Results 181 to 190 of about 13,735 (297)

Machine Learning Paradigm for Advanced Battery Electrolyte Development

Carbon Energy, EarlyView.
Electrolyte materials determine ion transport kinetics within the bulk and interphases, ultimately influencing the performance of battery systems. As data‐driven paradigms increasingly reshape materials discovery, this review provides an application‐oriented exploration of the intersection between machine learning and electrolyte science. By evaluating
Chang Su, Mengfan Pei, Keyu Chen, Xigao Jian, Fangyuan Hu   +4 more
wiley   +1 more source

PHA synthase variant design using a conditional variational autoencoder. [PDF]

PLoS Comput Biol
Tenkanen T, Ylinen A, Jouhten P, Penttilä M, Castillo S.   +4 more
europepmc   +1 more source

AI‐Enabled Precision Dosing in Pediatrics: Enhancing Model‐Informed Decision Making

Clinical Pharmacology &Therapeutics, EarlyView.
Ensuring safe and effective pharmacotherapy for children remains a central challenge in clinical pharmacology, yet rapid advances in AI have not translated into clinical practice. This Perspective highlights how AI‐enabled approaches can enhance model‐informed decision making for precision dosing.
Kei Irie, Tomoyuki Mizuno
wiley   +1 more source

A Supervised Contrastive Variational Autoencoder with Probabilistic Latent Alignment for Cross-Domain EEG Emotion Recognition. [PDF]

Sensors (Basel)
Wu L, Yang Y, Wang W, Xie Y, Zhou N, Shi K.   +5 more
europepmc   +1 more source

DinoFlow: Self‐supervised pretraining in flow cytometry enables accurate detection of common hematopathological disorders

Cytometry Part B: Clinical Cytometry, EarlyView.
Abstract Flow cytometry is an essential component of routine hematological lab testing. Many computational methods have been proposed for the analysis of flow cytometry data, but most have focused on supervised learning for just one or a few specific disorders.
Brendan O'Fallon, Muir Morrison, Mattia Medina Grespan, Nicholas C. Spies, David P. Ng   +4 more
wiley   +1 more source

Development of a variational autoencoder for handwritten digit recognition [PDF]

With each passing day, the application of neural networks becomes increasingly noticeable in various fields of activity. Scientists and researchers strive to develop new and improve existing neural networks to address issues in forecasting, creative ...
Filimonova, Т., Podoliak, В.
core   +1 more source

The Identification of Beckwith-Wiedemann Syndrome Through Swap Disentangled Variational Autoencoder. [PDF]

J Craniofac Surg
Rijlaarsdam T, Smith L, Rickart A, Schievano S, Dunaway D, Wolvius E, van de Lande L, Foti S, Ong J.   +8 more
europepmc   +1 more source

Research progress and current status of dynamic wave propagation characteristics in rock mass: A review

Deep Underground Science and Engineering, EarlyView.
This review elucidates the velocity–dispersion–attenuation coupling mechanisms of wave propagation in rock masses, compares six representative models, and reveals how pressure, temperature, mineral composition, and anisotropy jointly control dynamic responses in complex geological media.
Jiajun Shu, Tao Li, Ruiqi Yuan, Yue Li, Bingni Wu, Bo Liu, Zhengding Deng, Galindo Rubén, Fausto Molina‐Gómez   +8 more
wiley   +1 more source

Sidewalk Hazard Detection Using a Variational Autoencoder and One-Class SVM. [PDF]

Sensors (Basel)
Guzman ER, Howe RD.
europepmc   +1 more source

Advances in vital‐sign prediction and early‐warning models for underground coal mine workers integrating environmental factors

Deep Underground Science and Engineering, EarlyView.
This review synthesizes advances in predicting miners' vital signs by integrating environmental monitoring (dust, temperature, and gas) with physiological data. It highlights multi‐source data fusion techniques and early‐warning models for enhanced occupational safety in underground coal mines.
Junji Zhu, Dawei Cui, Yingjie Liu, Yuntao Liang, Yue Zhang   +4 more
wiley   +1 more source