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.

Generational Gap： Intrinsic （Non-monetary） Versus Extrinsic （Monetary） Rewards in the Workforce

Traditionally, organizations assume that compensation/pay and monetary benefits are what all employees need to work harder, be productive, or remain with the company. According to Abraham Maslow, within every person is a hierarchy of five needs： physiological needs, safety needs, social needs, esteem needs, and self-actualization needs Organizations must be able to identify what employees desire to secure optimum performance and to meet the needs of both employees and employers. This research focuses on the generational gap and the significance of intrinsic and extrinsic rewards in the workforce. The purpose and objective of this research are to test the significance of monetary versus non-monetary rewards among the different generations in the organization. A self-designed questionnaire distributed to a multi-generational group of employees of selected organizations was used to collect the analyzed data. Sixty-five （65%） responses were obtained. Secondary data were used to elucidate the needs in this area of study. Because the workforce is predicted to become more diverse in terms of age, organizations will be unlikely to implement one set of rewards for the multiple generations. This is due to the differing expectations and requirements among the generations. However, the results indicate no significant difference in monetary versus non-monetary rewards among the different generations in the workforce.

Rewards and Recognition Spark Revision Writing

If extrinsic rewards will spark student engagement with tasks of limited intrinsic motivation, such as writing revision, we owe it to the principles of best practices to draw forth curiosity and passion by every means possible. Including motivational situations such as contests, rewards, and recognition events to spark engagement in low interest tasks may tempt initially indecisive students, while driving students who are willing participants to perform at a deeper level. To revitalize the valid use of external performance motivations, more teachers can promote strategic activities tying core academics to student-oriented gains.

Policy of giving rewards and subsidies for grassland ecological conservation in Tibetan Plateau

This paper aims to explore the impact of policy of giving rewards and subsidies(GRS) for grassland ecological conservation in Tibetan Plateau implemented by the Chinese government since 2009.Taking Gerze County in Ngari Prefecture in the Tibetan Autonomous Region(TAR) as an example,it discusses the objective,implementation and outcome of that policy with regard to the ecological reconstruction and problems that have ensured.Located in the northern part of the Qiangtang Plateau,Gerze is the largest county in Ngari Prefecture.It covers more than 7.8 million acres of pastureland,of which 6.2 million acres are usable for pastoralism; 3.4 million acres,however,lack water source.In recent decades,due to the increased population and other reasons,pastures of the area have shown signs of overgrazing,thus leading to serious degradation,desertification and salinization of the grassland.Since 2009,when neighboring Coqin County was chosen as a pilot site for the national ecological incentive and subsidy policy(or: ecological compensation policy),Gerze has also started to adopt this policy and brought ful implementation in 2010.Its purpose is to solve the problem of overgrazing.But like other policies carried out in Gerze,its implementation is faced with many challenges.First,it is difficult to define the types and scopes of the incentives and subsidies,which have become a major source of complaints of the local herdsmen.Second,the local herdsmen are also concerned with the fairness of assigning rewards and subsidies.Third,the high cost of the policy's implementation and supervision reduces its effects.Fourth,the fact that the herdsmen are not willing to reduce livestock population makes it difficult for the policy to achieve actual results.The author thinks it's necessary to revise and improve the current ecological incentive and subsidy policy.

在一个非选择性冠状动脉疾病患者人群中比较西罗莫司洗脱支架与紫杉醇洗脱支架的远期临床结局和血栓形成率:REWARDS注册试验

与裸金属支架相比,西罗莫司洗脱支架(SES)和紫杉醇洗脱支架(PES)可显著减少再次介入的需要。在经过选择的患者及病变组中,使用这些支架系统的随机、对照、直接对比试验所得出的比较性结局资料不一致;因此。

Prisoners awarded——Henan Yudong Prison rewards accomplished inmates

Gao Pingyuan has seen new hopes of a new life after serving his terms for 12 years at the Yudong Prison in central China＇s Henan Province. He got the special class award for his accomplished teaching in prison.

Valiant Gaokao Rewards

New rules for this year＇s national college entrance examination, or gaokao in Mandarin, which takes place from June 7 to 9 every year, sparked heated debate among the public in China. Before gaokao in 2014, some provincial education authorities released a new policy stipulating that gaokao applicants may receive 10 to 20 extra points if they have ＂excellent morality＂ or have records of helping others for a just cause.

