An Adaptive Strategy via Reinforcement Learning for the Prisoner＇s Dilemma Game

The iterated prisoner's dilemma(IPD) is an ideal model for analyzing interactions between agents in complex networks. It has attracted wide interest in the development of novel strategies since the success of tit-for-tat in Axelrod's tournament. This paper studies a new adaptive strategy of IPD in different complex networks, where agents can learn and adapt their strategies through reinforcement learning method. A temporal difference learning method is applied for designing the adaptive strategy to optimize the decision making process of the agents. Previous studies indicated that mutual cooperation is hard to emerge in the IPD. Therefore, three examples which based on square lattice network and scale-free network are provided to show two features of the adaptive strategy. First, the mutual cooperation can be achieved by the group with adaptive agents under scale-free network, and once evolution has converged mutual cooperation, it is unlikely to shift. Secondly, the adaptive strategy can earn a better payoff compared with other strategies in the square network. The analytical properties are discussed for verifying evolutionary stability of the adaptive strategy.

Prevalence of Hepatitis B and Associated Factors in the Garoua Central Prison, Cameroon: A Cross-Sectional Study

Introduction: Hepatitis B virus (HBV) infection is a major public health problem in Cameroon. Garoua city is the headquarters of the North Region of Cameroon, where the HBV prevalence is among the highest of the country. The aim of this study was to determine the prevalence of HBsAg carriage and associated factors among persons incarcerated in the Garoua Central Prison. Methods: It was a cross-sectional study conducted from July 1 to July 31, 2023 at the Garoua Central prison. We included all prisoners willing to participate in the study and who gave their verbal consent. We collected data using a pre-established data entry form and we used rapid test for blood screening for HBV surface antigen (HBs Ag) with ELISA confirmation. Data were analyzed using the R® software for Windows. After the univariate analysis, we selected associated variables to HBV infection with p-value p-value was set at 5%. Results: We included 1389 prisoners out of which 97.6% were male. The median age (IQR) of the study population was 28 (23 - 35) years. The median (IQR) duration of incarceration was 12 (6 - 26) months and the mean (±sd) number of incarcerations was 1.24 (±0.6). HBV prevalence was estimated at 14.8% (95% CI: 13.0 - 16.7). Upon uni- and multivariate analysis, no risk factor was significantly associated with viral hepatitis B infection in our study population. Conclusion: The prevalence of Hepatitis B was high in the Garoua Central Prison, but there were no additional risk factors for HBV infection. There is a need to include the Garoua Central Prison and by the way other prisons in the country in the chronic viral hepatitis care program.

Prevalence and features of SARS-CoV-2 infection in prisons in Tuscany

BACKGROUND Prisons can be a reservoir for infectious diseases,including severe acute respiratory syndrome coronavirus 2(SARS-CoV-2),due to the very intimate nature of the living spaces and the large number of people forced to share them.AIM To investigate the SARS-CoV-2 epidemiology in prisons,this study evaluated the infection incidence rate in prisoners who underwent nasopharyngeal swabs.METHODS This is an observational cohort study.Data collection included information on prisoners who underwent nasopharyngeal swab testing for SARS-CoV-2 and the results.Nasopharyngeal swab tests for SARS-CoV-2 were performed between 15 February 2021 and 31 May 2021 for prisoners with symptoms and all new arrivals to the facility.Another section included information on the diagnosis of the disease according to the International Classification of Diseases,Ninth Revision,and Clinical Modification.RESULTS Up until the 31 May 2021,79.2%of the prisoner cohort(n=1744)agreed to a nasopharyngeal swab test(n=1381).Of these,1288 were negative(93.3%)and 85 were positive(6.2%).A significant association[relative risk(RR)]was found only for the risk of SARS-CoV-2 infection among foreigners compared to Italians[RR=2.4,95%confidence interval(CI):1.2-4.8].A positive association with SARS-CoV-2 infection was also found for inmates with at least one nervous system disorder(RR=4,95%CI:1.8-9.1).The SARS-CoV-2 incidence rate among prisoners is significantly lower than in the general population in Tuscany(standardized incidence ratio 0.7,95%CI:0.6-0.9).CONCLUSION In the prisoner cohort,screening and rapid access to health care for the immigrant population were critical to limiting virus transmission and subsequent morbidity and mortality in this vulnerable population.

