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.

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.

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.

Patient attitudes about financial incentives for diabetes self-management:A survey

AIM:To study the acceptability of incentives for behavior changes in individuals with diabetes,comparing financial incentives to self-rewards and non-financial incentives.METHODS:A national online survey of United States adults with diabetes was conducted in March 2013(n = 153).This survey was designed for this study,with iterative testing and modifications in a pilot population.We measured the demographics of individuals,their interest in incentives,as well as the perceived challenge of diabetes self-management tasks,and expectations of incentives to improve diabetes self-management(financial,non-financial and self-rewards).Using an ordered logistic regression model,we assessed the association between a 32-point score of the perceived challenge of the self-management tasks and the three types of rewards.RESULTS:Ninety-six percent of individuals were interested in financial incentives,60% in non-financial incentives and 72% in self-rewards.Patients were less likely to use financial incentives when they perceived the behavior to be more challenging(odds ratio of using financial incentives of 0.82(95%CI:0.72-0.93) for each point of the behavior score).While the effectiveness of incentives may vary according to the perceived level of challenge of each behavior,participants did not expect to need large amounts to motivate them to modify their behavior.The expected average amounts needed to motivate a 5 lb weight loss in our population and to maintain this weight change for a year was $258(interquartile range of $10-100) and $713(interquartile range of $25-250) for a 15 lb weight loss.The difference in mean amount estimates for 5 lb and 15 lb weight loss was significant(P < 0.001).CONCLUSION:Individuals with diabetes are willing to consider financial incentives to improve diabetes selfmanagement.Future studies are needed to explore incentive programs and their effectiveness for diabetes.

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.

The Potential Impact of Incentives on Managed Lane Travel

The use of managed lanes to control and maximize freeway throughput is increasing. One way of encouraging more managed lane use is through the implementation of incentives. In the Dallas-Fort Worth area, a managed lane is being added to the I-30 (Tom Landry freeway) and incentives to maximize the use of this lane were planned. Since the managed lanes were not yet open and the incentives were hypothetical, a stated preference survey was used to gauge the potential impact of the incentives on traveler behavior. The stated preference questions were designed using Db-efficient and random adaptive designs. The incentives were chosen by looking at other programs around the country and through discussion with transportation experts. Once ready, the survey was administered online to travelers in the area and a total of 898 usable responses were gathered. From the responses, a mixed-logit model was developed to describe and predict traveler behavior. From the model, elasticities were calculated to predict the impact of the incentives on mode choice. The model found that incentives with discounts and free trips (a transit fare discount, express bus service to downtown, a free trip for every X number of paid trips, and a discount offered to select businesses) were more effective at encouraging managed lane use. The other incentives (gift card worth $5 for every X number of trips and $5 in credit for every X number of trips taken by transit) had less of an impact.

Measuring the Effectiveness of Safety Incentives in Construction Sites in Korea

The purpose of this study is to investigate the effect of motivation factors that actively induce construction workers participate in safety prevention activities and to investigate the effect of incentive program as motivation factor by conducting questionnaire survey. The survey was conducted for construction workers from September, 2017 to October, 2018, and the number of respondents was 256. The results of this study are that the factor of the greatest role in encouraging workers to voluntarily participate in accident prevention activities is management factors and that the introduction of new management methods such as safety incentives to reduce safety accidents is necessary. These results indicate that the accident prevention facilities and safety education and training have their limitations. Therefore, it is necessary to thoroughly reconsider what has already been done, and to find more innovative methods to reduce construction accidents.

Payments for Ecosystem Services Incentives and Adoption of Land Use Interventions in Uluguru Mountains, Tanzania

The role played by Payments for ecosystem services (PES) in promoting land use interventions is increasingly being recognized as an important instrument for changing land use management worldwide. Despite the increase, adoption of land use interventions promoted by PES and factors influencing it are not well understood. This study was carried out to assess the adoption of land use interventions promoted by PES scheme four years after its implementation in the Uluguru Mountains, Tanzania. The specific objectives of this study were to assess the adoption and factors that influenced it. The study employed questionnaire survey method to collect data from 219 households selected randomly. Focus group discussions and key informant interviews were also conducted to complement information obtained through questionnaire surveys. Descriptive and inferential statistical analyses were employed. Binary logistic regression was used to analyse quantitative data obtained, while content analysis was applied to qualitative data. Results revealed that during the project implementation, 40% of the households did not adopt any of the promoted interventions. Unexpectedly, four years after the project ended, every household sampled had adopted the interventions. Households headed by younger heads and those with land ownership, households which received PES incentives and lived for a long time in the same area and those with more labour force and access to extension services were found to have adopted more interventions (p ≤ 0.05). Thus, the study concludes that socioeconomic characteristics, agricultural extension services and incentives initially provided to farmers are key factors influencing the adoption of land use interventions. Therefore, it is recommended to the government that it should support farmers to get land tenure and to provide them with more incentives to improve their farms through adopting technologies.

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.

Car Sales Service 4s Shop Incentives and Related Competitions

In order to improve the car sales service 4S shop management level,improve the economic and social benefits of enterprises,incentives are used to encourage their own 4s shops.In this paper,throughout the related analysis of the quarterly and annual awards incentive program in large-scale domestic automobile enterprises,entropy method with MATLAB software is applied to establish the dimensional fit function.In the data processing,the multiple linear regression function into Lingo software is employed to get the best solution;and C language is used to run the screening data program through the consistency test program in the sensitivity and error analysis.First of all,consider the company to maximize profits and maximize the amount of complaints in the store to maintain a low target;in terms of staff,we should consider providing more incentives for employees;in the customer,the overall satisfaction,service Attitude,vehicle cleanliness based should be improved.And two programs for the monthly profit and excess profits of progressive incentives should also be improved.

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.

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.

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.

Research on the Relationship between Salary Incentives for R&D Personnel and R&D Investment in China’s Pharmaceutical Industry

Objective To explore the relationship between salary incentives for R&D personnel and R&D investment in China’s pharmaceutical industry through an empirical analysis so as to propose suggestions for increasing more R&D investment from the perspective of stimulating R&D personnel.Methods Based on the relevant data of the China’s pharmaceutical industry in the“China High-tech Industry Statistical Yearbook”and other data from the National Bureau of Statistics from 1995 to 2018,co-integration tests was used to construct error correction models and Granger causality tests to explore the relationship between R&D personnel salary incentives and R&D investment.Results and Conclusion There is a long-term balanced relationship between the salary level of R&D personnel in China’s pharmaceutical industry and the investment of R&D.For the per capita salary of R&D personnel increases by 1 unit,the internal expenditure of R&D investment will increase by 2.2451 units.In the short term,the per capita salary of R&D personnel has a slight negative impact on the internal expenditure of R&D investment.In addition,the salary incentives for R&D personnel have the most significant role in promoting R&D investment after five years of implementation.

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.

Incentives for the Quality and Safety Traceability System of Agricultural Products

The quality and safety traceability system of agricultural products is an important measure to protect the quality and safety of agricultural products. Farmers and food enterprises are main operators of the traceability system. If they are effectively encouraged to practice traceability system,food safety can be guaranteed from the source. This paper studies the incentive problem of agricultural product traceability system from two aspects- vertical contract coordination and government external incentive.