ABSTRACT
This paper used regression and moderation approaches to evaluate the student's satisfaction with informatics towards the hybrid learning in their study. Multiple Linear Regression (MLR) identified student satisfaction based on hybrid learning difficulty and benefit ($p < 0.001$). Linear Regression (LR) found hybrid learning benefits impacted the student's satis-faction significantly $(p < 0.001$). Student's $t$-test also revealed that Overall Satisfaction (OS) significantly affected hybrid learning's satisfaction ($p < 0.001$). Analysis of Co-variants (ANCOVA) also proved that hybrid learning's benefit ($p < 0.001$) and OS ($p < 0.05$) significantly influenced student satisfaction. The paper also proved that hybrid learning's benefits positively correlate with student satisfaction (0.596). The slopes of 'Yes' and 'No' are substantially different from one another when the probability value of 0.22 $(p > 0.05$). Hence, no moderator (OS) affects the relationship's strength between the benefit and satisfaction of hybrid learning. The paper also revealed that hybrid learning's difficulty has a negative correlation (-.18), and the benefit of hybrid learning is positively associated with student satisfaction (.66). Implementing a hybrid learning mode during Covid-19 periods significantly impacted student satisfaction and the decision taken by the administration was also meaningful. © 2023 IEEE.
ABSTRACT
One of the most significant and well-publicized prevention practises for Covid 19 is hand cleanliness. Face masks and social withdrawal are useless without good hand hygiene. The healthcare professionals can only intervene and raise awareness to enhance the public's hand hygiene practises after they are aware of the public's perceptions of and barriers to hand hygiene. A private dental facility had 150 outpatients participate in this cross-sectional questionnaire survey. Ten questions addressing various facets of hand hygiene and perceived obstacles made up the survey. The information from Google Forms was then imported into SPSS Version 15 using Excel. Data were presented as frequencies and percentages after the chi square test, and a p value of 0.05 or less was regarded as statistically significant.. In our study, 92.62 percent of outpatients at a private facility said that they continue to take measures against COVID19. 83.89% of our patients agreed that good hand hygiene habits are crucial for preventing COVID19. Whereas 38.26% of outpatients claimed to only wash their hands for 30 seconds, 33.56% of outpatients claimed to wash their hands for a full minute. In contrast to the 48.32 percent who said hand sanitizer is best and important for hand hygiene, 51.68 percent of outpatients said soap and water is best and essential for hand hygiene. According to the study's findings, the participants had a reasonable understanding of hand hygiene and its significance. Yet, there is a need for greater awareness of the finishing details on touch surfaces. Thus, it is advised that media-based propaganda and awareness campaigns have a positive impact and should be kept up, with a stronger focus on the finer points. © 2023 IEEE.
ABSTRACT
In order to slow the COVID-19 pandemic's rapid spread and put an end to it, the world needs to take extraordinary action. The knowledge, attitude, and practises (KAP) of outpatients concerning COVID-19 have an impact on the adherence to control measures. As a result, this research serves as a baseline analysis to assess Knowledge, Attitude, and Practice and serve as the foundation for our mitigation efforts. The outpatients were given this self-administrated survey. The ten-item survey was created in a way that allowed for an accurate evaluation of the knowledge, attitude, and practise components. Using SPSS software, the statistical analysis was conducted. The replies from the Google sheet were loaded into SPSS after being exported to Excel. Data were described using frequency and percentages, and chi square analysis was conducted to see whether there was any correlation between the variables. 85 outpatients in total took part in the survey. While 80% of the participants were aware of the life trajectory of Covid-infected individuals and 77.6% of them paid close attention to government directives, the overall level of awareness about COVID-19 and its prevention was rather high. 54.12% of the participants used hand sanitizer and wore masks constantly. The outcomes indicated that the participants had sound knowledge and a positive outlook. To combat this epidemic, media propaganda and instructional video production must continue to be produced and distributed. © 2023 IEEE.
