Biometric characteristics are playing a vital role in security for the last few years.Human gait classification in video sequences is an important biometrics attribute and is used for security purposes.A new framework...Biometric characteristics are playing a vital role in security for the last few years.Human gait classification in video sequences is an important biometrics attribute and is used for security purposes.A new framework for human gait classification in video sequences using deep learning(DL)fusion assisted and posterior probability-based moth flames optimization(MFO)is proposed.In the first step,the video frames are resized and finetuned by two pre-trained lightweight DL models,EfficientNetB0 and MobileNetV2.Both models are selected based on the top-5 accuracy and less number of parameters.Later,both models are trained through deep transfer learning and extracted deep features fused using a voting scheme.In the last step,the authors develop a posterior probabilitybased MFO feature selection algorithm to select the best features.The selected features are classified using several supervised learning methods.The CASIA-B publicly available dataset has been employed for the experimental process.On this dataset,the authors selected six angles such as 0°,18°,90°,108°,162°,and 180°and obtained an average accuracy of 96.9%,95.7%,86.8%,90.0%,95.1%,and 99.7%.Results demonstrate comparable improvement in accuracy and significantly minimize the computational time with recent state-of-the-art techniques.展开更多
The graph-based representation of material structures,along with deep neural network models,often lacks locality and requires large datasets,which are seldom available in specialized materials research.To address this...The graph-based representation of material structures,along with deep neural network models,often lacks locality and requires large datasets,which are seldom available in specialized materials research.To address this challenge,we developed a more data-efficient center-environment(CE)structure representation that incorporates a predefined attention-focused mechanism.This approach was applied in a machine learning(ML)study to examine the local alloying effects on the structural stability of Nb alloys.In the CE feature model,the atomic environment type(AET)method was utilized,which effectively describes the low-symmetry physical shell structures of neighboring atoms.The optimized ML-CEAET models successfully predicted double-site substitution energies in Nb with a mean absolute error of 55.37 meV and identified Si-M pairs(where M=Ta,W,Re,and lanthanide rare-earth elements)as promising stabilizers for Nb.The ML-CE_(AET)model’s good transferability was further confirmed through accurate prediction of untrained alloying element Nb.Significantly,in cases involving small datasets,non-deep learning models with CE features outperformed deep learning models based on graph features reported in the literature.展开更多
This study aimed to address the challenge of accurately and reliably detecting tomatoes in dense planting environments,a critical prerequisite for the automation implementation of robotic harvesting.However,the heavy ...This study aimed to address the challenge of accurately and reliably detecting tomatoes in dense planting environments,a critical prerequisite for the automation implementation of robotic harvesting.However,the heavy reliance on extensive manually annotated datasets for training deep learning models still poses significant limitations to their application in real-world agricultural production environments.To overcome these limitations,we employed domain adaptive learning approach combined with the YOLOv5 model to develop a novel tomato detection model called as TDA-YOLO(tomato detection domain adaptation).We designated the normal illumination scenes in dense planting environments as the source domain and utilized various other illumination scenes as the target domain.To construct bridge mechanism between source and target domains,neural preset for color style transfer is introduced to generate a pseudo-dataset,which served to deal with domain discrepancy.Furthermore,this study combines the semi-supervised learning method to enable the model to extract domain-invariant features more fully,and uses knowledge distillation to improve the model's ability to adapt to the target domain.Additionally,for purpose of promoting inference speed and low computational demand,the lightweight FasterNet network was integrated into the YOLOv5's C3 module,creating a modified C3_Faster module.The experimental results demonstrated that the proposed TDA-YOLO model significantly outperformed original YOLOv5s model,achieving a mAP(mean average precision)of 96.80%for tomato detection across diverse scenarios in dense planting environments,increasing by 7.19 percentage points;Compared with the latest YOLOv8 and YOLOv9,it is also 2.17 and 1.19 percentage points higher,respectively.The model's average detection time per image was an impressive 15 milliseconds,with a FLOPs(floating point operations per second)count of 13.8 G.After acceleration processing,the detection accuracy of the TDA-YOLO model on the Jetson Xavier NX development board is 90.95%,the mAP value is 91.35%,and the detection time of each image is 21 ms,which can still meet the requirements of real-time detection of tomatoes in dense planting environment.The experimental results show that the proposed TDA-YOLO model can accurately and quickly detect tomatoes in dense planting environment,and at the same time avoid the use of a large number of annotated data,which provides technical support for the development of automatic harvesting systems for tomatoes and other fruits.展开更多
