Results 161 to 170 of about 29,948 (298)

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

Machine Learning Interatomic Potentials for Energy Materials: Architectures, Training Strategies, and Applications

Advanced Energy Materials, EarlyView.
Machine learning interatomic potentials bridge quantum accuracy and computational efficiency for materials discovery. Architectures from Gaussian process regression to equivariant graph neural networks, training strategies including active learning and foundation models, and applications in solid‐state electrolytes, batteries, electrocatalysts ...
In Kee Park, Saeed Pourasad, Jinhong Mun, Arunkumar Chitteth Rajan, Tae Hyeon Park, Anand Rohit, Mohammad Zafari, Tan‐Lien Pham, Eunjae Jung, Bonseung Koo, Miran Ha, Gi Beom Sim, Hyun Gyu Park, Won Woong Choi, Kwanghyeon Jo, Soohaeng Yoo Willow, David ChangMo Yang, Chang Woo Myung, Geunsik Lee, Kwang S. Kim   +19 more
wiley   +1 more source

Factors Affecting Recollapse of the Augmented Vertebrae After Percutaneous Vertebral Augmentation: A Prospective Study

AGING MEDICINE, EarlyView.
To investigate the relevant factors of augmented vertebrae recollapse after percutaneous vertebral augmentation (PVA) for the treatment of osteoporotic vertebral compression fractures (OVCFs). Intervertebral cleft (IVC) and those with PVP intervention are at higher risk of recollapse.
Yawen Zhang, Junchuan Liu, Maoyu Zhao, Yudian Qiu, Quan Ji, Nan Min, Qiang Wang   +6 more
wiley   +1 more source

Blockchain‐Based Smart Contracts in US Specialty Crop Marketing: Grower Preferences and Adoption Potential

Agribusiness, EarlyView.
ABSTRACT Using survey and discrete choice experiment data, we examined US specialty crop growers' preferences for marketing contract attributes in the context of emerging blockchain‐based technologies and expanding traceability initiatives. Results show that farmers preferred traditional written contracts but might be willing to accept digital ...
Elizabeth Canales, Alba J. Collart, Matthew G. Interis, Esther Mmenaa Agyemang‐Duah   +3 more
wiley   +1 more source

Impact of Web technologies on student-lecturer connectedness

In academic sense connectedness is recognized as students’ active engagement in the academic and social opportunities at their school based on their understanding that teachers at school care for them as individuals, as well as for their learning ...
Alshahrani, Saeed, Ward, Rupert
core  

The Impact of ESL Funding Restrictions on Student Academic Achievement [PDF]

ESL instruction is an important issue in Canada due to the large number of immigrants and has potentially impacts on both student academic progress and educational expenditures.
Cesar Furtado, Martin Dooley
core  

At-risk students and the dropout rate [PDF]

, 2021
openaire   +1 more source

SigmaFormer: Augmenting transformer encoders with COSMO sigma profiles for pure component property prediction

AIChE Journal, EarlyView.
Abstract Transformer‐based molecular models pretrained on SMILES strings demonstrate strong performance in property prediction. However, these model often lack explicit integration of molecular surface charge distributions that govern intermolecular interactions such as hydrogen bonding and polarity.
Tae Hyun Kim, Silabrata Pahari, Joseph Sang‐Il Kwon   +2 more
wiley   +1 more source

Using AI to forecast student dropout risk in technical education using a learning analytics approach. [PDF]

Sci Rep
Ovtšarenko O.
europepmc   +1 more source

Application of Neural Networks for Advanced Ir Spectroscopy Characterization of Ceria Catalysts Surfaces

Advanced Intelligent Discovery, EarlyView.
A novel convolutional neural network architecture enables rapid, unsupervised analysis of IR spectroscopic data from DRIFTS and IRRAS. By combining synthetic data generation with parallel convolutional layers and advanced regularization, the model accurately resolves spectral features of adsorbed CO, offering real‐time insights into ceria surface ...
Mehrdad Jalali, Lachlan Caulfield, Eric Sauter, Alexei Nefedov, Chengwu Yang, Christof Wöll   +5 more
wiley   +1 more source