The U -Radius And Hankel Determinant For Analytic Functions, And Product Of Logharmonic Mappings

This thesis studies geometric and analytic properties of complex-valued analytic functions and logharmonic mappings in the open unit disk D. It investigates four research problems. As a precursor to the first, let U be the class consisting of normalized analytic functions f satisfying |(z= f (z))2 f ′(z)−1| 0; or | f (z)=g(z)−1| < 1 in D; for g belonging to a certain class of analytic functions. In most instances, the exact U -radius are found. A recent conjecture by Obradovi´c and Ponnusamy concerning the radius of univalence for a product involving univalent functions is also shown to hold true. The second problem deals with the Hankel determinant of analytic functions. For a normalized analytic function f ; let z f ′(z)= f (z) or 1+z f ′′(z)= f ′(z) be subordinate to a given analytic function φ in D. Further let F be its kth-root transform, that is, F(z) = z[f(zk)=zk]1

Brain epilepsy seizure detection using bio-inspired krill herd and artificial alga optimized neural network approaches

© 2020, Springer-Verlag GmbH Germany, part of Springer Nature. Nowadays, Epilepsy is one of the chronic severe neurological diseases; it has been identified with the help of brain signal analysis. The brain signals are recorded with the help of electrocorticography (ECoG), Electroencephalogram (EEG). From the brain signal, the abnormal brain functions are a more challenging task. The traditional systems are consuming more time to predict unusual brain patterns. Therefore, in this paper, effective bio-inspired machine learning techniques are utilized to predict the epilepsy seizure from the EEG signal with maximum recognition accuracy. Initially, patient brain images are collected by placing the electrodes on their scalp. From the brain signal, different features are extracted that are analyzed with the help of the Krill Herd algorithm for selecting the best features. The selected features are processed using an artificial alga optimized general Adversarial Networks. The network recognizes the intricate and abnormal seizure patterns. Then the discussed state-of-art methods are examined simulation results

Enhancement of ciprofloxacin activity by incorporating it in solid lipid nanoparticles

Purpose: To incorporate ciprofloxacin (CIP) into solid lipid nanoparticles (SLN) in order to enhance its biopharmaceutical properties and antibacterial activity.Methods: A sonication melt-emulsification method was employed for the preparation of CIP-loaded SLN. The composition of the SLN was varied in order to investigate factors such as lipid type and combination ratio, drug to lipid ratio, and surfactant ratio. The produced SLN formulations wereevaluated for their particle size and shape, zeta potential, and entrapment efficiency. In addition, the effect of SLN formulation composition on its drug release profile and antimicrobial activity against Escherichia coli, Pseudomonas Aeruginosa, and Staphylococcus Aureus was also investigated.Results: The generated nanoparticles had particle size in the range of 165 to 320 nm. The zetapotential values were generally low within ± 5. All formulations exhibited entrapment efficiency between 50 and 90 %. CIP release exhibited a biphasic release profile with a low burst phase, followed by uniform controlled-release behavior of various rates. SLN-loaded CIP exhibited one-fold reduction in minimum inhibitory concentration (MIC) and caused significant inhibition of all the three bacterial strains tested, when compared with pure CIP.Conclusion: Loading of CIP into SLN significantly enhances its antimicrobial activity in vitro which can translate to significant enhancement of therapeutic outcomes by minimizing the dose-dependent adverse and side effects and/or enhancing the antimicrobial spectrum of activity. Keywords: Solid lipid nanoparticles, Sonication melt-emulsification, Ciprofloxacin, Escherichia coli, Pseudomonas aeruginos

Evaluation of the knowledge and practices of pregnant Yemeni Women regarding teratogens

Purpose: To investigate the knowledge and practice of pregnant women with regards to teratogens.Methods: A month-long cross-sectional study was carried out among 150 pregnant women selected from four Motherhood and Child Healthcare Centers (MCHCs) in Mukalla District of Yemen. Data collection was conducted during face-to-face interviews using a questionnaire. Descriptive and simple regression analyses were used.Results: Of the 150 pregnant women who participated in the study, 95.3 % of the pregnant women were &lt; 36 years old, 7.4 % had children with congenital malformations, 62 % indicated that they had heard about folic acid; however, only 16.6 % knew the significance of folic acid. Regarding toxoplasmosis, 94.7 % indicated that they had heard about toxoplasmosis, and 76 % knew about the serious consequences of the disease (congenital malformation and abortion) during pregnancy. Based on simple regression analysis, the results indicate that education and parity, irrespective of age or income level, were the major factors determining better knowledge and practices in pregnancy with regards to toxoplasmosis.Conclusion: Knowledge of folic acid deficiency among pregnant women in Mukalla District of Yemen is relatively low. Furthermore, preventive practices to avoid folic acid deficiency are minimal.Keywords: Knowledge, Practices, Teratogens, Pregnant Yemeni women, Folic acid deficienc

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

Clinical and biochemical characteristics of people experiencing post-coronavirus disease 2019-related symptoms: A prospective follow-up investigation

