• Nuclear Engineering and Technology
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• v.56 no.3
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• pp.993-1001
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• 2024

The TANDEM project is a European initiative funded under the EURATOM program. The project started on September 2022 and has a duration of 36 months. TANDEM stands for Small Modular ReacTor for a European sAfe aNd Decarbonized Energy Mix. Small Modular Reactors (SMRs) can be hybridized with other energy sources, storage systems and energy conversion applications to provide electricity, heat and hydrogen. Hybrid energy systems have the potential to strongly contribute to the energy decarbonization targeting carbon-neutrality in Europe by 2050. However, the integration of nuclear reactors, particularly SMRs, in hybrid energy systems, is a new R&D topic to be investigated. In this context, the TANDEM project aims to develop assessments and tools to facilitate the safe and efficient integration of SMRs into low-carbon hybrid energy systems. An open-source "TANDEM" model library of hybrid system components will be developed in Modelica language which, by coupling, will extend the capabilities of existing tools implemented in the project. The project proposes to specifically address the safety issues of SMRs related to their integration into hybrid energy systems, involving specific interactions between SMRs and the rest of the hybrid systems; new initiating events may have to be considered in the safety approach. TANDEM will study two hybrid systems covering the main trends of the European energy policy and market evolution at 2035's horizon: a district heating network and power supply in a large urban area, and an energy hub serving energy conversion systems, including hydrogen production; the energy hub is inspired from a harbor-like infrastructure. TANDEM will provide assessments on SMR safety, hybrid system operationality and techno-economics. Societal considerations will also be encased by analyzing European citizen engagement in SMR technology safety.

• International Journal of Computer Science & Network Security
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• v.23 no.12
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• pp.225-234
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• 2023

In this era of information and communication technology (ICT), tremendous improvements have been witnessed in our daily lives. The impact of these technologies is subjective and negative or positive. For instance, ICT has brought a lot of ease and versatility in our lifestyles, on the other hand, its excessive use brings around issues related to physical and mental health etc. In this study, we are bridging these both aspects by proposing the idea of AI based mental healthcare (AIMS). In this regard, we aim to provide a platform where the patient can register to the system and take consultancy by providing their assessment by means of a chatbot. The chatbot will send the gathered information to the machine learning block. The machine learning model is already trained and predicts whether the patient needs a treatment by classifying him/her based on the assessment. This information is provided to the mental health practitioner (doctor, psychologist, psychiatrist, or therapist) as clinical decision support. Eventually, the practitioner will provide his/her suggestions to the patient via the proposed system. Additionally, the proposed system prioritizes care, support, privacy, and patient autonomy, all while using a friendly chatbot interface. By using technology like natural language processing and machine learning, the system can predict a patient's condition and recommend the right professional for further help, including in-person appointments if necessary. This not only raises awareness about mental health but also makes it easier for patients to start therapy.

• The Research Journal of the Costume Culture
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• v.32 no.2
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• pp.247-259
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• 2024

This study assessed the effectiveness of brand image communication on consumer perceptions of cruelty-free fashion brands. Brand messaging data were gathered from postings on the official Instagram accounts of three cruelty-free fashion brands and consumer perception data were gathered from Tweets containing keywords related to each brand. Web crawling and natural language processing were performed using Python and sentiment analysis was conducted using the BERT model. By analyzing Instagram content from Stella McCartney, Patagonia, and Freitag from their inception until 2021, this study found these brands all emphasize environmental aspects but with differing focuses: Stella McCartney on ecological conservation, Patagonia on an active outdoor image, and Freitag on upcycled products. Keyword analysis further indicated consumers perceive these brands in line with their brand messaging: Stella McCartney as high-end and eco-friendly, Patagonia as active and environmentally conscious, and Freitag as centered on recycling. Results based on the assessment of the alignment between brand-driven images and consumer-perceived images and the sentiment evaluation of the brand confirmed the outcomes of brand communication performance. The study revealed a correlation between brand image and positive consumer evaluations, indicating that higher alignment of ethical values leads to more positive consumer assessments. Given that consumers tend to prioritize search keywords over brand concepts, it's important for brands to focus on using visual imagery and promotions to effectively convey brand communication information. These findings highlight the importance of brand communication by emphasizing the connection between ethical brand images and consumer perceptions.

