An Investigation on the Application of Online and Offline Hybrid Teaching in Local Anatomy Course of Acupuncture and Moxibustion Specialty

The purpose of this paper is to explore the application of online and offline blended teaching in local anatomy courses of acupuncture specialty.It introduces the concept and characteristics of blended teaching mode and analyzes the respective advantages of online and offline teaching as well as the advantages and application prospects of blended teaching mode.The teaching status quo of local anatomy courses in acupuncture and moxibustion is analyzed,pointing out the problems of the traditional teaching mode and the application prospect of the blended teaching mode.In the practical part,the preparation and design of online teaching resources,the design of offline practical teaching sessions,and the evaluation methods of teaching effect are introduced in detail.This study aims to provide new teaching modes and ideas for teaching acupuncture and moxibustion and promote the improvement of teaching quality.

Research on the Blended Online and Offline Teaching Mode of Ideological and Political Courses in Universities

With the development of information technology,the blended online and offline teaching mode has gradually become a new trend in the teaching of ideological and political theory courses in universities.This article analyzes the current situation and existing problems of blended online and offline teaching of ideological and political courses in universities,and explores how to effectively combine online and offline teaching resources to improve the teaching effectiveness of ideological and political courses in universities.

Construction Ideas of Musculoskeletal Rehabilitation First-class Undergraduate Course Based on Online and Offline Blended Teaching

In order to combine the advantages of online teaching and traditional offline classroom teaching,this paper optimizes the teaching design by taking Musculoskeletal Rehabilitation for undergraduates as the carrier,and reconstructs the course according to five parts:basic theory course,practical training course,standardized patient,case report,and course evaluation.Through analyzing the classroom quality and teaching effect,the innovation and practical effect of course reconstruction are explored.With students as the main body and goals as the guide,this model gives full play to the initiative and creativity of students,meets the individual needs of students at different levels,and provides reference ideas for improving the advanced,innovative and challenging creation of the course.

Online and Offline Integrated Teaching of Histology and Embryology Based on Concept of "Golden Course"

Taking construction of the online and offline integrated first-class undergraduate curriculum teaching modes of Histology and Embryology in Guangxi as an opportunity,under the guidance of student-centered teaching concept,efforts were made to innovate online and offline integrated teaching mode to overcome the shortcomings and dilemma of traditional Histology and Embryology teaching,with attention paid to the competence education in students including schematic knowledge,professional techniques,analytical thinking,and ideological and political theories,which would be of great significance for the cultivation of high-quality professionals specialized in traditional Chinese medicine.

On the “Online and Offline Blended Teaching” Mode for Ideological and Political Theory Courses in Universities—A Case of “Introduction to Basic Principles of Marxism”

With the deepening development of educational informatization, online and offline blended teaching, as a new teaching mode, is increasingly receiving widespread attention from educators [1]. At present, the reform of the “online and offline blended teaching” of ideological and political education courses directly affects the quality of talent cultivation in universities. The article takes the course “Introduction to Basic Principles of Marxism” as an example to explore the reform mode of “online and offline blended teaching” in ideological and political theory courses in universities from the aspects of reasonable allocation of class hours, design of online teaching activities, how to deepen classroom teaching offline, and diversified assessment modes. Furthermore, the article summarizes the experience of mode reform and promotes the deep development of the people-oriented education concept in ideological and political courses in universities, so as to achieve the ultimate goal of moral education in ideological and political education in universities.

Effect of Online Social Networking on Emotional Status and Its Interaction with Offline Reality during the Early Stage of the COVID-19 Pandemic in China

