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The results of effective irradiation swelling in a wide range of burnup levels are numerically obtained for an inert matrix fuel, which are verified with DART model. The fission gas swelling of fuel particles is calculated with a mechanistic model, which depends on the external hydrostatic pressure. Additionally, irradiation and thermal creep effects are included in the inert matrix. The effects of matrix creep strains, external hydrostatic pressure and temperature on the effective irradiation swelling are investigated. The research results indicate that (1) the above effects are coupled with each other; (2) the matrix creep effects at high temperatures should be involved; and (3) ranged from 0 to 300MPa, a remarkable dependence of external hydrostatic pressure can be found. Furthermore, an explicit multi-variable mathematic model is established for the effective irradiation swelling, as a function of particle volume fraction, temperature, external hydrostatic pressure and fuel particle fission density, which can well reproduce the finite element results. The mathematic model for the current volume fraction of fuel particles can help establish other effective performance models.

Although crowdsourcing has emerged as a paradigm for leveraging human intelligence and activity to solve a wide range of tasks, strategic workers will find enticement in their self-interest to free-ride and attack in a crowdsourcing contest dilemma game. Existing incentive mechanisms are not effective to avoid socially undesirable equilibrium due to the following features of competitive crowdsourcing: in the presence of imperfect monitoring, heterogeneous workers with competing interest tend to beat their opponents for larger self-profit, and the fact that they can freely and frequently change their opponents makes the situation much more complicated. Taking these features into consideration, this article proposes a mechanism design problem to enforce cooperation and extort selfish works simultaneously, with the objective of maximizing the requester’s utility. To solve the problem, we integrate binary ratings with differential pricing to develop a novel rating protocol. By establishing a mathematical model for the problem and quantifying necessary and sufficient conditions for a sustainable social norm, we provide design guidelines for optimal rating protocols and design a low-complexity algorithm to select optimal design parameters. Finally, extensive evaluation results demonstrate the performance of our proposed rating protocol and reveal how intrinsic parameters impact on design parameters.

With ever increasing people's awareness of low carbon and environmental protection, electric vehicles are gradually gaining wide popularity. However, the driving endurance of the electric vehicle is the biggest shortage that hinders the fully acceptance of this new vehicle technology. To deal with this shortage, this paper proposed a vehicle-to-vehicle (V2V) electricity trading scheme based on Bayesian game pricing in blockchain-enabled Internet of vehicles (BIoV). Specifically, the Bayesian game is adopted for pricing in the distributed BIoV with incomplete information sharing. The optimal pricing under the linear strategic equilibrium has been obtained which maximizes the utilities of both sides of electricity transaction. The transaction volume is determined from the formulated convex problem that maximizes the social welfare. Then, the pricing game is implemented by the dedicated smart contract. Blockchain guarantees its trustworthiness, security, and reliability. Finally, the experimental results show that referring to the benchmark of static game with complete information, the proposed Bayesian game with incomplete information can achieve approximate satisfaction of users. The degree of approximation can reach to 98% when the pricing ranges of buyers and sellers are close. Moreover, the proposed scheme has great advantages over the static game with complete information in terms of communication overhead and timeliness in the decentralized IoVs.

The core security of proof-of-work (PoW)-based blockchain networks, relies on PoW consensus algorithm and requires miners solving a crypto-puzzles of hash computation. However, the mining process in the blockchain is resource-intensive where only the first miner who completes full PoW will be rewarded. Therefore, miners exhibit malicious behaviors which cause a waste of distributed computation resource, even posing a threat on the efficiency of blockchain networks. In this brief, we propose a new reputation-based mechanism for the PoW computation in the blockchain, in which miners are incentivized to conduct honest mining. Based on the game theory, we design a reputation-based algorithm to encourage honest mining of miners, and thereby increase the overall revenue of the pool. In addition, numerical illustrations are also presented to support the performance of our proposed mechanism.

