Princess Nora bint Abdul Rahman University
Recent publications
The evolution of epidemics based on the Susceptible-Infected-Susceptible (SIS) model relies on the density of infected individuals ρ. Recent results show that the mean density ρ and its variance σ 2 can be regarded as canonical variables and obey Hamilton's equations. Using the Hamiltonian formulation, we study the SIS system coupled to a Nosé thermal bath. We reinterpret classical parameters like temperature in an epidemiological context. In contrast to classical epidemiological models, the thermal bath modifies the dynamical behavior of the system by introducing fluctuations, such as those seen in some infectious waves. We study the stability and show that ρ tends to be half of the value predicted by the original SIS model.
Breast cancer has replaced lung cancer as the most prevalent malignancy threatening human health. Early breast screening can help improve treatment success and reduce the risk of death. The analysis and diagnosis of breast cancer real images by computer-aided technology is the key link to early diagnosis. High-quality medical segmentation images can improve the accuracy of lesion area detection. This study used a multi-level threshold image segmentation framework based on novel differential evolution, two-dimensional Kapur's entropy, and the two-dimensional histogram to improve the efficiency of subsequent image analysis and diagnosis. We proposed an enhanced differential evolution in the framework based on the roundup search, the elite lévy-mutation, and the decentralized foraging strategy to explore the optimal thresholds. In this study, the enhanced differential evolution was compared to state-of-the-art methods for benchmark function experiments and breast cancer image segmentation experiments. It is shown that the proposed threshold search method accelerates convergence and reduces the problem of premature convergence. Quantitative results demonstrate that the proposed method can achieve an average peak signal-to-noise ratio and feature similarity index of 21.231 and 0.951, respectively, at the 5-level threshold, which is better than other methods. As a result, the proposed multi-level threshold image segmentation model can provide quality samples for subsequent image analysis and classification.
The non-linearity in medical image processing is a critical issue. Because the privacy of the medical image and loss of data is a major concern in recent years. Federated learning is a most advanced form of machine learning in which, rather than transmitting data to local server, a machine learning (ML) algorithm is installed to various devices to train on the information. The parameters from the separate modules will then be transferred to a master ML/ (deep learning) DL model for global training. The research of Image Quality Assessment (IQA) aims to simulate the process of human perception of image quality and construct an objective image quality model as consistent as possible with subjective assessment. The existing IQA methods can be roughly divided into traditional methods and deep learning methods. Traditional methods are knowledge-driven, using prior knowledge or assumptions about the human visual system (HVS) to heuristically design image quality index. Deep learning methods are data-driven, using a large amount of annotated data to learn the mapping from the image to its visual quality end-to-end. To effectively integrate traditional methods into deep networks and investigate the knowledge (model)-driven deep learning methods are the current mainstream trends in IQA research. In this paper, we take the contrary direction and improve traditional methods guided by the cue from deep learning methods. The main works include: 1. the employment of activation function ensure the nonlinear approximation ability of the neural network, here we first extend the two-stage framework widely used in the field of full-reference image quality assessment and propose a nonlinear two-stage framework. 2. Within this framework, we revisit the Edge Strength SIMlarity (ESSIM) algorithm that we previously published in IEEE Signal Processing Letters, and proposed the Nonlinear Edge Strength SIMlarity (NESSIM) algorithm. Experiments on public databases show that NESSIM can obtain good assessment results in traditional methods.
In this paper, we report on the semileptonic transitions of Bs→Ds2(2900)lν‾l, where l=e,μ,orτ with JP=2− by using the QCD sum rules within the standard model (SM). These channels which defined by the low energy Hamiltonian are calculated based on the transition form factors entering the hadronic matrix element. The form factors are obtained by using suitable fit functions that are then utilized to evaluate the total decay widths (TDW) and branching ratios of Bs→Ds2eν‾e=1.112×10−3,Bs→Ds2μν‾μ=1.093×10−3 and Bs→Ds2τν‾τ=7.412×10−6. The present results are consistent with the predications of the standard model as well as with the results of several studies on different channels. Therefore, such studied transition is promising to be achieved in the near future at LHCb experiment in CERN.
