Incentive-compatible and budget balanced AGV mechanism for peer-to-peer energy trading in smart grids

Peer-to-peer(P2P)energy trading refers to a type of decentralized transaction,where the energy from distributed energy resources is directly traded between peers.A key challenge in peer-to-peer energy trading is designing a safe,efficient,and transparent trading model and operating mechanism.In this study,we consider a P2P trading environment based on blockchain technology,where prosumers can submit bids or offers without knowing the reports of others.We propose an Arrow-d’Aspremont-Gerard-Varet(AGV)-based mechanism to encourage prosumers to submit their real reserve price and determine the P2P transaction price.We demonstrate that the AGV mechanism can achieve Bayesian incentive compatibility and budget balance.Kernel density estimation(KDE)is used to derive the prior distribution from the historical bid/offer information of the agents.Case studies are carried out to analyze and evaluate the proposed mechanism.Simulation results verify the effectiveness of the proposed mechanism in guiding agents to report the true reserve price while maximizing social welfare.Moreover,we discuss the advantages of budget balance for decentralized trading by comparing the Vickrey-Clarke-Groves(VCG)and AGV mechanisms.

An Incentive Mechanism Model for Crowdsensing with Distributed Storage in Smart Cities

Crowdsensing,as a data collection method that uses the mobile sensing ability of many users to help the public collect and extract useful information,has received extensive attention in data collection.Since crowdsensing relies on user equipment to consume resources to obtain information,and the quality and distribution of user equipment are uneven,crowdsensing has problems such as low participation enthusiasm of participants and low quality of collected data,which affects the widespread use of crowdsensing.This paper proposes to apply the blockchain to crowdsensing and solve the above challenges by utilizing the characteristics of the blockchain,such as immutability and openness.An architecture for constructing a crowdsensing incentive mechanism under distributed incentives is proposed.A multi-attribute auction algorithm and a k-nearest neighbor-based sensing data quality determination algorithm are proposed to support the architecture.Participating users upload data,determine data quality according to the algorithm,update user reputation,and realize the selection of perceived data.The process of screening data and updating reputation value is realized by smart contracts,which ensures that the information cannot be tampered with,thereby encouraging more users to participate.Results of the simulation show that using two algorithms can well reflect data quality and screen out malicious data.With the help of blockchain performance,the architecture and algorithm can achieve decentralized storage and tamper-proof information,which helps to motivate more users to participate in perception tasks and improve data quality.

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.

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.

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.

Analysis and Innovative Strategies of Tax Preferential Policies to Promote the Sustainable Development of Small-Scale Individual Businesses

This study conducted an in-depth analysis of the current tax preferential policies for small-scale individual businesses and compared them with similar policies both domestically and internationally,aiming to reveal the advantages and disadvantages of the current system.After examining the impact of these tax preferential policies on the economic status of individual business owners and the broader social economy,this article proposes a set of innovative tax preferential strategies based on theoretical foundations.By developing these innovative strategies and clarifying their implementation paths,the aim is to promote the sustainable and healthy development of small-scale individual businesses,thereby fostering comprehensive socio-economic progress.The conclusion of this study not only summarizes policy recommendations with practical significance but also provides theoretical support for the optimization and innovation of future related systems.

轨道交通TOD推进城市更新的机制保障与实现路径——以东京二子玉川站再开发为例

以公共交通为导向的城市开发(transit-oriented development,TOD)是实现特大城市有效更新的重要路径。针对轨道交通TOD推动城市更新的机制保障及实现路径问题,从制度特征、核心主体及更新规律3个层面总结东京轨道交通TOD推动城市更新发展的特征,分析东京二子玉川站再开发案例。研究结果表明:政府通过赋予以轨道主体为核心的再开发组织法定权利,将规划开发及土地利用的权利下放给市场,可以实现再开发组织自主进行项目设计和项目开发;实施“再开发促进地区”制度,放宽再开发区域内的容积率限制,能够激励轨道主体参与公共空间建设并获得更高的开发强度,推动土地利用价值最大化并实现参与主体的利益平衡。研究成果以期为我国特大城市TOD模式下的城市更新提供经验借鉴。

Lessons from the Real World: Financial Incentives to Improve Glycemic Control in Patients with Type 2 Diabetes