• Journal of Distribution Science
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• v.22 no.4
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• pp.11-22
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• 2024

Purpose: This research delves into the various factors that influence the performance of restaurant businesses on social commerce platforms in Bangkok, Thailand. The study considers both internal and external factors, including but not limited to business characteristics and location. Moreover, this research also analyzes the effects of employing multiple social commerce platforms on business efficiency and explores the underlying reasons for such effects. Research design, data, and methodology: Restaurants can be classified into different price ranges: low, medium, and high. To further investigate, we employed natural language processing AI to analyze online reviews and evaluate algorithm performance using machine learning techniques. We aimed to develop a model to gauge customer satisfaction with restaurants across different price categories effectively. Results: According to the research findings, several factors significantly impact restaurant groups in the low and mid-price ranges. Among these factors are population density and the number of seats at the restaurant. On the other hand, in the mid-and high-price ranges, the price levels of the food and drinks offered by the restaurant play a crucial role in determining customer satisfaction. Furthermore, the correlation between different social commerce platforms can significantly affect the business performance of high-price range restaurant groups. Finally, the level of online review sentiment has been found to influence customer decision-making across all restaurant types significantly. Conclusions: The study emphasizes that restaurants' characteristics based on their price level differ significantly, and social commerce platforms have the potential to affect one another. It is worth noting that the sentiment expressed in online reviews has a more significant impact on customer decision-making than any other factor, regardless of the type of restaurant in question.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.24 no.4
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• pp.9-14
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• 2024

This study developed a voice recognition chatbot system to address depression and loneliness among the elderly in an aging society. The system utilizes the Whisper model, GPT 2.5, and XTTS2 to provide high-performance voice recognition, natural language processing, and text-to-speech conversion. Users can express their emotions and states and receive appropriate responses, with voice recognition functionality using familiar voices for comfort and reassurance. The UX/UI design considers the cognitive responses, visual impairments, and physical limitations of the smart senior generation, using high contrast colors and readable fonts for enhanced usability. This research is expected to improve the quality of life for the elderly through voice-based interfaces.

• The Journal of Advanced Prosthodontics
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• v.16 no.4
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• pp.201-211
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• 2024

PURPOSE. The aim of this prospective clinical study was to compare the influence of palatal vault forms on accuracy and speed of intraoral (IO) scans in completely edentulous cases. MATERIALS AND METHODS. Based on the palatal vault form, participants were divided into three equal groups (n = 10 each); Class I: moderate; Class II: deep; Class III: flat palatal vault. A reference model was created for each patient using polyvinylsiloxane impression material. The poured models were digitized using an extraoral scanner. The resultant data were imported as a solid CAD file into 3D analysis software (GOM Inspect 2018; Gom GmbH, Braunschweig, Germany) and aligned using the software's coordinate system to determine its X, Y, and Z axes. Five digital impressions (DIs) of maxilla were captured for each patient using an intraoral scanner (TRIOS; 3Shape A/S, Copenhagen, Denmark) and the resultant Standard Tessellation Language (STL) scan files served as test models. Trueness was evaluated by calculating arithmetic mean deviation (AMD) of the vault area between reference and test files while precision was evaluated by calculating AMD between captured scans to measure repeatability of scan acquisition. The scan time taken for each participant was also recorded. RESULTS. There was no significant difference in trueness and precision among the groups (P = .806 and .950, respectively). Average scan time for Class I and III palatal vaults was 1 min 13 seconds and 1 min 37 seconds, respectively, while class II deep palatal vaults showed the highest scan time of 5 mins. CONCLUSION. Palatal vault form in edentulous cases has an influence on scan time. However, it does not have a substantial impact on the accuracy of the acquired scans.

