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.

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.