Application Effect of Blended Education Strategy Based on the Learning Pass Platform in Phase III Cardiac Rehabilitation of Patients with Coronary Artery Disease

Objective:To explore the application effect of the blended education strategy based on the Learning Pass platform in the phase III cardiac rehabilitation of patients with coronary artery disease.Methods:90 patients diagnosed with coronary artery disease in the Department of Cardiology of our hospital from January 2019 to January 2021 were selected and divided into the control group and the experimental group according to the method of randomized numerical table,with 45 cases in each group.Both the experimental group and the control group received pre-discharge cardiac rehabilitation education by conventional means.The control group received education and supervision information via WeChat after discharge,while the experimental group joined the Learning Pass platform to receive online and offline hybrid education and supervision,with online as the mainstay and offline as a supplement.The disease cognitive level,self-management skills,quality of life,medication adherence,and emotional status of the two groups were compared.Results:The disease cognitive levels in the experimental group were significantly higher than those of the control group(P<0.05);the scores of the experimental group in terms of quality of life,self-management skills,and medication adherence were significantly higher than those of the control group(P<0.05);and the scores of anxiety and depression in the experimental group were significantly lower than those of the control group(P<0.05).Conclusion:The blended education strategy based on the Learning Pass platform has a significant application effect in phase III cardiac rehabilitation of patients with coronary artery disease.It can improve patients’disease cognitive level,self-management skills,and quality of life,and provide a basis for improving patients’prognosis.

Study of The Technical Index of Online Learning Behavior Analysis of Nursing Majors on The Superstar Platform Based on The Kirkpatrick Evaluation Model

Objective:To analyze the technical indexes of students’online learning behavior analysis based on Kirkman’s evaluation model,sort out the basic indexes of online learning behavior,and extract scientific and efficient evaluation indexes of online learning effect through statistical analysis.Methods:The online learning behavior data of Physiology of nursing students from 2021-2023 and the first semester of 22 nursing classes(3 and 4)were collected and analyzed.The preset learning behavior indexes were analyzed by multi-dimensional analysis and a correlation analysis was conducted between the indexes and the final examination scores to screen for the dominant important indexes for online learning effect evaluation.Results:The study found that the demand for online learning of nursing students from 2021-2023 increased and the effect was statistically significant.Compared with the stage assessment results,the online learning effect was statistically significant.Conclusion:The main indicators for evaluating and classifying online learning behaviors were summarized.These two indicators can help teachers predict which part of students need learning intervention,optimize the teaching process,and help students improve their learning behavior and academic performance.

Deep Learning and Tensor-Based Multiple Clustering Approaches for Cyber-Physical-Social Applications

The study delves into the expanding role of network platforms in our daily lives, encompassing various mediums like blogs, forums, online chats, and prominent social media platforms such as Facebook, Twitter, and Instagram. While these platforms offer avenues for self-expression and community support, they concurrently harbor negative impacts, fostering antisocial behaviors like phishing, impersonation, hate speech, cyberbullying, cyberstalking, cyberterrorism, fake news propagation, spamming, and fraud. Notably, individuals also leverage these platforms to connect with authorities and seek aid during disasters. The overarching objective of this research is to address the dual nature of network platforms by proposing innovative methodologies aimed at enhancing their positive aspects and mitigating their negative repercussions. To achieve this, the study introduces a weight learning method grounded in multi-linear attribute ranking. This approach serves to evaluate the significance of attribute combinations across all feature spaces. Additionally, a novel clustering method based on tensors is proposed to elevate the quality of clustering while effectively distinguishing selected features. The methodology incorporates a weighted average similarity matrix and optionally integrates weighted Euclidean distance, contributing to a more nuanced understanding of attribute importance. The analysis of the proposed methods yields significant findings. The weight learning method proves instrumental in discerning the importance of attribute combinations, shedding light on key aspects within feature spaces. Simultaneously, the clustering method based on tensors exhibits improved efficacy in enhancing clustering quality and feature distinction. This not only advances our understanding of attribute importance but also paves the way for more nuanced data analysis methodologies. In conclusion, this research underscores the pivotal role of network platforms in contemporary society, emphasizing their potential for both positive contributions and adverse consequences. The proposed methodologies offer novel approaches to address these dualities, providing a foundation for future research and practical applications. Ultimately, this study contributes to the ongoing discourse on optimizing the utility of network platforms while minimizing their negative impacts.

