The Importance of Breathing Training in Folk Dance Teaching

The importance of breathing training in dance teaching is reflected in the two aspects of enhancing the quality of dance movements and sublimating the connotation of dance movements.For example,high-quality breathing can help performers complete the dance movements and improve the coordination of the movements;at the same time,the unique body rhythm formed by breathing can strengthen the visual effect of the performance and convey its spirit and soul to the audience.This requires folk dance teachers to carry out relevant training and teaching activities based on the categories and skills of dance breathing,such as changing students’ideological cognition,developing periodic breathing training courses,providing personalized guidance to students,and allowing students to adjust their learning and practice methods in the evaluation.

Discussion on the Core Strength Training Method for 100-Meter Event in Track and Field Sports

In track and field sports,the 100-meter race is an extremely intense sport that requires effective training of athletes’core strength.From the perspective of adolescents,in order to enhance core strength,it is necessary to effectively fix the pelvic position in the process of exercise,so that the core stability,balance,and coordination of athletes can be improved.The training process of the 100-meter event is mainly an anaerobic metabolic exercise,it is necessary to ensure that athletes maintain a high level of physical readiness during the exercises and concentrate highly on their core strength,so that they can achieve excellent results.This paper analyzes the core strength training for the 100-meter event in track and field sports,discusses its importance,and puts forward specific training methods,hoping to provide guidelines for relevant researchers.

Application and evaluation of improved surgical aseptic technique curriculum in specialty nurse training in Henan Province

Objective:To develop and apply a novel surgical aseptic technique curriculum in specialty training for operating room(OR)nurses.Methods:As surgical environments were improved,related specifications were revised.The proposed surgical aseptic procedure was discussed,improved,and recorded as procedure videos.In the new training program,traditional theoretical lecture training was replaced with sequential theoretical lessons and video demonstration,operation presentation,and skill competition.At the end of the training,the trainees were given questionnaires to assess training outcomes.Differences in student satisfaction regarding the proposed training program and conventional training programs were then compared.Results:A GPA of 3.61±0.58 was recorded after the new training program was implemented.The minimum and maximum average scores of each item were 3.05±0.43 and 4.61±0.33,respectively The satisfaction rate of the trainees in 2013 was significantly higher than that in 2011(t=2.14,P=0.039).Conclusion:Novel surgical aseptic technique and application in the curriculum design of training for OR nurses should be developed to enhance their mastery of theoretical and practical skills and to modify their behaviors.

Discussion on the Application of Stratified Evaluation in Middle School Football Teaching and Training

The evaluation of education in the training process is important for students, and its importance is at the level of students and thecorresponding level of evaluation. Understanding the guidance and training of the middle school football team is an important partof rapidly improving the level of Chinese football. It is necessary to adapt to the development characteristics of middle schoolstudents and conduct reasonable guidance training. Plans must be strengthened to implement training programs. On the trainingschedule, we must balance learning and training, and must fully consider the growth of students. As part of a layered education,stratified evaluation has the unique advantages of basic education and secondary education. The evaluation of the students whofocus on various levels of learning. Football coaching and training requires a full display of the student's personality. The teacher'sstratified evaluation method is very important for improving students' confidence in learning, enhancing students' enthusiasm forfootball training, and promoting students' continuous improvement. This study takes the application of stratified evaluation in theteaching and training of middle school football as the research direction, and puts forward reasonable suggestions by analyzing thetraining cases of the layered evaluation.

Randomized Controlled Trials and Ph.D. Level Training in Educational Research

This invited paper reviews recent initiatives in teaching about randomized field trials at the graduate level in education research. Reports in a book on randomized trials by Mosteller and Boruch are used as a benchmark. The initiatives have been driven heavily by recent governmental emphasis on ＂evidence based policy＂ in education, criminology, welfare and other sectors. This policy has been backed, in the US at least, by substantial investments in mounting randomized controlled trials to evaluate curriculum packages and programs in grades K-12, crime prevention programs, welfare, and other work. Accelerated growth in randomized trials has entailed cross discipline pre-doctoral and post-doctoral institutes and graduate education programs aimed at enhancing the quality in trial design, execution, and analysis of results. Specific illustrations of graduate education in this area of statistics are taken mainly from experience at the University of Pennsylvania＇s Graduate School of Education and selected other institutions.

Improved edge lightweight YOLOv4 and its application in on-site power system work

A“cloud-edge-end”collaborative system architecture is adopted for real-time security management of power system on-site work,and mobile edge computing equipment utilizes lightweight intelligent recognition algorithms for on-site risk assessment and alert.Owing to its lightweight and fast speed,YOLOv4-Tiny is often deployed on edge computing equipment for real-time video stream detection;however,its accuracy is relatively low.This study proposes an improved YOLOv4-Tiny algorithm based on attention mechanism and optimized training methods,achieving higher accuracy without compromising the speed.Specifically,a convolution block attention module branch is added to the backbone network to enhance the feature extraction capability and an efficient channel attention mechanism is added in the neck network to improve feature utilization.Moreover,three optimized training methods:transfer learning,mosaic data augmentation,and label smoothing are used to improve the training effect of this improved algorithm.Finally,an edge computing equipment experimental platform equipped with an NVIDIA Jetson Xavier NX chip is established and the newly developed algorithm is tested on it.According to the results,the speed of the improved YOLOv4-Tiny algorithm in detecting on-site dress code compliance datasets is 17.25 FPS,and the mean average precision(mAP)is increased from 70.89%to 85.03%.