Objectives: While the value of glycemic control to minimize adverse health outcomes among patients with diabetes is clear, achieving hemoglobin A1c (A1c) goals remain a challenge. We evaluated the use of financial incentives to increase enrollment and improve glycemic control among patients invited to participate in a monthly diabetes group appointment (DGA) as part of their enrollment in DaVita HealthCare Partners, a large southern California managed care organization. Methods: Adult diabetes patients (≥18 years) with a currently uncontrolled hemoglobin A1c level (>8.0% if 9.0% if ≥ 65 years) were randomized to 1) no DGA, 2) DGA with no financial incentives (non-incentive DGA) or 3) DGA with financial incentives (incentive DGA). Results: Nine sites among four regions of the greater Los Angeles area participated. Each site offered one non-incentive DGA and one incentive DGA. Over 1500 patients were identified for recruitment and at the peak of enrollment, 299 patients were enrolled in 18 DGAs. On average, hemoglobin A1c values dropped more for patients participating in the incentive DGA (9.9% to 8.7%, -1.2%) versus non-incentive DGA (9.7% to 9.0%, -0.7%) versus no DGA group (9.1% to 8.7%, -0.4%). Several unexpected implementation challenges arose which complicated evaluation but provide important learning lessons. Conclusions: Management of chronic diseases like diabetes is challenging for patients and the primary care system alike. Continuing to implement and evaluate programs under “real-world” conditions can provide further insight into how best to support patients with diabetes and their primary care teams in order to achieve glycemic control and avoid preventable complications.

Effective Incentive and Discipline Mechanisms for Top manage-ment in SOEs

Key to energize State-Owned-Enterprises (hereinafter SOEs) is to set up effective incentive and discipline mechanisms. First of all, the paper analyses the problems existing in the current incentive and discipline mechanism system in SOEs, including low transparency income and considerable covert income, insider control,corporate governance nominalization and so on; next,the paper explores the causes behind these problems,such as incomplete corporate governance and imperfect market mechanism; finally, the paper proposes a series of solutions from the aspects of incentive mechanism and discipline mechanism.

A PEER-TO-PEER INCENTIVE SCHEME FOR OVERCOMING FREE RIDING

Free riding has a great influence on the expandability,robustness and availability of Peer-to-Peer(P2P) network.Controlling free riding has become a hot research issue both in academic and industrial communities.An incentive scheme is proposed to overcoming free riding in P2P network in this paper.According to the behavior and function of nodes,the P2P network is abstracted to be a Distributed and Monitoring-based Hierarchical Structure Mechanism(DMHSM) model.A utility function based on several influencing factors is defined to determine the contribution of peers to the whole system.This paper also introduces reputation and permit mechanism into the scheme to guarantee the Quality of Service(QoS) and to reward or punish peers in the network.Finally,the simulation results verify the effectiveness and feasibility of this model.

An Improved Extreme Learning Machine Prediction Model for Ionospheric Total Electron Content

Earth’s ionosphere is an important medium for navigation,communication,and radio wave transmission.Total Electron Content(TEC)is a descriptive quantify for ionospheric research.However,the traditional empirical model could not fully consider the changes of TEC time series,the prediction accuracy level of TEC data performed not high.In this study,an improved Extreme Learning Machine(ELM)model is proposed for ionospheric TEC prediction.Improvements involved the use of Empirical Mode Decomposition(EMD)and a Fuzzy C-Means(FCM)clustering algorithm to pre-process data used as input to the ELM model.The proposed model fully uses the TEC data characteristics and expected to perform better prediction accuracy.TEC measurements provided by the Centre for Orbit Determination in Europe(CODE)were used to evaluate the performance of the improved ELM model in terms of prediction accuracy,applicable latitude,and the number of required training samples.Experimental results produced a Mean Relative Error(MRE)and a Root Mean Square Error(RMSE)of 8.5%and 1.39 TECU,respectively,outperforming the ELM algorithm(RMSE=2.33 TECU and MRE=17.1%).The improved ELM model exhibited particularly high prediction accuracy in mid-latitude regions,with a mean relative error of 7.6%.This value improved further as the number of available training data increased and when 20-doys data were trained,achieving a mean relative error of 4.9%.These results suggest the proposed model offers higher prediction accuracy than conventional algorithms.

Improvement of Incentive Mechanism on BitTorrent-like Peer-to-Peer File Sharing Systems

BitTorrent is a very popular Peer-to-Peer file sharing system, which adopts a set of incentive mechanisms to encourage contribution and prevent free-riding. However, we find that BitTorrent’s incentive mechanism can prevent free-riding effectively in a system with a relatively low number of seeds, but may fail in producing a disincentive for free-riding in a system with a high number of seeds. The reason is that BitTorrent does not provide effective mechanisms for seeds to guard against free-riding. Therefore, we propose a seed bandwidth allocation strategy for the BitTorrent system to reduce the effect of seeds on free-riding. Our target is that a downloader which provides more service to the system will be granted a higher benefit than downloaders which provide lower service when some downloaders ask for downloading file from a seed. Finally, simulation results are given, which validate the effectiveness of the proposed strategy.

