The Rural Minimum Living Standard Guarantee(Rural Dibao)is an important unconditional cash transfer program to alleviate poverty in rural China.Despite the importance of children’s nutrition in breaking poverty cycle...The Rural Minimum Living Standard Guarantee(Rural Dibao)is an important unconditional cash transfer program to alleviate poverty in rural China.Despite the importance of children’s nutrition in breaking poverty cycles,little is known about the impact of Rural Dibao on child nutrition outcomes.Using China Family Panel Studies(CFPS),this paper examines the effects of Rural Dibao on child nutrition outcomes and investigates potential pathways and heterogeneous effects.We exploit propensity score matching and difference-in-differences techniques to evaluate the effects of the Rural Dibao program on child nutrition outcomes.Our results suggest that Rural Dibao significantly impacts the nutrition outcomes of children up to 15 years of age.Specifically,our results suggest that Rural Dibao improves child height-to-age z-scores by 1.05 standard deviations and lowers the probability of stunting by 11.9 percentage points.Additional analyses suggest that increased protein intake is the main pathway through which Rural Dibao participation contributes to better nutrition outcomes.We also find that the effect of the program is more pronounced among girls,children who are non-left-behind or live with highly educated mothers,and those from low-income families and poor areas.Our findings suggest that Rural Dibao participation helps improve child nutrition outcomes through improving diet quality.展开更多
While autonomous vehicles are vital components of intelligent transportation systems,ensuring the trustworthiness of decision-making remains a substantial challenge in realizing autonomous driving.Therefore,we present...While autonomous vehicles are vital components of intelligent transportation systems,ensuring the trustworthiness of decision-making remains a substantial challenge in realizing autonomous driving.Therefore,we present a novel robust reinforcement learning approach with safety guarantees to attain trustworthy decision-making for autonomous vehicles.The proposed technique ensures decision trustworthiness in terms of policy robustness and collision safety.Specifically,an adversary model is learned online to simulate the worst-case uncertainty by approximating the optimal adversarial perturbations on the observed states and environmental dynamics.In addition,an adversarial robust actor-critic algorithm is developed to enable the agent to learn robust policies against perturbations in observations and dynamics.Moreover,we devise a safety mask to guarantee the collision safety of the autonomous driving agent during both the training and testing processes using an interpretable knowledge model known as the Responsibility-Sensitive Safety Model.Finally,the proposed approach is evaluated through both simulations and experiments.These results indicate that the autonomous driving agent can make trustworthy decisions and drastically reduce the number of collisions through robust safety policies.展开更多
The growing development of the Internet of Things(IoT)is accelerating the emergence and growth of new IoT services and applications,which will result in massive amounts of data being generated,transmitted and pro-cess...The growing development of the Internet of Things(IoT)is accelerating the emergence and growth of new IoT services and applications,which will result in massive amounts of data being generated,transmitted and pro-cessed in wireless communication networks.Mobile Edge Computing(MEC)is a desired paradigm to timely process the data from IoT for value maximization.In MEC,a number of computing-capable devices are deployed at the network edge near data sources to support edge computing,such that the long network transmission delay in cloud computing paradigm could be avoided.Since an edge device might not always have sufficient resources to process the massive amount of data,computation offloading is significantly important considering the coop-eration among edge devices.However,the dynamic traffic characteristics and heterogeneous computing capa-bilities of edge devices challenge the offloading.In addition,different scheduling schemes might provide different computation delays to the offloaded tasks.Thus,offloading in mobile nodes and scheduling in the MEC server are coupled to determine service delay.This paper seeks to guarantee low delay for computation intensive applica-tions by jointly optimizing the offloading and scheduling in such an MEC system.We propose a Delay-Greedy Computation Offloading(DGCO)algorithm to make offloading decisions for new tasks in distributed computing-enabled mobile devices.A Reinforcement Learning-based Parallel Scheduling(RLPS)algorithm is further designed to schedule offloaded tasks in the multi-core MEC server.With an offloading delay broadcast mechanism,the DGCO and RLPS cooperate to achieve the goal of delay-guarantee-ratio maximization.Finally,the simulation results show that our proposal can bound the end-to-end delay of various tasks.Even under slightly heavy task load,the delay-guarantee-ratio given by DGCO-RLPS can still approximate 95%,while that given by benchmarked algorithms is reduced to intolerable value.The simulation results are demonstrated the effective-ness of DGCO-RLPS for delay guarantee in MEC.展开更多
Infrastructure construction by Chinese enterprises in Malaysia began in 1984.In recent years,Chinese enterprises have continuously improved their technical abilities while keeping pricing reasonable and guaranteeing c...Infrastructure construction by Chinese enterprises in Malaysia began in 1984.In recent years,Chinese enterprises have continuously improved their technical abilities while keeping pricing reasonable and guaranteeing completion deadlines when building in Malaysia.Over the years,Malaysia has gradually recognized the professionalism and capability of Chinese enterprises.Since 2013,policy support from Malaysia has accelerated Chinese infrastructure construction there.Bilateral infrastructure cooperation in sectors of transportation,electricity,renewable energy,and the digital economy has helped Malaysia improve its development environment.展开更多