Construction of apricot variety search engine based on deep learning

Apricot has a long history of cultivation and has many varieties and types. The traditional variety identification methods are timeconsuming and labor-consuming, posing grand challenges to apricot resource management. Tool development in this regard will help researchers quickly identify variety information. This study photographed apricot fruits outdoors and indoors and constructed a dataset that can precisely classify the fruits using a U-net model (F-score:99%), which helps to obtain the fruit's size, shape, and color features. Meanwhile, a variety search engine was constructed, which can search and identify variety from the database according to the above features. Besides, a mobile and web application (ApricotView) was developed, and the construction mode can be also applied to other varieties of fruit trees.Additionally, we have collected four difficult-to-identify seed datasets and used the VGG16 model for training, with an accuracy of 97%, which provided an important basis for ApricotView. To address the difficulties in data collection bottlenecking apricot phenomics research, we developed the first apricot database platform of its kind (ApricotDIAP, http://apricotdiap.com/) to accumulate, manage, and publicize scientific data of apricot.

Quafu-RL:The cloud quantum computers based quantum reinforcement learning

With the rapid advancement of quantum computing,hybrid quantum–classical machine learning has shown numerous potential applications at the current stage,with expectations of being achievable in the noisy intermediate-scale quantum(NISQ)era.Quantum reinforcement learning,as an indispensable study,has recently demonstrated its ability to solve standard benchmark environments with formally provable theoretical advantages over classical counterparts.However,despite the progress of quantum processors and the emergence of quantum computing clouds,implementing quantum reinforcement learning algorithms utilizing parameterized quantum circuits(PQCs)on NISQ devices remains infrequent.In this work,we take the first step towards executing benchmark quantum reinforcement problems on real devices equipped with at most 136 qubits on the BAQIS Quafu quantum computing cloud.The experimental results demonstrate that the policy agents can successfully accomplish objectives under modified conditions in both the training and inference phases.Moreover,we design hardware-efficient PQC architectures in the quantum model using a multi-objective evolutionary algorithm and develop a learning algorithm that is adaptable to quantum devices.We hope that the Quafu-RL can be a guiding example to show how to realize machine learning tasks by taking advantage of quantum computers on the quantum cloud platform.

Application Effect of Blended Education Strategy Based on the Learning Pass Platform in Phase III Cardiac Rehabilitation of Patients with Coronary Artery Disease

Objective:To explore the application effect of the blended education strategy based on the Learning Pass platform in the phase III cardiac rehabilitation of patients with coronary artery disease.Methods:90 patients diagnosed with coronary artery disease in the Department of Cardiology of our hospital from January 2019 to January 2021 were selected and divided into the control group and the experimental group according to the method of randomized numerical table,with 45 cases in each group.Both the experimental group and the control group received pre-discharge cardiac rehabilitation education by conventional means.The control group received education and supervision information via WeChat after discharge,while the experimental group joined the Learning Pass platform to receive online and offline hybrid education and supervision,with online as the mainstay and offline as a supplement.The disease cognitive level,self-management skills,quality of life,medication adherence,and emotional status of the two groups were compared.Results:The disease cognitive levels in the experimental group were significantly higher than those of the control group(P<0.05);the scores of the experimental group in terms of quality of life,self-management skills,and medication adherence were significantly higher than those of the control group(P<0.05);and the scores of anxiety and depression in the experimental group were significantly lower than those of the control group(P<0.05).Conclusion:The blended education strategy based on the Learning Pass platform has a significant application effect in phase III cardiac rehabilitation of patients with coronary artery disease.It can improve patients’disease cognitive level,self-management skills,and quality of life,and provide a basis for improving patients’prognosis.