Decreasing the COVID spread of infection among patients at physical isolation hospitals during the coronavirus pandemic was the main aim of all governments in the world. It was required to increase isolation places in the hospital's rules to prevent the spread of infection. To deal with influxes of infected COVID-19 patients’ quick solutions must be explored. The presented paper studies converting natural rooms in hospitals into isolation sections and constructing new isolation cabinets using prefabricated components as alternative and quick solutions. Artificial Intelligence (AI) helps in the selection and making of a decision on which type of solution will be used. A Multi-Layer Perceptron Neural Network (MLPNN) model is a type of artificial intelligence technique used to design and implement on time, cost, available facilities, area, and spaces as input parameters. The MLPNN result decided to select a prefabricated approach since it saves 43% of the time while the cost was the same for the two approaches. Forty-five hospitals have implemented a prefabricated solution which gave excellent results in a short period of time at reduced costs based on found facilities and spaces. Prefabricated solutions provide a shorter time and lower cost by 43% and 78% in average values respectively as compared to retrofitting existing natural ventilation rooms.
Billions liters of wastewater are created every day from industrial and domestic locations, and while wastewater is frequently regarded as a concern, it includes the potential to be regarded as a rich supply of energy and materials. The creation of hydrogen gas by electrolysis utilizing a solar system is an alternate method for treating this residual water. Wastewater has four to five times the energy necessary to its treatment and a good source of bio-hydrogen as a feedstock chemical, clean energy vector, and a fuel generally acknowledged to play a part in the energy system's decarburization. The goal of this research was to see how well wastewater might be used to produce anaerobic hydrogen. Synthetic wastewater with high- and ordinary strength organic loadings as real-time residential wastewater with(out) a combination of food waste was examined. Hydrogen generation during sewage sludge and mining residue suspensions coupled was measured using electro dialytic methods at 50 and 100 mA. Hydrogen purity has been reached at 33 % along the electro dialytic treat of sewage sludge. Hydrogen purity reached 71 percent and 34 percent, respectively, while adding sewage sludge or effluent as enhancers in waste solutions. The maximum extraction ratios of phosphorus (71 %) and tungsten (62 %), respectively, were obtained while the technique has been performed to waste suspensions mixed with sewage sludge. This study's findings could be used to develop onsite household energy and wastewater recovery systems.
Direct-acting, indirect-acting, and mixed-acting sympathomimetics are the three types of sympathomimetic drugs. Direct-acting sympathomimetic medications bind to one or more adrenergic receptors directly. These drugs may have high selectivity for a single receptor subtype (e.g., phenylephrine for α1, terbutaline for β2) or no or low selectivity and operate on a variety of receptor types [e.g., epinephrine for α1, α2, β1, β2, and β3 receptors; norepinephrine (NE) for α1, α2, and β1 receptors]. The availability of NE or epinephrine (EP) to excite adrenergic receptors is increased by indirect acting. Mixed-acting sympathomimetic medications are those that release NE indirectly while also activating receptors directly (e.g., ephedrine, dopamine). Despite the convenience of this classification, there is likely a continuum of action from mostly direct-acting to predominantly indirect-acting medications. As a result, rather than being absolute, this classification is relative. Prior treatment with reserpine or guanethidine, which depletes NE from sympathetic neurons, has little effect on the responses of direct-acting sympathomimetic medications. Because the loss of the neurotransmitter causes compensatory alterations that up-regulate receptors or boost the signaling pathway, the activities of direct-acting sympathomimetic medications may actually increase after transmitter depletion. Prior treatment with reserpine or guanethidine, on the other hand, eliminates the effects of indirect-acting sympathomimetic medications (e.g., amphetamine, tyramine). Prior therapy with reserpine or guanethidine has the effect of blunting, but not eliminating the effects of mixed-acting sympathomimetic medications because the effects of NE are stronger on α and β1 receptors than on β2 receptors. The actions of noncatecholamines (CAs) that produce NE are mostly receptor mediated and cardiac. Non-CAs having direct and indirect adrenergic receptor actions, on the other hand, have considerable β2 activity and are employed clinically for these effects. Although part of its actions is dependent on the release of NE, ephedrine reduces bronchospasm by acting on β2 receptors in bronchial smooth muscle, an effect not seen with NE. Non-CAs, such as phenylephrine, also act predominantly and directly on target cells. As a result, predicting the effects of non-CAs merely based on their potential to cause NE release is impossible.