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In this paper, we study the epidemic situation in Kazakhstan and neighboring countries, taking into account territorial features in emergency situations. As you know, the excessive concentration of the population in large cities and the transition to a world without borders created ideal conditions for a global pandemic. The article also provides the results of a detailed analysis of the solution approaches to modeling the development of epidemics by types of models (basic SIR model, modified SEIR models) and the practical application of the SIR model using an example (Kazakhstan, Russia, Kyrgyzstan, Uzbekistan and other neighboring countries). The obtained processing results are based on statistical data from open sources on the development of the COVID-19 epidemic. The result obtained is a general solution of the SIR-model of the spread of the epidemic according to the fourth-order Runge-Kutta method. The parameters β, γ, which are indicators of infection, recovery, respectively, were calculated using data at the initial phase of the Covid 2019 epidemic. An analysis of anti-epidemic measures in neighboring countries is given. © 2022 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
ABSTRACT
The conventional methods used for the diagnostics of viral infection are either expensive and time-consuming or not accurate enough and dependent on consumable reagents. In the presence of pandemics, a fast and reagent-free solution is needed for mass screening. Recently, the diagnosis of viral infections using infrared spectroscopy has been reported as a fast and low-cost method. In this work a fast and low-cost solution for corona viral detection using infrared spectroscopy based on a compact micro-electro-mechanical systems (MEMS) device and artificial intelligence (AI) suitable for mass deployment is presented. Among the different variants of the corona virus that can infect people, 229E is used in this study due to its low pathogeny. The MEMS ATR-FTIR device employs a 6 reflections ZnSe crystal interface working in the spectral range of 2200-7000 cm-1. The virus was propagated and maintained in a medium for long enough time then cell supernatant was collected and centrifuged. The supernatant was then transferred and titrated using plaque titration assay. Positive virus samples were prepared with a concentration of 105 PFU/mL. Positive and negative control samples were applied on the crystal surface, dried using a heating lamp and the spectrum was captured. Principal component analysis and logistic regression were used as simple AI techniques. A sensitivity of about 90 % and a specificity of about 80 % were obtained demonstrating the potential detection of the virus based on the MEMS FTIR device. © 2023 SPIE.
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This work utilizes Sentinel-2A L1C remote sensing photographs from the years 2018, 2020, and 2022 to identify the different land use categories in the study area using the support vector machine (SVM) technique. The accuracy of categorization is greater than 90%. This research explores four factors of the dynamic change in land use in Hongta District from 2018 to 2022: the proportion of various types of land;the extent of something like the changing land usage;land use transfer;and the dynamic degree of the change in land use. According to the study's results, the proportion of cultivated and grassland land grew, while the quantity of barren and construction land fell by 1.90 percent, 0.03 percent, and 0.69 percent, respectively. The water system land portion of total area increased by 2.58 percent and 0.13 percent, respectively. After comparing the two research periods, the entire dynamic degree of the second stage is determined to be 3.5 percent lower than that of the first stage, and the pace of land use change is quite sluggish, which may be associated with the worldwide COVID-19 outbreak in 2020. The outcomes of the research may give the natural resources department the knowledge it needs to manage land resources properly. © 2023 SPIE.
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The COVID-19 pandemic was caused by the emergence of the new coronavirus (SARS-Cov2) in Wuhan, China, on December 12, 2019, and it has significantly impacted human health. It has also caused abrupt changes in lifestyle that have had social and economic repercussions, including social exclusion and isolation at home. This study aimed to investigate how COVID-19 has affected the food habits and lifestyle of the general population. A cross-sectional survey was conducted in Chennai to assess the awareness level of the population regarding the protective measures they take during the pandemic, and 500 participants of all ages were included in the study. Statistical analysis was performed using SPSS software. The study found that over 50% of the participants increased their intake of vegetables and fruits, and about 49.33% decreased their intake of fast food and snacks. Furthermore, more than 40% of the participants added immune-boosting ingredients to their diet. These results suggest that the study population adopted healthier dietary habits and behaviors, including a more nutritious diet with more vegetables, immune-boosting foods, and increased water intake. © 2023 IEEE.
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The COVID-19 pandemic has had a profound impact on the global community, and vaccination has been recognized as a crucial intervention. To gain insight into public perceptions of COVID-19 vaccines, survey studies and the analysis of social media platforms have been conducted. However, existing methods lack consideration of individual vaccination intentions or status and the relationship between public perceptions and actual vaccine uptake. To address these limitations, this study proposes a text classification approach to identify tweets indicating a user's intent or status on vaccination. A comparative analysis between the proportions of tweets from different categories and real-world vaccination data reveals notable alignment, suggesting that tweets may serve as a precursor to actual vaccination status. Further, regression analysis and time series forecasting were performed to explore the potential of tweet data, demonstrating the significance of incorporating tweet data in predicting future vaccination status. Finally, clustering was applied to the tweet sets with positive and negative labels to gain insights into underlying focuses of each stance. © 2023 ACM.