Hybrid precoding is considered as a promising low-cost technique for millimeter wave(mm-wave)massive Multi-Input Multi-Output(MIMO)systems.In this work,referring to the time-varying propagation circumstances,with semi...Hybrid precoding is considered as a promising low-cost technique for millimeter wave(mm-wave)massive Multi-Input Multi-Output(MIMO)systems.In this work,referring to the time-varying propagation circumstances,with semi-supervised Incremental Learning(IL),we propose an online hybrid beamforming scheme.Firstly,given the constraint of constant modulus on analog beamformer and combiner,we propose a new broadnetwork-based structure for the design model of hybrid beamforming.Compared with the existing network structure,the proposed network structure can achieve better transmission performance and lower complexity.Moreover,to enhance the efficiency of IL further,by combining the semi-supervised graph with IL,we propose a hybrid beamforming scheme based on chunk-by-chunk semi-supervised learning,where only few transmissions are required to calculate the label and all other unlabelled transmissions would also be put into a training data chunk.Unlike the existing single-by-single approach where transmissions during the model update are not taken into the consideration of model update,all transmissions,even the ones during the model update,would make contributions to model update in the proposed method.During the model update,the amount of unlabelled transmissions is very large and they also carry some information,the prediction performance can be enhanced to some extent by these unlabelled channel data.Simulation results demonstrate the spectral efficiency of the proposed method outperforms that of the existing single-by-single approach.Besides,we prove the general complexity of the proposed method is lower than that of the existing approach and give the condition under which its absolute complexity outperforms that of the existing approach.展开更多
The traditional concept of ”one-size-fits-all” educational and training programmes is no more fully adequate to meet the increasing demand worldwide. E-learning, as an alternative approach to traditional face-to-fac...The traditional concept of ”one-size-fits-all” educational and training programmes is no more fully adequate to meet the increasing demand worldwide. E-learning, as an alternative approach to traditional face-to-face education, is creating immense challenges for educational institutions to develop new approaches for the production and delivery of cost effective and efficient e-contents. Although, there have been many developments in web-based programmes, they have not fully attained their potential due to a variety of factors. These include: 1) lack of exchangeability between learning materials, 2) delivery mechanisms incompatible with the pedagogical design, 3) low student interaction and insensitive learning processes, 4) absence of intelligent online programme advice and guidance, 5) inflexibility in meeting diverse needs, and 6) institutionally centred ineffective implementation strategies. This paper addresses the critical elements for successful delivery of e-learning environments and then focuses on proposing a framework for the development of an integrated knowledge-based learning environment which has the potential to producer cost effective and personalised training programmes.展开更多
The paper analyzes the current condition of the use of virtual learning environment(VLE) in Zhejiang University of Chinese Medicine. It is indicated that students show a positive attitude toward this technology, but t...The paper analyzes the current condition of the use of virtual learning environment(VLE) in Zhejiang University of Chinese Medicine. It is indicated that students show a positive attitude toward this technology, but the use of it fails to meet students' perception. In light of this, recommendations are made with a view to enhance the use of VLE.展开更多
This paper, firstly, acknowledges the importance of classroom environment and the problems existing in the college English classroom. And then, it offers some ways of improving the classroom environment which is very ...This paper, firstly, acknowledges the importance of classroom environment and the problems existing in the college English classroom. And then, it offers some ways of improving the classroom environment which is very critical to evaluate educational programs and curriculum and provides guidance to teachers who are eager to boost their classroom teaching.展开更多
With the increasing development of economy and society,the 21st century will surely become an era of rapid development of information technology.Based on the macro and micro levels of education in China,this paper int...With the increasing development of economy and society,the 21st century will surely become an era of rapid development of information technology.Based on the macro and micro levels of education in China,this paper introduces the"affordance theory"to analyze and discuss the current situation of College English learning environment in China,and puts forward new goals and principles to promote the future development of College English learning environment in order to better promote its effective transformation.展开更多
