Reputational preference and other-regarding preference based rewarding mechanism promotes cooperation in spatial social dilemmas

To study the incentive mechanisms of cooperation, we propose a preference rewarding mechanism in the spatial prisoner’s dilemma game, which simultaneously considers reputational preference, other-regarding preference and the dynamic adjustment of vertex weight. The vertex weight of a player is adaptively adjusted according to the comparison result of his own reputation and the average reputation value of his immediate neighbors. Players are inclined to pay a personal cost to reward the cooperative neighbor with the greatest vertex weight. The vertex weight of a player is proportional to the preference rewards he can obtain from direct neighbors. We find that the preference rewarding mechanism significantly facilitates the evolution of cooperation, and the dynamic adjustment of vertex weight has powerful effect on the emergence of cooperative behavior. To validate multiple effects, strategy distribution and the average payoff and fitness of players are discussed in a microcosmic view.

The Use of a“Rewarding System”for Healthcare Personnel

This work aims to identify a method by the coordinator of the OU(operational unit)for the training of gratified personnel through the use of a rewarding system.The continuous transformations that concern the Italian healthcare scene lead the operators to face always new needs and problems.Professionals can not only be considered as workers but bearers of qualified intellectual,professional and cultural skills.Individual coordinators are required to be real leaders within their operational units and to use their managerial skills in achieving company objectives and in evaluating the personnel they manage.The main factor to which difficulties in the management of staff are related concerns the motivation,defined as a state of mind together with aspirations,needs,orientations,that pushes people to act and to use a behavior characterized by commitment,perseverance and determination.The need to better rationalize the resources available,to promote high quality health care,improving safety,efficiency and appropriateness has led the general management and coordinator of the OU to use the reward systems.With the introduction of this procedure aimed at enhancing the merit and encouraging virtuous behavior during the provision of health services,the public employment reform participates in the evolution of the regulatory framework and it turns on the change that is taking place in the world of work.

INTERNATIONAL COOPERATION IS A REWARDING ROUTE FOR HI-TECH DEVELOPMENT

The CAS Institute of Modern Physics is a center of pure basic research concerning nuclear physics, accelerator physics and related technology. In recent years, it succeeded in the construction of China’s first production line for manufacturing radiation-crosslinked (RC) wire and cable with the aid of international cooperation,achieving rewarding benefits from it.

REWARDING COOPERATION BETWEEN CAS AND YUNNAN TRIGGERS A SOCIO-ECONOMIC TAKE-OFF

Ⅰ. THE SUGGESTION OF THE STRATEGIC MEASURE Situated at the junction between the vast Eurasian landmass and the south Asian subcontinent, Yunnan Prov-

Rewarding and Revitalizing

The fiscal stimulus package continues to play the leading role in China’s economic prosperity After several nervous months, China is finally breathing a sigh of relief as the powerful stimulus shifts the nation’ s growth engine out of

Characterization of glutamatergic VTA neural population responses to aversive and rewarding conditioning in freely-moving mice

The Ventral Tegmental Area(VTA) is a midbrain structure known to integrate aversive and rewarding stimuli, but little is known about the role of VTA glutamatergic(VGluT2) neurons in these functions.Direct activation of VGluT2 soma evokes rewarding behaviors, while activation of their downstream projections evokes aversive behaviors. To facilitate our understanding of these conflicting properties, we recorded calcium signals from VTAVGluT2+ neurons using fiber photometry in VGluT2-cre mice to investigate how this population was recruited by aversive and rewarding stimulation, both during unconditioned and conditioned protocols. Our results revealed that, as a population, VTAVGluT2+neurons responded similarly to unconditioned-aversive and unconditioned-rewarding stimulation. During aversive and rewarding conditioning, the CS-evoked responses gradually increased across trials whilst the US-evoked response remained stable. Retrieval 24 h after conditioning, during which mice received only CS presentation, resulted in VTAVGluT2+ neurons strongly responding to CS presentation and to the expected-US but only for aversive conditioning. To help understand these differences based on VTAVGluT2+ neuronal networks, the inputs and outputs of VTAVGluT2+ neurons were investigated using Cholera Toxin B(CTB) and rabies virus. Based on our results, we propose that the divergent VTAVGluT2+ neuronal responses to aversion and reward conditioning may be partly due to the existence of VTAVGluT2+ subpopulations that are characterized by their connectivity.

