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Direct numerical simulations of a three-dimensional cylindrical Richtmyer–Meshkov instability with and without chemical reactions are carried out to explore the chemical reaction effects on the statistical characteristics of transition and turbulent mixing. We adopt 9-species and 19-reaction models of non-premixed hydrogen and oxygen separated by a multimode perturbed cylindrical interface. A new definition of mixing width suitable for a chemical reaction is introduced, and we investigate the spatio-temporal evolution of typical flow parameters within the mixing regions. After reshock with a fuller mixing of fuels and oxygen, the chemical reaction becomes sufficiently apparent at affecting the evolution of the flow fields. Because of the generation of a combustion wave within the combustion regions and propagation, the growth of the mixing width with a chemical reaction is accelerated, especially around the outer radius with large temperature gradient profiles. However, the viscous dissipation rate in the early stage of the chemical reaction is greater because of heat release, which results in weakened turbulent mixing within the mixing regions. We confirm that small-scale structures begin to develop after reshock and then decay over time. During the developing process, helicity also begins to develop, in addition to kinetic energy, viscous dissipation rate, enstrophy, etc. In the present numerical simulations with cylindrical geometry, the fluctuating flow fields evolve from quasi-two-dimensional perturbations, and the generations of helicity can capture this transition process. The weakened fluctuations during shock compression can be explained as the inverse energy cascade, and the chemical reaction can promote this inverse energy cascade process.
The extinct family Hylicellidae, as the ancestral group of modern cicadomorphans, had a wide distribution and a very high species-level biodiversity from the Triassic to Early Cretaceous. We herein report 11 new hylicellid specimens from the Jurassic Daohugou beds of Inner Mongolia, NE China, and execute geometric morphometric analysis (GMA) to elucidate their systematic position. Our GMA and subsequent morphometric statistics indicate that 10 of our new specimens can be compared to the holotype of Cycloscytina gobiensis, and one is new to science. Cycloscytina incompleta new species is erected based on this specimen, with the following discriminatory tegminal traits: C3 almost as long as and slightly narrower than C2, and the forking position of stem M distinctly migrates towards wing apex and much apicad of the stem CuA bifurcating. Additionally, Cycloscytina plachutai is herein transferred to the procercopid genus Procercopina, resulting in P. plachutai new combination. To date, just a few body structures of Hylicellidae have been revealed, and the new whole-bodied hylicellids reported herein provide some novel insights on the evolution of basal Clypeata. This study also emphasizes the use of morphometric analysis in the systematics of wing-bearing insects such as hylicellids.
Listeriosis is a rare but serious foodborne disease caused by Listeria monocytogenes. This matched case–control study (1:1 ratio) aimed to identify the risk factors associated with food consumption and food-handling habits for the occurrence of sporadic listeriosis in Beijing, China. Cases were defined as patients from whom Listeria was isolated, in addition to the presence of symptoms, including fever, bacteraemia, sepsis and other clinical manifestations corresponding to listeriosis, which were reported via the Beijing Foodborne Disease Surveillance System. Basic patient information and possible risk factors associated with food consumption and food-handling habits were collected through face-to-face interviews. One hundred and six cases were enrolled from 1 January 2018 to 31 December 2020, including 52 perinatal cases and 54 non-perinatal cases. In the non-perinatal group, the consumption of Chinese cold dishes increased the risk of infection by 3.43-fold (95% confidence interval 1.27–9.25, χ2 = 5.92, P = 0.02). In the perinatal group, the risk of infection reduced by 95.2% when raw and cooked foods were well-separated (χ2 = 5.11, P = 0.02). These findings provide important scientific evidence for preventing infection by L. monocytogenes and improving the dissemination of advice regarding food safety for vulnerable populations.
This study aimed to determine the risk factors for chronic diseases and to identify the potential influencing mechanisms from the perspectives of lifestyle and dietary factors. The findings could provide updated and innovative evidence for the prevention and control of chronic diseases.
A cross-sectional study.
1005 adults from Yangpu district of Shanghai participated in the study, and responded to questions on dietary habits, lifestyle and health status.
Residents suffering from chronic diseases accounted for about 34·99 % of the respondents. Logistic regression analysis showed that age, diet quality, amount of exercise and tea drinking were related to chronic diseases. Age > 60 and overeating (Diet Balance Index total score > 0) had negative additive interaction on the occurrence of chronic disease, while overexercise (Physical Activity Index > 17·1) and tea drinking had negative multiplicative interaction and negative additive interaction on the occurrence of chronic disease. Diet quality, physical activity and tea drinking were incomplete mediators of the relationship between types of medical insurance residents participating in and chronic diseases.
