Detection of brain lesion location in MRI images using convolutional neural network and robust PCA.

Purpose and aim: Detection of brain tumors plays a critical role in the treatment of patients. Before any treatment, tumor segmentation is crucial to protect healthy tissues during treatment and to destroy tumor cells. Tumor segmentation involves the detection, precise identification, and separation of tumor tissues. In this paper, we provide a deep learning method for the segmentation of brain tumors. Material and methods: In this article, we used a convolutional neural network (CNN) to segment tumors in seven types of brain disease consisting of Glioma, Meningioma, Alzheimer's, Alzheimer's plus, Pick, Sarcoma, and Huntington. First, we used the feature-reduction-based method robust principal component analysis to find tumor location and spot in a dataset of Harvard Medical School. Then we present an architecture of the CNN method to detect brain tumors. Results: Results are depicted based on the probability of tumor location in magnetic resonance images. Results show that the presented method provides high accuracy (96%), sensitivity (99.9%), and dice index (91%) regarding other investigations. Conclusion: The provided unsupervised method for tumor clustering and proposed supervised architecture can be potential methods for medical uses.

Studying the Effect of Taking Statins before Infection in the Severity Reduction of COVID-19 with Machine Learning.

Statins can help COVID-19 patients' treatment because of their involvement in angiotensin-converting enzyme-2. The main objective of this study is to evaluate the impact of statins on COVID-19 severity for people who have been taking statins before COVID-19 infection. The examined research patients include people that had taken three types of statins consisting of Atorvastatin, Simvastatin, and Rosuvastatin. The case study includes 561 patients admitted to the Razi Hospital in Ghaemshahr, Iran, during February and March 2020. The illness severity was encoded based on the respiratory rate, oxygen saturation, systolic pressure, and diastolic pressure in five categories: mild, medium, severe, critical, and death. Since 69.23% of participants were in mild severity condition, the results showed the positive effect of Simvastatin on COVID-19 severity for people that take Simvastatin before being infected by the COVID-19 virus. Also, systolic pressure for this case study is 137.31, which is higher than that of the total patients. Another result of this study is that Simvastatin takers have an average of 95.77 mmHg O2Sat; however, the O2Sat is 92.42, which is medium severity for evaluating the entire case study. In the rest of this paper, we used machine learning approaches to diagnose COVID-19 patients' severity based on clinical features. Results indicated that the decision tree method could predict patients' illness severity with 87.9% accuracy. Other methods, including the K-nearest neighbors (KNN) algorithm, support vector machine (SVM), Naïve Bayes classifier, and discriminant analysis, showed accuracy levels of 80%, 68.8%, 61.1%, and 85.1%, respectively.

The impact of artificial intelligence and digital style on industry and energy post-COVID-19 pandemic.

The SARS-CoV-2 virus caused crises in social, economic, and energy areas and medical life worldwide throughout 2020. This crisis had many direct and indirect effects on all areas of society. In the meantime, the digital and artificial intelligence industry can be used as a professional assistant to manage and control the outbreak of the virus. The present article's objective is to investigate the effects of COVID-19 on each of the various fields of medicine, industry, and energy. What sets this article apart is studying the impact of artificial intelligence and digital style on reducing the damage of this fatal virus. Energy and related industries are of the areas affected by the SARS-CoV-2 virus. The most exciting approach in this article is to encourage countries with economies based on non-renewable energy to develop solar and wind energies. Renewable energies can operate well in the event of another phenomenon such as COVID-19 and reduce the virus's destructive effects and lead to economic prosperity.

Experimental and numerical diagnosis of fatigue foot using convolutional neural network.

