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.

Effects of Grassland Eco-Protection Compensation and Reward System

Grassland ecological protection compensation and reward policy is the largest-scale investment concerning themost extensive areas since foundation of the PRC. It will be the long-term implementationpolicy for grassland ecological protection. In this study,based on macro-perspective, the policy effects ofgrasslandproductivity, ecological protection, animal husbandryoutput, pastoralists＇ income were ana- lyzed. The resultsshow that, afterimplementation of the policy, naturalgrass production and grasslandtheoretical stocking rateincreased. The averagenatural grasslandlivestockoverloading ratedecreased significantly, comprehensivenationalgrasslandvegetation coverageis increasing. Besides, adult cattleandbeef yield arefluctuated. Sheep head, adult sheep, sheep production, milk productionincreasedin varying degrees. The per capita netincomeof farmers and pastoralists, livestock income, the proportion oflivestockincomewere higher than those beforeimplementation of the policy.

Anhedonia and Reward System: Psychobiology, Evaluation, and Clinical Features

Anhedonia can be defined as a condition in which the hedonic capacity is totally or partially lost. From a psychobiological perspective, several researchers proposed that anhedonia has a putative neural substrate, the dopaminergic mesolimbic and mesocortical reward circuit, which involves the ventral tegmental area, the ventral striatum and part of the prefrontal cortex. Anhedonia is, besides depressed mood, one of the two core symptoms of depression;furthermore it is one of the most important negative symptom in schizophrenia. Anhedonia is also present in substance use disorders as part of the abstinence symptomatology, and interrelations between hedonic capability, craving and protracted withdrawal have been found, particularly in opiate-dependent subjects. Although anhedonia is regarded as an important symptom in psychopathology, so far it has received relatively little attention. In general, two main approaches have been utilized to investigate and assess anhedonia or hedonic capacity: laboratory-based measures and questionnaires. Among measurement scales, the most commonly used are the Snaith-Hamilton Pleasure Scale (SHAPS), the Fawcett-Clark Pleasure Scale (FCPS), and the Revised Chapman Physical Anhedonia Scale (CPAS). Nevertheless, other measurement scales, particularly used within broader psychopathological dimensions, are the Anhedonia-Asociality subscale (SANSanh) of the Scale for the Assessment of Negative Symptoms (SANS) and the Bech-Rafaelsen Melancholia Scale (BRMS). In this paper we analyze these different scales, individuating their strengths and limits and their current clinical applications.

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.

Usage of a Reward System for Dealing with Pediatric Dental Fear

Background： Pediatric dental fear, if left unchecked, can persist for a lifetime and adversely impact the physical and psychological health of a patient. In this study, a feasible nonmedical method for relieving pediatric dental fear was investigated. Methods： A randomized, single-blind, controlled trial model was applied. The juvenile patients experiencing dental fear, whose parents or guardian had signed an informed consent form, were randomly divided into two groups. Group A （n = 50） was the control group, while Group B （n = 50） was the reward group. Participants in Group A accepted routine treatment. Participants in Group B were told that they would obtain a gift as a reward for their good behavior if they were compliant during their dental treatments. The Chinese version of the Children＇s Fear Survey Schedule-Dental Subscale （CFSS-DS） was used to evaluate the level of dental fear of each patient both before and after each treatment. A contrast analysis and a correlation analysis of the results were used to assess the efficacy of the reward mechanism. Results： All participants in Group B, were obedient during the dental treatment, and they also successfully chose the present they wanted at the end of their dental treatment. Children at different ages showed different reward preferences. Significant difference in the fear scores of the participants in Group B before the treatment and after receiving the reward was found （independent samples t-test, t = 14.72, P 〈 0.001）. In Group A, 86% children＇s fear score did not undergo a noticeable change. Conclusions： A reward system is proved feasible to relieve pediatric dental fear, and the form of reward should meet the demand of patients.

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.

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.

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.

