Structural analysis of cotton stalk Puller and Shredder Machine

World scenario after pandemic COVID-19 has been drastically changing and researchers more focusing on, to minimize the post-pandemic effects on economy, energy sustainability and food security. Agriculture sector is playing pivotal role in world food security and energy sustain -ability. There is high need to optimize the mechanization technologies to increase the yield in limited energy inputs and operation time to fulfill the world growing food demand. This research is mainly focused on the design development and structural analysis aiding with Finite Element Analysis (FEA) approach for Cotton Stalk Puller and Shredder machine (CSPS) to cut the crop leftovers, soil conditioning (shredding the plant waste into soil) and sowing of next crop in single run by con-serving input resources. The experimental trials revealed that there is high pressure on cutting blades, chocking of shredder section and excessive pulling load on tractor hitches, which affected the machine's performance. To mitigate deficiencies and design optimization to improve the machine safety/reliability, the structure analysis carried out. Six core components of machine including baseplate, blade, gear system, root digger, pulley and shaft has investigated as per field conditions. The results revealed that the material of blade, root digger and teeth of gear system receiving the high stress under the operational conditions which results the edge wear and damage. The carbonization up to one-millimeter thickness can provide the extra strength to bear the exces-sive load on edge layers.(c) 2022 THE AUTHORS. Published by Elsevier BV on behalf of Faculty of Engineering, Alexandria University. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/ licenses/by-nc-nd/4.0/).

Spontaneous Haemothorax in a Patient with COVID-19.

The global pandemic of COVID-19 is caused by SARS-CoV-2 virus. We continue to discover the wide spectrum of complications associated with COVID-19. Some well-known complications include pneumonia, acute respiratory distress syndrome, pneumothorax, disseminated intravascular coagulation (DIC), chronic fatigue, multiorgan dysfunction, and long COVID-19 syndrome. We report a rare case of a 51-year-old man with severe COVID-19 pneumonia who developed haemorrhagic shock secondary to spontaneous haemothorax after 17 days of hospitalisation. Clinicians should be aware of such occurrence, and hence, high clinical suspicion, prompt recognition of signs and symptoms of shock, and adequate resuscitation will improve the outcomes of patients.

Machine Learning Model for Predicting Number of COVID-19 Cases in Countries with Low Number of Tests

Background: The COVID-19 pandemic has presented a series of new challenges to governments and healthcare systems. Testing is one important method for monitoring and controlling the spread of COVID-19. Yet with a serious discrepancy in the resources available between rich and poor countries, not every country is able to employ widespread testing. Methods and Objective: Here, we have developed machine learning models for predicting the prevalence of COVID-19 cases in a country based on multilinear regression and neural network models. The models are trained on data from US states and tested against the reported infections in European countries. The model is based on four features: Number of tests, Population Percentage, Urban Population, and Gini index. Result(s): The population and the number of tests have the strongest correlation with the number of infections. The model was then tested on data from European countries for which the correlation coefficient between the actual and predicted cases R2 was found to be 0.88 in the multi-linear regression and 0.91 for the neural network model Conclusion(s): The model predicts that the actual prevalence of COVID-19 infection in countries where the number of tests is less than 10% of their populations is at least 26 times greater than the reported numbers. Copyright © 2022 Hashim et al.

Using Liveworksheet as an Outline Teaching Platform to Evaluate Students' Listening Skills

This research used a liveworksheet platform to assess students' listening abilities and viewpoints. As a result of the COVID-19 epidemic affecting schools, we need appropriate media, particularly for listening skills, and one of the best venues is liveworksheets. As a result of Liveworksheet's numerous valuable features and its novel, interactive platform, online students are more engaged in assignments that need them to demonstrate their listening abilities. This research employs a quantitative approach, testing survey instruments via questionnaires. A total of 55 kids from Junior High School's 2nd grade participated in this research. Students' listening skills improved significantly after using liveworksheet, as shown by the results of the pre-and post-tests. Fifty-one percent of students strongly agree that listening lessons are essential, 45 percent strongly agree that the liveworksheet is an effective platform, 44 percent strongly agree that the platform needs to be maintained, 43 percent strongly agree that liveworksheets are the expect ed media, and 40 percent of students strongly agree that liveworksheets are easy to access media for listening skills. According to these findings, students' listening abilities improved as a consequence of using the liveworksheets platform. Copyright © 2022, Anka Publishers. All rights reserved.

