Introducing a new method for FDTD modeling of electromagnetic wave propagation in magnetized plasma

pre-printComputational investigations of electromagnetic wave propagation in the upper atmosphere are important for studying space weather hazards, such as geomagnetically induced currents (GICs). GICs are currents generated in gas/oil pipelines, railroads, and electric power networks due to solar storms and the consequent modification of the ionospheric current system. In the upper atmosphere where the collision frequency of the charged particles becomes negligible, the medium is magnetized and anisotropic. The difficulty in modeling wave propagation in magnetized plasma is due to the difficulty in accurately calculating the electric current. The electric current can be found from the momentum equation. However, a fast and efficient method is required to find the electric current perpendicular and parallel to the geomagnetic field

An Estimation of Tax Evasion in Iran

The aim of this research is to estimate the size of tax evasion between 1971 and 2007 in Iran. Among the present direct and indirect approaches, the indirect approach presented by Tanzi based on currency demand, is used to estimate the size of the underground economy, then taking the effective tax rate into consideration, the amount of the underground economy taxes. Our results show that the size of the underground economy is increasing in a long-term trend and also the ratio of the underground economy to gross domestic product has increased during the period under consideration. Our findings also indicate that tax evasion has markedly increased during the period. Therefore, policies to alleviate the tax evasion in the country should be implemented by policy makers

Predicting survival of Iranian COVID-19 patients infected by various variants including omicron from CT Scan images and clinical data using deep neural networks

Purpose: The rapid spread of the COVID-19 omicron variant virus has resulted in an overload of hospitals around the globe. As a result, many patients are deprived of hospital facilities, increasing mortality rates. Therefore, mortality rates can be reduced by efficiently assigning facilities to higher-risk patients. Therefore, it is crucial to estimate patients' survival probability based on their conditions at the time of admission so that the minimum required facilities can be provided, allowing more opportunities to be available for those who need them. Although radiologic findings in chest computerized tomography scans show various patterns, considering the individual risk factors and other underlying diseases, it is difficult to predict patient prognosis through routine clinical or statistical analysis. Method: In this study, a deep neural network model is proposed for predicting survival based on simple clinical features, blood tests, axial computerized tomography scan images of lungs, and the patients' planned treatment. The model's architecture combines a Convolutional Neural Network and a Long Short Term Memory network. The model was trained using 390 survivors and 108 deceased patients from the Rasoul Akram Hospital and evaluated 109 surviving and 36 deceased patients infected by the omicron variant. Results: The proposed model reached an accuracy of 87.5% on the test data, indicating survival prediction possibility. The accuracy was significantly higher than the accuracy achieved by classical machine learning methods without considering computerized tomography scan images (p-value &lt;= 4E-5). The images were also replaced with hand-crafted features related to the ratio of infected lung lobes used in classical machine-learning models. The highest-performing model reached an accuracy of 84.5%, which was considerably higher than the models trained on mere clinical information (p-value &lt;= 0.006). However, the performance was still significantly less than the deep model (p-value &lt;= 0.016). Conclusion: The proposed deep model achieved a higher accuracy than classical machine learning methods trained on features other than computerized tomography scan images. This proves the images contain extra information. Meanwhile, Artificial Intelligence methods with multimodal inputs can be more reliable and accurate than computerized tomography severity scores.</p

Differential privacy preserved federated transfer learning for multi-institutional 68Ga-PET image artefact detection and disentanglement.

PURPOSE Image artefacts continue to pose challenges in clinical molecular imaging, resulting in misdiagnoses, additional radiation doses to patients and financial costs. Mismatch and halo artefacts occur frequently in gallium-68 (68Ga)-labelled compounds whole-body PET/CT imaging. Correcting for these artefacts is not straightforward and requires algorithmic developments, given that conventional techniques have failed to address them adequately. In the current study, we employed differential privacy-preserving federated transfer learning (FTL) to manage clinical data sharing and tackle privacy issues for building centre-specific models that detect and correct artefacts present in PET images. METHODS Altogether, 1413 patients with 68Ga prostate-specific membrane antigen (PSMA)/DOTA-TATE (TOC) PET/CT scans from 3 countries, including 8 different centres, were enrolled in this study. CT-based attenuation and scatter correction (CT-ASC) was used in all centres for quantitative PET reconstruction. Prior to model training, an experienced nuclear medicine physician reviewed all images to ensure the use of high-quality, artefact-free PET images (421 patients' images). A deep neural network (modified U2Net) was trained on 80% of the artefact-free PET images to utilize centre-based (CeBa), centralized (CeZe) and the proposed differential privacy FTL frameworks. Quantitative analysis was performed in 20% of the clean data (with no artefacts) in each centre. A panel of two nuclear medicine physicians conducted qualitative assessment of image quality, diagnostic confidence and image artefacts in 128 patients with artefacts (256 images for CT-ASC and FTL-ASC). RESULTS The three approaches investigated in this study for 68Ga-PET imaging (CeBa, CeZe and FTL) resulted in a mean absolute error (MAE) of 0.42 ± 0.21 (CI 95%: 0.38 to 0.47), 0.32 ± 0.23 (CI 95%: 0.27 to 0.37) and 0.28 ± 0.15 (CI 95%: 0.25 to 0.31), respectively. Statistical analysis using the Wilcoxon test revealed significant differences between the three approaches, with FTL outperforming CeBa and CeZe (p-value < 0.05) in the clean test set. The qualitative assessment demonstrated that FTL-ASC significantly improved image quality and diagnostic confidence and decreased image artefacts, compared to CT-ASC in 68Ga-PET imaging. In addition, mismatch and halo artefacts were successfully detected and disentangled in the chest, abdomen and pelvic regions in 68Ga-PET imaging. CONCLUSION The proposed approach benefits from using large datasets from multiple centres while preserving patient privacy. Qualitative assessment by nuclear medicine physicians showed that the proposed model correctly addressed two main challenging artefacts in 68Ga-PET imaging. This technique could be integrated in the clinic for 68Ga-PET imaging artefact detection and disentanglement using multicentric heterogeneous datasets