Research on the Training Mode of “the Combination of College Students’ Extracurricular English Autonomous Learning and Chinese Culture” Based on WeChat Platform

In today’s college English extracurricular learning,college students often neglect the combination of English language learning and the input of Chinese traditional cultural knowledge,paying too much attention to Western culture learning,ignoring their traditional culture and lead to be speechless when encountering cultural exchange activities.The author believes that college students should pay attention to the input of Chinese traditional cultural content in the process of independent English learning outside the classroom.The creative team of college students led by the author through the WeChat platform,in Xinjiang Agricultural University,conducted a combination of Chinese culture input and English autonomous learning,aiming at strengthening the effectiveness of English learning,improving the self-learning ability and intercultural communication skills.

Research on Interactive Teaching Strategies of College English Teaching-Based on Super Star Learning Platform

Super Star Learning Platform is a learning platform which meets the needs of interactive teaching mode both in and out of the classroom.This paper analyzes the advantages of interactive teaching strategies and the existing problems to be solved.Super Star Learning Platform can effectively improve teaching efficiency by enhancing the interaction between teachers and students and motivating students’interest in learning.

Dynamic Pricing Model of E-Commerce Platforms Based on Deep Reinforcement Learning

With the continuous development of artificial intelligence technology,its application field has gradually expanded.To further apply the deep reinforcement learning technology to the field of dynamic pricing,we build an intelligent dynamic pricing system,introduce the reinforcement learning technology related to dynamic pricing,and introduce existing research on the number of suppliers(single supplier and multiple suppliers),environmental models,and selection algorithms.A two-period dynamic pricing game model is designed to assess the optimal pricing strategy for e-commerce platforms under two market conditions and two consumer participation conditions.The first step is to analyze the pricing strategies of e-commerce platforms in mature markets,analyze the optimal pricing and profits of various enterprises under different strategy combinations,compare different market equilibriums and solve the Nash equilibrium.Then,assuming that all consumers are naive in the market,the pricing strategy of the duopoly e-commerce platform in emerging markets is analyzed.By comparing and analyzing the optimal pricing and total profit of each enterprise under different strategy combinations,the subgame refined Nash equilibrium is solved.Finally,assuming that the market includes all experienced consumers,the pricing strategy of the duopoly e-commerce platform in emerging markets is analyzed.

Design of a WeChat Learning Platform for Syndrome Differentiation

Objective To design a WeChat mini program called Chinese Syndrome Differentiation Learning Platform(CSDLP)on smartphone to improve health literacy.Methods The developer tools of WeChat(Version:v1.01.170925)were used for designing and debugging the mini program.SPSS17.0 was used for statistical purposes.“View container”“Basic content”“Form component”“Navigation”and“Media components”were used for the development of the WeChat mini program.The detailed method was referred to https://mp.weixin.qq.com/debug/wxadoc/dev/.Results A WeChat mini program called CSDLP was developed.This program has three major functions which are WeChat reading,WeChat class and WeChat syndrome differentiation.The official test report showed that there were no functionality errors for the seven android smartphones(referred to as A,B,C,D,E,F and G)that CSDLP was tested on.Statistical analysis results showed that the average memory in D,E,F and G was lower than in A,B and C.Average ratio was the highest in F and the lowest in G.The average loading time was the same for all smartphones.The audio database for diagnostics using traditional Chinese medicine(TCM)and a lecture video database were based on diagnostic textbook.Our team built a syndrome differentiation database which included 51 diseases.Conclusion CSDLP can improve knowledge visualization,studying process,and information sharing in terms of the training and development of new techniques for syndrome differentiation and treatment in TCM,and it can provide a better illustration for people to understand TCM.

Research on the Construction of a Fully-participatory Extracurricular Learning Platform with Multiple Linkage Effects

In the existing research at home and abroad,the construction of extracurricular learning platform is still only focused on solving the problems of curriculum learning itself.At the same time,there are no cases of multiple linkage effects,including integrating alumni resources,promoting the construction of alumni association,promoting students'internship and employment,strengthening ties with enterprises and so on.On the basis of the original function of the alumni management system,this paper expands the sections and adds the main body of students to enrich the functions of the platform.This paper constructs a fully-participatory extracurricular learning platform with multiple linkage effect,which provides a reference for other majors inside and outside the school to establish extracurricular learning platform.