THE OPTIMAL CONTROL FOR PROMOTING THE COOPERATION IN EVOLUTION GAME GENERATED BY PRISONER'S DILEMMA

Natural selection opposes the evolution of cooperation unless specific mechanisms are at work in Prisoner＇s Dilemma. By taking advantage of the modern control theory, the controller design is discussed and the optimal control is designed for promoting cooperation based on the recent advances in mechanisms for the evolution of cooperation. Two con- trol strategies are proposed： compensation control strategy for the cooperator when playing against a defector and reward control strategy for cooperator when playing against a coop- erator. The feasibility and effectiveness of these control strategies for promoting cooperation in different stages are analyzed. The reward for cooperation can＇t prevent defection from being evolutionary stable strategy （ESS）. On the other hand, compensation for the coopera- tor can＇t prevent defection from emerging and sustaining. By considering the effect and the cost, an optimal control scheme with constraint on the admissible control set is put forward. By analyzing the special nonlinear system of replicator dynamics, the exact analytic solution of the optimal control scheme is obtained based on the maximum principle. Finally, the effectiveness of the proposed method is illustrated by examples.

Evolutionary prisoner's dilemma game on highly clustered community networks

This paper studies the evolutionary prisoner＇s dilemma game on a highly clustered community network in which the clustering coefficient and the community size can be tuned. It finds that the clustering coefficient in such a degree-homogeneous network inhibits the emergence of cooperation for the entire range of the payoff parameter. Moreover, it finds that the community size can also have a marked influence on the evolution of cooperation, with a larger community size leading to not only a lower cooperation level but also a smaller threshold of the payoff parameter above which cooperators become extinct.

Evolutionary prisoner’s dilemma on Newman-Watts social networks with an asymmetric payoff distribution mechanism

In this paper, we introduce an asymmetric payoff distribution mechanism into the evolutionary prisoner＇s dilemma game （PDG） on Newman Watts social networks, and study its effects on the evolution of cooperation. The asymmetric payoff distribution mechanism can be adjusted by the parameter α： if α〉 0, the rich will exploit the poor to get richer; if α 〈 0, the rich are forced to offer part of their income to the poor. Numerical results show that the cooperator frequency monotonously increases with c~ and is remarkably promoted when c~ 〉 0. The effects of updating order and self-interaction are also investigated. The co-action of random updating and self-interaction can induce the highest cooperation level. Moreover, we employ the Gini coefficient to investigate the effect of asymmetric payoff distribution on the the system＇s wealth distribution. This work may be helpful for understanding cooperative behaviour and wealth inequality in society.

PREFERENCE AND EVOLUTION IN THE ITERATED PRISONER'S DILEMMA

Game theory is extensively used to study strategy-making and actions of play- ers. The authors proposed an analysis method for study the evolutionary outcome and behaviors of players with preference in iterated prisoner＇s dilemma. In this article, a preference parameter k was introduced in the payoff matrix, wherein the value of k denotes the player＇s degree of egoism and altruism （preference）. Then, a game-theoretic dynamical model was formulated using Birth-and-Death process. The authors studied how preference influences the evolutionary equilibrium and behaviors of players. The authors get the general results： egoism leads to defection, and altruism can make players build trust and maintain cooperation, and so, the hope of the Pareto optimal solution. In the end, the simulation experiments proved the efficiency of the method.

