Knowledge towards Physical Medicine and Rehabilitation among Training Doctors and Medical Students at the Mohammed VI University Hospital of Marrakech

The aim of our study is to evaluate the knowledge regarding physical medicine and rehabilitation among physicians in training and medical students at the Mohammed VI University Hospital in Marrakech, to approach the knowledge, attitudes, and practices of doctors regarding physical medicine and rehabilitation and evaluate the knowledge in theoretical training related to PRM among the students. We conducted a monocentric cross-sectional analytical study, using a web-based anonymous survey, carried out among 558 undergraduate student and training doctors, randomly selected in the form of a survey on the knowledge towards Physical Medicine and rehabilitation. We received 558 survey duly completed by students of the Faculty of Medicine and Pharmacy of Marrakech (62.4%) and training doctors (37.6%). The mean age of the participants was 24.53 ± 3.9 years, with extremes ranging from 17 to 39 years. 52.7% of the participants were from the former educational reform, The predominance of participation was remarkable among pediatricians 23%, The population who knew PRM was the majority (79.3%), 40.7% of the participants were unaware of the availability of a PRM department at Mohamed VI University Hospital, 0.5% of all training doctors and medical students questioned strongly agreed with the sufficiency of their training in disability management were belonging to the new reform, 84.1% of participants had never attended or referred a patient to the PRM department. 23.2% of training doctors affirmed the referral of patients to PRM for further management. Despite the essential role of PRM in the management of diseases, it remains little known by training doctors and medical students. This lack of knowledge of PRM reflects the lack of the undergraduate and postgraduate of the medical education in the field of rehabilitation.

Entrepreneurship training:its influences on innovation potentials among nursing students

Objective:To evaluate the influences of entrepreneurship training on the innovation potential among nursing students.Methods:A quasi-experimental design was utilized to conduct this study.The study was conducted in the classroom at the faculty of nursing,and the“i Hub”center at Ain Shams University(ASU)in Cairo,Egypt.A purposive sample of 42 nursing students who participated in the“Ain Shams University-innovate”competition from the Faculty of Nursing of ASU were included in this study.Data collection tools included(1)Nursing students'innovation skills assessment questionnaire and(2)Innovative projects evaluation tool.Results:There was a statistically significant difference between the mean scores of nursing students'total innovation skills pre-and post-implementation of entrepreneurship training,where P-value<0.05.Conclusions:The results of the current study support the research hypothesis because the implementation of entrepreneurship training positively affects nursing students'innovation potential.Developing entrepreneurship education in nursing and integrating it into nursing programs will stimulate creativity,innovation,and entrepreneurship among nursing students and health care services.

Preparedness training programs improved the emergency response and crucial outcomes among nursing students:a scoping review

Objective:The study aims to review and discuss the outcomes of emergency preparedness training programs among nursing students.Methods:A scoping review was conducted based on the guidelines of Arskey and O’Malley.Databases through PubMed,Science Direct,and Google Scholar were used to obtain the relevant articles within the scoping review.Results:The researchers found 1175 articles.After manual review,28 articles were identified and assessed.The review reported that the emergency preparedness training program significantly affects preparedness,knowledge,performance,self-efficacy,learning motivation,self-confidence,satisfaction,critical decision-making,and problem-solving.Conclusions:Emergency training effectively increases knowledge,skills,confidence,satisfaction,and team performance.

Exploration of the Integrated Training Model for Information Technology Teachers

With the acceleration of the social information process,information awareness and information skills have become the basic qualities of every citizen.The establishment of the training mechanism for scientific and technological innovation talents from the beginning of higher education is insufficient to meet the needs of the development of the times.It is imperative to improve the training of information technology innovation talents and explore a new training model.This paper describes the general situation of the development of education in the field of information technology from a domestic and international perspective.It then analyzes the existing problems,explores new exploration models and implementation suggestions,and puts forward prospects at the end of the paper.

