Results 151 to 160 of about 3,045 (214)

Transfer Learning Approaches in Bioprocess Engineering: Opportunities and Challenges

Biotechnology and Bioengineering, Volume 123, Issue 6, Page 1417-1431, June 2026.
ABSTRACT Transfer learning (TL) has recently emerged as a promising approach to overcoming one of the key limitations of bioprocess engineering: data scarcity. By leveraging knowledge from one bioprocess to another, TL allows existing models and data sets to be reused efficiently, accelerating process development, improving prediction accuracy, and ...
Daniel Barón Díaz, Anna‐Lena Drommershausen, Alexander Grünberger, Dirk Holtmann   +3 more
wiley   +1 more source

Degradation Prediction of Proton‐Exchange Membrane Fuel Cell Tested Under Static and Quasi‐Dynamic Operating Conditions

Fuel Cells, Volume 26, Issue 3, June 2026.
ABSTRACT The viability and extended lifetime of proton‐exchange membrane (PEM) fuel cells are essential to enable hydrogen electric aviation. Their reliability, that is, the ability to maintain stable performance over long operational periods under varying environmental and load conditions, is a critical factor for their adoption in aviation systems ...
Matthew Georgio Dekkers, Marcia L. Baptista, Helmut Prendinger   +2 more
wiley   +1 more source

Deep Learning Unlocks Behavioral Prediction and Neurobehavioral Decoding

Med Research, Volume 2, Issue 2, Page 371-387, June 2026.
This review evaluates deep learning frameworks that surmount conventional limitations through high‐dimensional nonlinear modeling, spatiotemporal dependency capture, and multimodal information integration. Focusing on biological behavior forecasting and neural mechanism decoding, we delineate cutting‐edge applications, including real‐time action ...
Tianzhe Han, Yunhua Jia, Xiaomin Hu, Shuyue Wang, Ruiwen Wang, Zhijing Zhu   +5 more
wiley   +1 more source

An Interpretable TCN– Transformer Framework for Lithium‐Ion Battery State of Health Estimation Using SHAP Analysis

Quality and Reliability Engineering International, Volume 42, Issue 4, Page 1426-1442, June 2026.
ABSTRACT Accurate state of health (SOH) estimation of Li‐ion batteries is essential for ensuring safety, reliability, and prolonging battery lifespan in energy storage systems and electric vehicles. This study proposes a hybrid temporal convolutional network (TCN)–transformer framework that effectively captures both short‐term temporal dynamics and ...
Fusen Guo, Zhibo Zhang, Xuzhe Ma, Lin Li, Hailing Zhou, Chaojie Li, Huadong Mo   +6 more
wiley   +1 more source

Survey on AI‐Enabled Computer Vision Technologies and Applications for Space Robotic Missions

Journal of Field Robotics, Volume 43, Issue 4, Page 2553-2584, June 2026.
ABSTRACT This survey provides a comprehensive overview of recent advancements and challenges in Artificial Intelligence (AI)‐enabled computer vision (CV) techniques for space robotic missions, spanning critical phases such as Entry, Descent, and Landing (EDL), orbital operations, and planetary surface exploration.
Maciej Quoos, Steven Kay, Julia Wajoras, Robert Field, Evridiki V. Ntagiou, Fakher Mohammad, Yang Gao   +6 more
wiley   +1 more source

First Results of a New Inversion Tool for Thermospheric Neutral Mass Density Computations During Severe Geomagnetic Storms

AGU Advances, Volume 7, Issue 3, June 2026.
Abstract In Low Earth Orbit (LEO), atmospheric drag is the dominant source of trajectory prediction error below approximately 700 km altitude, primarily due to inaccuracies in thermospheric density models. This work presents a method for temporally deconvolving Energy Dissipation Rates (EDRs) to produce single‐satellite density estimates, which can be ...
S. Mutschler, M. Pilinski, E. Zesta, D. M. Oliveira, K. Delano, K. Garcia‐Sage, W. K. Tobiska   +6 more
wiley   +1 more source

Time‐Varying Cycling Rates of Size‐fractionated Particulate Organic Carbon During the Decay of a North Atlantic Spring Bloom

Global Biogeochemical Cycles, Volume 40, Issue 6, June 2026.
Abstract The concentrations of particulate organic carbon (POC) in the ocean are the result of a dynamic balance between multiple sources and sinks. Here, measurements of POC concentration in the small size fraction (SSF; 1–5 μm ${\upmu }\mathrm{m}$) and the large size fraction (LSF; >5μm ${ >} 5\ {\upmu }\mathrm{m}$) from the North Atlantic EXport ...
Daniel J. Clements, Phoebe J. Lam, Olivier Marchal, Amy E. Maas, Deborah K. Steinberg, Nicola L. Paul   +5 more
wiley   +1 more source

Ionosphere‐Thermosphere Responses to the March 2023 Geomagnetic Storm Using Observations and TIEGCM Simulations Driven by Data Assimilated Aurora and Electric Fields

Journal of Geophysical Research: Space Physics, Volume 131, Issue 6, June 2026.
Abstract We investigate Ionosphere‐Thermosphere (IT) responses to the March 2023 geomagnetic storm using GOLD and PFISR observations, along with TIEGCM simulations driven by data‐assimilated aurora and electric fields. A Lattice Kriging approach is implemented to assimilate auroral electron flux and characteristic energy from ground‐based (THEMIS/ASIs)
Prakash Poudel, Xian Lu
wiley   +1 more source

A Deep Learning‐Enhanced Ensemble Smoother for Non‐Gaussian Hydrological Data Assimilation Without Relying on Innovation Vectors

Journal of Geophysical Research: Machine Learning and Computation, Volume 3, Issue 3, June 2026.
Abstract Data assimilation (DA) plays a critical role in reducing simulation uncertainty in hydrological systems by leveraging available observations to estimate model states and/or parameters. Among DA methods, the ensemble smoother (ES) has emerged as an attractive option for parameter estimation due to its computational efficiency and ...
Jiangjiang Zhang, Junliang Jin, Lei Yao, Kun Zhou, Jianyun Zhang   +4 more
wiley   +1 more source

Improving Global Surface Soil Moisture Prediction Through Physics‐Guided Deep Learning and Cluster‐Based Regionalization

Journal of Geophysical Research: Machine Learning and Computation, Volume 3, Issue 3, June 2026.
Abstract Surface soil moisture (SSM) is essential to the hydrological cycle and land–atmosphere interactions, and its accurate simulation is crucial for climate prediction and resource management. This study developed an innovative modeling framework for global SSM prediction by integrating physics‐guided deep learning (PGDL) and clustering‐based ...
Xuan Xi, Qianlai Zhuang
wiley   +1 more source