Evolutionary prisoner＇s dilemma games with local interaction and best-response dynamics

This paper studies the long run behavior in evolutionary prisoner＇s dilemma games. All players are assumed to sit around a circle and to interact only with their neighbors. It is known that full-defection is the unique long run equilibrium as the probability of players＇ experimentation （or mutation） tends to zero in the best response dynamics. Here, it is shown that full-cooperation could emerge in the long run if one also cares for his neighbors in the best- response dynamics.

Payoff-based accumulative effect promotes cooperation in spatial prisoner’s dilemma

We study the effect of accumulative payoff on the evolution of cooperation in the evolutionary prisoner＇s dilemma on a square lattice. We introduce a decaying factor for the accumulative payoff, which characterizes the extent that the historical payoff is accumulated. It is shown that for fixed values of the temptation to defect, the density of cooperators increases with the value of the decaying factor. This indicates that the more the historical payoff is involved, the more favourable cooperators become. In the critical region where the cooperator density converges to zero, cooperators vanish according to a power-law-like behaviour. The associated exponents agree approximately with the two-dimensional directed percolation and depend weakly on the value of the decaying factor.

Adaptive co-evolution of strategies and network leading to optimal cooperation level in spatial prisoner's dilemma game

We study evolutionary prisoner＇s dilemma game on adaptive networks where a population of players co-evolves with their interaction networks. During the co-evolution process, interacted players with opposite strategies either rewire the link between them with probability p or update their strategies with probability 1 - p depending on their payoffs. Numerical simulation shows that the final network is either split into some disconnected communities whose players share the same strategy within each community or forms a single connected network in which all nodes are in the same strategy. Interestingly, the density of cooperators in the final state can be maximised in an intermediate range of p via the competition between time scale of the network dynamics and that of the node dynamics. Finally, the mean-field analysis helps to understand the results of numerical simulation. Our results may provide some insight into understanding the emergence of cooperation in the real situation where the individuals＇ behaviour and their relationship adaptively co-evolve.

A 2-stage strategy updating rule promotes cooperation in the prisoner's dilemma game

In this study,we propose a spatial prisoner＇s dilemma game model with a 2-stage strategy updating rule,and focus on the cooperation behavior of the system.In the first stage,i.e.,the pre-learning stage,a focal player decides whether to update his strategy according to the pre-learning factor β and the payoff difference between himself and the average of his neighbors.If the player makes up his mind to update,he enters into the second stage,i.e.,the learning stage,and adopts a strategy of a randomly selected neighbor according to the standard Fermi updating rule.The simulation results show that the cooperation level has a non-trivial dependence on the pre-learning factor.Generally,the cooperation frequency decreases as the pre-learning factor increases;but a high cooperation level can be obtained in the intermediate region of- 3〈 β 〈-1.We then give some explanations via studying the co-action of pre-learning and learning.Our results may sharpen the understanding of the influence of the strategy updating rule on evolutionary games.

妊娠糖尿病患者蛋白C、蛋白S和抗凝血酶Ⅲ水平及其与孕周的关系

目的探讨妊娠糖尿病(GDM)患者蛋白C(PC)、蛋白S(PS)和抗凝血酶Ⅲ(ATⅢ)水平及其与孕周的关系。方法选择2019年1月至2023年3月在医院就诊的232例GDM孕妇作为观察组,同时随机选取同期在医院进行孕检的268例正常孕妇作为对照组,比较两组一般资料和PC、PS和ATⅢ水平;按孕周将观察组和对照组分为孕中期和孕晚期,分别比较孕中期和孕晚期观察组和对照组PC、PS和ATⅢ水平。通过相关性分析,分析PC、PS和ATⅢ水平与孕周的相关性。结果与对照组相比,观察组PS和PC水平降低(P<0.05),但ATⅢ水平差异无统计学意义(P>0.05)。GDM患者PC和ATⅢ水平与孕周呈负相关(r=-0.156、-0.134,P<0.05)。结论GDM患者PS和PC水平降低,且随孕周增大,PC、PS和ATⅢ水平降低,应尽早进行干预,以减少血栓形成的风险。