Fatigue is an essential criterion for physiotherapy in injured athletes. Muscle fatigue mechanism also is a crucial matter in designing a workout program. It is mainly related to physical injury, cerebrovascular accident, spinal cord injury, and rheumatologic disease. The leg is one of the organs in the body where fatigue is visible, and usually, the first fatigue traces in the human body are shown. The main objective of the article is to diagnosis tired and untired feet base on digital footprint images. Therefore, the foot images of students in the age group of 20-30 were examined. The device is a digital footprint scanner. This device includes a plate screen equipped with pressure sensors and footprints in the image. A treadmill is used for 8 min to tire our test individuals. Therefore, six methods of k-nearest-neighbor classifier, multilayer perceptron, support vector machine, naïve Bayesian learning, decision tree, and convolutional neural network (CNN) architecture are presented to achieve the goal. First, the images are grayscale and divide into four regions, and the mean and variance of pressure in each of the four areas are extracted as features. Finally, the classification is accomplished using machine learning methods. Then, the results are compared with a proposed CNN architecture. The presented CNN method is outperforming other approaches and can be used for future fatigue diagnosis systems.

HSSAGA: Designation and scheduling of nurses for taking care of COVID-19 patients using novel method of Hybrid Salp Swarm Algorithm and Genetic Algorithm.

The COVID-19 pandemic is viewed as the most basic worldwide disaster that humankind has observed since the second World War. There is no report of any clinically endorsed antiviral medications or antibodies that are successful against COVID-19. It has quickly spread everywhere, presenting tremendous well-being, financial, ecological, and social difficulties to the whole human populace. The COVID flare-up is seriously disturbing the worldwide economy. Practically all the countries are battling to hinder the transmission of the malady by testing and treating patients, isolating speculated people through contact following, confining huge social affairs, keeping up total or incomplete lockdown, etc. Proper scheduling of nursing workers and optimal designation of nurses may significantly affect the quality of clinical facilities. It is delivered by eliminating unbalanced workloads or undue stress, which could lead to decreased nurse performance and potential human errors., Nurses are frequently asked to leave while caring for all sick patients. However, regular scheduling formulas are not thought to consider this possibility because they are out of scheduling control in typical scenarios. In this paper, a novel model of the Hybrid Salp Swarm Algorithm and Genetic Algorithm (HSSAGA) is proposed to solve nurses' scheduling and designation. The findings of the suggested test function algorithm demonstrate that this algorithm has outperformed state-of-the-art approaches.

Revealing the relationship between solar activity and COVID-19 and forecasting of possible future viruses using multi-step autoregression (MSAR).

The number of sunspots shows the solar activity level. During the high solar activity, emissions of matter and electromagnetic fields from the Sun make it difficult for cosmic rays to penetrate the Earth. When solar energy is high, cosmic ray intensity is lower, so that the solar magnetic field and solar winds affect the Earth externally and originate new viruses. In this paper, we assess the possible effects of sunspot numbers on the world virus appearance. The literature has no sufficient results about these phenomena. Therefore, we try to relate solar ray extremum to virus generation and the history of pandemics. First, wavelet decomposition is used for smoothing the sunspot cycle to predict past pandemics and forecast the future time of possible virus generation. Finally, we investigate the geographical appearance of the virus in the world to show vulnerable places in the world. The result of the analysis of pandemics that occurred from 1750 to 2020 shows that world's great viral pandemics like COVID-19 coincide with the relative extrema of sunspot number. Based on our result, 27 pandemic (from 36) incidences are on sunspot extrema. Then, we forecast future pandemics in the world for about 110 years or 10 cycles using presented multi-step autoregression (MSAR). To confirm these phenomena and the generation of new viruses because of solar activity, researchers should carry out experimental studies.

QAIS-DSNN: Tumor Area Segmentation of MRI Image with Optimized Quantum Matched-Filter Technique and Deep Spiking Neural Network.

