对照试验,21项自身前后对照试验,文献质量一般;进入森林对积极情绪影响的总效应量SMD=0.33,I^(2)=33%,95%置信区间Cl[0.23,0.44];进入森林环境对消极情绪影响总效应量SMD=-0.35,I^(2)=0,95%置信区间Cl[-0H46,-0.25];与城市环境相比,处于城市森林对积极情绪状态总效应量SMD=0.59,I^(2)=42%,95%置信区间Cl[0.44,0.75];对消极情绪的影响总效应量SMD=-0.45,I^(2)=0,95%置信区间Cl[-0.61,-0.30],城市森林对积极情绪影响效果的异质性来源主要为心理健康程度和城市森林自然环境。综上所述,城市森林环境对人的心理健康有积极促进效果,进入森林环境后可降低人的消极情绪,心理健康状态和城市森林自然环境影响城市森林对积极情绪的提升效果。
文摘In a rechargeable wireless sensor network,utilizing the unmanned aerial vehicle(UAV)as a mobile base station(BS)to charge sensors and collect data effectively prolongs the network’s lifetime.In this paper,we jointly optimize the UAV’s flight trajectory and the sensor selection and operation modes to maximize the average data traffic of all sensors within a wireless sensor network(WSN)during finite UAV’s flight time,while ensuring the energy required for each sensor by wireless power transfer(WPT).We consider a practical scenario,where the UAV has no prior knowledge of sensor locations.The UAV performs autonomous navigation based on the status information obtained within the coverage area,which is modeled as a Markov decision process(MDP).The deep Q-network(DQN)is employed to execute the navigation based on the UAV position,the battery level state,channel conditions and current data traffic of sensors within the UAV’s coverage area.Our simulation results demonstrate that the DQN algorithm significantly improves the network performance in terms of the average data traffic and trajectory design.
