ABSTRACT
The current COVID-19 pandemic is making a huge impact on society. Most projects are either abandoned or halted due to this pandemic, especially in developing countries. We have conducted this study to evaluate the impact of COVID-19 pandemic on construction projects by using the concept of rework projects. ‘Rework project’ is a class of projects that are initiated to achieve the intended objectives in the second attempt after failing to achieve the goals in the first attempt. People who were involved in the selected projects in different capacities were interviewed and analysis of the responses was performed. The unique challenges/risks such as time urgency, overburdened resources, and mobilisation of contractors, inappropriate documentation gaps, and technological changes were highly significant in rework projects. By having clear recognition to these highly significant risks, organisations will be well equipped in devising strategies to manage and complete the rework projects in the post-pandemic world. Copyright © 2022 Inderscience Enterprises Ltd.
ABSTRACT
Background: The clinical field has vast sick data that has not been analyzed. Discovering a way to analyze this raw data and turn it into an information treasure can save many lives. Using data mining methods is an efficient way to analyze this large amount of raw data. It can predict the future with accurate knowledge of the past, providing new insights into disease diagnosis and prevention. Several data mining methods exist but finding a suitable one is very important. Today, coronavirus disease (COVID-19) has become one of the causing deadly diseases in the world. The early diagnosis of pandemic coronavirus disease has a significant impact in preventing death. This study aims to extract the key indications of the disease and find the best data mining methods that enhance the accuracy of coronavirus disease diagnosis. Methods: In this study, to obtain high accuracy in diagnosing COVID-19 disease, a complete and effective workflow over data mining methods was proposed, which includes these steps: data pre-analyzing, indication selection, model creation, the measure of performance, and display of results. Data and related indications of patients with COVID-19 were collected from Kerman Afzalipour Hospital and Rafsanjan, Ali Ebn Abi Taleb Hospital. Prediction structures were made and tested via different combinations of the disease indications and seven data mining methods. To discover the best key indications, three criteria including accuracy, validation and F-value were applied and to discover the best data mining methods, accuracy and validation criteria were considered. For each data mining method, the criteria were measured independently and all results were reported for analysis. Finally, the best key indications and data mining methods that can diagnose COVID-19 disease with high accuracy were extracted. Results: 9 key indications and 3 data mining methods were obtained. Experimental results show that the discovered key indications and the best-operating data mining method (i.e. SVM) attain an accuracy of 83.19% for the diagnosis of coronavirus disease. Conclusion: Due to key indications and data mining methods obtained from this study, it is possible to use this method to diagnose coronavirus disease in different people of different clinical indications with high accuracy.
ABSTRACT
COVID-19 virus of the family Coronaviridae, is an enveloped virus with RNA nucleic acid. The virus spread rapidly from China and was declared a pandemic by the World Health Organization on March 11, 2020. Studies show that some patients who recovered from COVID-19 had a recurrence that arose concern among the scientists. The main purpose of this review is to provide potential hypotheses for the recurrence of COVID-19 infection in both clinical reactivation and reinfection. In reactivation recurrence, hypotheses such as hereditary immunodeficiency, the balance between angiotensin-converting enzyme (ACE) and angiotensin-converting enzyme2 (ACE2), the presence of extracellular exosomes were presented, and in the field of reinfection recurrence, hypotheses such as a false primary RT-PCR test deals with positive, low load of COVID-19 virus, acquired immune system defects and mutations in viral RNA that alter viral epitopes were presented in this review. From the hypotheses presented in this paper, it can be concluded that an imbalance between ACE/AngII/AT1R and ACE2/Ang1-7/MasR and the defects in the immune system, can cause defects in the proliferation of NK cells and T lymphocytes, and exosomes contain viral mRNA ultimately leads to reactivation recurrence of the disease. Reinfection recurrence of the COVID-19 may be as a result of the primary false positive report in RT-PCR test, low viral load and inactivation of the immune system, defects in the acquired immune system, and mutations in virus RNA.