مجله کنترل
سال چهاردهم شماره 5 (زمستان 1399)

Under the global pandemic of COVID-19 over the last year, the use of image processing techniques and the artificial intelligence algorithms to analysis chest X-ray (CXR) images is becoming important. Determining the lung involvement and percentage development of COVID-19 is one of most important requirements for the hospitalization centers. The most studies in this field belong to the articles based on the deep learning methodologies using convolution neural networks, which are usually implemented to facilitate the screening process. Only a few number of studies are about the determining the percentage of lung involvement and development of coronavirus based on CXR images. The lack of comprehensive datasets of CT images with a large amount of samples is one of the most important issues in this field. Determining of lung infection in COVID-19 patients, based on different CXR images in different days, has its own challenges such as different image sizes, illumination density, radiation dose of X-ray and angle of radiation, which makes it impossible to the implement a simple differential filter on two different images. Using an optimization self-adaptive algorithm with differential and multi-objective approach can improve the performance accuracy with a corresponding reduction in computation time.

Assessing the risk of transmitting the corona virus is important for protecting public health under the COVID-19 epidemic. Public transportation such as bus and metro wagon, are the most important source of COVID 19 dispersion. In the last decade, numerical simulation plays important roles in predicting. In this case study, by a combination of numerical simulation and artificial intelligence, tries to predict the droplet of sneezing process. Metro wagon as a case study was considered and dispersion of droplet along the bus was studied. The result indicated that the small diameter can easily transport along the wagon. It’s also shows that the large area under affected to particle deposition. In this case study, combination of numerical simulation and artificial intelligent has a great result.

The present study examined the level of knowledge, attitude, and practice of the people of Isfahan in the field of using electronic services during the corona outbreak.

The research was applied in terms of purpose and descriptive-correlational nature and was conducted on 384 people over 18 years old living in Isfahan. The tool was a researcher-made questionnaire consisting of 35 questions that were distributed among eligible people by snowball sampling method. . Data were analyzed by SPSS and Smart PLS software.
 

The knowledge, attitude, and practice of the studied individuals in the field of using electronic services during the corona outbreak are significantly different from the average and is higher than the average. This rate was 3.99 for knowledge, 4.11 for attitude and 3.75 for performance. There is a difference between women and men in the level of knowledge and attitude in the use of electronic services and it is more in men, but there is no significant difference between the performance of women and men in the use of electronic services. The level of knowledge and practice does not differ based on education. The attainment of people with Ph.D. education is stronger in the field of using electronic services. Also, there is a significant difference between the variables of knowledge, attitude and practice of Isfahan people in using electronic services based on job and there is more difference in faculty members. And are in the ages of 30 to 50 years old.

The results show that the knowledge, attitude and practice of the citizens of Isfahan in the use of electronic services are higher than average. The launch of various electronic service delivery systems in many organizations, organizations and commercial businesses and stores has enabled the citizens of Isfahan to receive various services provided by these organizations at home and through mobile or computer systems. During the Covid-19 pandemic, there is less need to be physically present in public places...

In this paper, we investigate the prevalence rate of smoking in COVID-19 patients and examine whether there is a difference in the distribution of smokers between the two statistical populations of critically ill COVID-19 patients and the entire Iranian population or not. To do this, we first prepared a sample of 300 COVID-19 patients admitted to hospitals in Tehran and Rasht. Then, through the non-parametric statistical runs test, we show that the sample was randomly selected and it is possible to generalize the result of tests on the sample to the community of hospitalized COVID-19 patients. In continuation, we examined the hypothesis that the prevalence of smoking among COVID-19 patients admitted to hospitals is equal to the prevalence rate of smoking in the whole Iranian society. For this purpose, we used the non-parametric chi-square test and it was observed that this hypothesis is rejected. The data show that there is a significant difference in the prevalence of smoking between critically ill COVID-19 patients and the whole of Iranian society. Also, it can be concluded that, the prevalence rate of smoking among COVID-19 hospitalized patients is lower than this rate in the whole Iranian society. The above results show the need for serious research in this field and confirm that while avoiding any positive or negative tendency towards smoking, the causes and factors affecting this phenomenon should be investigated and drugs can be prepared and produced accordingly.

The fatal SARS-COV-2 virus appeared in China at the end of 2019 for the first time. This virus has similar sequence with SARS-COV in 2002, but its infection is very high rate. On the other hand, SARS-COV-2 is a RNA virus and requires RNA-dependent RNA polymerase (RdRp) to transcribe its viral genome. Due to the availability of the active site of this enzyme, an effective treatment is targeting it to inhibit SARS-COV-2 reproduction. Remdesivir is an inhibitor for Hepatitis C and Ebola that is approved by Food and Drug Administration. Also, it has shown good results in inhibition of main protease and RdRp enzyme of SARS-COV-2. In this paper, the inhibitory of Remdesivir in various temperatures has been observed using steered molecular dynamics simulation. For this reason, the binding affinity of Remdesivir and RdRp were evaluated by molecular docking at four different temperatures (from 17 to 47 K). According to the results, the rupture force and pulling work to separate the Remdesivir from RdRp decrease with increasing temperature. It is also shown that at higher temperatures, Gibbs free energy is reduced due to its relation with pulling work.