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The study is devoted to a comparative analysis and retrospective evaluation of laboratory and instrumental data with the severity of lung tissue damage in COVID-19 of patients with COVID-19. An improvement was made in the methodology for interpreting and analyzing dynamic changes associated with COVID-19 on CT images of the lungs. The technique includes the following steps: pre-processing, segmentation with color coding, calculation and evaluation of signs to highlight areas with probable pathology (including combined evaluation of signs). Analysis and interpretation is carried out on the emerging database of patients. At the same time the following indicators are distinguished: the results of the analysis of CT images of the lungs in dynamics;the results of the analysis of clinical and laboratory data (severity course of the disease, temperature, saturation, etc.). The results of laboratory studies are analyzed with an emphasis on the values of the main indicator - interleukin-6. This indicator is a marker of significant and serious changes characterizing the severity of the patient's condition. © 2023 IEEE.
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The COVID-19 pandemic, caused by the novel coronavirus, has had a significant impact on daily life, education, business, and trade. The virus spreads quickly through direct contact with droplets, fecal-oral transmission, and water contamination. The consequences of the pandemic can be classified into three categories: health, economic, and social. The physical, mental, and psychological behaviors of individuals have also changed due to the pandemic. This study aimed to assess the impact of COVID-19 on the general population. A survey questionnaire with ten questions was distributed through an online portal, and the responses were analyzed using SPSS software. The results showed that healthcare workers were among the most affected, with the primary impact on their social and psychological well-being. Although previous research suggested that all fields were equally affected, this study found that healthcare workers were the most impacted group. The study concluded that the COVID-19 pandemic had a significant impact on the social and psychological well-being of the general population, with healthcare workers being the most affected. © 2023 IEEE.
ABSTRACT
The pandemic situation is affected in various ways in the education domain. The sudden transformation from offline to online teaching-learning process made students and teachers use different tools like WhatsApp for communication. The reason for this consideration is to investigate the impacts of WhatsApp utilized for instruction and decide the suppositions of understudies towards the method. The study is designed, keeping in mind the current COVID-19 situation and how it affected the education system turning it into online mode. On different questionnaires, regression and heatmap analysis is performed. The investigation showed that both learning situations have diverse impacts on the victory of understudies while supporting the conventional environment by utilizing WhatsApp is more successful for the increment of victory. The assessment moreover showed that students had superior pleasant reviews closer to the usage of WhatsApp in their courses. They requested the same workout in their one-of-a-kind courses as well. They expressed that picking up information can moreover take out unwittingly and the messages with pics were more prominent and viable for their picking up information. Be that as it may, some college understudies have communicated harming audits approximately the timing of a few posts and the repetitive posts within the bunch. At long last, it is supported that the utilization of WhatsApp within the preparing framework is to be energized as a steady innovation. . © 2023 IEEE.
ABSTRACT
Although many AI-based scientific works regarding chest X-ray (CXR) interpretation focused on COVID-19 diagnosis, fewer papers focused on other relevant tasks, like severity estimation, deterioration, and prognosis. The same holds for explainable decisions to estimate COVID-19 prognosis as well. The international hackathon launched during Dubai Expo 2020, aimed at designing machine learning solutions to help physicians formulate COVID-19 patients' prognosis, was the occasion to develop a machine learning model capable of predicting such prognoses and justifying them through interpretable explanations. The large hackathon dataset comprised subjects characterized by their CXR and numerous clinical features collected during triage. To calculate the prognostic value, our model considered both patients' CXRs and clinical features. After automatic pre-processing to improve their quality, CXRs were processed by a Deep Learning model to estimate the lung compromise degree, which has been considered as an additional clinical feature. Original clinical parameters suffered from missing values that were adequately handled. We trained and evaluated multiple models to find the best one and fine-tune it before the inference process. Finally, we produced novel explanations, both visual and numerical, to justify the model predictions. Ultimately, our model processes a CXR and several clinical data to estimate a patient's prognosis related to the COVID-19 disease. It proved to be accurate and was ranked second in the final rankings with 75%, 73.9%, and 74.4% in sensitivity, specificity, and balanced accuracy, respectively. In terms of model explainability, it was ranked first since it was agreed to be the most interpretable by health professionals. © 2023 SPIE.
