Results 51 to 60 of about 15,054,030 (290)
Predicting extreme defects in additive manufacturing remains a key challenge limiting its structural reliability. This study proposes a statistical framework that integrates Extreme Value Theory with advanced process indicators to explore defect–process relationships and improve the estimation of critical defect sizes. The approach provides a basis for
Muhammad Muteeb Butt +8 more
wiley +1 more source
Bootstrap for estimating the mean squared error of the spatial EBLUP [PDF]
This work assumes that the small area quantities of interest follow a Fay-Herriot model with spatially correlated random area effects. Under this model, parametric and nonparametric bootstrap procedures are proposed for estimating the mean squared ...
MOLINA ISABEL +6 more
core +1 more source
PurposesThis study aimed to develop a machine learning model to predict body mass index (BMI) in adolescents based on readily accessible daily information and to investigate the influence of modifiable factors on BMI changes through model interpretation ...
Zikang Zhang +10 more
doaj +1 more source
Segmentation of Printed Circuit Board Components Based on Nested U-shape Structures [PDF]
As an important modern electronic component, component segmentation of highly integrated Printed Circuit Boards (PCB) is a typical small-object image segmentation task. Owing to the excessive introduction of complex backgrounds, component segmentation of
LI Zhijin, FAN Xiaozhen, YAN Jinfeng
doaj +1 more source
Estimation and Prediction in Transformed Nested Error Regression Models
This manuscript is superseded by "Adaptively transformed mixed model prediction of general finite population parameters" by Sugasawa and Kubokawa (arXiv:1705.04136)
Sugasawa, Shonosuke, Kubokawa, Tatsuya
openaire +2 more sources
We develop a data‐driven method to derive the mathematical expressions of the Flory–Huggins interaction parameter χ for the swelling behavior of temperature–responsive hydrogels. Starting from initial assumptions of χ, our workflow combines Bayesian optimization, Flory–Rehner theory, and symbolic regression to generate candidate χ expressions.
Yawen Wang +2 more
wiley +1 more source
Empirical best linear unbiased predictors in multivariate nested-error regression models [PDF]
For analyzing unit-level multivariate data in small area estimation, we consider the multivariate nested error regression model (MNER) and provide the empirical best linear unbiased predictor (EBLUP) of a small area characteristic based on second-order unbiased and consistent estimators of the `within' and `between' multivariate components of variance.
Ito, Tsubasa, Kubokawa, Tatsuya
openaire +2 more sources
Nested sampling for Potts models [PDF]
Nested sampling is a new Monte Carlo method by Skilling [1] intended for general Bayesian computation. Nested sampling provides a robust alternative to annealing-based methods for computing normalizing constants.
Skilling, John +7 more
core
A Lightweight Procedural Layer for Hybrid Experimental–Computational Workflows in Materials Science
We unveil a prototype hybrid‐workflow framework that fuses automatedcomputation with hands‐on experiments. Built atop pyiron, a lightweight, parameterized layer translates procedure descriptions into executable manual steps, syncing instrument settings, human interventions, and data capture in real‐time today.
Steffen Brinckmann +8 more
wiley +1 more source
High demand for soil organic carbon data to support soil health and climate change mitigation efforts must be met with rapid, accurate, and inexpensive measurement methods.
Minerva J. Dorantes +2 more
doaj +1 more source