Comprehensive Evaluation of Talent Training Model for Modern Rehabilitation Therapy Technology:A Case Study of School Y

The purpose of this study is to comprehensively evaluate the modern training model of rehabilitation therapy technology talents.Selecting the third-year students of the rehabilitation therapy technology program in School Y as the research subject,300 questionnaires were collected and the effective response rate was 92%.The strengths and weaknesses of the modern training model were analyzed through a mixed qualitative and quantitative research method.It was found that 68%of the students thought that the modern model had obvious advantages in practical teaching,but 42%of the students thought that it still needed to be improved in personalized teaching.This study provides an empirical basis and specific suggestions for optimizing the cultivation of rehabilitation therapy technology talents.

An attention-based teacher-student model for multivariate short-term landslide displacement prediction incorporating weather forecast data

Predicting the displacement of landslide is of utmost practical importance as the landslide can pose serious threats to both human life and property.However,traditional methods have the limitation of random selection in sliding window selection and seldom incorporate weather forecast data for displacement prediction,while a single structural model cannot handle input sequences of different lengths at the same time.In order to solve these limitations,in this study,a new approach is proposed that utilizes weather forecast data and incorporates the maximum information coefficient(MIC),long short-term memory network(LSTM),and attention mechanism to establish a teacher-student coupling model with parallel structure for short-term landslide displacement prediction.Through MIC,a suitable input sequence length is selected for the LSTM model.To investigate the influence of rainfall on landslides during different seasons,a parallel teacher-student coupling model is developed that is able to learn sequential information from various time series of different lengths.The teacher model learns sequence information from rainfall intensity time series while incorporating reliable short-term weather forecast data from platforms such as China Meteorological Administration(CMA)and Reliable Prognosis(https://rp5.ru)to improve the model’s expression capability,and the student model learns sequence information from other time series.An attention module is then designed to integrate different sequence information to derive a context vector,representing seasonal temporal attention mode.Finally,the predicted displacement is obtained through a linear layer.The proposed method demonstrates superior prediction accuracies,surpassing those of the support vector machine(SVM),LSTM,recurrent neural network(RNN),temporal convolutional network(TCN),and LSTM-Attention models.It achieves a mean absolute error(MAE)of 0.072 mm,root mean square error(RMSE)of 0.096 mm,and pearson correlation coefficients(PCCS)of 0.85.Additionally,it exhibits enhanced prediction stability and interpretability,rendering it an indispensable tool for landslide disaster prevention and mitigation.

Low rank optimization for efficient deep learning:making a balance between compact architecture and fast training

Deep neural networks(DNNs)have achieved great success in many data processing applications.However,high computational complexity and storage cost make deep learning difficult to be used on resource-constrained devices,and it is not environmental-friendly with much power cost.In this paper,we focus on low-rank optimization for efficient deep learning techniques.In the space domain,DNNs are compressed by low rank approximation of the network parameters,which directly reduces the storage requirement with a smaller number of network parameters.In the time domain,the network parameters can be trained in a few subspaces,which enables efficient training for fast convergence.The model compression in the spatial domain is summarized into three categories as pre-train,pre-set,and compression-aware methods,respectively.With a series of integrable techniques discussed,such as sparse pruning,quantization,and entropy coding,we can ensemble them in an integration framework with lower computational complexity and storage.In addition to summary of recent technical advances,we have two findings for motivating future works.One is that the effective rank,derived from the Shannon entropy of the normalized singular values,outperforms other conventional sparse measures such as the?_1 norm for network compression.The other is a spatial and temporal balance for tensorized neural networks.For accelerating the training of tensorized neural networks,it is crucial to leverage redundancy for both model compression and subspace training.

