Innovation and Practice of Training Mode for Professional Postgraduates of Acupuncture and Tuina Based on Artificial Intelligence

In view of the common problems of integrating artificial intelligence into the training of postgraduates in Acupuncture and Tuina major,this paper reviews the related research progress both at home and abroad.It puts forward the innovative reform paths for integrating artificial intelligence into postgraduate training mode of Acupuncture and Tuina major:construct the teaching staff of artificial intelligence graduate students;innovating artificial intelligence to promote the integration of classics and scientific research;constructing the ideological and political case base of artificial intelligence courses;implementing artificial intelligence platform blended teaching;building a domestic and foreign exchange platform for artificial intelligence.Through practical research in teaching,it has achieved good teaching results and played a good demonstration,leading and radiation role in similar majors in China.

Teaching Design of Course Building Decoration Materials Based on Generative Artificial Intelligence

With the digital transformation of global education and China's emphasis on education digital,generative AI technology has been widely used in the field of higher education.In this paper,the development of generative AI technology and its potential in personalized learning,interactive content creation and adaptive assessment in education were introduced firstly.Then,the application case of generative AI tools in teaching content creation,scenario-based teaching content development,visual teaching content development,complex concept deconstruction and analogy,student-led application practice and other aspects in the teaching of Building Decoration Materials was discussed.Through the teaching experiment and effect evaluation,the positive influence of generative AI technology on the improvement of students'learning effect and teaching efficiency was verified.Finally,some thoughts and inspirations on the combination of educational theory and generative AI technology,the integration of teaching design and generative AI technology,and the practice cases and effect evaluation were put forward,and the importance of teacher role transformation and personalized learning path design was emphasized to provide theoretical and practical support for the innovative development of higher education.

Cultivation of Critical Thinking Skills: Exploring the Impact of Generative Artificial Intelligence- Enabled Instruction in English Essay Writing

This study explores the impact of generative artificial intelligence(AI)-enabled instruction on critical thinking in English essay writing among 1,050 first-year English majors across four colleges.Pedagogical strategies,including facilitating critical responses and emphasizing real-world application,are identified to enhance generative AI’s impact.Both qualitative and quantitative analyses reveal significant post-intervention improvements in critical thinking skills.This research contributes to understanding how generative AI can effectively foster critical thinking in educational settings.

Revisiting Educational Issues in the Age of Generative Artificial Intelligence

The emergence of generative artificial intelligence(AI)has had a huge impact on all areas of life,including the field of education.AI can assist teachers in cultivating talents and promoting personalized learning and teaching,but it also prevents individuals from thinking independently and creatively.In the era of generative AI,the rapid development of technology and its significant impact on the field of education are inevitable.There are many educational issues related to it,such as teaching methods,student training goals,teaching philosophy and purposes,and other educational issues,that require re-conceptualization and review.

Exploration of Industrial Internet Security Technology and Application from the Perspective of Generative Artificial Intelligence

In recent years,artificial intelligence technology has developed rapidly around the world is widely used in various fields,and plays an important role.The integration of industrial Internet security with new technologies such as big models and generative artificial intelligence has become a hot research issue.In this regard,this paper briefly analyzes the industrial Internet security technology and application from the perspective of generative artificial intelligence,hoping to provide some valuable reference and reference for readers.

Exploring Pedagogical Ideologies and Strategies for College English Writing Instruction from a Generative Artificial Intelligence Perspective

This study,drawing on the commonalities between generative artificial intelligence and foreign language writing instruction,outlines the core ideology of digital humanities-based college English writing instruction,including auxiliary use of generative artificial intelligence tools,primary focus on humanistic education,and the re-production of knowledge,aiming to foster students’critical thinking,collaborative skills,and creativity.Building on this foundation,the study delves into generative artificial intelligence tools applicable to different stages of process-genre writing and their strategic applications.The use of generative artificial intelligence tools is beneficial for students to present,discuss,and share writing content,encouraging them to enhance their writing,collaboration,critical thinking,and creative abilities through deep interaction with model essays and creative discourses.