The residents in Yangpu District of Shanghai have a high prevalence of chronic diseases. Strengthening access of residents to health education and interventions to prevent chronic diseases and cultivating healthy eating and exercise habits of residents are crucial. The nutritional environment of the elderly population should be considered, and the reimbursement level of different types of medical insurance should be designed reasonably to improve the accessibility of medical and health services and reduce the risk of chronic diseases.
Users in a social network are usually confronted with decision-making under uncertain network states. While there are some works in the social learning literature on how to construct belief in an uncertain network state, few studies have focused on integrating learning with decision-making for the scenario in which users are uncertain about the network state and their decisions influence each other. Moreover, the population in a social network can be dynamic since users may arrive at or leave the network at any time, which makes the problem even more challenging. In this chapter, we introduce a dynamic Chinese restaurant game to study how a user in a dynamic social network learns about the uncertain network state and makes optimal decisions by taking into account not only the immediate utility, but also subsequent users’ influence. We introduce a Bayesian learning-based method for users to learn the network state and discuss a multidimensional Markov decision process-based approach for users to make optimal decisions. Finally, we apply the dynamic Chinese restaurant game to cognitive radio networks and use simulations to verify the effectiveness of the scheme.
While peer-to-peer (P2P) video streaming systems have achieved promising results, they introduce a large number of unnecessary traverse links, leading to substantial network inefficiency. To address this problem, we discuss how to enable cooperation among “group peers,” which are geographically neighboring peers with large intragroup upload and download bandwidths. Considering the peers’ selfish nature, we formulate the cooperative streaming problem as an evolutionary game and introduce, for every peer, the evolutionarily stable strategy (ESS). Moreover, we discuss a simple and distributed learning algorithm for the peers to converge to the ESSs. With the discussed algorithm, each peer decides whether to be an agent who downloads data from the peers outside the group or a free-rider who downloads data from the agents by simply tossing a coin, where the probability of the coin showing a head is learned from the peer’s own past payoff history. Simulation results show that compared to the traditional noncooperative P2P schemes, the discussed cooperative scheme achieves much better performance in terms of social welfare, probability of real-time streaming, and video quality.
In cognitive networks, how to stimulate cooperation among nodes is very important. However, most existing game-theoretic cooperation stimulation approaches rely on the assumption that the interactions between any pair of players are long-lasting. When this assumption is not true, such as in the well-known Prisoner’s dilemma and the backward induction principle, the unique Nash equilibrium is to always play noncooperatively. In this chapter, we discuss a cooperation stimulation scheme for the scenario in which the number of interactions is finite. This scheme is based on indirect reciprocity game modeling where the key concept is “I help you not because you have helped me but because you have helped others.” The problem of finding the optimal action rule is formulated as a Markov decision process, and a modified value-iteration algorithm is utilized to find the optimal action rule. Using the packet forwarding game as an example, it is shown that with an appropriate cost-to-gain ratio, the strategy of forwarding the number of packets that is equal to the reputation level of the receiver is an evolutionarily stable strategy.
In the third part of this book, the third branch of modern game theory – sequential decision-making – is presented. The important components in sequential decision-making, such as network externality, information asymmetry, and user rationality, are presented and defined. The limitations of the existing approaches, such as social learning and multiarm bandit problems, are also presented.
The viability of cooperative communications depends on the willingness of users to help. Therefore, it is important to study incentive issues when designing such systems. In this chapter, we discuss a cooperation stimulation scheme for multiuser cooperative communications using an indirect reciprocity game. By introducing the notion of reputation and social norms, rational users who care about their future utility are incentivized to cooperate with others. Differently from existing works on reputation-based schemes that mainly rely on experimental verification, the effectiveness of the scheme is demonstrated in two steps. First, we conduct a steady-state analysis of the game and show that cooperating with users who have a good reputation can be sustained as an equilibrium when the cost-to-gain ratio is below a certain threshold. Then, by modeling the action spreading at transient states as an evolutionary game, we show that the equilibria we found in the steady-state analysis are stable and can be reached with proper initial conditions. Moreover, we introduce energy detection to handle the possible cheating behaviors of users and study its impact on the indirect reciprocity game.
