Results 151 to 160 of about 336,714 (315)

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

Volatility analysis and forecasting of vegetable prices using an ARMA‐GARCH model: An application of the CF filter and seasonal adjustment method to Korean green onions

Agribusiness, EarlyView.
Abstract The vegetable market experiences significant price fluctuations due to the complex interplay of trend, cyclical, seasonal, and irregular factors. This study takes Korean green onions as an example and employs the Christiano–Fitzgerald filter and the CensusX‐13 seasonal adjustment methods to decompose its price into four components: trend ...
Yiyang Qiao, Byeong‐il Ahn
wiley   +1 more source

Import Wheat Tenders and the Effects of the Russian Invasion

Agribusiness, EarlyView.
ABSTRACT Risk and volatility for many commodities escalated sharply following the Russian invasion of Ukraine, creating numerous uncertainties for trading firms and importers. The purpose of this study is to analyze the bidding behavior in Egyptian wheat import tenders in the pre‐ and post‐invasion periods.
William W. Wilson, Sumadhur Shakya, Isaac Dubovoy   +2 more
wiley   +1 more source

3D investigation and modeling of the geometric effects on porosity in packed beds

AIChE Journal, EarlyView.
Abstract In porous beds, physical boundaries restrict particle arrangement, leading to inhomogeneous porosity. This paper reports on the porosity profiles that are the result of geometric effects on monodisperse packed beds in cylindrical and cubic arrangements. Special focus is given to the influence of edges and corners in cubic geometries.
Bastian Oldach, Konrad E. R. Boettcher, Alexander S. Sommer‐Behr, Norbert Kockmann   +3 more
wiley   +1 more source

Evaluation du potentiel d’énergie éolienne au Liban

Revue des Énergies Renouvelables, 2014
Dans cette étude, les caractéristiques du vent sont analysées en utilisant les données de la vitesse du vent recueillies pour cinq stations météorologiques au Liban et disponibles pour au moins une année (2 ans pour Klaiaat, 10 ans pour les Cèdres, 7 ans
G. Al Zohbi, P. Hendrick, P. Bouillard   +2 more
doaj  

On the Mathematical Relationship Between RMSE and NSE Across Evaluation Scenarios

Model evaluation metrics play a crucial role in hydrology, where accurate prediction of continuous variables such as streamflow and rainfall–runoff is essential for sustainable water resources management and climate resilience. Among these metrics, the Nash–Sutcliffe efficiency (NSE) is the most widely adopted, while the Root Mean Squared Error (RMSE ...
openaire   +1 more source

Exploring Quantum Support Vector Regression for Predicting Hydrogen Storage Capacity of Nanoporous Materials

Advanced Intelligent Discovery, EarlyView.
In this study we employed support vector regressor and quantum support vector regressor to predict the hydrogen storage capacity of metal–organic frameworks using structural and physicochemical descriptors. This study presents a comparative analysis of classical support vector regression (SVR) and quantum support vector regression (QSVR) in predicting ...
Chandra Chowdhury
wiley   +1 more source

Feature Selection for Machine Learning‐Driven Accelerated Discovery and Optimization in Emerging Photovoltaics: A Review

Advanced Intelligent Discovery, EarlyView.
Feature selection combined with machine learning and high‐throughput experimentation enables efficient handling of high‐dimensional datasets in emerging photovoltaics. This approach accelerates material discovery, improves process optimization, and strengthens stability prediction, while overcoming challenges in data quality and model scalability to ...
Jiyun Zhang, Jiayi Tan, Qizhen Song, Tian Du, Jens Hauch, Christoph J. Brabec   +5 more
wiley   +1 more source

High resolution temperature forecasting using functional time series decomposition and advanced predictive models. [PDF]

Sci Rep
Alshanbari HM, Aldhabani MS, Iqbal N, Saeed AM, Noori N.   +4 more
europepmc   +1 more source

Decoding Tattoo and Permanent Makeup Pigments: Linking Physicochemical Properties to Absorption, Distribution, Metabolism, and Elimination Profiles Using Quantitative Structure–Activity Relationship (QSAR)‐Based New Approach Methodologies (NAMs)

Advanced Intelligent Discovery, EarlyView.
This study applies QSAR‐based new approach methodologies to 90 synthetic tattoo and permanent makeup pigments, revealing systemic links between their physicochemical properties and absorption, distribution, metabolism, and elimination profiles. The correlation‐driven analysis using SwissADME, ChemBCPP, and principal component analysis uncovers insights
Girija Bansod, Ajay Vikram Singh, Preeti Bhardwaj, Tulika Rai, Sweta Vijay Nakhale, Amruta Shelar, Rajendra Patil, Peter Laux, Andreas Luch, Christopher J. Osgood, Michael W. Stacey   +10 more
wiley   +1 more source