基于季节性碳交易机制的园区综合能源系统低碳经济调度

为有效提高碳排放配额分配的合理性,并且避免年度结算时碳排放量超标导致环境污染加剧问题,提出基于奖惩因子的季节性碳交易机制,以园区综合能源系统(park integrated energy system,PIES)为对象进行低碳经济调度。首先,构建包含能量层–碳流层–管理层的综合能源系统(integrated energy system,IES)运行框架,建立电气热多能流供需动态一致性模型;其次,分析系统内“日–季节–年度”碳排放特性,打破传统应用指标法的配额分配方法,采用灰色关联分析法建立碳排放配额分配模型,并基于奖惩阶梯碳价制定季节性碳交易机制;最后,以系统内全寿命周期运行成本及碳交易成本最小为目标,对执行季节性碳交易机制的PIES进行低碳经济调度,分析长时间尺度下季节性储能参与调度的减碳量。搭建IEEE 33节点电网5节点气网7节点热网的PIES,并基于多场景进行算例分析,验证此调度方法能够实现零碳经济运行,保证系统供能可靠性,为建立零碳园区奠定理论基础。

基于深度强化学习的SCR脱硝系统协同控制策略研究

针对选择性催化还原(SCR)脱硝系统大惯性、多扰动等特点,提出了一种基于多维状态信息和分段奖励函数优化的深度确定性策略梯度(DDPG)协同比例积分微分(PID)控制器的控制策略。针对SCR脱硝系统中存在部分可观测马尔可夫决策过程(POMDP),导致DDPG算法策略学习效率较低的问题,首先设计SCR脱硝系统的多维状态信息;其次,设计SCR脱硝系统的分段奖励函数;最后,设计DDPG-PID协同控制策略,以实现SCR脱硝系统的控制。结果表明:所设计的DDPG-PID协同控制策略提高了DDPG算法的策略学习效率,改善了PID的控制效果,同时具有较强的设定值跟踪能力、抗干扰能力和鲁棒性。

多巴胺奖赏系统参与抑郁环路机制及中医药干预研究思考与展望

在寻找提升抗抑郁疗效新策略的过程中,中医药干预逐渐展露出其独特的防治优势。多巴胺奖赏系统密切参与了抑郁症的病理发生发展过程,现今研究多聚焦于多巴胺奖赏系统中某一特定核团的功能机制,而着眼于神经环路功能机制所开展的研究较少。当前奖赏环路的微观研究中,奖赏环路异常和焦虑、抑郁等负面情绪的关联性已获得广泛认可,中医药干预可通过影响奖赏环路发挥抗抑郁作用。该文围绕多巴胺奖赏系统参与抑郁症的环路机制以及中医药干预研究做一综述。

联合机制补偿在国际足球转会中的实施及其启示

联合机制补偿是青训回报制度的重要组成。该研究对联合机制补偿在国际足球转会中的实施状况及实施机制展开研究,总结其实施经验,并提出联合机制补偿对国内足球转会实施的启示。研究发现:联合机制补偿在国际足球转会中的实施效果难符预期,联合机制补偿的实施机制有待完善;国际足联清算所建立后,联合机制补偿在国际足球转会中形成了包含启动条件判定、权益认定、补偿支付、争议申诉提交在内的实施机制,该机制以提升俱乐部认知为基点,以自动化补偿支付为依托,以数字化申诉提交为保障。联合机制补偿在我国国内足球转会实施中存在俱乐部认知较低、补偿支付保障性不足、球员转会监督机制有待强化的问题。为规范联合机制补偿在我国足球转会中的实施,参考联合机制补偿在国际足球转会中的实施经验,建议发挥中国足协信息化平台的效能,提升俱乐部对联合机制补偿的认知;委托国内清算机构,保障联合机制补偿的支付;强化中国足协的监督保障职责,严格球员转会交易审查。

A Geometric Process Repair Model for the Repairable System Consisting of One Component

This paper considers the optimal replacement problem of a repairable system consisting of one component and a single repairman, assume that the system after repair is not 'as good as new', by using the geometric process, we consider a placement policy T based on the age of the system. The problem is to determine the optimal replacement policy T * such that the long_run expected benefit per unit time is maximized. Also, the explicit expression of the long_run expected benefit per unit time can be found. In some conditions, the existence and uniqueness of the optimal policy T * can be proved, finally, we prove that the policy T * is better than the policy T * in .

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.