Ubiquitous learning is a new type of learning method with rich learning concepts and educational significance. The study of ubiquitous learning began in 1991, and has experienced three stages of gestation, start-up an...Ubiquitous learning is a new type of learning method with rich learning concepts and educational significance. The study of ubiquitous learning began in 1991, and has experienced three stages of gestation, start-up and formation and development.After entering the 21 st century, new technologies and new ideas have emerged endlessly. The change in learning methods has led to the flip of classroom teaching, and ubiquitous learning has become more known as the pace of social development. The current higher vocational education presents the characteristics of disjointed education content, misaligned learning roles, and single teaching form. The integration of ubiquitous learning environment into vocational education teaching is a new direction for the development of vocational education.展开更多
Metacognitive strategies are regarded as advanced strategies in all the learning strategies.This study focuses on the application of metacognitive strategies in English listening in the web-based self-access learning ...Metacognitive strategies are regarded as advanced strategies in all the learning strategies.This study focuses on the application of metacognitive strategies in English listening in the web-based self-access learning environment(WSLE) and tries to provide some references for those students and teachers in the vocational colleges.展开更多
Student engagement in a clinical learning environment is a vital component in the curricula of pre-licensure nursing students, providing an opportunity to combine cognitive, psychomotor, and affective skills. This pap...Student engagement in a clinical learning environment is a vital component in the curricula of pre-licensure nursing students, providing an opportunity to combine cognitive, psychomotor, and affective skills. This paper is significant in Arab world as there is a lack of knowledge, attitude and practice of student involvement in the new clinical learning environment. The purpose of this review article is to describe the experiences and perspectives of the nurse educator in facilitating pre-licensure nursing students’ engagement in the new clinical learning environment. The review suggests that novice students prefer actual engagement in clinical learning facilitated through diversity experiences, shared learning opportunities, student-faculty interaction and active learning. They expressed continuous supervision, ongoing feedback, interpersonal relationship and personal support from nurse educators useful in the clinical practice. However, the value of this review lies in a better understanding of what constitutes quality clinical learning environment from the students’ perspective of engagement in evidence-based nursing, reflective practice, e-learning and simulated case scenarios facilitated by the nurse educators. This review is valuable in planning and implementing innovative clinical and educational experiences for improving the quality of the clinical teaching-learning environment.展开更多
Preceding works tend to explicate affordance through supposing what is happening here and now.They seldom relate it to actual social,diachronic activities,such as foreign language learning.To tackle this issue,this st...Preceding works tend to explicate affordance through supposing what is happening here and now.They seldom relate it to actual social,diachronic activities,such as foreign language learning.To tackle this issue,this study explores how students actualize affordances in technology-enriched language learning environment(TELLE)by examining their perezhivanija(lived and emotional experience),a term borrowed from sociocultural theory.Because an individual’s social life is a developing process or a perezhivanie2,it is necessary to base the research in a dynamic development of language learning to figure out how the affordances are actualized.Narrative interviews were adopted to collect data from three Chinese college students who learn English as a foreign language in a Northeastern university in China.The results showed that due to the students’different past perezhivanija in English learning,their present interpretations of the perceived affordances in TELLE varied.This influenced hugely in their actions taken during their English learning in college to actualize the affordances.The findings indicated that the actualization of affordances is historical,dynamic and developmental instead of static.It does not lie in the autonomy of the students or the teachers,but in the institutional and cultural legitimacy of technology use in student’s social life.The paper contributes to the application of affordance theory in foreign language learning and provides implications to language teaching practice in TELLE.展开更多
This paper investigates the challenges associated with Unmanned Aerial Vehicle (UAV) collaborative search and target tracking in dynamic and unknown environments characterized by limited field of view. The primary obj...This paper investigates the challenges associated with Unmanned Aerial Vehicle (UAV) collaborative search and target tracking in dynamic and unknown environments characterized by limited field of view. The primary objective is to explore the unknown environments to locate and track targets effectively. To address this problem, we propose a novel Multi-Agent Reinforcement Learning (MARL) method based on Graph Neural Network (GNN). Firstly, a method is introduced for encoding continuous-space multi-UAV problem data into spatial graphs which establish essential relationships among agents, obstacles, and targets. Secondly, a Graph AttenTion network (GAT) model is presented, which focuses exclusively on adjacent nodes, learns attention weights adaptively and allows agents to better process information in dynamic environments. Reward functions are specifically designed to tackle exploration challenges in environments with sparse rewards. By introducing a framework that integrates centralized training and distributed execution, the advancement of models is facilitated. Simulation results show that the proposed method outperforms the existing MARL method in search rate and tracking performance with less collisions. The experiments show that the proposed method can be extended to applications with a larger number of agents, which provides a potential solution to the challenging problem of multi-UAV autonomous tracking in dynamic unknown environments.展开更多
