Results 171 to 180 of about 55,859 (291)

A selective Kalman filtering approach to online neural network updating under system drift. [PDF]

Sci Rep
Hammond JE, Soderstrom TA, Korgel BA, Baldea M.   +3 more
europepmc   +1 more source

Defective high‐density lipoprotein lipoprotection in type 2 diabetes during acute myocardial infarction is rescued by apolipoprotein M/sphingosine‐1‐phosphate loading

Diabetes, Obesity and Metabolism, Volume 28, Issue 3, Page 2175-2182, March 2026.
Abstract Aims Mortality of patients with type 2 diabetes (T2D) after acute myocardial infarction (AMI) is tremendous and massively increased compared to non‐T2D individuals. The reasons are unclear. High‐density lipoprotein (HDL) conducts lipoprotection during AMI, leading to improved outcomes.
Jens Vogt, Marcel Benkhoff, Nathalie H. Schröder, Hao Hu, Lisa Dannenberg, Petra Keul, Theresia Sarabhai, Kálmán Benedikt Bódis, Tobias Zeus, Philipp Wollnitzke, Malte Kelm, Amin Polzin, Bodo Levkau   +12 more
wiley   +1 more source

Integrating GAN-based machine learning with nonlinear Kalman filtering for enhanced state estimation. [PDF]

Sci Rep
Tobaly L, Yaniv E, Zalevsky Z.
europepmc   +1 more source

The Impact of OCO‐2 Seasonally Dependent Sampling on Carbon Flux Estimation in the Northern Tropical Africa

Geophysical Research Letters, Volume 53, Issue 4, 28 February 2026.
Abstract The large annual carbon source over northern tropical Africa (NTA), inferred from satellite CO2, remains highly debated. Using observing system simulation experiments with Orbiting Carbon Observatory‐2 (OCO‐2) sampling, we show that seasonally dependent sampling can lead to overestimated annual fluxes.
Junjie Liu, Sourish Basu, Brendan Byrne, Jeongmin Yun, Chris O’Dell   +4 more
wiley   +1 more source

Improved noise reduction for nonlinear PMDC motor dead zone and friction model using variants of extended Kalman filter with practical validation. [PDF]

PLoS One
Haider S, Ali S, Saqlain M, Rasool A, Sherefa A.   +4 more
europepmc   +1 more source

Evaluation of GFSv16 for Near‐Real‐Time Data Impact Studies During the Atmospheric River Reconnaissance Program 2022

Journal of Geophysical Research: Atmospheres, Volume 131, Issue 4, 28 February 2026.
Abstract Landfalling Atmospheric Rivers (ARs) are crucial for the water resources on the U.S. West Coast, but can also cause hazardous weather and water events there. Yet, accurately predicting AR impacts on precipitation remains a challenge. This study investigates the influence of dropsonde data from AR Reconnaissance (AR Recon) on improving weather ...
Vijay Tallapragada, Xingren Wu, Minghua Zheng, L. Delle Monache, F. Martin Ralph, Keqin Wu, Xiujun Wang, M. M. Nageswararao, Jia Wang, Anna M. Wilson, Jason Cordeira, Julie Kalansky, Daniel Steinhoff, Patrick Mulrooney   +13 more
wiley   +1 more source

Recursive Batch Smoother with Multiple Linearization for One Class of Nonlinear Estimation Problems: Application for Multisensor Navigation Data Fusion. [PDF]

Sensors (Basel)
Stepanov O, Isaev A, Dranitsyna E, Litvinenko Y.   +3 more
europepmc   +1 more source

How Can FengYun—3G Satellite Precipitation Radar and Microwave Imager Unveil Multi—Phase Hydrometeors?

Geophysical Research Letters, Volume 53, Issue 3, 16 February 2026.
Abstract The observations from Precipitation Measurement Radar (PMR) and Microwave Radiation Imager (MWRI‐RM) onboard FengYun‐3G satellite are first time utilized to derive multi‐phase hydrometeors in clouds. The FY‐3G hydrometeor retrieval algorithm utilizes the Advanced Radiative Transfer Modeling System (ARMS) as its core module. It incorporates PMR‐
Linjun Han, Fuzhong Weng, Xiuqing Hu
wiley   +1 more source

A hierarchical lateral stability control strategy of distributed drive electric vehicles based on extended Kalman filter and integral terminal sliding mode control. [PDF]

PLoS One
Wang J, Zhao Y, Gao W, Deng Z.
europepmc   +1 more source

Deep Learning Atmospheric Models Reliably Simulate Out‐of‐Sample Land Heat and Cold Wave Frequencies

Geophysical Research Letters, Volume 53, Issue 3, 16 February 2026.
Abstract Deep learning (DL)–based general circulation models (GCMs) are emerging as fast simulators, yet their ability to replicate extreme events outside their training range remains unknown. Here, we evaluate two such models—the hybrid Neural General Circulation Model (NGCM) and purely data‐driven Deep Learning Earth System Model (DLESyM)—against a ...
Zilu Meng, Gregory J. Hakim, Wenchang Yang, Gabriel A. Vecchi   +3 more
wiley   +1 more source