Visual Thinking for Art Librarians and Artists: Techniques for Unlocking Your Creativity to Generate Ideas and Solve Problems

This poster introduces visual thinking for art librarians and artists, and it looks at different techniques to promote creativity, conceptualize ideas, solve problems, and come up with new insights. It was presented virtually for the Art Libraries Society of North America's 48th Annual Conference held on July 29-31, 2020.
Date: June 30, 2020
Creator: Barham, Rebecca
Object Type: Poster
System: The UNT Digital Library
In-vitro biomineralization and biocompatibility of friction stir additively manufactured AZ31B magnesium alloy-hydroxyapatite composites (open access)

In-vitro biomineralization and biocompatibility of friction stir additively manufactured AZ31B magnesium alloy-hydroxyapatite composites

Article presents research where friction stir additive manufacturing technique was employed to fabricate AZ31B magnesium-hydroxyapatite composite. The study aims to evaluate effect of hydroxyapatite (HA, Ca₁₀(PO₄)₆OH₂), a ceramic similar to natural bone, into AZ31B Mg alloy matrix on biomineralization and biocompatibility.
Date: June 30, 2020
Creator: Ho, Yee-Hsien; Man, Kun; Joshi, Sameehan; Pantawane, Mangesh V.; Wu, Tso-Chang; Yang, Yong et al.
Object Type: Article
System: The UNT Digital Library
Thermally Tunable Dynamic and Static Elastic Properties of Hydrogel Due to Volumetric Phase Transition (open access)

Thermally Tunable Dynamic and Static Elastic Properties of Hydrogel Due to Volumetric Phase Transition

Article studying the temperature dependence of the mechanical properties of polyvinyl alcohol-based poly n-isopropyl acrylamide (PVA-PNIPAm) hydrogel from the static and dynamic bulk modulus of the material.
Date: June 30, 2020
Creator: Jin, Yuqi; Yang, Teng; Ju, Shuai; Zhang, Haifeng; Choi, Tae-Youl & Neogi, Arup
Object Type: Article
System: The UNT Digital Library
PPAD: a deep learning architecture to predict progression of Alzheimer’s disease (open access)

PPAD: a deep learning architecture to predict progression of Alzheimer’s disease

Article asserts that Alzheimer’s disease (AD) is a neurodegenerative disease that affects millions of people worldwide. The authors of the article propose two deep learning architectures based on RNN, namely Predicting Progression of Alzheimer’s Disease (PPAD) and PPAD-Autoencoder.
Date: June 30, 2023
Creator: Olaimat, Mohammad Al; Martinez, Jared; Saeed, Fahad & Bozdag, Serdar
Object Type: Article
System: The UNT Digital Library