Objective:The purpose of this study was to explore,describe and illuminate nursing students'best encounters of caring in the clinical learning environment.Caring for nursing students was emphasized and recommendat...Objective:The purpose of this study was to explore,describe and illuminate nursing students'best encounters of caring in the clinical learning environment.Caring for nursing students was emphasized and recommendations provided to enhance caring for nursing students within their clinical learning environment.Methods:Qualitative data was collected by the researcher using semi-structured individual interviews and an Appreciative Inquiry(AI)methodology.Ten second year nursing students undertaking the bridging course leading to registration as general nurses in terms of Regulation 683 of the South African Nursing Council(SANC)were purposively sampled from 3 private hospitals within the Western Cape.Data was analysed using Giorgi's method.Results:The main theme included the best and'least best'caring practices embedded in the centrality of the heart.The subthemes comprised of the nursing students'experiences of caring literacy and caring illiteracy.The second theme included the creation of best caring practices within a conducive clinical learning environment.Within this theme,the subthemes comprised of the caring attributes required in reflecting best caring practices,as well the creation of a clinical learning environment to optimise caring.Conclusions:The significance and necessity of caring for the nursing student were clearly illustrated and confirmed by participants.Caring was equated to the heart as the core to the nursing students'being.Recommendations for nursing education,management,practice and research were therefore specifically formulated to enhance caring towards nursing students.展开更多
Highly accurate positioning is a crucial prerequisite of multi Unmanned Aerial Vehicle close-formation flight for target tracking,formation keeping,and collision avoidance.Although the position of a UAV can be obtaine...Highly accurate positioning is a crucial prerequisite of multi Unmanned Aerial Vehicle close-formation flight for target tracking,formation keeping,and collision avoidance.Although the position of a UAV can be obtained through the Global Positioning System(GPS),it is difficult for a UAV to obtain highly accurate positioning data in a GPS-denied environment(e.g.,a GPS jamming area,suburb,urban canyon,or mountain area);this may cause it to miss a tracking target or collide with another UAV.In particular,UAV close-formation control in GPS-denied environments faces difficulties owing to the low-accuracy position,close distance between vehicles,and nonholonomic dynamics of a UAV.In this paper,on the one hand,we develop an innovative UAV formation localization method to address the formation localization issues in GPS-denied environments;on the other hand,we design a novel reinforcement learning based algorithm to achieve the high-efficiency and robust performance of the controller.First,a novel Lidar-based localization algorithm is developed to measure the localization of each aircraft in the formation flight.In our solution,each UAV is equipped with Lidar as the position measurement sensor instead of the GPS module.The k-means algorithm is implemented to calculate the center point position of UAV.A novel formation position vector matching method is proposed to match center points with UAVs in the formation and estimate their position information.Second,a reinforcement learning based UAV formation control algorithm is developed by selecting the optimal policy to control UAV swarm to start and keep flying in a close formation of a specific geometry.Third,the innovative collision risk evaluation module is proposed to address the collision-free issues in the formation group.Finally,a novel experience replay method is also provided in this paper to enhance the learning efficiency.Experimental results validate the accuracy,effectiveness,and robustness of the proposed scheme.展开更多
Monitoring students’ level of engagement during learning activities is an important challenge in the development of tutoring interventions. In this paper, we explore the feasibility of using electroencephalographic s...Monitoring students’ level of engagement during learning activities is an important challenge in the development of tutoring interventions. In this paper, we explore the feasibility of using electroencephalographic signals (EEG) as a tool to monitor the mental engagement index of novice medicine students during a reasoning process. More precisely, the objectives were first, to track students’ mental engagement evolution in order to investigate whether there were particular sections within the learning environment that aroused the highest engagement level among the students, and, if so, did these sections have an impact on learners’ performance. Experimental analyses showed the same trends in the different resolution phases as well as across the different regions of the environments. However, we noticed a higher engagement index during the treatment identification phase since it aroused more mental effort. Moreover statistically significant effects were found between mental engagement and students’ performance.展开更多
基金King Saud University,Grant/Award Number:RSP2024R157。
