Service-Cost-Sharing Contract Design for a Dual-Channel Supply Chain with Free Riding

This paper considers a dual channel supply chain,where a manufacturer sells a single product through his/her online channel and a traditional retailer,who provides consumers with pre-sale services.The manufacturer's online channel may free-ride the retailer's pre-sale service,which reduces the retailer's desired effort level,and hence may hurt the manufacturer's and the overall supply chain performance.Under both Manufacturer-and Retailer-Stackelberg settings,we study how the manufacturer designs a service-cost-sharing(SCS for brevity)contract to enhance the retailer's service effort level,and how free riding influences two members'optimal decisions.We design an algorithm for determining the two members’optimal decisions under each setting.The three main findings are found:(i)In the Manufacturer-Stackelberg setting,the SCS contract can enhance the retailer's service effort level and eliminate the negative impact incurred by free riding,but can't in the Retailer-Stackelberg setting,(ii)Under the SCS contract,the smaller the fraction of service cost the retailer is requested to share,the more detrimental to the retailer it will be under certain conditions.That is,the phenomenon called"counter-profit cost-sharing"appears,(iii)Both players like to act as a leader if the price competition between the two channels is not relatively very fierce,otherwise they both like to act as a follower.

Research on Alliance Decision of Dual-Channel Remanufacturing Supply Chain Considering Bidirectional Free-Riding and Cost-Sharing

This study delves into the formation dynamics of alliances within a closed-loop supply chain(CLSC)that encom-passes a manufacturer,a retailer,and an e-commerce platform.It leverages Stackelberg game for this exploration,contrasting the equilibrium outcomes of a non-alliance model with those of three differentiated alliance models.The non-alliance model acts as a crucial benchmark,enabling the evaluation of the motivations for various supply chain entities to engage in alliance formations.Our analysis is centered on identifying the most effective alliance strategies and establishing a coordination within these partnerships.We thoroughly investigate the consequences of diverse alliance behaviors,bidirectional free-riding and cost-sharing,and the resultant effects on the optimal decision-making among supply chain actors.The findings underscore several pivotal insights:(1)The behavior of alliances within the supply chain exerts variable impacts on the optimal pricing and demand of its members.In comparison to the non-alliance(D)model,the manufacturer-retailer(MR)and manufacturer-e-commerce platform(ME)alliances significantly lower both offline and online resale prices for new and remanufactured goods.This adjustment leads to an enhanced demand for products via the MR alliance’s offline outlets and the ME alliance’s online platforms,thereby augmenting the profits for those within the alliance.Conversely,retailer-e-commerce platform(ER)alliance tends to increase the optimal retail price and demand across both online and offline channels.Under specific conditions,alliance behavior can also increase the profits of non-alliance members,and the profits derived through alliance channels also exceed those from non-alliance channels.(2)The prevalence of bidirectional free-riding behavior largely remains constant across different alliance configurations.Across these models,bidirectional free-riding typically elevates the equilibrium prices in offline channel while negatively affecting the equilibrium prices in other channel.(3)The effect of cost-sharing shows relative uniformity across the various alliance models.Across all configurations,cost-sharing tends to reduce the manufacturer’s profits.Nonetheless,alliances initiated by the manufacturer can counteract these negative impacts,providing a strategic pathway to bolster CLSC profitability.

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.

Study of the Mode of Cost-sharing by Indi-viduals for Higher Education in China

In recent years, financial crises of higher education have become universal. The policy of cost-sharing by in- dividuals was tried out in many countries. However, empirical studies have shown that the policy has, to a certain de- gree, negative effects for the poor in accepting higher education costs. This paper points out it is the mismatch between tuition fees and the ability to pay that causes the problem. Using two dimensions of group and time to pay, a mode sys- tem, including seven modes for the individual to share the cost of higher education, was constructed. It is suggested that only three modes of pay-immediately have been carried out in China and they all have same shortcomings. To try new modes and exercise different modes synthetically will be meaningful for solving the questions that exist in the current policy.

