Results 111 to 120 of about 875,908 (286)
Stabilization of L‐PBF Ni50.7Ti49.3 under low‐cycle loading was investigated. Recoverable strain after cycling was dependent on the amount of applied load. Recovery ratio was 53.4% and 35.1% at intermediate and high load, respectively. The maximum total strain reached 10.3% at a high load of 1200 MPa.
Ondřej Červinek +5 more
wiley +1 more source
A Simpler Approach to Coefficient Regularized Support Vector Machines Regression
We consider a kind of support vector machines regression (SVMR) algorithms associated with lq (1 ...
Hongzhi Tong +2 more
doaj +1 more source
Insurance: an R-Program to Model Insurance Data [PDF]
Data sets from car insurance companies often have a high-dimensional complex dependency structure. The use of classical statistical methods such as generalized linear models or Tweedie?s compound Poisson model can yield problems in this case. Christmann (
Christmann, Andreas +1 more
core
Lipid nanoparticles (LNPs) are optimized to co‐deliver Cas9‐encoding messenger RNA (mRNA), a single guide RNA (sgRNA) targeting the endogenous cystic fibrosis transmembrane conductance regulator (CFTR) gene, and homologous linear double‐stranded donor DNA (ldsDNA) templates encoding CFTR.
Ruth A. Foley +12 more
wiley +1 more source
Statistical mechanics of support vector regression
A key problem in deep learning and computational neuroscience is relating the geometrical properties of neural representations to task performance. Here, we consider this problem for continuous decoding tasks where neural variability may affect task precision.
Abdulkadir Canatar, SueYeon Chung
openaire +2 more sources
Substrate Stress Relaxation Regulates Cell‐Mediated Assembly of Extracellular Matrix
Silicone‐based viscoelastic substrates with tunable stress relaxation reveal how matrix mechanics regulates cellular mechanosensing and cell‐mediated matrix remodelling in the stiff regime. High stress relaxation promotes assembly of fibronectin fibril‐like structures, increased nuclear localization of YAP and formation of β1 integrin‐enriched ...
Jonah L. Voigt +2 more
wiley +1 more source
Unleashing the Power of Machine Learning in Nanomedicine Formulation Development
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 +7 more
wiley +1 more source
Efficient Support Vector Regression with Reduced Training Data [PDF]
Ling Cen, Quang Hieu Vu, Dymitr Ruta
doaj +1 more source
In this strategy, a conductive nano‐probe is employed to induce nanoscale phase transitions and map the nanoscale conductivity and trap density of GST films. By utilizing the contrasting properties of phase‐change states, nano‐resonators are fabricated that exhibit plasmonic conduction and dramatically different transport characteristics.
Sunwoo Bang +4 more
wiley +1 more source
Micro‐injection laser‐assisted bioprinting enables ultrafast and precise patterning of small endothelial cell spheroids by injecting a highly concentrated single‐cell suspension into GelMA/ColMA hydrogels. In co‐culture with fibroblasts, controlled pre‐vasculogenic network formation is obtained at microscale resolution.
Charles Handschin +9 more
wiley +1 more source

