Results 181 to 190 of about 179,339 (238)

Experimental Nonevidence of Fragile-to-Strong Crossover. [PDF]

ACS Mater Lett
Koštál P, Barták J, Včeláková M, Slang S, Wieduwilt T, Schmidt MA, Málek J.   +6 more
europepmc   +1 more source

Spheres, tears, and spears: Regulating the perimeter and circularity of millimeter‐sized alginate hydrogel beads

AIChE Journal, EarlyView.
Abstract Generating hydrogel beads pertains to many engineering applications. We examined two alginate‐based fluids at three concentrations of alginate, cAG$$ {c}_{\mathrm{AG}} $$. We used the “Map of Misery” to determine which material property (viscosity, elasticity, and inertia) drives droplet formation.
Conor G. Harris, Katherine D. Bandettini, Hannah K. Gedde, Lewis Semprini, Kaitlin C. Fogg, Willie E. Rochefort   +5 more
wiley   +1 more source

Comparing Cardiovascular Morbidity and Mortality in Critically Ill Patients Undergoing Continuous Renal Replacement Therapy Versus Sustained Low-Efficiency Dialysis: A Systematic Review. [PDF]

Cureus
Lal K, Johal LK, Iram F, Nasrat AK, Palvia RN, Syeda SF, Raza A, Ullah I, Aliyev R, Nepesova J, Khan F.   +10 more
europepmc   +1 more source

Advanced Experiment Design Strategies for Drug Development

Advanced Intelligent Discovery, EarlyView.
Wang et al. analyze 592 drug development studies published between 2020 and 2024 that applied design of experiments methodologies. The review surveys both classical and emerging approaches—including Bayesian optimization and active learning—and identifies a critical gap between advanced experimental strategies and their practical adoption in ...
Fanjin Wang, Anthony Harker, Mohan Edirisinghe, Maryam Parhizkar   +3 more
wiley   +1 more source

Caloric density alters meiotic recombination rate in Drosophila melanogaster. [PDF]

Genetica
Novak TE, Rivera-Rincón N, Schneider C, Saurette E, Barnette CD, Stevison LS.   +5 more
europepmc   +1 more source

Machine Learning‐Assisted Second‐Order Perturbation Theory for Chemical Potential Correction Toward Hubbard U Determination

Advanced Intelligent Discovery, EarlyView.
In this work, the Doubao large language model (LLM) is involved in the formula derivation processes for Hubbard U determination regarding the second‐order perturbations of the chemical potential. The core ML tool is optimized for physical domain knowledge, which is not limited to parameter prediction but rather serves as an interactive physical theory ...
Mingzi Sun, Tong Wu, Baian Chen, Zikang Li, Qiuyang Lu, Lu Lu, Hon Ho Wong, Cheuk Hei Chan, Bolong Huang   +8 more
wiley   +1 more source

Location and capacity optimization of urban sanitation robot base stations using improved NSGA-II. [PDF]

Sci Rep
Ding J, Wang L, Pan K, Ji N, Cui N.
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

Uncovering Design and Assembly Rules for mRNA-DNA Origami. [PDF]

Nano Lett
Wang JY, Huzar J, Kim M, Ryu T, Lomeli NR, Delcassian D, Kim DN, Tikhomirov G.   +7 more
europepmc   +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