Cloudless-Training:基于serverless的高效跨地域分布式ML训练框架

跨地域分布式机器学习(ML)训练能够联合多区域的云资源协作训练,可满足许多新兴ML场景(比如大型模型训练、联邦学习)的训练需求。但其训练效率仍受2方面挑战的制约。首先,多区域云资源缺乏有效的弹性调度,这会影响训练的资源利用率和性能;其次,模型跨地域同步需要在广域网(WAN)上高频通信,受WAN的低带宽和高波动的影响,会产生巨大通信开销。本文提出Cloudless-Training,从3个方面实现高效的跨地域分布式ML训练。首先,它基于serverless计算模式实现,使用控制层和训练执行层的2层架构,支持多云区域的弹性调度和通信。其次,它提供一种弹性调度策略,根据可用云资源的异构性和训练数据集的分布自适应地部署训练工作流。最后,它提供了2种高效的跨云同步策略,包括基于梯度累积的异步随机梯度下降(ASGD-GA)和跨云参数服务器(PS)间的模型平均(MA)。Cloudless-Training是基于OpenFaaS实现的,并被部署在腾讯云上评估,实验结果表明Cloudless-Training可显著地提高跨地域分布式ML训练的资源利用率(训练成本降低了9.2%~24.0%)和同步效率(训练速度最多比基线快1.7倍),并能保证模型的收敛精度。

Disturbance rejection tube model predictive levitation control of maglev trains

Magnetic levitation control technology plays a significant role in maglev trains.Designing a controller for the levitation system is challenging due to the strong nonlinearity,open-loop instability,and the need for fast response and security.In this paper,we propose a Disturbance-Observe-based Tube Model Predictive Levitation Control(DO-TMPLC)scheme combined with a feedback linearization strategy for the levitation system.The proposed strategy incorporates state constraints and control input constraints,i.e.,the air gap,the vertical velocity,and the current applied to the coil.A feedback linearization strategy is used to cancel the nonlinearity of the tracking error system.Then,a disturbance observer is implemented to actively compensate for disturbances while a TMPLC controller is employed to alleviate the remaining disturbances.Furthermore,we analyze the recursive feasibility and input-to-state stability of the closed-loop system.The simulation results indicate the efficacy of the proposed control strategy.

Robust Parameter Identification Method of Adhesion Model for Heavy Haul Trains

A robust parameter identification method based on Kiencke model was proposed to solve the problem of the parameter identification accuracy being affected by the rail environment change and noise interference for heavy-duty trains. Firstly, a Kiencke stick-creep identification model was constructed, and the parameter identification task was transformed into a quadratic programming problem. Secondly, an iterative algorithm was constructed to solve the problem, into which a time-varying forgetting factor was added to track the change of the rail environment, and to solve the uncertainty problem of the wheel-rail environment. The Granger causality test was adopted to detect the interference, and then the weights of the current data were redistributed to solve the problem of noise interference in parameter identification. Finally, simulations were carried out and the results showed that the proposed method could track the change of the track environment in time, reduce the noise interference in the identification process, and effectively identify the adhesion performance parameters.

Comparative Analysis of Machine Learning Models for PDF Malware Detection:Evaluating Different Training and Testing Criteria

The proliferation of maliciously coded documents as file transfers increase has led to a rise in sophisticated attacks.Portable Document Format(PDF)files have emerged as a major attack vector for malware due to their adaptability and wide usage.Detecting malware in PDF files is challenging due to its ability to include various harmful elements such as embedded scripts,exploits,and malicious URLs.This paper presents a comparative analysis of machine learning(ML)techniques,including Naive Bayes(NB),K-Nearest Neighbor(KNN),Average One Dependency Estimator(A1DE),RandomForest(RF),and SupportVectorMachine(SVM)forPDFmalware detection.The study utilizes a dataset obtained from the Canadian Institute for Cyber-security and employs different testing criteria,namely percentage splitting and 10-fold cross-validation.The performance of the techniques is evaluated using F1-score,precision,recall,and accuracy measures.The results indicate that KNNoutperforms other models,achieving an accuracy of 99.8599%using 10-fold cross-validation.The findings highlight the effectiveness of ML models in accurately detecting PDF malware and provide insights for developing robust systems to protect against malicious activities.