A unique deep-learning-based model with chest X-ray image for diagnosing COVID-19

Later innovative advancements cleared the way for deep learning-based methods to be used in the therapeutic field due to its exactness for the detection and localization of different illnesses. Recently, the coronavirus widespread has put a parcel of weight on the health framework all around the world. Reverse transcription-polymerase chain reaction test and medical envisioning are both possible and effective techniques to determine the coronavirus infection. Since coronavirus is highly infection and reverse transcription-polymerase chain reaction is time-consuming, determination utilizing a chest X-ray to early diagnosing the infection is considered secure in different situations. A preprocessing step is done first to balance classes inside the dataset and increase the training data. A deep learning-based method is proposed in this study to determine some human lung infections and classify coronavirus from other non-coronavirus diseases accordingly. The proposed model is used for multi-class classification which is more complicated than binary classification especially in the medical image due to the inter classes' large similarity. The proposed procedure effectively classifies four classes that incorporate coronavirus, lung opacity, normal lung, and viral pneumonia with an accuracy of 97.5%. The proposed strategy appears excellent in terms of accuracy when compared with later strategies. © 2022 Institute of Advanced Engineering and Science. All rights reserved.

IMPACT OF COVID-19 ON AEROSOL OPTICAL DEPTH AND PARTICULATE MATTER OVER IRAQ

Aerosol Optical Depth (AOD) is a measure of aerosols that are small solid and liquid particles suspended in the atmosphere. Dust from wind, sea salts, volcanic ash, smoke from forest fires, and pollution from factories are all examples of aerosols. Depending on its size, type, and location, the aerosol can either cool or heat the surface. The high concentrations of droplets when inhaled lead to infection of the upper respiratory system, damage to people's health, and this is related to the spread of the Coronavirus. Optical depth data for aerosols AOD (496, 550, 675, 865, 1248) nm and Particulate matter (PM1, PM10. PM2.5) of different concentrations were taken from the European Centre for Medium-Range Weather Forecasts (ECMWF) for 2019 and 2020. The impact of COVID-19 on human health was studied by changing of Aerosols index and Particulate matter, and the relationship between them by comparing 2019, and 2020, while the results concluded that aerosols are less valuable in 2020 compared to last year, and the reason is due to the low percentage of pollutants. Such as carbon monoxide and nitrogen oxide, which are considered dangerous pollutants and affect human health, and it was observed in this study that the northern regions are almost devoid of aerosols and particles, as they are present at low rates for the year 2020 compared to in 2019, the central and southern regions recorded the largest An increase in cases, due to the variation in the proportions of aerosols and particles that help the spread of the Coronavirus (COVID-19).

The diagnosis of COVID-19 in CT images using hybrid machine learning approaches (CNN & SVM)

