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.

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.

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 repairable system model with two types repair based on extended geometric process

A simple repairable system with one repairman is considered. As the system working age is up to a specified time T, the repairman will repair the component preventively, and it will go back to work as soon as the repair finished. When the system failure, the repairman repair it immediately. The time interval of the preventive repair and the failure correction is described with the extended geometric process. Different from the available replacement policy which is usually based on the failure number or the working age of the system, the bivariate policy (T,N) is considered. The explicit expression of the long-run average cost rate function C(T,N) of the system is derived. Through alternatively minimize the cost rate function C(T,N), the optimal replacement policy (T?,N?) is obtained, and it proves that the optimal policy is unique. Numerical cases illustrate the conclusion, and the sensitivity analysis of the parameters is carried out.

A REPLACEMENT MODEL FOR QUEUEING SYSTEM WITH REPAIRABLE ELECTRICAL EQUIPMENT OF AUTOMATIC STEEL ROLLING

The optimal replacement model for the repairable queueing system con-sisting of single electrical equipment of automatic steel rolling is studied. Assumingthat the equipment after repair is not “as goed as new” , by using geometric pro-cess, we take the n

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.

A generalized geometric process based repairable system model with bivariate policy

The maintenance model of simple repairable system is studied.We assume that there are two types of failure,namely type Ⅰ failure(repairable failure)and type Ⅱ failure(irrepairable failure).As long as the type Ⅰ failure occurs,the system will be repaired immediately,which is failure repair(FR).Between the(n-1)th and the nth FR,the system is supposed to be preventively repaired(PR)as the consecutive working time of the system reaches λ^(n-1) T,where λ and T are specified values.Further,we assume that the system will go on working when the repair is finished and will be replaced at the occurrence of the Nth type Ⅰ failure or the occurrence of the first type Ⅱ failure,whichever occurs first.In practice,the system will degrade with the increasing number of repairs.That is,the consecutive working time of the system forms a decreasing generalized geometric process(GGP)whereas the successive repair time forms an increasing GGP.A simple bivariate policy(T,N)repairable model is introduced based on GGP.The alternative searching method is used to minimize the cost rate function C(N,T),and the optimal(T,N)^(*) is obtained.Finally,numerical cases are applied to demonstrate the reasonability of this model.

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.

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.

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-

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.

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.