Results 101 to 110 of about 236,656 (279)

In Situ Polymerized Composite Electrolytes for High‐Performance Solid‐State Lithium Batteries: A Review

Advanced Science, EarlyView.
This review systematically explores the recent advances in in situ polymerized composite polymer electrolytes (CPEs) for solid‐state lithium batteries. It covers the fundamentals of reaction mechanisms, monomer chemistry, and their impact on interfacial stability, ionic conductivity, and electrochemical performance.
Jialin Li, Yufen Yan, Xiaoju Wang, Yuhui Huang, Zhen Huang, Lingjie Zhang, Ningzhong Bao, Zijian Hong, Yongjun Wu, Dali Ji   +9 more
wiley   +1 more source

STAID: A Self‐Refining Deep Learning Framework for Spatial Cell‐Type Deconvolution with Biologically Informed Modeling

Advanced Science, EarlyView.
STAID is a unified deep learning framework that couples iterative pseudo‐spot refinement with neural network training through a feedback loop and exploits gene co‐expression information to model higher‐order interactions, achieving accurate and robust cell‐type deconvolution in spatial transcriptomics.
Jixin Liu, Shuli Sun, Zhengliang Lv, Xinyu Liu, Yihua Wang, Bingqiang Liu   +5 more
wiley   +1 more source

A bayesian-driven feedforward neural network model for Kafka cluster latency forecasting

Радіоелектронні і комп'ютерні системи
The subject matter of this article is the process of designing the architecture of a Feedforward neural network model based on the discrete Bayesian Network and a new method for setting the initial weights that connect neurons across layers.
Olha Solovei, Tetiana Honcharenko
doaj   +1 more source

Bayesian neural network learning for repeat purchase modelling in direct marketing. [PDF]

We focus on purchase incidence modelling for a European direct mail company. Response models based on statistical and neural network techniques are contrasted.
Baesens, Bart, Dedene, Guido, Van den Poel, D, Vanthienen, Jan, Viaene, Stijn   +4 more
core  

Restricted Bayesian Neural Network

Modern deep learning tools are remarkably effective in addressing intricate problems. However, their operation as black-box models introduces increased uncertainty in predictions. Additionally, they contend with various challenges, including the need for substantial storage space in large networks, issues of overfitting, underfitting, vanishing ...
Ganguly, Sourav, Bhattacharjee, Saprativa   +1 more
openaire   +2 more sources

Terahertz Channel Modeling, Estimation and Localization in RIS‐Assisted Systems

Advanced Electronic Materials, EarlyView.
Reconfigurable intelligent surfaces have become a recent intensive research focus. Based on practical applications, channel strategies for RIS‐assisted terahertz wireless communication systems are categorized into three different types: channel modeling, channel estimation, and channel localization.
Hongjing Wang, Weipeng Wang, Xuan Cong, Shiyu Wang, Gao Li, Sen Gong, Lin Huang, Hongxin Zeng, Yaxin Zhang, Ziqiang Yang   +9 more
wiley   +1 more source

Emerging Memory and Device Technologies for Hardware‐Accelerated Model Training and Inference

Advanced Electronic Materials, EarlyView.
This review investigates the suitability of various emerging memory technologies as compute‐in‐memory hardware for artificial intelligence (AI) applications. Distinct requirements for training‐ and inference‐centric computing are discussed, spanning device physics, materials, and system integration.
Yoonho Cho, Dae‐won Kim, Yujin Kim, Jung‐Heum Na, Yuwon Jeong, Dong‐Yeop Lee, Shinhyun Choi   +6 more
wiley   +1 more source

Neural network based approximations to posterior densities: a class of flexible sampling methods with applications to reduced rank models [PDF]

Likelihoods and posteriors of econometric models with strong endogeneity and weakinstruments may exhibit rather non-elliptical contours in the parameter space.This feature also holds for cointegration models when near non-stationarity occursand ...
Dijk, H.K. van, Hoogerheide, L.F., Kaashoek, J.F.   +2 more
core   +1 more source

Roadmap for High‐Throughput Ceramic Materials Synthesis and Discovery for Batteries

Advanced Energy Materials, EarlyView.
This work examines ceramic synthesis through the lens of high‐throughput synthesis and optimization, identifying opportunities for faster, adaptable routes. It emphasizes flexible liquid precursor–to–solid film methods over slower solid‐state approaches and highlights computer‐aided decision making to optimize both material properties and device ...
Jesse J. Hinricher, Katarzyna P. Sokol, Philipp Simons, Kun Joong Kim, Michael Foshey, Yunsheng Tian, Thorben Prein, Lincoln J. Miara, Elsa Olivetti, Wojciech Matusik, Jennifer L. M. Rupp   +10 more
wiley   +1 more source

Inverse solution of process parameters in gear grinding using hierarchical bayesian physics informed neural network (HBPINN)

Scientific Reports
Accurate inverse solution of process parameters by surface roughness is crucial for precision gear grinding processes. When inversely solving process parameters, model parameters are typically obtained by fitting experimental data.
Qi Zhang, Qiang Zhang, Yongsheng Zhao, Yanming Liu, Zhi Wang, Yali Ma   +5 more
doaj   +1 more source