The Education of Japanese Honorifics and Cultivating Intercultural Communication Competence

Japanese honorifics which symbolizes the social culture of Japan are linguistic forms that are indispensable in the Japanese language education.Hence,the Japanese language education requires an improvement of previously solidified teaching methods with the cultivation of students’intercultural communication competence,in order for students to understand Japanese honorifics in-depth and apply them in practice.This paper analyzes the necessity of cultivating students’intercultural communication competence,the difficulties in Japanese honorifics education,and proposes specific training methods to help students improve their competence.

Theoretical Research and Experimental Validation of Elastic Dynamic Load Spectra on Bogie Frame of High-speed Train

When a train runs at high speeds, the external exciting frequencies approach the natural frequencies of bogie critical components, thereby inducing strong elastic vibrations. The present international reliability test evaluation standard and design criteria of bogie frames are all based on the quasi-static deformation hypothesis. Structural fatigue damage generated by structural elastic vibrations has not yet been included. In this paper, theoretical research and experimental validation are done on elastic dynamic load spectra on bogie frame of high-speed train. The construction of the load series that correspond to elastic dynamic deformation modes is studied. The simplified form of the load series is obtained. A theory of simplified dynamic load–time histories is then deduced. Measured data from the Beijing–Shanghai Dedicated Passenger Line are introduced to derive the simplified dynamic load–time histories. The simplified dynamic discrete load spectra of bogie frame are established. Based on the damage consistency criterion and a genetic algorithm, damage consistency calibration of the simplified dynamic load spectra is finally performed. The computed result proves that the simplified load series is reasonable. The calibrated damage that corresponds to the elastic dynamic discrete load spectra can cover the actual damage at the operating conditions. The calibrated damage satisfies the safety requirement of damage consistency criterion for bogie frame. This research is helpful for investigating the standardized load spectra of bogie frame of high-speed train.

Towards robust neural networks via a global and monotonically decreasing robustness training strategy

Robustness of deep neural networks(DNNs)has caused great concerns in the academic and industrial communities,especially in safety-critical domains.Instead of verifying whether the robustness property holds or not in certain neural networks,this paper focuses on training robust neural networks with respect to given perturbations.State-of-the-art training methods,interval bound propagation(IBP)and CROWN-IBP,perform well with respect to small perturbations,but their performance declines significantly in large perturbation cases,which is termed“drawdown risk”in this paper.Specifically,drawdown risk refers to the phenomenon that IBPfamily training methods cannot provide expected robust neural networks in larger perturbation cases,as in smaller perturbation cases.To alleviate the unexpected drawdown risk,we propose a global and monotonically decreasing robustness training strategy that takes multiple perturbations into account during each training epoch(global robustness training),and the corresponding robustness losses are combined with monotonically decreasing weights(monotonically decreasing robustness training).With experimental demonstrations,our presented strategy maintains performance on small perturbations and the drawdown risk on large perturbations is alleviated to a great extent.It is also noteworthy that our training method achieves higher model accuracy than the original training methods,which means that our presented training strategy gives more balanced consideration to robustness and accuracy.

Study on the subgrade deformation under high-speed train loading and water–soil interaction

It is important to study the subgrade characteristics of high-speed railways in consideration of the water–soil coupling dynamic problem,especially when high-speed trains operate in rainy regions.This study develops a nonlinear water–soil interaction dynamic model of slab track coupling with subgrade under high-speed train loading based on vehicle–track coupling dynamics.By using this model,the basic dynamic characteristics,including water–soil interaction and without water induced by the high-speed train loading,are studied.The main factors-the permeability coefficien and the porosity-influencin the subgrade deformation are investigated.The developed model can characterize the soil dynamic behaviour more realistically,especially when considering the influenc of water-rich soil.

A deep Q-learning network based active object detection model with a novel training algorithm for service robots

This paper focuses on the problem of active object detection(AOD).AOD is important for service robots to complete tasks in the family environment,and leads robots to approach the target ob ject by taking appropriate moving actions.Most of the current AOD methods are based on reinforcement learning with low training efficiency and testing accuracy.Therefore,an AOD model based on a deep Q-learning network(DQN)with a novel training algorithm is proposed in this paper.The DQN model is designed to fit the Q-values of various actions,and includes state space,feature extraction,and a multilayer perceptron.In contrast to existing research,a novel training algorithm based on memory is designed for the proposed DQN model to improve training efficiency and testing accuracy.In addition,a method of generating the end state is presented to judge when to stop the AOD task during the training process.Sufficient comparison experiments and ablation studies are performed based on an AOD dataset,proving that the presented method has better performance than the comparable methods and that the proposed training algorithm is more effective than the raw training algorithm.

Building Case-Based Preliminary Design Systems: A Hopfield Network Approach

This paPer addresses the issue of building a case-based preliminary design system by using Hopfield networks. one limitation of Hopfield networks is that it cannot be tralned, i.e. the weights between two neurons must be set in advance. A pattern stored in Hopfield networks cannot be recalled if the pattern is not a local minimum.Two concepts are proposed to deal with this problem. They are the multiple training encoding method and the puppet encoding method. The multiple training encoding method, which guarantees to recall a single stored pattern under appropriate initial conditions of data, is theoretica-lly analyzed, and the minimal number of times for using a pattern for training to guarantee recalling of the pattern among a set of patterns is derived. The puppet encoding method is proved to be able to guarantee recalling of all stored patterns if attaching puppet data to the stored patterns is available.An integrated software PDS (Prelindnary Design System), which is developed from two aspects, is described. One is from a case-based expert system-CPDS (Case-based Prelindnary Design System), which is based on the algorithm of the Hopfield and developed for uncertain problems in PDS; the other is RPDS (Rule-based Preliminary Design System), which attacks logic or deduced problems in PDS. Based on the results of CPDS, RPDS can search for feasible solutioll in design model. CPDS is demonstrated to be useful in the domains of preliminary designs of cable-stayed bridges in this paper.