BackgroundPost-acute coronavirus disease 2019 (COVID-19) syndrome, also known as long COVID, is a prolonged illness after the acute phase of COVID-19. Hospitalized patients were known to have persisting symptoms of fatigue, headache, dyspnea, and anosmia. There is a need to describe the characteristics of individuals with post-COVID-19 symptoms in comparison to the baseline characteristics.PurposeTo investigate the clinical and biochemical characteristics of people who recovered from COVID-19 after 6 months of discharge from the hospital.MethodsThis was a prospective follow-up investigation of hospitalized and discharged COVID-19 patients. Adult patients admitted to King Saud University Medical City, Riyadh, Saudi Arabia, with laboratory-confirmed COVID-19 and discharged were recruited. The baseline demographic information, comorbidities, vital signs and symptoms, laboratory parameters, COVID-19 therapy, and outcomes were collected from the medical records. Blood samples were collected for cytokines estimation. A detailed interview about signs and symptoms was undertaken during the follow-up.ResultsHalf of the followed-up people reported experiencing at least one of the COVID-19-related symptoms. The mean blood pressure was found higher in follow-up. People with the symptoms were characterized by low lymphocyte count, lower serum calcium levels, and hyperglycemia compared to people without any post-COVID-19 symptoms. Cytokines IL-8, VEGF, and MCP-1 were higher in people with the most frequent symptoms.ConclusionPeople with post-COVID-19 symptoms were characterized by lower lymphocyte count, lower serum calcium levels, and hyperglycemia compared to people without symptoms. Individuals with the most frequent post-COVID-19 symptoms had higher baseline pro-inflammatory, chemotactic, and angiogenic cytokines

A fault-tolerant aware scheduling method for fog-cloud environments.

Fog computing is a promising technology that leverages the resources to provide services for requests of IoT (Internet of Things) devices at the cloud edge. The high dynamic and heterogeneous nature of devices at the cloud edge causes failures to be a popular event and therefore fault tolerance became indispensable. Most early scheduling and fault-tolerant methods did not highly consider time-sensitive requests. This increases the possibility of latencies for serving these requests which causes unfavorable impacts. This paper proposes a fault-tolerant scheduling method (FTSM) for allocating services' requests to the most sufficient devices in fog-cloud IoT-based environments. The main purpose of the proposed method is to reduce the latency and overheads of services and to increase the reliability and capacity of the cloud. The method depends on categorizing devices that can issue requests into three classes according to the type of service required. These classes are time-sensitive, time-tolerant and core. Each time-sensitive request is directly mapped to one or more edge devices using a pre-prepared executive list of devices. Each time-tolerant request may be assigned to one or more devices at the cloud edge or the cloud core. Core requests are assigned to resources at the cloud core. In order to achieve fault tolerance, the proposed method selects the most suitable fault-tolerant technique from replication, checkpointing and resubmission techniques for each request while most existing methods consider only one technique. The effectiveness of the proposed method is assessed using average service time, throughput, operation costs, success rate and capacity percentage as performance indicators

EVALUATION OF THE EFFECTIVENESS OF FREE NICOTINE PATCH THERAPY IN A CHARITY CLINIC FOR SMOKING CESSATION

The purpose of this study was to evaluate the efficacy of a free nicotine- patch therapy for smoking cessation in Saudi smokers. A single centered prospective study was carried out in a charity clinic for smoking cessation in Riyadh, Saudi Arabia.  A total of 31 subjects who attended the smoking cessation clinic from June 2014 to August 2014 were studied. All participants were male and their mean age was 31.1±6.4 years. The duration of history of smoking was 12.9±6.8 years. The nicotine- patch therapy outcomes were measured at baseline and at 6 weeks after using nicotine- patch therapy. At base line the number of cigarettes per day was 27±10 and carbon monoxide (CO) level was 20.2±8.3. The analyzed statistics revealed that there were significant decreased in the number of cigarette per day (p=0.001) and Carbon Monoxide (CO) level (p=0.001) over 6 weeks of nicotine- patch therapy. After 6 weeks of therapy, abstinence rate was 58 % (verified by CO level) and no serious adverse reactions were documented. The most common side effects were nausea, headache and local irritation sings. In addition, our finding revealed that smokers were likely to suffer from withdrawal symptoms following trying to quit. These withdrawal symptoms include sleep disturbance, loss of concentration and weight gain as well as irritability. Results of this study show that free nicotine-patch therapy is an effective measure for smoking cessation in Saudi population.Keywords: nicotine patch therapy, smoking cessation clinic, side effect

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

Box-Behnken Assisted Validation and Optimization of an RP-HPLC Method for Simultaneous Determination of Domperidone and Lansoprazole

A highly specific, accurate, and simple RP-HPLC technique was developed for the real-time quantification of domperidone (DOMP) and lansoprazole (LANS) in commercial formulations. Chromatographic studies were performed using a Luna C8(2), 5 &mu;m, 100&Aring;, column (250 &times; 4.6 mm, Phenomenex) with a mobile phase composed of acetonitrile/2 mM ammonium acetate (51:49 v/v), pH 6.7. The flow rate was 1 mL&middot;min&minus;1 with UV detection at 289 nm. Linearity was observed within the range of 4&ndash;36 &micro;g&middot;mL&minus;1 for domperidone and 2&ndash;18 &micro;g&middot;mL&minus;1 for lansoprazole. Method optimization was achieved using Box-Behnken design software, in which three key variables were examined, namely, the flow rate (A), the composition of the mobile phase (B), and the pH (C). The retention time (Y1 and Y3) and the peak area (Y2 and Y4) were taken as the response parameters. We observed that slight alterations in the mobile phase and the flow rate influenced the outcome, whereas the pH exerted no effect. Method validation featured various ICH parameters including linearity, limit of detection (LOD), accuracy, precision, ruggedness, robustness, stability, and system suitability. This method is potentially useful for the analysis of commercial formulations and laboratory preparations