The new coronavirus disease (2019) has spread quickly as an acute respiratory distress syndrome (ARDS) among millions of individuals worldwide. Furthermore, the number of COVID-19 checking obtainable in hospitals is very limited as compared to the rising number of infections every day. As an outcome, an automatic detection system must be implemented as a quick diagnostic tool for preventing or reducing the spread of COVID-19 among humans. The present paper aims to propose an automated system by means of a hybrid Deep Learning ("convolutional neural network "(CNN)) and "support vector machine (SVM) " approach for identifying COVID-19 pneumonia-infected patients on the basis of chest computed tomography (746 CT images of "COVID-19" and "non-COVID-19"). The proposed system is composed of three phases. The first, pre-processing phase begins with converting CT images into greyscale level CT images of equal size (256×256). The "contrast limited adaptive histogram equalization" technology is adopted to enhance the intensity levels, and demonstrate the feature of lung tissue. It is also necessary to normalize the division of the image elements by 255 to make the values between 0 and 1, as this will speed up the processing process. The second phase, the CNN (SimpNet model), was applied as a deep feature extraction technique to identify CT samples. The SVM classifier and SoftMax function are employed in the third phase to classify COVID-19 pneumonia-infected patients. Specificity, Sensitivity, "F-score ", Accuracy, and "area under curve" are used as criteria to estimate the efficiency of the classification. The results showed a high accuracy rate of COVID-19 classification which reached (98%) and (99.1%) for CNN-SoftMax and CNN-SVM classifier, respectively in the tested dataset (225 CT images). © The Author 2022. This work is licensed under a Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) that allows others to share and adapt the material for any purpose (even commercially), in any medium with an acknowledgement of the work's authorship and initial publication in this journal.

The Role of Knowledge, Emotion, and Intention in Influencing Students' Behaviors During COVID-19 Pandemic

This study aims to investigate the influence of students' knowledge, attitude, and behavioral intention on their behaviors during the COVID-19 pandemic. A survey study was designed using an online questionnaire involving 653 respondents from the first to final-year students at a Malaysian university. A CACQ-COV instrument was designed based on the Theory of Reasoned Action (TRA) model, comprising 67 items in four constructs: students' knowledge of the current pandemic, emotional engagement, behavioral intention, and behavioral action. The results show that the students learn most about the COVID-19 pandemic from the media and the internet platform;more than 50% of the students rated the television broadcast as the most trusted media. The mean scores of the students' knowledge about COVID-19 facts and symptoms;emotion, intention, and action are at high levels. In addition, knowledge, emotion, and behavioral intention have significantly influenced the students' behaviors and actions;it is noted that emotion has the greatest influence compared with knowledge and behavioral intention. The implication is that television broadcast should be the primary choice of media for carrying out future mass campaigns, in preference to social media, especially for announcing urgent matters and disseminating information related to the current issues.

Coronavirus disease 2019 in solid organ transplant recipients in a setting of proactive screening and contact tracing

Background: As of 26 June 2020, the global number of infections caused by Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2), had reached 11 million, with more than 500 thousand associated deaths1. Limited clinical information about COVID-19 on solid organ transplant (SOT) are available so far. We herein report our preliminary experience with COVID-19 in SOT recipients in the first few weeks of the outbreak in Qatar. Method: All SOT recipients with laboratory-confirmed COVID-19 up to 23 May 2020 were included. Baseline characteristics, antivirals and immunosuppressive management, complications, and outcomes were retrospectively extracted from the electronic health system. Categorical data are summarized as frequency and percentages, while continuous variables are presented as medians and ranges. Results: Twenty-four SOT patients with COVID-19 were included in this report (kidney: 16, liver: 6, heart: 1, and combined liver and kidney: 1). The median age was 57 years (range 24–72). Thanks to proactive screening, five (21%) asymptomatic cases were diagnosed (Table S1). Among the other 19 symptomatic patients, fever (15/19) and cough (13/19) were the most frequent presenting symptoms (Table S1). All patients were hospitalized;5 (21%) required invasive mechanical ventilation in the intensive care unit (ICU) (Table S2). Eleven (46%) patients developed acute kidney injury as a complication, including 3 in association with drug-drug interactions involving investigational COVID-19 therapies (Table S2). Maintenance of immunosuppressive therapy was changed in 18 (75%) patients, but systemic corticosteroids were not withdrawn in any. After a median follow up of 43 days (26–89), 18 (75%) patients had been discharged home, 3 (12.3%) were still hospitalized, 2 (8.3%) were still in ICU, and 1 (4.2%) had died (Table S2). Conclusion: Although higher mortality rates were observed in other reports,2,3 our results suggest that asymptomatic COVID-19 is possible in SOT recipients and that overall outcomes are not consistently worse than other immunocompetent patients. The results require validation in larger cohorts.