Contract Mechanism of Water Environment Regulation for Small and Medium Sized Enterprises Based on Optimal Control Theory

The small and scattered enterprise pattern in the county economy has formed numerous sporadic pollution sources, hindering the centralized treatment of the water environment, increasing the cost and difficulty of treatment. How enterprises can make reasonable decisions on their water environment behavior based on the external environment and their own factors is of great significance for scientifically and effectively designing water environment regulation mechanisms. Based on optimal control theory, this study investigates the design of contractual mechanisms for water environmental regulation for small and medium-sized enterprises. The enterprise is regarded as an independent economic entity that can adopt optimal control strategies to maximize its own interests. Based on the participation of multiple subjects including the government, enterprises, and the public, an optimal control strategy model for enterprises under contractual water environmental regulation is constructed using optimal control theory, and a method for calculating the amount of unit pollutant penalties is derived. The water pollutant treatment cost data of a paper company is selected to conduct empirical numerical analysis on the model. The results show that the increase in the probability of government regulation and public participation, as well as the decrease in local government protection for enterprises, can achieve the same regulatory effect while reducing the number of administrative penalties per unit. Finally, the implementation process of contractual water environmental regulation for small and medium-sized enterprises is designed.

Bipartite consensus problems of Lurie multi-agent systems over signed graphs: A contraction approach

This paper examines the bipartite consensus problems for the nonlinear multi-agent systems in Lurie dynamics form with cooperative and competitive communication between different agents. Based on the contraction theory, some new conditions for the nonlinear Lurie multi-agent systems reaching bipartite leaderless consensus and bipartite tracking consensus are presented. Compared with the traditional methods, this approach degrades the dimensions of the conditions, eliminates some restrictions of the system matrix, and extends the range of the nonlinear function. Finally, two numerical examples are provided to illustrate the efficiency of our results.

Smart Contract Vulnerability Detection Method Based on Feature Graph and Multiple Attention Mechanisms

The fast-paced development of blockchain technology is evident.Yet,the security concerns of smart contracts represent a significant challenge to the stability and dependability of the entire blockchain ecosystem.Conventional smart contract vulnerability detection primarily relies on static analysis tools,which are less efficient and accurate.Although deep learning methods have improved detection efficiency,they are unable to fully utilize the static relationships within contracts.Therefore,we have adopted the advantages of the above two methods,combining feature extraction mode of tools with deep learning techniques.Firstly,we have constructed corresponding feature extraction mode for different vulnerabilities,which are used to extract feature graphs from the source code of smart contracts.Then,the node features in feature graphs are fed into a graph convolutional neural network for training,and the edge features are processed using a method that combines attentionmechanismwith gated units.Ultimately,the revised node features and edge features are concatenated through amulti-head attentionmechanism.The result of the splicing is a global representation of the entire feature graph.Our method was tested on three types of data:Timestamp vulnerabilities,reentrancy vulnerabilities,and access control vulnerabilities,where the F1 score of our method reaches 84.63%,92.55%,and 61.36%.The results indicate that our method surpasses most others in detecting smart contract vulnerabilities.

A Review of Deep Learning-Based Vulnerability Detection Tools for Ethernet Smart Contracts

In recent years,the number of smart contracts deployed on blockchain has exploded.However,the issue of vulnerability has caused incalculable losses.Due to the irreversible and immutability of smart contracts,vulnerability detection has become particularly important.With the popular use of neural network model,there has been a growing utilization of deep learning-based methods and tools for the identification of vulnerabilities within smart contracts.This paper commences by providing a succinct overview of prevalent categories of vulnerabilities found in smart contracts.Subsequently,it categorizes and presents an overview of contemporary deep learning-based tools developed for smart contract detection.These tools are categorized based on their open-source status,the data format and the type of feature extraction they employ.Then we conduct a comprehensive comparative analysis of these tools,selecting representative tools for experimental validation and comparing them with traditional tools in terms of detection coverage and accuracy.Finally,Based on the insights gained from the experimental results and the current state of research in the field of smart contract vulnerability detection tools,we suppose to provide a reference standard for developers of contract vulnerability detection tools.Meanwhile,forward-looking research directions are also proposed for deep learning-based smart contract vulnerability detection.

