It is common to observe the epidemic risk perception(ERP)and a decline in subjective well-being(SWB)in the context of public health events,such as Corona Virus Disease 2019(COVID-19).However,there have been few studie...It is common to observe the epidemic risk perception(ERP)and a decline in subjective well-being(SWB)in the context of public health events,such as Corona Virus Disease 2019(COVID-19).However,there have been few studies exploring the impact of individuals’ERP within living space on their SWB,especially from a geographical and daily activity perspective after the resumption of work and other activities following a wave of the pandemic.In this paper,we conducted a study with 789 participants in urban China,measuring their ERP within living space and examining its influence on their SWB using path analysis.The results indicated that individuals’ERP within their living space had a significant negative effect on their SWB.The density of certain types of facilities within their living space,such as bus stops,subway stations,restaurants,fast food shops,convenience shops,hospitals,and public toilets,had a significantly negative impact on their SWB,mediated by their ERP within living space.Additionally,participation in out-of-home work and other activities not only increased individuals’ERP within living space,but also strengthened its negative effect on their SWB.展开更多
To meet the communication services with diverse requirements,dynamic resource allocation has shown increasing importance.In this paper,we consider the multi-slot and multi-user resource allocation(MSMU-RA)in a downlin...To meet the communication services with diverse requirements,dynamic resource allocation has shown increasing importance.In this paper,we consider the multi-slot and multi-user resource allocation(MSMU-RA)in a downlink cellular scenario with the aim of maximizing system spectral efficiency while guaranteeing user fairness.We first model the MSMURA problem as a dual-sequence decision-making process,and then solve it by a novel Transformerbased deep reinforcement learning(TDRL)approach.Specifically,the proposed TDRL approach can be achieved based on two aspects:1)To adapt to the dynamic wireless environment,the proximal policy optimization(PPO)algorithm is used to optimize the multi-slot RA strategy.2)To avoid co-channel interference,the Transformer-based PPO algorithm is presented to obtain the optimal multi-user RA scheme by exploring the mapping between user sequence and resource sequence.Experimental results show that:i)the proposed approach outperforms both the traditional and DRL methods in spectral efficiency and user fairness,ii)the proposed algorithm is superior to DRL approaches in terms of convergence speed and generalization performance.展开更多
基金Under the auspices of the National Natural Science Foundation of China(No.42271234,42101246,42101223)Hong Kong Research Grants Council General Research Fund Grant(No.14605920,14611621,14606922)+1 种基金Hong Kong Research Grants Council Collaborative Research Fund Grant(No.C4023-20GF)Hong Kong Research Grants Council Research Matching Grants RMG(No.8601219,8601242)。
文摘It is common to observe the epidemic risk perception(ERP)and a decline in subjective well-being(SWB)in the context of public health events,such as Corona Virus Disease 2019(COVID-19).However,there have been few studies exploring the impact of individuals’ERP within living space on their SWB,especially from a geographical and daily activity perspective after the resumption of work and other activities following a wave of the pandemic.In this paper,we conducted a study with 789 participants in urban China,measuring their ERP within living space and examining its influence on their SWB using path analysis.The results indicated that individuals’ERP within their living space had a significant negative effect on their SWB.The density of certain types of facilities within their living space,such as bus stops,subway stations,restaurants,fast food shops,convenience shops,hospitals,and public toilets,had a significantly negative impact on their SWB,mediated by their ERP within living space.Additionally,participation in out-of-home work and other activities not only increased individuals’ERP within living space,but also strengthened its negative effect on their SWB.
基金supported by the National Natural Science Foundation of China(No.62071354)the Key Research and Development Program of Shaanxi(No.2022ZDLGY05-08)supported by the ISN State Key Laboratory。
文摘To meet the communication services with diverse requirements,dynamic resource allocation has shown increasing importance.In this paper,we consider the multi-slot and multi-user resource allocation(MSMU-RA)in a downlink cellular scenario with the aim of maximizing system spectral efficiency while guaranteeing user fairness.We first model the MSMURA problem as a dual-sequence decision-making process,and then solve it by a novel Transformerbased deep reinforcement learning(TDRL)approach.Specifically,the proposed TDRL approach can be achieved based on two aspects:1)To adapt to the dynamic wireless environment,the proximal policy optimization(PPO)algorithm is used to optimize the multi-slot RA strategy.2)To avoid co-channel interference,the Transformer-based PPO algorithm is presented to obtain the optimal multi-user RA scheme by exploring the mapping between user sequence and resource sequence.Experimental results show that:i)the proposed approach outperforms both the traditional and DRL methods in spectral efficiency and user fairness,ii)the proposed algorithm is superior to DRL approaches in terms of convergence speed and generalization performance.
