Honeycomb oxide heterostructure: a new platform for Kitaev quantum spin liquid

Kitaev quantum spin liquid, massively quantum entangled states, is so scarce in nature that searching for new candidate systems remains a great challenge. Honeycomb heterostructure could be a promising route to realize and utilize such an exotic quantum phase by providing additional controllability of Hamiltonian and device compatibility, respectively. Here, we provide epitaxial honeycomb oxide thin film Na3Co2SbO6, a candidate of Kitaev quantum spin liquid proposed recently. We found a spin glass and antiferromagnetic ground states depending on Na stoichiometry, signifying not only the importance of Na vacancy control but also strong frustration in Na3Co2SbO6. Despite its classical ground state, the field-dependent magnetic susceptibility shows remarkable scaling collapse with a single critical exponent, which can be interpreted as evidence of quantum criticality. Its electronic ground state and derived spin Hamiltonian from spectroscopies are consistent with the predicted Kitaev model. Our work provides a unique route to the realization and utilization of Kitaev quantum spin liquid

The usefulness of noninvasive liver stiffness assessment using shear-wave elastography for predicting liver fibrosis in children

Background Pediatric patients with liver disease require noninvasive monitoring to evaluate the risk of fibrosis progression. This study aimed to identify the significant factors affecting liver stiffness values using two-dimensional shear-wave elastography (2D-SWE), and determine whether liver stiffness can predict the fibrosis stage of various childhood liver diseases. Methods This study included 30 children (22 boys and 8 girls; mean age, 5.1 ± 6.1 years; range, 7 days–17.9 years) who had undergone biochemical evaluation, 2D-SWE examination, histopathologic analysis of fibrosis grade (F0 to F3), assessment of necroinflammatory activity, and steatosis grading between August 2016 and March 2020. The liver stiffness from 2D-SWE was compared between fibrosis stages using Kruskal–Wallis analysis. Factors that significantly affected liver stiffness were evaluated using univariate and multivariate linear regression analyses. The diagnostic performance was determined from the area under the receiver operating curve (AUC) values of 2D-SWE liver stiffness. Results Liver stiffness at the F0-1, F2, and F3 stages were 7.9, 13.2, and 21.7 kPa, respectively (P < 0.001). Both fibrosis stage and necroinflammatory grade were significantly associated with liver stiffness (P < 0.001 and P = 0.021, respectively). However, in patients with alanine aminotransferase (ALT) levels below 200 IU/L, the only factor affecting liver stiffness was fibrosis stage (P = 0.030). The liver stiffness value could distinguish significant fibrosis (≥ F2) with an AUC of 0.950 (cutoff value, 11.3 kPa) and severe fibrosis (F3 stage) with an AUC of 0.924 (cutoff value, 18.1 kPa). The 2D-SWE values for differentiating significant fibrosis were 10.5 kPa (≥ F2) and 18.1 kPa (F3) in patients with ALT levels below 200 IU/L. Conclusion The liver stiffness values on 2D-SWE can be affected by both fibrosis and necroinflammatory grade and can provide excellent diagnostic performance in evaluating the fibrosis stage in various pediatric liver diseases. However, clinicians should be mindful of potential confounders, such as necroinflammatory activity or transaminase level, when performing 2D-SWE measurements for liver fibrosis staging.This work was supported by grant no 04–2020-0760 from the SNUH Research Fund and by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No. 2020R1C1C1008716). The funder had no involvement or infuence whatsoever in the study design at any stage, collec‑tion of the data or its analysis and interpretation, writing and preparation of the manuscript, or its submission for publication

Robust estimation of bacterial cell count from optical density

Optical density (OD) is widely used to estimate the density of cells in liquid culture, but cannot be compared between instruments without a standardized calibration protocol and is challenging to relate to actual cell count. We address this with an interlaboratory study comparing three simple, low-cost, and highly accessible OD calibration protocols across 244 laboratories, applied to eight strains of constitutive GFP-expressing E. coli. Based on our results, we recommend calibrating OD to estimated cell count using serial dilution of silica microspheres, which produces highly precise calibration (95.5% of residuals &lt;1.2-fold), is easily assessed for quality control, also assesses instrument effective linear range, and can be combined with fluorescence calibration to obtain units of Molecules of Equivalent Fluorescein (MEFL) per cell, allowing direct comparison and data fusion with flow cytometry measurements: in our study, fluorescence per cell measurements showed only a 1.07-fold mean difference between plate reader and flow cytometry data

