Data Visualization for Corona Patients Globally using Real-Time APIs

All the nations' administrative units are concerned about the COVID-19 outbreak in different regions of the world. India is also trying to control the spread of the virus with strict measures and has managed to slow down its growth rate. The administration of each country faces the challenge of maintaining records of corona patients. To address this challenge, this work builds a website from scratch using real-time APIs for data visualization. The website provides information on the number of active cases, death cases, recovery cases, and total cases of COVID-19 patients in each country. The data can be visualized using graphs, making it easier to compare the situation in different countries. The website allows for monitoring which country has a higher number of deaths, patients, favorable recovery rates, and a large number of active cases. The COVID-19 status regarding patients can be analyzed through graphical representation using real-time data. © 2023 IEEE.

A New Respiratory Diseases Detection Model in Chest X-Ray Images Using CNN

Convolutional Neural Network (CNN)-based deep learning techniques have recently demonstrated increased potential and effectiveness in image recognition applications, such as those involving medical images. Deep-learning models can recognize targets with performance comparable to radiologists when used with CXR. The primary goal of this research is to examine a deep learning technique used on the radiography dataset to detect COVID-19 in X-ray medical images. The proposed system consists of several stages, from pre-processing, passing through the feature reduction using more than one technique, to the classification stage based on a proposed model. The test was applied to the COVID-19 Radiography dataset of normal and three lung infections (COVID-19, Viral Pneumonia, and Lung Opacity). The proposed CNN model has shown its ability to classify COVID, normal, and other lung infections with perfect accuracy of 99.94%. Consequently, the AI-based early-stage detection algorithms will be enhanced, increasing the accuracy of the X-raybased modality for the screening of various lung diseases. © 2023 Lavoisier. All rights reserved.

Stability Analysis of COVID-19 SEIQR Model with Infectivity in Latent Period

In this paper, COVID-19 SEIQR model which can cause death is studied. The virus has infectivity in both latent period and infectious period, the existence, local stability and global asymptotic stability of disease-free equilibrium point and local equilibrium point are proved. © 2022 IEEE.

Efficient numerical simulations based on an explicit group approach for the time fractional advection–diffusion reaction equation

The time-fractional advection–diffusion reaction equation (TFADRE) is a fundamental mathematical model because of its key role in describing various processes such as oil reservoir simulations, COVID-19 transmission, mass and energy transport, and global weather production. One of the prominent issues with time fractional differential equations is the design of efficient and stable computational schemes for fast and accurate numerical simulations. We construct in this paper, a simple and yet efficient modified fractional explicit group method (MFEGM) for solving the two-dimensional TFADRE with suitable initial and boundary conditions. The proposed method is established using a difference scheme based on L1 discretization in temporal direction and central difference approximations with double spacing in spatial direction. For comparison purposes, the Crank–Nicolson finite difference method (CNFDM) is proposed. The stability and convergence of the presented methods are theoretically proved and numerically affirmed. We illustrate the computational efficiency of the MFEGM by comparing it to the CNFDM for four numerical examples including fractional diffusion and fractional advection–diffusion models. The numerical results show that the MFEGM is capable of reducing iteration count and CPU timing effectively compared to the CNFDM, making it well-suited to time fractional diffusion equations. © 2023, The Author(s) under exclusive licence to Sociedade Brasileira de Matemática Aplicada e Computacional.

Learning Analytics Powered Teacher Facing Dashboard to Visualize, Analyze Students' Academic Performance and give Key DL(Deep Learning) Supported Key Recommendations for Performance Improvement

COVID-19 has forced the government to close educational institutes to reduce the spread of the virus. As a result of this decision, students lose contact with teachers and a communication gap also arises. This survey attempts to bridge the gap between students and teachers. Through this survey, we sought to understand where the students are lacking and what are the different steps that can be taken by the teacher to improve the performance of the student and whether this concept should be reviewed or not. We found that most of the researchers who have published papers that we have read did the same mistake in their research, therefore we realized that the concept of AI should be studied again, and we should try not to repeat the same mistake in our research.The main aim of our project is to build 'Teacher facing dashboard' which can help the teacher to summarize,visualize and analyze the data of the education field(academics) and also understanding the students performance using Machine Learning(ML) and Deep Learning (DL). © 2023 IEEE.

