基于E—Learning环境下的教育传播模式研究

本文主要通过与传统教育传播模式的比较，得出在E-Learning环境下的一种新的教育传播模式。

E—Learning课程模式下口译教学的有效性分析

随着信息技术的不断进步及推广应用,传统的英语口译教学模式正面临着新的挑战。本文深入分析了E—Learning课程模式下口译教学的有效性,并在此基础上构建了E—Learning模式下的口译教学流程。同时,就如何进一步发挥E—Learning教学模式的有效性,提出了相关可行性建议。

《计算机网络》课程的E—learning教学模式研究

计算机网络是计算机相关专业的一门重要课程，而计算机网络的教学具有很强的实践性，需要大量的网络设备进行实验。该文分析了E—learning在《计算机网络》课程教学中相对于传统教学方式存在的优势，探讨了使用Boson Netsim进行仿真实验在E—learning教学中的应用。

语言心理学对大学外语e—learning教学的启示

E-learning作为一种技术手段，已经被越来越多的组织机构应用于各个学科知识的教育中。然而，外语作为一门特殊的人文学科，现有E-Learning技术，由于缺少深入地人人互动，不能因材施教，缺乏情景化，并不能很好地适应外语教学的特点。为了优化e—learning，本文汲取语言心理学“习惯论”、“认知论”、“自觉实践论”，提出了四点启示：增加场景、避免翻译、个性化和深入社交。希望能更有机地结合传统和e—learning，教师和机器，从而达到大学外语教学的终极目标。

高职院校E—Learning系统的设计与实现

高职院校以培养技术型人才为主要目标，即目标是实用化，是在完成中等教育的基础上培养出一批具有大学知识，而又有一定专业技术和技能的人才，其知识的讲授是以能用为度，实用为本。因此，高职院校不仅关注学生在校的成长，更要培养学生的职业能力、学习能力、发展能力，架构E—Learning在线自主学习平台已经成为了当前的一种发展趋势。

从学习理论的观点探讨E—Learning的发展

随着网络技术的发展，学习的方式方法也日益丰富。现在E—Learning已经成为企业、教育机构和政府机构一种新的学习方法。本文将E—Learning与学习理论相结合，探讨E—Learning的发展和走向。

E—Learning课程模式下口译教学的有效性分析

随着信息技术的不断进步及推广应用,传统的英语口译教学模式正面临着新的挑战。本文深入分析了E—Learning课程模式下口译教学的有效性,并在此基础上构建了E—Learning模式下的口译教学流程。同时,就如何进一步发挥E—Learning教学模式的有效性,提出了相关可行性建议。

e—learning网络大学

虽然网站倒闭和裁员的风潮继续席卷全球，但是互联网带来的一种全新教育理念——网络教育(e—Leorning)却开始深入人心。网络教育正在悄然改变着人们的学习内容和学习方式。据InterEd研究机构统计，2000年美国通过网络大学进行各类课程学习的人数为580万，有75％的大学提供在线学习的课程。

基于web的E—learning教学系统

基于我国高校E-Learning建设的现状和问题以及当前E-Learning平台的发展方向，系统地阐述了基于Web的E-Learning教学平台系统的实现。该平台充分考虑到我国当前高校信息化建设的阶段特点，同时也将当前世界先进的E-Leaming建设理念融入其中，以期为建设适应我国高校特点的、先进的网络教学一体化平台提供参考。本文前半部分主要是阐述了E-Learning的发展状况以及理论基础和技术基础，后半部分则介绍了基于Web的E-Learning教学平台系统的实现方案，包括系统的架构以及数据模型等。

E—Learning:一种全新开放的教学模式

在知识经济时代,E—Learning以其丰富的信息资源、友好的交互性能以及优良的开放性等特点而越来越受到人们的青睐。网络化教学已成为未来教育的发展趋势。文章从4个方面分析网络给教育带来的变化,指出E—Learning是适应信息社会高速发展的需要,加速人才培养的新型教学模式。

基于E—learning的电子商务网络化教学法的研究

E—Leaming是信息社会和网络经济下诞生的一种全新的学习方式，以计算机网络、信息搜索、专业内容网站，网上课堂等为依托，展示更加丰富的学习资料，提供多样化的学习形式这种全新的学习和教学环境，真正体现了“以学习者为中心”的教学思想。该文对E—Learning环境下电子商务课程网络化教学进行了研究，提出在E-Learning环境下教师在教学过程中大胆创新．拓宽思路是前提，改革教学模式是关键，网络教学平台是保证的观点。