Choice of discount rate in reinforcement learning with long-delay rewards

In the world, most of the successes are results of longterm efforts. The reward of success is extremely high, but before that, a long-term investment process is required. People who are “myopic” only value short-term rewards and are unwilling to make early-stage investments, so they hardly get the ultimate success and the corresponding high rewards. Similarly, for a reinforcement learning(RL) model with long-delay rewards, the discount rate determines the strength of agent’s “farsightedness”.In order to enable the trained agent to make a chain of correct choices and succeed finally, the feasible region of the discount rate is obtained through mathematical derivation in this paper firstly. It satisfies the “farsightedness” requirement of agent. Afterwards, in order to avoid the complicated problem of solving implicit equations in the process of choosing feasible solutions,a simple method is explored and verified by theoreti cal demonstration and mathematical experiments. Then, a series of RL experiments are designed and implemented to verify the validity of theory. Finally, the model is extended from the finite process to the infinite process. The validity of the extended model is verified by theories and experiments. The whole research not only reveals the significance of the discount rate, but also provides a theoretical basis as well as a practical method for the choice of discount rate in future researches.

Learning the continuous-time optimal decision law from discrete-time rewards

The concept of reward is fundamental in reinforcement learning with a wide range of applications in natural and social sciences.Seeking an interpretable reward for decision-making that largely shapes the system's behavior has always been a challenge in reinforcement learning.In this work,we explore a discrete-time reward for reinforcement learning in continuous time and action spaces that represent many phenomena captured by applying physical laws.We find that the discrete-time reward leads to the extraction of the unique continuous-time decision law and improved computational efficiency by dropping the integrator operator that appears in classical results with integral rewards.We apply this finding to solve output-feedback design problems in power systems.The results reveal that our approach removes an intermediate stage of identifying dynamical models.Our work suggests that the discrete-time reward is efficient in search of the desired decision law,which provides a computational tool to understand and modify the behavior of large-scale engineering systems using the optimal learned decision.

THE BOREL STATE SPACE SEMI-MARKOVDECISION PROCESS WITH EXPECTED TOTAL REWARDS IN A SEMI-MARKOV ENVIRONMENT

This paper investigates the Borel state space semi-Markov decision process (SMDP) with the criterion of expected total rewards in a semi-Markov environment. It describes a system which behaves like a SMDP except that the system is influenced by its environment modeled by a semi-Markov process. We transform the SMDP in a semiMarkov environment into an equivalent discrete time Markov decision process under the condition that rewards are all positive or all negative, and obtain the optimality equation and some properties for it.

New Rewards

China’s determination to be more innovative sparks a debate on how to reform the state’s incentive system On July 7, the National Office for Science and

Effect of Flavonoids in Scutellariae Radix on Depression-Like Behavior and Brain Rewards: Possible in Dopamine System

A series of animal models are used to investigate the anti-depression mechanism of flavonoids in scutellariae radix （SR） in vivo. Depression-like behavior in mice was studied after intraperitoneal administra- tion of SR. The results showed that SR administered to mice by the intraperitoneal route obviously short- ened the duration in the tail suspension test and the forced swimming test, aggravated the symptoms of eyelid ptosis, akinesia, and mortality caused by reserpine, prolonged climbing times, affected the condi- tioned place preference, and increased sugar consumption in mice. However the SR did not affect the head twitches induced by 5-HTP, locomotor activity in mice, the toxicity of yohimbine, and the body temperature decrease caused by high dosage of apomorphine. The tests show that SR has some anti-depression effect related to the dopamine system. Furthermore another anti-depression mechanism was possible that could affect the mechanism of brain reward, bring positive reinforcement, and increase the sensitivity to euphoria in mice.

Inspection strategies for quality products withrewards in a multi-stage production

In a multi-stage manufacturing system,defective components are generated due to deteriorating machine parts and failure to install the feed load.In these circumstances,the system requires inspection counters to distinguish imperfect items and takes a few discreet decisions to produce impeccable items.Whereas the prioritisation of employee appreciation and working on reward is one of the important policies to improve productivity.Here we look at the multistage manufacturing system as an M/PH/1 queue model and rewards are given for using certain inspection strategies to produce the quality items.A matrix analytical method is proposed to explain a continuous-time Markov process in which the reward points are given to the strategy of inspection in each state of the system.By constructing the value functions of this dynamic programming model,we derive the optimal policy and the optimal average reward of the entire system in the long run.In addition,we obtain the percentage of time spent on each system state for the probability of conformity and non-conformity of the product over the long term.The results of our computational experiments and case study suggest that the average reward increases due to the actions are taken at each decision epoch for rework and disposal of the non-conformity items.

Evolutionary dynamics of tax-based strong altruistic reward andpunishment in a public goods game

In public goods games, punishments and rewards have been shown to be effective mechanisms for maintaining individualcooperation. However, punishments and rewards are costly to incentivize cooperation. Therefore, the generation ofcostly penalties and rewards has been a complex problem in promoting the development of cooperation. In real society,specialized institutions exist to punish evil people or reward good people by collecting taxes. We propose a strong altruisticpunishment or reward strategy in the public goods game through this phenomenon. Through theoretical analysis and numericalcalculation, we can get that tax-based strong altruistic punishment (reward) has more evolutionary advantages thantraditional strong altruistic punishment (reward) in maintaining cooperation and tax-based strong altruistic reward leads toa higher level of cooperation than tax-based strong altruistic punishment.

