Improving Interventions on Cardiovascular Disease and Diet: A Policy Incentive Analysis

Various Cardiovascular Diseases (CVDs) can be catastrophic and can lead to irreversible outcomes. Despite improved interventions for CVD prevention awareness, there continues to be discussion and research on diet-related CVD and mortality without addressing the problem. Instead of prioritizing public guidelines and policies, policymakers should understand CVD and address population barriers to adhering to a healthy diet that decreases CVD risk. Therefore, this project aims to analyze federal healthy food incentive policies to promote healthy diet behaviors that reduce CVD risk. The method used was existing data for a comparative policy analysis that included a policy proposal process: phases of progression, measures, and a policy model with data collection and requirements. This analysis compared a current federal food incentive program versus the proposed program. Results of the final analysis derived from the literature review and collected data stated consuming foods from the Mediterranean and other low-fat and low-salt diets reduced CVD risks that also reduced other risks secondary to CVD, such as obesity, diabetes, and Cerebrovascular Accident (CVA). Comparatively, combined healthy food incentives and disincentives were more effective for improving healthy behaviors than, in some cases, even after incentives were removed. Therefore, this policy analysis supports the indication for incentive policy change. However, the lack of federal stakeholders’ response to key policy changes upon proposal submission may require other methods of proposal dissemination. Nonetheless, focusing analysis on various Food Insecurity Nutrition Incentive (FINI) programs instead of one, multi-state program, which may have improved analysis outcomes, was the lesson learned.

SRv6 Policy业务可靠性方案探讨

介绍了SRv6 Policy、SBFD技术及5G承载网络SRv6 Policy业务可靠性方案。针对SBFD误触发问题,提出了在被动端生成BSID、部署引流策略及设置不同检测周期等3种完善措施,这些措施不仅提高了业务的可靠性,还显著提升了网络服务能力.

Improving Federated Learning through Abnormal Client Detection and Incentive

Data sharing and privacy protection are made possible by federated learning,which allows for continuous model parameter sharing between several clients and a central server.Multiple reliable and high-quality clients must participate in practical applications for the federated learning global model to be accurate,but because the clients are independent,the central server cannot fully control their behavior.The central server has no way of knowing the correctness of the model parameters provided by each client in this round,so clients may purposefully or unwittingly submit anomalous data,leading to abnormal behavior,such as becoming malicious attackers or defective clients.To reduce their negative consequences,it is crucial to quickly detect these abnormalities and incentivize them.In this paper,we propose a Federated Learning framework for Detecting and Incentivizing Abnormal Clients(FL-DIAC)to accomplish efficient and security federated learning.We build a detector that introduces an auto-encoder for anomaly detection and use it to perform anomaly identification and prevent the involvement of abnormal clients,in particular for the anomaly client detection problem.Among them,before the model parameters are input to the detector,we propose a Fourier transform-based anomaly data detectionmethod for dimensionality reduction in order to reduce the computational complexity.Additionally,we create a credit scorebased incentive structure to encourage clients to participate in training in order tomake clients actively participate.Three training models(CNN,MLP,and ResNet-18)and three datasets(MNIST,Fashion MNIST,and CIFAR-10)have been used in experiments.According to theoretical analysis and experimental findings,the FL-DIAC is superior to other federated learning schemes of the same type in terms of effectiveness.

An Incentive Mechanism for Federated Learning:A Continuous Zero-Determinant Strategy Approach

As a representative emerging machine learning technique, federated learning(FL) has gained considerable popularity for its special feature of “making data available but not visible”. However, potential problems remain, including privacy breaches, imbalances in payment, and inequitable distribution.These shortcomings let devices reluctantly contribute relevant data to, or even refuse to participate in FL. Therefore, in the application of FL, an important but also challenging issue is to motivate as many participants as possible to provide high-quality data to FL. In this paper, we propose an incentive mechanism for FL based on the continuous zero-determinant(CZD) strategies from the perspective of game theory. We first model the interaction between the server and the devices during the FL process as a continuous iterative game. We then apply the CZD strategies for two players and then multiple players to optimize the social welfare of FL, for which we prove that the server can keep social welfare at a high and stable level. Subsequently, we design an incentive mechanism based on the CZD strategies to attract devices to contribute all of their high-accuracy data to FL.Finally, we perform simulations to demonstrate that our proposed CZD-based incentive mechanism can indeed generate high and stable social welfare in FL.

