Results 221 to 230 of about 562,901 (310)

Self‐Driving Laboratory Optimizes the Lower Critical Solution Temperature of Thermoresponsive Polymers

Advanced Intelligent Discovery, EarlyView.
A low‐cost, self‐driving laboratory is developed to democratize autonomous materials discovery. Using this "frugal twin" hardware architecture with Bayesian optimization, the platform rapidly converges to target lower critical solution temperature (LCST) values while self‐correcting from off‐target experiments, demonstrating an accessible route to data‐
Guoyue Xu, Renzheng Zhang, Tengfei Luo
wiley   +1 more source

Integrated energy in action: Practical learnings from integrating centralized and decentralized energy delivery models in Uganda. [PDF]

iScience
Mahomed S, Shirley R, Pan CI.
europepmc   +1 more source

Machine Learning Driven Inverse Design of Broadband Acoustic Superscattering

Advanced Intelligent Discovery, EarlyView.
Multilayer acoustic superscatterers are designed using machine learning to achieve broadband superscattering and strong sound insulation. By incorporating a weighted mean absolute error into the loss function, the forward and inverse neural networks accurately map structural parameters to spectral responses.
Lijuan Fan, Xiangliang Zhang, Ying Wu
wiley   +1 more source

Digital assets: risks, regulations, mitigation. [PDF]

Financ Innov
Teng HW, Härdle WK, Osterrieder J, Pele DT, Baals LJ, Papavassiliou V, Bolesta K, Kabašinskas A, Filipovska O, Thomaidis NS, Moukas AI, Goundar S, Nasir JA, Weinberg AI, Arakelian V, Truică CO, Akar M, Kabaklarlı E, Apostol ES, Iannario M, Bȩdowska-Sójka B, Skaftadóttir HK, Yildirim O, Shala A, Pisoni G, Coita IF, Korba S, Hafner CM, Schwendner P, Molnár B, Xhumari E.   +30 more
europepmc   +1 more source

RAMS: Residual‐Based Adversarial‐Gradient Moving Sample Method for Scientific Machine Learning in Solving Partial Differential Equations

Advanced Intelligent Discovery, EarlyView.
We propose a residual‐based adversarial‐gradient moving sample (RAMS) method for scientific machine learning that treats samples as trainable variables and updates them to maximize the physics residual, thereby effectively concentrating samples in inadequately learned regions.
Weihang Ouyang, Min Zhu, Wei Xiong, Si‐Wei Liu, Lu Lu   +4 more
wiley   +1 more source

Optimising regional care delivery systems targeting adults with heart failure and mental health interconnected conditions: a participatory action research study protocol. [PDF]

BMJ Open
Philip S, Dahrouge S, Valade RL, Hammond E, Aomreore A, Hosseini M, Archibald D, Bandk K, Antunes P, Assaouré J, Benoit P, Plourde D, Sabbagh R, Skaff E, Seguin J, Tanguay E, Welch V, Liu P, Kehoe MacLeod K.   +18 more
europepmc   +1 more source

Explaining the Origin of Negative Poisson's Ratio in Amorphous Networks With Machine Learning

Advanced Intelligent Discovery, EarlyView.
This review summarizes how machine learning (ML) breaks the “vicious cycle” in designing auxetic amorphous networks. By transitioning from traditional “black‐box” optimization to an interpretable “AI‐Physics” closed‐loop paradigm, ML is shown to not only discover highly optimized structures—such as all‐convex polygon networks—but also unveil hidden ...
Shengyu Lu, Xiangying Shen
wiley   +1 more source

Co-creating health system innovation with people who use drugs. [PDF]

Harm Reduct J
McBeth R, Sandberg C, Varewny V, Piggott B, Ajani A, Auger S, Campbell-Scherer D, Dong K, Hyshka E, Srinivasan C, Twan S, Umpherville L, Salvalaggio G.   +12 more
europepmc   +1 more source

Mobilizing Community Assets in Tikapur, Nepal: Applying Asset-based Community Development Strategy

Journal of South Asian Studies, 2014
null 신선영, null 정소윤, null Sang-Il Han   +2 more
openaire   +1 more source

Harnessing Machine Learning to Understand and Design Disordered Solids

Advanced Intelligent Discovery, EarlyView.
This review maps the dynamic evolution of machine learning in disordered solids, from structural representations to generative modeling. It explores how deep learning and model explainability transform property prediction into profound physical insight.
Muchen Wang, Yue Fan
wiley   +1 more source