Results 101 to 110 of about 21,063 (212)

A Highly Hydrophilic Air Electrode With Water‐Induced Surface Reconstruction for Efficient Reversible Protonic Ceramic Cell at Low Water Partial Pressures

Advanced Energy Materials, EarlyView.
This work demonstrates a new strategy for reversible protonic ceramic cells (R‐PCCs). By developing highly hydrophilic oxides, efficient operation is achieved under low water vapor pressures while maintaining high performance and stability. This approach addresses the challenge of hydrogen production in freshwater‐scarce regions.
Nai Shi, Yun Xie, Xueyu Hu, Junji Hyodo, Chun‐Kuo Peng, Gaopeng Zhou, Hanwen Liu, Bingbing Qiu, Daqin Guan, Jiayi Tang, Yan‐Gu Lin, Ranran Peng, Moses Oludayo Tadé, Meilin Liu, Yoshihiro Yamazaki, Zongping Shao   +15 more
wiley   +1 more source

Promoting Healthier Drinking: Evidence From a Vignette Experiment on Contextual and Informational Drivers of Dealcoholized Wine Choices

Applied Economic Perspectives and Policy, EarlyView.
ABSTRACT Growing demand for healthier beverages is driving innovation in the wine sector, with dealcoholized wine emerging as a promising alternative. However, little is known about the contextual conditions under which consumers would choose dealcoholized wine, particularly in countries with strong wine traditions. To fill this gap, this work examines
Giovanna Piracci, Giulia Maesano, Leonardo Casini, Maurizio Canavari, Marija Cerjak   +4 more
wiley   +1 more source

Farmers' Financial Literacy—Scale Development and Linkages to Accounting Practices and Financial Outcomes

Agribusiness, EarlyView.
ABSTRACT This study investigates the financial literacy (FL) of Swedish farmers, its linkages to farmer characteristics, management accounting practices and farm outcomes by surveying Swedish Farm Accountancy Data Network farmers. Using item response theory, we expand the existing FL measurement specifically to the farming context, assess measurement ...
Uliana Gottlieb, Helena Hansson
wiley   +1 more source

Dictionary‐based weak‐form training for noise‐robust series hybrid models with multiplicative unknowns

AIChE Journal, EarlyView.
ABSTRACT Hybrid modeling combines first‐principles equations with a data‐driven subcomponent. Training for the data‐driven part is sensitive to measurement noise when training targets are constructed using pointwise time derivatives. Beyond differentiation errors, hybrid models involve solving an inverse problem to estimate the data‐driven term, which ...
Hangjun Cho, Akshay Kudva, Parth Shah, Dongheon Lee, Joseph Sang‐Il Kwon   +4 more
wiley   +1 more source

Automated generative process synthesis via transformer‐based dual‐loop simulation and optimization

AIChE Journal, EarlyView.
Abstract This study presents a novel framework for automated generative process synthesis, addressing the complexity of simultaneously optimizing discrete topologies and continuous operating variables. To overcome conventional superstructure limitations, we propose a dual‐loop architecture integrating generative transformers with rigorous process ...
Yeong Woo Son, Chan Kim, Ji Hun Pak, Hain Lee, Jong Min Lee   +4 more
wiley   +1 more source

Cross-Architecture Knowledge Distillation for Histopathological Image Analysis. [PDF]

IEEE Access
Boudissa S, Kawanaka H, Aronow B, Prasath VBS.   +3 more
europepmc   +1 more source

Deep Learning Prediction of Surface Roughness in Multi‐Stage Microneedle Fabrication: A Long Short‐Term Memory‐Recurrent Neural Network Approach

Advanced Intelligent Discovery, EarlyView.
A sequential deep learning framework is developed to model surface roughness progression in multi‐stage microneedle fabrication. Using real‐world experimental data from 3D printing, molding, and casting stages, an long short‐term memory‐based recurrent neural network captures the cumulative influence of geometric parameters and intermediate outputs ...
Abdollah Ahmadpour, Sara Soltanabadi Farshi, Tuanna Ozcan, Amir Mohammad Ketabchi, Alper Kiraz, Savas Tasoglu   +5 more
wiley   +1 more source

Selective Correlation Based Knowledge Distillation for Ground Reaction Force Estimation. [PDF]

Measurement (Lond)
Jeon ES, Lee J, Lim H, Save OM, Lee H, Turaga P.   +5 more
europepmc   +1 more source

Artificial Intelligence‐Driven Insights into Electrospinning: Machine Learning Models to Predict Cotton‐Wool‐Like Structure of Electrospun Fibers

Advanced Intelligent Discovery, EarlyView.
Electrospinning allows the fabrication of fibrous 3D cotton‐wool‐like scaffolds for tissue engineering. Optimizing this process traditionally relies on trial‐and‐error approaches, and artificial intelligence (AI)‐based tools can support it, with the prediction of fiber properties. This work uses machine learning to classify and predict the structure of
Paolo D’Elia, Giovanna Pappalardo, Aldo R. Boccaccini, Liliana Liverani   +3 more
wiley   +1 more source

IMFLKD: an incentive mechanism for decentralized federated learning based on knowledge distillation. [PDF]

Sci Rep
Ying X, Yan K, Gao X, Huang J.
europepmc   +1 more source