Preserving Data Secrecy and Integrity for Cloud Storage Using Smart Contracts and Cryptographic Primitives

Cloud computing has emerged as a viable alternative to traditional computing infrastructures,offering various benefits.However,the adoption of cloud storage poses significant risks to data secrecy and integrity.This article presents an effective mechanism to preserve the secrecy and integrity of data stored on the public cloud by leveraging blockchain technology,smart contracts,and cryptographic primitives.The proposed approach utilizes a Solidity-based smart contract as an auditor for maintaining and verifying the integrity of outsourced data.To preserve data secrecy,symmetric encryption systems are employed to encrypt user data before outsourcing it.An extensive performance analysis is conducted to illustrate the efficiency of the proposed mechanism.Additionally,a rigorous assessment is conducted to ensure that the developed smart contract is free from vulnerabilities and to measure its associated running costs.The security analysis of the proposed system confirms that our approach can securely maintain the confidentiality and integrity of cloud storage,even in the presence of malicious entities.The proposed mechanism contributes to enhancing data security in cloud computing environments and can be used as a foundation for developing more secure cloud storage systems.

A Systematic Review and Performance Evaluation of Open-Source Tools for Smart Contract Vulnerability Detection

With the rise of blockchain technology,the security issues of smart contracts have become increasingly critical.Despite the availability of numerous smart contract vulnerability detection tools,many face challenges such as slow updates,usability issues,and limited installation methods.These challenges hinder the adoption and practicality of these tools.This paper examines smart contract vulnerability detection tools from 2016 to 2023,sourced from the Web of Science(WOS)and Google Scholar.By systematically collecting,screening,and synthesizing relevant research,38 open-source tools that provide installation methods were selected for further investigation.From a developer’s perspective,this paper offers a comprehensive survey of these 38 open-source tools,discussing their operating principles,installation methods,environmental dependencies,update frequencies,and installation challenges.Based on this,we propose an Ethereum smart contract vulnerability detection framework.This framework enables developers to easily utilize various detection tools and accurately analyze contract security issues.To validate the framework’s stability,over 1700 h of testing were conducted.Additionally,a comprehensive performance test was performed on the mainstream detection tools integrated within the framework,assessing their hardware requirements and vulnerability detection coverage.Experimental results indicate that the Slither tool demonstrates satisfactory performance in terms of system resource consumption and vulnerability detection coverage.This study represents the first performance evaluation of testing tools in this domain,providing significant reference value.

Mitigating Blackhole and Greyhole Routing Attacks in Vehicular Ad Hoc Networks Using Blockchain Based Smart Contracts

The rapid increase in vehicle traffic volume in modern societies has raised the need to develop innovative solutions to reduce traffic congestion and enhance traffic management efficiency.Revolutionary advanced technology,such as Intelligent Transportation Systems(ITS),enables improved traffic management,helps eliminate congestion,and supports a safer environment.ITS provides real-time information on vehicle traffic and transportation systems that can improve decision-making for road users.However,ITS suffers from routing issues at the network layer when utilising Vehicular Ad Hoc Networks(VANETs).This is because each vehicle plays the role of a router in this network,which leads to a complex vehicle communication network,causing issues such as repeated link breakages between vehicles resulting from the mobility of the network and rapid topological variation.This may lead to loss or delay in packet transmissions;this weakness can be exploited in routing attacks,such as black-hole and gray-hole attacks,that threaten the availability of ITS services.In this paper,a Blockchain-based smart contracts model is proposed to offer convenient and comprehensive security mechanisms,enhancing the trustworthiness between vehicles.Self-Classification Blockchain-Based Contracts(SCBC)and Voting-Classification Blockchain-Based Contracts(VCBC)are utilised in the proposed protocol.The results show that VCBC succeeds in attaining better results in PDR and TP performance even in the presence of Blackhole and Grayhole attacks.

