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.

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.

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.

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.

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.

Innovation and Practice of Teaching Methods in Digital and Adaptive Learning:Taking Communication Engineering Major as an Example

This paper proposes teaching reforms in communication engineering majors,emphasizing the implementation of digital and adaptive teaching methodologies,integrating emerging technologies,breaking free from the constraints of traditional education,and fostering high-caliber talents.The reform measures encompass fundamental data collection,recognition of individual characteristics,recommendation of adaptive learning resources,process-oriented teaching management,adaptive student guidance and early warning systems,personalized evaluation,and the construction of an integrated service platform.These measures,when combined,form a comprehensive system that is expected to enhance teaching quality and efficiency,and facilitate student development.

Extracting Eye Models from MRI Scans Using U-Net-Based Deep Learning Framework

.Abstracting eye models from MRI images is critical in advancing medical imaging, particularly for clinical diagnostics. Current methods often struggle with accuracy and efficiency, highlighting a gap this research aims to fill. This study investigates the application of machine learning methods, focusing on the U-net-based deep learning framework, to improve the accuracy of eye model extraction. The objectives include fitting measured eye data to models such as the Ellipsoid model, evaluating automated segmentation tools, and assessing the usability of machine learning-based extractions in clinical scenarios. We employed point cloud data of 202,872 points to fit eye models using ellipsoid, non-linear, and spherical fitting techniques. The fitting processes were optimized to ensure precision and reliability. We compared the performance of these models using mean squared error (MSE) as the primary metric. The non-linear model emerged as the most accurate, with a significantly lower MSE (1.186562) compared to the ellipsoid (781.0542) and spherical models. This finding indicates that the non-linear model provides a more detailed and precise representation of the eye’s geometry. These results suggest that machine learning methods, particularly non-linear models, can significantly enhance the accuracy and usability of eye model extraction in clinical diagnostics, offering a robust framework for future advancements in medical imaging.

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.

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.

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.

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.

U-Learning中个性化内容提取方法

提出了一种U-Learning中个性化内容提取方法,以帮助学生在泛在环境下获取个性化的学习对象。该方法可以在混合信息的基础上,按相似度的顺序生成个性化的搜索结果。使用学生的历史信息、当前地理位置信息及输入查询信息,用以过滤掉不相关的搜索结果,提高泛在环境下学习内容的获取效率。

U-Learning与终身学习的时代契合——浅谈U-Learning环境下的终身学习

随着现代移动技术和无线网络技术的不断发展,U-Learning时代的到来将极大地推进终身学习。本文在介绍"U-Learning"普适技术及其特点的基础上,探讨普适计算技术在当前终身学习领域的应用现状,展望发展的前景。

基于U-Learning的基础教育数字化学习资源应用空间模型和学科知识本体资源信息模型设计

随着教育信息化的不断深化,基础教育数字化学习资源的高共享高重用品质,已经成为基础教育数字化学习资源建设的瓶颈。在泛在学习理念的引领下,文章首先重构了数字化学习资源分类模型,然后建立其基于基础教育云平台下的"教师—学生—学科知识本体"三维空间应用模型,最后设计了学科知识本体的资源信息模型,充分体现了学习资源的进化性、分布性和开放性,为实现基础教育学习资源的进化收敛、语义聚合、小粒度重用和共享,提供了很好的保障。

基于U-Learning的学习资源建设研究

U-Learning即"泛在学习",作为一种新的学习方式已逐渐成为教育领域研究的热点,而基于U-Learning的学习资源建设是U-Learning环境建设的基础。主要研究高校校园网络资源在当前资源建设模式的基础上实现向U-Learning学习资源建设模式过渡的探索。