Results 41 to 50 of about 665,590 (295)

Sex‐Stratified Association of Regional Dopamine Transporter Binding With Disease Progression in Amyotrophic Lateral Sclerosis

open access: yesAnnals of Clinical and Translational Neurology, EarlyView.
ABSTRACT Objective To clarify the clinical relevance of dopamine transporter single‐photon emission computed tomography (DAT‐SPECT) abnormalities in amyotrophic lateral sclerosis (ALS), with a prespecified focus on sex‐stratified associations with disease progression and short‐term prognosis.
Tomoya Kawazoe   +7 more
wiley   +1 more source

Smoothing Module for Optimization Cranium Segmentation Using 3D Slicer

open access: yesInternational Journal of Applied Sciences and Smart Technologies, 2023
Anatomy is the most essential course in health and medical education to study parts of human body and also the function of it.  Cadaver is a media used by medical student to study anatomical subject.
Gilang Argya Dyaksa   +4 more
doaj   +1 more source

Stage‐Dependent β‐Synuclein Links MRI and Cognitive Decline in Alzheimer's Disease

open access: yesAnnals of Clinical and Translational Neurology, EarlyView.
ABSTRACT Objective Synaptic degeneration drives cognitive decline in Alzheimer's disease (AD), but synaptic biomarkers are scarce. Brain‐enriched β‐synuclein emerged as a synaptic damage marker. We investigated its diagnostic, prognostic, and structural correlates across the AD continuum.
Ulaş Ay   +15 more
wiley   +1 more source

Locally linear approximation for Kernel methods : the Railway Kernel [PDF]

open access: yes, 2008
In this paper we present a new kernel, the Railway Kernel, that works properly for general (nonlinear) classification problems, with the interesting property that acts locally as a linear kernel.
González, Javier, Muñoz, Alberto
core   +1 more source

dynoGP: Deep Gaussian Processes for Dynamic System Identification

open access: yesInternational Journal of Adaptive Control and Signal Processing, EarlyView.
This work introduces a novel class of deep models for system identification, dynamical deep Gaussian processes, which combine the strengths of data‐driven methods, such as those based on neural network architectures, with the ability to output a probability distribution for uncertainty representation.
Alessio Benavoli   +3 more
wiley   +1 more source

On the effect of fixed-bandwidth kernel density estimation on the exponential distribution

open access: yesJournal of Nigerian Society of Physical Sciences
Kernel density estimation (KDE) is widely used as a nonparametric smoothing operator in statistics. In this work, we study fixed-bandwidth KDE as a convolution operator applied to an exponential baseline distribution with rate parameter (beta > 0).
Anwar Bataihah
doaj   +1 more source

Characterization of Defect Distribution in an Additively Manufactured AlSi10Mg as a Function of Processing Parameters and Correlations with Extreme Value Statistics

open access: yesAdvanced Engineering Materials, EarlyView.
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

ks: Kernel Density Estimation and Kernel Discriminant Analysis for Multivariate Data in R

open access: yesJournal of Statistical Software, 2007
Kernel smoothing is one of the most widely used non-parametric data smoothing techniques. We introduce a new R package ks for multivariate kernel smoothing.
Tarn Duong
doaj  

Comparison of Methods for Smoothing Environmental Data with an Application to Particulate Matter PM10

open access: yesActa Universitatis Agriculturae et Silviculturae Mendelianae Brunensis, 2018
Data smoothing is often required within the environmental data analysis. A number of methods and algorithms that can be applied for data smoothing have been proposed.
Martina Čampulová
doaj   +1 more source

Symbolic Regression and Multi‐Objective Optimization of the Flory–Huggins Interaction Parameter for Hydrogels

open access: yesAdvanced Engineering Materials, EarlyView.
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

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