Results 201 to 210 of about 3,618 (265)

Forms of Organisational and Economic Cooperation of Auto Producers and Auto Components Suppliers on the Example of Leading World Auto Producers’ Experience

, 2015
Столярова, Екатерина Вячеславовна
openalex   +1 more source

Jaccard similarities (A- C) and average auto-correlations (D- F) of eight real-world physical contact networks for events of order as a function of time lag.

H.A. (Bart) Peters (21445736), Alberto Ceria (10023841), Huijuan Wang (157737)   +2 more
openalex   +1 more source

Domain‐Aware Implicit Network for Arbitrary‐Scale Remote Sensing Image Super‐Resolution

Advanced Intelligent Discovery, EarlyView.
Although existing arbitrary‐scale image super‐resolution methods are flexible to reconstruct images with arbitrary scales, the characteristic of training distribution is neglected that there exists domain shift between samples of various scales. In this work, a Domain‐Aware Implicit Network (DAIN) is proposed to handle it from the perspective of domain
Xiaoxuan Ren, Qian Jiang, Xin Jin, Puming Wang, Xing Chu, Shaowen Yao, Shengfa Miao   +6 more
wiley   +1 more source

Sociability of male cancer patients with stoma: a qualitative metasynthesis. [PDF]

Rev Bras Enferm
Santos JVALD, Rabelo AL, Conceição VMD, Silva LFSCD, Sousa AR, Araújo JS.   +5 more
europepmc   +1 more source

Places in the world a person could walk : auto-ethnobiographical explorations of family, stories, home and place

, 1997
David Syring
openalex   +1 more source

Comparison of OEE performance in different machine parameters such as auto-corrugation, flexo printing, auto glutter, and blinding, against world-class standards (10/12/23).

Abu M. Fuad (21300296), Nusra Akter Takia (21300299), Hasin Ahmad Zafir (21300302), Omar Farrok (10142033)   +3 more
openalex   +1 more source

What to Make and How to Make It: Combining Machine Learning and Statistical Learning to Design New Materials

Advanced Intelligent Discovery, EarlyView.
Combining machine learning and probabilistic statistical learning is a powerful way to discover and design new materials. A variety of machine learning approaches can be used to identify promising candidates for target applications, and causal inference can help identify potential ways to make them a reality.
Jonathan Y. C. Ting, Amanda S. Barnard
wiley   +1 more source

Mindfulness practices for alleviating distress among postpartum women. [PDF]

Rev Bras Enferm
Feitosa FVV, Silva RMD, Carvalho WBO, Paixão DP, Herculano MMS, Presado MHCV, Albuquerque VKO, Melo LPT.   +7 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

Ethics and Technology in Nursing Education: Rethinking Artificial Intelligence. [PDF]

Invest Educ Enferm
Canales Morales P.
europepmc   +1 more source