Stability analysis and approximate solution of interval mathematical model for the COVID-19 pandemic

In this paper, an interval solution has been constructed for the system of differential equations (SDEs) governing the COVID-19 pandemic with uncertain parameters, namely, interval. The imposition of lockdown on infective has been considered as an interval parameter. As a result, the complete system of first-order differential equations is transformed into interval form. The resulting interval system of differential equations (ISDEs) has been solved with help of the parametric concept and the Runge–Kutta method of order 4. Obtained results are compared with existing crisp results, and they are found to be in good agreement. © 2023 John Wiley & Sons, Ltd.

CIVIL ENGINEERING STUDENTS' MISCONCEPTIONS ON PRINCIPLES OF GEOLOGY

Previous studies have shown that secondary school students may have misconceptions about geological scientific information. By the end of secondary education these misconceptions may remain unresolved. As a result, students enter university studies and still hold them. Students of engineering, as for example civil engineering, are no exception. The aim of this study was to investigate and analyse misconceptions of this specific target group. A closed questionnaire was designed and given to 102 University students who attended the 2nd semester course "Geology for Civil Engineers” in the Department of Civil Engineering at the University of Patras. The questionnaire was designed and validated according to previous research findings and implemented through google forms that were prepared and given electronically to the students to fill them online. The participants completed the questionnaire on the principles of geology electronically due to Covid-19 conditions. The results showed that in some questions most of the students answered correctly while in others there were many wrong answers, which revealed their misconceptions in geology. Many misconceptions were traced especially regarding mineral properties like color and luster. An important observation was that a notable number of students confused hardness with brittleness and as a result they expressed their belief that hard minerals are hard to break. Gender and age differences were tested using appropriate statistical tests. In cases that there was a significant difference between the genders, women were the ones with higher percentage of correct answers. The results may be seen in relation to educational practices. © 2022 International Multidisciplinary Scientific Geoconference. All rights reserved.

WHAT ARE THE IMPLICATIONS OF UKRAINE CONFLICT O EU FOREST PRODUCTS TRADE?

After the changes in globally traded forest products patterns and supply chains caused by the COVID 19 pandemic, currently, there is another geopolitical event that has a direct impact on a whole spectrum of markets, not least the timber market. The current situation in Ukraine resulted in many sanctions imposed by the global community and regional groups on Russia, some of them directly targeting the trade with forest products. EU is a significant global player on forest products market. In 2020 it contributed to 42% of the world total export and 32% of the world total import of forest products. As the EU member states to a large extent depends on international trade there is a need to examine what may be the implications of such sanctions on the EU wood trade patterns and consequently on the supplies for forest based industries. Therefore, the aim of the paper is to quantify the EU's dependence on timber from Russia and to indicate the possible impacts on international timber trade. © 2022 15th International Scientific Conference WoodEMA 2022 - Crisis Management and Safety Foresight in Forest-Based Sector and SMES Operating in the Global Environment. All rights reserved.

Comparison of Machine Learning Models for predicting Covid-19 patients' recovery in India

The pandemic Covid-19 is a name coined by WHO on 31st December 2019. This devastating illness was carried on by a new coronavirus known as SARS-COV-2. Most of the research has focused on estimating the total number of cases and mortality rate of COVID-19. Due to this, people across the world were stressed out by observing the growing number of cases every day. As a means of maintaining equilibrium, this paper aims to identify the best way to predict the number of recovered cases of Coronavirus in India. Dataset was divided into two parts: training and testing. The training dataset utilised 70% of the dataset, and the testing dataset utilised 30%. In this paper, we applied 10 machine learning techniques i.e. Random Forest Classifier (RF), Naive Bayes (NB), Quadratic Discriminant Analysis (QDA), Gradient Boosting Classifier (GBM), Linear Discriminant Analysis (LDA), Logistic Regression (LR), K Neighbour Classifier (KNN), Decision Tree Classifier (DT), SVM - Linear and Ada-Boost Classifier in order to predict recovered patients in India. Our study suggests that Random Forest Classifier outperforms other machine learning models for predicting the recovered Coronavirus patients having an accuracy of 0.9632, AUC of 0.9836, Recall of 0.9640, Precision of 0.9680, F1 Score of 0.9617 and Kappa of 0.9558. © 2022 IEEE.

SARS-CoV-2 structures detection in artificial saliva using ATR-FTIR associated with Linear Discriminant Analysis

Here, we used ATR-FTIR platform supported by artificial intelligence algorithms to identify unique infrared vibrational modes of a pseudotyped human immunodeficiency virus type-1 (HIV-1) coupled to Spike (S) protein of SARS-CoV-2 (HIV/NanoLuc-SARS-CoV-2 pseudotype virus). © Optica Publishing Group 2022 The Authors.