Evolutionary analysis of green credit and automobile enterprises under the mechanism of dynamic reward and punishment based on government regulation

To explore the green development of automobile enterprises and promote the achievement of the“dual carbon”target,based on the bounded rationality assumptions,this study constructed a tripartite evolutionary game model of gov-ernment,commercial banks,and automobile enterprises;introduced a dynamic reward and punishment mechanism;and analyzed the development process of the three parties’strategic behavior under the static and dynamic reward and punish-ment mechanism.Vensim PLE was used for numerical simulation analysis.Our results indicate that the system could not reach a stable state under the static reward and punishment mechanism.A dynamic reward and punishment mechanism can effectively improve the system stability and better fit real situations.Under the dynamic reward and punishment mechan-ism,an increase in the initial probabilities of the three parties can promote the system stability,and the government can im-plement effective supervision by adjusting the upper limit of the reward and punishment intensity.Finally,the implementa-tion of green credit by commercial banks plays a significant role in promoting the green development of automobile enter-prises.

Enhancing Cross-Lingual Image Description: A Multimodal Approach for Semantic Relevance and Stylistic Alignment

Cross-lingual image description,the task of generating image captions in a target language from images and descriptions in a source language,is addressed in this study through a novel approach that combines neural network models and semantic matching techniques.Experiments conducted on the Flickr8k and AraImg2k benchmark datasets,featuring images and descriptions in English and Arabic,showcase remarkable performance improvements over state-of-the-art methods.Our model,equipped with the Image&Cross-Language Semantic Matching module and the Target Language Domain Evaluation module,significantly enhances the semantic relevance of generated image descriptions.For English-to-Arabic and Arabic-to-English cross-language image descriptions,our approach achieves a CIDEr score for English and Arabic of 87.9%and 81.7%,respectively,emphasizing the substantial contributions of our methodology.Comparative analyses with previous works further affirm the superior performance of our approach,and visual results underscore that our model generates image captions that are both semantically accurate and stylistically consistent with the target language.In summary,this study advances the field of cross-lingual image description,offering an effective solution for generating image captions across languages,with the potential to impact multilingual communication and accessibility.Future research directions include expanding to more languages and incorporating diverse visual and textual data sources.

UAV maneuvering decision-making algorithm based on deep reinforcement learning under the guidance of expert experience

Autonomous umanned aerial vehicle(UAV) manipulation is necessary for the defense department to execute tactical missions given by commanders in the future unmanned battlefield. A large amount of research has been devoted to improving the autonomous decision-making ability of UAV in an interactive environment, where finding the optimal maneuvering decisionmaking policy became one of the key issues for enabling the intelligence of UAV. In this paper, we propose a maneuvering decision-making algorithm for autonomous air-delivery based on deep reinforcement learning under the guidance of expert experience. Specifically, we refine the guidance towards area and guidance towards specific point tasks for the air-delivery process based on the traditional air-to-surface fire control methods.Moreover, we construct the UAV maneuvering decision-making model based on Markov decision processes(MDPs). Specifically, we present a reward shaping method for the guidance towards area and guidance towards specific point tasks using potential-based function and expert-guided advice. The proposed algorithm could accelerate the convergence of the maneuvering decision-making policy and increase the stability of the policy in terms of the output during the later stage of training process. The effectiveness of the proposed maneuvering decision-making policy is illustrated by the curves of training parameters and extensive experimental results for testing the trained policy.

New Idea Generation and Individual Motivation： A Conceptual Framework

The adoption of specific idea management programs is becoming a strategic asset for organizations, as they are increasingly trying to adopt specific organizational solutions to detect, fertilize, evaluate, and promote new idea generation within and across their boundaries. The centrality of the ideas generation is linked to its vital characteristic of being the main source for new products, services, processes, and drivers of change. This paper copes with the controversial role of the general organizational setting and closely focuses on the rewards mechanisms that could further nurture creativity. By formulating a set of propositions, the paper submits that the understanding of the motivational drivers and the organizational settings is paramount to distill the links between idea generation and incentive structures. This paper aims also to critically analyze and assess the impact of extrinsic and intrinsic motivation on idea generation both at an individual and team level, and develop a framework within which it explores the necessary change to be adopted by firms in managing the idea generation. What is new to the field is the recognition of the impact of the individual locus of control on creative performance. In this vein, the paper sees its ultimate aim in uncovering the dynamics of individual and collective motivation related to creativity, considered as the main source for innovation. The paper concludes that new ideas could be nurtured through the adoption of routine system aligned with the companies＇ human resource management policy.