Due to the continuation of the pandemic of Coronavirus in the whole world, the number of deaths has reached over one million, based on the World Health Organization reports. Early diagnosis of the illness can be great assistance in order to break the chain of disease transmission. Nowadays, COVID-19 test kits are so limited in numbers, and expensive in terms of cost, which slows down the diagnosis procedure and makes it difficult, thus, it is necessary to diagnose the disease in the early stages, to prevent its incidence. Therefore, we decided to propose a self-assessment method for COVID-19 disease, using a type-2 Sugeno fuzzy inference system, which causes conservation in time and costs. The system is prepared based on 98 rules, according to the World Health Organization instructions, using MATLAB software to simulate and diagnose the disease. The results show that Sugeno fuzzy with the better correlation coefficient  R2=0.94  and error squared RMSE = 0.045, respectively, has acceptable accuracy for estimating and identifying COVID-19 disease. The self-assessment consequences are very promising and can prevent the further spread of the disease.

According to the global prevalence of coronavirus (COVID-19) pandemic, mathematical models can predict and control the dynamic behavior of the pandemic. Therefore, in this study, a comprehensive model is considered to examine the trend of COVID-19 based on Susceptible, Exposed, Infected (Symptomatic and Asymptomatic), and Recovered individuals. In the absence of a curative treatment or vaccination campaign, the group of "quarantined people" is added to the model. Then, a positivity analysis of states is examined, and the threshold criterion is determined. The equilibrium points (disease-free and endemic) are also calculated, and their stability is investigated using the Jacobin matrix. The quarantine rate is regulated as the only control input using the fuzzy sliding mode controller. The efficiency of the controller is also investigated in the presence of uncertainty in model parameters. Also, the impact of the infected community on other communities, considering the controller, will be examined. Finally, the performance and efficiency of the proposed controller are evaluated.

Accurate prediction and diagnosis of COVID-19 disease is very important for everyone, especially for medical professionals. On the other hand, the use of fuzzy systems in medicine is increasing rapidly. In this study, a fuzzy system was designed using the information of 375 patients suspected of having COVID-19 disease who referred to Imam Khomeini(Tehran), Alborz(Karaj) and Kowsar(Karaj) hospitals. For this purpose, 300 people were considered to extract the rules and 75 people were considered as test data. Information on 12 important parameters of COVID-19 disease including fever, cough,  headache, gastrointestinal symptoms,  skin rash,  sense of smell and taste, underlying disease, chest CT, blood oxygen level,  lethargy, age, family history and severity of COVID-19 disease received. The fuzzy expert system was designed with 29 rules after reviewing the rules and removing similar and contradictory rules by using their degree calculation. In this system, by integrating some factors, finally 8 input variables and one output variable were considered that was used by product inference engine, singleton fuzzifier and center average defuzzifier. It was observed that the designed fuzzy expert system provides very good results, so that it detects 93% of Covid-19 disease with high accuracy and also the sensitivity of the system is more than 95% and the specificity of the designed system is more than 87%.

In this paper, a model is proposed based on the different levels of social restrictions for the COVID-19.spread restraint in Iran. Also, a Genetic Algorithm (GA) identifies parameters of model using reported main data from the Iranian Ministry of Health and simulated data based on proposed model. Whereas Model Predictive Control (MPC) is a popular method which has been widely used in process control, after the discretization of model by a common method like Euler method, then we can consider the appropriate constraints and solve online optimization problem. In this paper, we have shown that the MPC controller able to flatten infected (symptomatic) individual curve and decrease its peak by applying the different levels of social restrictions. Numerical example and simulation results, based on main data, are given to illustrate the capability of this method.

In this paper modeling, analysis and prediction of novel epidemic of COVID-19 are concerned to identify effective spread parameters of it in Iran. For this purpose, the basic susceptible-infected-removed (SIR) model is used which has two parameters: the infection rate and remove rate. The occurrence of several maximum points in the Iranian data and the single peak of the SIR model makes it impossible to use a model with the same parameters for all times. For this reason, the Iranian data are divided into five time periods and then the parameters of each period are obtained. In addition to adapting to the behavior of disease-related data, these time periods are consistent with the realities of society, including the timing of government decisions and the changing patterns of individuals in society. Finally, an analysis based on the obtained parameters and the trend of disease spread in the continuation of this year is presented. Since the economic,social and health consequences of this virus are catastrophic, using the results of mathematical modeling to identify the factors affecting the spread of the disease can be a step towards future actions to control the disease.

On February 2020, Iran reported its first confirmed cases of infections COVID-19 pandemic and since than its effects have been widely spread across in Iran and worldwide. The devastating consequences of the COVID-19 pandemic showed that science and technologies play an important role in ensuring national security and social stability is such crises and we must learn from this event to better prepare for similar cases. In this paper, we will review the developed technologies in Iran to deal with the COVID-19 pandemic. We tried to gather the information of the startup companies and their presented technologies across Iran. After validating the gathered information, the startup companies have been classified based on their fields and their geographical locations.