The safety of agricultural industry in Hunan Province shows an upward trend from"basically safe"to"very safe",but the state in the"safe"or"very safe"range is still unstable.In v...The safety of agricultural industry in Hunan Province shows an upward trend from"basically safe"to"very safe",but the state in the"safe"or"very safe"range is still unstable.In view of this,Hunan Province should guarantee the agricultural production ability,cultivate and enhance the core competitiveness of agriculture,firmly grasp the agricultural control power,attach importance to the export quality of agri-cultural products and other aspects to ensure the safety of agricultural industry.展开更多
In the case of major public emergencies, the ability of cities to resist shocks becomes very important and reflects urban resilience. This paper selected the supply guarantee facilities during the city-wide lockdown o...In the case of major public emergencies, the ability of cities to resist shocks becomes very important and reflects urban resilience. This paper selected the supply guarantee facilities during the city-wide lockdown of Shanghai during March-May 2022 due to COVID-19 pandemic as the research object. In this research, the per capita supply guarantee capacity and supply guarantee overlay intensity were analyzed based on the official list of facilities, and the actual role of the supply guarantee facilities was investigated through a public research questionnaire. The thermodynamic diagram map that was generated based on phone visits reflected the actual distribution of the population. Then the level of “supply guarantee resilience” was evaluated and graded for each district of Shanghai. It was concluded that the overall level of supply guarantee resilience was high in the central city, divergence in the near suburban districts, and generally low in the remote suburban districts. Based on this, the paper summarized the problems of supply guarantee revealed in Shanghai during the epidemic lockdown as well as proposed strategies to improve the resilience of similar scenarios in the future. With the help of big data and public research tools, this paper aimed to provide ways and methods to evaluate the resilience of cities under major public emergencies.展开更多
相对科学的分类体系是实现科技文献资源学科特征有效揭示的重要依据。面向国家科技图书文献中心(National Science and Technology Library,NSTL)资源集成与知识服务的总体需求,通过主体及重点学科揭示、多体系融合、多维同位类设置、...相对科学的分类体系是实现科技文献资源学科特征有效揭示的重要依据。面向国家科技图书文献中心(National Science and Technology Library,NSTL)资源集成与知识服务的总体需求,通过主体及重点学科揭示、多体系融合、多维同位类设置、多学科列类、新兴学科类目扩展、综合性类目设置、双重语义编码等方法,编制可基本覆盖NSTL主体资源的NSTL科技文献分类体系,共包含61个基本大类,类目深度为4~5级,类目数达5350个。同时建立与现用核心分类体系的映射关系,支撑资源分类数据规范处理、馆藏目录系统服务等方面的应用。该分类体系可支撑国家重点扶持产业资源保障分析与学科布局评估。展开更多
基金The authors are grateful for support from the National Social Science Fund of China(21AJL015).
文摘The Rural Minimum Living Standard Guarantee(Rural Dibao)is an important unconditional cash transfer program to alleviate poverty in rural China.Despite the importance of children’s nutrition in breaking poverty cycles,little is known about the impact of Rural Dibao on child nutrition outcomes.Using China Family Panel Studies(CFPS),this paper examines the effects of Rural Dibao on child nutrition outcomes and investigates potential pathways and heterogeneous effects.We exploit propensity score matching and difference-in-differences techniques to evaluate the effects of the Rural Dibao program on child nutrition outcomes.Our results suggest that Rural Dibao significantly impacts the nutrition outcomes of children up to 15 years of age.Specifically,our results suggest that Rural Dibao improves child height-to-age z-scores by 1.05 standard deviations and lowers the probability of stunting by 11.9 percentage points.Additional analyses suggest that increased protein intake is the main pathway through which Rural Dibao participation contributes to better nutrition outcomes.We also find that the effect of the program is more pronounced among girls,children who are non-left-behind or live with highly educated mothers,and those from low-income families and poor areas.Our findings suggest that Rural Dibao participation helps improve child nutrition outcomes through improving diet quality.
基金supported in part by the Start-Up Grant-Nanyang Assistant Professorship Grant of Nanyang Technological Universitythe Agency for Science,Technology and Research(A*STAR)under Advanced Manufacturing and Engineering(AME)Young Individual Research under Grant(A2084c0156)+2 种基金the MTC Individual Research Grant(M22K2c0079)the ANR-NRF Joint Grant(NRF2021-NRF-ANR003 HM Science)the Ministry of Education(MOE)under the Tier 2 Grant(MOE-T2EP50222-0002)。
文摘While autonomous vehicles are vital components of intelligent transportation systems,ensuring the trustworthiness of decision-making remains a substantial challenge in realizing autonomous driving.Therefore,we present a novel robust reinforcement learning approach with safety guarantees to attain trustworthy decision-making for autonomous vehicles.The proposed technique ensures decision trustworthiness in terms of policy robustness and collision safety.Specifically,an adversary model is learned online to simulate the worst-case uncertainty by approximating the optimal adversarial perturbations on the observed states and environmental dynamics.In addition,an adversarial robust actor-critic algorithm is developed to enable the agent to learn robust policies against perturbations in observations and dynamics.Moreover,we devise a safety mask to guarantee the collision safety of the autonomous driving agent during both the training and testing processes using an interpretable knowledge model known as the Responsibility-Sensitive Safety Model.Finally,the proposed approach is evaluated through both simulations and experiments.These results indicate that the autonomous driving agent can make trustworthy decisions and drastically reduce the number of collisions through robust safety policies.