Policy Gradient Adaptive Dynamic Programming for Model-Free Multi-Objective Optimal Control

Dear Editor,In this letter,the multi-objective optimal control problem of nonlinear discrete-time systems is investigated.A data-driven policy gradient algorithm is proposed in which the action-state value function is used to evaluate the policy.In the policy improvement process,the policy gradient based method is employed.

Accelerating the establishment of a new science-policy panel to address the triple planetary crisis

he triple planetary crisis—climate change,biodiversity loss,and pollution—threatens planetary health.In response to these challenges,the Intergovernmental Panel on Climate Change(IPCC)was established in 1988,followed by the formation of the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services(IPBES)in 2012.Ongoing global initiatives through IPCC and IPBES have significantly advanced scientific understanding,raised public awareness,and informed policy-making in relation to climate change and biodiversity loss.However,pollution remains a pressing concern in all three crises.

Research on Policy Diffusion Path and Reproduction of River Chief System

The River Chief System (RCS) has evolved from local innovative practices to a national water governance strategy to address the current challenges in China’s water environmental management. In contrast to existing research that focuses on the strengths, weaknesses, and improvements of RCS, this study uses literature study to reveal the dynamic evolution of RCS through three phases, with RCS spreading from developed coastal areas to central and western inland regions. RCS’s diffusion path involves vertical diffusion between central and local levels and horizontal diffusion among local governments. Moreover, RCS has also achieved conceptual spillover, gradually expanding into other governance domains, such as the Lake Chief System, the Field Chief System, the Forestry Chief System, and the integration of multiple chief roles. However, it is essential to scrutinize the phenomenon of applying similar governance mechanisms to different areas, as it may result in challenges such as overburdening local governments, insufficient public participation, oversimplification of differences in natural resource endowments, and limited applicability. This study also provides suggestions on how to address these challenges. The study contributes theoretical insights and policy implications, providing a foundation for practical policy innovation.

Inactivated H9N2 vaccines developed with early strains do not protect against recent H9N2 viruses:Call for a change in H9N2 control policy

H9N2 virus has been widely distributed in wild birds and poultry around the world since its first emergence in the United States of America in 1966(Gu et al.2017;Carnaccini and Perez 2020).The virus appeared in chickens in China in the early 1990s,and over the last two decades has gradually become the dominant epidemic subtype(Sun and Liu 2015;Bi et al.2020).Although H9N2 virus infection alone cannot cause severe disease or death in poultry,H9N2 virus-infected birds experience a degree of egg production drop and can be easily infected by other pathogens,thus causing economic losses for poultry industry.

A quantitative study of disruptive technology policy texts:An example of China’s artificial intelligence policy

Purpose:The transformative impact of disruptive technologies on the restructuring of the times has attracted widespread global attention.This study aims to analyze the characteristics and shortcomings of China’s artificial intelligence(AI)disruptive technology policy,and to put forward suggestions for optimizing China’s AI disruptive technology policy.Design/methodology/approach:Develop a three-dimensional analytical framework for“policy tools-policy actors-policy themes”and apply policy tools,social network analysis,and LDA topic model to conduct a comprehensive analysis of the utilization of policy tools,cooperative relationships among policy actors,and the trends in policy theme settings within China’s innovative AI technology policy.Findings:We find that the collaborative relationship among the policy actors of AI disruptive technology in China is insufficiently close.Marginal subjects exhibit low participation in the cooperation network and overly rely on central subjects,forming a“center-periphery”network structure.Policy tool usage is predominantly focused on supply and environmental types,with a severe inadequacy in demand-side policy tool utilization.Policy themes are diverse,encompassing topics such as“Intelligent Services”“Talent Cultivation”“Information Security”and“Technological Innovation”,which will remain focal points.Under the themes of“Intelligent Services”and“Intelligent Governance”,policy tool usage is relatively balanced,with close collaboration among policy entities.However,the theme of“AI Theoretical System”lacks a comprehensive understanding of tool usage and necessitates enhanced cooperation with other policy entities.Research limitations:The data sources and experimental scope are subject to certain limitations,potentially introducing biases and imperfections into the research results,necessitating further validation and refinement.Practical implications:The study introduces a three-dimensional analysis framework for disruptive technology policy texts,which is significant for formulating and enhancing disruptive technology policies.Originality/value:This study utilizes text mining and content analysis techniques to quantitatively analyze disruptive technology policy texts.It systematically evaluates China’s AI policies quantitatively,focusing on policy tools,policy actors,policy themes.The study uncovers the characteristics and deficiencies of current AI policies,offering recommendations for formulating and enhancing disruptive technology policies.

