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

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

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

Geophysical monitoring of the groundwater resources in the Southern Arabian Peninsula using satellite gravity data

In recent decades, geophysical and remote sensing monitoring techniques have advanced to the point where they can be utilized. It is possible to investigate the spatiotemporal mass fluctuations induced by groundwater changes over the Southern Arabian Peninsula (SAP) by combining time-variable gravity data with land surface model outputs and rainfall data. Here are the findings: The average annual precipitation rates for the whole study region were 91.11, 87.6, and 96.61 mm yr−1 during the entire period (2002–2021), period before 2013, and period after 2012, respectively. The southern and eastern parts (Zone I) of the investigated region show modest rainfall rates of 109.6, 105, and 117 mm yr−1 during the whole period, period before 2013, and period after 2012, respectively. The Rub El Khali region (Zone II) is receiving lower precipitation rates of 54.6, 53.3, and 56.5 mm yr−1 throughout the whole period, period before 2013, and period after 2012, respectively. Based on the three distinct gravity solutions, the average Terrestrial Water Storage (ΔTWS) values are computed through the entire period to be − 0.21 ± 0.011, − 0.15 ± 0.013, and − 0.32 ± 0.0107 cm yr−1 for the whole study region, Zone of the southern and eastern regions, and Zone of Rub El Khali, respectively. The whole study region, Zone of the southern and eastern parts, and Zone of Rub El Khali are showing highly negative ΔTWS in the period before 2013, in comparison to slightly negative to slightly positive ΔTWS trends in period after 2012. The average annual change in groundwater storage for the entire study area was calculated at − 0.21 ± 0.011, − 0.29 ± 0.024, and − 0.091 ± 0.038 cm yr−1 throughout the investigated period, period before 2013, and period after 2012, respectively. Zone of Rub El Khali is showing higher negative groundwater storage trend (ΔGWS) averaged at − 0.32 ± 0.104 cm yr−1 throughout the investigated period, whereas Zone of southern and eastern regions is showing lower negative groundwater storage trend of − 0.15 ± 0.013 cm yr−1. Most of the recharge rate occurs in Zone of the southern and eastern regions reaching up to + 0.77 ± 0.092 cm yr−1 by taking the average groundwater withdrawal rate of + 0.92 ± 0.092 cm yr−1 during the whole period. This integrated approach is a valuable and economical method for more effectively assessing the variations of groundwater resources across wide areas

Simulation of Surface and Subsurface Water Quality in Hyper-Arid Environments

Forty-eight water samples (30 groundwater and 18 surface water samples) were collected from the study region. Physical and chemical examinations were performed on the water samples to determine the values of various variables. Several graphs, sheets, and statistical measures, including the sodium solubility percentage (SSP), the sodium absorption ratio (SAR), and Piper’s diagram, were used to plot the concentration of the principal ions and the chloride mass balance (CMB). The contents of the variables were compared with the contents in other local areas and the standard allowable safe limits as recommended by the World Health Organization (WHO). Water pH values were neutral for all water samples. Electric conductivity (EC) readings revealed that water samples vacillated from slightly mineralized to excessively mineralized. Water salinities were fresh and very fresh according to the total dissolved solids (TDS) amounts. The hardness of water ranged from medium to hard in the surface water and from medium to very hard in the groundwater samples. Bicarbonate, sodium, and calcium made up the highest amounts in the surface water samples. The highest concentrations of bicarbonate, sulfate, chloride, and sodium were found in the groundwater. Diagrams show the major ion relationships as well as the type and origin of the water. According to Piper’s plots, most of the water samples under investigation were Ca-HCO3 type, Mg water types, followed by SO4.Ca-Cl water types. This highlighted the elemental preponderance of bicarbonate and alkaline earth (Ca2+ + Mg2+). This dominance is caused by evaporite and carbonate minerals dissolving in water because of anthropogenic activities and interaction processes. The groundwater recharge was estimated to be 0.89–1.6 mm/yr based on Chloride Mass Balance. The examined water samples can also be used for cattle, poultry, and irrigation. Additionally, the groundwater is of poorer quality than the surface water, although both types of water are adequate for various industries, with a range of 14 to 94 percent. With the exception of a few groundwater samples, the tested water samples are suitable for a number of applications

Geophysical investigations for the identification of subsurface features influencing mineralization zones

The numerous hydrothermal alteration zones and subsurface structures affecting the mineralized deposits of the Dungash region were identified using aeromagnetic data. The Center of Exploration Targeting (CET) approach and several filters, such as reduction-to-pole, Tilt derivative, First Vertical Derivative, Horizontal gradient map, Downward continuation, analytical signal methods, regional, and residual separation, were used to analyze the aeromagnetic data. The research region is impacted by several structural trends running in the N-S, E-W, NW-SE, and NE-SW directions, and these trends are strongly related to the gold mineralization and surrounding hydrothermal alteration zones. In the NW-SE direction, four alteration zones have been identified. The research region's northern and eastern regions have shallower basement relief, with depths of only approximately 100 m, and those depths show that the area is rootless. Conversely, the basement relief and surface depths are lower in the study region's western and southern regions. The routes taken by the ascending hydrothermal fluids can be seen as aeromagnetic lineaments at the hydrothermal alteration zones. Mineralization appears to be linked to structural lineaments, as evidenced by airborne magnetic data. For gold prospecting, the aeromagnetic technique seems to be the most effective and efficient geophysical method because gold is typically found in severely deformed shear zones and faults.Water Resource

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.Water Resource

Monoallelic characteristic-bearing heterozygous L1053X in BRCA2 gene among Sudanese women with breast cancer

Abstract Background Breast cancer (BC) is the most common type of cancer in women. Among many risk factors of BC, mutations in BRCA2 gene were found to be the primary cause in 5–10% of cases. The majority of deleterious mutations are frameshift or nonsense mutations. Most of the reported BRCA2 mutations are protein truncating mutations. Methods The study aimed to describe the pattern of mutations including single nucleotide polymorphisms (SNPs) and variants of the BRCA2 (exon11) gene among Sudanese women patients diagnosed with BC. In this study a specific region of BRCA2 exon 11 was targeted using PCR and DNA sequencing. Results Early onset cases 25/45 (55.6%) were premenopausal women with a mean age of 36.6 years. Multiparity was more frequent within the study amounting to 30 cases (66.6%), with a mean parity of 4.1. Ductal type tumor was the predominant type detected in 22 cases (48.8%) among the reported histotypes. A heterozygous monoallelic nonsense mutation at nucleotide 3385 was found in four patients out of 9, where TTA codon was converted into the stop codon TGA. Conclusion This study detected a monoallelic nonsense mutation in four Sudanese female patients diagnosed with early onset BC from different families. Further work is needed to demonstrate its usefulness in screening of BC