基金supported in part by the National Natural Science Foundation of China under Grant 61901128,62273109the Natural Science Foundation of the Jiangsu Higher Education Institutions of China(21KJB510032).
文摘The growing development of the Internet of Things(IoT)is accelerating the emergence and growth of new IoT services and applications,which will result in massive amounts of data being generated,transmitted and pro-cessed in wireless communication networks.Mobile Edge Computing(MEC)is a desired paradigm to timely process the data from IoT for value maximization.In MEC,a number of computing-capable devices are deployed at the network edge near data sources to support edge computing,such that the long network transmission delay in cloud computing paradigm could be avoided.Since an edge device might not always have sufficient resources to process the massive amount of data,computation offloading is significantly important considering the coop-eration among edge devices.However,the dynamic traffic characteristics and heterogeneous computing capa-bilities of edge devices challenge the offloading.In addition,different scheduling schemes might provide different computation delays to the offloaded tasks.Thus,offloading in mobile nodes and scheduling in the MEC server are coupled to determine service delay.This paper seeks to guarantee low delay for computation intensive applica-tions by jointly optimizing the offloading and scheduling in such an MEC system.We propose a Delay-Greedy Computation Offloading(DGCO)algorithm to make offloading decisions for new tasks in distributed computing-enabled mobile devices.A Reinforcement Learning-based Parallel Scheduling(RLPS)algorithm is further designed to schedule offloaded tasks in the multi-core MEC server.With an offloading delay broadcast mechanism,the DGCO and RLPS cooperate to achieve the goal of delay-guarantee-ratio maximization.Finally,the simulation results show that our proposal can bound the end-to-end delay of various tasks.Even under slightly heavy task load,the delay-guarantee-ratio given by DGCO-RLPS can still approximate 95%,while that given by benchmarked algorithms is reduced to intolerable value.The simulation results are demonstrated the effective-ness of DGCO-RLPS for delay guarantee in MEC.
文摘Infrastructure construction by Chinese enterprises in Malaysia began in 1984.In recent years,Chinese enterprises have continuously improved their technical abilities while keeping pricing reasonable and guaranteeing completion deadlines when building in Malaysia.Over the years,Malaysia has gradually recognized the professionalism and capability of Chinese enterprises.Since 2013,policy support from Malaysia has accelerated Chinese infrastructure construction there.Bilateral infrastructure cooperation in sectors of transportation,electricity,renewable energy,and the digital economy has helped Malaysia improve its development environment.
基金Supported by Hunan Provincial Philosophy and Social Science Fund Project"Hunan Agricultural Industry Safety Assessment and Early Warning Research"(22YBA161).
文摘The safety of agricultural industry in Hunan Province shows an upward trend from"basically safe"to"very safe",but the state in the"safe"or"very safe"range is still unstable.In view of this,Hunan Province should guarantee the agricultural production ability,cultivate and enhance the core competitiveness of agriculture,firmly grasp the agricultural control power,attach importance to the export quality of agri-cultural products and other aspects to ensure the safety of agricultural industry.
文摘In the case of major public emergencies, the ability of cities to resist shocks becomes very important and reflects urban resilience. This paper selected the supply guarantee facilities during the city-wide lockdown of Shanghai during March-May 2022 due to COVID-19 pandemic as the research object. In this research, the per capita supply guarantee capacity and supply guarantee overlay intensity were analyzed based on the official list of facilities, and the actual role of the supply guarantee facilities was investigated through a public research questionnaire. The thermodynamic diagram map that was generated based on phone visits reflected the actual distribution of the population. Then the level of “supply guarantee resilience” was evaluated and graded for each district of Shanghai. It was concluded that the overall level of supply guarantee resilience was high in the central city, divergence in the near suburban districts, and generally low in the remote suburban districts. Based on this, the paper summarized the problems of supply guarantee revealed in Shanghai during the epidemic lockdown as well as proposed strategies to improve the resilience of similar scenarios in the future. With the help of big data and public research tools, this paper aimed to provide ways and methods to evaluate the resilience of cities under major public emergencies.
文摘相对科学的分类体系是实现科技文献资源学科特征有效揭示的重要依据。面向国家科技图书文献中心(National Science and Technology Library,NSTL)资源集成与知识服务的总体需求,通过主体及重点学科揭示、多体系融合、多维同位类设置、多学科列类、新兴学科类目扩展、综合性类目设置、双重语义编码等方法,编制可基本覆盖NSTL主体资源的NSTL科技文献分类体系,共包含61个基本大类,类目深度为4~5级,类目数达5350个。同时建立与现用核心分类体系的映射关系,支撑资源分类数据规范处理、馆藏目录系统服务等方面的应用。该分类体系可支撑国家重点扶持产业资源保障分析与学科布局评估。