Results 61 to 70 of about 50,503 (261)

Neuromorphic Electronics for Intelligence Everywhere: Emerging Devices, Flexible Platforms, and Scalable System Architectures

Advanced Materials, EarlyView.
The perspective presents an integrated view of neuromorphic technologies, from device physics to real‐time applicability, while highlighting the necessity of full‐stack co‐optimization. By outlining practical hardware‐level strategies to exploit device behavior and mitigate non‐idealities, it shows pathways for building efficient, scalable, and ...
Kapil Bhardwaj, Roshni Satheesh Babu, Yuxin Xia, Eva Bestelink, Radu Sporea, Nikos Hastas, Ioannis Zeimpekis, Dimitra G. Georgiadou, Sayani Majumdar   +8 more
wiley   +1 more source

Organic Materials of Tomorrow: Horizons of Artificial Intelligence

Advanced Materials, EarlyView.
This review examines machine learning techniques accelerating the discovery of organic semiconductors by linking molecular structure to properties. Key methods include graph neural networks, generative models, and active learning. Applications to organic photovoltaics demonstrate practical impact.
Harold Mena, J. Terence Blaskovits, Kun‐Han Lin, Denis Andrienko   +3 more
wiley   +1 more source

Bayesian hierarchical models combining different study types and adjusting for covariate imbalances: a simulation study to assess model performance.

PLoS ONE, 2011
BackgroundBayesian hierarchical models have been proposed to combine evidence from different types of study designs. However, when combining evidence from randomised and non-randomised controlled studies, imbalances in patient characteristics between ...
C Elizabeth McCarron, Eleanor M Pullenayegum, Lehana Thabane, Ron Goeree, Jean-Eric Tarride   +4 more
doaj   +1 more source

Modeling the probability of a batter/pitcher matchup event: A Bayesian approach. [PDF]

PLoS ONE, 2018
We develop a Bayesian hierarchical log5 model to predict the probability of a particular batter/pitcher matchup event in baseball by extending the log5 model which is widely used for describing matchup events.
Woojin Doo, Heeyoung Kim
doaj   +1 more source

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

Advanced 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, Chongyang Hao, Karol Viviana Mejia‐Centeno, Malik Dilshad Khan, Shang Wang, Longqiu Li, Yanhong Tian, Andreu Cabot, Qing Sun   +8 more
wiley   +1 more source

A Bayesian Hierarchical Cox Model with Elastic Net Regularization for Improved Survival Prediction and Feature Selection

Mathematics
In recent years, the growing availability of large-scale data across a wide range of disciplines has created new opportunities for developing models that improve the predictive accuracy of statistical models.
Bulus I. Doroh, Kazeem A. Dauda, Rasheed K. Lamidi   +2 more
doaj   +1 more source

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

Advanced 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, Jiaxiang Yi, Hieu Nguyen‐Duc, Tien Nu Hoang Lo, Weili Zhao, Chaoqun Dong, Fabien Sorin, Baris Caglar, Lei Wei, Tung Nguyen‐Dang   +9 more
wiley   +1 more source

Projecting malaria elimination in Thailand using Bayesian hierarchical spatiotemporal models

Scientific Reports, 2023
Thailand has set a goal of eliminating malaria by 2024 in its national strategic plan. In this study, we used the Thailand malaria surveillance database to develop hierarchical spatiotemporal models to analyze retrospective patterns and predict ...
Chawarat Rotejanaprasert, Saranath Lawpoolsri, Patiwat Sa-angchai, Amnat Khamsiriwatchara, Chantana Padungtod, Rungrawee Tipmontree, Lynette Menezes, Jetsumon Sattabongkot, Liwang Cui, Jaranit Kaewkungwal   +9 more
doaj   +1 more source

Hard‐Magnetic Soft Millirobots in Underactuated Systems

Advanced Robotics Research, EarlyView.
This review provides a comprehensive overview of hard‐magnetic soft millirobots in underactuated systems. It examines key advances in structural design, physics‐informed modeling, and control strategies, while highlighting the interplay among these domains.
Qiong Wang, Zixuan Zhang, Bingyan Li, Junkai Wang, Hongcheng Gu   +4 more
wiley   +1 more source

Bayesian Prior Choice in IRT estimation using MCMC and Variational Bayes

Frontiers in Psychology, 2016
This study investigated the impact of three prior distributions: matched, standard vague, and hierarchical in Bayesian estimation parameter recovery in two and one parameter models. Two Bayesian estimation methods were utilized: Markov chain Monte Carlo (
Prathiba Natesan, Ratna Nandakumar, Tom Minka, Jonathan Rubright   +3 more
doaj   +1 more source