This paper examines the grain of content in junior high mathematics digital textbooks from People’s Education Press(PEP)using Cognitive Load Theory(CLT)in a sequential explanatory mixed-methods design:(1)bibliometric...This paper examines the grain of content in junior high mathematics digital textbooks from People’s Education Press(PEP)using Cognitive Load Theory(CLT)in a sequential explanatory mixed-methods design:(1)bibliometric analysis of 2008–2023142 publications found substantial gaps in cognitive-aligned pedagogical design;then,(2)large-scale surveys of 231 teachers and 102 students found critical gaps in navigation intuitiveness(71.3%),interactive affordance deficiency(68.9%),and personal pathway rigidity(76.5%).(3)Interviews with 6 teachers and 3 developers further revealed these deficiencies lay in:(1)content fragmentation serving procedural skills at the expense of conceptual integration;(2)sequence disruption violating CLT’s intrinsic load tenets;and(3)passive multimodal serving static text/images(82%of resources)limiting germane processing.We thus innovated a CLT-driven framework to reduce intrinsic load by animating schema builders chunking complex concepts,minimize extraneous load by Gestalt-principled UI redesign serving spatial consistency,and enhance germane load by adaptive analytics serving personal pathways.Empirical results showed 34%more knowledge retention(p<0.01,d=1.87)and 28%less perceived cognitive load(NASA-TLX)relative to conventional textbooks.Our work contributed both a theoretically grounded resource optimization model and an advancement of CLT in technology-enhanced mathematics instruction.展开更多
Mechanical metamaterials such as auxetic materials have attracted great interest due to their unusual properties that are dictated by their architectures.However,these architected materials usually have low stiffness ...Mechanical metamaterials such as auxetic materials have attracted great interest due to their unusual properties that are dictated by their architectures.However,these architected materials usually have low stiffness because of the bending or rotation deformation mechanisms in the microstructures.In this work,a convolutional neural network(CNN)based self-learning multi-objective optimization is performed to design digital composite materials.The CNN models have undergone rigorous training using randomly generated two-phase digital composite materials,along with their corresponding Poisson's ratios and stiffness values.Then the CNN models are used for designing composite material structures with the minimum Poisson's ratio at a given volume fraction constraint.Furthermore,we have designed composite materials with optimized stiffness while exhibiting a desired Poisson's ratio(negative,zero,or positive).The optimized designs have been successfully and efficiently obtained,and their validity has been confirmed through finite element analysis results.This self-learning multi-objective optimization model offers a promising approach for achieving comprehensive multi-objective optimization.展开更多
In this work,a physics-informed neural network(PINN)designed specifically for analyzing digital mate-rials is introduced.This proposed machine learning(ML)model can be trained free of ground truth data by adopting the...In this work,a physics-informed neural network(PINN)designed specifically for analyzing digital mate-rials is introduced.This proposed machine learning(ML)model can be trained free of ground truth data by adopting the minimum energy criteria as its loss function.Results show that our energy-based PINN reaches similar accuracy as supervised ML models.Adding a hinge loss on the Jacobian can constrain the model to avoid erroneous deformation gradient caused by the nonlinear logarithmic strain.Lastly,we discuss how the strain energy of each material element at each numerical integration point can be calculated parallelly on a GPU.The algorithm is tested on different mesh densities to evaluate its com-putational efficiency which scales linearly with respect to the number of nodes in the system.This work provides a foundation for encoding physical behaviors of digital materials directly into neural networks,enabling label-free learning for the design of next-generation composites.展开更多
This paper addresses the challenges in the teaching of electrical and electronic technology in higher vocational colleges and proposes specific countermeasures to improve teaching quality and effectiveness.The counter...This paper addresses the challenges in the teaching of electrical and electronic technology in higher vocational colleges and proposes specific countermeasures to improve teaching quality and effectiveness.The countermeasures include optimizing teaching content,emphasizing practical application and innovation,innovating teaching methods,introducing modern instructional approaches,strengthening the teaching team,enhancing teacher quality and practical expertise,upgrading experimental equipment and facilities,enriching curriculum resources,and incorporating digital teaching materials.These measures aim to cultivate high-quality skilled talents,promote social and economic development,and enhance national competitiveness.By adjusting the course structure,incorporating real-world industry cases,and fostering collaboration with enterprises,students can better understand and apply electrical and electronic technology.The introduction of project-based teaching,flipped classrooms,and multimedia technology can enhance student engagement and facilitate independent learning.Furthermore,the improvement of experimental resources and the establishment of online teaching platforms can enhance students’practical skills and provide a variety of learning resources.These measures contribute to the overall improvement of electrical and electronic technology teaching in higher vocational colleges.展开更多
Subject Code:B04Under the financial support of the National Natural Science Foundation of China,the research team led by Prof.Xie Tao(谢涛)at the State Key Laboratory of Chemical Engineering,College of Chemical and Bi...Subject Code:B04Under the financial support of the National Natural Science Foundation of China,the research team led by Prof.Xie Tao(谢涛)at the State Key Laboratory of Chemical Engineering,College of Chemical and Biological Engineering,Zhejiang University,developed an ultrafast process to produce shape changing materials with complex 3Dgeometries.This work was published in Advanced Materials(2016,DOI:10.展开更多
基金funded by the Macao Science and Technology Development Fund(FDCT)under Grant(No.0071/2023/RIB3&No.0003-2024-AGJ)by the Macao Foundation under Grant(No.MF2342).