基于AIGC的天津工业老字号品牌数字化转型策略研究

目的 助力老字号品牌数字化转型,构建AIGC背景下品牌数字化转型策略,增强市场竞争力并优化消费者体验。方法 通过案例分析法、TOE框架分析法对天津工业老字号品牌数字化转型所需的数字技术、组织结构与发展环境进行客观分析;通过案例分析法对AIGC在品牌数字化转型的作用方面加以总结,并说明其适用性与局限性。结果 通过TOE框架分析数字技术与天津工业老字号品牌创新的关系。结合国内外百年品牌数字化成果与案例分析,提出基于AIGC技术的天津工业老字号品牌数字化转型策略,为其他品牌提供参考。结论 数字化转型对天津工业老字号品牌来说是一次重要的发展机遇,不仅可以提升自身实力和竞争力,还可以为消费者带来更优质的体验和服务,从而提升我国优秀传统品牌的影响力。

生成式AI在金融领域的应用、风险与监管建议

近年来,以信息技术为基础,新一轮科技革命方兴未艾,人工智能尤其是生成式人工智能广泛应用于人类社会生活不同领域,促进相关产业链快速发展。现有研究成果显示,人工智能在金融领域的广泛应用,大大提升经济效益与效率,但也蕴含不少风险。对此,境外国家和地区、国际监管机构为因应当前金融科技发展的需求,妥善处理金融科技创新与安全之间的关系,积极主动制定监管制度,提升监管机构的能力。我国作为人工智能技术和金融业发展的大国,亦应多措并举制定人工智能监管制度,这不仅有助于控制相关领域潜在风险,而且有助于争夺相关领域的制度性话语权。

AIGC在高校图书馆情报服务中的作用:以查新业务为视角

人工智能生成内容(AIGC)技术的快速发展和应用,为传统情报服务的转型升级带来了新机遇。探索情报服务中AIGC的应用场景和发展方向,有助于提升服务效率和质量,推动服务创新和升级。文章聚焦于情报服务中的查新业务,选取当前国内外被广泛讨论的五种AIGC工具:ChatGPT、文心一言、智谱清言、Kimi和百小应,根据科技查新服务的流程,分别测试和评估这些工具在文献检索与筛选、文献分析、报告撰写中的实际性能和表现。基于测试结果,从服务理念更新、服务流程优化、技术平台创新以及人员专业能力提升等方面,为我国高校图书馆提升情报服务效能提出相关建议。

Towards data-efficient mechanical design of bicontinuous composites usinggenerative AI

The distribution of material phases is crucial to determine the composite’s mechanical property.While the full structure-mechanics relationship of highly ordered material distributions can be studied with finite number of cases,this relationship is difficult to be revealed for complex irregular distributions,preventing design of such material structures to meet certain mechanical requirements.The noticeable developments of artificial intelligence(AI)algorithms in material design enables to detect the hidden structure-mechanics correlations which is essential for designing composite of complex structures.It is intriguing how these tools can assist composite design.Here,we focus on the rapid generation of bicontinuous composite structures together with the stress distribution in loading.We find that generative AI,enabled through fine-tuned Low Rank Adaptation models,can be trained with a few inputs to generate both synthetic composite structures and the corresponding von Mises stress distribution.The results show that this technique is convenient in generating massive composites designs with useful mechanical information that dictate stiffness,fracture and robustness of the material with one model,and such has to be done by several different experimental or simulation tests.This research offers valuable insights for the improvement of composite design with the goal of expanding the design space and automatic screening of composite designs for improved mechanical functions.

面向AIoT的协同智能综述

深度学习和物联网的融合发展有力地促进了AIoT生态的繁荣.一方面AIoT设备为深度学习提供了海量数据资源,另一方面深度学习使得AIoT设备更加智能化.为保护用户数据隐私和克服单个AIoT设备的资源瓶颈,联邦学习和协同推理成为了深度学习在AIoT应用场景中广泛应用的重要支撑.联邦学习能在保护隐私的前提下有效利用用户的数据资源来训练深度学习模型,协同推理能借助多个设备的计算资源来提升推理的性能.引入了面向AIoT的协同智能的基本概念,围绕实现高效、安全的知识传递与算力供给,总结了近十年来联邦学习和协同推理算法以及架构和隐私安全3个方面的相关技术进展,介绍了联邦学习和协同推理在AIoT应用场景中的内在联系.从设备共用、模型共用、隐私安全机制协同和激励机制协同等方面展望了面向AIoT的协同智能的未来发展.

A Deep Learning-Based Computational Algorithm for Identifying Damage Load Condition: An Artificial Intelligence Inverse Problem Solution for Failure Analysis

In this work,we have developed a novel machine(deep)learning computational framework to determine and identify damage loading parameters(conditions)for structures and materials based on the permanent or residual plastic deformation distribution or damage state of the structure.We have shown that the developed machine learning algorithm can accurately and(practically)uniquely identify both prior static as well as impact loading conditions in an inverse manner,based on the residual plastic strain and plastic deformation as forensic signatures.The paper presents the detailed machine learning algorithm,data acquisition and learning processes,and validation/verification examples.This development may have significant impacts on forensic material analysis and structure failure analysis,and it provides a powerful tool for material and structure forensic diagnosis,determination,and identification of damage loading conditions in accidental failure events,such as car crashes and infrastructure or building structure collapses.