Tumor segmentation in brain MRI images is a noted process that can make the tumor easier to diagnose and lead to effective radiotherapy planning. Providing and building intelligent medical systems can be considered as an aid for physicians. In many cases, the presented methods' reliability is at a high level, and such systems are used directly. In recent decades, several methods of segmentation of various images, such as MRI, CT, and PET, have been proposed for brain tumors. Advanced brain tumor segmentation has been a challenging issue in the scientific community. The reason for this is the existence of various tumor dimensions with disproportionate boundaries in medical imaging. This research provides an optimized MRI segmentation method to diagnose tumors. It first offers a preprocessing approach to reduce noise with a new method called Quantum Matched-Filter Technique (QMFT). Then, the deep spiking neural network (DSNN) is implemented for segmentation using the conditional random field structure. However, a new algorithm called the Quantum Artificial Immune System (QAIS) is used in its SoftMax layer due to its slowness and nonsegmentation and the identification of suitable features for selection and extraction. The proposed approach, called QAIS-DSNN, has a high ability to segment and distinguish brain tumors from MRI images. The simulation results using the BraTS2018 dataset show that the accuracy of the proposed approach is 98.21%, average error-squared rate is 0.006, signal-to-noise ratio is 97.79 dB, and lesion structure criteria including the tumor nucleus are 80.15%. The improved tumor is 74.50%, and the entire tumor is 91.92%, which shows a functional advantage over similar previous methods. Also, the execution time of this method is 2.58 seconds.

The prognostic effect of hyponatraemia in determining mortality in patients with heart failure hospitalised in AL-Yarmouk Teaching Hospital.

OBJECTIVE: To assess the prognostic effect of hyponatraemia in determining mortality in patients with heart failure. METHODS: The prospective observational study was conducted at the Internal Medicine Unit of Al-Yarmouk Teaching Hospital, Baghdad, Iraq, from September 1, 2019, to February 1, 2020, and comprised adult patients of either gender who were hospitalised due to heart failure and left ventricular ejection fraction <49% at admission. The endpoint was inpatient mortality from the index hospitalisation for decompensated heart failure according to the serum sodium levels. RESULTS: Of the 70 patients, 46(65.7%) were males and 24(34.3%) were females. A total of 30(42.9%) patients were aged 60-69 years. Serum sodium level was <135mmol/L in 40(57%) patients, and, among them, 29(67.4%) had normal body mass index and 21(70%) were ex-smokers. There was a significant association of serum sodium with body mass index and smoking status (p<0.05). Of the 25(35.7%) cases of inpatient mortality, 19(76%) had serum sodium level <135mmol/L (p= 0.004). CONCLUSIONS: Hyponatraemia was found to be common in patients with heart failure. It is a significant modifiable risk factor in patients with heart failure.

The effect of serum uric acid in hospitalised patients with acute coronary syndrome in Al-Yarmouk Teaching Hospital.

OBJECTIVE: To evaluate the efficacy of serum uric acid on inpatient morbidity and mortality among acute coronary syndrome patients. METHODS: The hospital-based study was conducted in the Coronary Care Unit of Al-Yarmouk Teaching Hospital, Baghdad, Iraq, from June 1, 2019, to February 28, 2020, and comprised acute coronary syndrome patients of either gender aged >18 years. Other than the demographics, data was collected on echocardiography findings, cardiac markers and serum uric acid level. Data was analysed using SPSS 20. RESULTS: Of the 70 patients, 49(70%) were male and 21(30%) were female. Overall, 44(62.8%) patients had normal uric acid level and 26(37.2%) had hyperuricaemia. Mean serum uric acid concentration was 5.6±1.6mg/dl (range: 2.5-9.3mg/dl). There was a significant association between patients presenting with heart failure and hyperuricaemia (p<0.05). Complications, such as arrhythmia and heart failure, occurred more in patients with hyperuricaemia (p<0.05). CONCLUSIONS: There was an association between hyperuricaemia and in-hospital complications of patients with acute coronary syndrome in comparison with patients with normal serum uric acid levels.

Presentation of a developed sub-epidemic model for estimation of the COVID-19 pandemic and assessment of travel-related risks in Iran.

The COVID-19 pandemic is one of the contagious diseases involving all the world in 2019-2020. Also, all people are concerned about the future of this catastrophe and how the continuous outbreak can be prevented. Some countries are not successful in controlling the outbreak; therefore, the incidence is observed in several peaks. In this paper, firstly single-peak SIR models are used for historical data. Regarding the SIR model, the termination time of the outbreak should have been in early June 2020. However, several peaks invalidate the results of single-peak models. Therefore, we should present a model to support pandemics with several extrema. In this paper, we presented the generalized logistic growth model (GLM) to estimate sub-epidemic waves of the COVID-19 outbreak in Iran. Therefore, the presented model simulated scenarios of two, three, and four waves in the observed incidence. In the second part of the paper, we assessed travel-related risk in inter-provincial travels in Iran. Moreover, the results of travel-related risk show that typical travel between Tehran and other sites exposed Isfahan, Gilan, Mazandaran, and West Azerbaijan in the higher risk of infection greater than 100 people per day. Therefore, controlling this movement can prevent great numbers of infection, remarkably.