占领教育现代化的制高点——论建设我校E—Learning网络及E—VOD学习体系

本文对教育技术在IT时代高等院校的地位、发展目标进行分析和比较，结合我校近年来工作发展的实践提出了作者的观点和计划。

E—Learning环境下的Web课本设计理论与技术基础

随着计算机和网络技术的不断发展,虚拟学堂、虚拟教室、虚拟学校相继出现,Web课本(教材)、课件等以各种形式出现在各级网站上,供不同层次的学习者学习和利用。Web课本制作时采用各种复杂技术,不同的表现方式显示丰富的教学内容,实现人机交互。本文从理论和技术的角度探讨了个别化学习模式下的Web课本页面设计与应用。

E—Learning系统中基于Adaboost算法的注意力识别

E-Learning．也称电子学习，是指通过因特网或其他数罕化媒体进行的学习与教学活动。它使得人们可以随时随地学习该文针对e—Learning系统中的情感缺失问题．采用适合实时表情识别的Adaboost算法实现快运正面人脸的检测与人眼定位，并根据人脸移动变化情况及眼睛睁闭情况判断学习者注意力类型。

改进Q-Learning的路径规划算法研究

针对Q-Learning算法学习效率低、收敛速度慢且在动态障碍物的环境下路径规划效果不佳的问题,本文提出一种改进Q-Learning的移动机器人路径规划算法.针对该问题,算法根据概率的突变性引入探索因子来平衡探索和利用以加快学习效率;通过在更新函数中设计深度学习因子以保证算法探索概率;融合遗传算法,避免陷入局部路径最优同时按阶段探索最优迭代步长次数,以减少动态地图探索重复率;最后提取输出的最优路径关键节点采用贝塞尔曲线进行平滑处理,进一步保证路径平滑度和可行性.实验通过栅格法构建地图,对比实验结果表明,改进后的算法效率相较于传统算法在迭代次数和路径上均有较大优化,且能够较好的实现动态地图下的路径规划,进一步验证所提方法的有效性和实用性.

Machine learning applications in stroke medicine:advancements,challenges,and future prospectives

Stroke is a leading cause of disability and mortality worldwide,necessitating the development of advanced technologies to improve its diagnosis,treatment,and patient outcomes.In recent years,machine learning techniques have emerged as promising tools in stroke medicine,enabling efficient analysis of large-scale datasets and facilitating personalized and precision medicine approaches.This abstract provides a comprehensive overview of machine learning’s applications,challenges,and future directions in stroke medicine.Recently introduced machine learning algorithms have been extensively employed in all the fields of stroke medicine.Machine learning models have demonstrated remarkable accuracy in imaging analysis,diagnosing stroke subtypes,risk stratifications,guiding medical treatment,and predicting patient prognosis.Despite the tremendous potential of machine learning in stroke medicine,several challenges must be addressed.These include the need for standardized and interoperable data collection,robust model validation and generalization,and the ethical considerations surrounding privacy and bias.In addition,integrating machine learning models into clinical workflows and establishing regulatory frameworks are critical for ensuring their widespread adoption and impact in routine stroke care.Machine learning promises to revolutionize stroke medicine by enabling precise diagnosis,tailored treatment selection,and improved prognostication.Continued research and collaboration among clinicians,researchers,and technologists are essential for overcoming challenges and realizing the full potential of machine learning in stroke care,ultimately leading to enhanced patient outcomes and quality of life.This review aims to summarize all the current implications of machine learning in stroke diagnosis,treatment,and prognostic evaluation.At the same time,another purpose of this paper is to explore all the future perspectives these techniques can provide in combating this disabling disease.

Use of machine learning models for the prognostication of liver transplantation: A systematic review

BACKGROUND Liver transplantation(LT)is a life-saving intervention for patients with end-stage liver disease.However,the equitable allocation of scarce donor organs remains a formidable challenge.Prognostic tools are pivotal in identifying the most suitable transplant candidates.Traditionally,scoring systems like the model for end-stage liver disease have been instrumental in this process.Nevertheless,the landscape of prognostication is undergoing a transformation with the integration of machine learning(ML)and artificial intelligence models.AIM To assess the utility of ML models in prognostication for LT,comparing their performance and reliability to established traditional scoring systems.METHODS Following the Preferred Reporting Items for Systematic Reviews and Meta-Analysis guidelines,we conducted a thorough and standardized literature search using the PubMed/MEDLINE database.Our search imposed no restrictions on publication year,age,or gender.Exclusion criteria encompassed non-English studies,review articles,case reports,conference papers,studies with missing data,or those exhibiting evident methodological flaws.RESULTS Our search yielded a total of 64 articles,with 23 meeting the inclusion criteria.Among the selected studies,60.8%originated from the United States and China combined.Only one pediatric study met the criteria.Notably,91%of the studies were published within the past five years.ML models consistently demonstrated satisfactory to excellent area under the receiver operating characteristic curve values(ranging from 0.6 to 1)across all studies,surpassing the performance of traditional scoring systems.Random forest exhibited superior predictive capabilities for 90-d mortality following LT,sepsis,and acute kidney injury(AKI).In contrast,gradient boosting excelled in predicting the risk of graft-versus-host disease,pneumonia,and AKI.CONCLUSION This study underscores the potential of ML models in guiding decisions related to allograft allocation and LT,marking a significant evolution in the field of prognostication.