Efficient Optimal Routing Algorithm Based on Reward and Penalty for Mobile Adhoc Networks

Mobile adhoc networks have grown in prominence in recent years,and they are now utilized in a broader range of applications.The main challenges are related to routing techniques that are generally employed in them.Mobile Adhoc system management,on the other hand,requires further testing and improvements in terms of security.Traditional routing protocols,such as Adhoc On-Demand Distance Vector(AODV)and Dynamic Source Routing(DSR),employ the hop count to calculate the distance between two nodes.The main aim of this research work is to determine the optimum method for sending packets while also extending life time of the network.It is achieved by changing the residual energy of each network node.Also,in this paper,various algorithms for optimal routing based on parameters like energy,distance,mobility,and the pheromone value are proposed.Moreover,an approach based on a reward and penalty system is given in this paper to evaluate the efficiency of the proposed algorithms under the impact of parameters.The simulation results unveil that the reward penalty-based approach is quite effective for the selection of an optimal path for routing when the algorithms are implemented under the parameters of interest,which helps in achieving less packet drop and energy consumption of the nodes along with enhancing the network efficiency.

Detecting Icing on the Blades of a Wind Turbine Using a Deep Neural Network

The blades of wind turbines located at high latitudes are often covered with ice in late autumn and winter,where this affects their capacity for power generation as well as their safety.Accurately identifying the icing of the blades of wind turbines in remote areas is thus important,and a general model is needed to this end.This paper proposes a universal model based on a Deep Neural Network(DNN)that uses data from the Supervisory Control and Data Acquisition(SCADA)system.Two datasets from SCADA are first preprocessed through undersampling,that is,they are labeled,normalized,and balanced.The features of icing of the blades of a turbine identified in previous studies are then used to extract training data from the training dataset.A middle feature is proposed to show how a given feature is correlated with icing on the blade.Performance indicators for the model,including a reward function,are also designed to assess its predictive accuracy.Finally,the most suitable model is used to predict the testing data,and values of the reward function and the predictive accuracy of the model are calculated.The proposed method can be used to relate continuously transferred features with a binary status of icing of the blades of the turbine by using variables of the middle feature.The results here show that an integrated indicator systemis superior to a single indicator of accuracy when evaluating the prediction model.

Magnetic Field-Based Reward Shaping for Goal-Conditioned Reinforcement Learning

Goal-conditioned reinforcement learning(RL)is an interesting extension of the traditional RL framework,where the dynamic environment and reward sparsity can cause conventional learning algorithms to fail.Reward shaping is a practical approach to improving sample efficiency by embedding human domain knowledge into the learning process.Existing reward shaping methods for goal-conditioned RL are typically built on distance metrics with a linear and isotropic distribution,which may fail to provide sufficient information about the ever-changing environment with high complexity.This paper proposes a novel magnetic field-based reward shaping(MFRS)method for goal-conditioned RL tasks with dynamic target and obstacles.Inspired by the physical properties of magnets,we consider the target and obstacles as permanent magnets and establish the reward function according to the intensity values of the magnetic field generated by these magnets.The nonlinear and anisotropic distribution of the magnetic field intensity can provide more accessible and conducive information about the optimization landscape,thus introducing a more sophisticated magnetic reward compared to the distance-based setting.Further,we transform our magnetic reward to the form of potential-based reward shaping by learning a secondary potential function concurrently to ensure the optimal policy invariance of our method.Experiments results in both simulated and real-world robotic manipulation tasks demonstrate that MFRS outperforms relevant existing methods and effectively improves the sample efficiency of RL algorithms in goal-conditioned tasks with various dynamics of the target and obstacles.

