On-Line Multi-Frequency Electrical Resistance Tomography (mfERT) Device for Crystalline Phase Imaging in High-Temperature Molten Oxide.

An on-line multi-frequency electrical resistance tomography (mfERT) device with a melt-resistive sensor and noise reduction hardware has been proposed for crystalline phase imaging in high-temperature molten oxide. The melt-resistive sensor consists of eight electrodes made of platinum-rhodium (Pt-20mass%Rh) alloy covered by non-conductive aluminum oxide (Al2O3) to prevent an electrical short. The noise reduction hardware has been designed by two approaches: (1) total harmonic distortion (THD) for the robust multiplexer, and (2) a current injection frequency pair: low fL and high fH, for thermal noise compensation. THD is determined by a percentage evaluation of k-th harmonic distortions of ZnO at f=0.1~10,000 Hz. The fL and fH are determined by the thermal noise behavior estimation at different temperatures. At  f <100 Hz, the THD percentage is relatively high and fluctuates; otherwise, THD dramatically declines, nearly reaching zero. At the determined fL≥ 10,000 Hz and fH≈ 1,000,000 Hz, thermal noise is significantly compensated. The on-line mfERT was tested in the experiments of a non-conductive Al2O3 rod dipped into conductive molten zinc-borate (60ZnO-40B2O3) at 1000~1200 °C. As a result, the on-line mfERT is able to reconstruct the Al2O3 rod inclusion images in the high-temperature fields with low error, ςfL, T = 5.99%, at 1000 °C, and an average error ⟨ςfL⟩ = 9.2%.

Distinction Between Precapillary and Postcapillary Pulmonary Hypertension by the Atrial Volume Ratio on Transthoracic Echocardiography.

OBJECTIVES: The differential diagnosis between precapillary and postcapillary pulmonary hypertension (PH) is important for deciding on the appropriate therapeutic strategy. The aim of this study was to assess whether the atrial volume ratio can differentiate precapillary and post-capillary PH. METHODS: Seventy-seven patients with PH who underwent transthoracic echocardiography (TTE) and right heart catheterization were retrospectively studied. Pulmonary hypertension was defined as a mean pulmonary arterial pressure of 25 mm Hg or higher by right heart catheterization. Patients with a pulmonary capillary wedge pressure higher than 15 mm Hg were classified as having postcapillary PH, and patients with a pulmonary capillary wedge pressure of 15 mm Hg or lower were classified as having precapillary PH. The atrial volume ratio derived from TTE was defined as right atrial volume divided by left atrial volume. RESULTS: Forty-four (57%) of 77 patients had precapillary PH by the right heart catheterization classification. The atrial volume ratio was significantly higher in precapillary PH than in postcapillary PH (1.03 ± 0.69 versus 0.50 ± 0.19; P < .001). The area under the receiver operating characteristic curve of the atrial volume ratio for detecting postcapillary PH was 0.84 (95% confidence interval: 0.75-0.93). Adding the atrial volume ratio to the left ventricular ejection fraction yielded a high area under the curve of 0.90 (95% confidence interval, 0.83-0.96) for distinguishing precapillary and postcapillary PH. CONCLUSIONS: The atrial volume ratio assessed by TTE might be useful for differential diagnosis between precapillary and postcapillary PH.

A case of complete double aortic arch visualized by transthoracic echocardiography.

A case of double aortic arch that was well visualized using transthoracic echocardiography is reported. A 38-year-old man underwent transthoracic echocardiography for the evaluation of dyspnea. A suprasternal view of transthoracic echocardiography showed the ascending aorta bifurcate to left and right aortic arches, with blood flow from the ascending aorta to bilateral aortic arches. The diagnosis of right side-dominant double aortic arch was made, and the patient's symptom was conceivably related to compression of the trachea due to a vascular ring. This report indicates the potential usefulness of transthoracic echocardiography for noninvasive detection of double aortic arch in adults.

Einstein-Roscoe regression for the slag viscosity prediction problem in steelmaking.

In classical machine learning, regressors are trained without attempting to gain insight into the mechanism connecting inputs and outputs. Natural sciences, however, are interested in finding a robust interpretable function for the target phenomenon, that can return predictions even outside of the training domains. This paper focuses on viscosity prediction problem in steelmaking, and proposes Einstein-Roscoe regression (ERR), which learns the coefficients of the Einstein-Roscoe equation, and is able to extrapolate to unseen domains. Besides, it is often the case in the natural sciences that some measurements are unavailable or expensive than the others due to physical constraints. To this end, we employ a transfer learning framework based on Gaussian process, which allows us to estimate the regression parameters using the auxiliary measurements available in a reasonable cost. In experiments using the viscosity measurements in high temperature slag suspension system, ERR is compared favorably with various machine learning approaches in interpolation settings, while outperformed all of them in extrapolation settings. Furthermore, after estimating parameters using the auxiliary dataset obtained at room temperature, an increase in accuracy is observed in the high temperature dataset, which corroborates the effectiveness of the proposed approach.

Oxygen Speciation in Multicomponent Silicate Glasses Using Through Bond Double Resonance NMR Spectroscopy.

The description of the structure of aluminosilicate glasses is more often centered on its cationic constituents, and oxygen ions determine their connectivity, directly impacting the physical properties of those disordered materials. A very powerful approach to ascertain this short- to medium-range order is to use 17O NMR, but up to now the speciation of the chemical bonds was only ambiguously achieved for multicomponent glasses. Here, we propose to directly probe the very scarcely explored through-bond correlations using 17O{27Al} and 17O{23Na} solid-state nuclear magnetic resonance (NMR) double-resonance experiments. Our approach allows quantifying the strongly overlapping components of the 17O NMR spectra of a quaternary aluminosilicate glass. We observe a cooperative location of alkali and aluminum ions in the neighborhood of bridging oxygens, which is consistent with the modified random network model where the glass structure is composed of two regions: network structure and breakage region (i.e., channel).