考虑双层奖惩型碳交易机制的源网荷分布协同低碳经济调度

在“双碳”目标下,设计合理的碳交易机制使源网荷共同参与碳市场,对节能减排具有重要意义。在此背景下,提出了考虑双层奖惩型碳交易机制的源网荷分布协同低碳经济调度策略。首先,建立双层奖惩型碳交易机制下综合能源供应商-园区协同的低碳调度模型,综合能源供应商直接参与外部奖惩型阶梯碳交易市场,园区通过向综合能源供应商支付碳费用或获取碳收益间接参与碳市场,从而激发各主体积极参与节能减排。其次,建立奖惩型阶梯碳价格模型,并提出奖惩型阶梯碳价格机制下综合能源供应商-多园区的碳成本/收益分摊方法,保证了碳成本/收益分摊的有效性与合理性。再次,分别构建综合能源供应商与多园区低碳调度模型,并基于纳什协商刻画园区间的合作博弈,通过各园区间功率互济降低碳排放并提高社会效益。然后,提出了基于自适应调节机制的嵌套交替方向乘子法的双层分布式求解方法。最后,以IEEE 14节点配电网与12线路热管网组成的源网荷综合能源系统为例,验证了所提模型和方法的有效性。

高校实验室安全年度考核评价机制的探索和实践

建立校级实验室安全年度考核机制是落实教育部对高等学校建立健全实验室安全管理奖惩机制要求的重要手段。通过构建学校实验室安全年度考核指标体系、量化实验室安全管理评价要素、优化考核工作实施流程、强化考评结果运用和反馈,学校可较好地客观评价二级单位实验室安全管理工作状况;通过建立合理的奖励机制,对管理优秀、贡献突出或取得进步的二级单位实施奖励,激励二级单位持续改进实验室安全管理工作。对中山大学连续5年的实验室安全年度考核实践进行总结,为完善高校实验室安全管理体系建设和提升管理服务水平提供参考。

FOCUS-PDCA结合标准化奖惩制度模式在医院病历管理中的应用

目的:分析标准化奖惩制度模式应用FOCUS-PDCA在提高医院病历管理质量中的应用价值。方法:回顾性纳入山东省第二人民医院2021—2022年实施标准化奖惩制度模式应用FOCUS-PDCA前病历资料200份为对照组,选取2022—2023年实施标准化奖惩制度模式应用FOCUS-PDCA后病历资料200份为观察组,比较不同管理模式在医院病历管理质量的应用价值。结果:观察组病历资料合格率高于对照组(P<0.05);观察组病历资料综合管理质量、病案管理满意度高于对照组(P<0.05)。结论:标准化奖惩制度模式应用FOCUS-PDCA能显著提升医院病历资料合格率、综合管理质量及患者满意度。

Delta EEG Activity in Left Orbitofrontal Cortex in Rats Related to Food Reward and Craving

The orbitofrontal cortex （OFC） is particularly important for the neural representation of reward value. Previous studies indicated that electroencephalogram （EEG） activity in the OFC was involved in drug administration and withdrawal. The present study investigated EEG activity in the OFC in rats during the development of food reward and craving. Two environments were used separately for control and food-related EEG recordings. In the food-related environment rats were first trained to eat chocolate peanuts; then they either had no access to this food, but could see and smell it （craving trials）, or had free access to this food （reward trials）. The EEG in the left OFC was recorded during these trials. We showed that, in the food-related environment the EEG activity peaking in the delta band （2-4 Hz） was significantly correlated with the stimulus, increasing during food reward and decreasing during food craving when compared with that in the control environment. Our data suggests that EEG activity in the OFC can be altered by food reward; moreover, delta rhythm in this region could be used as an index monitoring changed signal underlying this reward.

The Effect of Performance Management System on Employee Productivity in Cross-Border E-Commerce Enterprises in China

The aim of the current research was to analyze how the performance management system of China’s cross-border e-commerce enterprises affects employee productivity.The study was guided by the following research objectives:to investigate the performance management system on employee productivity in cross-border e-commerce enterprises in China;to determine the relationship between the performance management system and employee productivity in cross-border e-commerce enterprises in China.The study adopted a quantitative approach to the effects of performance management practices on employee productivity.The dependent variables included performance appraisals,reward systems,and performance feedback,and the implications on employee productivity as the independent variable.The target population is comprised of 400 employees in China’s cross-border e-commerce enterprises.Descriptive statistics were utilized as a data analysis tool.The demographic profiles of the respondents were analyzed using percentages and frequencies.Inferential statistics such as correlation and regression analysis established the relationship between dependent and independent variables.The study recommends that the performance management practices should be optimized to improve employee performance.Performance reviews should be focused on the contributions of the individual employees to meet the organizational objectives.For every possible opportunity,the manager should formally recognize good employee efforts for enhanced work performance.Effective performance management practices that edify appraisal and reward should be used to achieve organization goals and enhance employee productivity.