A UAV collaborative defense scheme driven by DDPG algorithm

The deep deterministic policy gradient(DDPG)algo-rithm is an off-policy method that combines two mainstream reinforcement learning methods based on value iteration and policy iteration.Using the DDPG algorithm,agents can explore and summarize the environment to achieve autonomous deci-sions in the continuous state space and action space.In this paper,a cooperative defense with DDPG via swarms of unmanned aerial vehicle(UAV)is developed and validated,which has shown promising practical value in the effect of defending.We solve the sparse rewards problem of reinforcement learning pair in a long-term task by building the reward function of UAV swarms and optimizing the learning process of artificial neural network based on the DDPG algorithm to reduce the vibration in the learning process.The experimental results show that the DDPG algorithm can guide the UAVs swarm to perform the defense task efficiently,meeting the requirements of a UAV swarm for non-centralization,autonomy,and promoting the intelligent development of UAVs swarm as well as the decision-making process.

Effectiveness of Reward System on Assessment Outcomes in Mathematics

As assessment outcomes provide students with a sense of accomplishment that is boosted by the reward system,learning becomes more effective.This research aims to determine the effects of reward system prior to assessment in Mathematics.Quasi-experimental research design was used to examine whether there was a significant difference between the use of reward system and students’level of performance in Mathematics.Through purposive sampling,the respondents of the study involve 80 Grade 9 students belonging to two sections from Gaudencio B.Lontok Memorial Integrated School.Based on similar demographics and pre-test results,control and study group were involved as participants of the study.Data were treated and analyzed accordingly using statistical treatments such as mean and t-test for independent variables.There was a significant finding revealing the advantage of using the reward system compare to the non-reward system in increasing students’level of performance in Mathematics.It is concluded that the use of reward system is effective in improving the assessment outcomes in Mathematics.It is recommended to use reward system for persistent assessment outcomes prior to assessment,to be a reflection of the intended outcomes in Mathematics.

Networked Evolutionary Model of Snow-Drift Game Based on Semi-Tensor Product

This paper investigates the networked evolutionary model based on snow-drift game with the strategy of rewards and penalty. Firstly, by using the semi-tensor product of matrices approach, the mathematical model of the networked evolutionary game is built. Secondly, combined with the matrix expression of logic, the mathematical model is expressed as a dynamic logical system and next converted into its evolutionary dynamic algebraic form. Thirdly, the dynamic evolution process is analyzed and the final level of cooperation is discussed. Finally, the effects of the changes in the rewarding and penalty factors on the level of cooperation in the model are studied separately, and the conclusions are verified by examples.

An Efficient Billing Scheme for Trusted Nodes Using Fuzzy Logic in Wireless Sensor Networks

Extending the lifetime of the wireless sensor networks (WSNs), where recharging sensors is not always possible, has been a major concern for researchers for the past decade. In this paper, we study the cooperation between nodes in wireless sensor networks in forwarding packets to others, and we propose a new collaboration technique which stimulates intermediate nodes to forward packets toward their destination. Some nodes show selfish behavior by denying the forwarding packets to other nodes in commercial networks in an effort to preserve their own energy. This paper applies a technique which is used to prolong the network lifetime, based on a node’s energy and trust value, and additionally incorporates fuzzy logic, which stimulates nodes to forward packets by rewarding cooperation. According to simulation results, the proposed approach surpasses the Nuglets (virtual currency) approach and the Reputation approach in network energy and thus prolongs the network lifetime. Additionally, our proposed approach demonstrates better results in the number of dropped packets, PDR and forwarded packets to neighboring nodes.

薪金、奖金、罚金

一、薪金1.wage工资,工钱。一般按每小时、每天或每周计算,以蓝领工人、半技术工人为对象,通常会给现金;此外,该词还用来泛指工资这一概念。使用时多用复数形式。例: (1)The postal workers have asked for wage rise of＄5 a week.邮政工人要求周薪提高5美元。(2)current wage system现行工资制度2.salary多指“月薪”或“年俸”,以公职人员、公司职员、白领职工等为对象,通常通过支票付给。例: The union leaders enjoy great prestige and authorityand large salaries.工会领袖享有很高威望和很大权力,而且领取丰厚的薪水。3.stipend专指酬劳牧师、教师、行政官员的俸给。例如:

Teaching Guided By the Cognitive Principle

It is necessary to invite the cognitive principle to improve our language teaching method and make the learning process more effectively.Compared with the traditional teacher-centered teaching method,language teaching guided by the cognitive principle can change the role of teacher and students.Teachers should pay more attention to form students' autonomy learning ability,increase their motivation and help students find balance between investing efforts and getting results.Meaningful learning should be adopted during teaching and anticipation of reward also works effectively.