• Journal of Internet Computing and Services
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• v.25 no.4
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• pp.7-21
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• 2024

There are various machine learning techniques such as Reinforcement Learning, Deep Learning, Neural Network Learning, and so on. In recent, Large Language Models (LLMs) are popularly used for Generative AI based on Reinforcement Learning. It makes decisions with the most optimal rewards through the fine tuning process in a particular situation. Unfortunately, LLMs can not provide any explanation for how they reach the goal because the training is based on learning of black-box AI. Reinforcement Learning as black-box AI is based on graph-evolving structure for deriving enhanced solution through adjustment by human feedback or reinforced data. In this research, for mutually exclusive decision-making, Mutually Exclusive Learning (MEL) is proposed to provide explanations of the chosen goals that are achieved by a decision on both ends with specified conditions. In MEL, decision-making process is based on the tree-based structure that can provide processes of pruning branches that are used as explanations of how to achieve the goals. The goal can be reached by trade-off among mutually exclusive alternatives according to the specific contextual conditions. Therefore, the tree-based structure is adopted to provide feasible solutions with the explanations based on the pruning branches. The sequence of pruning processes can be used to provide the explanations of the inferences and ways to reach the goals, as Explainable AI (XAI). The learning process is based on the pruning branches according to the multi-dimensional contextual conditions. To deep-dive the search, they are composed of time window to determine the temporal perspective, depth of phases for lookahead and decision criteria to prune branches. The goal depends on the policy of the pruning branches, which can be dynamically changed by configured situation with the specific multi-dimensional contextual conditions at a particular moment. The explanation is represented by the chosen episode among the decision alternatives according to configured situations. In this research, MEL adopts the tree-based learning model to provide explanation for the goal derived with specific conditions. Therefore, as an example of mutually exclusive problems, employment process is proposed to demonstrate the decision-making process of how to reach the goal and explanation by the pruning branches. Finally, further study is discussed to verify the effectiveness of MEL with experiments.

• Journal of Intelligence and Information Systems
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• v.26 no.3
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• pp.109-125
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• 2020

The Ministry of National Defense is pushing for the Defense Acquisition Program to build strong defense capabilities, and it spends more than 10 trillion won annually on defense improvement. As the Defense Acquisition Program is directly related to the security of the nation as well as the lives and property of the people, it must be carried out very transparently and efficiently by experts. However, the excessive diversification of laws and regulations related to the Defense Acquisition Program has made it challenging for many working-level officials to carry out the Defense Acquisition Program smoothly. It is even known that many people realize that there are related regulations that they were unaware of until they push ahead with their work. In addition, the statutory statements related to the Defense Acquisition Program have the tendency to cause serious issues even if only a single expression is wrong within the sentence. Despite this, efforts to establish a sentence comparison system to correct this issue in real time have been minimal. Therefore, this paper tries to propose a "Comparison System between the Statement of Military Reports and Related Laws" implementation plan that uses the Siamese Network-based artificial neural network, a model in the field of natural language processing (NLP), to observe the similarity between sentences that are likely to appear in the Defense Acquisition Program related documents and those from related statutory provisions to determine and classify the risk of illegality and to make users aware of the consequences. Various artificial neural network models (Bi-LSTM, Self-Attention, D_Bi-LSTM) were studied using 3,442 pairs of "Original Sentence"(described in actual statutes) and "Edited Sentence"(edited sentences derived from "Original Sentence"). Among many Defense Acquisition Program related statutes, DEFENSE ACQUISITION PROGRAM ACT, ENFORCEMENT RULE OF THE DEFENSE ACQUISITION PROGRAM ACT, and ENFORCEMENT DECREE OF THE DEFENSE ACQUISITION PROGRAM ACT were selected. Furthermore, "Original Sentence" has the 83 provisions that actually appear in the Act. "Original Sentence" has the main 83 clauses most accessible to working-level officials in their work. "Edited Sentence" is comprised of 30 to 50 similar sentences that are likely to appear modified in the county report for each clause("Original Sentence"). During the creation of the edited sentences, the original sentences were modified using 12 certain rules, and these sentences were produced in proportion to the number of such rules, as it was the case for the original sentences. After conducting 1 : 1 sentence similarity performance evaluation experiments, it was possible to classify each "Edited Sentence" as legal or illegal with considerable accuracy. In addition, the "Edited Sentence" dataset used to train the neural network models contains a variety of actual statutory statements("Original Sentence"), which are characterized by the 12 rules. On the other hand, the models are not able to effectively classify other sentences, which appear in actual military reports, when only the "Original Sentence" and "Edited Sentence" dataset have been fed to them. The dataset is not ample enough for the model to recognize other incoming new sentences. Hence, the performance of the model was reassessed by writing an additional 120 new sentences that have better resemblance to those in the actual military report and still have association with the original sentences. Thereafter, we were able to check that the models' performances surpassed a certain level even when they were trained merely with "Original Sentence" and "Edited Sentence" data. If sufficient model learning is achieved through the improvement and expansion of the full set of learning data with the addition of the actual report appearance sentences, the models will be able to better classify other sentences coming from military reports as legal or illegal. Based on the experimental results, this study confirms the possibility and value of building "Real-Time Automated Comparison System Between Military Documents and Related Laws". The research conducted in this experiment can verify which specific clause, of several that appear in related law clause is most similar to the sentence that appears in the Defense Acquisition Program-related military reports. This helps determine whether the contents in the military report sentences are at the risk of illegality when they are compared with those in the law clauses.

