Beliefs and Expectations about Engineering Preparation Exhibited by High School STEM Teachers

Background If we are to effect change in teacher practices and decision making regarding instruction, college preparation, and career success in engineering, then knowledge of teachers' beliefs and expectations about engineering needs to be understood. Purpose (Hypothesis ) The primary purpose was to develop a statistically reliable survey instrument to document teachers' beliefs and expectations about pre‐college engineering instruction, college preparation, and career success in engineering, called the Engineering Education Beliefs and Expectations Instrument (EEBEI), and to compare teachers' views. Design /Method Using two samples of teachers, EEBEI was established as a statistically reliable survey and was used to examine the beliefs and expectations of Project Lead the Way (PLTW) and non‐PLTW teachers. The results were used to further examine teachers' decisions in advising fictional students (described in vignettes) with varying academic and socioeconomic profiles. Results High school STEM teachers report their instruction was influenced by students' interests, family background, and prior academic achievement. Comparisons between PLTW and non‐PLTW teachers revealed that non‐PLTW teachers agreed more strongly that an engineer must demonstrate high scholastic achievement in math and science whereas PLTW teachers were more likely to report that science and math content was integrated into engineering activities. Although teachers report that students' socioeconomic status was not influential when asked explicitly, it did influence situated decision‐making tasks using fictional student vignettes. Conclusions Findings address challenges of STEM integration and reveal conflicting purposes of K‐12 engineering education as being for a select few or to promote technological literacy for all students, which affects recruitment, instruction, and assessment practices.     

Role of Artificial Intelligence and Machine Learning in Nanosafety

Robotics and automation provide potentially paradigm shifting improvements in the way materials are synthesized and characterized, generating large, complex data sets that are ideal for modeling and analysis by modern machine learning (ML) methods. Nanomaterials have not yet fully captured the benefits of automation, so lag behind in the application of ML methods of data analysis. Here, some key developments in, and roadblocks to the application of ML methods are reviewed to model and predict potentially adverse biological and environmental effects of nanomaterials. This work focuses on the diverse ways a range of ML algorithms are applied to understand and predict nanomaterials properties, provides examples of the application of traditional ML and deep learning methods to nanosafety, and provides context and future perspectives on developments that are likely to occur, or need to occur in the near future that allow artificial intelligence to make a deeper contribution to nanosafety.     

Artificial Intelligence in Material Engineering: A Review on Applications of Artificial Intelligence in Material Engineering

The role of artificial intelligence (AI) in material science and engineering (MSE) is becoming increasingly important as AI technology advances. The development of high-performance computing has made it possible to test deep learning (DL) models with significant parameters, providing an opportunity to overcome the limitation of traditional computational methods, such as density functional theory (DFT), in property prediction. Machine learning (ML)-based methods are faster and more accurate than DFT-based methods. Furthermore, the generative adversarial networks (GANs) have facilitated the generation of chemical compositions of inorganic materials without using crystal structure information. These developments have significantly impacted material engineering (ME) and research. Some of the latest developments in AI in ME herein are reviewed. First, the development of AI in the critical areas of ME, such as in material processing, the study of structure and material property, and measuring the performance of materials in various aspects, is discussed. Then, the significant methods of AI and their uses in MSE, such as graph neural network, generative models, transfer of learning, etc. are discussed. The use of AI to analyze the results from existing analytical instruments is also discussed. Finally, AI's advantages, disadvantages, and future in ME are discussed.     

知识论框架通向信息-知识-智能统一的理论

知识是人类所创造的宝贵财富,但至今没有形成系统的知识理论。章旨在提出和建立知识论的框架体系,包括它的基础和主体两部分。基础部分主要给出知识的概念、定义、表示、度量、推理和决策规则;主体部分的核心是阐明由信息提炼知识(知识生成)以及由知识形成智能(知识激活)的机理。知识论的建立将为信息论-知识论-智能论的统一理论奠定坚实的基础,促进人们在更高的水平上利用信息和知识,研究、设计和应用各种智能机器,推动经济和社会的发展。     

人工智能在感性工学研究中的应用与趋势

目的 梳理人工智能(AI)技术在感性工学研究中的应用现状,对关键技术、存在问题、研究趋势进行分析。方法 通过归纳整理国内外相关文献,分析人工智能基础研究领域,以感性工学研究的一般流程为主线,探讨人工智能在用户情感意向获取、产品设计特征提取、映射模型构建3个环节中的应用。结论 人工智能在感性工学研究中的广泛应用,极大地提高了设计效率,加快了设计的自动化和智能化的步伐,但也存在着局限性。在未来,感性工学通过与生成式AI相结合将成为新的趋势,更加强大和高效的人工智能将会给设计行业带来新的机遇和挑战。     

知行学引论——信息"知识"智能的统一理论

资源乃人类生存之源。科学技术的任务就要揭示资源的性质及其转换规律，以创造先进工具，扩展人的能力，改善人类的生存发展条件。近代科学揭示了物质和能量两类资源的性质和转换规律，创造和不断改进了人力工具和动力工具，创造了辉煌的工业时代文明：文章试图总结信息资源的性质及其转换规律，阐明信息—知识—智能的统一理论，构建知行学，为创造各种智能工具奠定理论基础。     