In a minimum partial set multi-cover problem (MinPSMC), given an element set E, a collection of subsets $${\mathcal {S}} \subseteq 2^E$$, a cost $$w_S$$ on each set $$S\in {\mathcal {S}}$$, a covering requirement $$r_e$$ for each element $$e\in E$$, and an integer k, the goal is to find a sub-collection $${\mathcal {F}} \subseteq {\mathcal {S}}$$ to fully cover at least k elements such that the cost of $${\mathcal {F}}$$ is as small as possible, where element e is fully covered by $${\mathcal {F}}$$ if it belongs to at least $$r_e$$ sets of $${\mathcal {F}}$$. On the application side, the problem has its background in the seed selection problem in a social network. On the theoretical side, it is a natural combination of the minimum partial (single) set cover problem (MinPSC) and the minimum set multi-cover problem (MinSMC). Although both MinPSC and MinSMC admit good approximations whose performance ratios match those lower bounds for the classic set cover problem, previous studies show that theoretical study on MinPSMC is quite challenging. In this paper, we prove that MinPSMC cannot be approximated within factor $$O(n^\frac{1}{2(\log \log n)^c})$$ for some constant c under the ETH assumption. Furthermore, assuming $$r_{\max }$$ is a constant, where $$r_{\max } =\max _{e\in E} r_e$$ is the maximum covering requirement and f is the maximum number of sets containing a common element, we present a primal-dual algorithm for MinPSMC and show that its performance ratio is $$O(\sqrt{n})$$. We also improve the ratio for a restricted version of MinPSMC which possesses a graph-type structure.

Weighted vertex cover (WVC) is a classical combinatorial optimization problem, which has rich practice significance. Indeed, the existing WVC studies based on game theory has introduced the traditional expected utility theory (EUT), where each vertex is treated as a rational player. However, the uncertainty caused by the weight factor is neglected. So how does this uncertainty affect players' behaviour? In this paper, the complete theoretical analysis is shown. Specifically, we model the WVC problem as an non-cooperative game on weighted networks and respectively establish EUT-based and prospect theory-based utility functions. Then, we prove the existence of mixed NE on this weighted game. Further, by designing a state function, we find that the vertices' state under mixed NE satisfies weighted vertex cover state of the network. Finally, a fictitious play algorithm is proposed to get a NE of the game. Our findings will pave the way for the future numerical simulation, which can demonstrate the impact of the prospect theory on weighted vertex cover.

It’s known that one player adopts the zero-determinant strategy can enforce a linear relation between players in two-player Iterated Prisoner’s Dilemma (IPD) games. In this case, the player takes an unfair share which makes her opponent no incentive for cooperation, even leads to a lower system welfare. In this paper, we study the fair solution of zero-determinant strategies and adopt price of fairness to measure the quality of a fair solution. We first consider the situation that one player adopts zero-determinant strategies in IPD, in which some properties holding for zero-determinant strategies are provided. Furthermore, we explore the bounds about price of fairness under three different definitions of fairness, namely maximin, Kalai-Smorodinski and proportional fairness.

Macrotasking crowdsourcing systems (MCSs), such as Google Helpouts and Elance have emerged as an effective paradigm for improving human intelligence and activity to solve a wide variety of tasks. Requesters often post tasks to the MCS and competitive workers solve the tasks to earn the reward. However, rational and selfish workers in the MCS aim to strategically maximize their own benefit by exhibiting malicious behaviors, thereby decreasing the efficiency of systems. Herein, we present a novel game-theoretic mechanism to incentivize the competitive and selfish workers to provide high-quality solutions in the MCS. We first formulate the crowdsourcing problem as a multiplayer iterated game with incomplete information, where each worker has certain private information (such as solution quality), but does not know what other workers do. Subsequently, we propose an incentive mechanism in terms of zero-determinant (ZD) strategies aiming to improve the social welfare of the MCS, which serves to incentivize the competitive selfish workers toward high-quality solutions. Moreover, we find the conditions for reaching the maximum social welfare of the MCS. Numerical illustrations demonstrate a high and stable social welfare of the MCS with the proposed ZD strategies mechanism.

In proof-of-work-based (PoW-based) blockchain networks, the miners participate in a crypto-puzzle solving competition to win the reward by publishing a new block. Open mining pools attract a large number of miners for solving difficult problems together. Although the open strategy is likely to be more efficient, it makes pools susceptible to attack at the same time. In this paper, we present a game-theoretic analysis of mining pool strategy selection in order to explore the trade-off between the efficiency of openness and the vulnerability of attacks in a PoW-based blockchain network. We first model the pool mining process as a two-stage game, wherein the pools might decide whether to open or not and to attack or not. Based on the two-stage game model, we analyze the Nash equilibrium and the evolutionary stability of the mining games among pools, which uncovers the pool selection dynamics of PoW-based blockchain networks. In particular, we find that the attack behavior is the norm for a weak pool and triggers lower expected utilities when punishing the attacks more severely. Numerical simulations also support our theoretical findings as well as demonstrate the stability of the pools’ strategy selection.

