Scheme Based on Multi-Level Patch Attention and Lesion Localization for Diabetic Retinopathy Grading

Early screening of diabetes retinopathy(DR)plays an important role in preventing irreversible blindness.Existing research has failed to fully explore effective DR lesion information in fundus maps.Besides,traditional attention schemes have not considered the impact of lesion type differences on grading,resulting in unreasonable extraction of important lesion features.Therefore,this paper proposes a DR diagnosis scheme that integrates a multi-level patch attention generator(MPAG)and a lesion localization module(LLM).Firstly,MPAGis used to predict patches of different sizes and generate a weighted attention map based on the prediction score and the types of lesions contained in the patches,fully considering the impact of lesion type differences on grading,solving the problem that the attention maps of lesions cannot be further refined and then adapted to the final DR diagnosis task.Secondly,the LLM generates a global attention map based on localization.Finally,the weighted attention map and global attention map are weighted with the fundus map to fully explore effective DR lesion information and increase the attention of the classification network to lesion details.This paper demonstrates the effectiveness of the proposed method through extensive experiments on the public DDR dataset,obtaining an accuracy of 0.8064.

Deep neural network based on multi-level wavelet and attention for structured illumination microscopy

Structured illumination microscopy(SIM)is a popular and powerful super-resolution(SR)technique in biomedical research.However,the conventional reconstruction algorithm for SIM heavily relies on the accurate prior knowledge of illumination patterns and signal-to-noise ratio(SNR)of raw images.To obtain high-quality SR images,several raw images need to be captured under high fluorescence level,which further restricts SIM’s temporal resolution and its applications.Deep learning(DL)is a data-driven technology that has been used to expand the limits of optical microscopy.In this study,we propose a deep neural network based on multi-level wavelet and attention mechanism(MWAM)for SIM.Our results show that the MWAM network can extract high-frequency information contained in SIM raw images and accurately integrate it into the output image,resulting in superior SR images compared to those generated using wide-field images as input data.We also demonstrate that the number of SIM raw images can be reduced to three,with one image in each illumination orientation,to achieve the optimal tradeoff between temporal and spatial resolution.Furthermore,our MWAM network exhibits superior reconstruction ability on low-SNR images compared to conventional SIM algorithms.We have also analyzed the adaptability of this network on other biological samples and successfully applied the pretrained model to other SIM systems.

From Big Data to Intelligent Tutoring: Exploring the Application of Big Data Technology in the Ideological and Political Education for College Counselors

This paper explores the application of big data technology in ideological and political education for college counselors.Big data-driven personalized counseling for students,social network analysis,and public opinion monitoring can optimize the efficiency of counselor work.However,there are still challenges concerning data privacy protection,technical support,and training.By scientifically evaluating and addressing these challenges,big data technology is expected to enhance the effectiveness of college counselors and improve the quality of ideological and political education.In the future,with technological advancements,big data technology will become an essential tool for higher education management.

Peer-tutoring理论及其在护理教学中应用的意义

Peer-tutoring作为一种辅助性教学方法,是同学之间的相互帮助学习,学生承担教与学的责任。护理教学中应用Peer-tutoring,对护生认知获得、促进交流、提高自信以及社会支持方面非常有益。

The Investigation into Problems of College Students’ Individual Tutoring on the Subject of English

More and more college students start their social practice by tutoring children. However, quite a few do not have a clear conception about individual tutoring at the beginning of their practice, and not everyone involved in individual tutoring can end up with fruitful results. In this paper, the author did a research to find out the problems and the efficient ways to carry on this social practice in the hope that college students can finish their practice efficiently and thus form their career goal gradually.