On Principle of Rewards in English Learning

There is no question that learning a foreign language like English is different from learning other subjects, mainly because it is new to us Chinese and there is no enough environment. But that doesn’t mean we have no way to learn it and do it well .If asked to identify the most powerful influences on learning, motivation would probably be high on most teachers’ and learners’ lists. It seems only sensible to assume that English learning is most likely to occur when the learners want to learn. That is, when motivation such as interest, curiosity, or a desire achieves, the learners would be engaged in learning. However, how do we teachers motivate our students to like learning and learn well? Here, rewards both extrinsic and intrinsic are of great value and play a vital role in English learning.

上海市三级甲等医院科研竞争力评价

目的评价上海市38家三级甲等医院2018年的科研竞争力,以期为政府在医疗卫生领域的宏观决策提供参考,为各医疗单位的管理者了解自身科研实力及同行水平,进而在工作中发挥优势、改进劣势提供科学依据。方法用德尔菲法确定医院科研竞争力评价指标,收集相关数据,用因子分析法评估科研竞争力。结果科研竞争力评价指标共纳入15个评价指标,提取到科研人才建设、奖励性成果、产权性成果3个公因子,累积方差贡献率为83.895%。科研竞争力综合得分排名前5位的医院分别为A医院、B医院、C医院、D医院和E医院。结论上海市三级甲等医院的科研竞争力存在一定的差距,科研人才建设对于科研发展至关重要。各家医院应结合自身发展现状,完善科研人才培养机制和科研管理机制,鼓励并引导高质量科研成果的产出,提高科研竞争力。

Co-effect of Demand-control-support Model and Effort-reward Imbalance Model on Depression Risk Estimation in Humans: Findings from Henan Province of China

Objective To investigate the co-effect of Demand-control-support （DCS） model and Effort-reward Imbalance （ERI） model on the risk estimation of depression in humans in comparison with the effects when they are used respectively. Methods A total of 3 632 males and 1 706 females from 13 factories and companies in Henan province were recruited in this cross-sectional study. Perceived job stress was evaluated with the Job Content Questionnaire and Effort-Reward Imbalance Questionnaire （Chinese version）. Depressive symptoms were assessed by using the Center for Epidemiological Studies Depression Scale （CES-D）. Results DC （demands/job control ratio） and ERI were shown to be independently associated with depressive symptoms. The outcome of low social support and overcommitment were similar. High DC and low social support （SS）, high ERI and high overcommitment, and high DC and high ERI posed greater risks of depressive symptoms than each of them did alone. ERI model and SS model seem to be effective in estimating the risk of depressive symptoms if they are used respectively. Conclusion The DC had better performance when it was used in combination with low SS. The effect on physical demands was better than on psychological demands. The combination of DCS and ERI models could improve the risk estimate of depressive symptoms in humans.

Evidence and explanation for the involvement of the nucleus accumbens in pain processing

The nucleus accumbens(NAc)is a subcortical brain structure known primarily for its roles in pleasure,reward,and addiction.Despite less focus on the NAc in pain research,it also plays a large role in the mediation of pain and is effective as a source of analgesia.Evidence for this involvement lies in the NAc’s cortical connections,functions,pharmacology,and therapeutic targeting.The NAc projects to and receives information from notable pain structures,such as the prefrontal cortex,anterior cingulate cortex,periaqueductal gray,habenula,thalamus,etc.Additionally,the NAc and other pain-modulating structures share functions involving opioid regulation and motivational and emotional processing,which each work beyond simply the rewarding experience of pain offset.Pharmacologically speaking,the NAc responds heavily to painful stimuli,due to its high density ofμopioid receptors and the activation of several different neurotransmitter systems in the NAc,such as opioids,dopamine,calcitonin gene-related peptide,γ-aminobutyric acid,glutamate,and substance P,each of which have been shown to elicit analgesic effects.In both preclinical and clinical models,deep brain stimulation of the NAc has elicited successful analgesia.The multi-functional NAc is important in motivational behavior,and the motivation for avoiding pain is just as important to survival as the motivation for seeking pleasure.It is possible,then,that the NAc must be involved in both pleasure and pain in order to help determine the motivational salience of positive and negative events.