Diagnosis and detection of infected tissue of COVID-19 patients based on lung x-ray image using convolutional neural network approaches.

COVID-19 pandemic has challenged the world science. The international community tries to find, apply, or design novel methods for diagnosis and treatment of COVID-19 patients as soon as possible. Currently, a reliable method for the diagnosis of infected patients is a reverse transcription-polymerase chain reaction. The method is expensive and time-consuming. Therefore, designing novel methods is important. In this paper, we used three deep learning-based methods for the detection and diagnosis of COVID-19 patients with the use of X-Ray images of lungs. For the diagnosis of the disease, we presented two algorithms include deep neural network (DNN) on the fractal feature of images and convolutional neural network (CNN) methods with the use of the lung images, directly. Results classification shows that the presented CNN architecture with higher accuracy (93.2%) and sensitivity (96.1%) is outperforming than the DNN method with an accuracy of 83.4% and sensitivity of 86%. In the segmentation process, we presented a CNN architecture to find infected tissue in lung images. Results show that the presented method can almost detect infected regions with high accuracy of 83.84%. This finding also can be used to monitor and control patients from infected region growth.

Investigation of Microwave Absorption Performance of CoFe2O4/NiFe2O4/Carbon Fiber Composite Coated with Polypyrrole in X-Band Frequency.

The current research reports the preparation of a microwave absorber containing CoFe2O4/NiFe2O4/Carbon fiber (H/S/CF) coated with polypyrrole polymer (PPy@H/S/CF) through sol-gel and in-situ polymerization processes. X-ray diffraction (XRD), field emission scanning electron microscopy (FESEM), vibrating sample magnetometer (VSM), and a vector network analyzer (VNA) are utilized to evaluate the features of the prepared composite. The microstructure analysis results revealed carbon fibers well decorated with submicron-size particles having hard/soft magnetic phases and thoroughly coated with polymer. The paraffin-based microwave absorber sample filled with 45 wt.% of PPy@H/S/CF has simultaneously both magnetic and dielectric losses in the 8.2-12.4 GHz frequency range. The absorber is used in a Salisbury screen configuration aiming at reducing the radar cross-section of objects. A minimum reflection loss of -55 dB at 10.6 GHz frequency with 5 GHz bandwidth is obtained for the sample with a 2 mm thickness. Different mechanisms, such as interfacial polarization, ferromagnetic resonance, and electron hopping, are the main factors for achieving such an appropriate microwave absorption. These results suggest that the PPy@H/S/CF composite is an ideal candidate for microwave absorption applications requiring high performance and low thickness.

Investigation of effective climatology parameters on COVID-19 outbreak in Iran.

SARS CoV-2 (COVID-19) Coronavirus cases are confirmed throughout the world and millions of people are being put into quarantine. A better understanding of the effective parameters in infection spreading can bring about a logical measurement toward COVID-19. The effect of climatic factors on spreading of COVID-19 can play an important role in the new Coronavirus outbreak. In this study, the main parameters, including the number of infected people with COVID-19, population density, intra-provincial movement, and infection days to end of the study period, average temperature, average precipitation, humidity, wind speed, and average solar radiation investigated to understand how can these parameters effects on COVID-19 spreading in Iran? The Partial correlation coefficient (PCC) and Sobol'-Jansen methods are used for analyzing the effect and correlation of variables with the COVID-19 spreading rate. The result of sensitivity analysis shows that the population density, intra-provincial movement have a direct relationship with the infection outbreak. Conversely, areas with low values of wind speed, humidity, and solar radiation exposure to a high rate of infection that support the virus's survival. The provinces such as Tehran, Mazandaran, Alborz, Gilan, and Qom are more susceptible to infection because of high population density, intra-provincial movements and high humidity rate in comparison with Southern provinces.