文摘Biometric characteristics are playing a vital role in security for the last few years.Human gait classification in video sequences is an important biometrics attribute and is used for security purposes.A new framework for human gait classification in video sequences using deep learning(DL)fusion assisted and posterior probability-based moth flames optimization(MFO)is proposed.In the first step,the video frames are resized and finetuned by two pre-trained lightweight DL models,EfficientNetB0 and MobileNetV2.Both models are selected based on the top-5 accuracy and less number of parameters.Later,both models are trained through deep transfer learning and extracted deep features fused using a voting scheme.In the last step,the authors develop a posterior probabilitybased MFO feature selection algorithm to select the best features.The selected features are classified using several supervised learning methods.The CASIA-B publicly available dataset has been employed for the experimental process.On this dataset,the authors selected six angles such as 0°,18°,90°,108°,162°,and 180°and obtained an average accuracy of 96.9%,95.7%,86.8%,90.0%,95.1%,and 99.7%.Results demonstrate comparable improvement in accuracy and significantly minimize the computational time with recent state-of-the-art techniques.
基金supported by the National Natural Science Foundation of China(Nos.52373227,52201016)the National Key Research and Development Program of China(Nos.2017YFB0702901,2017YFB0701502,2023YFB4606200)+1 种基金Shanghai Technical Service Center for Advanced Ceramics Structure Design and Precision Manufacturing,China(No.20DZ2294000)Key Program of Science and Technology of Yunnan Province,China(No.202302AB080020).
文摘The graph-based representation of material structures,along with deep neural network models,often lacks locality and requires large datasets,which are seldom available in specialized materials research.To address this challenge,we developed a more data-efficient center-environment(CE)structure representation that incorporates a predefined attention-focused mechanism.This approach was applied in a machine learning(ML)study to examine the local alloying effects on the structural stability of Nb alloys.In the CE feature model,the atomic environment type(AET)method was utilized,which effectively describes the low-symmetry physical shell structures of neighboring atoms.The optimized ML-CEAET models successfully predicted double-site substitution energies in Nb with a mean absolute error of 55.37 meV and identified Si-M pairs(where M=Ta,W,Re,and lanthanide rare-earth elements)as promising stabilizers for Nb.The ML-CE_(AET)model’s good transferability was further confirmed through accurate prediction of untrained alloying element Nb.Significantly,in cases involving small datasets,non-deep learning models with CE features outperformed deep learning models based on graph features reported in the literature.
基金The National Natural Science Foundation of China (32371993)The Natural Science Research Key Project of Anhui Provincial University(2022AH040125&2023AH040135)The Key Research and Development Plan of Anhui Province (202204c06020022&2023n06020057)。
文摘This study aimed to address the challenge of accurately and reliably detecting tomatoes in dense planting environments,a critical prerequisite for the automation implementation of robotic harvesting.However,the heavy reliance on extensive manually annotated datasets for training deep learning models still poses significant limitations to their application in real-world agricultural production environments.To overcome these limitations,we employed domain adaptive learning approach combined with the YOLOv5 model to develop a novel tomato detection model called as TDA-YOLO(tomato detection domain adaptation).We designated the normal illumination scenes in dense planting environments as the source domain and utilized various other illumination scenes as the target domain.To construct bridge mechanism between source and target domains,neural preset for color style transfer is introduced to generate a pseudo-dataset,which served to deal with domain discrepancy.Furthermore,this study combines the semi-supervised learning method to enable the model to extract domain-invariant features more fully,and uses knowledge distillation to improve the model's ability to adapt to the target domain.Additionally,for purpose of promoting inference speed and low computational demand,the lightweight FasterNet network was integrated into the YOLOv5's C3 module,creating a modified C3_Faster module.The experimental results demonstrated that the proposed TDA-YOLO model significantly outperformed original YOLOv5s model,achieving a mAP(mean average precision)of 96.80%for tomato detection across diverse scenarios in dense planting environments,increasing by 7.19 percentage points;Compared with the latest YOLOv8 and YOLOv9,it is also 2.17 and 1.19 percentage points higher,respectively.The model's average detection time per image was an impressive 15 milliseconds,with a FLOPs(floating point operations per second)count of 13.8 G.After acceleration processing,the detection accuracy of the TDA-YOLO model on the Jetson Xavier NX development board is 90.95%,the mAP value is 91.35%,and the detection time of each image is 21 ms,which can still meet the requirements of real-time detection of tomatoes in dense planting environment.The experimental results show that the proposed TDA-YOLO model can accurately and quickly detect tomatoes in dense planting environment,and at the same time avoid the use of a large number of annotated data,which provides technical support for the development of automatic harvesting systems for tomatoes and other fruits.