Intelligent Tutoring System of Linear Programming

There is a growing technological development in intelligent teaching systems. This field has become interesting to many researchers. In this paper, we present an intelligent tutoring system for teaching mathematics that helps students understand the basics of linear programming using Linear Program Solver and Service for Solving Linear Programming Problems, through which students will be able to solve economic problems. It comes down to determining the minimum or maximum value of a linear function, which is called the objective function, according to pre-set limiting conditions expressed by linear equations and inequalities. The goal function and the limiting conditions represent a mathematical model of the observed problem. Working as a professor of mathematics in high school, I felt the need for one such work and dealing with the study of linear programming as an integral part of mathematics. There are a number of papers in this regard, but exclusively related to traditional ways of working, as stated in the introductory part of the paper. The center of work as well as the final part deals with the study of linear programming using programs that deal with this topic.

FPGA based hardware platform for trapped-ion-based multi-level quantum systems

We report a design and implementation of a field-programmable-gate-arrays(FPGA)based hardware platform,which is used to realize control and signal readout of trapped-ion-based multi-level quantum systems.This platform integrates a four-channel 2.8 Gsps@14 bits arbitrary waveform generator,a 16-channel 1 Gsps@14 bits direct-digital-synthesisbased radio-frequency generator,a 16-channel 8 ns resolution pulse generator,a 10-channel 16 bits digital-to-analogconverter module,and a 2-channel proportion integration differentiation controller.The hardware platform can be applied in the trapped-ion-based multi-level quantum systems,enabling quantum control of multi-level quantum system and highdimensional quantum simulation.The platform is scalable and more channels for control and signal readout can be implemented by utilizing more parallel duplications of the hardware.The hardware platform also has a bright future to be applied in scaled trapped-ion-based quantum systems.

A Fuzzy Expert System Architecture for Intelligent Tutoring Systems:A Cognitive Mapping Approach

An Intelligent Tutoring System (ITS) is a computer based instruction tool that attempts to provide individualized instructions based on learner’s educational status. Advances in development of these systems have rose and fell since their emergence. Perhaps the main reason for this is the absence of appropriate framework for ITS development. This paper proposes a framework for designing two main parts of ITSs. Besides development framework, the second main reason for lack of significant advances in ITS development is its development cost. In general, this cost for instructional material is quite high and it becomes more in ITS development. The proposed method can significantly reduce the development cost. The cost reduction mainly is because of characteristics of applied mapping techniques. These maps are human readable and easily understandable by people who are not aware of knowledge representation techniques. The proposed framework is implemented for a graduate course at a technical university in Asia. This experiment provides an individualized instruction which is the main designing purpose of the ITSs.

Deep reinforcement learning based multi-level dynamic reconfiguration for urban distribution network:a cloud-edge collaboration architecture

With the construction of the power Internet of Things(IoT),communication between smart devices in urban distribution networks has been gradually moving towards high speed,high compatibility,and low latency,which provides reliable support for reconfiguration optimization in urban distribution networks.Thus,this study proposed a deep reinforcement learning based multi-level dynamic reconfiguration method for urban distribution networks in a cloud-edge collaboration architecture to obtain a real-time optimal multi-level dynamic reconfiguration solution.First,the multi-level dynamic reconfiguration method was discussed,which included feeder-,transformer-,and substation-levels.Subsequently,the multi-agent system was combined with the cloud-edge collaboration architecture to build a deep reinforcement learning model for multi-level dynamic reconfiguration in an urban distribution network.The cloud-edge collaboration architecture can effectively support the multi-agent system to conduct“centralized training and decentralized execution”operation modes and improve the learning efficiency of the model.Thereafter,for a multi-agent system,this study adopted a combination of offline and online learning to endow the model with the ability to realize automatic optimization and updation of the strategy.In the offline learning phase,a Q-learning-based multi-agent conservative Q-learning(MACQL)algorithm was proposed to stabilize the learning results and reduce the risk of the next online learning phase.In the online learning phase,a multi-agent deep deterministic policy gradient(MADDPG)algorithm based on policy gradients was proposed to explore the action space and update the experience pool.Finally,the effectiveness of the proposed method was verified through a simulation analysis of a real-world 445-node system.