Global prevalence of intestinal protozoan contamination in vegetables and fruits: A systematic review and meta-analysis

A B S T R A C T Environmental contamination of vegetables and fruits with intestinal protozoan trophozoites, cysts and oocysts is a means of transmitting parasitic agents of public health importance. The purpose of this systematic review and meta-analysis was to determine the global prevalence of intestinal protozoan parasite contamination in vege- tables and fruits. Several databases (Web of Science, PubMed, Scopus, ProQuest and Google Scholar) were searched for literature published up to August 2021. Pooled prevalence was determined using the meta-package in R (version 3.6.1). Out of 90,404 publications, 189 articles (202 datasets) met the inclusion criteria. Among these, 183 investigations documented protozoan contamination in vegetables and 20 in fruits. The pooled prevalence (95% confidence interval) was 20% (16%–24%) for vegetables and 13% (7%–21%) for fruits. The highest pooled prevalence was found in South-East Asian WHO region 37% (6%–76%). The most prevalent protozoan parasite in vegetables was Cryptosporidium spp. (11%, 7%–15%). As well, Entamoeba histolytica was the most common agent found in fruits (9%, 4%–14%). Furthermore, the unwashed samples had the highest pooled prevalence of contamination (22%, 3%–49%). Our data suggest a possible risk of protozoan infection in humans via unwashed vegetables and fruits. Accidental ingestion of protozoa occurs through consumption of contami- nated vegetables and fruits that have been improperly washed and prepared under poor sanitation. Using san- itary irrigation water, consuming properly cleaned and cooked vegetables, and practicing good hygiene can all assist to reduce the risk of protozoa infection Keywords: Vegetables Fruits, Protozoan contamination, Public health, Food-borne, disease

Global prevalence of intestinal protozoan contamination in vegetables and fruits: A systematic review and meta-analysis

Environmental contamination of vegetables and fruits with intestinal protozoan trophozoites, cysts and oocysts is a means of transmitting parasitic agents of public health importance. The purpose of this systematic review and meta-analysis was to determine the global prevalence of intestinal protozoan parasite contamination in vegetables and fruits. Several databases (Web of Science, PubMed, Scopus, ProQuest and Google Scholar) were searched for literature published up to August 2021. Pooled prevalence was determined using the meta-package in R (version 3.6.1). Out of 90,404 publications, 189 articles (202 datasets) met the inclusion criteria. Among these, 183 investigations documented protozoan contamination in vegetables and 20 in fruits. The pooled prevalence (95% confidence interval) was 20% (16%–24%) for vegetables and 13% (7%–21%) for fruits. The highest pooled prevalence was found in South-East Asian WHO region 37% (6%–76%). The most prevalent protozoan parasite in vegetables was Cryptosporidium spp. (11%, 7%–15%). As well, Entamoeba histolytica was the most common agent found in fruits (9%, 4%–14%). Furthermore, the unwashed samples had the highest pooled prevalence of contamination (22%, 3%–49%). Our data suggest a possible risk of protozoan infection in humans via unwashed vegetables and fruits. Accidental ingestion of protozoa occurs through consumption of contaminated vegetables and fruits that have been improperly washed and prepared under poor sanitation. Using sanitary irrigation water, consuming properly cleaned and cooked vegetables, and practicing good hygiene can all assist to reduce the risk of protozoa infection

Bond and Option Prices under Skew Vasicek Model with Transaction Cost

This paper studies the European option pricing on the zero-coupon bond in which the Skew Vasicek model uses to predict the interest rate amount. To do this, we apply the skew Brownian motion as the random part of the model and show that results of the model predictions are better than other types of the model. Besides, we obtain an analytical formula for pricing the zero-coupon bond and find the European option price by constructing a portfolio that contains the option and a share of the bond. Since the skew Brownian motion is not a martingale, thus we add transaction costs to the portfolio, where the time between trades follows the exponential distribution. Finally, some numerical results are presented to show the efficiency of the proposed model

Samimi A J. Long Memory Forecasting of Stock Price Index Using a Fractionally Differenced Arma Model [J

Tehran stock Exchange. Furthermore, we compared the forecasting outcome of ARFIMA and ARIMA models. The results show that the series is long memory and therefore it can become stationary with fractional differencing. After processing fractional differencing and determining the number of lags of the autoregressive and moving average components, the models were specified as ARFIMA (2,0.4767,18) and ARIM