Results 61 to 70 of about 2,854,398 (286)

Modeling of Tunneling Total Loads Based on Symbolic Regression Algorithm

Applied Sciences, 2021
The tunneling total load is one of the core control parameters for safe and efficient construction using tunneling machines. However, because the tunneling process involves complex coupling relationships between the equipment and the local geology ...
Liting Zhang, Qian Zhang, Siyang Zhou, Shanglin Liu   +3 more
doaj   +1 more source

Subjective Cognitive Concerns and Cognitive Trajectories in Parkinson's Disease: Biomarker Impact

Annals of Clinical and Translational Neurology, EarlyView.
ABSTRACT Objective To examine the relationship of subjective cognitive concerns (SCC) with biomarkers of Alzheimer's disease (AD), neurodegeneration, and Parkinson's disease (PD) and domain‐specific cognitive trajectories among cognitively unimpaired persons with de novo PD. Method Cognitively unimpaired participants with SCC (n = 294) and without SCC (
Francesca V. Lopez, Fareshte Erani, Katherine J. Bangen, David G. Coughlin, Douglas Galasko, Kelsey R. Thomas   +5 more
wiley   +1 more source

Taylor genetic programming for symbolic regression

Proceedings of the Genetic and Evolutionary Computation Conference, 2022
Genetic programming (GP) is a commonly used approach to solve symbolic regression (SR) problems. Compared with the machine learning or deep learning methods that depend on the pre-defined model and the training dataset for solving SR problems, GP is more focused on finding the solution in a search space.
Baihe He, Qiang Lu 0005, Qingyun Yang, Jake Luo, Zhiguang Wang   +4 more
openaire   +4 more sources

Parkinson Disease Subtype Stability and Eventual Convergence

Annals of Clinical and Translational Neurology, EarlyView.
ABSTRACT Objective Previous research identified distinct Parkinson disease (PD) clinical subtypes of “Motor Only,” “Psychiatric & Motor,” and “Cognitive & Motor” based on comprehensive, multidomain classification. Whether these subtypes reflect distinct trajectories or progressive disease stages remains unknown.
Laura M. Rosok, Christina N. Lessov‐Schlaggar, Abdulmunaim M. Eid, Erin R. Foster, Baijayanta Maiti, Hailee Domash, Helen Hwang, Richard J. Perrin, Paul T. Kotzbauer, Joel S. Perlmutter, Meghan C. Campbell   +10 more
wiley   +1 more source

Matching Large Biomedical Ontologies Using Symbolic Regression Using Symbolic Regression

Journal of Data Intelligence, 2022
The problem of ontology matching consists of finding the semantic correspondences between two ontologies that, although belonging to the same domain, have been developed separately. Ontology matching methods are of great importance today since they allow us to find the pivot points from which an automatic data integration process can be established ...
Jorge Martinez-Gil, Shaoyi Yin, Josef Küng, Franck Morvan   +3 more
openaire   +2 more sources

Impact of Hydrogen Incorporation on Electronic and Magnetic Structure of X2CrNi18‐9 Stainless Steel

Advanced Engineering Materials, EarlyView.
Hydrogen uptake in austenitic stainless steel alters both its electronic and magnetic structure. Seebeck measurements reveal hydrogen‐induced shifts in the electronic density of states, while neutron scattering uncovers nanoscale inhomogeneities that grow upon hydrogen incorporation, with the most pronounced effect observed in the PBF as‐built state ...
Torben Tappe, Louis Becker, Gaurav Kanu, Thomas F. Headen, Dirk Honecker, Gabi Schierning, Santiago Benito, Sebastian Weber, Klara Lünser, Sabrina Disch   +9 more
wiley   +1 more source

Investigation of Resonance Characteristics of More Than 10 000 Metal‐Coated Silicon MEMS Cantilevers Using Machine Learning

Advanced Engineering Materials, EarlyView.
More than 10 000 metal‐coated silicon MEMS cantilevers are fabricated and characterized to build a large‐scale experimental dataset spanning multiple vibrational modes. Machine learning algorithms are benchmarked to predict resonance frequency and quality factor, with ensemble based models achieving accuracy approaching experimental uncertainty.
Sina Zare Pakzad, Saurabh Thorat, Andre Loch Gesing, Dominik Huber, Christopher Zellner, Patrick Egger, Georg Pfusterschmied, Ulrich Schmid, Michael Schneider   +8 more
wiley   +1 more source

salmon: A Symbolic Linear Regression Package for Python

Journal of Statistical Software
One of the most attractive features of R is its linear modeling capabilities. We describe a Python package, salmon, that brings the best of R's linear modeling functionality to Python in a Pythonic way - by providing composable objects for specifying ...
Alex Boyd, Dennis L. Sun
doaj   +1 more source

Counterion Dependent Side‐Chain Relaxation Stiffens a Chemically Doped Thienothiophene Copolymer

Advanced Functional Materials, EarlyView.
Oxidation of a thienothiophene copolymer, p(g3TT‐T2), via different doping strategies and dopant molecules resulted in materials with similar oxidation levels and a high electrical conductivity of ≈100 S cm−1. However, mechanical properties varied significantly, with sub‐glass transition temperatures and elastic moduli spanning from –44°C to –3°C and ...
Mariavittoria Craighero, Meghna Jha, Elsa Veronica Flores‐Vela, Joost Kimpel, Andreas Schaefer, Jiali Guo, Jesika Asatryan, Alberto Peinador Veiga, Shuichi Haraguchi, Per‐Anders Carlsson, Jaime Martín, Mariano Campoy‐Quiles, Christian Müller   +12 more
wiley   +1 more source

Symbolic regression for defect interactions in 2D materials

Materials & Design
Machine learning models have become firmly established across all scientific fields. Extracting features from data and making inferences based on them with neural network models often yields high accuracy; however, this approach has several drawbacks ...
Mikhail Lazarev, Andrey Ustyuzhanin
doaj   +1 more source