The COVID-19 virus's rapid global spread has caused millions of illnesses and deaths. As a result, it has disastrous consequences for people's lives, public health, and the global economy. Clinical studies have revealed a link between the severity of COVID-19 cases and the amount of virus present in infected people's lungs. Imaging techniques such as computed tomography (CT) and chest x-rays can detect COVID-19 (CXR). Manual inspection of these images is a difficult process, so computerized techniques are widely used. Deep Convolutional Neural Networks (DCNN) are a type of machine learning that is frequently used in computer vision applications, particularly in medical imaging, to detect and classify infected regions. These techniques can assist medical personnel in the detection of COVID-19 patients. In this paper, a Bayesian optimized DCNN and explainable AI based framework is proposed for the classification of COVID-19 from the Chest-Xray images. The proposed method starts with a multi-filter contrast enhancement technique that increases the visibility of infected part. Two pre-trained deep models such as EfficientNet-B0 and MobileNet-V2 are fine-tuned according to the target classes and then trained by employing Bayesian optimization (BO). Through BO, hyperparameters have been selected instead of static initialization. Features are extracted from the trained model and fused using a slicing-based serial fusion approach. The fused features are classified using machine learning classifiers for the final classification. Moreover, visualization is performed using a Grad-CAM that highlights the infected part in the image. Three publically available COVID-19 datasets are used for the experimental process to obtain the improved accuracies of 98.8%, 97.9%, and 99.4%, respectively.
The mechanisms underlie increased stress tolerance in plants of salinity stress in plants by arbuscular mycorrhizal fungi (AMF) are poorly understood, particularly the role of polyamine metabolism. The current study was conducted to investigate how inoculation with the AMF, Funneliformis constrictum, affects maize plant tolerance to salt stress. To this end, we investigated the changes in photosynthesis, redox status, primary metabolites (amino acids) and secondary metabolism (phenolic and polyamine metabolism). Control and inoculated maize plants were grown using different concentrations of diluted seawater (0, 10, 20 and 40%). Results revealed that treatment with 10% seawater had a beneficial effect on AMF and its host growth. However, irrigation with 20% and 40% significantly reduced plant growth and biomass. As seawater concentration increased, the plants' reliance on mycorrhizal fungi increased resulting in enhanced growth and photosynthetic pigments contents. Under higher seawater concentrations, inoculation with AMF reduced salinity induced oxidative stress and supported redox homeostasis by reducing H 2 O 2 and MDA levels as well as increasing antioxidant-related enzymes activities (e.g., CAT, SOD, APX, GPX, POX, GR, and GSH). AMF inoculation increased amino acid contents in shoots and roots under control and stress conditions. Amino acids availability provides a route for polyamines biosynthesis, where AMF increased polyamines contents (Put, Spd, Spm, total Pas) and their metabolic enzymes associated (ADC, SAMDC, Spd synthase, and Spm synthase), particularly under 40% seawater irrigation. Consistently, the transcription of genes, involved in polyamine metabolism was also up regulated in salinity-stressed plants. AMF further increased the expression in genes involved in polyamine biosynthesis (ODC, SAMDC, SPDS2 and decreased expression of those in catabolic biosynthesis (ADC and PAO). Overall, inoculation with Funneliformis constrictum could be adopted as a practical strategy to alleviate salinity stress.
