Results 211 to 220 of about 61,380 (270)

Data‐Driven Prediction of Block Copolymer Morphology Using Coarse‐Grained Modeling and Machine Learning

open access: yesJournal of Polymer Science, EarlyView.
An integrated framework combining high‐throughput CG‐MD simulations, automated image‐based feature extraction, and supervised machine learning enables efficient, interpretable, and data‐driven prediction of block copolymer morphological behaviors.
Lan Xu, Zhaofan Li, Wenjie Xia
wiley   +1 more source

Exploding Bridgewire (EBW) Detonators: An Example of Synergistic Multiphysics

open access: yesPropellants, Explosives, Pyrotechnics, EarlyView.
ABSTRACT Exploding bridgewire (EBW) detonators are highly temporally reproducible explosive devices that require the rapid discharge of a high‐voltage capacitance to operate and so are immune to most of the accidental hazards associated with traditional electric detonators.
P. J. Rae   +3 more
wiley   +1 more source

Generalizing the Gaussian Network Model: Spanning‐Tree Thermodynamics Shows Entropy‐Driven KRAS Activation

open access: yesProteins: Structure, Function, and Bioinformatics, EarlyView.
ABSTRACT The GTPase KRAS executes a conformational switch between a GTP‐bound active state and a GDP‐bound inactive state, a process central to oncogenic signaling. However, the structural basis of this switching at the level of residue‐contact organization remains incompletely characterized by traditional binary structural models.
Fatma Senguler Ciftci, Burak Erman
wiley   +1 more source

Phase-Specific Parameter Estimation in Chiral HPLC Using 1-Site, 2-Site Stochastic Models, and Unified Equation Approach. [PDF]

open access: yesAnal Chem
Mirzahosseini A   +12 more
europepmc   +1 more source

Sensitivity of flower trade‐wind cloud organisation to mesoscale atmospheric heterogeneities

open access: yesQuarterly Journal of the Royal Meteorological Society, EarlyView.
Trade‐wind cloud organisation is insensitive to dynamical and thermal heterogeneities but very sensitive to humidity mesoscale heterogeneities, especially in the cloud layer, where they form moist patches, and not in the sub‐cloud layer. Clouds and rain develop in moist patches, then cold pools develop progressively, initiating mesoscale circulations ...
Thibaut Dauhut   +2 more
wiley   +1 more source

How consistently do ensemble prediction systems represent the growth of atmospheric uncertainty?

open access: yesQuarterly Journal of the Royal Meteorological Society, EarlyView.
Spread‐based diagnostics calculated for 12 ensemble prediction systems are compared to understand the consistency with which they represent atmospheric uncertainty growth. Good correlation between all these systems is found in the extratropics for a lead time range from 48 hr to between 96 hr and 192 hr.
Douglas Wood   +3 more
wiley   +1 more source

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