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Ha-Linh Quach,Thai Quang Pham,Ngoc-Anh Hoang,Dinh Cong Phung,Viet-Cuong Nguyen,Son Hong Le,Thanh Cong Le,Dang Hai Le,Anh Duc Dang,Duong Nhu Tran,Nghia Duy Ngu,Florian Vogt,Cong-Khanh Nguyen 대한의료정보학회 2022 Healthcare Informatics Research Vol.28 No.4
Objectives: Online misinformation has reached unprecedented levels during the coronavirus disease 2019 (COVID-19) pandemic. This study analyzed the magnitude and sentiment dynamics of misinformation and unverified information about public health interventions during a COVID-19 outbreak in Da Nang, Vietnam, between July and September 2020. Methods: We analyzed user-generated online information about five public health interventions during the Da Nang outbreak. We compared the volume, source, sentiment polarity, and engagements of online posts before, during, and after the outbreak using negative binomial and logistic regression, and assessed the content validity of the 500 most influential posts. Results: Most of the 54,528 online posts included were generated during the outbreak (n = 46,035; 84.42%) and by online newspapers (n = 32,034; 58.75%). Among the 500 most influential posts, 316 (63.20%) contained genuine information, 10 (2.00%) contained misinformation, 152 (30.40%) were non-factual opinions, and 22 (4.40%) contained unverifiable information. All misinformation posts were made during the outbreak, mostly on social media, and were predominantly negative. Higher levels of engagement were observed for information that was unverifiable (incidence relative risk [IRR] = 2.83; 95% confidence interval [CI], 1.33–0.62), posted during the outbreak (before: IRR = 0.15; 95% CI, 0.07–0.35; after: IRR = 0.46; 95% CI, 0.34-0.63), and with negative sentiment (IRR = 1.84; 95% CI, 1.23–2.75). Negatively toned posts were more likely to be misinformation (odds ratio [OR] = 9.59; 95% CI, 1.20–76.70) or unverified (OR = 5.03; 95% CI, 1.66–15.24). Conclusions: Misinformation and unverified information during the outbreak showed clustering, with social media being particularly affected. This indepth assessment demonstrates the value of analyzing online “infodemics” to inform public health responses.
Nghi Do Huu,Kellner Harald,Büttner Enrico,Huong Le Mai,Duy Le Xuan,Giap Vu Dinh,Quynh Dang Thu,Hang Tran Thi Nhu,Verberckmoes An,Diels Ludo,Liers Christiane,Hofrichter Martin 한국응용생명화학회 2021 Applied Biological Chemistry (Appl Biol Chem) Vol.64 No.5
From the biotechnological viewpoint, the enzymatic disintegration of plant lignocellulosic biomass is a promising goal since it would deliver fermentable sugars for the chemical sector. Cellobiose dehydrogenase (CDH) is a vital component of the extracellular lignocellulose-degrading enzyme system of fungi and has a great potential to improve catalyst efficiency for biomass processing. In the present study, a CDH from a newly isolated strain of the agaricomycete Coprinellus aureogranulatus (CauCDH) was successfully purified with a specific activity of 28.9 U mg− 1. This pure enzyme (MW = 109 kDa, pI = 5.4) displayed the high oxidative activity towards β-1–4-linked oligosaccharides. Not least, CauCDH was used for the enzymatic degradation of rice straw without chemical pretreatment. As main metabolites, glucose (up to 165.18 ± 3.19 mg g− 1), xylose (64.21 ± 1.22 mg g− 1), and gluconic acid (5.17 ± 0.13 mg g− 1) could be identified during the synergistic conversion of this raw material with the fungal hydrolases (e.g., esterase, cellulase, and xylanase) and further optimization by using an RSM statistical approach.
Nguyen Duc Anh,Pham Van Thanh,Doan Tu Lap,Nguyen Tuan Khai,Tran Van An,Tran Duc Tan,Nguyen Huu An,Dang Nhu Dinh 한국인터넷정보학회 2022 KSII Transactions on Internet and Information Syst Vol.16 No.2
Forest fires inflict great losses of human lives and serious damages to ecological systems. Hence, numerous fire detection methods have been proposed, one of which is fire detection based on sensors. However, these methods reveal several limitations when applied in large spaces like forests such as high cost, high level of false alarm, limited battery capacity, and other problems. In this research, we propose a novel forest fire detection method based on image processing and correlation coefficient. Firstly, two fire detection conditions are applied in RGB color space to distinguish between fire pixels and the background. Secondly, the image is converted from RGB to YCbCr color space with two fire detection conditions being applied in this color space. Finally, the correlation coefficient is used to distinguish between fires and objects with fire-like colors. Our proposed algorithm is tested and evaluated on eleven fire and non-fire videos collected from the internet and achieves up to 95.87% and 97.89% of F-score and accuracy respectively in performance evaluation.