结合主动光源和改进YOLOv5s模型的夜间柑橘检测方法

【目的】解决夜间环境下遮挡和较小柑橘难以准确识别的问题,实现采摘机器人全天候智能化作业。【方法】提出一种结合主动光源的夜间柑橘识别方法。首先,通过分析主动光源下颜色特征不同的夜间柑橘图像,选择最佳的光源色并进行图像采集。然后,提出一种夜间柑橘检测模型BI-YOLOv5s,该模型采用双向特征金字塔网络(Bi-FPN)进行多尺度交叉连接和加权特征融合,提高对遮挡和较小果实的识别能力;引入Coordinate attention(CA)注意力机制模块,进一步加强对目标位置信息的提取;采用融入Transformer结构的C3TR模块,在减少计算量的同时更好地提取全局信息。【结果】本文提出的BI-YOLOv5s模型在测试集上的精准率、召回率、平均准确率分别为93.4%、92.2%和97.1%,相比YOLOv5s模型分别提升了3.2、1.5和2.3个百分点。在所采用的光源色环境下,模型对夜间柑橘识别的正确率为95.3%,相比白光环境下提高了10.4个百分点。【结论】本文提出的方法对夜间环境下遮挡和小目标柑橘的识别具有较高的准确性,可为夜间果蔬智能化采摘的视觉精准识别提供技术支持。

HBsAg和HBsAb共存的慢性乙肝患者的血清学特征和S区突变位点

目的探讨HBsAg/HBsAb双阳患者和HBsAg单阳性患者的血清学差异,并对两组患者的HBV S区突变位点进行比较分析,探寻HBsAg/HBsAb共阳性患者可能的分子机制。方法选取首都医科大学附属北京佑安医院2022年6月至2023年3月的284例HBsAg/HBsAb双阳患者和519例HBsAg单阳患者,统计Age、ALT、AST、AST/ALT、TBiL、ALb、r-GT、HBsAg和HBsAg滴度、HBsAb、HBeAg、HBeAb、HBcAb、WBC,对两组患者的血清学指标进行比较分析。对HBV S区扩增成功的19例HBsAg/HBsAb双阳患者和19例HBsAg单阳患者进行测序分析,统计高频突变位点,分析HBV S区突变特征,探寻HBsAg/HBsAb双阳患者可能发生的分子机制。结果HBsAg/HBsAb双阳患者和HBsAg单阳患者在性别、HBeAg、ALT、AST、TBiL、r-GT差异有统计学意义(P<0.05)。两组患者在Age、HBeAb、HBcAb、AST/ALT、Alb、WBC差异无统计学意义(P>0.05)。HBsAg/HBsAb双阳组患者C基因型占78.95%(15/19),其中A113D/T、A317C/S、A45T、A86G、A87T、A96V、A97P等突变频率较高,HBsAg单阳组C基因型占84.21%(16/19),V159A、M47T、I213L、A317S、E44G、Q267L突变频率较高,两组患者的高频突变位点明显不同。结论双阳和单阳CHB患者血清学和基因突变位点存在显著差异。建议针对HBV S区不同的突变位点进一步研究HBsAg和HBsAb共存的机制。

三维各向异性TI介质中的P/S波快速解耦技术及在弹性波逆时偏移中的应用

随着多分量采集技术的发展,弹性波逆时偏移技术在三维各向异性介质复杂地质构造成像中得到了广泛的应用.然而耦合的P波场和S波场,会在传播过程中产生串扰噪声,降低弹性波逆时偏移的成像精度.为了解决这一问题,本研究针对具有倾斜各向异性对称轴的三维横向各向同性(Transverse Isotropy,TI)介质,提出了一种矢量弹性波场快速解耦方法,可以有效提高偏移剖面的成像质量.该方法首先通过坐标转换,将观测系统坐标系的垂直轴旋转到TI介质的对称轴方向,在新坐标系下,根据具有垂直对称轴的三维横向各向同性(Vertical Transverse Isotropy,VTI)介质中的分解算子,推导出三维TI介质解耦算子表达式.接着引入一种在空间域快速计算分解波场的方法,来实现空间域矢量P波场和S波场分离,极大地提高了计算效率.最后,通过点积成像条件,将提出的P/S波分解方法引入到三维TI介质弹性波逆时偏移中,得到高精度的PP和PS成像.与以往的波场分解方法相比,本文方法具有数值稳定和计算效率高的特点.数值算例表明,应用上述三维TI分解算子得到的偏移剖面有效压制了噪声,提高了成像质量.

