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MOOCs (massive open online courses) have attracted considerable attention from researchers. Fueled by constant change and developments in educational technology, the trends of MOOCs have varied greatly over the years. To detect and visualize the developments and changes in MOOC research, 4,652 articles published between 2009 and 2021 were retrieved from Web of Science and Scopus with the aid of CiteSpace. This study sought to explore the number of publications, co-citation network, cluster analysis, timeline analysis, burstness analysis, and dual-map overlays based on co-citation relationships. The first finding was that the number of publications on MOOCs had increased consistently, and grew especially quickly between 2013 and 2015. Second, the main topic of the top 10 co-cited studies revolved around the problem of learner continuance. Third, blended programs, task-technology fit, and comparative analysis have emerged as popular subjects. Fourth, the development of MOOC research has followed distinct phases, with 2009 to 2012 the starting phase, 2013 to 2015 the high growth phase, 2016 to 2018 the plateau phase, and 2019 to 2021 another peak phase. Lastly, both cluster analysis and dual-map overlays provided empirical evidence of cross-disciplinary research. Our findings provided an in-depth and dynamic understanding of the development and evolution of MOOC research and also proposed novel ideas for future studies.

Considering the importance of group member familiarity in collaborative learning in classroom learning environments, this study examined the impact of group member familiarity on CSCL (computer-supported collaborative learning) in a networked setting. Also, the differences between CSCL in the online environments and FtF (face-to-face) collaborative learning were also compared. An analysis using structural equation modeling revealed that group member familiarity increased teamwork satisfaction, which in turn increased student engagement and perceived knowledge construction. A multi-group analysis revealed that while FtF collaborative learning exhibits higher levels of group member familiarity, teamwork satisfaction, student engagement, and perceived knowledge construction, the mediating effect of teamwork satisfaction was more pronounced in online settings. The study findings provided insights for teachers to improve collaborative learning experiences and adapt different teaching strategies.

Chatbots have shown great potential for language learning. However, previous studies have reported mixed results on the efficiency of chatbot-assisted language learning (CALL). This study integrated the results of previous experimental studies on CALL by using meta-analysis to explore its effectiveness. A total of 61 samples from 18 studies were examined. The results showed that CALL had a moderate average effect (g = 0.527). In addition, nine potential moderating variables (educational level, target language, language domain, learning outcome, instruction duration, chatbot interface, chatbot development, task dominance, and interaction way) were identified and discussed. The results of this study provided insights into the use and design of chatbots for language learning.

Introduction: The present study examines how community disaster resilience and social solidarity concerning the Sewol ferry disaster, one of South Korea's most tragic maritime disasters that occurred on April 16, 2014, were portrayed on and manifested by social media during a 1-year span at three time periods. Method: Data was collected from Korean social media platforms Naver blog and Naver café regarding the keyword 'Sewol ferry' during three time periods: April 16, 2014 to May 16, 2014, October 1, 2014 to October 31, 2014 and March 16, 2015 to April 16, 2015. Analysis: A semantic network analysis was conducted on the data collected to show the most significant words pertaining to the Sewol ferry disaster. Results: During the first period, people used social media to share news information and factual information, including some words indicating sympathy, positive emotions and hopeful sentiments. For the second period, the central words were similar to those of the first period, but more optimistic and emotional. In the last period, a noticeable change in the central words was found for the community's reminiscence and commemoration of the victims and their families' grief. Conclusions: Words relevant to community disaster resilience and social solidarity were revealed on social media. Furthermore, there was a significant change in the sentiments of the community, as well as the way of forming resilience and solidarity over time.

The insulated gate bipolar transistor (IGBT) is crucial in high-voltage applications due to its characteristics, like breakdown voltage (BV) and on-state voltage V CE(sat). However, its slower turn-off time, attributed to hole mobility, restricts its frequency range. Techniques such as the carrier storage layer (CSL) and super-junction (SJ) structures aim to optimize BV and V CE(sat) through hole density and field distribution. Combining CSL and SJ offers advantages, yet challenges remain regarding E-field concentration. In this work, the split CSL concept introduces a solution by optimizing BV and E off through effective field distribution and hole extraction acceleration respectively while maintaining V CE(sat). Split CSL, which is divided into a high doping layer (HDL) and a low doping layer (LDL), reduces the burden on the gate oxide by distributing the E-field evenly when in the off-state due to the difference in doping concentration. During the turn-off, the hole current is concentrated on the LDL, which has relatively low resistance, thereby accelerating hole extraction. Simulation-based results showcase improvements in the proposed structure's properties. Further optimization of HDL and LDL concentrations enhances the structure's performance. It is clear that the split CSL structure presents the potential for advancing IGBT capabilities. The application of the split CSL structure resulted in significant improvements: the turn-off time was reduced by 32.4% and the BV increased by 32.5 V compared to conventional CSL-SJ structures. These enhancements highlight the effectiveness of the split CSL design in optimizing the IGBT's performance attribute. [ABSTRACT FROM AUTHOR]