The new coronavirus (COVID-19) is caused by the acute respiratory syndrome 2. The disease was first diagnosed in December 2019 in Hubei Province, Wuhan City, China. On March 11, the World Health Organization declared the virus an epidemic. Until Mid-February, 2021, about 112 million people worldwide have been infected with the virus, of which about 2.500.000 people have lost their lives, according to the latest global statistics from the Worldometer database. The economic shock of the coronavirus outbreak has also led to negative economic impacts, including declining sales of many related businesses. In this paper, the effects of coronavirus on the Iranian economy are examined with a systems dynamics approach. First, the relationships of important variables affecting GDP in SPSS software are obtained and then the data is analyzed using Vensim software. In the following, by analyzing the sensitivity and providing recommendations, the impact of managerial insights to control the conditions of this new crisis will be examined. The results show that the correct use of the mask and the observance of social distance as well as air flow in spaces and avoidance of closed spaces have a significant effect on reducing the spread of this disease and reducing mortality.

This paper presents a survey on haptic technology, virtual reality, and artificial intelligence applications in medical training during the COVID-19 pandemic. Over the last few decades, there has been a great deal of interest in using new technologies to establish capable approaches for medical training purposes. These methods are intended to minimize surgerychr('39')s adverse effects, mostly when done by an inexperienced surgeon. Due to the worldchr('39')s unique situation during the pandemic, which causes several cities to be locked up, these methodologies are becoming more critical. They eliminate the physical contact requirement between medical personnel and fellows, which decreases the risk of being infected with the virus. This study aims to present new applications for haptic technology, virtual reality, artificial intelligence, and new fields where they can provide a viable solution in the COVID-19 pandemic or any other similar crises.

Covid-19 disease is caused by a new type of coronavirus, which is an infectious disease that has so far infected more than 90 million people and killed about 2 million. Hygiene programs, treatment, education, restrictions and other preventive measures are the inputs that can limit the spread of the disease, which in turn reduces the incidence of the disease. In this paper, in order to investigate some of the influential parameters, a nonlinear model is analyzed to investigate the effect of hygiene and education on the transmission dynamics of Covid-19 disease. Stability analysis and equilibrium points are examined to determine the generation number (R0), the number that determines whether the disease disappears or not. The first part of the results shows that proper education and hygiene will reduce the number of patients. Also, in the second part of the results, the experimental data of three countries, Iran, Germany and Turkey from the beginning of the outbreak until January 10th, are examined and statistically compared. Then, the performance of these three countries at the peak of the disease outbreak (November, December and January) is analyzed. The results show that Iran has performed relatively better, despite sanctions and a lack of facilities, especially in the last three months.

Coronavirus Disease 2019 (COVID-19) caused by the SARS-CoV-2 virus is spreading rapidly worldwide and has led to widespread deaths globally. As a result, the early diagnosis of patients with COVID-19 is vital to control this dangerous viruschr('39')s release. There are two common diagnosing methods, chest computed tomography scan (CT-scan) and Reverse Transcription Polymerase Chain Reaction (RT-PCR) test. The most significant disadvantages of RT-PCR molecular tests are the high cost and the long waiting time for test results. The common weaknesses of chest CT-scan are the need for a radiologist to analyze, a misdiagnosis of flu disease due to its similarity, and risky for pregnancy and infants. This article presents a low-cost, highly available method for early detection of COVID-19 based on Artificial Intelligence (AI) systems and blood tests. In this study, 6635 patientchr('39')s blood tests are used. Experiments conducted using three machine learning algorithms. The results show that the proposed method can detect COVID-19 with an accuracy of %84 and an F1-score of %83. The trained model is being used in a real-world product through an online website called CODAS.

These days almost all the countries around the world are struggling with coronavirus outbreak. This virus made a lot of challenges in almost all aspects of societies including the economy, transportation, education, etc. If the governments and public health care systems donchr(chr('39')39chr('39'))t take any action against this outbreak, it would have severe effects on human life, now and in the future. By doing so, there are several intervention strategies that could be implemented and as the result, the societies become more secure from the casualties of this virus. In this paper, we used a mathematical model of coronavirus epidemic transmission and by use of some LMIs, a robust LPV controller is designed which helps us to choose and use the intervention methods, effectively. The model of a coronavirus contains some uncertainties with some states and parameter values. So, by use of the proposed robust controller, the robustness and stability of the model against a wide range of uncertainties are approved. Then, we compare the results of an uncontrolled system with the controlled system and also the optimal control problem, to observe the effects of our controller on the system. The final objective of this control design is to minimize the number of exposed and infected individuals in the compartmental model. In the end, it can be seen that the control strategies which are preventive action, good medical care, and sterilization of the environment can highly reduce the negative effects of the coronavirus.