Astrocytic endothelin-1 overexpression impairs learning and memory ability in ischemic stroke via altered hippocampal neurogenesis and lipid metabolism

Vascular etiology is the second most prevalent cause of cognitive impairment globally.Endothelin-1,which is produced and secreted by endothelial cells and astrocytes,is implicated in the pathogenesis of stroke.However,the way in which changes in astrocytic endothelin-1 lead to poststroke cognitive deficits following transient middle cerebral artery occlusion is not well understood.Here,using mice in which astrocytic endothelin-1 was overexpressed,we found that the selective overexpression of endothelin-1 by astrocytic cells led to ischemic stroke-related dementia(1 hour of ischemia;7 days,28 days,or 3 months of reperfusion).We also revealed that astrocytic endothelin-1 overexpression contributed to the role of neural stem cell proliferation but impaired neurogenesis in the dentate gyrus of the hippocampus after middle cerebral artery occlusion.Comprehensive proteome profiles and western blot analysis confirmed that levels of glial fibrillary acidic protein and peroxiredoxin 6,which were differentially expressed in the brain,were significantly increased in mice with astrocytic endothelin-1 overexpression in comparison with wild-type mice 28 days after ischemic stroke.Moreover,the levels of the enriched differentially expressed proteins were closely related to lipid metabolism,as indicated by Kyoto Encyclopedia of Genes and Genomes pathway analysis.Liquid chromatography-mass spectrometry nontargeted metabolite profiling of brain tissues showed that astrocytic endothelin-1 overexpression altered lipid metabolism products such as glycerol phosphatidylcholine,sphingomyelin,and phosphatidic acid.Overall,this study demonstrates that astrocytic endothelin-1 overexpression can impair hippocampal neurogenesis and that it is correlated with lipid metabolism in poststroke cognitive dysfunction.

Selective and Adaptive Incremental Transfer Learning with Multiple Datasets for Machine Fault Diagnosis

The visions of Industry 4.0 and 5.0 have reinforced the industrial environment.They have also made artificial intelligence incorporated as a major facilitator.Diagnosing machine faults has become a solid foundation for automatically recognizing machine failure,and thus timely maintenance can ensure safe operations.Transfer learning is a promising solution that can enhance the machine fault diagnosis model by borrowing pre-trained knowledge from the source model and applying it to the target model,which typically involves two datasets.In response to the availability of multiple datasets,this paper proposes using selective and adaptive incremental transfer learning(SA-ITL),which fuses three algorithms,namely,the hybrid selective algorithm,the transferability enhancement algorithm,and the incremental transfer learning algorithm.It is a selective algorithm that enables selecting and ordering appropriate datasets for transfer learning and selecting useful knowledge to avoid negative transfer.The algorithm also adaptively adjusts the portion of training data to balance the learning rate and training time.The proposed algorithm is evaluated and analyzed using ten benchmark datasets.Compared with other algorithms from existing works,SA-ITL improves the accuracy of all datasets.Ablation studies present the accuracy enhancements of the SA-ITL,including the hybrid selective algorithm(1.22%-3.82%),transferability enhancement algorithm(1.91%-4.15%),and incremental transfer learning algorithm(0.605%-2.68%).These also show the benefits of enhancing the target model with heterogeneous image datasets that widen the range of domain selection between source and target domains.

Social Media-Based Surveillance Systems for Health Informatics Using Machine and Deep Learning Techniques:A Comprehensive Review and Open Challenges

Social media(SM)based surveillance systems,combined with machine learning(ML)and deep learning(DL)techniques,have shown potential for early detection of epidemic outbreaks.This review discusses the current state of SM-based surveillance methods for early epidemic outbreaks and the role of ML and DL in enhancing their performance.Since,every year,a large amount of data related to epidemic outbreaks,particularly Twitter data is generated by SM.This paper outlines the theme of SM analysis for tracking health-related issues and detecting epidemic outbreaks in SM,along with the ML and DL techniques that have been configured for the detection of epidemic outbreaks.DL has emerged as a promising ML technique that adaptsmultiple layers of representations or features of the data and yields state-of-the-art extrapolation results.In recent years,along with the success of ML and DL in many other application domains,both ML and DL are also popularly used in SM analysis.This paper aims to provide an overview of epidemic outbreaks in SM and then outlines a comprehensive analysis of ML and DL approaches and their existing applications in SM analysis.Finally,this review serves the purpose of offering suggestions,ideas,and proposals,along with highlighting the ongoing challenges in the field of early outbreak detection that still need to be addressed.