A huge amount of information, created and forwarded by millions of people with various characteristics, propagates through online social networks every day. Understanding the mechanisms of information diffusion over social networks is critical to various applications, including online advertisements and website management. Differently from most existing works in this area, we investigate information diffusion from an evolutionary game-theoretic perspective and try to reveal the underlying principles dominating the complex information diffusion process over heterogeneous social networks. Modeling the interactions among the heterogeneous users as a graphical evolutionary game, we derive the evolutionary dynamics and the evolutionarily stable states (ESSs) of the diffusion. The different payoffs of the heterogeneous users lead to different diffusion dynamics and ESSs among them, in accordance with the heterogeneity observed in real-world data sets. The theoretical results are confirmed by simulations. We also test the theory on the Twitter hashtag data set. We observe that the evolutionary dynamics fit the data well and can predict future diffusion data.
Network service acquisition in a wireless environment requires the selection of a wireless access network. A key problem in wireless access network selection is studying rational strategies that consider negative network externality. In this chapter, we formulate the wireless network selection problem as a stochastic game with negative network externality and show that finding the optimal decision rule can be modeled as a multidimensional Markov decision process. A modified value-iteration algorithm is utilized to efficiently obtain the optimal decision rule with a simple threshold structure. We further investigate the mechanism design problem with incentive compatibility constraints, which force the networks to reveal truthful state information. The formulated problem is a mixed-integer programming problem that, in general, lacks an efficient solution. Exploiting the optimality of substructures, we introduce a dynamic programming algorithm that can optimally solve the problem in the two-network scenario. For the multinetwork scenario, the dynamic programming algorithm can outperform the heuristic greedy approach in polynomial-time complexity.
In a social network, agents are intelligent and have the capacity to make decisions so as to maximize their utility. They can either make wise decisions by taking advantages of other agents’ experiences through learning or make decisions earlier to avoid competition from huge crowds. Both of these effects – social learning and negative network externality – play important roles in the decision-making process of an agent. In this chapter, a new game called the Chinese restaurant game is introduced to formulate the social learning problem with negative network externality. Through analyzing the Chinese restaurant game, we derive the optimal strategy of each agent and provide a recursive method to achieve the optimal strategy. How social learning and negative network externality influence each other under various settings is studied through simulations. We also illustrate the spectrum access problem in cognitive radio networks as one application of the Chinese restaurant game. We find that the Chinese restaurant game-theoretic approach indeed helps users make better decisions and improves overall system performance.
Many spectrum sensing methods and dynamic access algorithms have been proposed to improve secondary users’ access opportunities. However, few of them have considered integrating the design of spectrum sensing and access algorithms together by taking into account the mutual influence between them. In this chapter, we focus on jointly analyzing the spectrum sensing and access problem. Due to their selfish nature, secondary users tend to act selfishly to access the channel without contributing to spectrum sensing. Moreover, they may employ out-of-equilibrium strategies because of the uncertainty of others’ strategies. To model the complicated interactions among secondary users, the joint spectrum sensing and access problem is formulated as an evolutionary game and the evolutionarily stable strategy (ESS) that no one will deviate from is studied. Furthermore, a distributed learning algorithm for the secondary users to converge to the ESS is introduced. Simulation results shows that the system can quickly converge to the ESS and such an ESS is robust to the sudden unfavorable deviations of the selfish secondary users.
Cooperation is a promising approach to simultaneously achieving efficient spectrum resource use and improving the quality of service of primary users in dynamic spectrum access networks. However, due to their selfish nature, how to stimulate secondary users to play cooperatively is an important issue. In this chapter, we discuss a reputation-based spectrum access framework where the cooperation stimulation problem is modeled as an indirect reciprocity game. In this game, secondary users choose how to help primary users relay information and gain reputation, based on which they can access a certain amount of vacant licensed channels in the future. By formulating a secondary user's decision-making as a Markov decision process, the optimal action rule can be obtained, according to which the secondary user will use maximal power to help the primary user relay data and thus greatly improve the primary user's quality of service as well as the spectrum utilization efficiency. Moreover, the uniqueness of the stationary reputation distribution is proved, and the conditions under which the optimal action rule is evolutionarily stable are theoretically derived.
The motivation of this book and necessary background knowledge of this book are provided. First, a brief introduction to competition and cooperation in wireless and social networks is provided, along with examples and a literature review. Then, the limitations of traditional game theory in this area are presented. Finally, the three branches of modern game theory – indirect reciprocity, evolutionary games, and sequential decision-making – will be briefly mentioned to illustrate their strengths for overcoming the highlighted limitations.