Advances in the application of action learning in nursing practice

In this paper, we elucidate the concept and characteristics of action learning as well as summarize the re？exivity, cooperativeness, and subjectivity of this approach. Furthermore, we describe the effects and limitations of action learning when applied in nursing management, nursing education, and clinical practice, among various ？elds.

Using Vector Representation of Propositions and Actions for STRIPS Action Model Learning

Action model learning has become a hot topic in knowledge engineering for automated planning.A key problem for learning action models is to analyze state changes before and after action executions from observed"plan traces".To support such an analysis,a new approach is proposed to partition propositions of plan traces into states.First,vector representations of propositions and actions are obtained by training a neural network called Skip-Gram borrowed from the area of natural language processing(NLP).Then,a type of semantic distance among propositions and actions is defined based on their similarity measures in the vector space.Finally,k-means and k-nearest neighbor(kNN)algorithms are exploited to map propositions to states.This approach is called state partition by word vector(SPWV),which is implemented on top of a recent action model learning framework by Rao et al.Experimental results on the benchmark domains show that SPWV leads to a lower error rate of the learnt action model,compared to the probability based approach for state partition that was developed by Rao et al.

Some Applications of the Learning of the Research on Teacher Development in College English Class

Nowadays much concern has been showed on the research of teacher development and an effective way of teacher development, the action research. From the learning of these researches we get clearer that English teaching and learning is a continuous process in which many interrelated and intrinsic factors work together. Furthermore, we know that teachers＇ beliefs on teaching and learning play an important role in class. During the process of learning and teaching, a teacher＇s own learning from relevant books on education and learning helps a lot in increase teachers＇ awareness about English teaching and learning, and improving the classroom teaching. In this paper three effective applications of the results of the research of teacher development are introduced, which shows that a teacher is a decisive factor in the classroom setting and the research and learning about teacher development is useful for a teacher to improve himself or herself as a teacher.

Learning by Researching:Achievements and Actions of Teacher Learning in a School-University Collaborative Project

Purpose:This study explores the achievements and process of a group of Chinese primary school teachers learning from a research-based school-university collaborative project.Design/Approach/Methods:We used qualitative methods to construct our research design,collecting data through participatory observations of weekly meetings,teacher interviews,and participants'reflective journals.Both thematic analysis and discursive analysis were employed as strategies to scrutinize the data.Findings:We categorize teachers'learning into five achievements:outcome,processual,democratic,catalytic,and dialogic achievement.A further examination highlights seven successive learning actions composing an implicit mechanism to facilitate these achievements:questioning,analyzing,modeling,examining,implementing,reflecting,and consolidating.Originality/Value:Through this longitudinal study,we more comprehensively record details about teachers'learning as they conduct their own research.Although school-university heterogeneous collaboration has potential conflicts,teachers can improve their problem-solving and knowledge creation and sharing abilities,promoting a sense of professional accomplishment.These findings also suggest the need to reconsider the authentic process of teacher research,a task equally significant for international educators.

A Reinforcement-learning-based Bidding Strategy for Power Suppliers with Limited Information

The power market is a typical imperfectly competitive market where power suppliers gain higher profits through strategic bidding behaviors.Most existing studies assume that a power supplier is accessible to the sufficient market information to derive an optimal bidding strategy.However,this assumption may not be true in reality,particularly when a power market is newly launched.To help power suppliers bid with the limited information,a modified continuous action reinforcement learning automata algorithm is proposed.This algorithm introduces the discretization and Dyna structure into continuous action reinforcement learning automata algorithm for easy implementation in a repeated game.Simulation results verify the effectiveness of the proposed learning algorithm.

An enhanced eco-driving strategy based on reinforcement learning for connected electric vehicles:cooperative velocity and lane-changing control

Purpose–This study aims to propose an enhanced eco-driving strategy based on reinforcement learning(RL)to alleviate the mileage anxiety of electric vehicles(EVs)in the connected environment.Design/methodology/approach–In this paper,an enhanced eco-driving control strategy based on an advanced RL algorithm in hybrid action space(EEDC-HRL)is proposed for connected EVs.The EEDC-HRL simultaneously controls longitudinal velocity and lateral lane-changing maneuvers to achieve more potential eco-driving.Moreover,this study redesigns an all-purpose and efficient-training reward function with the aim to achieve energy-saving on the premise of ensuring other driving performance.Findings–To illustrate the performance for the EEDC-HRL,the controlled EV was trained and tested in various traffic flow states.The experimental results demonstrate that the proposed technique can effectively improve energy efficiency,without sacrificing travel efficiency,comfort,safety and lane-changing performance in different traffic flow states.Originality/value–In light of the aforementioned discussion,the contributions of this paper are two-fold.An enhanced eco-driving strategy based an advanced RL algorithm in hybrid action space(EEDC-HRL)is proposed to jointly optimize longitudinal velocity and lateral lane-changing for connected EVs.A full-scale reward function consisting of multiple sub-rewards with a safety control constraint is redesigned to achieve eco-driving while ensuring other driving performance.