The abuse of personal identity information is one of the most serious problems worldwide. Most social services or businesses use the identity authorization to confirm their validity and legality and the copies of users’ identity certification are usually recorded by the service providers. It is easy to leak the users’ identity information due to the untrustworthy service provider or single-point security failure, and various social problems are then caused. To deal with such problems, this paper proposes a Blockchain-based Identity Authorization mechanism (BIA). First, an Identity Authorization Module (IAM) is devised, which reads the identity certificate and transform the identity plaintext to ciphertext under the authorization by the user’s identity certificate entity and password. IAM guarantees the security of identity information by keeping its plaintext offline. Second, a Business Contract Module (BCM) is designed, which provides a general smart contract framework for identity authorization that can be adopted by most of social services or businesses. Third, a double-chain blockchain infrastructure is developed, whereby the encrypted identity information and service smart contracts are respectively recorded in the tamper-resistant, non-repudiable, and publicly verifiable way. Finally, a prototype system has been developed to verify the security, feasibility and effectiveness of the proposed BIA.

The cladding acts as the first barrier to prevent the release of radioactive fission products, requiring its structural integrity to be maintained throughout the whole operation period of nuclear reactor. Therefore, cladding failure due to PCI (pellet claading mechanical interaction) should be avoided as much as possible in fuel design and operating conditions. At the same time, it is necessary to achieve effective control of the cladding stress by limiting the power growth rate etc. However, in the manufacturing process of fuel rod, the MPS (missing pellet surface) defect is inevitably generated. This defect may lead to a substantial increase in the local stress of the cladding, which in turn exceeds its corresponding stress limit, resulting in cladding failure. Accurate simulation of fuel performance caused by such defects will help prevent such failures. The traditional fuel performance analysis codes are based on a 1.5D analysis framework and cannot handle the local asymmetry problem of fuel such as the MPS defect. In order to accurately simulate the PCI phenomenon caused by the MPS defect, this research establishes a fuel performance analysis code based on the ABAQUS software and this code is suit for the 2D and 3D conditions. Based on the established analysis code, the irradiation-thermal-mechanical behavior of nuclear fuel under typical II transient conditions was studied, and the sensitivity analysis of the influence of different MPS sizes on the local stress of cladding was carried out. The simulation results show that :(1)the mises stress, contact pressure and equivalent creep strain of the cladding may be unevenly distributed due to the MPS defect.(2)the MPS defect will result in a more severe contact pressure on cladding during power transient period, which may lead to failure of cladding and should be prevented. The simulation method established in this research could be very help for the performance analysis for the nuclear fuel rods.

Crowdsourcing technology has been widely used in the online matching system, e.g., Uber and Airbnb, which provides an efficient matching service and enables a balance between service supply and demand. The current crowdsourcing platform generally leverages a centralized architecture through a third party for trust endorsement. However, this kind of architecture brings several challenges such as the quality of service (QoS) and trustability. In this paper, a novel blockchain-based crowdsourcing system (BCS) is proposed to guarantee QoS and fight with the malicious behaviors which employs a new consensus protocol Proof-of-Strategy to solve the well-known fork issue in the blockchain. Proof-of-Strategy also enables a fully distributed implementation of a crowdsourcing platform which prevents the damage of the information during the matching service. By this tamper-proof design, the task of matching supply and demand can prevent malicious behaviors, e.g., plagiarism and fraud. Moreover, a Quality Rating Protocol (QRP) is introduced to jointly work with Proof-of-Strategy for the guaranteed service quality. The existence of Nash equilibrium regards to BCS is given by the game theoretical analysis. The performance evaluation is presented to illustrate the effectiveness of our proposed method.

Weighted vertex cover (WVC), a generalized type of vertex cover, is one of the most important combinatorial optimization problems. In this paper, we provide a novel solution to the WVC problem from the view of network engineering. We model the WVC problem as an asymmetric game on weighted networks, where each vertex is treated as an intelligent rational agent rather than an inanimate one. Under the framework of asymmetric game, we find that strict Nash equilibriums of the asymmetric game are the intermediate states between the WVC states and the minimum WVC (MWVC) states. Besides, we propose best response algorithms with memory and feedback to solve the WVC problem, and find that a better approximate solution to the MWVC can be obtained under the feedback-based best response algorithm. Numerical illustrations verify the performance of the proposed game solution on weighted networks. Our findings pave a new way to solve the WVC problem from the perspective of asymmetric game, which opens a bottom-up avenue to address the combinatorial optimization problems.

Locating sources in a large network is of paramount importance to reduce the spreading of disruptive behavior. Based on the backward diffusion-based method and integer programming, we propose an efficient approach to locate sources in complex networks with limited observers. The results on model networks and empirical networks demonstrate that, for a certain fraction of observers, the accuracy of our method for source localization will improve as the increase of network size. Besides, compared with the previous method (the maximum–minimum method), the performance of our method is much better with a small fraction of observers, especially in heterogeneous networks. Furthermore, our method is more robust against noise environments and strategies of choosing observers.

