Results 51 to 60 of about 26,484 (294)

Fluid Biomarkers of Disease Burden and Cognitive Dysfunction in Progressive Supranuclear Palsy

Annals of Clinical and Translational Neurology, EarlyView.
ABSTRACT Objective Identifying objective biomarkers for progressive supranuclear palsy (PSP) is crucial to improving diagnosis and establishing clinical trial and treatment endpoints. This study evaluated fluid biomarkers in PSP versus controls and their associations with regional 18F‐PI‐2620 tau‐PET, clinical, and cognitive outcomes.
Roxane Dilcher, Charles B. Malpas, Stuart J. McDonald, William T. O’Brien, Craig Despott, Kelly L. Bertram, Matthew P. Pase, Lukas Frontzkowski, Meng Law, Terence J. O’Brien, Lucy Vivash   +10 more
wiley   +1 more source

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

Advanced 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, Varma Kvvssn, Hossein Laieghi, Zia Uddin, Peyman Ansari, E. Kıvanç Sadak, Can Barış Toprak, Hüseyin Kızıl, Enrico Salvati   +8 more
wiley   +1 more source

Carboxylic‐Acid Functionalized Multiwalled Carbon Nanotube‐Alkane‐Based Resistive Temperature Sensor for Cold Chain Applications

Advanced Engineering Materials, EarlyView.
This study presents a reversible temperature sensor with high switching ratio, ∼103. The device is fabricated using PET‐ITO and carbon nanotube dispersions in alkane. Considering its application in cold chain logistics, a proof‐of‐concept with LED is showcased. Thus, a temperature drop below the threshold temperature (crystallization temperature of the
Sunil Kumar Behera, Philippe Marrec, A. Jorge Parola, Madalena Dionísio, Hugo Águas, Andreia F. M. Santos, Hamed Pourkheirollah, Donald Lupo, César A. T. Laia   +8 more
wiley   +1 more source

A New Approach to Improve the Topological Stability in Non-Linear Dimensionality Reduction

IEEE Access, 2020
Dimensionality reduction in the machine learning field mitigates the undesired properties of high-dimensional spaces to facilitate classification, compression, and visualization of high-dimensional data.
Mohammed Elhenawy, Mahmoud Masoud, Sebastien Glaser, Andry Rakotonirainy   +3 more
doaj   +1 more source

Application of Linear and Nonlinear Dimensionality Reduction Methods

, 2012
Dimensionality reduction methods have proved to be important tools in exploratory analysis as well as confirmatory analysis for data mining in various fields of science and technology. Where ever applications involve reducing to fewer dimensions, feature
Sun, Mingui, Vinjamuri, Ramana, Wang, Wei, Mao, Zhi-Hong   +3 more
core   +1 more source

Planar Solid‐State Nanopores Toward Scalable Nanofluidic Integration Based on CMOS Technology

Advanced Engineering Materials, EarlyView.
We present a scalable silicon‐based fabrication strategy for planar solid‐state nanopores to enable their integration with complex nanofluidic systems. Prototype devices demonstrate normal voltage‐current characteristics, good noise performance, and appreciable streaming currents. Our CMOS‐compatible fabrication process offers precise geometric control
Ngan Hoang Pham, Chenyu Wen, Mohammad Hadi Khaksaran, Won‐Yong Lee, Javier Cruz, Klas Hjort, Dongping Wu, Shi‐Li Zhang   +7 more
wiley   +1 more source

On the Lightweight Potential of Laser Additive Manufactured NiTi Triply Periodic Minimal Sheet Lattices

Advanced Engineering Materials, EarlyView.
This study explores the lightweight potential of laser additive‐manufactured NiTi triply periodic minimal surface sheet lattices. It systematically investigates the effects of relative density and unit cell size on surface quality, deformation recovery, compression behavior, and energy absorption.
Haoming Mo, Shuo Qu, Shiming Gao, Xu Song   +3 more
wiley   +1 more source

Locality constrained dictionary learning for non‐linear dimensionality reduction and classification

IET Computer Vision, 2017
In view of the incremental dimensionality reduction problem of existing non‐linear dimensionality reduction methods, a novel algorithm, based on locality constrained dictionary learning (LCDL), is proposed in this study.
Lina Liu, Shiwei Ma, Ling Rui, Jian Lu
doaj   +1 more source

Nonlinear dimensionality reduction of gene expression data [PDF]

, 2006
Using microarray measurements techniques, it is possible to measure the activity of genes simultaneously across the whole genome. Since genes influence each others activity levels through complex regulatory networks, such gene expression measurements are
Nilsson, Jens
core  

Geodesic distances in the intrinsic dimensionality estimation using packing numbers

, 2014
Dimensionality reduction is a very important tool in data mining. An intrinsic dimensionality of a data set is a key parameter in many dimensionality reduction algorithms. When the intrinsic dimensionality of a data set is known, it is possible to reduce
Dzemyda, Gintautas, Karbauskaitė, Rasa
core   +1 more source