基于多目标遗传算法的8×8 S盒的优化设计方法

混沌系统具有非线性、伪随机性、初始值敏感等特性,为基于动力系统构造性能良好的S盒提供了基础,进一步保证了分组加密算法安全性.目前,基于混沌构造S盒的方法大多数针对单个性能指标进行优化,难以获得全面的性能提升.针对此问题,结合混沌映射与多目标遗传算法,提出了一种新的S盒设计方法.首先,利用混沌映射的特性产生初始S盒种群;然后,以S盒的非线性度和差分均匀性为优化目标,基于遗传算法框架对上述两指标进行优化.针对S盒的特点,在优化算法中引入了交换操作,设计了新的变异操作以及非支配序集计算,有效提升了S盒的非线性度和差分均匀性.实验结果表明该算法产生的S盒其差分均匀度为6,非线性度值至少为110,有效提升了S盒的综合性能.

宽负荷下切圆燃煤锅炉H_(2)S分布特性的数值模拟

锅炉采用空气分级燃烧降低NO_(x)排放的同时也提高了主燃区H_(2)S体积分数。炉墙壁面过高的H_(2)S体积分数是加剧水冷壁高温腐蚀的重要因素。为保障新能源并网发电,大型燃煤机组灵活调峰的需求增加,不同负荷下的水冷壁近壁面H_(2)S分布特性值得关注。通过正交试验分析了切圆燃煤锅炉运行参数对水冷壁近壁面H_(2)S体积分数分布的影响。选取一台超临界600 MW切圆燃煤锅炉建立数值模型,设计L_(16)(4^(5))正交工况,覆盖100%BMCR、75%THA,50%THA以及35%BMCR四种负荷。建立了自定义SO_(x)生成模型以确定燃料硫的析出和转化路径,模型包含多表面反应子模型以描述焦炭与O_(2)/CO_(2)/H_(2)O等3种气体的异相反应,并确定焦炭气化反应消耗量占总消耗量的比例,进而对炉膛H_(2)S空间分布进行了模拟计算。研究表明,近壁面高体积分数H_(2)S区域主要位于投运燃烧器层中最下层燃烧器以下以及最上层燃烧器以上至SOFA层之间,烟气切圆沿炉膛高度增加逐渐增大是造成后一区域H_(2)S体积分数较高的重要原因。35%BMCR负荷下水冷壁重点区域的H_(2)S平均体积分数为364μL/L,明显低于其他负荷。锅炉运行参数对重点区域H_(2)S体积分数影响程度的排序为:锅炉负荷>一次风率>主燃区空气过量系数>假想切圆直径>燃烧器竖直摆角。

基于S变换双阈值法的汽车零部件载荷谱加速编辑

针对S变换编辑法加速编辑效果不佳的问题,提出了一种基于S变换双阈值编辑方法。该方法先对载荷谱进行S变换以获取最大幅值谱,在以双阈值识别并保留的幅值谱片段为依据保留对应的载荷谱片段后,再将其拼接成加速后的载荷谱,最后对比分析S变换双阈值编辑法与S变换编辑法编辑的加速谱的统计参数、功率谱密度、穿级计数和疲劳仿真结果。研究结果表明,S变换双阈值编辑法可明显压缩原始载荷时间,且其压缩效率高于S变换编辑法,转向节的疲劳损伤和寿命分析误差更小,适用于汽车零部件载荷谱加速编辑研究。