SARS-CoV-2 Vaccine-Induced Immunogenicity among Hematopoietic Stem Cell Transplant Recipients (HSCT)

Background. SARS-CoV-2 vaccination reduces the risk and severity of coronavirus disease 2019 (COVID-19), but immunogenicity may be reduced in patients undergoing hematopoietic stem cell transplantation (HSCT). The variables that impact the humoral response, such as age, gender, disease and transplant type, prior treatments, and vaccine type, have not been comprehensively described. Methods. A retrospective review was conducted at a single-centre of HSCT recipients who received COVID-19 vaccinations between 2020 and 2021. Participants were included if >18 years and had received at least a single dose of Pfizer, Moderna or Johnson & Johnson (J&J) vaccine. Anti-Spike (S) IgG titers were quantitatively measured at provider discretion during routine care using the Roche Elecsys Anti-SARS-CoV-2 spike immunoassay and categorized as Responders (< 0.8U/mL) and Non-responder (>0.8). Multivariate logistic regression was used to estimate odds ratios (ORs) and 95% confidence intervals (CIs) for Responders vs Non-responders. Controlled risk factors included;Age, disease, treatments, and history of graft-versus-host disease (GVHD). Results. Of 117 HSCT patients assessed, 59 (50.4%) were female, 106 (90.6%) were white, and the median age was 62.5 years (interquartile range [IQR, 49.9-67.8). Vaccinations were administered at a median of 179 days post-transplant (IQR 319 - 105) and antibody responses were measured at a median of 135.5 days post-vaccination (IQR 190-50). 106(90.6%) were responders with a mean titre of 1141.5U/mL (SD=1095.3). 35% had Low (< 100U/mL) titres. Being Female (OR 0.02, 95%CI 0.003 - 0.6) was associated with a slightly higher odds of being a responder. Conclusion. Hematopoietic stem cell transplant recipients demonstrated a high prevalence of anti-S IgG antibody positivity following COVID vaccination. However, neither patient characteristics nor treatment regimens were seen to be strongly associated with anti-S protein positivity among HSCT recipients. More studies are needed to further characterize patient and treatment characteristics that correlate with seroprotection among these patients.

CT quantitative analysis in patients with severe coronavirus disease 2019 and its correlation with laboratory examination results

OBJECTIVE: To quantitatively analyze the longitudinal changes of ground-glass opacity (GGO), consolidation and total lesion in patients infected with severe coronavirus disease 2019 (COVID-19), and its correlation with laboratory examination results. MATERIALS AND METHODS: All 76 computed tomography (CT) images and laboratory examination results from the admission to discharge of 15 patients confirmed with severe COVID-19 were reviewed, whereas the GGO volume ratio, consolidation volume ratio, and total lesion volume ratio in different stages were analyzed. The correlations of lesions volume ratio and laboratory examination results were investigated. RESULTS: Four stages were identified based on the degree of lung involvement from day 1 to day 28 after disease onset. GGO was the most common CT manifestation in the four stages. The peak of lung involvement was at around stage 2, and corresponding total lesion volume ratio, GGO volume ratio, and consolidation volume ratio were 17.48 (13.44−24.33), 12.11 (7.34−17.08), and 5.51 (2.58−8.58), respectively. Total lesion volume ratio was positively correlated with neutrophil percentage, C-reactive protein (CRP), high-sensitivity CRP (Hs-CRP), procalcitonin, lactate dehydrogenase (LD), and creatine kinase isoenzyme MB (CK-MB), but negatively correlated with lymphocyte count, lymphocyte percentage, arterial oxygen saturation, and arterial oxygen tension. Consolidation volume ratio was correlated with most above laboratory examination results except Hs-CRP, LD, and CK-MB. GGO, however, was only correlated with lymphocyte count. CONCLUSION: CT quantitative parameters could show longitudinal changes well. Total lesion volume ratio and consolidation volume ratio are well correlated with laboratory examination results, suggesting that CT quantitative parameters may be an effective tool to reflect the changes in the condition.

