Results 191 to 200 of about 35,702 (279)

Identifying the spatial pattern and driving factors of nitrate in groundwater using a novel framework of interpretable stacking ensemble learning. [PDF]

Environ Geochem Health
Li X, Liang G, Wang L, Yang Y, Li Y, Li Z, He B, Wang G.   +7 more
europepmc   +1 more source

Permafrost Terrain Disturbance Susceptibility in the Nacho Nyäk Tagé (Stewart River) Watershed, Yukon, Canada

Permafrost and Periglacial Processes, EarlyView.
ABSTRACT The Nacho Nyäk Tagé (Stewart River) watershed in central Yukon (Canada) is characterized by discontinuous permafrost that is locally highly sensitive to thaw. This study aims to map the spatial distribution of permafrost terrain disturbances (PTDs) in the watershed and model thaw susceptibility to support community‐led land‐use planning by the
Frederic Brieger, Murray Richardson, Shawn Kenny   +2 more
wiley   +1 more source

Multimodal MRI radiomics-based stacking ensemble learning model with automatic segmentation for prognostic prediction of HIFU ablation of uterine fibroids: a multicenter study. [PDF]

Front Physiol
Wen B, Li C, Cai Q, Shen D, Bu X, Zhou F.   +5 more
europepmc   +1 more source

Climate requirements for cultivated Liberica coffee (Coffea liberica) and consequences for its use and development as a crop species

PLANTS, PEOPLE, PLANET, EarlyView.
The global coffee industry, supporting 25 million smallholder farmers, is vulnerable to climate change. Diversifying the coffee species portfolio beyond Arabica and robusta is a promising intervention. Liberica coffee could provide adaptive capacity, although its climate parameters for cultivation are poorly known.
Isobel M. J. Wild, Aaron P. Davis, Kenny Wee Ting Lee, Justin Moat   +3 more
wiley   +1 more source

¹⁸F-FDG PET/CT-based habitat radiomics combining stacking ensemble learning for predicting prognosis in hepatocellular carcinoma: a multi-center study. [PDF]

BMC Cancer
Sui C, Su Q, Chen K, Tan R, Wang Z, Liu Z, Xu W, Li X.   +7 more
europepmc   +1 more source

A pilot variational coupled reanalysis based on the CESAM climate model

Quarterly Journal of the Royal Meteorological Society, EarlyView.
Variational data assimilation of in‐situ and satellite ocean data and reanalysis atmospheric data into an intermediate complexity Earth system model is possible by adjusting the surface fluxes and internal model parameters. This pilot application requires nearly complete information on the atmospheric state for synchronization.
Armin Köhl, Iuliia Polkova, Guokun Lyu, Frank Lunkeit, Abhirup Banerjee, Silke Schubert, Detlef Stammer   +6 more
wiley   +1 more source

Detection of fake face images using lightweight convolutional neural networks with stacking ensemble learning method. [PDF]

PeerJ Comput Sci
Şafak E, Barışçı N.
europepmc   +1 more source

Polar‐low track prediction using machine‐learning methods

Quarterly Journal of the Royal Meteorological Society, EarlyView.
Machine‐learning models are developed to produce reliable and efficient forecasts of polar‐low (PL) trajectories 12 hours ahead. A temporal model (RLSTM) benefiting from the rolling‐forecast strategy, improves overall prediction accuracy and is suitable for quick experimentation, while a spatiotemporal model (PL‐UNet), incorporating both historical and
Ziying Yang, Rune Grand Graversen, Filippo Maria Bianchi, Jiping Liu, Ruibo Lei   +4 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, EarlyView.
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

Heterogeneous Multilayer Nanopores via Chemically Tuned Dielectric Breakdown for Single‐Molecule Sensing

Small, EarlyView.
A scalable fabrication method based on chemically tuned controlled dielectric breakdown (CT‐CDB) enables heterogeneous multilayer nanopores integrating SiNx with 2D materials such as hBN, MoS2, and graphene. The resulting nanopores exhibit distinct electrokinetic behaviors and low noise, allowing structure‐dependent single‐protein sensing.
Chaoming Gu, Kamruzzaman Joty, Navod Thyashan, Ivan Vlassiouk, Liam Collins, Xingye Zhang, Christopher T. Nelson, Nathan Taton, Sangyoup Lee, Min Jun Kim   +9 more
wiley   +1 more source