This study presents a comprehensive exploration of topic modeling methods tailored for large language model (LLM) using data obtained from Web of Science and LexisNexis from June 1, 2020, to December 31, 2023. The data collection process involved queries focusing on LLMs, including "Large language model," "LLM," and "ChatGPT." Various topic modeling approaches were evaluated based on performance metrics, including diversity and coherence. latent Dirichlet allocation (LDA), nonnegative matrix factorization (NMF), combined topic models (CTM), and bidirectional encoder representations from Transformers topic (BERTopic) were employed for performance evaluation. Evaluation metrics were computed across platforms, with BERTopic demonstrating superior performance in diversity and coherence across both LexisNexis and Web of Science. The experiment result reveals that news articles maintain a balanced coverage across various topics and mainly focus on efforts to utilize LLM in specialized domains. Conversely, research papers are more concise and concentrated on the technology itself, emphasizing technical aspects. Through the insights gained in this study, it becomes possible to investigate the future path and the challenges that LLMs should tackle. Additionally, they could offer considerable value to enterprises that utilize LLMs to deliver services. [ABSTRACT FROM AUTHOR]

In this study, a frequency doubler that consists of a tunnel field-effect transistor (TFET) with dual pocket doping is proposed, and its operation is verified using technology computer-aided design (TCAD) simulations. The frequency-doubling operation is important to having symmetrical current characteristics, which eliminate odd harmonics and the need for extra filter circuitry. The proposed TFET has intrinsically bidirectional and controllable currents that can be implemented by pocket doping, which is located at the junction between the source/drain (S/D) and the channel region, to modify tunneling probabilities. The source-to-channel (ISC) and channel-to-drain currents (ICD) can be independently changed by managing each pocket doping concentration on the source and drain sides (NS,POC and ND,POC). After that, the current matching process was investigated through NS,POC and ND,POC splits, respectively. However, it was found that the optimized doping condition achieved at the device level (namely, a transistor evaluation) is not suitable for a frequency doubler operation because the voltage drop generated by a load resistor in the frequency doubler circuit configuration causes the currents to be unbalanced between ISC and ICD. Therefore, after symmetrical current matching was performed by optimizing NS,POC and ND,POC at the circuit level, it was clearly seen that the output frequency was doubled in comparison to the input sinusoidal signal. In addition, the effects of the S/D and pocket doping variations that can occur during process integration were investigated to determine how much frequency multiplications are affected, and these variations have the immunity of S/D doping and pocket doping length changes. Furthermore, the impact of device scaling with gate length (LG) variations was evaluated. Based on these findings, the proposed frequency doubler is anticipated to offer benefits for circuit design and low-power applications compared to the conventional one. [ABSTRACT FROM AUTHOR]

The science of social network analysis has co-evolved with the development of online environments and computer-mediated communication. Unique and precise data available from computer and information systems have allowed network scientists to explore novel social phenomena and develop new methods. Additionally, advances in the structural analysis and visualization of computer-mediated social networks have informed developers and shaped the design of social media tools. This article reviews some examples of research that highlight the ways that social network analysis has evolved with online data. Examples include the international hyperlink network, political blogs and hyperlinks, social media, and multi-user virtual environments. The data available from online environments makes several important contributions to network science, including reliable network flow data, unique forms of relational data across a myriad of contexts, and dynamic data allowing for longitudinal analysis and the animation of social networks.

The use of a second screen can enhance information processing and the execution of search tasks within a given period. In this study, we examined the learner's attentional shift (AS) between two screens and controlled secondary tasks (STs) in the media multitasking setting and its effect on the learning process. In particular, we analyzed how cognitive and emotional learning effects can be explored depending on the role of this setting. A between-subject experiment was conducted with 69 participants (simultaneous vs. sequential use of a second screen in terms of open versus closed STs). The findings showed that there was a greater difference in emotional learning effects than in cognitive learning effects for participants who used a second screen. In terms of estimated emotional learning effects of media multitasking, participants engaged in sequential use demonstrated lower anxiety levels and higher competence beliefs during learning compared to participants who were engaged in simultaneous use. Moreover, an interaction effect occurred in the AS between the two screens and in controlled STs of recognition memory. These results indicate that attentional change and controlled STs are key predictors of learning effects in the media multitasking environment.

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To stay competitive, retail companies have introduced innovative retailing strategies for enhancing the consumer experience. Among these, omnichannel retailing services are one of the most critical services. However, studies on the loyalty required to secure the market using omnichannel strategies are limited. Instead of arbitrarily selecting key factors for users' loyalty based on previous research data used by users in social networking services were evaluated in this study, and the validity of the selection of variables was verified through network analysis. A structural equation model based on the results of network analysis and the factors influencing online loyalty and offline loyalty were analysed using the model. Therefore, independent variables except for perceived usability exhibited a positive effect on user satisfaction, and user satisfaction had a positive effect on loyalty. Furthermore, offline user satisfaction exhibited a positive effect on online loyalty, but online user satisfaction did not. Suggestions for future research and corporate strategy were provided. [ABSTRACT FROM AUTHOR]

