Results 81 to 90 of about 175 (174)

How Experts Adapt Their Gaze Behavior When Modeling a Task to Novices. [PDF]

open access: yesCogn Sci, 2020
Emhardt SN   +5 more
europepmc   +1 more source

Correcting Apparent Priming Bias Unveils Fertilizer Nitrogen‐Risk Archetypes of Surplus and Depletion Across Asian Rice Systems

open access: yesAdvanced Science, EarlyView.
Correcting the apparent priming effect resolves systematic biases in Asian rice fertilizer nitrogen accounting. Net soil retention drops below 7%, while 48% of fertilizer escapes, inflicting US$98.53 billion in annual reactive‐nitrogen damages. High‐resolution mapping uncovers N‐risk archetypes across 42% of the rice area, delivering a spatially ...
Xiuyun Liu   +5 more
wiley   +1 more source

A systematic review of fuzzing based on machine learning techniques. [PDF]

open access: yesPLoS One, 2020
Wang Y, Jia P, Liu L, Huang C, Liu Z.
europepmc   +1 more source

An Integrative Strategy Delineates Modular Metabolic Remodeling and Potential Therapeutic Targets Across Metabolic Diseases

open access: yesAdvanced Science, EarlyView.
An integrative single‐cell atlas across multiple metabolic diseases reveals coordinated metabolic modules and disease‐shared versus disease‐specific pathway activities. By systematically comparing scoring strategies, a robust RankAve framework is established. Coupled with network analysis and drug‐target prediction, this resource uncovers cross‐disease
Kuan Yang   +10 more
wiley   +1 more source

Optimizing Stratification in Binary Colloidal Supraparticles

open access: yesAdvanced Science, EarlyView.
Supraparticles from binary particle populations show stratification when dried at high Péclet numbers. In spray‐dried systems at high initial particle concentration and fast drying conditions optimal combinations of particle size ratios and volume fractions that produce maximal stratification are found both in experiment and simulation, contrasting the
Frederic Rudlof   +5 more
wiley   +1 more source

Frontier Advances of Terpyridine–Zn(II) Complexes: From Molecular Design to Smart Functional Materials

open access: yesAdvanced Science, EarlyView.
Tpy–Zn complexes serve as versatile building blocks for the modular assembly of functional materials, including polymers, gels, MOFs, cages, and composites through programmed noncovalent interactions, empowering practical applications in visual molecular recognition, smart functional materials, and photocatalytic transformations. ABSTRACT As one of the
Lixin Duan   +4 more
wiley   +1 more source

Twisted MoS2 Bilayers as Functional Elements in Memtransistors: Hysteresis, Optical Signatures, and Photocurrent Kinetics

open access: yesAdvanced Electronic Materials, EarlyView.
ABSTRACT Layered 2D materials are considered as promising for memristive applications due to their ultimate vertical scalability compared to conventional semiconductor films and pronounced hysteresis properties. Bias‐resolved Raman and Photoluminescence mapping is used to quantify strain from phonon shifts and carrier density from the exciton‐trion ...
Vladislav Kurtash   +4 more
wiley   +1 more source

A Pattern-based Foundation for Language-Driven Software Engineering

open access: yes
This work brings together two fundamental ideas for modelling, programming and analysing software systems. The first idea is of a methodological nature: engineering software by systematically creating and relating languages.
Reichert, Tim
core  

Smart Exploration of Perovskite Photovoltaics: From AI Driven Discovery to Autonomous Laboratories

open access: yesAdvanced Energy Materials, EarlyView.
In this review, we summarize the fundamentals of AI in automated materials science, and review AI applications in perovskite solar cells. Then, we sum up recent progress in AI‐guided manufacturing optimization, and highlight AI‐driven high‐throughput and autonomous laboratories.
Wenning Chen   +4 more
wiley   +1 more source

Machine Learning Interatomic Potentials for Energy Materials: Architectures, Training Strategies, and Applications

open access: yesAdvanced Energy Materials, EarlyView.
Machine learning interatomic potentials bridge quantum accuracy and computational efficiency for materials discovery. Architectures from Gaussian process regression to equivariant graph neural networks, training strategies including active learning and foundation models, and applications in solid‐state electrolytes, batteries, electrocatalysts ...
In Kee Park   +19 more
wiley   +1 more source

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