文摘This paper examines the grain of content in junior high mathematics digital textbooks from People’s Education Press(PEP)using Cognitive Load Theory(CLT)in a sequential explanatory mixed-methods design:(1)bibliometric analysis of 2008–2023142 publications found substantial gaps in cognitive-aligned pedagogical design;then,(2)large-scale surveys of 231 teachers and 102 students found critical gaps in navigation intuitiveness(71.3%),interactive affordance deficiency(68.9%),and personal pathway rigidity(76.5%).(3)Interviews with 6 teachers and 3 developers further revealed these deficiencies lay in:(1)content fragmentation serving procedural skills at the expense of conceptual integration;(2)sequence disruption violating CLT’s intrinsic load tenets;and(3)passive multimodal serving static text/images(82%of resources)limiting germane processing.We thus innovated a CLT-driven framework to reduce intrinsic load by animating schema builders chunking complex concepts,minimize extraneous load by Gestalt-principled UI redesign serving spatial consistency,and enhance germane load by adaptive analytics serving personal pathways.Empirical results showed 34%more knowledge retention(p<0.01,d=1.87)and 28%less perceived cognitive load(NASA-TLX)relative to conventional textbooks.Our work contributed both a theoretically grounded resource optimization model and an advancement of CLT in technology-enhanced mathematics instruction.
文摘Mechanical metamaterials such as auxetic materials have attracted great interest due to their unusual properties that are dictated by their architectures.However,these architected materials usually have low stiffness because of the bending or rotation deformation mechanisms in the microstructures.In this work,a convolutional neural network(CNN)based self-learning multi-objective optimization is performed to design digital composite materials.The CNN models have undergone rigorous training using randomly generated two-phase digital composite materials,along with their corresponding Poisson's ratios and stiffness values.Then the CNN models are used for designing composite material structures with the minimum Poisson's ratio at a given volume fraction constraint.Furthermore,we have designed composite materials with optimized stiffness while exhibiting a desired Poisson's ratio(negative,zero,or positive).The optimized designs have been successfully and efficiently obtained,and their validity has been confirmed through finite element analysis results.This self-learning multi-objective optimization model offers a promising approach for achieving comprehensive multi-objective optimization.
文摘In this work,a physics-informed neural network(PINN)designed specifically for analyzing digital mate-rials is introduced.This proposed machine learning(ML)model can be trained free of ground truth data by adopting the minimum energy criteria as its loss function.Results show that our energy-based PINN reaches similar accuracy as supervised ML models.Adding a hinge loss on the Jacobian can constrain the model to avoid erroneous deformation gradient caused by the nonlinear logarithmic strain.Lastly,we discuss how the strain energy of each material element at each numerical integration point can be calculated parallelly on a GPU.The algorithm is tested on different mesh densities to evaluate its com-putational efficiency which scales linearly with respect to the number of nodes in the system.This work provides a foundation for encoding physical behaviors of digital materials directly into neural networks,enabling label-free learning for the design of next-generation composites.
基金The Project of China Vocational Education Association(Project number:ZJS2022YB024)。
文摘This paper addresses the challenges in the teaching of electrical and electronic technology in higher vocational colleges and proposes specific countermeasures to improve teaching quality and effectiveness.The countermeasures include optimizing teaching content,emphasizing practical application and innovation,innovating teaching methods,introducing modern instructional approaches,strengthening the teaching team,enhancing teacher quality and practical expertise,upgrading experimental equipment and facilities,enriching curriculum resources,and incorporating digital teaching materials.These measures aim to cultivate high-quality skilled talents,promote social and economic development,and enhance national competitiveness.By adjusting the course structure,incorporating real-world industry cases,and fostering collaboration with enterprises,students can better understand and apply electrical and electronic technology.The introduction of project-based teaching,flipped classrooms,and multimedia technology can enhance student engagement and facilitate independent learning.Furthermore,the improvement of experimental resources and the establishment of online teaching platforms can enhance students’practical skills and provide a variety of learning resources.These measures contribute to the overall improvement of electrical and electronic technology teaching in higher vocational colleges.
文摘Subject Code:B04Under the financial support of the National Natural Science Foundation of China,the research team led by Prof.Xie Tao(谢涛)at the State Key Laboratory of Chemical Engineering,College of Chemical and Biological Engineering,Zhejiang University,developed an ultrafast process to produce shape changing materials with complex 3Dgeometries.This work was published in Advanced Materials(2016,DOI:10.
