Moving object localization using frequency measurements

This research investigates the ability of locating a moving object using the Doppler shifts of a carrier frequency signal sent or re ected by the object and observed by several fixed or moving sensors spatially distributed in the 2-D or 3-D space. The idea was previously studied and several solutions are proposed based on exhaustive grid search or numerical polynomial optimization. We shall formulate the problem as a constrained optimization and propose two efficient solutions. The first is by using linear optimization method to reach a closed-form solution and the second is through semi-definite relaxation technique to achieve a noise resilient estimate. The solutions are derived first for the single-time measurement and then developed to multipletime observations collected during a short time interval in which the object motion is linear. Several scenarios are considered including 2-D and 3-D localization geometry, the sensors are fixed or moving along nonlinear trajectory with random speed, the presence of errors in the carrier frequency and the sensor positions, and the noncooperative object scenario where the frequency of the carrier signal is completely not known. Analysis validates the algebraic closed-form solution in reaching the Cramer- Rao Lower Bound accuracy under Gaussian noise within the small error region. The simulations show good performance for the proposed algorithms and support the theoretical analysis.Includes bibliographical references

Estimating Pressure Drop in Vertical Wells Using Group Method of Data Handling (GMDH) Approach; a Comparative Study

A reliable estimation of the pressure drop in well tubing is essential for the solution of a number of important production engineering and reservoir analysis problems. Different methods have been discussed and analyzed in the literature. This includes the basics of each method, the variables used and the limitations and constraints. This project aims to construct a tool that can estimate the pressure drop in vertical well using the minimum possible variables. In this project group method of data handling (GMDH) approach is used in order to build the model. And for the optimization of the model, Trend analysis is also used for the sake of having a physically sound model. The developed model GMDH has shown an outstanding results and it has outperformed all empirical correlations and mechanistic models which have been used in the comparison. The analysis of the results also confirmed with the testing set which has not seen by the GMDH during the development of the model which could still achieve an accurate estimation of the pressure drop. The GMDH model is developed and the objective is successfully achieved. Moreover, the simplicity and good functionality of the model made it a better choice when it comes to predict a pressure drop in any multiphase vertical well

Regulation of the Potassium channels Kv1.3, Kv1.5 and Kir2.1 by Human Parvovirus B19 Capsid Protein VP1

Parvovirus B19 (B19V) can cause inflammatory cardiomyopathy and endothelial dysfunction. Pathophysiological mechanisms involved include lysophosphatidylcholine producing phospholipase A2 (PLA2) activity of the B19V capsid protein VP1. Most recently, VP1 and lysophosphatidylcholine have been shown to inhibit Na+/K+ ATPase. The present study explored whether VP1 modifies the activity of Kv1.3, Kv1.5 and Kir2.1 K+ channels. The first part of study explored, whether expression of VP1 modifies the activity of Kv1.3 and Kv1.5 K+ channels. cRNA encoding Kv1.3 or Kv1.5 was injected into Xenopus oocytes without or with cRNA encoding VP1,which was isolated from a patient suffering from fatal B19V induced myocarditis or the VP1 mutant H153AVP1 lacking a functional PLA2 activity. K+ channel activity was determined by dual electrode voltage clamp. Injection of cRNA encoding Kv1.3 or Kv1.5 into Xenopus oocytes was followed by appearance of Kv K+ channel activity, which was significantly decreased by additional injection of cRNA encoding VP1, but not by additional injection of cRNA encoding PLA2-negative VP1 mutant H153AVP1. The effect of VP1 on Kv current was not significantly modified by transcription inhibitor actinomycin (10 µM for 36 hours) but was mimicked by lysophosphatidylcholine (1 μg/ml). The B19V capsid protein VP1 inhibits host cell Kv channels, an effect at least partially due to phospholipase A2 (PLA2) dependent formation of lysophosphatidylcholine. The second part of study explored, whether expression of VP1 influences the activity of the inwardly rectifying Kir2.1 K+ channels. cRNA encoding Kir2.1 was injected into Xenopus oocytes without or with cRNA encoding VP1 or the VP1 mutant H153AVP1. K+ channel activity was determined by dual electrode voltage clamp. Injection of cRNA encoding Kir2.1 into Xenopus oocytes was followed by appearance of inwardly rectifying K+ channel activity (IK), which was significantly decreased by additional injection of cRNA encoding VP1, but not by additional injection of cRNA encoding H153AVP1. The effect of VP1 on IK was mimicked by lysophosphatidylcholine (1 μg/ml) and by inhibition of Na+/K+-ATPase with 0.1 mM ouabain. In the presence of lysophosphatidylcholine, IK was not further decreased by additional treatment with ouabain. The B19V capsid protein VP1 inhibits Kir2.1 channels, an effect at least partially due to phospholipase A2 (PLA2) dependent formation of lysophosphatidylcholine with subsequent inhibition of Na+/K+-ATPase activity