Mapping Climate Services for Disaster Risk Management: A Systematic Review and Research Gaps from a Policy Process Perspective

Climate services (CS) are crucial for mitigating and managing the impacts and risks associated with climate-induced disasters. While evidence over the past decade underscores their effectiveness across various domains, particularly agriculture, to maximize their potential, it is crucial to identify emerging priority areas and existing research gaps for future research agendas. As a contribution to this effort, this paper employs the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology to review the state-of-the-art in the field of climate services for disaster risk management. A comprehensive search across five literature databases combined with a snowball search method using ResearchRabbit was conducted and yielded 242 peer-reviewed articles, book sections, and reports over 2013-2023 after the screening process. The analysis revealed flood, drought, and food insecurity as major climate-related disasters addressed in the reviewed literature. Major climate services addressed included early warning systems, (sub)seasonal forecasts and impact-based warnings. Grounded in the policy processes’ theoretical perspective, the main focus identified and discussed three prevailing policy-oriented priority areas: 1) development of climate services, 2) use-adoption-uptake, and 3) evaluation of climate services. In response to the limitations of the prevalent supply-driven and top-down approach to climate services promotion, co-production emerges as a cross-cutting critical aspect of the identified priority areas. Despite the extensive research in the field, more attention is needed, particularly pronounced in the science-policy interface perspective, which in practice bridges scientific knowledge and policy decisions for effective policy processes. This perspective offers a valuable analytical lens as an entry point for further investigation. Hence, future research agendas would generate insightful evidence by scrutinizing this critical aspect given its importance to institutions and climate services capacity, to better understand intricate facets of the development and the integration of climate services into disaster risk management.

Provincial-level analysis of electrification feasibility and climate policy interactions

Improving electrification feasibility is essential for reducing emissions from non-electric energy sources,thereby enhancing air quality and public health.Concurrently,climate mitigation actions,such as carbon pricing policies,have significant potential to alleviate increasing carbon dioxide(CO_(2))and other co-emitted air pollutants.However,the interactions between climate policy and the improvement of electrification feasibility at the provincial level remain unclear,collectively impacting the net-zero transition of energy-intensive sectors.Here we combine a technologically rich economic-energy-environment model with air quality modeling across China to examine the health,climate,and economic implications of large-scale upgrades in electrification feasibility and climate policies from 2017 to 2030.The results indicate that advancing electrification feasibility,coupled with adopting carbon pricing policies,is likely to facilitate a transition towards electricity-dominant energy systems.Improved electrification feasibility is projected to yield a 7-25%increase in nationwide climate benefits and a 5-14%increase in health benefits by 2030.These incremental benefits,coupled with reduced economic costs,result in a 22-68%increase in net benefits.However,regionally,improvements in electrification feasibility will lead to heightened power demand and unintended emissions from electric energy production in certain provinces(e.g.,Nei Mongol)due to the coal-dominated power system.Additionally,in major coal-producing provinces like Shanxi and Shaanxi,enhanced electrification feasibility exacerbates the negative economic impacts of climate policies.This study provides quantitative insights into how improving electrification feasibility reshapes energy evolution and the benefit-cost profile of climate policy at the provincial level.The findings underscore the necessity of a well-designed compensation scheme between affected and unaffected provinces and coordinated emission mitigation across the power and other end-use sectors.

Carbon Abatement Cost-Sharing Strategy for Electric Power Sector Based on Incentive and Subsidy Mechanisms

The green and low carbon transition and development of the electricity industry is the most crucial task in realizing the“dual-carbon target”,and it is urgent to explore the incentive and subsidy mechanism to promote green electricity consumption and the cost-sharing strategy of carbon reduction,to alleviate the pressure of carbon abatement cost of each subject of the electricity supply chain.Against this background,this paper takes into account the low-carbon subsidies provided by the government and the incentive subsidies for users,and studies the optimal decision-making of each subject in the electricity supply chain,so that each of them can obtain the optimal profit and achieve carbon emission reduction at the same time.Firstly,taking into account the direct power purchase mode of large users and the electricity-selling companies emerging after the reform of the power sales side,we have established a cooperative mechanism for sharing the cost of carbon emission reduction in the electricity supply chain and clarified the relationship between the supply and demand of electricity among the main parties.Subsequently,considering government low-carbon subsidies and user incentive subsidies,the optimal decisionmaking model is established under two scenarios of decentralized and centralized cooperative games in the supply chain,respectively,with the objective of maximizing profits and carbon reduction rates.Solving for the optimal proportion of carbon abatement costs shared by each participant in the electricity supply chain in achieving game equilibrium.Finally,we analyze the role of the government’s low-carbon subsidies,users’incentive subsidies,and other factors on the profit and carbon reduction effect of the electricity industry through the example analysis and further analyze the impact of carbon abatement cost-sharing measures to provide recommendations for the electricity industry to realize low-carbon abatement and make decisions.

