Results 111 to 120 of about 47,518 (218)

Harnessing machine learning and optimization for informed chemical engineering decisions: A styrene reactor analysis

The Canadian Journal of Chemical Engineering, EarlyView.
This study shows that integrating multiple machine learning models with optimization and decision‐making improves chemical process design, and that a consensus‐based strategy across models provides more robust and reliable operating recommendations than any single model, especially under limited or noisy data conditions.
Farough Agin, Clémence Fauteux‐Lefebvre, Jules Thibault   +2 more
wiley   +1 more source

A Co‐Designed Implicit‐Reset Path PFD and Current Mismatch Compensated CP Enabling Sub‐0.5% Mismatch and Dead Zone‐Free for Low‐Spur PLLs

International Journal of Circuit Theory and Applications, EarlyView.
A co‐designed implicit‐reset phase‐frequency detector (PFD) and nonlinear amplifier‐assisted charge pump (CP) achieve dead zone‐free operation from 10 MHz to 4.7 GHz with < 0.5% current mismatch without calibration, enabling −80 dBc of reference spur and 0.50 ps of RMS jitter in a 180 nm of CMOS PLL.
A. Ghaemnia, Jincheng Yang, Xiaojiong Fei, G. Zhang   +3 more
wiley   +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

Dynamic geo‐hydrogeological monitoring‐driven situational awareness for real‐time floor water inrush risk prediction in deep mining

Deep Underground Science and Engineering, EarlyView.
The fused data extracted from the distributed monitoring system as the data basis, combined with dynamic geological data, are imported into a deep learning model. As the geological conditions of mining and excavation change, the risk of water inrush at the working face is retrieved in real time.
Yongjie Li, Huiyong Yin, Fangying Dong, Tao Wu, Chao Zhang   +4 more
wiley   +1 more source

Comparison of volume‐ and surface‐based magnetic resonance imaging morphometry algorithms in the detection of focal cortical dysplasia

Epilepsia, EarlyView.
Abstract Objective Detection of focal cortical dysplasia (FCD) remains a major challenge in presurgical epilepsy diagnostics. Magnetic resonance imaging (MRI) morphometry increasingly improves lesion detection and postsurgical outcomes. The volume‐based Morphometric Analysis Program, version 2018 (MAP18) with integrated artificial neural network and ...
Lara Bücheler, Marco Reisert, Theo Demerath, Florian Mayer, Christian Moeller, Joshua Reß, Yiwen Li Hegner, Dirk‐Matthias Altenmüller, Victoria San Antonio‐Arce, Christian Scheiwe, Mukesch Shah, Jürgen Beck, Soroush Doostkam, Thomas Bast, Bernhard J. Steinhoff, Andreas Schulze‐Bonhage, Horst Urbach, Marcel Heers   +17 more
wiley   +1 more source

Hippocampal network activity changes during early epileptogenesis predict subsequent epilepsy

Epilepsia, EarlyView.
Abstract Objective Despite decades of research, the circuit mechanisms that underlie focal epileptogenesis remain incompletely understood. In this study, we aimed to characterize the changes in hippocampal network activity induced by an epileptogenic insult.
Michael Strüber, Tristan Manfred Stöber, Vanessa Schütz, Lara Costard, Valentin Neubert, Sebastian Bauer, Parsa Gharavi, Julika Pitsch, Nico Melzer, Adam Strzelczyk, Jochen Triesch, Kai Siebenbrodt, Felix Rosenow, Ricardo Kienitz   +13 more
wiley   +1 more source

Learning Rocking Dynamics From Sparse Shake‐Table Data With Interpretable Physics‐Informed Neural Networks

Earthquake Engineering &Structural Dynamics, EarlyView.
ABSTRACT We present a hybrid interpretable Physics‐Informed Neural Network Long‐Short Term Memory (Hybrid PINN LSTM) framework for predicting the seismic response of rocking blocks. Existing analytical models rely on uncertain idealizations, while purely data‐driven and machine‐learning approaches lack physical consistency and interpretability.
Shirley Shen, Christian Málaga‐Chuquitaype   +1 more
wiley   +1 more source

Hybrid Simulation–Machine Learning Surrogates for Coordinate‐Based Solar and Wind Energy Yield Assessment in Iraq: A Streamlit Decision‐Support Tool

Energy Science &Engineering, EarlyView.
This study integrates climatic simulations with machine learning to predict solar and wind energy across Iraq. Results show Random Forest excels for solar (R2 = 0.98) and neural networks for wind (R2 = 0.97), enabling a practical web tool for renewable energy planning. ABSTRACT Driven by the global shift away from fossil fuels, solar and wind resources
Bassam Musheer Kareem, Abdolsalam Ebrahimpour, Jafar Ghafouri, Roghayyeh Motallebzadeh   +3 more
wiley   +1 more source

Providing affective and supportive video feedback in a multidisciplinary unit during the pandemic

Journal of Learning Development in Higher Education, 2021
Abdul Razeed, Pat Norman, Kristna Gurney
doaj   +1 more source

Predicting Solar Photovoltaic Power Output in Saudi Arabia's Jazan Region: Performance Comparison of Machine Learning Models

Energy Science &Engineering, EarlyView.
Workflow of the PV power estimation and ML forecasting methodology. ABSTRACT Accurate prediction of solar panel energy output is vital for managing power systems effectively and maintaining a stable electrical grid. This is especially important in regions that rely heavily on renewable sources. This research provides a direct comparison of five machine
Abdoalateef Alzhrani, Abdullahi Abubakar Mas'ud, Mohd Khairul Amri Kamarudin, Firdaus Muhammad‐Sukki, Abdullahi Mohammad   +4 more
wiley   +1 more source