Results 91 to 100 of about 43,843 (275)

Phase Diagrams and Piezoelectric Properties of Wurtzite Al1−x−yScxGdyN Heterostructural Alloys

open access: yesAdvanced Science, EarlyView.
This study demonstrates ferroelectricity and piezoelectric properties improvement of quaternary wurtzite Al1−x−yScxGdyN${\rm Al}_{1-x-y}{\rm Sc}_x{\rm Gd}_y{\rm N}$ films, guided by density functional theory calculations. Wurtzite Al1−x−yScxGdyN${\rm Al}_{1-x-y}{\rm Sc}_x{\rm Gd}_y{\rm N}$ films have a high optical bandgap, enhanced piezoelectric ...
Julia L. Martin   +11 more
wiley   +1 more source

B-spline polynomials models for analyzing growth patterns of Guzerat young bulls in field performance tests [PDF]

open access: yesAnimal Bioscience
Objective The aim of this study was to identify suitable polynomial regression for modeling the average growth trajectory and to estimate the relative development of the rib eye area, scrotal circumference, and morphometric measurements of Guzerat young ...
Ricardo Costa Sousa   +5 more
doaj   +1 more source

Smooth-car mixed models for spatial count data [PDF]

open access: yes
Penalized splines (P-splines) and individual random effects are used for the analysis of spatial count data. P-splines are represented as mixed models to give a unified approach to the model estimation procedure.
Dae-Jin Lee, Maria Durban
core  

Nonparametric Bayes modeling of count processes [PDF]

open access: yes, 2013
Data on count processes arise in a variety of applications, including longitudinal, spatial and imaging studies measuring count responses. The literature on statistical models for dependent count data is dominated by models built from hierarchical ...
Canale, Antonio, Dunson, David B.
core   +2 more sources

Kinesin‐Induced Buckling Reveals the Limits of Microtubule Self‐Repair

open access: yesAdvanced Science, EarlyView.
This study shows that kinesin‐driven buckling induces extensive microtubule lattice damage that often exceeds intrinsic self‐repair and leads to filament failure. While curvature, motor motility, and force individually cause limited damage, their combination overwhelms repair.
Shweta Nandakumar   +9 more
wiley   +1 more source

spikeSlabGAM: Bayesian Variable Selection, Model Choice and Regularization for Generalized Additive Mixed Models in R

open access: yesJournal of Statistical Software, 2011
The R package spikeSlabGAM implements Bayesian variable selection, model choice, and regularized estimation in (geo-)additive mixed models for Gaussian, binomial, and Poisson responses.
Fabian Scheipl
doaj  

Joint Dispersion Model with a Flexible Link [PDF]

open access: yes, 2015
The objective is to model longitudinal and survival data jointly taking into account the dependence between the two responses in a real HIV/AIDS dataset using a shared parameter approach inside a Bayesian framework.
Martins, Rui
core   +2 more sources

Gibbs sampling for Bayesian P-splines

open access: yes
P-splines provide a flexible setting for modeling nonlinear model components based on a discretized penalty structure with a relatively simple computational backbone. Under a Bayesian inferential framework based on Markov chain Monte Carlo, estimates of model coefficients in P-splines models are typically obtained by means of Metropolis-type algorithms.
Gressani, Oswaldo, Eilers, Paul H. C.
openaire   +2 more sources

Leveraging Artificial Intelligence and Large Language Models for Cancer Immunotherapy

open access: yesAdvanced Science, EarlyView.
Cancer immunotherapy faces challenges in predicting treatment responses and understanding resistance mechanisms. Artificial intelligence (AI) and machine learning (ML) offer powerful solutions for cancer immunotherapy in patient stratification, biomarker discovery, treatment strategy optimization, and foundation model development.
Xinchao Wu   +4 more
wiley   +1 more source

Nonlinear association structures in flexible Bayesian additive joint models

open access: yes, 2017
Joint models of longitudinal and survival data have become an important tool for modeling associations between longitudinal biomarkers and event processes.
Greven, Sonja   +2 more
core   +1 more source

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