Geographical Distribution of Atmospheric Nitrogen Deposition in China and Its Response to Emission Control Policy

Atmospheric nitrogen(N)deposition has experienced significant change because of anthropogenic emissions,thereby exert-ing a pronounced impact on global ecosystem services.With the rapid development of industry and agriculture and the swift expansion of urban areas in China since the 1980s,reactive nitrogen(Nr)emissions and N deposition have substantially increased.In pursuit of im-proving air quality,China has implemented a series of environmental protection policies and undertaken diverse measures to reduce pol-lutant emissions.This paper is a review of multivariate data sources of atmospheric N deposition based on the results of literature from 1980 to 2023,and the original data from 1980 to 2020 are summarized,counted and calculated.The main findings are as follows:1)the annual average atmospheric N deposition ranged from approximately 20-40 kg/(ha·yr),with the variability primarily linked to different assessment methods;2)regional disparities were evident in the spatial distribution of N deposition,with elevated values concentrated in areas with intense Nr emissions;3)atmospheric N deposition significantly declined after 2010,particularly the deposition of oxidized N,while reduced N deposition remained stable.These results reflect the effects of China's serious control policies on nitrogen oxide(NO.)emissions and strengthen the importance of agricultural NH3 emission mitigation.This study contributes to a comprehensive understand-ing of the N dynamics in the emission-deposition process,and provides a scientific foundation for the research of environmental protec-tion,climate change,and sustainable development.

Stackelberg Game-Based Optimal Dispatch for PEDF Park and Power Grid Interaction under Multiple Incentive Mechanisms

The integration of photovoltaic,energy storage,direct current,and flexible load(PEDF)technologies in building power systems is an importantmeans to address the energy crisis and promote the development of green buildings.The friendly interaction between the PEDF systems and the power grid can promote the utilization of renewable energy and enhance the stability of the power grid.For this purpose,this work introduces a framework of multiple incentive mechanisms for a PEDF park,a building energy system that implements PEDF technologies.The incentive mechanisms proposed in this paper include both economic and noneconomic aspects,which is the most significant innovation of this paper.By modeling the relationship between a PEDF park and the power grid into a Stackelberg game,we demonstrate the effectiveness of these incentive measures in promoting the friendly interaction between the two entities.In this game model,the power grid determines on the prices of electricity trading and incentive subsidy,aiming to maximize its revenue while reducing the peak load of the PEDF park.On the other hand,the PEDF park make its dispatch plan according to the prices established by the grid,in order to reduce electricity consumption expense,improve electricity utility,and enhance the penetration rate of renewable energy.The results show that the proposed incentive mechanisms for the PEDF park can help to optimize energy consumption and promote sustainable energy practices.

Perception Enhanced Deep Deterministic Policy Gradient for Autonomous Driving in Complex Scenarios

Autonomous driving has witnessed rapid advancement;however,ensuring safe and efficient driving in intricate scenarios remains a critical challenge.In particular,traffic roundabouts bring a set of challenges to autonomous driving due to the unpredictable entry and exit of vehicles,susceptibility to traffic flow bottlenecks,and imperfect data in perceiving environmental information,rendering them a vital issue in the practical application of autonomous driving.To address the traffic challenges,this work focused on complex roundabouts with multi-lane and proposed a Perception EnhancedDeepDeterministic Policy Gradient(PE-DDPG)for AutonomousDriving in the Roundabouts.Specifically,themodel incorporates an enhanced variational autoencoder featuring an integrated spatial attention mechanism alongside the Deep Deterministic Policy Gradient framework,enhancing the vehicle’s capability to comprehend complex roundabout environments and make decisions.Furthermore,the PE-DDPG model combines a dynamic path optimization strategy for roundabout scenarios,effectively mitigating traffic bottlenecks and augmenting throughput efficiency.Extensive experiments were conducted with the collaborative simulation platform of CARLA and SUMO,and the experimental results show that the proposed PE-DDPG outperforms the baseline methods in terms of the convergence capacity of the training process,the smoothness of driving and the traffic efficiency with diverse traffic flow patterns and penetration rates of autonomous vehicles(AVs).Generally,the proposed PE-DDPGmodel could be employed for autonomous driving in complex scenarios with imperfect data.