In the last few years, artificial intelligence has shown a lot of promise in the medical domain for the diagnosis and classification of human infections. Several computerized techniques based on artificial intelligence (AI) have been introduced in the literature for gastrointestinal (GIT) diseases such as ulcer, bleeding, polyp, and a few others. Manual diagnosis of these infections is time consuming, expensive, and always requires an expert. As a result, computerized methods that can assist doctors as a second opinion in clinics are widely required. The key challenges of a computerized technique are accurate infected region segmentation because each infected region has a change of shape and location. Moreover, the inaccurate segmentation affects the accurate feature extraction that later impacts the classification accuracy. In this paper, we proposed an automated framework for GIT disease segmentation and classification based on deep saliency maps and Bayesian optimal deep learning feature selection. The proposed framework is made up of a few key steps, from preprocessing to classification. Original images are improved in the preprocessing step by employing a proposed contrast enhancement technique. In the following step, we proposed a deep saliency map for segmenting infected regions. The segmented regions are then used to train a pre-trained fine-tuned model called MobileNet-V2 using transfer learning. The fine-tuned model’s hyperparameters were initialized using Bayesian optimization (BO). The average pooling layer is then used to extract features. However, several redundant features are discovered during the analysis phase and must be removed. As a result, we proposed a hybrid whale optimization algorithm for selecting the best features. Finally, the selected features are classified using an extreme learning machine classifier. The experiment was carried out on three datasets: Kvasir 1, Kvasir 2, and CUI Wah. The proposed framework achieved accuracy of 98.20, 98.02, and 99.61% on these three datasets, respectively. When compared to other methods, the proposed framework shows an improvement in accuracy
In this work, N-doped graphene quantum dots (N-GQDs) are used as luminescent downshifting layers, which enhanced the performance of the CIGS solar cells. For providing mechanical strength and chemical stability, a poly(methyl methacrylate) (PMMA) polymer-based matrix is used. However, the PMMA layer creates photoluminescence (PL) quenching, so N-GQDs/PMMA layer is annealed at various temperatures (20–80 °C) to obtain the best performance. These layers were applied on the top of the CIGS solar cells and the performance of the cell is evaluated. The best value of η is obtained for 60 °C. The Jsc and η values are enhanced to 36.03 mA/cm² and 16.13% from 34.05 mA/cm² and 14.70%, respectively. Furthermore, the PV cell parameters (photogenerated current density (Jph), shunt resistance (Rsh), series resistance (Rs), diode ideality factor (n), and reverse saturation current density (J0)) were also determined and analyzed to investigate the reduction in losses in the cell.
Different 2,4-thiazolidinedione-tethered coumarins 5a–b, 10a–n and 11a–d were synthesised and evaluated for their inhibitory action against the cancer-associated hCAs IX and XII, as well as the physiologically dominant hCAs I and II to explore their selectivity. Un-substituted phenyl-bearing coumarins 10a, 10 h, and 2-thienyl/furyl-bearing coumarins 11a–c exhibited the best hCA IX (KIs between 0.48 and 0.93 µM) and hCA XII (KIs between 0.44 and 1.1 µM) inhibitory actions. Interestingly, none of the coumarins had any inhibitory effect on the off-target hCA I and II isoforms. The sub-micromolar compounds from the biochemical assay, coumarins 10a, 10 h and 11a–c, were assessed in an in vitro antiproliferative assay, and then the most potent antiproliferative agent 11a was tested to explore its impact on the cell cycle phases and apoptosis in MCF-7 breast cancer cells to provide more insights into the anticancer activity of these compounds.
The current research elucidates the nuclear shielding capacity of germinate tellurite glasses: 41.7GeO2–41.7TeO2–16.6Ga2O3, 37.5GeO2–62.5TeO2, 10.4GeO2–72.9TeO2–16.7Ga2O3 and 12.5 GeO2–87.5TeO2. Gamma-ray photon, fast neutron and electron shielding parameters of the present glassy materials were evaluated and studied via the Geant4 Monte Carlo, Phy-X/PSD software, ESTAR and analytic computations. In addition, Makishima–Mackenzie’s theory was applied to assess the elastic properties of the studied tellurite glass system containing Ga2O3 and/or GeO2. The effective atomic number of the glasses varies from 19.14 to 44.08 for 41.7GeO2–41.7TeO2–16.6Ga2O3, 20.63–48.02 for 37.5GeO2–62.5TeO2, 21.15–48.15 for 10.4GeO2–72.9TeO2–16.7Ga2O3 and 22.42–50.29 for 12.5GeO2–87.5TeO2. The obtained fast neutron removal cross sections of the glasses were 0.0991, 0.0966, 0.1024 and 0.1021 cm−1, respectively, for 41.7GeO2–41.7TeO2–16.6Ga2O3, 37.5GeO2–62.5TeO2, 10.4GeO2–72.9TeO2–16.7Ga2O3 and 12.5GeO2–87.5TeO2. Also, an equilibrium is reached between total stopping power (TSP) due to radiation and collision for electrons at energy T = 1.0 MeV where the TSP was minimum in the investigated glasses. Computed Young’s modulus for 37.5GeO2–62.5TeO2 was the lowest with a value of 0.218 GPa while the other three glass samples have almost equal value of 0.226 GPa. The present glasses’ shielding ability outclassed some conventional shields, hence have potential for radiation safety/shielding purposes in nuclear facilities.
