Results 151 to 160 of about 5,999 (239)

Learning Continuous Decomposable Models Using Mutual Information and Statistical Copulas. [PDF]

open access: yesEntropy (Basel)
Desuó Neto L   +3 more
europepmc   +1 more source

Finite monodromy of some two-parameter families of exponential sums

open access: yes
We determine the set of polynomials $f(x)\in k[x]$, where $k$ is a finite field, such that the local system on $\mathbb G_m^2$ which parametrizes the family of exponential sums $(s,t)\mapsto\sum_{x\in k}\psi(sf(x)+tx)$ has finite monodromy, in two cases:
Rojas-León, Antonio   +1 more
core  

Additive Manufacturing of Ordered Polymer Nanostructures

open access: yesAdvanced Materials, EarlyView.
A new 3D printing strategy, Polymerization‐Induced Arrangement of Nanostructures with Order‐tunability (PIANO), enables the formation of ordered nanostructures in polymer materials by enhancing chain mobility during photopolymerization, overcoming the kinetic arrest that leads to disordered morphologies in the conventional Polymerization‐Induced ...
Di Wu   +6 more
wiley   +1 more source

Advancing Lithium–Oxygen Batteries: Pioneering Cathode Catalyst Innovation and Artificial Intelligence‐Driven Design Paradigms

open access: yesAdvanced Materials, EarlyView.
This review summarizes the principles and challenges of nonaqueous lithium‐oxygen batteries and recent advances in cathode catalysts, including carbon‐based materials, metals, oxides, sulfides, nitrides, carbides, and redox mediators. It highlights emerging design strategies and artificial intelligence‐driven approaches, emphasizing data‐assisted ...
Yuqing Yao   +8 more
wiley   +1 more source

Weaving Intelligence: Thermally Drawn Multimaterial Fibers Toward AI‐Enabled Smart Textiles

open access: yesAdvanced Materials, EarlyView.
Thermally drawn multimaterial fibers are rapidly advancing as intelligent structural units for next‐generation smart textiles. Integrating multimaterial architectures with neuromorphic and spiking‐neural‐network principles enables fabrics that can sense, compute, and adapt autonomously.
Vuong Dinh Trung   +9 more
wiley   +1 more source

Machine Learning Accelerated Computational Design of Bio‐Inspired Catalysts in the Nitrogen Reduction Reaction

open access: yesAdvanced Materials, EarlyView.
We introduce a computational workflow that combines quantum chemical calculations and machine learning techniques to predict the catalytic performance of a wide range of catalysts in the nitrogen reduction reaction (NRR). The analysis of the trained models provides insights into the complex structure–activity relationship in experimental catalytic ...
Leonardo Di Ciano   +5 more
wiley   +1 more source

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