Tracking direct and indirect impact on technology and policy of transformative research via ego citation network

Purpose:The disseminating of academic knowledge to nonacademic audiences partly relies on the transition of subsequent citing papers.This study aims to investigate direct and indirect impact on technology and policy originating from transformative research based on ego citation network.Design/methodology/approach:Key Nobel Prize-winning publications(NPs)in fields of gene engineering and astrophysics are regarded as a proxy for transformative research.In this contribution,we introduce a network-structural indicator of citing patents to measure technological impact of a target article and use policy citations as a preliminary tool for policy impact.Findings:The results show that the impact on technology and policy of NPs are higher than that of their subsequent citation generations in gene engineering but not in astrophysics.Research limitations:The selection of Nobel Prizes is not balanced and the database used in this study,Dimensions,suffers from incompleteness and inaccuracy of citation links.Practical implications:Our findings provide useful clues to better understand the characteristics of transformative research in technological and policy impact.Originality/value:This study proposes a new framework to explore the direct and indirect impact on technology and policy originating from transformative research.

Flexible Load Participation in Peaking Shaving and Valley Filling Based on Dynamic Price Incentives

Considering the widening of the peak-valley difference in the power grid and the difficulty of the existing fixed time-of-use electricity price mechanism in meeting the energy demand of heterogeneous users at various moments or motivating users,the design of a reasonable dynamic pricing mechanism to actively engage users in demand response becomes imperative for power grid companies.For this purpose,a power grid-flexible load bilevel model is constructed based on dynamic pricing,where the leader is the dispatching center and the lower-level flexible load acts as the follower.Initially,an upper-level day-ahead dispatching model for the power grid is established,considering the lowest power grid dispatching cost as the objective function and incorporating the power grid-side constraints.Then,the lower level comprehensively considers the load characteristics of industrial load,energy storage,and data centers,and then establishes a lower-level flexible load operation model with the lowest user power-consuming cost as the objective function.Finally,the proposed method is validated using the IEEE-118 system,and the findings indicate that the dynamic pricing mechanism for peaking shaving and valley filling can effectively guide users to respond actively,thereby reducing the peak-valley difference and decreasing users’purchasing costs.

China’s Proactive Fiscal Policy

In contrast to the traditional Western approach to macro-fiscal management,China’s proactive fiscal policy is founded on a people-centered development philosophy and,with distinctive Chinese characteristics,is a significant policy innovation of macroeconomic management in the Chinese modernization.Although there are notable distinctions between the Western“Keynesian”and the“nonKeynesian”schools of thought,both of these approaches’core policy goals and methodological roots are the same,composing the traditional Western macro-fiscal approach.This approach faces increasing real dilemmas.China’s proactive fiscal policy,however,places greater emphasis on future potential growth rates in addition to equilibrium between supply and demand,achieving a fiscal policy transformation with a new approach.In this paper we argue that with such a new approach,China should reconsider the nature and reasonable level of the fiscal deficit,the function and risk assessment criteria of government debt,the scope and effects of reductions in taxes and fees,its approach and focus of demand management,and the costs and resulting efficiencies of policies in order to develop a new fiscal policy paradigm that is more in line with its stated goals.

Review of China's Energy Policy in 2023

In 2023,China's energy policy primarily focused on deepening systemic and institutional reforms,enhancing energy security capabilities,strengthening energy conservation and carbon reduction,and improving the standard system.It has laid the foundation for China's policy direction,which is predicated on ensuring energy security,centered on economic construction,and aimed at achieving the carbon peak and carbon neutrality goals on schedule.In the current key tasks,China has accelerated the construction of a big unified electricity market,vigorously promoted upgrading industries for low-carbon,high-end,and intelligent development,and established carbon markets and standard systems aligned with international practices,achieving substantial progress.

HELLO ASEAN Visa-free policy sparks surge of Chinese tourists to Southeast Asia

As of March 1,Malaysia,Singapore,and Thailand have all implemented visa-free entry policy for Chinese citizens,allowing stays of up to 30 days.The three Southeast Asian countries have been popular destinations for Chinese outbound tourists since the late 1980s and early 1990s.The implementation of the visa-free entry policy is expected to attract more Chinese tourists,especially young people,to travel to Southeast Asia and boost the recovery of the tourism industry in the region.