Results 51 to 60 of about 13,394,361 (202)

Data‐Guided Photocatalysis: Supervised Machine Learning in Water Splitting and CO2 Conversion

Advanced Intelligent Discovery, EarlyView.
This review highlights recent advances in supervised machine learning (ML) for photocatalysis, emphasizing methods to optimize photocatalyst properties and design materials for solar‐driven water splitting and CO2 reduction. Key applications, challenges, and future directions are discussed, offering a practical framework for integrating ML into the ...
Paul Rossener Regonia, Christian Mark Pelicano   +1 more
wiley   +1 more source

Energy‐Aware Perturbation Optimization for Memristor‐Array Convolutional Neural Networks

Advanced Intelligent Systems, EarlyView.
Memristor‐array inference becomes more energy efficient when layer inputs are reshaped before computation. Sinusoidal perturbation encoding with dual‐threshold screening reduces active voltage pulses and contracts ADC input‐current ranges, jointly lowering crossbar and peripheral energy while preserving accuracy across hardware MNIST validation, deep ...
Ao Xu, Lun Lu, Youpeng Wu, Yao Guo, Yi Li, Yinan Wang, Qingjiang Li   +6 more
wiley   +1 more source

Artificial Intelligence for Advanced Functional Materials: Progress and Emerging Frontiers

Advanced Intelligent Systems, EarlyView.
Artificial intelligence is transforming the discovery of functional materials by linking synthesis, characterization, simulation, and design in unified workflows. Advances in machine learning, autonomous experimentation, and foundation models are accelerating innovation across energy, electronics, and biomedicine, while revealing new frontiers for ...
Cristiano Malica, Kostya S. Novoselov, Seongmin Kim, Yousung Jung, Silvana Botti, Miguel A. L. Marques, Tigany Zarrouk, Miguel A. Caro, Sanggyu Chong, Michele Ceriotti, Marivi Fernandez‐Serra, Sara Navarro‐Rodriguez, Aakash Ashok Naik, Janine George, Ömer Hasan Omar, Victor Trinquet, Gian‐Marco Rignanese, Andrey Ustyuzhanin, Sonia Conesa‐Boj, Juan Rojo, Minh‐Tuan Dau, Simon M.‐M. Dubois, Jean‐Christophe Charlier, Jose H. Garcia, Romain Gautier, Rachid Laref, Junfeng Qiao, Giovanni Pizzi, Nicola Marzari, Camilla Coletti, Antonio Rossi, Jan‐Lucas Uslu, Chiara Zanardi, Vincenzo Palermo, Wide Hogenhout, Natalia Konchakova, Peter Klein, Tejs Vegge, Stephan Roche   +38 more
wiley   +1 more source

Arithmetic progressions in binary quadratic forms and norm forms [PDF]

, 2019
We prove an upper bound for the length of an arithmetic progression represented by an irreducible integral binary quadratic form or a norm form, which depends only on the form and the progression's common difference.
Elsholtz, Christian, Frei, Christopher
core   +1 more source

A Compact Multijoint Finger Motion Sensing Module for Flexible Exoskeleton Applications

Advanced Intelligent Systems, EarlyView.
Addressing the motion estimation gap in underactuated wearable electronics, a highly integrated sensor alongside a customized readout circuit is proposed. Featuring a screen‐printed layout and optimized horseshoe geometry, this design establishes an optimal feedback mechanism for flexible hand exoskeletons while eliminating wiring clutter.
Huawei Zheng, Frédéric Lamarque, Hani Al Hajjar, Andreas Dietzel   +3 more
wiley   +1 more source

A Scalable and Resource‐Efficient Pipelined p‐Computer for Probabilistic Ising Machines

Advanced Intelligent Systems, EarlyView.
(a) Block diagram of the portfolio optimization problem: given M assets, the goal is to determine the optimal weights w that maximize the expected return (based on the mean historical assets return u), while minimizing the risk, quantified by the assets covariance matrix S.
Deborah Volpe, Eleonora Raimondo, Andrea Grimaldi, Pedram Khalili Amiri, Stefano Chiappini, Anna Giordano, Mario Carpentieri, Hyunsoo Yang, Massimo Chiappini, Giovanni Finocchio   +9 more
wiley   +1 more source

Accelerating Materials Discovery: A Review of Machine Learning in X‐Ray Absorption Spectroscopy

Advanced Intelligent Systems, EarlyView.
This review systematically details how machine learning transforms X‐ray absorption spectroscopy (XAS) analysis. It covers advanced deep learning architectures for structure‐spectra mapping and inverse tasks, while discussing key challenges like the simulation‐to‐reality gap.
Melaku Lake Tegegne, Haodong Yao, Liyuan Wu, Yihe Pang, Fazhi Qi, Lina Zhao   +5 more
wiley   +1 more source

Energy methods for lossless systems using quadratic differential forms

, 2008
In this thesis, we study the properties of lossless systems using the concept of quadratic differential forms (QDFs). Based on observation of physical linear lossless systems, we define a lossless system as one for which there exists a QDF known as an ...
Rao, Shodhan
core   +1 more source

Preferential trading in agriculture: New insights from a structural gravity analysis and machine learning

American Journal of Agricultural Economics, EarlyView.
Abstract Preferential trade agreements (PTAs) contain various non‐tariff provisions, yet identifying their trade effects remains challenging because these commitments are high‐dimensional and strongly correlated within agreements. We estimated a theory‐consistent structural gravity model with domestic flows for 26 agricultural subsectors over 1988–2017
Dongin Kim, Sandro Steinbach
wiley   +1 more source

Numerical Simulation of Electrodialysis Desalination for Multicomponent Salt Lake Brine

Asia-Pacific Journal of Chemical Engineering, EarlyView.
ABSTRACT Electrodialysis (ED) technology is presently recognized as an effective approach for extracting lithium from salt lake brine. This study concentrates on modeling the ED desalination process applied to multicomponent salt lake brine and equivalent NaCl solutions.
Kang Li, Qian Zhang, Yun Ding, Xiaoyan Wei, Wanqi Wang, Yuan Sui, Ruizhi Hu, Bing Pan, Qing Yang   +8 more
wiley   +1 more source