基金supported by the National Science Foundation of China under Grant No.62101467.
文摘Hybrid precoding is considered as a promising low-cost technique for millimeter wave(mm-wave)massive Multi-Input Multi-Output(MIMO)systems.In this work,referring to the time-varying propagation circumstances,with semi-supervised Incremental Learning(IL),we propose an online hybrid beamforming scheme.Firstly,given the constraint of constant modulus on analog beamformer and combiner,we propose a new broadnetwork-based structure for the design model of hybrid beamforming.Compared with the existing network structure,the proposed network structure can achieve better transmission performance and lower complexity.Moreover,to enhance the efficiency of IL further,by combining the semi-supervised graph with IL,we propose a hybrid beamforming scheme based on chunk-by-chunk semi-supervised learning,where only few transmissions are required to calculate the label and all other unlabelled transmissions would also be put into a training data chunk.Unlike the existing single-by-single approach where transmissions during the model update are not taken into the consideration of model update,all transmissions,even the ones during the model update,would make contributions to model update in the proposed method.During the model update,the amount of unlabelled transmissions is very large and they also carry some information,the prediction performance can be enhanced to some extent by these unlabelled channel data.Simulation results demonstrate the spectral efficiency of the proposed method outperforms that of the existing single-by-single approach.Besides,we prove the general complexity of the proposed method is lower than that of the existing approach and give the condition under which its absolute complexity outperforms that of the existing approach.
文摘The traditional concept of ”one-size-fits-all” educational and training programmes is no more fully adequate to meet the increasing demand worldwide. E-learning, as an alternative approach to traditional face-to-face education, is creating immense challenges for educational institutions to develop new approaches for the production and delivery of cost effective and efficient e-contents. Although, there have been many developments in web-based programmes, they have not fully attained their potential due to a variety of factors. These include: 1) lack of exchangeability between learning materials, 2) delivery mechanisms incompatible with the pedagogical design, 3) low student interaction and insensitive learning processes, 4) absence of intelligent online programme advice and guidance, 5) inflexibility in meeting diverse needs, and 6) institutionally centred ineffective implementation strategies. This paper addresses the critical elements for successful delivery of e-learning environments and then focuses on proposing a framework for the development of an integrated knowledge-based learning environment which has the potential to producer cost effective and personalised training programmes.
文摘The paper analyzes the current condition of the use of virtual learning environment(VLE) in Zhejiang University of Chinese Medicine. It is indicated that students show a positive attitude toward this technology, but the use of it fails to meet students' perception. In light of this, recommendations are made with a view to enhance the use of VLE.
文摘This paper, firstly, acknowledges the importance of classroom environment and the problems existing in the college English classroom. And then, it offers some ways of improving the classroom environment which is very critical to evaluate educational programs and curriculum and provides guidance to teachers who are eager to boost their classroom teaching.
文摘With the increasing development of economy and society,the 21st century will surely become an era of rapid development of information technology.Based on the macro and micro levels of education in China,this paper introduces the"affordance theory"to analyze and discuss the current situation of College English learning environment in China,and puts forward new goals and principles to promote the future development of College English learning environment in order to better promote its effective transformation.
