Recent studies have suggested a link between executive function(EF)and obesity.Studies oftenadopt body mass index(BM),which reflects the distribution of subcutaneous fat,as the solemarker of obesity;however,BMI is ina...Recent studies have suggested a link between executive function(EF)and obesity.Studies oftenadopt body mass index(BM),which reflects the distribution of subcutaneous fat,as the solemarker of obesity;however,BMI is inappropriate to distinguish central obesity,which indicatesthe centralized distribution of visceral fat.Visceral fat compared with subcutaneous fat repre-sents greater relative lipid turnover and may increase the risk of cognitive decline in older adults.However,the relationship between EF and central obesity is largely unknown,particularly inyoung adults.Therefore,we used waist circumference(WC)as a marker of central obesity andinvestigated diferent sensitivities between BMI and WC in the brain function.A total of 26healthy young adults(aged 18-25 years;42%female)underwent functional near-infrared spec-troscopy assessments.EF was assessed using the Stroop task,which is a classical measurementof EF.A significant Stroop effect was observed in the behavioral and hemodynamic data.Inaddition,we observed that behavioral interference on the Stroop task varied much more insubjects with higher BMI and WC than those subjects with lower.Elevated BMI and WC wereassociated with a decreased hemodynamic response during the Stroop task specifically in the pre-frontal cortex(PFC).Compared to BMI,WC was more closely connected with inhibitory controland revealed right lateralized PFC activation.Our findings suggest that WC is a reliable indicatorof brain function in young adults and propose a relationship bet ween EF and central obesity.展开更多
Esteemed Deputies,The Ministry of Finance has been entrusted by the State Council to submit this report on the execution of the central and local budgets for 2024 and on the draft central and local budgets for 2025 to...Esteemed Deputies,The Ministry of Finance has been entrusted by the State Council to submit this report on the execution of the central and local budgets for 2024 and on the draft central and local budgets for 2025 to the present Third Session of the 14th National People's Congress(NPC)for your deliberation and for comments from members of the National Committee of the Chinese People's Political Consultative Conference(CPPCC).展开更多
Opportunistic mobile crowdsensing(MCS)non-intrusively exploits human mobility trajectories,and the participants’smart devices as sensors have become promising paradigms for various urban data acquisition tasks.Howeve...Opportunistic mobile crowdsensing(MCS)non-intrusively exploits human mobility trajectories,and the participants’smart devices as sensors have become promising paradigms for various urban data acquisition tasks.However,in practice,opportunistic MCS has several challenges from both the perspectives of MCS participants and the data platform.On the one hand,participants face uncertainties in conducting MCS tasks,including their mobility and implicit interactions among participants,and participants’economic returns given by the MCS data platform are determined by not only their own actions but also other participants’strategic actions.On the other hand,the platform can only observe the participants’uploaded sensing data that depends on the unknown effort/action exerted by participants to the platform,while,for optimizing its overall objective,the platform needs to properly reward certain participants for incentivizing them to provide high-quality data.To address the challenge of balancing individual incentives and platform objectives in MCS,this paper proposes MARCS,an online sensing policy based on multi-agent deep reinforcement learning(MADRL)with centralized training and decentralized execution(CTDE).Specifically,the interactions between MCS participants and the data platform are modeled as a partially observable Markov game,where participants,acting as agents,use DRL-based policies to make decisions based on local observations,such as task trajectories and platform payments.To align individual and platform goals effectively,the platform leverages Shapley value to estimate the contribution of each participant’s sensed data,using these estimates as immediate rewards to guide agent training.The experimental results on real mobility trajectory datasets indicate that the revenue of MARCS reaches almost 35%,53%,and 100%higher than DDPG,Actor-Critic,and model predictive control(MPC)respectively on the participant side and similar results on the platform side,which show superior performance compared to baselines.展开更多
基金supported by the Science Fund for Hubei Superior Discipline Groups of Physical Education and Health Promotion,the project from General Administration of Sport of China(Grant No.2014B094)Hubei Provincial Department of Education Program(Grant No.D20123304).
文摘Recent studies have suggested a link between executive function(EF)and obesity.Studies oftenadopt body mass index(BM),which reflects the distribution of subcutaneous fat,as the solemarker of obesity;however,BMI is inappropriate to distinguish central obesity,which indicatesthe centralized distribution of visceral fat.Visceral fat compared with subcutaneous fat repre-sents greater relative lipid turnover and may increase the risk of cognitive decline in older adults.However,the relationship between EF and central obesity is largely unknown,particularly inyoung adults.Therefore,we used waist circumference(WC)as a marker of central obesity andinvestigated diferent sensitivities between BMI and WC in the brain function.A total of 26healthy young adults(aged 18-25 years;42%female)underwent functional near-infrared spec-troscopy assessments.EF was assessed using the Stroop task,which is a classical measurementof EF.A significant Stroop effect was observed in the behavioral and hemodynamic data.Inaddition,we observed that behavioral interference on the Stroop task varied much more insubjects with higher BMI and WC than those subjects with lower.Elevated BMI and WC wereassociated with a decreased hemodynamic response during the Stroop task specifically in the pre-frontal cortex(PFC).Compared to BMI,WC was more closely connected with inhibitory controland revealed right lateralized PFC activation.Our findings suggest that WC is a reliable indicatorof brain function in young adults and propose a relationship bet ween EF and central obesity.
文摘Esteemed Deputies,The Ministry of Finance has been entrusted by the State Council to submit this report on the execution of the central and local budgets for 2024 and on the draft central and local budgets for 2025 to the present Third Session of the 14th National People's Congress(NPC)for your deliberation and for comments from members of the National Committee of the Chinese People's Political Consultative Conference(CPPCC).
基金sponsored by Qinglan Project of Jiangsu Province,and Jiangsu Provincial Key Research and Development Program(No.BE2020084-1).
文摘Opportunistic mobile crowdsensing(MCS)non-intrusively exploits human mobility trajectories,and the participants’smart devices as sensors have become promising paradigms for various urban data acquisition tasks.However,in practice,opportunistic MCS has several challenges from both the perspectives of MCS participants and the data platform.On the one hand,participants face uncertainties in conducting MCS tasks,including their mobility and implicit interactions among participants,and participants’economic returns given by the MCS data platform are determined by not only their own actions but also other participants’strategic actions.On the other hand,the platform can only observe the participants’uploaded sensing data that depends on the unknown effort/action exerted by participants to the platform,while,for optimizing its overall objective,the platform needs to properly reward certain participants for incentivizing them to provide high-quality data.To address the challenge of balancing individual incentives and platform objectives in MCS,this paper proposes MARCS,an online sensing policy based on multi-agent deep reinforcement learning(MADRL)with centralized training and decentralized execution(CTDE).Specifically,the interactions between MCS participants and the data platform are modeled as a partially observable Markov game,where participants,acting as agents,use DRL-based policies to make decisions based on local observations,such as task trajectories and platform payments.To align individual and platform goals effectively,the platform leverages Shapley value to estimate the contribution of each participant’s sensed data,using these estimates as immediate rewards to guide agent training.The experimental results on real mobility trajectory datasets indicate that the revenue of MARCS reaches almost 35%,53%,and 100%higher than DDPG,Actor-Critic,and model predictive control(MPC)respectively on the participant side and similar results on the platform side,which show superior performance compared to baselines.