The present research aims to identify the role of future planning scenarios in reducing the social impacts of the Coronavirus (COVID-19) pandemic in Saudi Arabia. It defines the foundations of the success, types, approaches for using, as well as criteria and indicators of designing and building future planning scenarios to reduce the social impacts of COVID-19. It also explores the differences in the participants’ responses to the criteria and indicators of designing and building future planning scenarios to reduce the social impacts of the COVID-19 pandemic in Saudi Arabia due to academic degree, position in the Ministry of Human Resources and Social Development, and years of experience. The author adopted the descriptive analytical approach and used a questionnaire to collect data. The results provide a ranking of each item in the five domains according to the participants’ responses regarding the benefits of planning scenarios, the foundations of success, types of planning scenarios, methodological steps of using them, and criteria and indicators of designing and building them. Additionally, the results showed no differences in the responses to the criteria and indicators of designing and building the scenarios that reduce the social impacts of the COVID-19 pandemic due to academic degree, and years of experience, whereas there were differences due to position.
Moringa oleifera is a nutrient-rich plant, also referred to as a miracle tree, and is commonly used in the preparation of functional foods including herbal biscuits. Despite having a wide range of biomolecules, M. oleifera has not been studied for its nutritional benefits in Nepal. To fill this gap, five different formulations of flower and leaf powder ratios of 11:4, 11.75:3.25, 12.5:2.5, 13.25:1.75, 14:1 named as A, B, C, D, E, and control formulations were tested for their sensory and chemical characteristics. The results showed that calcium content (115.73 mg/100 g) was higher in biscuits with a higher percentage of the leaf (11:4) while TPC was minimum. Further, biscuits containing a higher percentage of flower powder contained fewer tannins. The sensory analysis concluded that D was deemed the best in overall attributes by panelists upon statistical analysis, however formulations A and B were superior to other samples regarding the chemical properties. These findings confirm that there is a huge potential for improving herbal biscuits.
This study aimed to investigate the effect of different concentrations (0, 0.5, 1, 1.5, and 2%) of clove powder on the physicochemical, nutritional, and sensorial quality and storage stability of cookies. The results showed significantly (P ≤ .05) increases in the peak viscosity, breakdown, final viscosity, setback, hardness, cohesiveness, springiness, adhesiveness, chewiness, water holding capacity, and oil holding capacity, and reduction of pasting temperature of cookie flour containing clove powder compared to control. In cookies, the incorporation of clove powder significantly (p ≤ .05) increased diameter, thickness, hardness, factorability, redness (a*), and moisture. The significant increasing significantly (p ≤ .05) in cookies content was: macronutrients (Protein (10.65 ± 0.03 to 10.62 ± 0.07), Fat (14.52 ± 0.15 to 15.48 ± 0.07), Ash (1.08 ± 0.05 to 1.35 ± 0.00)%), minerals (K (12.04 ± 0.78 to 35.16 ± 0.76), Mg (12.62 ± 0.45 to 17.63 ± 0.04), Fe (8.14 ± 0.06 to 8.61 ± 0.02), P (10.51 ± 0.94 to 13.52 ± 0.12), Zn (0.31 ± 0.01 to 0.53 ± 0.00), Ca (56.89 ± 0.31 to 66.97 ± 0.43) (mg/kg)), also, TPC (12.93 ± 1.8 to 34.07 ± 1.9 (mg GAE/g)), and DPPH (% inhibition) was increased from (15.73 ± 1.4 to 28.51 ± 1.7) in control and 2% clove cookies respectively. However, it reduced the spread ratio, water activity, lightness (L*), and yellowness (b*) of cookies (p ≤ .05). Clove powder also improves the storage stability of cookies. Overall, incorporating clove powder into cookies enhanced the physicochemical, nutritional, bioactive properties, and storability without a major effect on the sensory acceptability of the developed product.
