Results 101 to 110 of about 88,482 (262)

Predicting Materials Thermodynamics Enabled by Large Language Model‐Driven Dataset Building and Machine Learning

Advanced Intelligent Systems, EarlyView.
Illustration of text data mining of rare earth mineral thermodynamic parameters with the large language model‐powered LMExt. A dataset is built with mined thermodynamic properties. Subsequently, a machine learning model is trained to predict formation enthalpy from the dataset.
Juejing Liu, Haydn Anderson, Noah I. Waxman, Vsevolod Kovalev, Byron Fisher, Elizabeth Li, Xiaofeng Guo   +6 more
wiley   +1 more source

Procrustean pseudo‐landmark methods in Python to measure massive quantities of leaf shape data

Applications in Plant Sciences, EarlyView.
Abstract Premise When examining leaf shapes that are different from one another, it can be difficult to compare both the overall leaf shape and points along the leaf margin in biologically and statistically meaningful ways. Methods To address this problem, we present a simple and user‐friendly leaf shape analysis method in Jupyter Notebook and Python ...
Asia Hightower, Svea Hall, Ricardo Urquidi Camacho, Alexandra Papamichail, Evan Adamski, Claudia Colligan, Aidan Deneen, Gabriel Dunn, Justine Haziza, Claire Henley, Arnan Pawawongsak, Lachlann Simms, Sean Ward, Manica Balant, Christopher Blackwood, Charles Cannon, Andrea Case, Aman Y. Husbands, Emily B. Josephs, Zoë Migicovsky, Eric Patterson, Yenny Alejandra Saavedra‐Rojas, Daniel H. Chitwood   +22 more
wiley   +1 more source

A method for generating stochastic 3D tree models with Python in Autodesk Maya

Journal of Graphic Engineering and Design, 2016
This paper introduces a method for generating 3D tree models using stochastic L-systems with stochastic parameters and Perlin noise. L-system is the most popular method for plant modeling and Perlin noise is extensively used for generating high detailed ...
Nemanja Stojanović
doaj  

Quantifying the Impact of Relativistic Precession on Tidal Disruption Event Light Curves

Astronomische Nachrichten, EarlyView.
ABSTRACT The tidal field of a black hole can turn a star into a gas stream whose orbit can precess, especially if the a black hole is rapidly spinning. In this work, we investigate the impact of precession on the light curves of tidal disruption events (TDE).
Diego Calderón, Ondřej Pejcha, Brian D. Metzger, Paul C. Duffell, Stephan Rosswog   +4 more
wiley   +1 more source

The quantitative impact of metabolism‐inhibiting drugs on the occurrence of adverse drug reactions—A backward selection approach

British Journal of Clinical Pharmacology, EarlyView.
Abstract Aim The quantitative effect of several inhibitory drugs on the development of adverse drug reactions (ADRs) is currently difficult to estimate. Our aim was to identify metabolic pathways, which, when inhibited, increase the risk for certain ADRs, and to use this system to consider comedication at individual level. Methods Data of a prospective
Judith Berres, Justyna Wozniak, Hannah Fölsch, Anja Knueppel‐Ruppert, Verena Graeff, Andrea Kriegisch‐Stumpf, Harald Dormann, Julia C. Stingl, Katja S. Just   +8 more
wiley   +1 more source

opseestools: A Python library to streamline OpenSeesPy workflows

SoftwareX
OpenSees, a framework for nonlinear structural analysis, has significantly advanced seismic research and practice. The introduction of OpenSeesPy in 2018, which integrated Python as an interpreter alongside TCL, greatly enhanced the framework's utility ...
Orlando Arroyo, Dirsa Feliciano, Daniela Novoa, Jairo Valcárcel   +3 more
doaj   +1 more source

Mediated Electrochemical Probing and Machine Learning for Cysteine and Reduced Monoclonal Antibody Quantification

Biotechnology and Bioengineering, EarlyView.
ABSTRACT Biopharmaceutical manufacturing requires robust analytics and process controls throughout production to insure high yield of quality products. New methodologies for rapidly accessing and integrating data‐rich information from complex dynamic biological environments are of great interest.
Kayla Chun, Varun Kochar, Evelyn Jaskowiak, Chen‐Yu Chen, Divya Muthusamy, Dana Motabar, Eunkyoung Kim, Gregory F. Payne, William E. Bentley   +8 more
wiley   +1 more source

AsymIntervals: A Python library for uncertainty modeling with asymmetric interval numbers

SoftwareX
Moore interval arithmetic represents uncertainty using symmetric bounds, yet many real-world quantities and operations exhibit asymmetric behavior. Asymmetric Interval Numbers (AINs) generalize Moore intervals by introducing an expected value within the ...
Wojciech Sałabun
doaj   +1 more source

Rapid development of a transferable Raman model using high‐throughput cell culture for monitoring monoclonal antibody titer

Biotechnology Progress, EarlyView.
Abstract Monoclonal antibody (mAb) titer monitoring is a key capability during process development and optimization, enabling timely decision making and increasing the speed of development. Raman spectroscopy is a prominent process analytical technology (PAT), but resource‐efficient calibration strategies for the development of transferable models are ...
Alexandra Umprecht, Nicholas Uth, Haenah Kim, Oliver Spadiut, Yang Yang   +4 more
wiley   +1 more source

Machine Learning‐Driven Prediction and Optimization of Cu‐Based Catalysts for CO2 Hydrogenation to Methanol

Carbon and Hydrogen, EarlyView.
A machine‐learning framework integrating multimodel prediction, feature selection, and SHAP interpretability is developed to uncover structure–performance relationships of Cu‐based CO2‐to‐methanol catalysts. The optimized XGBoost model and an online prediction platform enable accurate selectivity prediction and data‐driven catalyst design.
Conglong Su, Zhangkai Jing, Qiuming Zhou, Yuan Zhu, Sishi Ye, Tianye Wang, Kuo Chen, Xuebin Luan, Zhigang Tang, Run Xu, Chaopeng Hou, Hongwei Li   +11 more
wiley   +1 more source