Strategic Contracting for Software Upgrade Outsourcing in Industry 4.0

The advent of Industry 4.0 has compelled businesses to adopt digital approaches that combine software toenhance production efficiency. In this rapidly evolving market, software development is an ongoing process thatmust be tailored to meet the dynamic needs of enterprises. However, internal research and development can beprohibitively expensive, driving many enterprises to outsource software development and upgrades to externalservice providers. This paper presents a software upgrade outsourcing model for enterprises and service providersthat accounts for the impact of market fluctuations on software adaptability. To mitigate the risk of adverseselection due to asymmetric information about the service provider’s cost and asymmetric information aboutthe enterprise’s revenues, we propose pay-per-time and revenue-sharing contracts in two distinct informationasymmetry scenarios. These two contracts specify the time and transfer payments for software upgrades. Througha comparative analysis of the optimal solutions under the two contracts and centralized decision-making withfull-information, we examine the characteristics of the solutions under two information asymmetry scenarios andanalyze the incentive effects of the two contracts on the various stakeholders. Overall, our study offers valuableinsights for firms seeking to optimize their outsourcing strategies and maximize their returns on investment insoftware upgrades.

Revisiting the Electron Radius in Light of Length Contraction

In previous work, the electron radius was identified as the “actual electron radius.” However, this is more accurately described as the electron radius at rest. This study reexamines the electron with an emphasis on the electron radius under motion, incorporating the effects of length contraction. The findings suggest that the radius is subject to Lorentz contraction, which has interesting implications for relativistic effects at the subatomic level.

Design and pricing of maintenance service contract based on Nash non-cooperative game approach

Nowadays manufacturers are facing fierce challenge.Apart from the products,providing customers with multiple maintenance options in the service contract becomes more popular,since it can help to improve customer satisfaction,and ultimately promote sales and maximize profit for the manufacturer.By considering the combinations of corrective maintenance and preventive maintenance,totally three types of maintenance service contracts are designed.Moreover,attractive incentive and penalty mechanisms are adopted in the contracts.On this basis,Nash non-cooperative game is applied to analyze the revenue for both the manufacturer and customers,and so as to optimize the pricing mechanism of maintenance service contract and achieve a win-win situation.Numerical experiments are conducted.The results show that by taking into account the incentive and penalty mechanisms,the revenue can be improved for both the customers and manufacturer.Moreover,with the increase of repair rate and improvement factor in the preventive maintenance,the revenue will increase gradually for both the parties.

Allocation of Public Service Facilities in Villages and Towns from the Perspective of Smart Contraction: A Case Study of Zixi County, Jiangxi Province

The limitation of traditional layout system and the continuous contraction of villages and towns have brought challenges to the layout of public service facilities.It is an imperative task against this background to integrate space resources and allocate public service facilities scientifically and reasonably.This paper took the system and method of rural public service facility allocation from the perspective of smart contraction as the theoretic support,and Zixi County in Jiangxi Province for example,built the four-level life circle of“county-fundamental-basic-routine”based on the influence factors of facility allocation,village and town contraction strength,rural comprehensive assessment results,residents’trip willingness and demands on public service facilities,finally proposed the suggestions for optimizing the allocation of village and town public service facilities.

The Impact of Corporate Governance and Fair Value Accounting on Debt Contracts

This paper investigates the theoretical relationship between corporate governance,fair value accounting,and debt contracts.It primarily examines the individual impacts of corporate governance and fair value accounting on debt contracts,while also exploring the influence of corporate governance on fair value accounting.The study emphasizes the importance of considering the interests and legal status of creditors in the context of debt contracts.The findings indicate that strong corporate governance can reduce the likelihood of debt default and that the company’s restructuring costs in the event of a default determine whether improved corporate governance will increase or decrease debt costs.Additionally,the study reveals that the strength of corporate governance affects the value relevance of fair value accounting.However,the impact of fair value accounting on debt contracts is not inherently positive or negative;for instance,companies may use fair value adjustments with manipulative intent to enhance performance.Ultimately,the research highlights that discussions about corporate governance should not prioritize shareholder interests exclusively but also consider the legitimate position of creditors.