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Assessing the generalizability of deep learning algorithms based on the size and diversity of the training data is not trivial. This study uses the mapping of samples in the image data space to the decision regions in the prediction space to understand how different subgroups in the data impact the neural network learning process and affect model generalizability. Using vicinal distribution-based linear interpolation, a plane of the decision region space spanned by the random 'triplet' of three images can be constructed. Analyzing these decision regions for many random triplets can provide insight into the relationships between distinct subgroups. In this study, a contrastive self-supervised approach is used to develop a 'base' classification model trained on a large chest x-ray (CXR) dataset. The base model is fine-tuned on COVID-19 CXR data to predict image acquisition technology (computed radiography (CR) or digital radiography (DX) and patient sex (male (M) or female (F)). Decision region analysis shows that the model's image acquisition technology decision space is dominated by CR, regardless of the acquisition technology for the base images. Similarly, the Female class dominates the decision space. This study shows that decision region analysis has the potential to provide insights into subgroup diversity, sources of imbalances in the data, and model generalizability. © 2023 SPIE.
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A casing leak repair alternative is presented to allow continued hydraulic fracture treatment of an unconventional formation. Analysis of diagnostic operations, selection of the best alternative, and the results are detailed. This paper details the diagnostic operations (annular circulation test, multifinger caliper log, leak chase with hydraulic packer on Coiled Tubing, fluid transit evaluation, and real-time camera downhole images acquisition) to identify the casing leak zone and the analyzed repair alternatives with the final selection of a casing patch. To verify the pipe body shield strength and burst pressure post-patch expansion, a finite element analysis in dynamic condition was carried out to limit the hydraulic fracture pumping parameters. This paper covers details on repair operations executed, verification analysis to confirm original frac treatment continuity, and lower & upper completion installation. The diagnostics operations allowed pinpointing casing leak detection and selection of possible repair alternatives. The repair was carried out as planned involving many services companies. A solution was implemented with local staff and services considering the COVID context with travel restrictions of the patch owners. Web broadcasting CT surface parameters allowed real-time support from casing patch suppliers during the entire intervention. The completion plan with 24 frac stages performed through the casing patch was successfully executed. The production packer with an OD of 99.5% of the casing patch drift was run through the casing patch and wireline set without any problem. Considering well integrity conditions throughout the entire well production life as the main intervention objective, this paper introduces a successful alternative to repair casing failures on an unconventional well that allowed hydraulic fractures continuity to accomplish the original frac plan. The well production was higher than the Estimated Ultimate Recovery (EUR) expected for the landing zone. Copyright 2023, Society of Petroleum Engineers.
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The capability to infer emotional insights from emojis found in social media has projected emoji analysis into the spotlight of current emoji-based research. Previous studies mainly used text-surrounding emojis to estimate sentimentality scores. However, trying to conclude the same score based solely on emojis is challenging. In this paper this challenge was welcomed, and with it we created a new concept. This revolutionary scoring method, named the EmojiSets Sentiment Score Rank, proposes using sets of emojis taken from tweets along with information from previous studies [1] to find a sentiment score. This bottom-up scoring approach gives each emoji a sentiment score. It then calculates the context-level sentiment score of a tweet solely dependent on the emojis found within it. To the best of the authors' knowledge, no such approach has been researched in the Emojis Sentiment Analysis area. We tested our model against over 1.2 million tweets concerning Covid-19 and compared it to the VADER model [7] to validate our assumption. Our model corrected around 72% of the tweets that the other model scored as neutral. To succor these findings, 32 human annotators were given the task of annotating 8040 randomly chosen tweets. When calculating similarity using the Jaccard Index, their results were consistent with our approach in over 70% of cases © 2022 IEEE.
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Disinformation spread on social media generates a truly massive amount of content on a daily basis, much of it not quite duplicated but repetitive and related. In this paper, we present an approach for clustering social media posts based on topic modeling in order to identify and formalize an underlying structure in all the noise. This would be of great benefit for tracking evolving trends, analyzing large-scale campaigns, and focusing efforts on debunking or community outreach. The steps we took in particular include harvesting through CrowdTangle huge collection of Facebook posts explicitly identified as containing disinformation by debunking experts, following those links back to the people, pages and groups where they were shared then collecting all posts shared on those channels over an extended period of time. This generated a very large textual dataset which was used in the topic modeling experiments attempting to identify the larger trends in the available data. Finally, the results were transformed and collected in a Knowledge Graph for further study and analysis. Our main goal is to investigate different trends and common patterns in disinformation campaigns, and whether there exist some correlations between some of them. For instance, for some of the most recent social media posts related to COVID-19 and political situation in Ukraine. © 2022 Copyright for this paper by its authors.
