This study aims to classify R&D activities related to the nuclear fuel cycle using the deep learning methodology. First, R&D data of the Republic of Korea were collected from the National Science & Technology Information Service (NTIS) for the years 2021, 2022, and 2023. We use keywords such as ‘nuclear,’ ‘uranium,’ ‘plutonium,’ and ‘thorium’ to find nuclear-related R&D projects in the NTIS database. Among the numerous R&D projects found through keyword searches, overlapping and medical-related R&D projects were excluded. Finally, 495 R&D projects conducted in 2021, 430 R&D projects conducted in 2022, and 296 R&D projects conducted in 2023 were obtained for analysis. After that, Safeguards experts determine whether the R&D projects are subject to declaration under the AP. The values of the content validity index (CVI) and content validity ratio (CVR) were used to verify whether the experts’ judgments were valid. The 1,218 collected and labeled data were then divided 8:2 into training and test datasets to see if deep learning could be applied to classify nuclear fuel cycle-related R&D activities. We use the Python and TensorFlow packages, including RNN, GRU, and CNN methods. First, the collected text information was preprocessed to remove punctuation marks and then tokenized to make it suitable for deep learning. After 20 epochs of training to classify the nuclear fuel cycle-related R&D activities, the RNN model achieved 97.30% accuracy and a 5.85% error rate on the validation dataset. The GRU model achieved 96.53% accuracy and a 9.06% error rate on the validation dataset. In comparison, the CNN model achieved 94.61% accuracy and a 2.57% error rate on the validation dataset. When applying the test dataset to each model, the RNN model had a test accuracy of 83.20%, the GRU test accuracy of 82.79%, and the CNN model had a test accuracy of 85.66% for the same dataset. This study applied deep learning models to labeled data judged by various experts, and the CNN model showed the best results. In the future, this study will continue to develop an optimum deep learning model that can classify nuclear fuel cycle-related R&D activities to achieve the purpose of safeguards measures from open-source data such as papers and articles.

In case a spent nuclear fuel transport cask is lost in the sea due to an accident during maritime transport, it is necessary to evaluate the critical depth by which the pressure resistance of the cask is maintained. A licensed type B package should maintain the integrity of containment boundary under water up to 200 m of depth. However, if the cask is damaged during accidents of severity excessing those of design basis accidents, or it is submerged in a sea deeper than 200 m, detailed analyses should be performed to evaluated the condition of the cask and possible scenarios for the release of radioactive contents contained in the cask. In this work, models to evaluate pressure resistance of an undamaged cask in the deep sea are developed and coded into a computer module. To ensure the reliability of the models and to maintain enough flexibility to account for a variety of input conditions, models in three different fidelities are utilized. A very sophisticated finite element analysis model is constructed to provide accurate response of containment boundary against external pressure. A simplified finite element model which can be easily generated with parameters derived from the dimensions and material properties of the cask. Lastly, mathematical formulas based on the shell theory are utilized to evaluate the stress and strain of cask body, lid and the bolts. The models in mathematical formula will be coded into computer model once they show good agreement with the other two model with much higher fidelity. The evaluation of the cask was largely divided into the lid, body, and bottom, bolts of the cask. It was confirmed that the internal stress of the cask was increased in accordance with the hydrostatic pressure. In particular, the lid and bottom have a circular plate shape and showed a similar deformation pattern with deflection at the center. The maximum stress occurred where the lid was in the center and the bottom was in contact with the body. Because the body was simplified and evaluated as a cylinder, only simple compression without torsion and bending was observed. The maximum stress occurred in the tangential direction from the inner side of the cylinder. The bolt connecting the lid and the body was subjected to both bending and tension at the same time, and the maximum stress was evaluated considering both tension and bending loads. In general, the results calculated by the formulas were evaluated to have higher maximum stresses than the analysis results of the simplified model. The results of the maximum stress evaluation in this study confirms that the mathematical models provide conservative results than the finite element models and can be used in the computer module.

Not only does this paper examine the etymology of yuan元, yuan原, yuan源 and ben本 through Sinoxenic vocabulary but conducts comparative analyses of similarities and differences in them as well. yuan元 means ‘beginning, first’ and ‘root, origin’ in Sinoxenic words. However, its role was replaced with yuan原 at the beginning of the Ming Dynasty. yuan原was originally used to mean ‘first, beginning’ and ‘origin, source’, the former of which has strengthened and the latter of which has become transferred to yuan源to which more meanings of ‘root, by nature’ have been added since then. ben本 means ‘nature, essence, basis’ referring to the essence or nature itself, thereby differentiated both from yuan源, whose meaning indicates the origin of such essence or nature, and from yuan原, which means ‘first’ and ‘beginning (initial appearance or status)’.

This paper examines the effects of the mergers and acquisitions (M&A) announcement through social media on the consumer perception of the luxury brand consumption. A M&A is becoming more wide spread in the luxury market. Yet, the academic research examining the M&A in the luxury brand context has been sparse albeit the growing interests. Moreover, previous research has not paid attention to the effect of social media as a vehicle to communicate the M&A deal with consumers although social media is increasingly used by luxury brands in their brand communication these days. We aim to fill the gap in the luxury brand literature by examining how a horizontal M&A announcement delivered through social media would affect the brand loyalty derived from the luxury consumption values. Specifically, our research focuses on the four distinctive luxury brand values, which are symbolic, experiential, economic and quality values as well as the perceived sustainability of the M&A deal. We examine how a M&A announcement would affect these five values which in turn influence the brand loyalty, as well as examining the differential effect of social media and non-social media as a brand communication vehicle. In addition, we examine how the vertically differentiated luxury brand perceptions (i.e. different luxury tiers) between acquiring and acquired brands influence the consumption values and brand loyalty. Using a scenario-based online survey, our results reveal several interesting insights on the luxury brand M&A. First, our results show that use of social media as a communication vehicle has differential effects on how the M&A announcement influences consumption values and brand loyalty, comparing with the non-social media communication vehicle. Second, we find that a M&A announcement via social media has a positive impact on the consumer values. Third, the symbolic and experiential values have a positive influence on the brand loyalty, regardless of the luxury tier difference between brands. Fourth, our results show that the perceived sustainability has a positive impact on the brand loyalty as long as the M&A was completed between brands at different tiers. Fourth, the perceived quality has a positive impact on the brand loyalty only if the brand is acquired by a less prestigious brand. Lastly, economic value has a positive impact on the brand loyalty only if the acquiring brand is of more luxurious. In sum, our paper provides useful insights to both academics as well as practitioners in the luxury brand M&A context.