We consider the general problem of learning from both labeled and unlabeled data, which is often called semi-supervised learning (SSL) or transductive inference. A principled approach to SSL is to design a classification function that is sufficiently smooth with respect to the underlying structure collectively revealed by known labeled and unlabeled data. Combining transductive learning and inductive learning together, we present a simple and scalable algorithm to obtain such a smooth function, namely, Consistent Unlabeled Probability of Identical Distribution (CUPID). The labels of unlabeled data are taken as the probability, consistent to their identical distribution based on geometric structure of the unlabeled. The proposed algorithm yields encouraging experimental results on a number of image classification problems and demonstrates effective use of unlabeled data.

Despite the increasing popularity and the perceived promise of crowdsourcing, its openness presents individuals with an opportunity to exhibit antisocial behavior, such as free-ride and attack to decrease the social welfare, which is considered as a crowdsourcing contest dilemma. Hence, incentive mechanisms are needed to compel rational and selfish individuals to contribute well behavior in tasks. In this paper, we integrate the pricing and reputation schemes to design a novel socially optimal rating protocol based on game theory, in which each player is tagged with a rating to represent its social status, and players are encouraged to contribute good behaviors to increase their ratings, thus receive higher rewards. In particular, we analyze how the players’ behaviors are influenced by the incurred costs and the designed payment, as well as their long-term utilities. By quantifying the sufficient and necessary conditions under which all players comply with the social norm in their self-interests, we formulate the rating protocol design problem, and analyze the impacts of the design parameters in order to characterize the optimal design, that maximizes the social welfare to achieve the social optimum. Finally, illustrative results show the validity and effectiveness of our proposed protocol design for crowdsourcing contest dilemma.

Locating the source node that initiates a diffusion process is an increasingly popular topic that contributes new insights into the maintenance of cyber security, rumor detection in social media, digital surveillance of infectious diseases, etc. Existing studies select the observers randomly or select them heuristically according to the network centrality or community measures. However, there still lacks a method to identify the minimum set of observers for accurately locating the source node of information diffusion in cyber physical networks. Here, we fill this knowledge gap by proposing a greedy optimization algorithm by analyzing the differences of the propagation delay. We use extensive simulations with both synthetic and empirical networks to show that the number of observers can be substantially decreased: Our method only uses a small fraction of nodes (10%–20%) as observers in most networks, whereas the conventional random selection methods have to use 2–3 times more nodes as observers. Interestingly, if a network has a large proportion of low-degree nodes (e.g., karate network), it is necessary to recruit more observers. In particular, the periphery nodes that are only connected with one edge must be observers. Combining our greedy optimization algorithm with the diffusion-back method, the performance of source localization is robust against noise.

Blockchain is a decentralized billing system that guarantees mutual trust among parties through decentralized data storage, sophisticated encryption and signature technologies. The basis of blockchain is P2P distributed network, encryption algorithm and consensus mechanism. To improve the reward during the process of mining, the mining pool will choose to cooperate or attack with other mining pools. In this paper, the zero-determinant (ZD) strategy is applied to to optimize the strategy selection during the mining process, which enforces mining pools selecting cooperative mining and thereby increase the overall revenue of the pools. In addition, numerical illustrations are also presented to support our theoretical analysis.

A player taking zero-determinant (ZD) strategies could enforce a linear relationship between his payoff and opponents’ payoff, thereby control the total excepted payoff of opponents. On the other hand, the winner-take-all (WTA) behaviors are universal in society. This paper focuses on the evolution dynamics of ZD strategies in WTA game, where we explore how the focused player controls the sum of the opponent’s payoff under the dynamic rule of WTA. Numerical simulations also illustrate the effect of parameters on total payoff.

In proof-of-work (PoW)-based blockchain networks, the miners contribute their distributed computation in solving a crypto-puzzle competition to win the reward. To secure stable profits, some miners organize mining pools and share the rewards from the pool in proportion to each miner’s contribution. However, some miners may exhibit malicious behaviors which cause a waste of distributed computation resource, even posing a threat on the efficiency of blockchain networks. In this paper, we propose a new game-theoretic framework to incentivize miners mining honestly and help to bring about a higher total welfare of blockchain networks. We first formulate the mining process as a noncooperative iterated game. We then propose a mechanism in terms of zero-determinant strategies (ZD strategies) to encourage the cooperative mining and improve the efficiency of mining in PoW-based blockchain networks. In addition, we theoretically analyze the maximum system welfare of the target pool through the method of optimization. Numerical illustrations are also presented to support our theoretical results.