Results 161 to 170 of about 5,191,119 (260)

A transcription factor regulatory atlas for activity inference and perturbation prediction. [PDF]

Nucleic Acids Res
Sugimoto H, Tsuyuzaki K, Zou Z, Oki S, Ohta T, Kawakami E.   +5 more
europepmc   +1 more source

An Explicit Constitutive Reconstruction Framework for Mechanical Characterization of Miniaturized 3D‐Printed Specimens

Advanced Engineering Materials, EarlyView.
An explicit constitutive reconstruction framework enables accurate tensile characterization of miniaturized additively manufactured specimens using only the grip‐to‐grip displacement measurements. Progressive validation through self‐consistency, analytical comparison, and inverse finite element method (iFEM) benchmarking demonstrates constitutive ...
Junqing Leng, Caralyn Collins, Pengpeng Zhang, Joanna Zhang, Cheng Sun   +4 more
wiley   +1 more source

Inference in Constrained Quantile Regression

, 2018
I investigate the asymptotic distribution of linear quantile regression coefficient estimates when the parameter may lie on the boundary of the parameter space, and related inference procedures when the null hypothesis asserts that the parameters lie on ...
Parker, Tom
core  

varGuid: R and Python Implementations of Variance-Guided Regression for Robust Effect-Size Estimation in Linear Models. [PDF]

Appl Psychol Meas
Liu S, Wang Z, Lu M.
europepmc   +1 more source

Leveraging Symbolic Artificial Intelligence and Fuzzy Logic for Materials Science: A Review of Methods, Challenges, and Applications to Scarce and Imperfect Experimental Data

Advanced Engineering Materials, EarlyView.
This article explores the transformative potential of symbolic artificial intelligence (AI) in the field of materials science, particularly in leveraging experimental data. The article presents several symbolic AI models and discusses their applications in materials science.
Ahmed Amrani, Mohamed‐Ali Belloum, Laurence Boudet, Valentin Fouillard, Edwin Friedmann, Guillaume Lacharme, Ali Mahmoud, Jean‐Philippe Poli   +7 more
wiley   +1 more source

Optimal design for prediction using local linear regression and the DSI-criterion

, 2013
When it is anticipated that data to be collected from an experiment cannot be adequately described by a low-order polynomial, alternative modelling and new design methods are required.
Fisher, Verity A., Woods, David C., Lewis, Susan M.   +2 more
core  

Fusion of MLP, XGBoost, and QAT-Optimized PointNet++ for Predicting Short-Term Dendrometer-Derived Stem Dynamics: An Edge-Oriented Computational Framework. [PDF]

Sensors (Basel)
Bolikulov F, Zohirov K, Mannonov G, Khudayorov U, Temirov Z, Mingboev U, Hafizov E, Abdusalomov A, Cho YI.   +8 more
europepmc   +1 more source

Supporting AI Readiness Through Digital Workflows in Materials Science

Advanced Engineering Materials, EarlyView.
Digitalization drives innovation in materials science by connecting data silos and turning heterogeneous processes into reusable research pipelines. Across 13 MaterialDigital projects, digital workflows reveal complementary pathways toward AI‐ready materials research, founded on structured data, persistent artifacts, executable orchestration, and ...
Marian Bruns, Lateef Abdul, Stephan Barth, Martin Glauer, Manuel Günther, Fabian Neuhaus, Jan‐Ole Perschewski, Thomas Bjarsch, Henrik Abel, Johannes Bosch, Niklas Meyer, Peter Hofmann, Marc Drechsler, Alexander Dyck, Dhanunjaya Kumar Nerella, Marvin Tegeler, Oleg Shchyglo, Ingo Steinbach, Timo Klecker, Henk Birkholz, Ehsan Borzabadi Farahani, Bernard Fedelich, Cetin Haftaoglu, Tarakeshwar Lakshmipathy, Elena Garcia Trelles, Christoph Schweizer, Reza Darvishi Kamachali, Lukas Koschmieder, Janin Eiken, Markus Apel, Sung‐Min Wi, Jiangdong Zhao, Andreas Fezer, Nicolas Christ, Patrick Schneider, Eldor Urinov, Veit Königer, Volker Knoblauch, Janik Harter, Thomas Seifert, Thitichai Janpheng, Florian Fuchs, Fabian Teichert, Jörg Schuster, Anke Bardehle, Andreas Limper, Ulrich Giese, Carlos Lucke, Mario Beiner, Christoph Gögelein, Lisa Leuchtenberger‐Engel, Christian Hopmann, Felix Wentzien, Heike Wittek, Benjamin Klie, Alexander Röhrs, Ludger Overmeyer, Achraf Atila, Marcel Sadowski, Leopold Talirz, Jan Janssen, Jesper R. Pedersen, Peter Beck, Tejs Vegge, Ivano E. Castelli, Wolfgang Wenzel, Tilmann Hickel, Jörg Schaarschmidt   +67 more
wiley   +1 more source

Deconfounded-Debiased Estimation and Inference for High-Dimensional Mediation Analysis With Pervasive Hidden Confounders. [PDF]

Stat Med
Li Z, Pan L, Yu Y, Qin G, Fu B.
europepmc   +1 more source

A Multi‐Scale Machine Learning Framework for the Inverse Design of High Entropy Alloys

Advanced Engineering Materials, EarlyView.
High‐entropy alloys offer vast potential for various applications, including electrocatalysis; however, their compositional complexity challenges conventional screening. We introduce an inverse‐design framework combining two neural networks to determine optimal compositions and reconstruct nanoparticle geometry from targeted properties and conventional
Mikael Takoutsin, Marta Campolucci, Nicolas de Andrade Ishiki, Nathan Mirgot, Nadir Zhamantay, Frédéric Kanoufi, Jennifer Peron, Jean Charlety, Emiliano Fonda, Valérie Briois, Marco Faustini, Maria‐Letizia De Marco, Christine Goyhenex, Ovidiu Ersen, Hervé Bulou   +14 more
wiley   +1 more source