ECONOMIC IMPACT OF COVID-19 ON THE COMPANY “MAFESA S.A.” AM-BATO-ECUADOR BRANCH OFFICE

Due to the covid-19 pandemic, the economy of companies worldwide has declined, making it a challenge to continue with activities, with the option of closing activities due to fear of contagion. In turn, companies that work with technology chose to use direct distribution channels such as Customer Relationship Management, which allowed them to stay within the market, showing a slight increase in their sales during 2020 and 2021. The objective is focused on analyze the economic impact of COVID-19 on the company “MAFESA S.A. The method of analysis has been used, taking the company MAFESA S.A. as a reference, where we will apply the documentary review techniques, which allows us to analyze the economic effect suffered by the company. It is concluded that sales have been affected during the health crisis, although strategies motivated by public policies allowed these losses to not be significant for the company MAFESA S.A. © 2022, University of Cienfuegos, Carlos Rafael Rodriguez. All rights reserved.

Using Deep Learning to Identify Linguistic Features that Facilitate or Inhibit the Propagation of Anti- and Pro-Vaccine Content on Social Media

Anti-vaccine content is rapidly propagated via social media, fostering vaccine hesitancy, while pro-vaccine content has not replicated the opponent's successes. Despite this dis-parity in the dissemination of anti- and pro-vaccine posts, linguistic features that facilitate or inhibit the propagation of vaccine-related content remain less known. Moreover, most prior machine-learning algorithms classified social-media posts into binary categories (e.g., misinformation or not) and have rarely tackled a higher-order classification task based on divergent perspectives about vaccines (e.g., anti-vaccine, pro-vaccine, and neutral). Our objectives are (1) to identify sets of linguistic features that facilitate and inhibit the propagation of vaccine-related content and (2) to compare whether anti-vaccine, pro-vaccine, and neutral tweets contain either set more frequently than the others. To achieve these goals, we collected a large set of social media posts (over 120 million tweets) between Nov. 15 and Dec. 15, 2021, coinciding with the Omicron variant surge. A two-stage framework was developed using a fine-tuned BERT classifier, demonstrating over 99 and 80 percent accuracy for binary and ternary classification. Finally, the Linguistic Inquiry Word Count text analysis tool was used to count linguistic features in each classified tweet. Our regression results show that anti-vaccine tweets are propagated (i.e., retweeted), while pro-vaccine tweets garner passive endorsements (i.e., favorited). Our results also yielded the two sets of linguistic features as facilitators and inhibitors of the propagation of vaccine-related tweets. Finally, our regression results show that anti-vaccine tweets tend to use the facilitators, while pro-vaccine counterparts employ the inhibitors. These findings and algorithms from this study will aid public health officials' efforts to counteract vaccine misinformation, thereby facilitating the delivery of preventive measures during pandemics and epidemics. © 2022 IEEE.

Self-interest Attribution Behavior in Annual Reports under the COVID-19 Pandemic-Taking restaurant companies for example

The outbreak of the COVID-19 pandemic at the end of 2019 has caused a profound impact on economic development. The catering, logistics and tourism industries have suffered a huge blow. This paper selects the catering industry as the research object, selects the 2019 and 2020 annual reports of five representative listed catering companies, classifies and summarizes the stated criteria for determination of the occurrence of self-interest attribution, calculates the degree of self-interest attribution, and compares and analyzes whether the self-interest attribution behavior of the five case companies before and after the COVID-19 pandemic stands out or amplifies the self-interest attribution behavior of the companies. The case studies showed that the degree of self-interest attribution was higher in the poor-performing companies, and that the impact of the COVID-19 pandemic on the self-interest behavior of restaurant companies was prominent, and that the poor external environment was more likely to lead to a higher degree of self-interest attribution behavior. © 2022 IEEE.

A Machine Learning Model for Healthcare Stocks Forecasting in the US Stock Market during COVID-19 Period

This paper study the nowcasting and forecasting for the healthcare stock price in the united states during the Covid-19 period including the google trend data information. The data is collected in monthly data from 2015 to 2020 which are five interested stock price indexes in the healthcare sector. Empirically, the finding reveals that the Bayesian structural time series analysis can be used to investigate the stock price indexes with the google trend data is becoming useful for the prediction in term of current movement. In term of the machine learning algorithms, the unsupervised learning k-Mean algorithm is employed to cluster the cycle regimes of the stock market which provided three regimes such as Bull market, Sideways and Bear market. There are twenty-nine months stand for bull market, thirty-seven months are predictively provided sideways market and five months are referred as the bear market. Additionally, the supervised learning algorithms by using the Linear Discriminant Analysis (LDA), k-Nearest Neighbors (kNN) and Support vector machine (SVM) are used to investigate the cycle regimes of healthcare stock in next five year. The results indicated that LDA is chosen by the highest coefficient validation which represented the the regimes of stock in the healcare sector of the unites states of America will stay on the sideways periods in the next five years. Thus, the finding in this paper can be the useful information for investor to manage their portfolio especially, in healthcare sector during the Covid-19 period. © Published under licence by IOP Publishing Ltd.