Particulate matter is emitted from diverse sources and affect the human health very badly. Dust particles exposure from the stated environment can affect our heart and lungs very badly. The particle pollution exposure creates a variety of problems including nonfatal heart attacks, premature deaths in people with lung or heart disease, asthma, difficulty in breathing, etc. In this article, we developed an automated tool by computing multimodal features to capture the diverse dynamics of ambient particulate matter and then applied the Chi-square feature selection method to acquire the most relevant features. We also optimized parameters of robust machine learning algorithms to further improve the prediction performance such as Decision Tree, SVM with Linear and Regression, Naïve Bayes (NB), Random Forest (RF), Ensemble Classifier, K-Nearest Neighbor, and XGBoost for classification. The classification results with and without feature selection methods yielded the highest detection performance with random forest, and GBM yielded 100% of accuracy and AUC. The results revealed that the proposed methodology is more robust to provide an efficient system that will detect the particulate matters automatically and will help the individuals to improve their lifestyle and comfort. The concerned department can monitor the individual’s healthcare services and reduce the mortality risk. © 2022 The Author(s). Published with license by Taylor & Francis Group, LLC.
Purpose: The aim of this study was to assess the antibacterial potential and ex vivo skin permeation kinetics of cefixime from bionanocomposite films. Methods: The films were prepared by solvent casting method by using chitosan and starch. The fabricated films were tested for their antibacterial potential against three bacteria i.e. Escherichia coli, Klebsiella pneumonia, and Acetobacter aceti. In vitro permeation studies of cefixime from the films across rat skin was conducted using Franz diffusion cell. Results: The highest antibacterial effect was exhibited by F5 formulation (non-irradiated film) against Escherichia coli and Klebsiella pneumonia; however, antibacterial activity of the films was significantly (p < 0.05) reduced after their irradiation. F5 formulation showed the highest cumulative amount of permeated drug after 24 h, while F1 (100% chitosan) showed the lowest amount of permeated drug. Non-Fickian diffusion (anomalous) was the main mode of drug release from all films. The cross-linking of films by γ-radiations improved their mechanical properties. The percentage swelling ratio was the highest in non-irradiated films having a polymeric blend (50:50). Water uptake of irradiated films was appreciably reduced as compared to non-irradiated films. Conclusion: The synthesized bionanocomposites are promising therapeutic moieties which not only improve drug permeability across but also ameliorates antibacterial potential of cefixime.
Epigallocatechin-3-gallate (EGCG) was isolated from Cycas thouarsii leaves for the first time and encapsulated in aqueous core poly(lactide-co-glycolide) (PLGA) nanocapsules (NCs). This work investigates antimicrobial activity and in vivo reno-protective effects of EGCG-PLGA NCs in cisplatin-induced nephrotoxicity. A double emulsion solvent evaporation process was adopted to prepare PLGA NCs loaded with EGCG. Particle size, polydispersity index (PDI), zeta potential, percent entrapment efficiency (%EE), structural morphology, and in vitro release platform were all studied in vitro. The optimum formula (F2) with particle size (61.37 ± 5.90 nm), PDI (0.125 ± 0.027), zeta potential (–11.83 ± 3.22 mV), %EE (85.79 ± 5.89%w/w), initial burst (36.85 ± 4.79), and percent cumulative release (87.79 ± 9.84) was selected for further in vitro/in vivo studies. F2 exhibited an enhanced antimicrobial activity against uropathogens as it had lower minimum inhibitory concentration (MIC) values and a more significant impact on bacterial growth than free EGCG. Forty male adult mice were randomly allocated into five groups: control vehicle, untreated methotrexate, MTX groups treated with a daily oral dose of free EGCG, placebo PLGA NCs, and EGCG PLGA NCs (F2) for 10 days. Results showed that EGCG PLGA NCs (F2) exerted promising renoprotective effects compared to free EGCG. EGCG PLGA NCs group induced a significant decrease in kidney index, serum creatinine, kidney injury molecule-1 (KIM-1), NGAL serum levels, and pronounced inhibition of NLPR-3/caspase-1/IL/1β inflammasome pathway. It also significantly ameliorated oxidative stress and decreased NFκB, Bax expression levels. Aqueous core PLGA NCs are a promising formulation strategy that provides high polymeric protection and sustained release pattern for hydrophilic therapeutic agents. © 2022 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.
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Abdul Razak
  • College of Medicine
Sireen Shilbayeh
  • Clinical Pharmacy Practice
Mahmoud Abo-Sinna
  • Mathematical Science
Fatima M. Al- Oboudi
  • Department of Mathematical Sciences
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