Results 71 to 80 of about 426,517 (268)

Microstructure Evolution of a VMnFeCoNi High‐Entropy Alloy After Synthesis, Swaging, and Annealing

Advanced Engineering Materials, EarlyView.
The synthesis and processing (rotary swaging and annealing) of the novel VMnFeCoNi alloy is investigated, alongside the estimation of the grain size effect on hardness. Analysis of a wide grain size range of recrystallized microstructures (12–210 µm) reveals a low annealing twin density.
Aditya Srinivasan Tirunilai, Zeki Ayrildi, Buse Şen, Maik Rajkowski, Mathias Redondo Garcia, Markus Stricker, Guillaume Laplanche   +6 more
wiley   +1 more source

Visual Detection of Structural Changes in Time-Varying Graphs Using Persistent Homology

, 2017
Topological data analysis is an emerging area in exploratory data analysis and data mining. Its main tool, persistent homology, has become a popular technique to study the structure of complex, high-dimensional data.
Hajij, Mustafa, Rosen, Paul, Scheidegger, Carlos, Wang, Bei   +3 more
core   +1 more source

Direct Metal Deposition of Graphene–Ti28Nb35.4Zr Matrix Composites With Enhanced Mechanical, Corrosion, and Biocompatibility Properties for Bone Implants

Advanced Engineering Materials, EarlyView.
Graphene nanoplatelet (0.1 wt.%) reinforcement significantly enhances the performance of β Ti‐28Nb‐35.4Zr alloy. Grain refinement, reduced water contact angle, and improved surface characteristics promote osteoblast adhesion and complete surface coverage after 7 days.
Khurram Munir, Jixing Lin, Yuncang Li, Li Zhu, Paul F. A. Wright, Cuie Wen   +5 more
wiley   +1 more source

Topology Assisted Clustering of Temporal fMRI Brain Networks With Use-Case in Mitigating Non-Neural Multi-Site Variability

IEEE Access
Using temporal analysis of fMRI (functional Magnetic Resonance Imaging) data, we can characterize dynamic changes in brain connectivity over time. However, dynamic temporal analysis of fMRI data is challenging due to the high dimensionality of the ...
Ahmedur Rahman Shovon, Sidharth Kumar, Gopikrishna Deshpande   +2 more
doaj   +1 more source

Topological Data Analysis of C. elegans Locomotion and Behavior

Frontiers in Artificial Intelligence, 2021
We apply topological data analysis to the behavior of C. elegans, a widely studied model organism in biology. In particular, we use topology to produce a quantitative summary of complex behavior which may be applied to high-throughput data.
Ashleigh Thomas, Kathleen Bates, Alex Elchesen, Iryna Hartsock, Hang Lu, Peter Bubenik   +5 more
doaj   +1 more source

Magnetic Control of Chiral Hybridized Phonon Magnetic Moments in Ferrimagnets Fe2‐xZnxMo3O8

Advanced Functional Materials, EarlyView.
Helicity‐resolved magneto‐Raman spectroscopy reveals magnetic control of chiral phonon magnetic moments in polar ferrimagnet (ZnxFe2−xMo3O₈). Large spontaneous zero‐field phonon splittings, selective phonon–magnon coupling, and asymmetric Zeeman responses demonstrate that phonon chirality is governed by magnon‐phonon coupling and magnetization.
Youngsu Choi, Dirk Wulferding, Amit Pawbake, Clément Faugeras, Sivasakthi Kuppusamy, Raman Sankar, Gichan Son, Jeongwoo Kim, Kwang‐Yong Choi   +8 more
wiley   +1 more source

Disambiguating flat spots in discrete scalar fields

Graphical Models
We consider 2D scalar fields sampled on a regular grid. When the gradient is low relative to the resolution of the dataset’s range, the signal may contain flat spots: connected areas where all points share the same value.
L. Rocca, F. Iuricich, E. Puppo
doaj   +1 more source

Topological Data Analysis of Task-Based fMRI Data from Experiments on Schizophrenia

, 2020
We use methods from computational algebraic topology to study functional brain networks, in which nodes represent brain regions and weighted edges encode the similarity of fMRI time series from each region.
Emerson, Tegan, Harrington, Heather A., Nahkuri, Satu, Porter, Mason A., Stolz, Bernadette J.   +4 more
core  

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, Cristiano Pesce, Antonietta Greco, Claudia Pisante, Greta Avancini, Valentina Di Francesco, Yosi Shamay, Paolo Decuzzi   +7 more
wiley   +1 more source

Emergent Spin‐Glass Behavior in an Iron(II)‐Based Metal–Organic Framework Glass

Advanced Functional Materials, EarlyView.
A one‐pot, solvent‐free synthesis yields an Fe2+‐based metal‐organic framework (MOF) glass featuring a continuous random network structure. The material exhibits spin‐glass freezing at 14 K, driven by topological‐disorder and short‐range magnetic frustration, showcasing the potential of MOF glasses as a plattform for cooperative magnetic phenomena in ...
Chinmoy Das, Soma Salamon, Joachim Landers, Jan‐Benedikt Weiß, Wen‐Long Xue, Pascal Kolodzeiski, Roman Pallach, Heiko Wende, Sebastian Henke   +8 more
wiley   +1 more source