ABSTRACT
Owing to the COVID-19 outbreak, sterilization of deep-ultraviolet light-emitting diodes (DUV LEDs) has attracted increasing attention. Effectively improving the radiative recombination efficiency and mitigating the efficiency degradation, mainly caused by electron leakage and nonradiative recombination, have also emerged as two of the main issues to be addressed. In this study, a DUV LED epitaxial structure with a novel electron-blocking layer (EBL) is proposed. The DUV LED with a luminescence wavelength of similar to 297 nm was formed by the stepwise variation of the Al component. Through the simulation and analysis of its performance parameters, we found that, compared to the conventional EBL structure, this new EBL structure not only reduces the electron leakage to the p-region effectively but also increases the hole injection into the active region, resulting in an increase in carrier concentration in the active region, a two-to-three-fold increase in the radiative recombination rate, and a 58% increase in the internal quantum efficiency, thus alleviating the efficiency droop and achieving a more efficient operation at high current densities. (C) 2022 Author(s).
ABSTRACT
Cybercriminals often register many pornographic or gambling domains (known as abusive domains) with similar character compositions in bulk to reduce their investment in buying domains and make it easier for clients to remember and spread them. Therefore, this study combines the ideas of text similarity and text generation and proposes an abusive domain generation model based on GRU for rapidly generating new abusive domain names from known ones. Additionally, we develop a two-layer detection system for pornography and gambling domains using fastText and CNN models to obtain an abusive domain dataset for model training and validation. In the end, our detection system identifies pornographic and gambling domains with 99% precision while balancing correctness and speed. By inputting 40,000 random keywords into the abusive domain generation model, we obtained 130,220 online domains that served web pages, of which about 66% were pornographic or gambling domains. The results show that by exploiting cybercriminals' behaviors in registering abusive domain names, such as bulk registration of similar domain names, we can prospectively acquire a large number of new abusive domains based on known ones. This study demonstrates that predicting new abusive domains not only expands the domain blacklist but also allows researchers to target the generated suspicious domains and dispose of them in time before they show abusive behavior.
ABSTRACT
In the context of the COIVD-19 pandemic, everyone must maintain social distances in public. Our ability to perceive each other through "distances" in public places has been compromised. However, "distance" is an essential factor in communication that cannot be ignored, like "facial expressions" and "body movements". This paper reflects on the current fixed "social distancing" in the context of the current viral pandemic. The innovative point of our research is to detect and calculate the differences of brainwave signal data between two people and visualize the differences through programmed 2D moving images. In terms of the research process, first, we explored a new way of interaction, using brainwave signals to express "distances" and moire patterns as visual representations. Then we wrote an algorithm to generate the dynamic responses of the moire patterns to different stimuli in real-time to represent the concepts of distances and visualize people's reactions. Finally, we developed an interactive device to imagine "electroencephalography (EEG) signal discrepancy" to perceive the "distances" in social situations. Nowadays, online meetings, classes, etc., are becoming more and more popular, and the distances between people in virtual spaces will be more ambiguous. In light of this, we plan to explore the visualization of electroencephalography (EEG) signal discrepancy in remote communication to enhance people's perceptions of each other in the future.
ABSTRACT
Rarely in recent history has there been a challenge to public health as great as the COVID-19 pandemic. With newer cases still being reported on a daily basis, the media has maintained close track of the development of the outbreak in their news reports. This study analyzes representations of the European Union and the United States in China Daily, one of the major Chinese state media, through a content analysis of 88 news reports concerning the pandemic situations in 2020 from a broad perspective without explicitly elucidating the individual differences of the countries and states within these two global players. It reveals that China Daily represents the EU and the US differently within the global context of the COVID-19 pandemic, particularly in the intensity of the outbreak, coordination and response, and most notably, their amicable and antagonistic relations with China. The results of the analysis epitomize China's interactions with major global actors from the West, which are increasingly shifting away from the traditional "East versus West" dichotomy, and the EU and the US are viewed as differentiated "others" from China's perspective according to the reports involved in this study.
ABSTRACT
The COVID-19 pandemic has received extensive media coverage, with a vast variety of claims made about different aspects of the virus. In order to track these claims, we present COVID-19 Claim Radar(1), a system that automatically extracts claims relating to COVID-19 in news articles. We provide a comprehensive structured view of such claims, with rich attributes (such as claimers and their affiliations) and associated knowledge elements (such as events, relations and entities). Further, we use this knowledge to identify inter-claim connections such as equivalent, supporting, or refuting relations, with shared structural evidence like claimers, similar centroid events and arguments. In order to consolidate claim structures at the corpus-level, we leverage Wikidata(2) as the hub to merge coreferential knowledge elements, and apply machine translation to aggregate claims from news articles in multiple languages. The system provides users with a comprehensive exposure to COVID-19 related claims, their associated knowledge elements, and related connections to other claims. The system is publicly available on GitHub(3) and DockerHub(4), with complete documentation(5).
ABSTRACT
The global financial market shocks have intensified due to the COVID-19 epidemic and other impacts, and the impacts of economic policy uncertainty on the financial system cannot be ignored. In this paper, we construct asymmetric risk spillover networks of Chinese financial markets based on five sectors: bank, securities, insurance, diversified finance, and real estate. We investigate the complexity of the risk spillover effect of Chinese financial markets and the impact of economic policy uncertainty on the level of network contagion of financial risk. The study yields three findings. First, the cross-sectoral risk spillover effects of Chinese financial markets are asymmetric in intensity. The bank sector is systemically important in the risk spillover network. Second, the level of risk stress in the real estate sector has increased in recent years, and it plays an important role in the path of financial risk contagion. Third, Economic policy uncertainty has a significant positive impact on the level of network contagion of financial risk of Chinese financial markets.
ABSTRACT
In the face of the sudden covid-19 outbreak, the Ministry of Education pointed out that we should promote the application of the"three types of classrooms"in Working Focus for 2020. Sichuan Technology and Business University earnestly implements the overall scheme of the provincial department of education on epidemic prevention and control, and puts forward the "three groups in the cloud"ideology model centering on the overall work of the school and the epidemic situation. According to this model, teachers take active actions to strengthen the teaching reform, promote the block chain technology, upgrade the MOOC to SPOC, strengthen in-class practice teaching and the construction of "three teams"of extracurricular practice teaching and improve the quality of teaching. © 2020 IEEE.