Listening Barriers Among Chinese Students And Training Tactics

Listening is very important in the learning of English language. Most of our Chinese students regard listening comprehension as the most difficult part among all kinds of examinations. So if we can find the reasons why the students exist such barriers that prevent them from understanding, and give them ways of training it, I’m sure it will help the students to improve their ability of language learning.

基于Tri-training GPR的半监督软测量建模方法

集成学习因通过构建并结合多个学习器,常获得比单一学习器显著优越的泛化能力。但是在标记数据比例较少时,建立高性能的集成学习软测量模型依然是个挑战。针对这一个问题,提出一种基于半监督集成学习的软测量建模方法——Tri-training GPR模型。该建模策略充分发挥了半监督学习的优势,减轻建模过程对标记样本数据的需求,在低数据标签率下,仍能通过对无标记数据进行筛选从而扩充可用于建模的有标记样本数据集,并进一步结合半监督学习和集成学习的优势,提出一种新的选择高置信度样本的思路。将所提方法应用于青霉素发酵和脱丁烷塔过程,建立青霉素和丁烷浓度预测软测量模型,与传统的建模方法相比获得了更优的预测结果,验证了模型的有效性。

The Role and features of Student Peer Feedback in Interpreting Training——Preliminary Findings of a Survey of both trainers and students

The project delves into the preliminary findings of a survey of both trainers and students on the practice of using student peer feedback in interpreting practice.It first explains the theoretical foundation which justifies the use of peer feedback in interpreting practice,the research methodology and data collection.Then it brings forth specific findings concerning the implementation of peer feedback in the interpreting class followed by discussions of the role and features of student peer feedback as a means to help students ready for the booth.Analysis of the results shows that peer feedback in interpreting practice keeps students on-task,attentive and help them spot their own problems.Trainers and students themselves point to similar features of student peer feedback as focusing on comprehension of the original,word choice and numbers.The preliminary findings of the survey demonstrate the roles and features of student peer feedback in interpreting practice and point to the possible way of enhancing student’s learning curve through more effective peer feedback.

Topic Modelling and Sentimental Analysis of Students’Reviews

Globally,educational institutions have reported a dramatic shift to online learning in an effort to contain the COVID-19 pandemic.The fundamental concern has been the continuance of education.As a result,several novel solutions have been developed to address technical and pedagogical issues.However,these were not the only difficulties that students faced.The implemented solutions involved the operation of the educational process with less regard for students’changing circumstances,which obliged them to study from home.Students should be asked to provide a full list of their concerns.As a result,student reflections,including those from Saudi Arabia,have been analysed to identify obstacles encountered during the COVID-19 pandemic.However,most of the analyses relied on closed-ended questions,which limited student involvement.To delve into students’responses,this study used open-ended questions,a qualitative method(content analysis),a quantitative method(topic modelling),and a sentimental analysis.This study also looked at students’emotional states during and after the COVID-19 pandemic.In terms of determining trends in students’input,the results showed that quantitative and qualitative methods produced similar outcomes.Students had unfavourable sentiments about studying during COVID-19 and positive sentiments about the face-to-face study.Furthermore,topic modelling has revealed that the majority of difficulties are more related to the environment(home)and social life.Students were less accepting of online learning.As a result,it is possible to conclude that face-to-face study still attracts students and provides benefits that online study cannot,such as social interaction and effective eye-to-eye communication.

