Teaching Reform of “Nursing of Traditional Chinese Medicine” Course Based on OBE Concept

Traditional Chinese medicine(TCM)nursing is one of the important disciplines in TCM.It is based on TCM theory and combined with modern nursing theory and technology,aiming to provide comprehensive and individualized nursing services.With the changes in the medical environment and the continuous improvement of people’s health needs,the teaching of TCM nursing is facing new challenges and opportunities.This paper aims to discuss the teaching reform of TCM nursing course based on the concept of outcome-based education(OBE)or student-centered teaching.The significance of this study is to provide theoretical basis and practical guidance for the teaching reform of TCM nursing course,and to promote the development and progress of TCM nursing education.Through the teaching reform based on OBE concept,we can better cultivate TCM nursing talents with innovative spirit and practical skills,and contribute to the development of TCM.

Authentic Assessment of Compulsory Subjects in Primary Schools:A Case Study in the Western Area of China

This article discusses a case study that explores the relationship between 21st century learning and curriculum alignment.It investigates three compulsory subjects:Chinese,mathematics,and English.By inquiring the survey on individual perspectives on curriculum content and authentic assessment in different schools within the scope of western Chinese primary schools,this article concluded that 21st century learning skills are well developed in primary schools.Although authentic assessment from parents5 perspective is diminutive,it is progressive for some students.Numerous factors can influence the actual practice of authentic assessment as teachers and pedagogies significantly contribute to studentsJ learning outcomes.The authentic assessment in China still has a long way to go in order to boost these learning skills to a higher level.

Intelligent learning model-based skill learning and strategy optimization in robot grinding and polishing

With the rapid advancement of manufacturing in China,robot machining technology has become a popular research subject.An increasing number of robots are currently being used to perform complex tasks during manual operation,e.g.,the grinding of large components using multi-robot systems and robot teleoperation in dangerous environments,and machining conditions have evolved from a single open mode to a multisystem closed mode.Because the environment is constantly changing with multiple systems interacting with each other,traditional methods,such as mechanism modeling and programming are no longer applicable.Intelligent learning models,such as deep learning,transfer learning,reinforcement learning,and imitation learning,have been widely used;thus,skill learning and strategy optimization have become the focus of research on robot machining.Skill learning in robot machining can use robotic flexibility to learn skills under unknown working conditions,and machining strategy research can optimize processing quality under complex working conditions.Additionally,skill learning and strategy optimization combined with an intelligent learning model demonstrate excellent performance for data characteristics learning,multisystem transformation,and environment perception,thus compensating for the shortcomings of the traditional research field.This paper summarizes the state-of-the-art in skill learning and strategy optimization research from the perspectives of feature processing,skill learning,strategy,and model optimization of robot grinding and polishing,in which deep learning,transfer learning,reinforcement learning,and imitation learning models are integrated into skill learning and strategy optimization during robot grinding and polishing.Finally,this paper describes future development trends in skill learning and strategy optimization based on an intelligent learning model in the system knowledge transfer and nonstructural environment autonomous processing.

Skill Learning for Robotic Insertion Based on One-shot Demonstration and Reinforcement Learning

In this paper,an efficient skill learning framework is proposed for robotic insertion,based on one-shot demonstration and reinforcement learning.First,the robot action is composed of two parts:expert action and refinement action.A force Jacobian matrix is calibrated with only one demonstration,based on which stable and safe expert action can be generated.The deep deterministic policy gradients(DDPG)method is employed to learn the refinement action,which aims to improve the assembly efficiency.Second,an episode-step exploration strategy is developed,which uses the expert action as a benchmark and adjusts the exploration intensity dynamically.A safety-efficiency reward function is designed for the compliant insertion.Third,to improve the adaptability with different components,a skill saving and selection mechanism is proposed.Several typical components are used to train the skill models.And the trained models and force Jacobian matrices are saved in a skill pool.Given a new component,the most appropriate model is selected from the skill pool according to the force Jacobian matrix and directly used to accomplish insertion tasks.Fourth,a simulation environment is established under the guidance of the force Jacobian matrix,which avoids tedious training process on real robotic systems.Simulation and experiments are conducted to validate the effectiveness of the proposed methods.

Changes in brain activation patterns according to cross-training effect in serial reaction time task An functional MRI study

Cross-training is a phenomenon related to motor learning, where motor performance of the untrained limb shows improvement in strength and skill execution following unilateral training of the homologous contralateral limb. We used functional MRI to investigate whether motor performance of the untrained limb could be improved using a serial reaction time task according to motor sequential learning of the trained limb, and whether these skill acquisitions led to changes in brain activation patterns. We recruited 20 right-handed healthy subjects, who were randomly allocated into training and control groups. The training group was trained in performance of a serial reaction time task using their non-dominant left hand, 40 minutes per day, for 10 days, over a period of 2 weeks. The control group did not receive training. Measurements of response time and percentile of response accuracy were performed twice during pre- and post-training, while brain functional MRI was scanned during performance of the serial reaction time task using the untrained right hand. In the training group, prominent changes in response time and percentile of response accuracy were observed in both the untrained right hand and the trained left hand between pre- and post-training. The control group showed no significant changes in the untrained hand between pre- and post-training. In the training group, the activated volume of the cortical areas related to motor function （i.e., primary motor cortex, premotor area, posterior parietal cortex） showed a gradual decrease, and enhanced cerebellar activation of the vermis and the newly activated ipsilateral dentate nucleus were observed during performance of the serial reaction time task using the untrained right hand, accompanied by the cross-motor learning effect. However, no significant changes were observed in the control group. Our findings indicate that motor skills learned over the 2-week training using the trained limb were transferred to the opposite homologous limb, and motor skill acquisition of the untrained limb led to changes in brain activation patterns in the cerebral cortex and cerebellum.

Learning A New Skill

相信同学们都有在语文上写命题作文的经历,国外英语课上的命题作文是什么样呢?下面就是10岁的马茗泉在美国某小学5年级班上写的一篇课堂命题作文。大家不妨感受一下美国小学生的课堂作文。

浅析报刊文章《是时候尝试新事物了》Time to try new things

这篇文章选自 21 世纪报刊共同战“疫”的文章,面对疫情,如何摆脱生活之寂寞和无趣,是时候该尝试新事物了。文章首先引出话题,面对疫情,生活倍感无聊。其次,建议大家保持积极的心态;可以学习一项新技能,如烹饪、编程等;还要保持幽默感。总之,现在有更多的时间去做之前太忙碌而从未开始的事情。

A Novel Heterogeneous Actor-critic Algorithm with Recent Emphasizing Replay Memory

Reinforcement learning(RL) algorithms have been demonstrated to solve a variety of continuous control tasks. However,the training efficiency and performance of such methods limit further applications. In this paper, we propose an off-policy heterogeneous actor-critic(HAC) algorithm, which contains soft Q-function and ordinary Q-function. The soft Q-function encourages the exploration of a Gaussian policy, and the ordinary Q-function optimizes the mean of the Gaussian policy to improve the training efficiency. Experience replay memory is another vital component of off-policy RL methods. We propose a new sampling technique that emphasizes recently experienced transitions to boost the policy training. Besides, we integrate HAC with hindsight experience replay(HER) to deal with sparse reward tasks, which are common in the robotic manipulation domain. Finally, we evaluate our methods on a series of continuous control benchmark tasks and robotic manipulation tasks. The experimental results show that our method outperforms prior state-of-the-art methods in terms of training efficiency and performance, which validates the effectiveness of our method.