EXPERIMENTAL AND NUMERICAL INVESTIGATION ON THE FRACTURING BEHAVIOR AND SCALING OF DISCONTINUOUS FIBER COMPOSITE STRUCTURES

Thesis (Ph.D.)--University of Washington, 2021Discontinuous fiber composites (DFCs) have received significant attention from many industries due to their unique advantages in manufacturing capabilities. Compare to the continuous fiber composites, DFCs can be formed into complex contours. Also, they are optimized to be used for the compression molding process which is a suitable manufacturing method for high volume production. DFCs possess these manufacturing capabilities because of their unique meso-structures. They are composed of randomly oriented, and deposited platelets or chips made of prepregs. Because they contain fiber volume fractions similar to their pristine prepregs, they possess similar stiffness compared to the quasi-isotropic laminate made of identical prepregs. They are a suitable material form to be used for complex shapes yet stiff and lightweight structures.Despite their strong manufacturing advantages, engineers and designers do not fully appreciate DFCs' capabilities. The main reason is that DFCs possess strong stochastic fracturing behaviors. The complex mechanisms make DFCs far more difficult to analyze and predict their mechanical responses compare to the continuous fiber composites. As a result, many industries are heavily relied on costly physical tests. The physical experiment should only be proceeded when it is necessary. Also, they only provide insights to the meso-structures that they tested. Therefore, computational models based on the experimental data must be established in order to expand the applications of DFCs. In this study, we analyze the fracturing behaviors of DFCs and develop computational tools to understand the failure mechanisms. We experimentally investigate and numerically analyze the unique relationship between the meso-structures and macro-scale material behaviors. We focus on the tensile behaviors of DFCs with and without notches. We study various meso-structures composed of different platelet sizes and structural thicknesses. By leveraging the computational tools validated against the experiment, we can create safer and more efficient design guidelines to broaden the applications of DFCs

A Deep Learning Method for Monitoring Vehicle Energy Consumption with GPS Data

A monitoring method for energy consumption of vehicles is proposed in the study. It is necessary to have parameters estimating fuel economy with GPS data obtained while driving in the proposed method. The parameters are trained by fuel consumption data measured with a data logger for the reference cars. The data logger is equipped with a GPS sensor and OBD connection capability. The GPS sensor measures vehicle speed, acceleration rate and road gradient. The OBD connector gathers the fuel consumption signaled from OBD port built in the car. The parameters are trained by a 5-layer deep-learning construction with input data (speed, acceleration, gradient) and labels (fuel consumption data) in the typical classification approach. The number of labels is about 6–8 and the number of neurons for hidden layers increases in proportionate to the label numbers. There are about 160–200 parameters. The parameters are calibrated to consider the wide range of fuel efficiency and deterioration degree in age for various test cars. The calibration factor is made from the certified fuel economy and model year taken from the car registration form. The error range of the estimated fuel economy from the measured value is about −6% to +7% for the eight test cars. It is accurate enough to capture the vehicle dynamics for using the input and output data in point-to-point classification style for training steps. Further, it is simple enough to hit fuel economy of the other test cars because fuel economy is a kind of averaged value of fuel consumption for the time period or driven distance for monitoring steps. You can predict or monitor energy consumption for any vehicle with the GPS-measured speed/acceleration/gradient data by the pre-trained parameters and calibration factors of the reference vehicles according to fuel types such as gasoline, diesel and electric. The proposed method requires just a GPS sensor that is cheap and common, and the calculating procedure is so simple that you can monitor energy consumption of various vehicles in real-time with ease. However, it does not consider weight, weather and auxiliary changes and these effects will be addressed in the future works with a monitoring service system under preparation