Research Methodology and Analysis of Innovative Pedagogical Models in Mechanical Engineering Courses for International Students at the School of Mechanical Engineering and Automation, Beihang University

Taking the engineering courses“Introduction to Mechanical Engineering”and“Machining Process”taught by the author at Beihang University’s International School as examples,this article systematically analyzed the current status and background of international student education,focused on research strategies for interdisciplinary education and industry-education integration innovation models from the perspectives of technical demands,student needs,and societal requirements.This article aims to cultivate highly qualified international students who come to China for their studies,help them better adapt to China’s social employment environment,and enhance their capabilities for career development in China.This article proposes a comprehensive set of innovative teaching models for mechanical engineering courses for international students at Beihang University,which encompasses eight key aspects,including talent development programs,curriculum outline reforms,optimization of classroom teaching content,respect for individual student characteristics,the establishment of practical teaching components,the use of open-ended assignments to stimulate enthusiasm for learning,the cultivation of international students’interest in China,their belief in developing friendships with China,and their goals of staying in China,and the incorporation of external experts to facilitate industry-education integration.

Discussion on the Impact of“Shift System”Teaching Model of Physical Education on Students’Mental Health

The“shift system”teaching model of physical education is an emerging education model that aims to improve students’independent choice and personalized development.However,there are also some challenges in the practical application of this model.For example,there are mental health issues for some students including difficulty in adaptation,social interaction,high psychological pressure,etc.Based on this,this article analyzes the impact of the“shift system”teaching model of physical education on students’mental health and explores the optimization path of the physical education“shift system”teaching model in order to promote students’mental health and all-round development.

Research on the Evaluation of VBSE Practical Training Teaching Effectiveness Based on CIPP Model at X University

Based on the CIPP(context,input,process,and product)model,this paper constructs an index system suitable for evaluating the teaching effect of VBSE(virtual business social environment)practical training from four aspects:context evaluation,input evaluation,process evaluation,and product evaluation.Taking the students participating in VBSE practical training in X university as the survey population,381 valid sample data were obtained through an online questionnaire survey,and the index weights were determined by factor analysis method.The score value of the VBSE practical training teaching effect was calculated based on the evaluation mean value of three indexes.The results showed that the context evaluation score was 1.56,the input evaluation score was 1.54,the process evaluation score was 1.51,and the product evaluation score was 1.48.Subsequently,this paper put forward some countermeasures from the aspects of optimizing course arrangement,improving hardware facilities,and enhancing team cooperation to provide a guideline for improving the effect of VBSE practical training.

Study of the Highly-skilled Talent Training Mode for Business English Major Rural Students:A Case Study of Higher Vocational Colleges in Anhui Province

This paper makes an in-depth study of the reasons for employment problems of Business English major rural students in Anhui Province,and sets forth the appropriate highly-skilled Business English talent training mode and the corresponding recommendations.

Effectiveness of combining manual external defibrillator and automated external defibrillator training for third-year nurse students

Purpose:To assess the effectiveness of automated external defibrillator(AED)and manual external defibrillator(MED)training for third-year nurse students.Methods:We conducted post-demonstration and post-practice evaluation for MED defibrillation,and pre-training,post-demonstration,and post-practice evaluation for AED defibrillation.Results:Following MED training,time and confidence to defibrillate were improved significantly post-practice(p<0.001,p<0.001,respectively).In post-demonstration and postpractice evaluation,most students placed electrodes correctly(84.21%vs.80.70%),cleared before defibrillation(75.44%vs.89.47%),and performed cardiopulmonary resuscitation immediately after defibrillation(81.81%vs.94.44%);the evaluations were not statistically different(p=0.806,p=0.094,p=0.198,respectively).For AED training,time and confidence to defibrillate post-demonstration and post-practice were significantly improved(p<0.001 vs.p<0.001;p<0.001 vs.p<0.001,respectively)compared to that of pre-training;there was no obvious difference between the post-demonstration and postpractice evaluation(p=0.235,=0.346,respectively).Post-AED demonstration,most students could place electrodes correctly(85.96%),clear(91.23%),and perform CPR immediately after defibrillation(85.96%),which remained at a high level post-practice(94.74%,85.96%,82.46%,respectively);there was no significant difference between the two evaluations(p=0.203,p=0.557,p=0.776,respectively).Conclusion:Combining MED and AED defibrillation training is effective and feasible for third-year nurse students.Minimal training is effective for AED,while MED requires additional practice.