Scheduling on tractor and trailer transportation considering the influence of disrupted events based on the contract net and simulated annealing algorithm

To provide a much more resilient transport scheme for tractor and trailer transportation systems,this paper explores the generation method of tractor and trailer transport schemes considering the influence of disrupted events.Three states of tractors including towing loaded trailers,towing empty trailers,and idle driving are taken into account.Based on the disruption management theory,a scheduling model is constructed to minimize the total deviation cost including transportation time,transportation path,and number of used vehicles under the three states of tractors.A heuristics based on the contract net and simulated annealing algorithm is designed to solve the proposed model.Through comparative analysis of examples with different numbers of newly added transportation tasks and different types of road networks,the performance of the contract net algorithm in terms of deviations in idle driving paths,empty trailer paths,loaded trailer paths,time,number of used vehicles,and total deviation cost are analyzed.The results demonstrate the effectiveness of the model and algorithm,highlighting the superiority of the disruption management model and the contract net annealing algorithm.The study provides a reference for handling unexpected events in the tractor and trailer transportation industry.

Construction of the“Micro-Ideological and Political”Model for Ideological and Political Teaching Reform in Bidding and Contract Management Courses

In the context of deepening educational reforms,the significance of ideological and political education has become increasingly prominent,emerging as a crucial aspect of enhancing students’comprehensive qualities.To ensure the orderly progress of ideological and political education in courses,it is essential to actively promote the integration of ideological and political elements within coursework.This integration aims to align professional course education with ideological and political education,fostering a collaborative educational model.Therefore,in the teaching of bidding and contract management courses,constructing a“micro-ideological and political”teaching model has emerged as a critical component.The implementation of this model facilitates the seamless integration of bidding and contract management coursework with ideological and political education,thereby elevating the teaching quality of these courses.Additionally,it leverages the synergistic role of coursework and ideology in educating students,ultimately enhancing the quality and level of talent cultivation.Hence,this paper proposes strategies for constructing a“microideological and political”model within the context of ideological and political teaching reforms in bidding and contract management courses,inviting discussion and exchange on this topic.

Effect of contract choice on upstream carbon emission reduction considering carbon taxation

In order to curb the manufacturer’s product carbon emission levels, the leading retailer usually offers three contracts to the manufacturer, i.e., wholesale-price contract(WC), cost-sharing contract(CC) and revenue-sharing contract(RC). The results of implementing the three contracts are discussed and compared. The results reveal that as long as the government levies carbon taxations, all the three contracts can effectively stimulate the manufacturer to invest in carbon emission reduction. Among the three contracts, RC can achieve the highest level of carbon emission reduction of products and the maximum profits for both the manufacturer and retailer in a supply chain. However, the RC fails to reach the level of the centralized supply chain(CSC), thus it cannot coordinate the supply chain. The supply chain members’ contract choices are consistent. Both members prefer RC to the other two contracts. In order to effectively reduce the manufacturer’s carbon emission levels, the government should impose the highest carbon taxation level under RC, the medium carbon taxation level under CC, the lowest carbon taxation level under WC, and the same carbon taxation level as RC under the CSC.

Research on telecommunication resale service supply chain coordination with revenue-sharing contract

The competition and cooperation between traditional telecom operators and resale operators in the supply chain are discussed, when the telecom industry developed telecommunication resale service. Supply chain contract is an effective way to coordinate the supply chain. This paper analyzes the revenue-sharing contract between telecom operators and resale operators. The study shows that by setting up a rational revenue-sharing ratio and wholesale price, a revenue-sharing contract can coordinate this supply chain, and telecom operators and resale operators can achieve a 'win-win' solution.