Field training is the backbone of the teacher-preparation process.Its importance stems from the goals that colleges of education aim to achieve,which include bridging the gap between theory and practice and aligning w...Field training is the backbone of the teacher-preparation process.Its importance stems from the goals that colleges of education aim to achieve,which include bridging the gap between theory and practice and aligning with contemporary educational trends during teacher training.Currently,trainee students attendance in field training is recordedmanually through signatures on attendance sheets.However,thismethod is prone to impersonation,time wastage,and misplacement.Additionally,traditional methods of evaluating trainee students are often susceptible to human errors during the evaluation and scoring processes.Field training also lacks modern technology that the supervisor can use in case of his absence from school to monitor the trainee students’implementation of the required activities and tasks.These shortcomings do not meet the needs of the digital era that universities are currently experiencing.As a result,this paper presents a smart management system for field training based on Internet of Things(IoT)and mobile technology.It includes three subsystems:attendance,monitoring,and evaluation.The attendance subsystem uses an R307 fingerprint sensor to record trainee students’attendance.The Arduino Nano microcontroller transmits attendance data to the proposed Android application via an ESP-12F Wi-Fi module,which then forwards it to the Firebase database for storage.The monitoring subsystem utilizes Global Positioning System(GPS)technology to continually track trainee students’locations,ensuring they remain at the school during training.It also enables remote communication between trainee students and supervisors via audio,video,or text by integrating video call and chat technologies.The evaluation subsystem is based on three items:an online exam,attendance,and implementation of required activities and tasks.Experimental results have demonstrated the accuracy and efficiency of the proposed management system in recording attendance,as well as in monitoring and evaluating trainee students during field traiing.展开更多
As we all know, peoples intrinsic enthusiasm is the key factor to achieve their goals and it is also true for track and field training. From teaching, we can easily find that most students enthusiasm for track and fie...As we all know, peoples intrinsic enthusiasm is the key factor to achieve their goals and it is also true for track and field training. From teaching, we can easily find that most students enthusiasm for track and field training is not high. Therefore, the process of students training is also forced training at the request of teachers and this mode of training will inevitably be difficult to achieve the ideal training effect. Applying motivation theory to training can arouse students enthusiasm for training, make students devote themselves to training in a full mental state and promote students to move forward unswervingly towards the training goal. This is of great benefit to improve the training efficiency and quality.展开更多
Summary What is already known about this topic?Trainees in the China Field Epidemiology Training Program(CFETP)constitute a vital workforce in addressing global public health emergencies.Developing intercultural commu...Summary What is already known about this topic?Trainees in the China Field Epidemiology Training Program(CFETP)constitute a vital workforce in addressing global public health emergencies.Developing intercultural communication competence is essential for their future participation in international public health efforts.However,within China’s existing public health training system,this aspect has not yet received adequate attention or been systematically strengthened.展开更多
Gas-insulated switchgear(GIS)plays a critical role in ensuring the reliability of power systems,but partial discharge(PD)is a primary cause of failures within GIS equipment.Traditional PD diagnostic methods rely heavi...Gas-insulated switchgear(GIS)plays a critical role in ensuring the reliability of power systems,but partial discharge(PD)is a primary cause of failures within GIS equipment.Traditional PD diagnostic methods rely heavily on laboratory data,which differ signifi-cantly from that under the complex conditions of field data,leading to a marked drop in recognition accuracy when they are applied to field PD diagnosis.This study addresses the challenge by integrating field data into the training process,utilising a deep transfer learning approach that combines laboratory and field data to improve diagnostic accuracy for GIS PD.The research collected PD data from laboratory models representing five defect types and field data gathered from operational GIS equipment.A deep residual network(ResNet50)was pretrained using laboratory data and fine-tuned with field data through deep transfer learning to optimise the recognition of PD in field conditions.The results show that the proposed model achieves a significantly higher recognition accuracy(93.7%)for field data compared to traditional methods(60%-70%).The integration of deep transfer learning ensures that both low-dimensional general features from labora-tory data and high-dimensional specific features from field data are effectively utilised.This research significantly contributes to improving the diagnostic accuracy of PD in GIS under field conditions,providing a robust method for defect detection in operational equipment.展开更多
