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- McCauley, Brian, et al.
(författare)
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Facebook in Vietnam : Uses, gratifications & narcissism
- 2016
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Ingår i: Open Journal of Social Sciences. - : Scientific Research Publishing. - 2327-5960 .- 2327-5952. ; 4:11, s. 69-79
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Tidskriftsartikel (refereegranskat)abstract
- The purpose of this study was to create a conceptual framework and to collect some pilot data in order to underpin future research on how the Vietnamese use Facebook in their day-to-day lives. A number of key points were observed in this study, which informed the framework. Firstly, there is a paucity of research on this topic, that Facebook users in Vietnam (population 90 million) rank as some of the heaviest consumers in the world, and Vietnamese cultural traditions and values need to be acknowledged given these differences when compared to other nations and how this might influence Facebook use. Given the studies focus on users, the theory on “uses and gratifications” was employed in order to understand how Facebook satisfies the needs of its Vietnamese users. An important component in this theory is the way in which Facebook allows posting of material related to the enhancement of the “self”, which has the potential to satisfy ego driven needs in the form of narcissism. However, narcissism and its links with Facebook have only recently been systematically studied in Asian countries, predominately in China. In conclusion, the conceptual framework and analysis of the pilot data produced a number of interrelated constructs (e.g. socializing, social enhancement, entertainment) that provide a baseline or foundation from which a longer-term program of empirical research can be conducted on Facebook use in Vietnam.
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- McCauley, Brian, et al.
(författare)
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From Digital Subcultures to Destination Tourism : Profiling Attendees at Multi Genre Festivals
- 2023
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Ingår i: Proceedings of the Annual Hawaii International Conference on System Sciences. - Honolulu : HICSS Conference Office. - 9780998133164 ; , s. 3974-3983
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Konferensbidrag (refereegranskat)abstract
- The rise and connectivity of digital subcultures are increasingly influencing destination tourism. This study provides an understanding of a multi genre festival within the wider context of popular ‘geek’ culture and its increasing role in events and destination tourism. Through profiling the characteristics and experiences of visitors attending Nordsken, an annual festival in Northern Sweden, we profile segments and provide insights on attendees. Based on a survey of festival visitors, this study revealed five distinct clusters (Digital Gamer, Enthusiastic Nerd, Analogue Fan, Spectator & Follower and Creative Player) based on interests and activities. Experiences of the event were relatively similar for all clusters indicating that multi genre festivals can create memorable experiences for a broad audience with a variety of interests rooted in digital cultures. Through understanding and developing target audiences, regions can leverage multi genre festivals as platforms to enhance regional digitalization.
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- Neshat, M., et al.
(författare)
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Short-term solar radiation forecasting using hybrid deep residual learning and gated LSTM recurrent network with differential covariance matrix adaptation evolution strategy
- 2023
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Ingår i: Energy. - : Elsevier Ltd. - 0360-5442 .- 1873-6785. ; 278
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Tidskriftsartikel (refereegranskat)abstract
- Developing an accurate and robust prediction of long-term average global solar irradiation plays a crucial role in industries such as renewable energy, agribusiness, and hydrology. However, forecasting solar radiation with a high level of precision is historically challenging due to the nature of this source of energy. Challenges may be due to the location constraints, stochastic atmospheric parameters, and discrete sequential data. This paper reports on a new hybrid deep residual learning and gated long short-term memory recurrent network boosted by a differential covariance matrix adaptation evolution strategy (ADCMA) to forecast solar radiation one hour-ahead. The efficiency of the proposed hybrid model was enriched using an adaptive multivariate empirical mode decomposition (MEMD) algorithm and 1+1EA-Nelder–Mead simplex search algorithm. To compare the performance of the hybrid model to previous models, a comprehensive comparative deep learning framework was developed consisting of five modern machine learning algorithms, three stacked recurrent neural networks, 13 hybrid convolutional (CNN) recurrent deep learning models, and five evolutionary CNN recurrent models. The developed forecasting model was trained and validated using real meteorological and Shortwave Radiation (SRAD1) data from an installed offshore buoy station located in Lake Michigan, Chicago, United States, supported by the National Data Buoy Centre (NDBC). As a part of pre-processing, we applied an autoencoder to detect the outliers in improving the accuracy of solar radiation prediction. The experimental results demonstrate that, firstly, the hybrid deep residual learning model performed best compared with other machine learning and hybrid deep learning methods. Secondly, a cooperative architecture of gated recurrent units (GRU) and long short-term memory (LSTM) recurrent models can enhance the performance of Xception and ResNet. Finally, using an effective evolutionary hyper-parameters tuner (ADCMA) reinforces the prediction accuracy of solar radiation.
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