Neighborhood Effects and Political Trust: A Multi-level Analysis of Chinese Rural-to-Urban Migrants’ Trust in County Government

Massive rural-to-urban migration in China is consequential for political trust: rural-to-urban migrants have been found to hold lower levels of trust in local government than their rural peers who choose to stay in the countryside (mean 4.92 and 6.34 out of 10, respectively, p < 0.001). This article explores why migrants have a certain level of political trust in their county-level government. Using data of rural-to-urban migrants from the China Family Panel Survey, this study performs a hierarchical linear modeling (HLM) to unpack the multi-level explanatory factors of rural-to-urban migrants’ political trust. Findings show that the individual-level socio-economic characteristics and perceptions of government performance (Level-1), the neighborhood-level characteristics-the physical and social status and environment of neighborhoods (Level-2), and the objective macroeconomic performance of county-level government (Level-3), work together to explain migrants’ trust levels. These results suggest that considering the effects of neighborhood-level factors on rural-to-urban migrants’ political trust merits policy and public management attention in rapidly urbanizing countries.

下一代个性化学习:生成式人工智能增强智能辅导系统

长期以来,教育技术领域的研究者致力于利用计算机开展个性化自适应教学。智能辅导系统作为这一领域的核心,其目标是通过计算机技术提供个性化学习体验和支持。近年来,生成式人工智能,尤其是大语言模型的发展,为智能辅导系统带来了突破性机遇,为个性化学习开辟了新的路径。本文回顾了研究者为实现个性化学习所作的努力及其成果和局限,探讨了大语言模型在个性化学习中的潜力和影响,介绍了“苏格拉底游乐园”——一个基于对话的智能辅导系统,如何利用大语言模型实施苏格拉底式教学,揭示了生成式人工智能如何革新个性化学习方式。针对大语言模型的潜在误用,文章提出了相应的建议,并构建了框架,旨在引导研发者在生成式人工智能和大语言模型范式下开发高效的智能教育应用。

全科预备导师5A胜任力模型的构建与探索

目的为进一步实现高质量培养全科医学人才的目标,构建全科研究生预备导师评估模型,探索全科人才培养新途径。方法构建全科预备导师5A胜任力模型,包括一级指标、二级指标、三级指标体系,5A一级指标包含Ⅰ育人能力、Ⅱ教学能力、Ⅲ专业能力、Ⅳ科研能力、Ⅴ评估能力;每个一级指标分解为三个维度的二级指标,每个二级指标再分解为包含三个维度的三级指标。遴选12名全科预备导师,在培养过程中实施全科预备导师5A胜任力模型,培训前后进行胜任力达标评价。结果通过构建及实施全科预备导师5A胜任力模型,全面提升了全科医学预备导师的综合能力。12名全科预备导师培训后育人能力、教学能力、专业能力、研究能力、评估能力均有明显提升(t分别=8.07、12.22、7.65、11.63、9.71,P均<0.05)。结论构建的全科预备导师5A胜任力模型有利于提升全科预备导师的综合能力,在全科研究生教育中发挥了重要作用,为提升全科人才培养质量提供保障。

“双导师制”在病理专硕研究生实践创新能力培养中的探索

专业学位研究生教育是培养高层次应用型专门人才的主渠道,学校自从2015年开始招收病理专硕研究生以来,在病理专硕培养与住院医师规范化培训“双轨合一”过程中遇到培训模式单一、缺乏科研思维训练、基础病理与临床病理隔离等。针对以上问题,本课题整合基础医学院病理学系和医院病理科的骨干力量,组建双导师团队,将“双导师制”培养模式贯穿于临床病理学研究生培养全过程,包括研究生录取、培养方案制定、病理规范化培训、科研训练、开题、毕业答辩等各个环节,充分发挥学校和医院的优势,取得一定成效。该培养模式不仅促进基础与临床的深度融合,而且对培养具有较高病理诊断水平、解决临床问题能力的高素质临床病理人才有积极的推动作用,有力提升了病理专硕研究生的实践创新能力。

