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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]

Although previous studies have highlighted the problematic artificial intelligence (AI) usage behaviors in educational contexts, such as overreliance on AI, no study has explored the antecedents and potential consequences that contribute to this problem. Therefore, this study investigates the causes and consequences of AI dependency using ChatGPT as an example. Using the Interaction of the Person-Affect-Cognition-Execution (I-PACE) model, this study explores the internal associations between academic self-efficacy, academic stress, performance expectations, and AI dependency. It also identifies the negative consequences of AI dependency. Analysis of data from 300 university students revealed that the relationship between academic self-efficacy and AI dependency was mediated by academic stress and performance expectations. The top five negative effects of AI dependency include increased laziness, the spread of misinformation, a lower level of creativity, and reduced critical and independent thinking. The findings provide explanations and solutions to mitigate the negative effects of AI dependency. [ABSTRACT FROM AUTHOR]

Prior research has explored the impact of diverse anthropomorphic interventions on the effectiveness of AI (artificial intelligence) instructors. However, the exploration of interpersonal communication skills (e.g., self-disclosure) as anthropomorphic conversational cues for AI instructors is rare. Considering the positive impact of the self-disclosure of human instructors and guided by the social penetration theory (Altman & Taylor, 1973) and computers are social actors (CASA) paradigm (Nass & Moon, 2000), this study explores the role of self-disclosure by AI instructors and the mediating role of emotional attachment between AI instructors' self-disclosure and students' learning experiences (learning interest and knowledge gain). Additionally, it examines the differences in students' emotional attachment, learning interest, and knowledge gain between AI and human instructors. Through a 2 (AI instructor vs. human instructor) × 2 (self-disclosure: yes or no) experiment, this study concluded that 1) consistent with human instructors, self-disclosure by AI instructors led to higher emotional attachment, learning interest, and knowledge gain; 2) emotional attachment played an important mediating role in AI instructor self-disclosure and students' learning interest and knowledge gain; and 3) in the context of self-disclosure, students exhibited similar levels of emotional attachment to both AI and human instructors, with no significant differences observed. Regarding learning outcomes, while students demonstrated a greater interest in learning during courses taught by AI instructors, the difference in knowledge gained from AI and human instructors was not significant. The results of this study contribute to the understanding of the anthropomorphic cues of AI instructors and provide recommendations and insights for the future use of AI instructors in educational settings. [ABSTRACT FROM AUTHOR]

Background: Intermediate cell carcinoma (Int-CA) is a rare and enigmatic primary liver cancer characterized by uniform tumor cells exhibiting mixed features of both HCC and intrahepatic cholangiocarcinoma. Despite the unique pathological features of int-CA, its molecular characteristics remain unclear yet., Methods: RNA sequencing and whole genome sequencing profiling were performed on int-CA tumors and compared with those of HCC and intrahepatic cholangiocarcinoma., Results: Int-CAs unveiled a distinct and intermediate transcriptomic feature that is strikingly different from both HCC and intrahepatic cholangiocarcinoma. The marked abundance of splicing events leading to intron retention emerged as a signature feature of int-CA, along with a prominent expression of Notch signaling. Further exploration revealed that METTL16 was suppressed within int-CA, showing a DNA copy number-dependent transcriptional deregulation. Notably, experimental investigations confirmed that METTL16 suppression facilitated invasive tumor characteristics through the activation of the Notch signaling cascade., Conclusions: Our results provide a molecular landscape of int-CA featured by METTL16 suppression and frequent intron retention events, which may play pivotal roles in the acquisition of the aggressive phenotype of Int-CA., (Copyright © 2024 The Author(s). Published by Wolters Kluwer Health, Inc. on behalf of the American Association for the Study of Liver Diseases.)

As sustainability emerges as a crucial factor in the development of modern enterprises, integrating environmental, social, and governance (ESG) information into financial assessments has become essential. ESG indicators serve as important metrics in evaluating a company's sustainable practices and governance effectiveness, influencing investor trust and future growth potential, ultimately affecting stock prices. This study proposes an innovative approach that combines ESG sentiment index extracted from news with technical indicators to predict the S&P 500 index. By utilizing a deep learning model and exploring optimal window sizes, the study explores the best model through mean absolute percentage error (MAPE) as an evaluation metric. Additionally, an ablation test clarifies the influence of ESG and its causality with the S&P 500 index. The experimental results demonstrate improved predictive accuracy when considering ESG sentiment compared to relying solely on technical indicators or historical data. This comprehensive methodology enhances the advantage of stock price prediction by integrating technical indicators, which consider short-term fluctuations, with ESG information, providing long-term effects. Furthermore, it offers valuable insights for investors and financial market experts, validating the necessity to consider ESG for financial assets and introducing a new perspective to develop investment strategies and decision-making processes., (© 2024. The Author(s).)