Results 151 to 160 of about 36,481,271 (303)

A Computational Approach for Biomimetic Design of Liver‐On‐A‐Chip

Advanced Intelligent Discovery, EarlyView.
A biomimetic liver‐on‐a‐chip with antiparallel perfusion was designed using COMSOL‐guided simulation to replicate hepatic acinus transport. Full‐scale modelling revealed diffusion‐dominated, length‐dependent nutrient gradients and low‐shear conditions.
Zhenxu Yang, Qiankun Yin, Qingxin Liu, Zhejun Xu, Ye Zhang, Christopher Vega‐Sánchez, Xiaochen Liu, Daniele Vigolo, Jiao Jiao Li, Ken‐Tye Yong   +9 more
wiley   +1 more source

DATA MINING TECHNOLOGIES [PDF]

Knowledge discovery and data mining software (Knowledge Discovery and Data Mining - KDD) as an interdisciplinary field emersion have been in rapid growth to merge databases, statistics, industries closely related to the desire to extract valuable ...
Titrade Cristina-Maria
core  

Advances in Thermal Modeling and Simulation of Lithium‐Ion Batteries with Machine Learning Approaches

Advanced Intelligent Discovery, EarlyView.
Heat generation in lithium‐ion batteries affects performance, aging, and safety, requiring accurate thermal modeling. Traditional methods face efficiency and adaptability challenges. This article reviews machine learning‐based and hybrid modeling approaches, integrating data and physics to improve parameter estimation and temperature prediction ...
Qi Lin, Kang Fu, Junjie Ding, Kai Sun, Peng Tan   +4 more
wiley   +1 more source

Time Series Data Mining for Linking the Shape of Bacterial Growth Curves to Biological Functions. [PDF]

Comput Struct Biotechnol J
Lao Z, Ying BW.
europepmc   +1 more source

Terrain-Identification-from-Time-Series-Data

Terrain Identification from Time Series Data. Public copy of my NCSU class repo at: https://github.ncsu.edu/kajadhav/Terrain-Identification-from-Time-Series-DataIf you use this software, please cite it as ...
Jadhav, Kaushik
core   +1 more source

Accelerating Biosensor Discovery: A Computationally‐Driven Pipeline for Microplastics Monitoring

Advanced Intelligent Discovery, EarlyView.
A computationally guided pipeline unites molecular simulation, synthetic biology, electrochemical engineering, and machine learning to accelerate biosensor discovery. A Bacillus anthracis carbohydrate‐binding module is used to develop a high‐performance micro‐ and nanoplastics sensor with greatly reduced error and variability.
Gabriel X. Pereira, Marcos de C. Leite, Pedro H. M. Zanineli, Juliana N. Y. Costa, Débora M. Cunha, Sophia P. Serra, Pedro H. Sophia, Flávio M. Shimizu, Yasmin W. Moura, Célio C. Oliveira, Gabriel R. Schleder, Gabriela F. Persinoti, Leandro Merces, Renato S. Lima   +13 more
wiley   +1 more source

Research on engine power-loss fault diagnosis method based on time-series data mining. [PDF]

Sci Rep
Feng L, Liu L, Xu H, Gao S, Tang H, Cui J, Zhang B, Rao Y.   +7 more
europepmc   +1 more source

Fuzzy clustering of univariate and multivariate time series by genetic multiobjective optimization [PDF]

Given a set of time series, it is of interest to discover subsets that share similar properties. For instance, this may be useful for identifying and estimating a single model that may fit conveniently several time series, instead of performing the usual
S. Bandyopadhyay, R. Baragona, U. Maulik
core  

Self‐Driving Laboratory Optimizes the Lower Critical Solution Temperature of Thermoresponsive Polymers

Advanced Intelligent Discovery, EarlyView.
A low‐cost, self‐driving laboratory is developed to democratize autonomous materials discovery. Using this "frugal twin" hardware architecture with Bayesian optimization, the platform rapidly converges to target lower critical solution temperature (LCST) values while self‐correcting from off‐target experiments, demonstrating an accessible route to data‐
Guoyue Xu, Renzheng Zhang, Tengfei Luo
wiley   +1 more source

A Mining Algorithm for Extracting Decision Process Data Models [PDF]

The paper introduces an algorithm that mines logs of user interaction with simulation software. It outputs a model that explicitly shows the data perspective of the decision process, namely the Decision Data Model (DDM). In the first part of the paper we
Cristina-Claudia DOLEAN, Razvan PETRUSEL
core