Geometry Analysis of Ratawi Field

Ratawi Field is a promising hydrocarbon bearing structure conforming several reservoirs. Ratawi Field lies northwest of the Basrah city and west of Northern Rumaila Field.The study focuses on the structural analysis (geometric analysis) of Ratawi Structure. Reinterpretation of Seismic Data proved that no presence of any Fault in Ratawi Structure. The Fold Classifications (Geometric Analysis) proved that Ratawi Structure is Dome and Anticline shape, Gentle, Upright and Supratenuous Fold

Rare Presentation of Wilson Disease in an 11-year-old Sudanese Girl

Background: Wilson disease is an inherited disorder in which excessive amount of copper accumulates in various tissues of the body. Clinical features related to copper deposition in the liver may appear in the first and second decades followed by neurologic and psychiatric thereafter; however, many patients have a combination of these symptoms. Case: We report a case of 11 year-old girl, admitted to Wad Medani Pediatric Teaching Hospital with generalized body swellings for four days. Initial investigations showed proteinuria and hypoalbuminemia, thought to be due to nephrotic syndrome. Days later, patient developed jaundice and neuropsychiatric manifestations. A slit lamb examination confirmed the presence of Kayser–Fleischer ring (KF ring) and she scored high in the scoring system for the diagnosis of Wilson disease. Dpenicillamine treatment therapy was started and unfortunately the patient’s clinical condition deteriorated gradually, and eventually went into deep coma and died. Wilson disease mainly affects the liver, but the initial presentation was completely compatible with nephrotic syndrome. Conclusion: Diagnosis of Wilson disease should be suspected in a child presenting with generalized body swellings even in the absence of clinical evidence of hepatic and/or neuropsychiatric involvements

Sero-molecular Epidemiology of Hepatitis E Virus in Blood Donors, Gezira State, Sudan: A Cross-sectional Study

Background: Hepatitis E virus (HEV) is a hepatotropic pathogen that causes significant morbidity and mortality in humans. It is an important causative agent of viral hepatitis outbreaks. This study investigates the serological and molecular prevalence of HEV in blood donors attending the Central Blood Bank in Wad Medani City in Gezira State, Sudan. Methods: The study adopted a cross-sectional descriptive design. A structured questionnaire was used to collect data concerning demographic information and risk factors associated with HEV transmission. All enrolled participants (N = 300) were screened for HEV IgG antibodies using commercial ELISA kits, then strong positive samples (N = 84) were selected and rescreened for HEV IgM and HEV RNA by RT PCR. SPSS version 24.0 was used for analysis. Results: Out of 300 male participants, 36.3% (109/300) were positive for HEV IgG. However, only one participant was IgM positive, while the HEV RNA was negative. The highest prevalence rates of the virus were 42 (44.6%) among the age group of 31–40 years, 20 (48.8%) in those who consumed food from outside, 13 (50%) in three to four multiple blood donations, and 5 (62.5%) in those who consumed water from the river source. A significant association of HEV IgG prevalence concerning the occupation of the participants being students or farmers was detected using univariate and multivariate analysis (P-value = 0.007). Conclusion: High prevalence of HEV IgG was demonstrated among the healthy blood donors in this study. Given the possibility of HEV transmission by transfusion from donors to recipients, we recommend that routine screening for HEV should be adopted by blood banks in Sudan

Adherence to Immunosuppressive Medications in Kidney Transplant Patients at Three Centers in Khartoum State, Sudan: A Cross-sectional Hospital Study

Background: Graft survival post-kidney transplantation is of paramount importance to patients and nephrologists. Nonadherence to immunosuppressive therapy can be associated with deterioration of renal function and graft rejection. This study aimed to evaluate the adherence to immunosuppressive medications in kidney transplant patients at three centers in Khartoum, Sudan. Methods: In this descriptive cross-sectional hospital-based survey, 277 post-kidneytransplant patients were recruited. Data were collected using a questionnaire and analyzed using the SPSS v.23. Our scoring method was calculated based on Morisky Medication Adherence Scale (MMAS-8) related to immunosuppressive medications and was expressed as questions in the questionnaire; every correct answer was given one mark, then the marks were gathered and their summation was expressed. Results: Overall, 33% ,45%, and 22% of the studied participants reported high, medium, and low adherence, respectively. The major factor for nonadherence was forgetfulness affecting 36.1% of those who did not adhere. The cost of the immunosuppressive medications did not negatively affect any of the participants’ adherence (100%). However, a significant association was seen between adherence and occupational status, duration of transplantation, shortage of immunosuppressants, recognizing the name of immunosuppressant, side effect, and forgetfulness (P-values = 0.002, 0.01, 0.006 , 0.000, 0.022, and 0.000, respectively). Logistic regression analysis showed a significant association with occupational status, side effects, and forgetfulness Conclusion: Only one-third of the participants were classed as “highly adherent” to their immunosuppressant medications. Factors that can affect adherence to immunosuppressant medications were occupational status, side effects, and forgetfulness