Background:During the early stages of the COVID-19 pandemic in China,social interactions shifted to online spaces due to lockdowns and social distancing measures.As a result,the impact of online social networking on users’emotional status has become stronger than ever.This study examines the association between online social networking and Internet users’emotional status and how offline reality affects this relationship.Methods:The study utilizes cross-sectional online survey data(n=3004)and Baidu Migration big data from the first 3 months of the pandemic.Two dimensions of online networking are measured:social support and information sources.Results:First,individuals’online social support(β=0.16,p<0.05)and information sources(β=0.08,p<0.01)are both positively associated to their emotional status during the epidemic.Second,these positive associations are moderated by social status and provincial pandemic control interventions.With regards to the moderation effect of social status,the constructive impact of information sources on emotional well-being is more pronounced among individuals from vulnerable groups compared to those who are not.With regard to the moderation effect of provincial interventions,online social support has the potential to alleviate the adverse repercussions of high rates of confirmed COVID-19 cases and strict lockdown measures while simultaneously augmenting the favorable effects of recovery.Conclusion:The various dimensions of social networking exert distinct effects on emotional status through diverse mechanisms,all of which must be taken into account when designing and adapting pandemic-control interventions.

Research on Online Teaching and Offline Course Construction for Power Electronics

Due to the impact of the novel coronavirus outbreak,universities have adopted online teaching and carried out remote teaching.With the improvement of the epidemic and the approaching of the new school year,the organic connection between online teaching during the epidemic and offline course construction after the epidemic is not only a challenge for tertiary education teachers,but also an urgent issue to be addressed.Therefore,the power electronics course is taken as an example to explore this connection.

基于Online to Offline的高职航海技术专业现代学徒制教学生态构建

以高职航海技术专业为例,分析现代学徒制培养模式下的瓶颈问题,探究引入O2O(Online to Offline)教学方式,在线上线下形成学生、学校和企业共同参与的工学结合、产教融合的教学生态,提高航海技术专业学徒制学生的自主学习能力、专业技能和综合素质,培养满足社会和航运企业需求的航海人才。

在线课程背景下高职英语online+offline教学模式的应用研究

通过对当前英语教学中存在问题的统计分析,依托在线开放课程平台,利用英语课前、课上和课后教学实践活动验证英语online+offline教学新模式下的教学设计、教学组织形式、教学方法手段、考核评价方式等对英语教学效果的提升作用。

基于在线开放课程平台的高职英语“online+offline”学习方式研究

随着教育信息化理念指导各学科教学的不断深入,有关信息化课堂教学中学生学习方式的研究成为不可忽视的关注点。基于在线开放课程信息化平台对英语教学及学习的支持,以建构主义学习理论为指导,探讨现代信息环境下英语主要教学环节与学习方式的关系,阐释通过自主学习、合作学习等方式在英语"online+offline"课堂教学中有效开展学习活动,提升学生学习效率,为英语课堂教学改革提供参考。

TPACK框架下GeoScene Online与地理教学融合的实践

技术革新影响学科教学方式选择,TPACK模式为解决当下学科教学应用新技术“两张皮”问题提供了新思路。本文从科勒和米什拉的理论出发,尝试将GeoScene Online平台与地理课堂教学融合,提出循序渐进、跨学科、基于真实情境和交互式的融合原则,进而在TPACK模式下将GeoScene Online功能特点与高中地理必修内容进行融合分析,构建以PCK、TCK、TPK三条子路径为导向的GeoScene Online与地理教学融合模式,并以“耕地”为主题进行案例实践探索。

Online Consensus Control of Nonlinear Affine Systems From Disturbed Data

Dear Editor,In this letter, we introduce a novel online distributed data-driven robust control approach for learning controllers of unknown nonlinear multi-agent systems(MASs) using state-dependent representations.

Optimized operation scheme of flash-memory-based neural network online training with ultra-high endurance

With the rapid development of machine learning,the demand for high-efficient computing becomes more and more urgent.To break the bottleneck of the traditional Von Neumann architecture,computing-in-memory(CIM)has attracted increasing attention in recent years.In this work,to provide a feasible CIM solution for the large-scale neural networks(NN)requiring continuous weight updating in online training,a flash-based computing-in-memory with high endurance(10^(9) cycles)and ultrafast programming speed is investigated.On the one hand,the proposed programming scheme of channel hot electron injection(CHEI)and hot hole injection(HHI)demonstrate high linearity,symmetric potentiation,and a depression process,which help to improve the training speed and accuracy.On the other hand,the low-damage programming scheme and memory window(MW)optimizations can suppress cell degradation effectively with improved computing accuracy.Even after 109 cycles,the leakage current(I_(off))of cells remains sub-10pA,ensuring the large-scale computing ability of memory.Further characterizations are done on read disturb to demonstrate its robust reliabilities.By processing CIFAR-10 tasks,it is evident that~90%accuracy can be achieved after 109 cycles in both ResNet50 and VGG16 NN.Our results suggest that flash-based CIM has great potential to overcome the limitations of traditional Von Neumann architectures and enable high-performance NN online training,which pave the way for further development of artificial intelligence(AI)accelerators.