Beyond p-y method:A review of artificial intelligence approaches for predicting lateral capacity of drilled shafts in clayey soils

In 2023,pivotal advancements in artificial intelligence(AI)have significantly experienced.With that in mind,traditional methodologies,notably the p-y approach,have struggled to accurately model the complex,nonlinear soil-structure interactions of laterally loaded large-diameter drilled shafts.This study undertakes a rigorous evaluation of machine learning(ML)and deep learning(DL)techniques,offering a comprehensive review of their application in addressing this geotechnical challenge.A thorough review and comparative analysis have been carried out to investigate various AI models such as artificial neural networks(ANNs),relevance vector machines(RVMs),and least squares support vector machines(LSSVMs).It was found that despite ML approaches outperforming classic methods in predicting the lateral behavior of piles,their‘black box'nature and reliance only on a data-driven approach made their results showcase statistical robustness rather than clear geotechnical insights,a fact underscored by the mathematical equations derived from these studies.Furthermore,the research identified a gap in the availability of drilled shaft datasets,limiting the extendibility of current findings to large-diameter piles.An extensive dataset,compiled from a series of lateral loading tests on free-head drilled shaft with varying properties and geometries,was introduced to bridge this gap.The paper concluded with a direction for future research,proposes the integration of physics-informed neural networks(PINNs),combining data-driven models with fundamental geotechnical principles to improve both the interpretability and predictive accuracy of AI applications in geotechnical engineering,marking a novel contribution to the field.

Real-Time Prediction of Urban Traffic Problems Based on Artificial Intelligence-Enhanced Mobile Ad Hoc Networks(MANETS)

Traffic in today’s cities is a serious problem that increases travel times,negatively affects the environment,and drains financial resources.This study presents an Artificial Intelligence(AI)augmentedMobile Ad Hoc Networks(MANETs)based real-time prediction paradigm for urban traffic challenges.MANETs are wireless networks that are based on mobile devices and may self-organize.The distributed nature of MANETs and the power of AI approaches are leveraged in this framework to provide reliable and timely traffic congestion forecasts.This study suggests a unique Chaotic Spatial Fuzzy Polynomial Neural Network(CSFPNN)technique to assess real-time data acquired from various sources within theMANETs.The framework uses the proposed approach to learn from the data and create predictionmodels to detect possible traffic problems and their severity in real time.Real-time traffic prediction allows for proactive actions like resource allocation,dynamic route advice,and traffic signal optimization to reduce congestion.The framework supports effective decision-making,decreases travel time,lowers fuel use,and enhances overall urban mobility by giving timely information to pedestrians,drivers,and urban planners.Extensive simulations and real-world datasets are used to test the proposed framework’s prediction accuracy,responsiveness,and scalability.Experimental results show that the suggested framework successfully anticipates urban traffic issues in real-time,enables proactive traffic management,and aids in creating smarter,more sustainable cities.

The enlightenment of artificial intelligence large-scale model on the research of intelligent eye diagnosis in traditional Chinese medicine

Eye diagnosis is a method for inspecting systemic diseases and syndromes by observing the eyes.With the development of intelligent diagnosis in traditional Chinese medicine(TCM);artificial intelligence(AI)can improve the accuracy and efficiency of eye diagnosis.However;the research on intelligent eye diagnosis still faces many challenges;including the lack of standardized and precisely labeled data;multi-modal information analysis;and artificial in-telligence models for syndrome differentiation.The widespread application of AI models in medicine provides new insights and opportunities for the research of eye diagnosis intelli-gence.This study elaborates on the three key technologies of AI models in the intelligent ap-plication of TCM eye diagnosis;and explores the implications for the research of eye diagno-sis intelligence.First;a database concerning eye diagnosis was established based on self-su-pervised learning so as to solve the issues related to the lack of standardized and precisely la-beled data.Next;the cross-modal understanding and generation of deep neural network models to address the problem of lacking multi-modal information analysis.Last;the build-ing of data-driven models for eye diagnosis to tackle the issue of the absence of syndrome dif-ferentiation models.In summary;research on intelligent eye diagnosis has great potential to be applied the surge of AI model applications.

