Virtual Earthquake Engineering Laboratory Capturing Nonlinear Shear, Localized Damage and Progressive Buckling of Bar

We embarked upon developing a novel parallel simulation platform that is rooted in microphysical mechanisms. Primarily aiming at large-scale reinforced-concrete structures exposed to cyclic loading, we sought to settle the question as to how to capture nonlinear shear, localized damage and progressive buckling of reinforcing bar. We proposed a tribology-inspired three-dimensional (3-D) interlocking mechanism in the well-established framework of multidirectional smeared crack models. Strong correlation between random material property and localized damage has been shown, notably at the global system level. An automated platform has been suggested to capture progressive buckling phenomena. Validation and applications straddle a wide range, from small laboratory tests to large-scale 3-D experiments, successfully offering a clear causal pathway between underlying physical mechanisms and the unresolved issues addressed above

Gauss curvature-based unique signatures of individual large earthquakes and its implications for customized data-driven prediction

Statistical descriptions of earthquakes offer important probabilistic information, and newly emerging technologies of high-precision observations and machine learning collectively advance our knowledge regarding complex earthquake behaviors. Still, there remains a formidable knowledge gap for predicting individual large earthquakes’ locations and magnitudes. Here, this study shows that the individual large earthquakes may have unique signatures that can be represented by new high-dimensional features—Gauss curvature-based coordinates. Particularly, the observed earthquake catalog data are transformed into a number of pseudo physics quantities (i.e., energy, power, vorticity, and Laplacian) which turn into smooth surface-like information via spatio-temporal convolution, giving rise to the new high-dimensional coordinates. Validations with 40-year earthquakes in the West U.S. region show that the new coordinates appear to hold uniqueness for individual large earthquakes (Mw ≥ 7.0), and the pseudo physics quantities help identify a customized data-driven prediction model. A Bayesian evolutionary algorithm in conjunction with flexible bases can identify a data-driven model, demonstrating its promising reproduction of individual large earthquake’s location and magnitude. Results imply that an individual large earthquake can be distinguished and remembered while its best-so-far model can be customized by machine learning. This study paves a new way to data-driven automated evolution of individual earthquake prediction.This article is published as Cho, In Ho. "Gauss curvature-based unique signatures of individual large earthquakes and its implications for customized data-driven prediction." Scientific Reports 12, no. 1 (2022): 8669. doi: https://doi.org/10.1038/s41598-022-12575-w. © Te Author(s) 2022. Open Access. This article is licensed under a Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/)

Multiphysics machine learning framework for on-demand multi-functional nano pattern design by light-controlled capillary force lithography

Nature finds ways to realize multi-functional surfaces by modulating nano-scale patterns on their surfaces, enjoying transparent, bactericidal, and/or anti-fogging features. Therein height distributions of nanopatterns play a key role. Recent advancements in nanotechnologies can reach that ability via chemical, mechanical, or optical fabrications. However, they require laborious complex procedures, prohibiting fast mass manufacturing. This paper presents a computational framework to help design multi-functional nano patterns by light. The framework behaves as a surrogate model for the inverse design of nano distributions. The framework’s hybrid (i.e., human and artificial) intelligence-based approach helps learn plausible rules of multi-physics processes behind the UV-controlled nano patterning and enriches training data sets. Then the framework’s inverse machine learning (ML) model can describe the required UV doses for the target heights of liquid in nano templates. Thereby, the framework can realize multiple functionalities including the desired nano-scale color, frictions, and bactericidal properties. Feasibility test results demonstrate the promising capability of the framework to realize the desired height distributions that can potentially enable multi-functional nano-scale surface properties. This computational framework will serve as a multi-physics surrogate model to help accelerate fast fabrications of nanopatterns with light and ML.This article is published as Chapagain, Ashish, and In Ho Cho. "Multiphysics machine learning framework for on-demand multi-functional nano pattern design by light-controlled capillary force lithography." Communications Physics 7, no. 1 (2024): 213. doi: https://doi.org/10.1038/s42005-024-01703-9. © The Author(s) 2024. This Open Access article is licensed under a Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/)

Replica molding-based nanopatterning of tribocharge on elastomer with application to electrohydrodynamic nanolithography

Replica molding often induces tribocharge on elastomers. To date, this phenomenon has been studied only on untextured elastomer surfaces even though replica molding is an effective method for their nanotexturing. Here we show that on elastomer surfaces nanotextured through replica molding the induced tribocharge also becomes patterned at nanoscale in close correlation with the nanotexture. By applying Kelvin probe microscopy, electrohydrodynamic lithography, and electrostatic analysis to our model nanostructure, poly(dimethylsiloxane) nanocup arrays replicated from a polycarbonate nanocone array, we reveal that the induced tribocharge is highly localized within the nanocup, especially around its rim. Through finite element analysis, we also find that the rim sustains the strongest friction during the demolding process. From these findings, we identify the demolding-induced friction as the main factor governing the tribocharge’s nanoscale distribution pattern. By incorporating the resulting annular tribocharge into electrohydrodynamic lithography, we also accomplish facile realization of nanovolcanos with 10 nm-scale craters

