Results 121 to 130 of about 2,574 (191)

Viscosity Reduction in Diluted Polyethylene Melts: A Comparative Study of Semiempirical, Viscoelastic, and Equation‐of‐State Modeling Frameworks

open access: yesPolymer Engineering &Science, EarlyView.
Schematic of the in‐line rheometry setup and the evaluation of three distinct modeling frameworks (semiempirical, viscoelastic, and EOS) to predict viscosity reduction in diluted polyethylene melts via time‐concentration superposition (Illustration generated via ChatGPT).
Ernst Georg Viehböck   +5 more
wiley   +1 more source

Deciphering cell-fate trajectories using spatiotemporal single-cell transcriptomic data. [PDF]

open access: yesNPJ Syst Biol Appl
Zhang Z   +6 more
europepmc   +1 more source

Negative Stiffness Induced and Controlled by Constriction

open access: yesphysica status solidi (b), EarlyView.
Structures with negative stiffness can be stabilized by constriction of external displacement. Furthermore, constriction can make a conventional positive stiffness material exhibit negative stiffness, either bidirectional or unidirectional (shown in the figure).
Elena Pasternak, Arcady V. Dyskin
wiley   +1 more source

A Review of Metal–Organic Framework (MOF) Based Active Food Packaging: Materials Selection, Cellulose Matrices Current Advances, Synthesis and Characterizations

open access: yesPackaging Technology and Science, EarlyView.
Active packaging offers an effective approach to extending food shelf life. This review summarizes the past decade of progress in metal‐organic framework (MOF)‐based active food packaging, highlighting material selection, characterization, challenges, and future prospects.
Belladini Lovely   +4 more
wiley   +1 more source

An objective Bayesian method for including parameter uncertainty in ensemble model output statistics

open access: yesQuarterly Journal of the Royal Meteorological Society, EarlyView.
Conventional model output statistics and ensemble model output statistics methods for calibrating ensemble forecasts lead to severe underestimation of the probabilities of ensemble extremes (in blue). This is because they ignore statistical parameter uncertainty.
Stephen Jewson   +4 more
wiley   +1 more source

Loss Behavior in Supervised Learning With Entangled States

open access: yesAdvanced Quantum Technologies, EarlyView.
Entanglement in training samples supports quantum supervised learning algorithm in obtaining solutions of low generalization error. Using analytical as well as numerical methods, this work shows that the positive effect of entanglement on model after training has negative consequences for the trainability of the model itself, while showing the ...
Alexander Mandl   +4 more
wiley   +1 more source

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