Artificial intelligence for disease diagnostics still has a long way to go

Artificial intelligence(AI)can sometimes resolve difficulties that other advanced technologies and humans cannot.In medical diagnostics,AI has the advantage of processing figure recognition,especially for images with similar characteristics that are difficult to distinguish with the naked eye.However,the mechanisms of this advanced technique should be well-addressed to elucidate clinical issues.In this letter,regarding an original study presented by Takayama et al,we suggest that the authors should effectively illustrate the mechanism and detailed procedure that artificial intelligence techniques processing the acquired images,including the recognition of non-obvious difference between the normal parts and pathological ones,which were impossible to be distinguished by naked eyes,such as the basic constitutional elements of pixels and grayscale,special molecules or even some metal ions which involved into the diseases occurrence.

Advantage Competition of Air and Space in Artificial Intelligence Era

Air and space is one of the most intense fields of science and technology competition for powerful countries.This paper focuses on the competition to achieve mastery of air and space,and analyzes the impact of fast developing intelligent technologies from six basic contradictions of the war,including hiding and finding,understanding and confusion,network resilience and network degradation,hitting and intercepting,speed of action and decisionmaking,and shaping the perceptions of key crowd.On this basis,aiming at securing competitive advantage in the future,the development directions of intelligent technologies are proposed for the air and space competition.

Advances in teleophthalmology and artificial intelligence for diabetic retinopathy screening:a narrative review

Background and Objective:Advances in teleophthalmology and artificial intelligence(AI)for diabetic retinal screening is of growing public health interest.Currently,only 30–40%of patients with diabetes adhere to recommended diabetes screening guidelines.To enhance early detection and reduce vision threatening complications,there has been a growing number of teleophthalmology programs and novel AI algorithms with the aim to improve eye care access.The purpose of this review is to assess current literature on teleophthalmology and AI for use in diabetic retinopathy(DR)screening,and to discuss advances and barriers to these innovative technologies.Methods:Literature review involving teleophthalmology and AI for DR screening,with focus on the past decade.Key Content and Findings:Teleophthalmology has demonstrated the ability to increase DR screening rates,enable earlier eye care access,and reduce healthcare costs.Novel AI-based DR screening programs appear accurate and effective,but detection of other ocular pathologies is still under development and not yet approved in the United States.Logistical,technological,financial,and legal barriers limit widespread adoption and long-term sustainability of teleophthalmology programs.Conclusions:The use of teleophthalmology and AI algorithms expands eye care access and helps prevent vision loss from DR and potentially other sight threatening conditions.Transparency in the process utilized for arriving at a particular diagnosis or decision to refer,often referred to as the“black box”,remains a multifaceted issue within the field of telemedicine for developing trust and improving patient-centered outcomes.

Potential and limitations of ChatGPT and generative artificial intelligence in medical safety education

The primary objectives of medical safety education are to provide the public with essential knowledge about medications and to foster a scientific approach to drug usage.The era of using artificial intelligence to revolutionize medical safety education has already dawned,and ChatGPT and other generative artificial intelligence models have immense potential in this domain.Notably,they offer a wealth of knowledge,anonymity,continuous availability,and personalized services.However,the practical implementation of generative artificial intelligence models such as ChatGPT in medical safety education still faces several challenges,including concerns about the accuracy of information,legal responsibilities,and ethical obligations.Moving forward,it is crucial to intelligently upgrade ChatGPT by leveraging the strengths of existing medical practices.This task involves further integrating the model with real-life scenarios and proactively addressing ethical and security issues with the ultimate goal of providing the public with comprehensive,convenient,efficient,and personalized medical services.

面向AI生成的产品概念设计方案智能评估方法

面向批量生成的产品概念设计方案,为了实现在方案初筛阶段的高效且精准的智能化评估,提出一种基于改进的卷积神经网络的方案选择方法和评估模型。首先通过主成分法进行指标降维,筛选出产品概念设计方案具有代表性的指标,其次为具有更高的适用性,通过调查问卷建立结构方程模型,验证评估认知逻辑的合理性并得到评估指标的权重,作为数据集标注的依据。以头戴式耳机为研究案例,分别构建了带有方案感知价值标签的三分类和二分类数据集进行二分类对比实验,验证了方案图像分类效果与各评估指标的相关性。然后基于ResNet算法和卷积注意力机制对三分类数据集进行训练,获得方案图像智能评估模型,输出结果通过SHAP(Shapley Additive Explanations)算法进行可解释性分析,以助于设计师明确设计重点,为产品设计方案的初步筛选与设计迭代提供参考。将模型与其他经典卷积神经网络模型进行对比实验,结果表明产品概念设计方案评估模型的有效性和可行性。