As visual marketing gains a more critical role in marketing communications, consumer eye-tracking data has been utilized to assess the effectiveness of those marketing efforts (Croll, 2016; Glazer, 2012). With eye-tracking data, researchers can capture consumers’ visual attention effectively and may predict their behavior better than with traditional memory measures (Wedel & Pieters, 2008). However, due to the complexity of data: its volume, velocity and variety, known as 3Vs of Big Data, marketing scholars have been slow in fully utilizing eye-tracking data. These data properties may pose a challenge for researchers to analyze eye-tracking data, especially gaze sequence data, with traditional statistical approaches. Commonly, researchers may analyze gaze sequences by computing average probabilities of gaze transitions from a particular area of interest to another area of interest. When the variance of gaze sequence data in the sample is small, this method would uncover a meaningful “global” trend, a trend consistent across all the individuals. However, when the variance is large, this method may not enable researchers to understand the nature of the variance, or the “messiness” of data. In this paper, first, to overcome this challenge, we propose an innovative method of analyzing gaze sequence data. Utilizing the singular value decomposition, our proposed method enables researchers to reveal a “local” trend, a trend shared by only some individuals in the sample. Second, we illustrate the benefits of our method through analyzing gaze sequence data collected in an advertising study. Finally, we discuss the implications of our proposed method, including its capability of uncovering a hidden “local” trend in “messy” gaze sequence data.

In recent years, safety recalls have occurred frequently in the biopharmaceutical industry, which affects the health of consumers. This article attempts to use the fsQCA method to draw a conclusion through the study of ESG and its quantification system, as well as the study of data samples related to MES and GMP processes, that is, MES-DPT has a positive impact on process safety management, and GMP-DPT has a positive impact on process safety. Management has a positive and positive impact, and ESG-DPT has a positive and positive impact on process safety management. Finally, this article puts forward suggestions for improving ESG-DPT, MES-DPT, GMP-DPT and the biomedical ESG-DPT model. Future research hopes to further study ESG -DPT model and ESG biomedical industry indicators.

The deep space orbit determination software (DSODS) is a part of a flight dynamic subsystem (FDS) for the Korean Pathfinder Lunar Orbiter (KPLO), a lunar exploration mission expected to launch after 2018. The DSODS consists of several sub modules, of which the orbit determination (OD) module employs a weighted least squares algorithm for estimating the parameters related to the motion and the tracking system of the spacecraft, and subroutines for performance improvement and detailed analysis of the orbit solution. In this research, DSODS is demonstrated and validated at lunar orbit at an altitude of 100 km using actual Lunar Prospector tracking data. A set of a priori states are generated, and the robustness of DSODS to the a priori error is confirmed by the NASA planetary data system (PDS) orbit solutions. Furthermore, the accuracy of the orbit solutions is determined by solution comparison and overlap analysis as about tens of meters. Through these analyses, the ability of the DSODS to provide proper orbit solutions for the KPLO are proved.

We estimated the orbit of the Communication, Ocean and Meteorological Satellite (COMS), a Geostationary Earth Orbit (GEO) satellite, through data from actual optical observations using telescopes at the Sobaeksan Optical Astronomy Observatory (SOAO) of the Korea Astronomy and Space Science Institute (KASI), Optical Wide field Patrol (OWL) at KASI, and the Chungbuk National University Observatory (CNUO) from August 1, 2014, to January 13, 2015. The astrometric data of the satellite were extracted from the World Coordinate System (WCS) in the obtained images, and geometrically distorted errors were corrected. To handle the optically observed data, corrections were made for the observation time, light-travel time delay, shutter speed delay, and aberration. For final product, the sequential filter within the Orbit Determination Tool Kit (ODTK) was used for orbit estimation based on the results of optical observation. In addition, a comparative analysis was conducted between the precise orbit from the ephemeris of the COMS maintained by the satellite operator and the results of orbit estimation using optical observation. The orbits estimated in simulation agree with those estimated with actual optical observation data. The error in the results using optical observation data decreased with increasing number of observatories. Our results are useful for optimizing observation data for orbit estimation.