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A Form Evaluation System and Its data structure for Brush-Written Chinese Characters

Journal of Computer Science & Technology 1995年第1期10卷 35-41页

作者：曾建超 HidehikoSanada YoshikazuTezuka Laboratory of Information Processing and Its Applications Department of Computer Science & Technology Tsinghua University Beijing Department of Communication Engineering Faculty of Engineering Osaka University Suita City Japan

A form evaluation system for brush-written Chinese characters is *** knowledge used in the system is represented in the form of ruleswith the help of a data structure proposed in this paper. Reflecting the spe-cific hierarchical relations among radicals and strokes of Chinese characters,the proposed data structure is based upon a character model that can generatebrush-written Chinese characters on a computer. Through evaluation experi-ments using the developed system, it is shown that representation of calligraphicknowledge and form evaluation of Chinese characters can be smoothly realizedif the data structure is utilized.

关键词： Brush-written Chinese characters form evaluation calligraphic knowledge hierarchical data structure character generation model

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Predictors of the Aggregate of COVID-19 Cases and Its Case-Fatality: A Global Investigation Involving 120 Countries

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Open Journal of Statistics 2021年第2期11卷 259-277页

作者： Sarah Al-Gahtani Mohamed Shoukri Maha Al-Eid Department of Internal Medicine King Fahd Medical City Riyadh Saudi Arabia Department of Epidemiology and Biostatistics Schulich School of Medicine and Dentistry University of Western Ontario London Ontario Canada Department of Biostatistics Epidemiology and Scientific Computing King Faisal Specialist Hospital and Research Center Riyadh Saudi Arabia

Objective: Since the identification of COVID-19 in December 2019 as a pandemic, over 4500 research papers were published with the term “COVID-19” contained in its title. Many of these reports on the COVID-19 pandemic suggested that the coronavirus was associated with more serious chronic diseases and mortality particularly in patients with chronic diseases regardless of country and age. Therefore, there is a need to understand how common comorbidities and other factors are associated with the risk of death due to COVID-19 infection. Our investigation aims at exploring this relationship. Specifically, our analysis aimed to explore the relationship between the total number of COVID-19 cases and mortality associated with COVID-19 infection accounting for other risk factors. Methods: Due to the presence of over dispersion, the Negative Binomial Regression is used to model the aggregate number of COVID-19 cases. Case-fatality associated with this infection is modeled as an outcome variable using machine learning predictive multivariable regression. The data we used are the COVID-19 cases and associated deaths from the start of the pandemic up to December 02-2020, the day Pfizer was granted approval for their new COVID-19 vaccine. Results: Our analysis found significant regional variation in case fatality. Moreover, the aggregate number of cases had several risk factors including chronic kidney disease, population density and the percentage of gross domestic product spent on healthcare. The Conclusions: There are important regional variations in COVID-19 case fatality. We identified three fac

关键词： Intraclass Correlation Coefficient hierarchical data structure Negative Binomial Regression data Splitting Mixed Effects Linear Regression Model

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