基于Online-GRU信道预测的星上自适应功率控制方法

针对传统卫星功率控制方法存在资源浪费、时延长的问题,提出一种基于在线-门控循环单元(Online-GRU)信道预测的星上自适应功率控制方法,通过在线训练更新网络参数来解决离线预测算法存在的累积误差的问题。仿真结果表明,提出的在线训练算法比离线算法预测精确度提升了38.30%,相比在线-长短期记忆网络(Online-LSTM)节约了63.21%的训练时间;提出的自适应功率控制方法比固定发射功率的方法节约了55.74%的发射功率;同时,相比基于地面定时反馈信道状态的自适应功率控制方法具备更好的鲁棒性。

QoE oriented intelligent online learning evaluation technology in B5G scenario

Students'demand for online learning has exploded during the post-COvID-19 pandemic era.However,due to their poor learning experience,students'dropout rate and learning performance of online learning are not always satisfactory.The technical advantages of Beyond Fifth Generation(B5G)can guarantee a good multimedia Quality of Experience(QoE).As a special case of multimedia services,online learning takes into account both the usability of the service and the cognitive development of the users.Factors that affect the Quality of Online Learning Experience(OL-QoE)become more complicated.To get over this dilemma,we propose a systematic scheme by integrating big data,Machine Learning(ML)technologies,and educational psychology theory.Specifically,we first formulate a general definition of OL-QoE by data analysis and experimental verification.This formula considers both the subjective and objective factors(i.e.,video watching ratio and test scores)that most affect OLQoE.Then,we induce an extended layer to the classic Broad Learning System(BLS)to construct an Extended Broad Learning System(EBLS)for the students'OL-QoE prediction.Since the extended layer can increase the width of the BLS model and reduce the redundant nodes of BLS,the proposed EBLS can achieve a trade-off between the prediction accuracy and computation complexity.Finally,we provide a series of early intervention suggestions for different types of students according to their predicted OL-QoE values.Through timely interventions,their OL-QoE and learning performance can be improved.Experimental results verify the effectiveness oftheproposed scheme.

Will Online Car-Hailing Affect Consumers’ Decisions about Automobile Purchase?—An Empirical Study Based on Questionnaire Investigation

The online car-hailing industry, which provides the right of use, has a certain impact on the traditional automobile market, but there is no unified theory on whether it has a positive impact or a negative impact. Based on 362 consumer questionnaire data, this study builds a structural equation model to discuss the driving factors of residents’ choice of online car-hailing and whether the development of online car-hailing will have a certain substitution impact on the sales of private cars. From the perspective of consumers’ purchase intention, the research results show that consumers’ price consciousness, convenience consciousness, environmental protection consciousness and possession tendency will affect their choice of travel mode, and the use of online car-hailing is positively correlated with consumers’ willingness to replace private car ownership with online car-hailing.

Research on the Collaborative Governance of Social Responsibility in Online Audiovisual Enterprises

This paper aims to analyze the present conditions of the social responsibility ecosystem in online audiovisual enterprises in the digital age. It focuses on the governance of social responsibility in these enterprises and conducts an in-depth analysis of the problems and influencing factors related to the social responsibility aberrations of online audiovisual enterprises. Drawing upon social responsibility theory and collaborative governance theory, this research constructs a social responsibility guidance and governance system guided by the public, supported by the voluntary fulfillment of responsibilities by online audiovisual enterprises, and based on the collaborative participation of diverse stakeholders. It explores and optimizes the implementation pathways of this system, providing theoretical support and practical guidance for promoting the sustainable development of online audiovisual enterprises. Furthermore, it aims to contribute to the creation of a harmonious Internet ecosystem.