ABSTRACT
The COVID-19 pandemic has greatly affected our society badly. It has been a subject of discussion since 2019 due to the increased prevalence of social media and its extensive use, and it has been a source of tension, fear, and disappointment for people all over the world. In this research, we took data from COVID-19 tweets from 10 different regions from July 25, 2020, to August 29, 2020. Using the well-known word embedding technique count-vectorizer, we experimented with different machine learning classifiers on data to train deep neural networks to improve the accuracy of predicted opinions with a low elapsed time. In addition, we collected PCR results from these regions for the same time interval. We compared the opinions in the form of positive or negative responses with the results of the PCR tests per million people. With the help of the results, We figured out a real-time international measure to detect these regions' behaviors for any future pandemic. If we know how a region thinks about an upcoming pandemic, then we can predict the region's real-time behavior for the particular pandemic. This would happen if we had past case studies to compare, like in our proposed research. Copyrights © 2023 The Institute of Electronics and Information Engineers.
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The COVID-19 pandemic disrupted typical travel behavior worldwide. In the United States (U.S.), government entities took action to limit its spread through public health messaging to encourage reduced mobility and thus reduce the spread of the virus. Within statewide responses to COVID-19, however, there were different responses locally. Likely some of these variations were a result of individual attitudes toward the government and health messaging, but there is also likely a portion of the effects that were because of the character of the communities. In this research, we summarize county-level characteristics that are known to affect travel behavior for 404 counties in the U.S., and we investigate correlates of mobility between April and September (2020). We do this through application of three metrics that are derived via changepoint analysis—initial post-disruption mobility index, changepoint on restoration of a ‘‘new normal,'' and recovered mobility index. We find that variables for employment sectors are significantly correlated and had large effects on mobility during the pandemic. The state dummy variables are significant, suggesting that counties within the same state behaved more similarly to one another than to counties in different states. Our findings indicate that few travel characteristics that typically correlate with travel behavior are related to pandemic mobility, and that the number of COVID-19 cases may not be correlated with mobility outcomes. © National Academy of Sciences: Transportation Research Board 2021.
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This article considers the problem of the impact of the pandemic on medical personnel in the Russian Federation. During the pandemic, one of the most acute problems is the shortage of medical personnel. The study of the issue of shortage of medical personnel is relevant both for the whole world and for Russia. The authorities of several Russian regions at once, against the backdrop of an increase in the incidence of coronavirus, announced an acute shortage of doctors and mid-level health workers. In Russia, first of all, there was a shortage of primary health care doctors - general practitioners, general practitioners, and pediatricians. Staffing problems were discussed even before the pandemic, but during the pandemic, the workload on the medical staff increased and existing specialists began to leave. This paper presents possible solutions. Such as increasing wages and attracting students to increase staff. The purpose of the study is to analyze the reasons for the lack of personnel, identify existing problems, and formulate recommendations for their elimination. © 2023 IEEE.
ABSTRACT
Classifying Covid-19 and Pneumonia is one of the most important and challenging tasks in the field of the medical sector since manual classification with human assistance can lead to incorrect prediction and diagnosis. Additionally, it is a difficult operation when there is a lot of data that need to be analyzed thoroughly. Due to the similarity in symptoms as well as in chest X-ray images of Covid-19 and Pneumonia diseases, it is difficult to distinguish those. The study presents a technological solution to build a mixed-data model using customized neural networks to discriminate between Covid-19 and Pneumonia. The proposed method is applied to the chest X-ray images and symptoms of patients of Covid-19 and Pneumonia. This helps to perform immediate prediction of Covid-19 and Pneumonia providing fast and specialized treatment to the patients appropriately. This prediction also helps the radiologist or doctors in making quick decisions. In this work, imaging data (such as Chest X-ray images) and text data (such as disease symptoms like cough, body pain, short breathing, fever, etc.) are taken for detecting Covid-19, Pneumonia and Normal patients. Data Synthesis is carried out due to the unavailability of mixed data and it has created dataset of 450 entries of Covid-19, Normal and Pneumonia cases. The goal is to design a system that accurately classifies Covid19, Pneumonia, and Normal patients by utilizing convolutional neural networks (CNN) and multi-layer perceptron (MLP) algorithms. An accuracy of 93.33% is obtained for the mixed-data model using a deep neural network, that is designed by combining custom CNN and MLP architectures. © 2022 IEEE.