Knowledge Engineering Application in maintainability Design and Analysis

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人工智能在分布式储能技术中的应用

分布式储能是智能配电网和微电网中的关键组成部分。作为目前最具颠覆性的科学技术之一,人工智能有望改变传统分布式储能建模、分析和控制方式,营造更智能化的应用前景。针对人工智能在分布式储能技术中的应用问题,简要回顾了人工智能在电力系统的发展历程,分析了其在分布式储能中的应用适配性问题,归纳总结微电网、智能楼宇和车网协同3种不同空间尺度场景下,人工智能在分布式储能中的具体应用方向和研究成果,并对未来发展趋势进行了展望,以期为分布式储能的智能化研究和发展提供有益参考。     

人工智能在设计产业中的应用及发展

目的 对人工智能在设计领域的应用进行梳理与总结,分析当下人工智能对设计流程和设计师的影响,展望未来人工智能对设计行业的影响趋势。方法 使用VOSviewer工具和文献计量法对Web of Science数据库中关于“人工智能在设计领域的创新与应用”的文献进行详细的可视化和聚类分析,深入探讨文献中的核心观点和案例。结果 基于四个主要聚类(AI+技术应用、AI+设计流程、AI+创意协作、AI+影响反思)来展开讨论。特别关注生成式人工智能(AIGC)技术对设计方法和设计流程的影响,指出生成式人工智能在促进设计创新和提升设计效率方面发挥着至关重要的作用。此外,生成式人工智能对设计师的传统角色及设计原创性提出了新的挑战并重新定义需求。预测未来人工智能将进一步整合进设计流程,促进设计创新,更加关注人工智能的原创性、责任边界问题,探讨人工智能与设计师合作的新模式。结论 通过对人工智能在设计领域应用的全面综述,为未来设计创新与人工智能融合提供了有价值的理论参考和发展方向。     

人工智能在非物质文化遗产保护与传承中的应用研究现状

目的 对当下人工智能在非物质文化遗产中的研究现状进行梳理、归纳和分析,为更好地保护和传承非物质文化遗产提供思路和参考。方法 详细解读了非物质文化遗产对中华文化产生的深远影响;论述了当下非物质文化遗产知识库构建、分类检索、创新设计三方面国内外发展的现状,归纳并阐述了基于人工智能的工业设计的特点;总结并分析了智能时代下非物质文化遗产领域的发展趋势,对未来智能化的研究方向及研究重点进行了展望。结论 随着智能技术的不断发展,人工智能的应用在非物质文化遗产的保护与传承方面所占的比例也会逐渐增加,而人工智能技术的运用并不是对传统技术的否定,而是为了更好地满足多方面的需求,充分发挥传统技术与人工智能技术的优势互补作用,未来运用人工智能技术对非物质文化遗产进行保护和传承是一种必然趋势。     

基于混合智能的电火花加工电参数学习模型的研究

提出了一个基于混合智能的电火花加工电参数学习模型,它模仿熟练操作者的决策过程,由于工艺数据库、加工规则库、学习模块和推理模块组成。在学习模块中利用遗传算法从工艺数据库中抽取出反映电参数和加工结果之间关系的模糊产生式规则,存储在规则库中。推理模块基于这些规则利用模糊拄是对新加的要求提供合适的电参数。     

The Relationships between Students' Conceptions of Learning Engineering and their Preferences for Classroom and Laboratory Learning Environments

This study developed a survey entitled Conceptions of Learning Engineering (CLE), to elicit undergraduate engineering students' conceptions of learning engineering. The reliability and validity of the CLE survey were confirmed through a factor analysis of 321 responses of undergraduate students majoring in electrical engineering. A series of ANOVA analyses revealed that students who preferred a classroom setting tended to conceptualize learning engineering as “testing” and “calculating and practicing,” whereas students who preferred a laboratory setting expressed conceptions of learning engineering as “increasing one's knowledge,” “applying,” “understanding,” and “seeing in a new way.” A further analysis of student essays suggested that learning environments which are student‐centered, peer‐interactive, and teacher‐facilitated help engineering students develop more fruitful conceptions of learning engineering.     

中国人对说谎行为和谎言识别的信念及其群体差异

本文采用问卷调查的方法对中国被试对说谎行为、谎言识别的信念及其群体差异进行了探索性研究.研究结果表明:法律工作者、学生群体对说谎者行为和谎言识别的信念与其他群体存在显著差异;中国被试对说谎行为的认识存在显著的性别偏见、年龄偏见、文化偏见和亲疏偏见;中国被试对说谎频率的报告相对比较保守,对自己的谎言识别能力的估计相对来说...     

Factors Influencing the Self‐Efficacy Beliefs of First‐Year Engineering Students

A survey incorporating qualitative measures of student self‐efficacy beliefs was administered to 1,387 first‐year engineering students enrolled in ENGR 106, Engineering Problem‐Solving and Computer Tools, at Purdue University. The survey was designed to identify factors related to students' self‐efficacy beliefs, their beliefs about their capabilities to perform the tasks necessary to achieve a desired outcome. Open‐ended questions prompted students to list factors affecting their confidence in their ability to succeed in the course. Students were then asked to rank these factors based on the degree to which their self‐efficacy beliefs were influenced. Gender trends emerged in student responses to factors that affect confidence in success. These trends are discussed in light of the categories identified by efficacy theorists as sources of self‐efficacy beliefs. The results presented here provide a useful look at the first‐year engineering experiences that influence students' efficacy beliefs, an important consideration in explaining student achievement, persistence, and interest.