This study examined differences in the Seven Network’s primetime coverage of the 2018 PyeongChang Winter Olympic Games on all of its channels. Over 102 hr of total coverage was analyzed for clock time, name mentions, and the descriptions of athletes by announcers divided by gender. Results found that male athletes received the bulk of the clock time; 13 of the top 20 most-mentioned athletes were men. There were also gender differences in the word for word descriptors of success, failure, physicality, and personality. From a theoretical perspective, results found the framing of the 2018 PyeongChang Winter Olympic Games to favor male Olympians. The top three sports that were broadcast featuring women were ice hockey, freestyle skiing, and snowboarding, which differs from other studies in this line of scholarship, so differences in the sports covered in the Australian context provides a unique context to study the Winter Olympics. Theoretical and practical implications are provided.
The purpose of this paper is to present a comprehensive multibody system dynamics model of a multiple launch rocket system (MLRS), and implement its simulation and experimental studies. The new version of transfer matrix method of multibody system and the launch dynamics theory are used in deriving the equations of motion coupled with rockets and barrels. The obtained model accounts for the complete process of the rockets’ ignition, movement in the barrels, airborne flight and landing. Launch dynamics of an 18-tube 122mm MLRS is investigated in this paper. Considering the effects of random factors, such as the impact and clearance between the rockets and barrels, the mass eccentricity and dynamic unbalance of the rockets and the thrust misalignment in this model, and combining the Monte Carlo method, the simulation of the dynamics of MLRS is carried out. Finally, the experimental implementation is proposed and the experimental results emphasize the feasibility of the multibody system launch dynamics model as a viable alternative for modeling accurately the dynamics characteristics of a practical MLRS. Meanwhile, the correctness of the numerical results is validated.
Tongue diagnosis is an important part of the diagnostic process in traditional Chinese medicine (TCM). It primarily relies on the expertise and experience of TCM practitioners in identifying tongue features, which are subjective and unstable. We proposed a tongue feature classification framework based on convolutional neural networks to reduce the differences in diagnoses among TCM practitioners. Initially, we used our self-designed instrument to capture 482 tongue photos and created 11 data sets based on different features. Then, the tongue segmentation task was completed using an upgraded facial landmark detection method and UNET. Finally, we used ResNet34 as the backbone to extract features from the tongue photos and classify them. Experimental results show that our framework has excellent results with an overall accuracy of over 86 percent and is particularly sensitive to the corresponding feature regions, and thus it could assist TCM practitioners in making more accurate diagnoses.
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