Molecular Line Search in Archival ALMA Imaging of M87

We present a molecular line search in spectral imaging of the elliptical galaxy M87 using archival data from the Atacama Large Millimeter/sub-millimeter Array (ALMA). The primary goal of this project is to verify and characterize possible CO transition lines, which can be used for future studies to determine the dynamics of the system, or the mass of the supermassive black hole (BH) in the center of the galaxy. After performing extensive atmospheric modeling, we found that most of the absorption features have a corresponding atmospheric ozone transition, so it is unlikely that these features are either Galactic or extragalactic. We also found and corrected a calibration error that introduced an overly sharp absorption feature. After careful continuum subtraction, we calculated line strengths of these absorption features and identified CO emission from high-velocity gas very near the central BH. After analyzing the CO absorption and emission properties, we present a pathway forward for future spectral imaging programs of M87

Framing of Business in China: East vs. West

China is one of the largest and most promising markets for new global business ventures. This exploratory research investigates the current news-related themes regarding business in China by comparing news originating from eastern countries to western countries. This study applied agenda-setting and framing theoretical frameworks. A mixed-method approach of both qualitative and quantitative content analyses was used to generate key themes and their related concepts. The results indicate some thematic similarities as well as differences in agenda and news framing between eastern and western news samples. This exploratory study contributes to agenda-setting and framing literature as well as by creating a media agenda benchmark for future agenda-setting studies focused doing business in China

Fatigue life prognostic for medium-carbon steel based S-N curve computation and deep autoencoder

Medium-carbon steel is widely used in architecture, rail, machinable steel and so on. So, there is a huge significance in the analysis and research of its fatigue life. In this paper, the fatigue experiment with different surface roughness was set up. In the experiment, there were three type of roughness and a total of 75 experiments were performed. Then a deep autoencoder to model the relationship of roughness (R), stress (S), and fatigue life (N). It has been found that the method can automatically extract the features which can be used to effectively model the relationship of R, S and N. Finally, the approach presented was compared with the existing model based on Tanaka-Mura theory and got an unexpected better result

Numerical simulation of flow and heat transfer of continous cast steel slab under traveling magnetic field

A unified numerical model for simulating solidification transport phenomena (STP) of steel slab in electromagnetic continuous casting (EMCC) process was developed. In order to solve the multi-physics fields coupled problem conveniently, the complicated bidirectional coupled process between EM and STP was simplified as a unidirectional one, and a FEM/FVM-combined numerical simulation technique was adopted. The traveling magnetic fields (TMFs) applied to the EMCC process were calculated using the ANSYS11.0 software, and then the EM-data output by ANSYS were converted to FVM-format using a data-format conversion program developed previously. Thereafter, the governing equations were solved using a pressure-based Direct-SIMPLE algorithm. The simulation results of the STP in CC-process show that, due to the influences of Lorentz force and Joule heat, the two strong circulating flows and the temperature field can be obviously damped and changed once TMF with one pair of poles (1-POPs) or 2-POPs is applied, which would accordingly improve the quality of casting. It was found in the present research that the integrated actions of 2-POPs TMF are superior to 1-POPs. All the computations indicate that the present numerical model of EM-STP as well as the FEM/FVM-combined technique is successful

Research on Keywords Variations in Linguistics Based on TF-IDF and N-gram

The rapid development of natural language processing (NLP) holds great promise for bridging the divide among languages. One of its main innovative applications is to use broad data to explore the historical trend of a subject. However, since Saussure pioneered modern linguistics, there is relatively inadequate research work done in the linguistic research on the field\u27s variations to comprehensively reveal the linguistic trends. To trace the changes in linguistic research hotspots, we use a dataset of more than 30,000 linguistics-related literature with their titles from the Web of Science and apply NLP techniques to the data consisting of their keywords and publication years. It is found that the co-occurrence relationship between keywords, NGRAM, and their relationship with years can effectively present changes in linguistic research themes. This research is supposed to provide further insights and new methods that can be applied in the field of linguistics and related disciplines

Gear pitting level diagnosis using vibration signals with an improved inception structure

Gear pitting fault is common in mechanical devices. At present, most of the gear pitting fault detection methods are based on the manual extraction of the frequency domain features from vibration signals. This paper presents a method for gear pitting fault level diagnosis using vibration signals with an improved inception network. The presented method directly applies to the vibration signals to automatically extract features and diagnose the level of the gear pitting fault using deep learning. The presented method has been validated with vibration data collected for 7 gear pitting conditions from gear pitting fault tests. The validation results have shown that the presented method can effectively classify the levels of the gear pitting faults. In comparison with traditional convolutional neural network, the diagnosis accuracy has been significantly improved with the presented method

Nonlinear transient engagement characteristics of planetary gear train

Based on the engagement principle of the gear drive, the nonlinear transient dynamic characteristics of the planetary gearing are researched and the corresponding dynamic equilibrium equations for sun gear, planet gear and internal gear are respectively derived. Then transient engagement simulation analysis of the planetary gear train is performed in ANSYS. The corresponding stress-time curves of sun gear, planet gear and internal gear are obtained, and the worst engaging location and the ultimate stress in every gear are calculated accurately. The simulation experiment shows that the engagement characteristics of the planetary gearing at any time and in any location can be accurately analyzed by transient engagement analysis. It provides a reliable guaranty for the subsequent fatigue analysis and structural optimization of the planetary gear train