The Office of the Leading Team of Product Quality and Food Safety of the State Council held its third meeting on September 10. Li Changjiang, Minister of AQSIQ, presided over the meeting, and Pu Changcheng, Deputy... The Office of the Leading Team of Product Quality and Food Safety of the State Council held its third meeting on September 10. Li Changjiang, Minister of AQSIQ, presided over the meeting, and Pu Changcheng, Deputy Director of AQSIQ, also attended.……展开更多
Background:Teamwork is essential to provide the highest quality of care for patients.Feeling supported within a nursing unit is a significant factor in nursing satisfaction,intention to remain in the job,and the capac...Background:Teamwork is essential to provide the highest quality of care for patients.Feeling supported within a nursing unit is a significant factor in nursing satisfaction,intention to remain in the job,and the capacity to provide safe patient care by Aiken et al[1].Purpose:This study examined mutual support among a nursing team to measure the influence of an educational intervention focusing on Mutual Support from the Team STEPPS curriculum by Agency for Healthcare Research and Quality,AHRQ[2].Methods:The study design used a Likert scale survey,the Nursing Teamwork Survey,before and following an education intervention adapted from the Team STEPPS curriculum on Mutual Support.Demographic data from the 41 participants were analyzed for impact on educational background,roles and responsibilities,age,and other factors.Results:Pre-Post education intervention results varied among the survey items,although scores demonstrated heightened awareness of teamwork following the educational intervention.The subscale of Backup illustrated the strongest improvement.Conclusion:The study demonstrates that education can have an impact on perceptions and awareness of mutual support among nursing team members.The survey instrument can be used effectively to inform leadership areas for improvement and staff development in the effort to improve team coordination and mutual support.展开更多
Autonomous agents can explore the environment around them when equipped with advanced hardware and software systems that help intelligent agents minimize collisions.These systems are developed under the term Artificia...Autonomous agents can explore the environment around them when equipped with advanced hardware and software systems that help intelligent agents minimize collisions.These systems are developed under the term Artificial Intelligence(AI)safety.AI safety is essential to provide reliable service to consumers in various fields such asmilitary,education,healthcare,and automotive.This paper presents the design of an AI safety algorithmfor safe autonomous navigation using Reinforcement Learning(RL).Machine Learning Agents Toolkit(ML-Agents)was used to train the agentwith a proximal policy optimizer algorithmwith an intrinsic curiositymodule(PPO+ICM).This training aims to improve AI safety and minimize or prevent any mistakes that can cause dangerous collisions by the intelligent agent.Four experiments have been executed to validate the results of our research.The designed algorithmwas tested in a virtual environment with four differentmodels.A comparison was presented in four cases to identify the best-performing model for improvingAI safety.The designed algorithmenabled the intelligent agent to perform the required task safely using RL.A goal collision ratio of 64%was achieved,and the collision incidents were minimized from 134 to 52 in the virtual environment within 30min.展开更多
文摘 The Office of the Leading Team of Product Quality and Food Safety of the State Council held its third meeting on September 10. Li Changjiang, Minister of AQSIQ, presided over the meeting, and Pu Changcheng, Deputy Director of AQSIQ, also attended.……
文摘Background:Teamwork is essential to provide the highest quality of care for patients.Feeling supported within a nursing unit is a significant factor in nursing satisfaction,intention to remain in the job,and the capacity to provide safe patient care by Aiken et al[1].Purpose:This study examined mutual support among a nursing team to measure the influence of an educational intervention focusing on Mutual Support from the Team STEPPS curriculum by Agency for Healthcare Research and Quality,AHRQ[2].Methods:The study design used a Likert scale survey,the Nursing Teamwork Survey,before and following an education intervention adapted from the Team STEPPS curriculum on Mutual Support.Demographic data from the 41 participants were analyzed for impact on educational background,roles and responsibilities,age,and other factors.Results:Pre-Post education intervention results varied among the survey items,although scores demonstrated heightened awareness of teamwork following the educational intervention.The subscale of Backup illustrated the strongest improvement.Conclusion:The study demonstrates that education can have an impact on perceptions and awareness of mutual support among nursing team members.The survey instrument can be used effectively to inform leadership areas for improvement and staff development in the effort to improve team coordination and mutual support.
基金the United States Air Force Office of Scientific Research(AFOSR)contract FA9550-22-1-0268 awarded to KHA,https://www.afrl.af.mil/AFOSR/.The contract is entitled:“Investigating Improving Safety of Autonomous Exploring Intelligent Agents with Human-in-the-Loop Reinforcement Learning,”and in part by Jackson State University.
文摘Autonomous agents can explore the environment around them when equipped with advanced hardware and software systems that help intelligent agents minimize collisions.These systems are developed under the term Artificial Intelligence(AI)safety.AI safety is essential to provide reliable service to consumers in various fields such asmilitary,education,healthcare,and automotive.This paper presents the design of an AI safety algorithmfor safe autonomous navigation using Reinforcement Learning(RL).Machine Learning Agents Toolkit(ML-Agents)was used to train the agentwith a proximal policy optimizer algorithmwith an intrinsic curiositymodule(PPO+ICM).This training aims to improve AI safety and minimize or prevent any mistakes that can cause dangerous collisions by the intelligent agent.Four experiments have been executed to validate the results of our research.The designed algorithmwas tested in a virtual environment with four differentmodels.A comparison was presented in four cases to identify the best-performing model for improvingAI safety.The designed algorithmenabled the intelligent agent to perform the required task safely using RL.A goal collision ratio of 64%was achieved,and the collision incidents were minimized from 134 to 52 in the virtual environment within 30min.
