Results 71 to 80 of about 853,208 (277)

A One-Sample Test for Normality with Kernel Methods [PDF]

, 2015
We propose a new one-sample test for normality in a Reproducing Kernel Hilbert Space (RKHS). Namely, we test the null-hypothesis of belonging to a given family of Gaussian distributions.
Celisse, Alain, Kellner, Jérémie
core   +2 more sources

Influence of Test Temperature and Test Frequency on Fatigue Life of Aluminum Alloy EN AW‐2618A

Advanced Engineering Materials, EarlyView.
The influence of test temperature and test frequency on the fatigue life of EN AW‐2618A is investigated. High‐cycle fatigue tests are performed at different test temperatures and frequencies on the 1000 h/230°C overaged state. Both test parameters reduce fatigue life due to time‐dependent damage mechanisms.
Ying Han, Julian M. Rosalie, Jan Radners, Mauro Madia, Philipp von Hartrott, Birgit Skrotzki   +5 more
wiley   +1 more source

Operating System’s Kernel Hooking Methods (Study Case of Linux Kernel)

Безопасность информационных технологий, 2014
The article presents an overview of dynamic integration in the kernel Linux, allowed to modify (add, change) its functionality. Traditional methods of integration based on changing in the kernel code (patching), and methods based on using system ...
Ilya Vladimirovich Matveychikov
doaj  

An Experimental High‐Throughput Approach for the Screening of Hard Magnet Materials

Advanced Engineering Materials, EarlyView.
An entire workflow for the high‐throughput characterization and analysis of compositionally graded magnetic films is presented. Characterization protocols, data management tools and data analysis approaches are illustrated with test case Sm(Fe, V)12 based films.
William Rigaut, Pierre Le Berre, Hamdi Jaballah, Lukas Fink, Victor Poline, Richard Haettel, Stéphane Grenier, Gilbert Chahine, Ewen Bellec, Guillaume Buatier, Wilfried Hortschitz, Santa Pile, Thomas Schrefl, Nils Blanc, Yuan Hong, Thibaut Devillers, Nora M. Dempsey   +16 more
wiley   +1 more source

Locally-Scaled Kernels and Confidence Voting

Machine Learning and Knowledge Extraction
Classification, the task of discerning the class of an unlabeled data point using information from a set of labeled data points, is a well-studied area of machine learning with a variety of approaches.
Elizabeth Hofer, Martin v. Mohrenschildt
doaj   +1 more source

On the combination of kernels for support vector classifiers [PDF]

, 2005
The problem of combining different sources of information arises in several situations, for instance, the classification of data with asymmetric similarity matrices or the construction of an optimal classifier from a collection of kernels.
Martín de Diego, Isaac, Moguerza, Javier M., Muñoz, Alberto   +2 more
core   +1 more source

3D (Bio) Printing Combined Fiber Fabrication Methods for Tissue Engineering Applications: Possibilities and Limitations

Advanced Functional Materials, EarlyView.
Biofabrication aims at providing innovative technologies and tools for the fabrication of tissue‐like constructs for tissue engineering and regenerative medicine applications. By integrating multiple biofabrication technologies, such as 3D (bio) printing with fiber fabrication methods, it would be more realistic to reconstruct native tissue's ...
Waseem Kitana, Indra Apsite, Leonid Ionov   +2 more
wiley   +1 more source

Biodegradable and Recyclable Luminescent Mixed‐Matrix‐Membranes, Hydrogels, and Cryogels based on Nanoscale Metal‐Organic Frameworks and Biopolymers

Advanced Functional Materials, EarlyView.
The study presents biodegradable and recyclable mixed‐matrix membranes (MMMs), hydrogels, and cryogels using luminescent nanoscale metal‐organic frameworks (nMOFs) and biopolymers. These bio‐nMOF‐MMMs combine europium‐based nMOFs as probes for the status of the materials with the biopolymers agar and gelatine and present alternatives to conventional ...
Moritz Maxeiner, Julian Burkhart, Kateryna Borysova, Chiara Mazzariol, Klaus Müller‐Buschbaum   +4 more
wiley   +1 more source

Unleashing the Power of Machine Learning in Nanomedicine Formulation Development

Advanced Functional Materials, EarlyView.
A random forest machine learning model is able to make predictions on nanoparticle attributes of different nanomedicines (i.e. lipid nanoparticles, liposomes, or PLGA nanoparticles) based on microfluidic formulation parameters. Machine learning models are based on a database of nanoparticle formulations, and models are able to generate unique solutions
Thomas L. Moore, Cristiano Pesce, Antonietta Greco, Claudia Pisante, Greta Avancini, Valentina Di Francesco, Yosi Shamay, Paolo Decuzzi   +7 more
wiley   +1 more source

Kernel methods for in silico chemogenomics

, 2007
Predicting interactions between small molecules and proteins is a crucial ingredient of the drug discovery process. In particular, accurate predictive models are increasingly used to preselect potential lead compounds from large molecule databases, or to
Jacob, Laurent, Vert, Jean-Philippe
core   +1 more source