Effects of a multi-herbal extract on type 2 diabetes

<p>Abstract</p> <p>Background</p> <p>An aqueous extract of multi-hypoglycemic herbs of <it>Panax ginseng </it>C.A.Meyer, <it>Pueraria lobata, Dioscorea batatas Decaisne, Rehmannia glutinosa, Amomum cadamomum Linné, Poncirus fructus </it>and <it>Evodia officinalis </it>was investigated for its anti-diabetic effects in cell and animal models.</p> <p>Methods</p> <p>Activities of PPARγ agonist, anti-inflammation, AMPK activator and anti-ER stress were measured in cell models and in <it>db/db </it>mice (a genetic animal model for type 2 diabetes).</p> <p>Results</p> <p>While the extract stimulated PPARγ-dependent luciferase activity and activated AMPK in C2C12 cells, it inhibited TNF-α-stimulated IKKβ/NFkB signaling and attenuated ER stress in HepG2 cells. The <it>db/db </it>mice treated with the extract showed reduced fasting blood glucose and HbA_1clevels, improved postprandial glucose levels, enhanced insulin sensitivity and significantly decreased plasma free fatty acid, triglyceride and total cholesterol.</p> <p>Conclusion</p> <p>The aqueous extract of these seven hypoglycemic herbs demonstrated many therapeutic effects for the treatment of type 2 diabetes in cell and animal models.</p

Comparison of the Plasma Metabolome Profiles Between the Internal Thoracic Artery and Ascending Aorta in Patients Undergoing Coronary Artery Bypass Graft Surgery Using Gas Chromatography Time-of-Flight Mass Spectrometry.

BackgroundThe left internal thoracic artery (LITA) has been used as the first conduit of choice in coronary artery bypass grafting (CABG) because of excellent long-term patency and outcomes. However, no studies have examined substances other than nitric oxide that could be beneficial for the bypass conduit, native coronary artery or ischemic myocardium. This study was conducted to evaluate differences in metabolic profiles between the LITA and ascending aorta using gas chromatography-time of flight-mass spectrometry (GC-TOF-MS).MethodsTwenty patients who underwent CABG using the LITA were prospectively enrolled. Plasma samples were collected simultaneously from the LITA and ascending aorta. GC-TOF-MS based untargeted metabolomic analyses were performed and a 2-step volcano plot analysis was used to identify distinguishable markers from two plasma metabolome profiles. Semi-quantitative and quantitative analyses were performed using GC-TOF-MS and enzyme-linked immunosorbent assay, respectively, after selecting target metabolites based on the metabolite set enrichment analysis.ResultsInitial volcano plot analysis demonstrated 5 possible markers among 851 peaks detected. The final analysis demonstrated that the L-cysteine peak was significantly higher in the LITA than in the ascending aorta (fold change = 1.86). The concentrations of intermediate metabolites such as L-cysteine, L-methionine and L-cystine in the 'cysteine and methionine metabolism pathway' were significantly higher in the LITA than in the ascending aorta (2.0-, 1.4- and 1.2-fold, respectively). Quantitative analysis showed that the concentration of hydrogen sulfide (H₂S) was significantly higher in the LITA.ConclusionThe plasma metabolome profiles of the LITA and ascending aorta were different, particularly higher plasma concentrations of L-cysteine and H₂S in the LITA

Flexible and interpretable generalization of self-evolving computational materials framework

The recent innovations of computational material models by machine learning (ML) methods face formidable challenges. Incorporating internal heterogeneity and diverse boundary conditions (BC’s) into existing ML methods remains difficult, and the weak interpretability of ML remains unresolved. To tackle these challenges, this paper generalizes a recently developed self-evolving computational material models framework built upon Bayesian update and evolutionary algorithm. This paper proposes a new material-specific information index (II), which is capable of autonomously quantifying the internal heterogeneity and diverse BC’s. Also, this paper introduces highly flexible cubic regression spline (CRS)-based link functions which can offer mathematical expressions of salient material coefficients of the existing computational material models in terms of convolved II. Thereby, this paper suggests a novel means by which ML can directly leverage internal heterogeneity and diverse BC’s to autonomously evolve computational material models while keeping interpretability. Validations using a wide spectrum of large-scale reinforced composite structures confirm the favorable performance of the generalization. Example expansions of nonlinear shear of quasi-brittle materials and progressive compressive buckling of reinforcing steel underpin efficiency and accuracy of the generalization. This paper adds a meaningful avenue for accelerating the fusion of computational material models and ML.This is a manuscript of the article Published as Bazroun, Mohammed, Yicheng Yang, and In Ho Cho. "Flexible and interpretable generalization of self-evolving computational materials framework." Computers & Structures 260 (2022): 106706. doi: https://doi.org/10.1016/j.compstruc.2021.106706. © 2021 Elsevier. This manuscript is made available under the Elsevier user license (https://www.elsevier.com/open-access/userlicense/1.0/). CC BY-NC-N

SECBlock-IIoT : A Secure Blockchain-enabled Edge Computing Framework for Industrial Internet of Things

This work was supported by a National Research Foundation of Korea (NRF) grant funded by Korean Government (MSIT) (No. 2021R1A2C2014333).Postprin