Temporal and spatial analysis of the impact of epidemic situation on tourists' emotional fluctuation in China based on text mining

After the outbreak of COVID in Wuhan, it has had an impact on all aspects of tourism industry. Tourists' sentiment is an important factor for people to make tourism decisions. The implementation of tourism decisions affects the development of tourism to a certain extent. In order to explore the impact of the COVID-19 on the tourism industry from the micro level of tourist sentiment. Firstly, the text mining algorithm is used to analyze the emotion of tourism microblog text, and the tourism emotion index TSI is constructed. Then combined with the tourism heat index THI, the tourist sentiment TS comprehensive index is constructed. The temporal and spatial differences of the impact of the epidemic on tourists' emotion are analyzed by comparing the tourists' emotion and epidemic data in different regions and stages. From the temporal and spatial distribution of tourist sentiment and epidemic situation, they are not completely parallel related, and there is spatial heterogeneity. Tourist sentiment is affected by multiple factors such as economic level and geographical location. The change of tourists' mood does not only depend on the change of epidemic data, but also related to many factors such as economic level and geographical location. © COPYRIGHT SPIE. Downloading of the is permitted for personal use only.

The SMILE, A Cyber Pedagogy based Learning Management System Models

The study's purpose is to create an LMS model that is adapted to the characteristics of university students to enhance the learning experience by utilizing various multidimensional learning resources in Cyber Pedagogy. This research and development study used the Analyze, Design, Develop, Implement, and Evaluate (ADDIE) instructional design framework as well as the Waterfall system development model to develop learning materials and infrastructure. The study involves 50 students from the Bali Institute of Technology and Business, as well as five lecturers and six judges at the expert test stage, namely learning media experts, learning design experts, and teaching experts, who were chosen through purposive sampling. The SMILE Model (Simple, Multidimensional, and Interactive Learning Ecosystem) is designed to meet the learning needs and expectations of today's largest market share of higher education, the millennial generation. The SMILE Model was developed successfully with ongoing assistance from researchers' students, particularly in the E-Tourism course. The implementation is accomplished through the combination of university E-Learning and the use of Microsoft Teams as a virtual learning platform alternative. During the COVID-19 pandemic, this was considered the new face-to-face norm. © 2022. All Rights Reserved.

Impacts of COVID-19 Pandemic on Mental Health Condition of PnC Engineering Students: A Basis for Development of Intervention Program Using DMADV

The study aimed to determine the mental health condition of engineering students of Pamantasan ng Cabuyao (PnC) during COVID-19 pandemic and create an intervention program to mitigate the impact of pandemic on mental health condition. The DMADV (Define-Measure-Analyze-Design-Verify) methodology was utilized to clearly discuss the data gathered. The online survey and PHQ (Patient Health Questionnaire)-9 questionnaire were used to determine the mental health condition of the students. The researchers found out that before pandemic there is only a mild experience of negative emotions and feelings. During the pandemic, the score increased which indicated the moderate experience. The three main factors that affects the mental health condition such as personal, social and academic life were also considered. Based on the findings, the pandemic affected the mental health condition of the engineering students. The intervention program was proposed that helped the students coped up with any related mental health issues arising. © 2021 IEEE.

Machine Learning Applications in Forecasting of COVID-19 Based on Patients' Individual Symptoms

Predicting the COVID-19 outbreak has been studied by many researchers in recent years. Many machine learning models have been used for the prediction of the transmission in a country or region, but few studies aim to predict whether an individual has been infected by COVID-19. However, due to the gravity of this global pandemic, prediction at an individual level is critical. The objective of this paper is to predict if an individual has COVID-19 based on the symptoms and features. The prediction results can help the government better allocate the medical resources during this pandemic. Data of this study was taken on June 18th from the Israeli Ministry of Health on COVID-19. The purpose of this study is to compare and analyze different models, which are Support Vector Machine (SVM), Logistic Regression (LR), Naive Bayesian (NB), Decision Tree (DT), Random Forest (RF) and Neural Network (NN). © 2021 ACM.