Incentives to Invest in Improving Quality in the Telecommunications Industry

This paper investigates the incentives of invest in improving quality （as opposed to investments in new activities） in the telecommunications industry, based on the example of wireless markets. What is the impact of competition on incentives to invest, and on capacities to invest？ What is the role of the rate of penetration and technical progress？ This paper highlights the fact that investment incentives are positively related to potential for technical progress. Investment incentives also depend on market structure, competition intensity, and penetration rate, but not monotonically. This paper consists of a theoretical part which, under assumptions of full market coverage and market share symmetry, shows that for each national market, there is a target level of investment which companies strive to achieve but had not exceeded, and an empirical part that confirms the findings of the theoretical part and explains the differences with the theoretical part by relaxing the assumptions of full coverage and market share symmetry. This target level on the one hand depends on the potential for technical progress and on the other hand, depends on the rate of penetration. From a social perspective, this target level is the best amount that companies are encouraged to invest. Non-achievement of the target level entails underinvestment and a decrease in consumer surplus and welfare and may slow down technical progress. A data set covering 30 countries over a period of eight years is used to empirically prove the existence of a change in investment behavior depending on whether or not the target level is achieved. A low margin per user may hamper achievement of the target level. As a result, maximum consumer surplus and welfare occur under imperfect competition but not under perfect competition.

Enhancing customers prevention efforts:An incentive feedback mechanism design

To improve the inefficient prevention caused by customers unwillingness to adopt prevention strategies in health management,an incentive feedback mechanism that is based on game theory and contract design theory is introduced.The conditions for making customers and health maintenance organizations(HMOs)willing to participate in the proposed mechanism are given.A dual nonlinear programming model is used to identify the optimal prevention effort of customers and the pricing strategy of HMOs.Results show that to generate increased benefits,HMOs need to consider cost sharing when customers are not familiar with the proposed health services.When health services are gradually accepted,the cost sharing factor can be gradually reduced.Simulation shows that under random circumstances in which the market reaches a certain size,the proposed method exhibits a positive network externality.Motivated by network externality,HMOs only need to make their customers understand that the larger the number of participants,the greater the utility of each person.Such customers may then spontaneously invite others to purchase insurance.

Study on Financial and Taxation Incentives to Promote Innovation and Entrepreneurship in China

Mass entrepreneurship and innovation are the key to the continued economic and social development in China For long,the nation's financial and taxation policies have been playing an active role in promoting innovation,entrepreneurship and overall social and economic growth in China.This paper makes an analysis of the strengths and weaknesses of the current financial and taxation policies,and explores the opportunities and challenges faced by the current financial and taxation policies.To cultivate a sound environment across China for sustainable innovation and entrepreneurship,it calls for a multi-layered financial and taxation incentive mechanism featuring dynamically monitored taxation policies on hi-tech enterprisos,more favorable tax incentives for medium-and small-enterprises.

Do Research Incentives Promote Researchers’Mental Health?

Background:Researchers have a higher risk of anxiety and depression than the general population,so it is important to promote researchers’mental health.Method:Based on the data from 3210 global researchers surveyed by the journal Nature in 2021,confirmatory factor analysis,OLS regression and other regressions were used to explore the research incentive dimensions and their effects on researchers’mental health.Results:(1)Material incentive factors,work-family life balance factors,good organizational environment and spiritual motivation had significant positive effects on researchers’mental health.(2)The spiritual motivation could better promote researchers’mental health than the other factors.(3)Heterogeneity analysis showed that material incentive factors and spiritual motivation created more significant stimulating effects on the mental health of humanities and social sciences researchers.Work-family life balance factors were more effective in promoting the mental health of the mid-career group and the overtime group.Conclusion:Application of the four research incentives resulted in lower likelihood of anxiety or depression among researchers,and special attention should be paid to the role of the spiritual motivation.In order to promote researchers’mental health,different incentives should be applied to different researcher groups to better improve researchers’mental health.

A dynamic incentive and reputation mechanism for energy-efficient federated learning in 6G

As 5G becomes commercial,researchers have turned attention toward the Sixth-Generation(6G)network with the vision of connecting intelligence in a green energy-efficient manner.Federated learning triggers an upsurge of green intelligent services such as resources orchestration of communication infrastructures while preserving privacy and increasing communication efficiency.However,designing effective incentives in federated learning is challenging due to the dynamic available clients and the correlation between clients'contributions during the learning process.In this paper,we propose a dynamic incentive and reputation mechanism to improve energy efficiency and training performance of federated learning.The proposed incentive based on the Stackelberg game can timely adjust optimal energy consumption with changes in available clients during federated learning.Meanwhile,clients’contributions in reputation management are formulated based on the cooperative game to capture the correlation between tasks,which satisfies availability,fairness,and additivity.The simulation results show that the proposed scheme can significantly motivate high-performance clients to participate in federated learning and improve the accuracy and energy efficiency of the federated learning model.

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.