• Cartoon and Animation Studies
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• s.37
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• pp.107-142
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• 2014

The purpose of this study is to examine the effects of the teaching English factors on student's communicative competence and motivation by using animation at the College. To achieve this purpose, this study presented an effective integrative teaching model to develop students communicative competence. The study created animation based teaching English model by using the animation of Frozen and applied it to lectures. Using animation in the classroom was a creative English teaching technique involving authentic activities like English dram, English guide contest, and various communicative activities A case study on the use of the animation in English classes at was examined and the language teaching syllabus were provided. In order to investigate the motivation and proficiency of learners, the writer chose 79 students who took the lecture. The study discovered the students' motivation and proficiency in English improved significantly. The results of experiment are as follows: First, using animation in the English class was found to have meaningful influence student's intrinsic motivation to learn English. Second, using animation in the English class was found to be effective for developing student's English proficiency. Third, appropriate materials should be selected and applied it to the real classroom activities. In conclusion, one of disadvantages of learning is less communication and the authentic interaction in a real life, so that the integrative teaching methodology which is combined English content and English animation content is also the effective method to improve student's intrinsic motivations in the age of global village.

• Sijohaknonchong
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• v.20
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• pp.141-171
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• 2004

Even though only 3 sijo are in high school textbook. through these 3 sijo each type can be understood in that each represents pyung sijo, sasul sijo, and present sijo. To learn with learner-centered activities, which aim for full knowledge acquisition regarding literary works, as the preparing stage, students can learn what theyll learn by teachers. Sijo are, so to speak, formed with three chapters, and stand for the world that is colorless, scentless, and flavorless. So, the theme can be found with ease. Compared with other genres, sijo can be formed creating background with ease. Moreover, sijo are not too long, so learners can paraphrase it. Sijo that express private experiences with the everyday language can be related to other genres or everyday language. So, sijo are last to present. In the teaching phase, on the gradation of concretion and gradation, writing or presentation activities are presented. After classroom, learners keep a reaction journal. In the phase of concretion and gradation, learners can apprehend that typical differences of the emotions of poetic speakers is from typical differences, even though emotions of poetic speakers of (1)$\cdot$(2)$\cdot$(3) that is each stand for pyung sijo, sasul sijo, and present sijo are roughly summarized loneliness, desolateness, and gloominess. Moreover, these typical differences are from social, political. and cultural settings, namely, the differences of contexts. In this teaching model. learners should prepare for content regarding context and text before the class. Teachers should act as an assistant to help learners pre-understand their subjective experiences and imaginations.