导师制在血液内科规范化培训护士带教中的应用

目的 探索导师制在血液内科规范化培训护士带教工作中的应用效果。方法 研究以2021年3月—2023年3月轮转到重庆医科大学附属第二医院血液内科的规范化培训护士70名为研究对象,对每轮规范化培训护士采用随机数表法分为对照组和观察组,其中对照组34名,观察组36名。对照组接受传统的带教模式,由资深护士指导学习和实践;观察组实施导师制带教模式,由专业导师负责培养和辅导。通过对比2组护士的理论知识、操作技能、综合素质和教学满意度等指标,评价导师制教学模式在规范化培训护士培训中的应用效果。结果 观察组的理论知识成绩为(93.00±2.28)分、操作技能成绩为(93.36±1.66)分、综合素质考核成绩为(97.11±1.47)分、教学满意度为100%,高于对照组[理论知识成绩(89.65±6.71)分、操作技能成绩(91.44±3.93)分、综合素质考核成绩(94.18±2.89)分、教学满意度83.60%],差异有统计学意义(P <0.05)。结论 对血液科规范化培训护士实施导师制教学模式,可以使规范化培训护士的专科理论知识、操作技能、综合素质等方面的能力得到全面的提高。

基于OBE理论的体育研究生导师团队课程思政评价体系研究

该文运用文献资料法、问卷调查法、专家访谈法和层次分析法等方法,基于OBE理论构建了体育研究生导师团队课程思政评价体系,充分发挥了评价的预测、诊断、反馈、导向、激励的整体功能。该体系能够发挥导师团队潜能,不断促进导师团队的发展,发挥其主观能动性和创造性,使导师团队自我创新和持续发展能力得到增强。

位育视角下导生关系的冲突与弥合

和谐的导生关系是研究生教育高质量发展的重要保障,而实践中导生关系时常出现或隐性或公开的冲突。导生关系包括教育关系、伦理关系和心理关系,归根结底是人与人之间的关系。根源于我国传统文化的“位育”理论能为研究中国式导生关系提供切实性视角。在“位育”理论视角下,导生关系冲突主要缘于教育关系“错位”、伦理关系“越位”、心理关系“缺位”等三个方面。弥合导生冲突,需要基于导生之间的应有之“位”,促进导生双方安其所,进而遂其生,具体需要“以导育人,弥合教育关系”“以界明伦,匡正伦理关系”“以情化人,修缮心理关系”。

日本政府与校外培训机构合作:路径、成效及思考

21世纪以来,为推动教育改革并促进区域振兴,日本地方政府与校外培训机构之间的合作愈发深入、模式多元且成效显著。通过分析日本地方政府与校外培训机构合作的三个典型案例,可以看出政府与培训机构之间的合作提升了课后教育服务质量,促进了学校教师的专业发展,推动了新型公共教育模式的生成,并深化了家校社协同。“和而不同、求同存异”是日本政府寻求与培训机构合作的理论基础,在开展合作的过程中日本政府尤其注重妥善处理政府与培训机构、政府与学校、学校教师与机构讲师等利益相关方之间的关系,而这种合作也在促使日本校外培训业走向更加可持续的发展之路,并进一步为日本教育乃至日本社会的发展向前贡献力量。

立德树人视域下研究生导师团队育人模式探索——以数学学科为例

研究生导师团队制度是提高研究生培养质量和推进研究生培养模式改革的一种有效形式,也是研究生导师进行学术紧密合作和跨学科交流的一个良好的平台。在立德树人视域下,以烟台大学数学学科研究生培养为例,针对研究生导师团队制度建设、课程思政融入研究生教育课程体系建设等进行了研究和探索,在教学过程中进行了全过程育人引导,完善了研究生的人格,夯实了其价值观,提升了其专业素养,形成了数学学科研究生导师团队育人新模式,并取得了初步成效。