文摘Ubiquitous learning is a new type of learning method with rich learning concepts and educational significance. The study of ubiquitous learning began in 1991, and has experienced three stages of gestation, start-up and formation and development.After entering the 21 st century, new technologies and new ideas have emerged endlessly. The change in learning methods has led to the flip of classroom teaching, and ubiquitous learning has become more known as the pace of social development. The current higher vocational education presents the characteristics of disjointed education content, misaligned learning roles, and single teaching form. The integration of ubiquitous learning environment into vocational education teaching is a new direction for the development of vocational education.
文摘Metacognitive strategies are regarded as advanced strategies in all the learning strategies.This study focuses on the application of metacognitive strategies in English listening in the web-based self-access learning environment(WSLE) and tries to provide some references for those students and teachers in the vocational colleges.
文摘Student engagement in a clinical learning environment is a vital component in the curricula of pre-licensure nursing students, providing an opportunity to combine cognitive, psychomotor, and affective skills. This paper is significant in Arab world as there is a lack of knowledge, attitude and practice of student involvement in the new clinical learning environment. The purpose of this review article is to describe the experiences and perspectives of the nurse educator in facilitating pre-licensure nursing students’ engagement in the new clinical learning environment. The review suggests that novice students prefer actual engagement in clinical learning facilitated through diversity experiences, shared learning opportunities, student-faculty interaction and active learning. They expressed continuous supervision, ongoing feedback, interpersonal relationship and personal support from nurse educators useful in the clinical practice. However, the value of this review lies in a better understanding of what constitutes quality clinical learning environment from the students’ perspective of engagement in evidence-based nursing, reflective practice, e-learning and simulated case scenarios facilitated by the nurse educators. This review is valuable in planning and implementing innovative clinical and educational experiences for improving the quality of the clinical teaching-learning environment.
基金part of the work for the National Project on Social Sciences“Efficacy of Ecological Affordances Actualization in Language Learning Environment in China in the Technology Era”(16BYY093)
文摘Preceding works tend to explicate affordance through supposing what is happening here and now.They seldom relate it to actual social,diachronic activities,such as foreign language learning.To tackle this issue,this study explores how students actualize affordances in technology-enriched language learning environment(TELLE)by examining their perezhivanija(lived and emotional experience),a term borrowed from sociocultural theory.Because an individual’s social life is a developing process or a perezhivanie2,it is necessary to base the research in a dynamic development of language learning to figure out how the affordances are actualized.Narrative interviews were adopted to collect data from three Chinese college students who learn English as a foreign language in a Northeastern university in China.The results showed that due to the students’different past perezhivanija in English learning,their present interpretations of the perceived affordances in TELLE varied.This influenced hugely in their actions taken during their English learning in college to actualize the affordances.The findings indicated that the actualization of affordances is historical,dynamic and developmental instead of static.It does not lie in the autonomy of the students or the teachers,but in the institutional and cultural legitimacy of technology use in student’s social life.The paper contributes to the application of affordance theory in foreign language learning and provides implications to language teaching practice in TELLE.
基金supported by the National Natural Science Foundation of China(Nos.12272104,U22B2013).
文摘This paper investigates the challenges associated with Unmanned Aerial Vehicle (UAV) collaborative search and target tracking in dynamic and unknown environments characterized by limited field of view. The primary objective is to explore the unknown environments to locate and track targets effectively. To address this problem, we propose a novel Multi-Agent Reinforcement Learning (MARL) method based on Graph Neural Network (GNN). Firstly, a method is introduced for encoding continuous-space multi-UAV problem data into spatial graphs which establish essential relationships among agents, obstacles, and targets. Secondly, a Graph AttenTion network (GAT) model is presented, which focuses exclusively on adjacent nodes, learns attention weights adaptively and allows agents to better process information in dynamic environments. Reward functions are specifically designed to tackle exploration challenges in environments with sparse rewards. By introducing a framework that integrates centralized training and distributed execution, the advancement of models is facilitated. Simulation results show that the proposed method outperforms the existing MARL method in search rate and tracking performance with less collisions. The experiments show that the proposed method can be extended to applications with a larger number of agents, which provides a potential solution to the challenging problem of multi-UAV autonomous tracking in dynamic unknown environments.
基金The research study was financially supported by the researcher and the partial funding of Supervisor bursaries as awarded by the University of Johannesburg.