Early warning method for thermal runaway of lithium-ion batteries under thermal abuse condition based on online electrochemical impedance monitoring

Early warning of thermal runaway(TR)of lithium-ion batteries(LIBs)is a significant challenge in current application scenarios.Timely and effective TR early warning technology is urgently required considering the current fire safety situation of LIBs.In this work,we report an early warning method of TR with online electrochemical impedance spectroscopy(EIS)monitoring,which overcomes the shortcomings of warning methods based on traditional signals such as temperature,gas,and pressure with obvious delay and high cost.With in-situ data acquisition through accelerating rate calorimeter(ARC)-EIS test,the crucial features of TR were extracted using the RReliefF algorithm.TR mechanisms corresponding to the features at specific frequencies were analyzed.Finally,a three-level warning strategy for single battery,series module,and parallel module was formulated,which can successfully send out an early warning signal ahead of the self-heating temperature of battery under thermal abuse condition.The technology can provide a reliable basis for the timely intervention of battery thermal management and fire protection systems and is expected to be applied to electric vehicles and energy storage devices to realize early warning and improve battery safety.

Ghost Module Based Residual Mixture of Self-Attention and Convolution for Online Signature Verification

Online Signature Verification (OSV), as a personal identification technology, is widely used in various industries.However, it faces challenges, such as incomplete feature extraction, low accuracy, and computational heaviness. Toaddress these issues, we propose a novel approach for online signature verification, using a one-dimensionalGhost-ACmix Residual Network (1D-ACGRNet), which is a Ghost-ACmix Residual Network that combines convolutionwith a self-attention mechanism and performs improvement by using Ghost method. The Ghost-ACmix Residualstructure is introduced to leverage both self-attention and convolution mechanisms for capturing global featureinformation and extracting local information, effectively complementing whole and local signature features andmitigating the problem of insufficient feature extraction. Then, the Ghost-based Convolution and Self-Attention(ACG) block is proposed to simplify the common parts between convolution and self-attention using the Ghostmodule and employ feature transformation to obtain intermediate features, thus reducing computational costs.Additionally, feature selection is performed using the random forestmethod, and the data is dimensionally reducedusing Principal Component Analysis (PCA). Finally, tests are implemented on the MCYT-100 datasets and theSVC-2004 Task2 datasets, and the equal error rates (EERs) for small-sample training using five genuine andforged signatures are 3.07% and 4.17%, respectively. The EERs for training with ten genuine and forged signaturesare 0.91% and 2.12% on the respective datasets. The experimental results illustrate that the proposed approacheffectively enhances the accuracy of online signature verification.

AMachine Learning Approach to User Profiling for Data Annotation of Online Behavior

The user’s intent to seek online information has been an active area of research in user profiling.User profiling considers user characteristics,behaviors,activities,and preferences to sketch user intentions,interests,and motivations.Determining user characteristics can help capture implicit and explicit preferences and intentions for effective user-centric and customized content presentation.The user’s complete online experience in seeking information is a blend of activities such as searching,verifying,and sharing it on social platforms.However,a combination of multiple behaviors in profiling users has yet to be considered.This research takes a novel approach and explores user intent types based on multidimensional online behavior in information acquisition.This research explores information search,verification,and dissemination behavior and identifies diverse types of users based on their online engagement using machine learning.The research proposes a generic user profile template that explains the user characteristics based on the internet experience and uses it as ground truth for data annotation.User feedback is based on online behavior and practices collected by using a survey method.The participants include both males and females from different occupation sectors and different ages.The data collected is subject to feature engineering,and the significant features are presented to unsupervised machine learning methods to identify user intent classes or profiles and their characteristics.Different techniques are evaluated,and the K-Mean clustering method successfully generates five user groups observing different user characteristics with an average silhouette of 0.36 and a distortion score of 1136.Feature average is computed to identify user intent type characteristics.The user intent classes are then further generalized to create a user intent template with an Inter-Rater Reliability of 75%.This research successfully extracts different user types based on their preferences in online content,platforms,criteria,and frequency.The study also validates the proposed template on user feedback data through Inter-Rater Agreement process using an external human rater.