文摘Field training is the backbone of the teacher-preparation process.Its importance stems from the goals that colleges of education aim to achieve,which include bridging the gap between theory and practice and aligning with contemporary educational trends during teacher training.Currently,trainee students attendance in field training is recordedmanually through signatures on attendance sheets.However,thismethod is prone to impersonation,time wastage,and misplacement.Additionally,traditional methods of evaluating trainee students are often susceptible to human errors during the evaluation and scoring processes.Field training also lacks modern technology that the supervisor can use in case of his absence from school to monitor the trainee students’implementation of the required activities and tasks.These shortcomings do not meet the needs of the digital era that universities are currently experiencing.As a result,this paper presents a smart management system for field training based on Internet of Things(IoT)and mobile technology.It includes three subsystems:attendance,monitoring,and evaluation.The attendance subsystem uses an R307 fingerprint sensor to record trainee students’attendance.The Arduino Nano microcontroller transmits attendance data to the proposed Android application via an ESP-12F Wi-Fi module,which then forwards it to the Firebase database for storage.The monitoring subsystem utilizes Global Positioning System(GPS)technology to continually track trainee students’locations,ensuring they remain at the school during training.It also enables remote communication between trainee students and supervisors via audio,video,or text by integrating video call and chat technologies.The evaluation subsystem is based on three items:an online exam,attendance,and implementation of required activities and tasks.Experimental results have demonstrated the accuracy and efficiency of the proposed management system in recording attendance,as well as in monitoring and evaluating trainee students during field traiing.
文摘As we all know, peoples intrinsic enthusiasm is the key factor to achieve their goals and it is also true for track and field training. From teaching, we can easily find that most students enthusiasm for track and field training is not high. Therefore, the process of students training is also forced training at the request of teachers and this mode of training will inevitably be difficult to achieve the ideal training effect. Applying motivation theory to training can arouse students enthusiasm for training, make students devote themselves to training in a full mental state and promote students to move forward unswervingly towards the training goal. This is of great benefit to improve the training efficiency and quality.
基金Supported by funds from African countries to enhance their public health capacitybuilding projects[No.OPP1161303(GAT/16/303)].
文摘Summary What is already known about this topic?Trainees in the China Field Epidemiology Training Program(CFETP)constitute a vital workforce in addressing global public health emergencies.Developing intercultural communication competence is essential for their future participation in international public health efforts.However,within China’s existing public health training system,this aspect has not yet received adequate attention or been systematically strengthened.
基金Key Program of Joint Funds of the National Natural Science Foundation of China,Grant/Award Number:U22B20118。
文摘Gas-insulated switchgear(GIS)plays a critical role in ensuring the reliability of power systems,but partial discharge(PD)is a primary cause of failures within GIS equipment.Traditional PD diagnostic methods rely heavily on laboratory data,which differ signifi-cantly from that under the complex conditions of field data,leading to a marked drop in recognition accuracy when they are applied to field PD diagnosis.This study addresses the challenge by integrating field data into the training process,utilising a deep transfer learning approach that combines laboratory and field data to improve diagnostic accuracy for GIS PD.The research collected PD data from laboratory models representing five defect types and field data gathered from operational GIS equipment.A deep residual network(ResNet50)was pretrained using laboratory data and fine-tuned with field data through deep transfer learning to optimise the recognition of PD in field conditions.The results show that the proposed model achieves a significantly higher recognition accuracy(93.7%)for field data compared to traditional methods(60%-70%).The integration of deep transfer learning ensures that both low-dimensional general features from labora-tory data and high-dimensional specific features from field data are effectively utilised.This research significantly contributes to improving the diagnostic accuracy of PD in GIS under field conditions,providing a robust method for defect detection in operational equipment.