Application of GIS-based machine learning algorithms for prediction of irrigational groundwater quality indices

Agriculture is considered one of the primary elements for socioeconomic stability in most parts of Sudan. Consequently, the irrigation water should be properly managed to achieve sustainable crop yield and soil fertility. This research aims to predict the irrigation indices of sodium adsorption ratio (SAR), sodium percentage (Na%), permeability index (PI), and potential salinity (PS) using innovative machine learning (ML) techniques, including K-nearest neighbor (KNN), random forest (RF), support vector regression (SVR), and Gaussian process regression (GPR). Thirty-seven groundwater samples are collected and analyzed for twelve physiochemical parameters (TDS, pH, EC, TH, Ca+2, Mg+2, Na+, HCO3−, Cl, SO4−2, and NO3−) to assess the hydrochemical characteristics of groundwater and its suitability for irrigation purposes. The primary investigation indicated that the samples are dominated by Ca-Mg-HCO3 and Na-HCO3 water types resulted from groundwater recharge and ion exchange reactions. The observed irrigation indices of SAR, Na%, PI, and PS showed average values of 7, 42.5%, 64.7%, and 0.5, respectively. The ML modeling is based on the ion’s concentration as input and the observed values of the indices as output. The data is divided into two sets for training (70%) and validation (30%), and the models are validated using a 10-fold cross-validation technique. The models are tested with three statistical criteria, including mean square error (MSE), root means square error (RMSE), and correlation coefficient (R2). The SVR algorithm showed the best performance in predicting the irrigation indices, with the lowest RMSE value of 1.45 for SAR. The RMSE values for the other indices, Na%, PI, and PS, were 6.70, 7.10, and 0.55, respectively. The models were applied to digital predictive data in the Nile River area of Khartoum state, and the uncertainty of the maps was estimated by running the models 10 times iteratively. The standard deviation maps were generated to assess the model’s sensitivity to the data, and the uncertainty of the model can be used to identify areas where a denser sampling is needed to improve the accuracy of the irrigation indices estimates

Knowledge and attitudes toward COVID-19 vaccination in Sudan: A cross-sectional study

Background: Vaccines are an essential part of public health interventions to mitigate the devastating health and non-health impacts of COVID-19 pandemic. Despite the fact that Sudan launched the COVID-19 vaccination program in March 2021, only 10% of the population received their two primary doses of vaccines by the end of May 2022. This delayed uptake of vaccines obviously warrants investigation. Therefore, we have conducted this study to evaluate the knowledge, attitude and acceptance of the general population in Sudan toward COVID-19 vaccines. Methodology: A descriptive cross-sectional community-based study. The data were collected using an electronic questionnaire from 403 individuals living in Khartoum, Sudan. The data were processed using the Statistical Package for Social Sciences (SPSS), and data analysis was performed using appropriate tests. Results: 51% of the participants were found to have sufficient knowledge about the COVID-19 vaccine, and the knowledge level is higher among those educated beyond the secondary school and those who were employed. Among those unvaccinated, only 47% of the participants expressed their intention to take the vaccine when offered to them. The major reason for not trusting the vaccine is safety concerns expressed by 65.5% of the unvaccinated. Conclusion: Higher education levels and employment were associated with an increase in sufficient knowledge about the vaccine in around half of the participants. However, most of participants had not taken the vaccine at the time of the study, and the trust in vaccines is not high. Effective interventions by the health authorities are needed to address these issues in order to accelerate the COVID-19 vaccination program in Sudan

Moving source localization using doppler frequency shift measurements in sensor network

[ACCESS RESTRICTED TO THE UNIVERSITY OF MISSOURI AT REQUEST OF AUTHOR.] This thesis studies and develops a solution for locating a moving source using Doppler frequency shift measurements in sensor network. The study inquires the problems of inaccurate sensor positions, unknown source frequency, and inaccurate source frequency, on the quality of the location estimate. Iterative solutions that need initial guesses have been considered since the frequency measurements are nonlinearly related to the source trajectories (position and velocity). In this thesis we present three solutions to localize the source, iterative maximum likelihood estimator (IMLE), grid search estimator (GSE), and closed form estimator (CFE). The IMLE achieved the Cramer Rao lower bound (CRLB) performance but it needs initial guess sufficiently close to the actual source trajectories. The GSE searches over only the source position and this makes the processing time much less than normal full dimensional GSE. The proposed CFE does not need initial guess and has very small processing time and it can achieve the CRLB performance under low level of measurements noise