Results 211 to 220 of about 1,304,075 (309)

Flexible Memory: Progress, Challenges, and Opportunities

Advanced Intelligent Discovery, EarlyView.
Flexible memory technology is crucial for flexible electronics integration. This review covers its historical evolution, evaluates rigid systems, proposes a flexible memory framework based on multiple mechanisms, stresses material design's role, presents a coupling model for performance optimization, and points out future directions.
Ruizhi Yuan, Sen Chen, Minghui Guo, Ziliang Cui, Yujie Du, Jing Liu   +5 more
wiley   +1 more source

AI-assisted design synthesis and human creativity in engineering education. [PDF]

Front Artif Intell
Tsakalerou M, Akhmadi S, Balgynbayeva A, Kumisbek Y.   +3 more
europepmc   +1 more source

Deep Learning Prediction of Surface Roughness in Multi‐Stage Microneedle Fabrication: A Long Short‐Term Memory‐Recurrent Neural Network Approach

Advanced Intelligent Discovery, EarlyView.
A sequential deep learning framework is developed to model surface roughness progression in multi‐stage microneedle fabrication. Using real‐world experimental data from 3D printing, molding, and casting stages, an long short‐term memory‐based recurrent neural network captures the cumulative influence of geometric parameters and intermediate outputs ...
Abdollah Ahmadpour, Sara Soltanabadi Farshi, Tuanna Ozcan, Amir Mohammad Ketabchi, Alper Kiraz, Savas Tasoglu   +5 more
wiley   +1 more source

High performance work system and innovative work behaviour: A moderated mediation analysis of knowledge sharing and employee creativity in Nigerian higher education institutions. [PDF]

PLoS One
Amoozegar A, Esohwode O, Falahat M, Krishnasamy HN, Anjum T, Raju V, Hasan MA.   +6 more
europepmc   +1 more source

CrossMatAgent: AI‐Assisted Design of Manufacturable Metamaterial Patterns via Multi‐Agent Generative Framework

Advanced Intelligent Discovery, EarlyView.
CrossMatAgent is a multi‐agent framework that combines large language models and diffusion‐based generative AI to automate metamaterial design. By coordinating task‐specific agents—such as describer, architect, and builder—it transforms user‐provided image prompts into high‐fidelity, printable lattice patterns.
Jie Tian, Martin Taylor Sobczak, Dhanush Patil, Jixin Hou, Lin Pang, Arunachalam Ramanathan, Libin Yang, Xianyan Chen, Yuval Golan, Xiaoming Zhai, Hongyue Sun, Kenan Song, Xianqiao Wang   +12 more
wiley   +1 more source

Creativity and transition to bipolar disorder: a prospective analysis from the early-bipolife study. [PDF]

Int J Bipolar Disord
Michaelis E, Bauer M, Bechdolf A, Bermpohl F, Berndt C, Bröckel-Bundt KL, Burkhardt E, Correll CU, Dannlowski U, Falkenberg I, Fallgatter AJ, Fusar-Poli P, Hadji S, Jansen A, Juckel G, Kircher T, Kittel-Schneider S, Krüger-Özgürdal S, Lambert M, Leopold K, Maicher B, Matura S, Mennigen E, Mikolas P, Reif A, Ritter P, Sauer C, Stamm T, Martini J, Pfennig A.   +29 more
europepmc   +1 more source

Deep Learning‐Assisted Design of Mechanical Metamaterials

Advanced Intelligent Discovery, EarlyView.
This review examines the role of data‐driven deep learning methodologies in advancing mechanical metamaterial design, focusing on the specific methodologies, applications, challenges, and outlooks of this field. Mechanical metamaterials (MMs), characterized by their extraordinary mechanical behaviors derived from architected microstructures, have ...
Zisheng Zong, Pu Wen, Zhiping Chai, Dunxuan Wu, Han Ding, Zhigang Wu   +5 more
wiley   +1 more source

Embodied dynamics of creative placemaking: conceptualizing the spaces, places, and environments of creativity. [PDF]

Front Psychol
Malinin LH.
europepmc   +1 more source

Large Language Model in Materials Science: Roles, Challenges, and Strategic Outlook

Advanced Intelligent Discovery, EarlyView.
Large language models (LLMs) are reshaping materials science. Acting as Oracle, Surrogate, Quant, and Arbiter, they now extract knowledge, predict properties, gauge risk, and steer decisions within a traceable loop. Overcoming data heterogeneity, hallucinations, and poor interpretability demands domain‐adapted models, cross‐modal data standards, and ...
Jinglan Zhang, Xinyi Chen, Xu Ye, Yulin Yang, Bin Ai   +4 more
wiley   +1 more source

The SECI model of knowledge creation as an enabler of students' creative behavior through the lens of absorptive capacity. [PDF]

Front Psychol
Han L, Zhao Y.
europepmc   +1 more source