A Cross-cultural Analysis of Brand Personality: Comparisons of China’s and the US Energy Companies’ English Websites

With the rapid development of economic globalization, projecting a positive image overseas and creating world famous brands have become vital to enhance industrial and national core competencies and execute the "Going out" strategy. To achieve the goals, corporates attach significance to establish and maintain corporate websites in view of its convenience, autonomy and interactivity while encountering cross-cultural challenges. This study employs corpus analytical tools to conduct content analysis on the existing cross-cultural differences and the linguistic and cultural features, between Chinese and US energy companies’ websites based on Aaker’s brand personality framework and Hofstede’s cultural dimension theory. Findings reveal that there is a significant difference between occurrence frequencies of brand personality dimensions between China and US, and their websites linguistic discrepancies are relevant to their cultural differences. The study may provide meaningful implications on employing linguistic theories and methods to conduct multidisciplinary studies on corporate communication online

The ensemble photometric variability of over 10510^5 quasars in the Dark Energy Camera Legacy Survey and the Sloan Digital Sky Survey

We present the ensemble variability analysis results of quasars using the Dark Energy Camera Legacy Survey (DECaLS) and the Sloan Digital Sky Survey (SDSS) quasar catalogs. Our dataset includes 119,305 quasars with redshifts up to 4.89. Combining the two datasets provides a 15-year baseline and permits analysis of the long timescale variability. Adopting a power-law form for the variability structure function, $V=A(t/1yr)^{\gamma}$, we use the multi-dimensional parametric fitting to explore the relationships between the quasar variability amplitude and a wide variety of quasar properties, including redshift (positive), bolometric luminosity (negative), rest-frame wavelength (negative), and black hole mass (uncertain). We also find that $\gamma$ can be also expressed as a function of redshift (negative), bolometric luminosity (positive), rest-frame wavelength (positive), and black hole mass (positive). Tests of the fitting significance with the bootstrap method show that, even with such a large quasar sample, some correlations are marginally significant. The typical value of $\gamma$ for the entire dataset is $\gtrsim 0.25$, consistent with the results in previous studies on both the quasar ensemble variability and the structure function. A significantly negative correlation between the variability amplitude and the Eddington ratio is found, which may be explained as an effect of accretion disk instability.Comment: 13 pages, 8 figures, 4 tables, accepted for publication in Ap

The Extremely Luminous Quasar Survey (ELQS) in the SDSS footprint I.: Infrared Based Candidate Selection

Studies of the most luminous quasars at high redshift directly probe the evolution of the most massive black holes in the early Universe and their connection to massive galaxy formation. However, extremely luminous quasars at high redshift are very rare objects. Only wide area surveys have a chance to constrain their population. The Sloan Digital Sky Survey (SDSS) has so far provided the most widely adopted measurements of the quasar luminosity function (QLF) at $z>3$. However, a careful re-examination of the SDSS quasar sample revealed that the SDSS quasar selection is in fact missing a significant fraction of $z\gtrsim3$ quasars at the brightest end. We have identified the purely optical color selection of SDSS, where quasars at these redshifts are strongly contaminated by late-type dwarfs, and the spectroscopic incompleteness of the SDSS footprint as the main reasons. Therefore we have designed the Extremely Luminous Quasar Survey (ELQS), based on a novel near-infrared JKW2 color cut using WISE AllWISE and 2MASS all-sky photometry, to yield high completeness for very bright ($m_{\rm{i}} < 18.0$) quasars in the redshift range of $3.0\leq z\leq5.0$. It effectively uses random forest machine-learning algorithms on SDSS and WISE photometry for quasar-star classification and photometric redshift estimation. The ELQS will spectroscopically follow-up $\sim 230$ new quasar candidates in an area of $\sim12000\,\rm{deg}^2$ in the SDSS footprint, to obtain a well-defined and complete quasars sample for an accurate measurement of the bright-end quasar luminosity function at $3.0\leq z\leq5.0$. In this paper we present the quasar selection algorithm and the quasar candidate catalog.Comment: 16 pages, 8 figures, 9 tables; ApJ in pres

Model-based Optimal Control of Variable Air Volume Terminal Box

In the U.S. A Variable Air Volume (VAV) system is one of most commonly used air system for multiple-zone commercial buildings due to its capability to meet the varying heating and cooling loads of different building thermal zones. One of key component of VAV system is the terminal VAV box. There are an air damper and a reheat coil in the box. How to effectively and efficiently control the VAV box plays a significant role to reduce energy consumption and maintain acceptable indoor environment in buildings. Currently, there are two control logics used for controlling VAV box, namely, single maximum and dual maximum control logics. The single maximum logic is the most common, where the room temperature setpoint is maintained by only adjusting the reheat coil valve position in the heating model. The damper position is kept as the minimal to satisfy the ventilation requirement only. On the other hand, the more advanced dual maximum control logic realizes the room air temperature control by adjusting both damper position and reheat coil valve position in the heating model. For the cooling model, both control logics have the same action to maintain room air temperature setpoint through adjusting the damper position. Â In this study, a model-based optimal control is explored to minimize the energy consumption of the VAV box with a hot water reheat coil. Data driven approach based on an Autoregressive exogenous (ARX) model is investigated to represent dynamics of the room thermal response. The similar data-driven approach is used to develop an energy consumption model of the VAV box. Measured data for the VAV box from a real building is used to train and test data-driven model. Such data includes room air temperature, outdoor air temperature, supply air temperature, supply air flow rate, damper position, reheat coil valve position and VAV box energy consumption. A platform of AMPL (A Modeling Language for Mathematical Programming) is used to for mathematical modeling and links to different optimization solvers. Â In addition, uncertainty analysis and sensitivity analysis are conducted to help understand the model behaviors and performance. In this study, the Monte Carlo sampling method is applied to generate samples for model inputs including supply air temperature, outdoor conditions, etc. A quantified sensitivity index of Sobol is calculated to indicate the impact level from different inputs or disturbances

A Multi-Criteria Group Decision-Making Method with Possibility Degree and Power Aggregation Operators of Single Trapezoidal Neutrosophic Numbers

Single valued trapezoidal neutrosophic numbers (SVTNNs) are very useful tools for describing complex information, because of their advantage in describing the information completely, accurately and comprehensively for decision-making problems. In the paper, a method based on SVTNNs is proposed for dealing with multi-criteria group decision-making (MCGDM) problems. Firstly, the new operations SVTNNs are developed for avoiding evaluation information aggregation loss and distortion