Numerical Simulation of Magnetic Drug Targeting to the Stenosis Vessel Using Fe3O4 Magnetic Nanoparticles Under the Effect of Magnetic Field of Wire.

PURPOSE: In the present paper, the magnetic drug targeting using drug coated Fe3O4 nanoparticles to the stenosis region of the vessel was investigated. The problem was solved for various magnetic numbers. Moreover, the effect of the location of the wire, as a magnetic source, on the MDT was studied. METHODS: The governing equations of continuity, momentum and volume fraction were solved by taking into account the effects of kelvin force and magnetophoresis. Finite volume method is used for discretization of unsteady two-phase flow equations. RESULTS: In low magnetic numbers, the most important phenomenon is the gradual formation of drug droplet on the location of the wire. The drug drop holds the drug near the target tissue for a long time and has a positive role in the MDT as a source of drug over time. Also, in high magnetic numbers, the amount of drug in the tissue is also high at the time of the formation of the droplet. However, the number of vortices formed in the flow increases, and this leads to get the target further away from the tissue. Two main phenomena of drug droplet formation and vortices generation were observed as positive and negative factors in MDT, respectively. The results showed that in a specific magnetic number, the MDT function could be optimal. If the wire is located in the upstream region of the stenosis, it will have a small positive effect on the concentration of the drug in the target tissue.

Microalbuminuria and left ventricular hypertrophy in patients with essential hypertension.

OBJECTIVE: To assess the relationship between microalbuminuria and left ventricular hypertrophy in patients with essential hypertension. METHODS: The case-control study was conducted at Al-Yarmouk Teaching Hospital, Baghdad, Iraq, from June 1 to December 31, 2016, and comprised patients with essential hypertension. Based on echocardiography, the patients were divided into 2 equal groups of those with and without left ventricular hypertrophy. Spot urine sample for the patients in the 2 groups was collected to assess microalbuminuria. Blood pressure, smoking status, family history of hypertension, serum creatinine, total cholesterol and blood sugar levels were evaluated. SPSS 22 was used for data analysis. RESULTS: Of the 100 subjects, 47(47%) were males and 53(53%) were females. The overall mean age was 59}7.2 years. The case and control groups had 50(50%) patients each. The mean albumin-to-creatinine ratio of the patients was 70.5}4.6µg/mg compared to 30.3}16.6µg/mg of the controls (p<0.05). The mean systolic blood pressure of patients with microalbuminuria was 163.6}10.5 mmHg, mean diastolic blood pressure was 104.7}7.3mmHg, and mean albumin-to-creatinine ratio was 74}43µg/mg compared to 157.1}0.2mmHg, 96.5}6.8mmHg and 23}13µg/mg, respectively, in patients without microalbuminuria (p<0.05 each). CONCLUSIONS: There was found a positive relationship between left ventricular hypertrophy and microalbuminuria in patients with essential hypertension.

Modeling and sensitivity analysis of NOx emissions and mechanical efficiency for diesel engine.

This paper involves discovering effective and better reaction of the diesel engine at various velocities by having ideal values in a short period. Therefore, gene expression programming is used for modeling and presenting governing expression for the related factors. The effective parameters consist of engine speed, intake air temperature, rate of air over fuel, fuel mass, NOx emission, mechanical efficiency, and immediate infusion diesel engine used as a part of demonstrating. Gene expression programming and its values exactly predict output results and present precise formula. Moreover, the sensitivity analysis was performed to recognize the effectiveness of the factors for reducing NOx and increasing mechanical efficiency. In the sensitivity analysis, the methods such as partial correlation coefficient, standard regression coefficient, and the Sobol'-Jansen and distributed evaluation of local sensitivity analysis are all used. The obtained results show that air/fuel rate is more influential factor in both NOx emission and mechanical efficiency. Moreover, the difference between results of standard regression or partial correlation coefficients and Sobol'-Jansen or distributed evaluation methods is in nonlinearity effect or interactions among the factors.