A Study of Learning Strategies on the Internet-Based College English Self-Study Platform

The learning strategy is one of the important inner factors that may affect the students＇ self-study. And the self-study ability is more and more emphasized. The Foreign Department of Harbin Engineering University has put the the Internet-based self-study platform in use among the Chen Geng class for years. Based on the observation of the using condition of the platform and the results from a survey, this paper will analyze the influence of the learning strategies after the use of the platform, and at the same time, some suggestions will be provided for the teachers to help students have a better command of learning strategies. As proved by the data, for the college English education in all Chinese universities, it is both a trend and a necessity to cultivate the self-study ability of students through the Internet-based platform.

The Application of Moodle-Based Platform in College English Learning

After a brief introduction to current situation of College English teaching, the paper explores the theoretical foundations for designing a Moodle-based College English learning environment. Through a quantitative and qualitative research, the author proves its effectiveness in College English learning. In this new teaching environment, the learners can take initiative to construct knowledge with the assist of modules provided on Moodle platform. Their comprehensive language ability is greatly improved in this harmonious and equal environment through the interaction and communication with peers and the teacher.

A Study on the College English Learning Strategies Based on Intellectual Teaching Platforms

Taking Leshan Normal University as an example,the investigation has been conducted in this paper on the use of learning strategies of more than 3,000 non-English majors in the process of English learning.Based on the analysis of the survey results,this paper discusses the necessity of college English learning strategy cultivation under the new situation,focusing on the cultivation approaches to college English learning strategy through intelligent teaching platforms,aiming to cultivate student’s awareness of using learning strategies,strengthen their according ability,and improve their English level.

Designing Higher Vocational College English Blended Learning Mode based on Wechat Platform

As a platform bridging classroom teaching and online teaching for higher vocational college(HVC)English,WeChat expands the dimension of HVC English teaching.This paper first introduces basic functions and characteristics of WeChat,then analyzes problems existing in current HVC English teaching and finally puts forward specific ways to design HVC English blended learning mode based on WeChat platform in four steps:warming-up,text analysis,exercises and post-course expansion.

Analysis on the Education Mechanism of the"Learning Power"Platform from the Perspective of Media Convergence

As a media learning platform,the"Learning Power55 platform integrates the advantages of the internet,big data,and new media.Through the supply of massive explicit and implicit learning resources as well as the construction of the interactive space of"Learning Power/5 it fully embodies the education mechanism of moral education.Specifically,it is reflected in the distinctive political position and the education goal mechanism of"moral education,55 the education operation mechanism of"explicit and implicit unity,"the learning mechanism of'"autonomy and cooperation integTation,"and the feedback incentive mechanism of"gamification."The organic combination and interactive operation of these four mechanisms form a collaborative education mechanism system of goal orientation,education operation,learning process,and feedback incentive.

E-learning网络平台的构建及应用效果分析

目的:传统课程以面对面授课为主,不能满足现代教学发展的需要。Elearning是在线学习新方式,通过计算机互联网或手机无线网络进行网络授课、答疑和论坛交流。方法:通过E-learning网络平台的构建及应用,对比分析网络平台应用前后学生掌握知识的程度和师生交流的密切程度,实验组与对照组医学生的实验考核、理论考试成绩输入Excel 2007,并用Stata 19.0统计分析。结果:常规课堂教学以外开发E-learning平台并强化利用,确能提高学生成绩,促进师生互动,不失为传统授课方式的有益补充。结论:课堂教学为主,E-learning学习为辅的教学模式符合当前信息社会的发展趋势和学生的学习规律,相辅相成,共同促进医学生知识的掌握和临床技能水平的提高。但E-learning平台建设是一项系统工程,必须长远规划,循序渐进,逐步完善,方能发挥更大效能。

影像科数字化趋势下的医学影像E-learning网络教学平台研究

对影像科数字化趋势下的医学影像有效利用E-learning网络教学平台进行教学的途径进行研究,通过查阅相关的文献资料,根据教学经验对医学影像E-learning网络教学平台进行研究,对E-learning网络教学平台的要素、在教学中的优势以及平台建设重点进行分析,最终得出医学影像科有效利用E-learning平台时教学的途径,揭示医学影像教学应从对平台教学中的形式和内容进行妥善处理、对学生的学习情况进行及时监督以及E-learning平台上的内容要符合学生需求的三个角度来对E-learning平台进行有效利用。