Growing expectations and interest in the metaverse have increased the need to explore the public hype. This study measured the hype in the South Korean metaverse context and analyzed its temporal pattern. To this end, 129,032 tweets from Korean users who used the "metaverse" keyword were collected, and 86,901 tweets were analyzed. Using BERT-based topic modeling, a content analysis was performed. The extracted topics were classified into three expectation frameworks: specific expectations, generalized expectations, and frames. Our results imply that the pre-emptive inflation of expectations by the Korean government caused the public's excessive expectations of the metaverse. Additionally, by using Twitter as a source for analyzing user-perceived hype, it was confirmed that the public responds to the expectations of other actors about the technology rather than expecting the technology itself. Furthermore, pronounced hype dynamics were observed by analyzing the distribution of topics over time. [ABSTRACT FROM AUTHOR]

ABSTRACT Background: The bulb of Fritillaria thunbergii has been utilised as mucoregulators and expectorants for controlling the airway inflammatory diseases in folk medicine. Hypothesis/Purpose: We investigated whether verticine, ebeiedine and [...]

An improved structure for an Insulated Gate Bipolar Transistor (IGBT) with a separated buffer layer is presented in order to improve the trade-off between the turn-off loss (Eoff) and on-state voltage (Von). However, it is difficult to set efficient parameters due to the increase in the new buffer doping concentration variable. Therefore, a machine learning (ML) algorithm is proposed as a solution. Compared to the conventional Technology Computer-Aided Design (TCAD) simulation tool, it is demonstrated that incorporating the ML algorithm into the device analysis could make it possible to achieve high accuracy and significantly shorten the simulation time. Specifically, utilizing the ML algorithm could achieve coefficients of determination (R2) of Von and Eoff of 0.995 and 0.968, respectively. In addition, it enables the optimized design to fit the target characteristics. In this study, the structure proposed for the trade-off improvement was targeted to obtain the minimum Eoff at the same Von, especially by adjusting the concentration of the separated buffer. We could improve Eoff by 36.2% by optimizing the structure, which was expected to be improved by 24.7% using the ML approach. In another way, it is possible to inversely design four types of structures with characteristics close to the target characteristics (Eoff = 1.64 μJ, Von = 1.38 V). The proposed method of incorporating machine learning into device analysis is expected to be very strategic, especially for power electronics analysis (where the transistor size is comparatively large and requires significant computation). In summary, we improved the trade-off using a separated buffer, and ML enabled optimization and a more precise design, as well as reverse engineering. [ABSTRACT FROM AUTHOR]

In this paper, a crown-shaped trench gate formed by a sidewall spacer in insulated gate bipolar transistors (IGBT) is proposed to improve breakdown voltage. When a sidewall spacer is added to trench bottom corners, the electric field is distributed to the surface of the sidewall spacer and decreased to 48% peak value of the electric field. Thus, the sidewall spacer IGBT improved to 5% breakdown voltage. Another study proposed an additional oxide layer for trench bottom corners and improved breakdown voltage similar to the proposed IGBT. Previous studies have shown degradation in other electrical characteristics. However, this study shows a sidewall spacer IGBT that increases the current over 3% compared to a conventional trench IGBT when the applied gate voltage is under 4 V. Additionally, the turn-off loss characteristic is similar to conventional trench IGBT. Therefore, the breakdown voltage of the IGBT was improved while maintaining similar electrical properties to existing IGBTs through the crown-shaped gate. [ABSTRACT FROM AUTHOR]

Sleep disturbance is prevalent in patients with degenerative spinal disease, and recent studies have reported that surgical treatment is more effective for improving sleep quality than conservative treatment. We aimed to investigate the perioperative changes of sleep problems in patients who underwent surgical treatment for degenerative spinal disease with a concurrent sleep disorder, and presented them according to various clinical profiles possibly associated with sleep disturbance. In addition, we identified factors associated with poor sleep improvement after surgery. This study used data from the Korea Health Insurance Review and Assessment Service database from 2016 to 2018. We included 3183 patients aged ≥19 years who underwent surgery for degenerative spinal disease and had a concurrent sleep disorder. Perioperative changes in the two target outcomes, including the use of sleep medication and hospital visits owing to sleep disorders, were precisely investigated according to factors known to be associated with sleep disturbance, including demographics, comorbidities, and spinal regions. Logistic regression analysis was performed to identify factors associated with poor improvement in terms of sleep medication after surgery. All estimates were validated using bootstrap sampling. During the 1-year preoperative period, the use of sleep medications and hospital visits owing to sleep disorder increased continuously. However, they abruptly decreased shortly after surgical treatment, and throughout the 1-year postoperative period, they remained lower than those in the late preoperative period. At the 1-year follow-up, 75.6% (2407 of 3183) of our cohort showed improvement in sleep medication after surgery. Multivariable analysis identified only two variables as significant factors associated with non-improvement in sleep medication after surgery: depressive disorder (odds ratio (OR) = 1.25 [1.06–1.48]; p = 0.008), and migraine (OR = 1.42 [1.04–1.94]; p = 0.028). We could not investigate the actual sleep quality and resultant quality of life; however, our results justify the necessity for further high-quality studies that include such information and would arouse clinicians' attention to the importance of sleep disturbance in patients with degenerative spinal disease. [ABSTRACT FROM AUTHOR]