文摘Objective:The purpose of this study was to explore,describe and illuminate nursing students'best encounters of caring in the clinical learning environment.Caring for nursing students was emphasized and recommendations provided to enhance caring for nursing students within their clinical learning environment.Methods:Qualitative data was collected by the researcher using semi-structured individual interviews and an Appreciative Inquiry(AI)methodology.Ten second year nursing students undertaking the bridging course leading to registration as general nurses in terms of Regulation 683 of the South African Nursing Council(SANC)were purposively sampled from 3 private hospitals within the Western Cape.Data was analysed using Giorgi's method.Results:The main theme included the best and'least best'caring practices embedded in the centrality of the heart.The subthemes comprised of the nursing students'experiences of caring literacy and caring illiteracy.The second theme included the creation of best caring practices within a conducive clinical learning environment.Within this theme,the subthemes comprised of the caring attributes required in reflecting best caring practices,as well the creation of a clinical learning environment to optimise caring.Conclusions:The significance and necessity of caring for the nursing student were clearly illustrated and confirmed by participants.Caring was equated to the heart as the core to the nursing students'being.Recommendations for nursing education,management,practice and research were therefore specifically formulated to enhance caring towards nursing students.
基金This work was co-funded by the National Natural Science Foundation of China(No.52072309)Key Research and Development Program of Shaanxi,China(No.2019ZDLGY14-02-01)+5 种基金Shenzhen Fundamental Research Program,China(No.JCYJ20190806152203506)Aeronautical Science Foundation of China(No.ASFC-2018ZC53026)Funding Project with Beijing Institute of Spacecraft System Engineering,China(No.JSZL2020203B004)the Basic Research Program of Taicang,China(No.TC2021JC09)the Natural Science Foundation of Shaanxi Province,China(No.2023-JC-QN-0003)Innovation Foundation for Doctor Dissertation of Northwestern Polytechnical University,China(No.CX2021033).
文摘Highly accurate positioning is a crucial prerequisite of multi Unmanned Aerial Vehicle close-formation flight for target tracking,formation keeping,and collision avoidance.Although the position of a UAV can be obtained through the Global Positioning System(GPS),it is difficult for a UAV to obtain highly accurate positioning data in a GPS-denied environment(e.g.,a GPS jamming area,suburb,urban canyon,or mountain area);this may cause it to miss a tracking target or collide with another UAV.In particular,UAV close-formation control in GPS-denied environments faces difficulties owing to the low-accuracy position,close distance between vehicles,and nonholonomic dynamics of a UAV.In this paper,on the one hand,we develop an innovative UAV formation localization method to address the formation localization issues in GPS-denied environments;on the other hand,we design a novel reinforcement learning based algorithm to achieve the high-efficiency and robust performance of the controller.First,a novel Lidar-based localization algorithm is developed to measure the localization of each aircraft in the formation flight.In our solution,each UAV is equipped with Lidar as the position measurement sensor instead of the GPS module.The k-means algorithm is implemented to calculate the center point position of UAV.A novel formation position vector matching method is proposed to match center points with UAVs in the formation and estimate their position information.Second,a reinforcement learning based UAV formation control algorithm is developed by selecting the optimal policy to control UAV swarm to start and keep flying in a close formation of a specific geometry.Third,the innovative collision risk evaluation module is proposed to address the collision-free issues in the formation group.Finally,a novel experience replay method is also provided in this paper to enhance the learning efficiency.Experimental results validate the accuracy,effectiveness,and robustness of the proposed scheme.
文摘Monitoring students’ level of engagement during learning activities is an important challenge in the development of tutoring interventions. In this paper, we explore the feasibility of using electroencephalographic signals (EEG) as a tool to monitor the mental engagement index of novice medicine students during a reasoning process. More precisely, the objectives were first, to track students’ mental engagement evolution in order to investigate whether there were particular sections within the learning environment that aroused the highest engagement level among the students, and, if so, did these sections have an impact on learners’ performance. Experimental analyses showed the same trends in the different resolution phases as well as across the different regions of the environments. However, we noticed a higher engagement index during the treatment identification phase since it aroused more mental effort. Moreover statistically significant effects were found between mental engagement and students’ performance.
