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 +7 more
wiley +1 more source
The Priestley-Chao Estimator of Conditional Density with Uniformly Distributed Random Design [PDF]
Statistika: Statistics and Economy Journal, 2018 The present paper is focused on non-parametric estimation of conditional density. Conditional density can be regarded as a generalization of regression thus the kernel estimator of conditional density can be derived from the kernel estimator of the ...
Kateřina Konečná
doaj
ROBUST KERNEL ESTIMATOR FOR DENSITIES OF UNKNOWN [PDF]
Results on nonparametric kernel estimators of density differ according to the assumed degree of density smoothness; it is often assumed that the density function is at least twice differentiable.
Victoria Zinde-Walsh, Yulia Kotlyarova
core
Crack‐Growing Interlayer Design for Deep Crack Propagation and Ultrahigh Sensitivity Strain Sensing
Advanced Functional Materials, EarlyView.A crack‐growing semi‐cured polyimide interlayer enabling deep cracks for ultrahigh sensitivity in low‐strain regimes is presented. The sensor achieves a gauge factor of 100 000 at 2% strain and detects subtle deformations such as nasal breathing, highlighting potential for minimally obstructive biomedical and micromechanical sensing applications ...
Minho Kim +11 more
wiley +1 more source
Small-Sample Comparison of the Gamma Kernel and the Orthogonal Series Methods of Density Estimation
پژوهشهای ریاضی, 2020 Introduction Estimation of a probability density function is an important area of nonparametric statistical inference that has received much attention in recent decades.
Muhyiddin Izadi, Abdollah Jalilian
doaj
Regular and Modified Kernel-Based Estimators of Integrated Variance: The Case with Independent Noise [PDF]
We consider kernel-based estimators of integrated variances in the presence of independent market microstructure effects. We derive the bias and variance properties for all regular kernel-based estimators and derive a lower bound for their asymptotic ...
Asger Lunde +3 more
core
Consumed by Abdominal Distention
Arthritis Care &Research, Accepted Article.
Abimbola Fadairo‐Azinge +3 more
wiley +1 more source
Local Thermal Conductivity Patterning in Rotating Lattice Crystals of Anisotropic Sb2S3
Advanced Functional Materials, EarlyView.Microscale control of thermal conductivity in Sb2S3 is demonstrated via laser‐induced rotating lattice crystals. Thermal conductivity imaging reveals marked thermal transport anisotropy, with the c axis featuring amorphous‐like transport, whereas in‐plane directions (a, b) exhibit 3.5x and 1.7x larger thermal conductivity.
Eleonora Isotta +13 more
wiley +1 more source
AIMS MathematicsThe integrals of diffusion processes are of significant importance in the field of finance, particularly in relation to stochastic volatility models, which are frequently employed to represent the temporal variability of stock prices.
Yue Li , Yunyan Wang
doaj +1 more source
Kernel Methods for Small Sample and Asymptotic Tail Inference for Dependent, Heterogeneous Data [PDF]
This paper considers tail shape inference techniques robust to substantial degrees of serial dependence and heterogeneity. We detail a new kernel estimator of the asymptotic variance and the exact small sample mean-squared-error, and a simple ...
Jonathan Hill
core