Results 91 to 100 of about 42,095 (293)

Comparison between nested grids and unstructured grids for a high-resolution wave forecasting system in the western Mediterranean sea [PDF]

, 2017
This is an Accepted Manuscript of an article published by Taylor & Francis Group in Journal of Operational Oceanography on 2017, available online at: http://www.tandfonline.com/10.1080/1755876X.2016.1260389Traditionally wave modelling uses a downscaling ...
Espino Infantes, Manuel, López Marco, Jaime, Pallarès López, Elena, Sánchez-Arcilla Conejo, Agustín   +3 more
core   +2 more sources

Assessing Mesoscale Heterogeneities in Hard Carbon Electrodes Through Deep Learning‐Assisted FIB‐SEM Characterization, Manufacturing and Electrochemical Modeling

Advanced Energy Materials, EarlyView.
A combination of discrete and finite element method models for the current collector deformation and electrochemical performance analysis, respectively. The models are calibrated and validated with electrochemical and imaging data of hard carbon electrodes. These electrodes were manufactured with different parameters (slurry solid contents of 35 and 40
Soorya Saravanan, Imelda Cardenas‐Sierra, Pei Sun, Hari Raj, Daniele Duranti, Mahnoor Nafees, Edoardo Rossi, Marco Sebastiani, Paul Vigneaux, Martin Petit, Maxime Moreaud, Francisco Fernandez, Alejandro A. Franco   +12 more
wiley   +1 more source

Designing Cost‐Effective Carbon Payments to Induce Cellulosic Feedstock Production for Sustainable Aviation Fuel

Applied Economic Perspectives and Policy, EarlyView.
ABSTRACT Perennial bioenergy crops, such as miscanthus and switchgrass, and crop residues have the potential to scale up sustainable aviation fuel (SAF) production and mitigate carbon emissions. However, high establishment costs, delayed returns, and risk–return profiles that diverge from those of conventional crops can hinder incentives to adopt ...
Fahd Majeed, Madhu Khanna, Ruiqing Miao
wiley   +1 more source

Harnessing Phase Dynamics Across Diverse Frequencies with Multifrequency Oscillatory Neural Networks

Advanced Intelligent Discovery, EarlyView.
Oscillatory Neural Networks (ONNs) are an emerging computing paradigm that encodes information in the phases of coupled oscillators. Traditionally, ONNs have been investigated using homogeneous frequency oscillators. However, physical hardware implementations are inherently subject to frequency mismatches, device variability, and nonuniformities.
Nil Dinç, Filip Sabo, Aida Todri‐Sanial   +2 more
wiley   +1 more source

Temporal and spatial downscaling for solar radiation

35 pages, 14 ...
Bailey, Maggie, Nychka, Doug, Sengupta, Manajit, Yang, Jaemo, Bandyopadhyay, Soutir   +4 more
openaire   +2 more sources

Harnessing Digital Microstructure for Simulation‐Guided Optimization of Permanent Magnets

Advanced Intelligent Discovery, EarlyView.
An experimental‐to‐computational workflow is presented that transforms experimental 3D focused ion beam‐scanning electron microscopy data into a simulation‐ready digital microstructure for multiphase functional materials. Using heavy‐rare‐earth‐free Nd–Fe–B magnets as a model system, the approach quantifies grain connectivity across complex secondary ...
Nikita Kulesh, Anton Bolyachkin, Hajime Nakamura, Tadakatsu Ohkubo, Hossein Sepehri‐Amin   +4 more
wiley   +1 more source

A multiscale geographically weighted regression kriging method for spatial downscaling of satellite-based ozone datasets

Frontiers in Environmental Science
Accurate monitoring of ozone (O3) concentrations by remote sensing is essential for achieving pollution control and ecological protection. However, the existing O3 remote sensing data with a low spatial resolution do not facilitate fine-grained studies ...
Shuang Cheng, Guoqiao Zhang, Xuexi Yang, Bingfeng Lei   +3 more
doaj   +1 more source

Spatial and Temporal Downscaling of TRMM Precipitation with Novel Algorithms Spatial and Temporal Downscaling of TRMM Precipitation with Novel Algorithms [PDF]

, 2020
Abstract: Tropical Rainfall Measuring Mission (TRMM) satellite products constitute valuable precipitation datasets over regions with sparse rain gauge networks. Downscaling is an effective approach to estimating the precipitation over ungauged areas with high spatial resolution. However, a large bias and low resolution of original TRMM satellite images
Zhang, Huihui, Loáiciga, Hugo A, Ha, Da, Du, Qingyun   +3 more
openaire   +1 more source

Data‐Driven Modeling of Forces Exerted by Pneumatic Actuators for a Pediatric Exosuit

Advanced Intelligent Systems, EarlyView.
This work presents the experimental analysis and data‐driven modeling of the interaction forces between soft pneumatic actuators designed to assist upper‐extremity motion in a pediatric exosuit and an engineered test rig, across different experimental conditions: (A) force profiling of shoulder actuators, with varying actuator anchoring points and ...
Mehrnoosh Ayazi, Ipsita Sahin, Caio Mucchiani, Elena Kokkoni, Konstantinos Karydis   +4 more
wiley   +1 more source

Differences between Machine Learning and Traditional Downscaling Method in Processing Summer Meteorological Elements in the Yellow River Basin

Gaoyuan qixiang
Global Climate Models （GCMs） are the primary tools currently used to predict future climate change； however， their coarse spatial resolution limits their ability to assess localized impacts of climate change.To address this issue， statistical downscaling
Han CHEN, Xiaodan GUAN, Tingting MA
doaj   +1 more source