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The adsorptive properties of UiO-66 towards organic dyes: A record adsorption capacity for the anionic dye Alizarin red S.

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Chinese Journal of Chemical Engineering 2018年第4期26卷 731-739页

作者： Marwa S.Embaby saber D.Elwany Widiastuti Setyaningsih mohamed r.saber Central laboratories Fayoum Drinking Water and Sanitation Company (FDWaSC) Food and Agricultural Product Technology Department Faculty of Agricultural Technology Gadjah Mada University Chemistry Department Faculty of Science Fayoum University

In order to decisively determine the adsorption selectivity of zirconium MOF(UiO-66) towards anionic versus cationic species, the adsorptive removal of the anionic dyes(Alizarin red S.(ArS), Eosin(E), Fuchsin Acid(FA)and Methyl Orange(MO)) and the cationic dyes(Neutral red(Nr), Fuchsin Basic(FB), Methylene Blue(MB),and Safranine T(ST)) has been evaluated. The results clearly reveal a significant selectivity towards anionic dyes. Such an observation agrees with a plethora of reports of UiO-66 superior affinity towards other anionic species(Floride, PO_4^(3-), Diclofenac sodium, Methylchlorophenoxy-propionic acid, Phenols, CrO_4^(2-), SeO_3^(2-), and AsO_4^-). The adsorption process of ArS as an example has been optimized using the central composite design(CCD). The resultant statistical model indicates a crucial effect of both pH and sorbent mass. The optimum conditions were determined to be initial dye concentration 11.82 mg.L^(-1), adsorbent amount 0.0248 g, shaking time of 36 min and pH 2. The adsorption process proceeds via pseudo-second order kinetics(r^2= 0.999). The equilibrium data were fit to Langmuir and Tempkin models(r^2= 0.999 and 0.997 respectively). The results reveal an exceptional removal for the anionic dye(Alizarin red S.) with a record adsorption capacity of400 mg·g^(-1). The significantly high adsorption capacity of UiO-66 towards ArS adds further evidence to the recently reported exceptional performance of MOFs in pollutants removal from water.

关键词：红色染料吸附能力茜素红器官性质电荷耦合器件吸附过程模型显示

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Exploring dielectric phenomena in sulflower-like nanostructures via Monte Carlo technique

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Communications in Theoretical Physics 2024年第4期76卷 188-198页

作者： N saber Z Fadil Hussein Sabbah A Mhirech B Kabouchi L Bahmad Chaitany Jayprakash raorane Siva Sankar Sana Hassan Fouad mohamed Hashem Laboratory of Condensed Matter and Interdisciplinary Sciences(LaMCScI) Research Unit Labelled CNRSTURL-CNRST-17.Faculty of SciencesPO Box 1014Mohammed V University in RabatMorocco College of Engineering and Technology American University of the Middle EastEgaila 54200Kuwait School of Chemical Engineering Yeungnam UniversityGyeongsan38541Republic of Korea Applied Medical Science Department Community CollegeKing Saud UniversityPO Box 11433Riyadh Saudi Arabia Department of Dental Health College of Applied Medical SciencesKing Saud UniversityPO Box 12372RiyadhSaudi Arabia

This research focuses on the electric behavior of a mixed ferrielectric sulflower-like nanostructure.The structure includes a core with spin S^(Z)_(i)-1 atoms and a shell with spinσ^(Z)_(j)-5/2 atoms.The Blume–Capel model and the Monte Carlo technique(MCt)with the Metropolis algorithm are employed.Diagrams are established for absolute zero,investigating stable spin configurations correlated with various physical parameters.The MCt method explores phase transition behavior and electric hysteresis cycles under different physical parameters.

关键词： sulfower-like nanostructure dielectric characteristics Monte Carlo technique phase transition phase diagrams electric hysteresis

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Predictors and Effects of Persistent Hypertension after Successful Treatment of Coarctation of the Aorta

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Congenital Heart Disease 2022年第3期17卷 325-333页

作者： mohamed saber Hafez Alaa roushdy Dina Ezzeldin Ain Shams University CairoEgypt

Background:Coarctation of the aorta is a congenital heart disease that sometimes remains clinically silent until adulthood,usually presenting with arterial hypertension.It is well known that after coarctation treatment,many patients remain hypertensive despite successful repair.In this study we approached the predictors and effects of residual hypertension after successful coarctation therapy.Methods:It was a cross sectional observational study involving 50 patients who underwent Coarctation repair/angioplasty in Ain Shams university hospitals.We divided the patients into two groups(hypertensive and normotensive)and we studied their demographic data(e.g.,age,gender,age at first repair,follow up period,etc.)to conclude the possible predictors of persistent hypertension in such cases.We also performed full echocardiographic assessment including 3D speckle tracking echocardiography to detect subtle changes in left ventricular systolic functions.results:Hypertensive patients had significantly higher BMI(29.1 kg/m^(2))as compared to normotensives(24 kg/m^(2)),prst CoA intervention was significantly higher in hypertensive patients.Hypertensive patients had significantly lower 3D left ventricular strain measures(Global area strain,global circumferential strain and global longitudinal strain).The incidence of bicuspid aortic valve was significantly higher in the hypertensive group,yet it was not an independent predictor of persistent hypertension.Conclusions:The predictors for residual hypertension post successful CoA repair were late intervention and higher BMI.Bicuspid aortic valve was more common in the hypertensive group.regarding the effects of such disease domain,treated CoA patients with persistent hypertension tended to have higher LV mass index and lower values of LV deformation.

关键词： Coarctation arterial hypertension speckle tracking left ventricular strain

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Metaheuristic Optimization Algorithm for Signals Classification of Electroencephalography Channels

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Computers, Materials & Continua 2022年第6期71卷 4627-4641页

作者： Marwa M.Eid Fawaz Alassery Abdelhameed Ibrahim mohamed saber Department of Communications and Electronics Delta Higher Institute of Engineering and TechnologyMansoura35111Egypt Department of Computer Engineering College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia Computer Engineering and Control Systems Department Faculty of EngineeringMansoura UniversityMansoura35516Egypt Department of Electronics and Communications Engineering Faculty of EngineeringDelta University for Science and TechnologyMansoura11152Egypt

Digital signal processing of electroencephalography(EEG)data is now widely utilized in various applications,including motor imagery classification,seizure detection and prediction,emotion classification,mental task classification,drug impact identification and sleep state classification.With the increasing number of recorded EEG channels,it has become clear that effective channel selection algorithms are required for various applications.Guided Whale Optimization Method(Guided WOA),a suggested feature selection algorithm based on Stochastic Fractal Search(SFS)technique,evaluates the chosen subset of channels.This may be used to select the optimum EEG channels for use in Brain-Computer Interfaces(BCIs),the method for identifying essential and irrelevant characteristics in a dataset,and the complexity to be eliminated.This enables(SFS-Guided WOA)algorithm to choose the most appropriate EEG channels while assisting machine learning classification in its tasks and training the classifier with the dataset.The(SFSGuided WOA)algorithm is superior in performance metrics,and statistical tests such as ANOVA and Wilcoxon rank-sum are used to demonstrate this.

关键词： Signals metaheuristics optimization feature selection multilayer perceptron support vector machines

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robust ACO-Based Landmark Matching and Maxillofacial Anomalies Classification

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Intelligent Automation & Soft Computing 2023年第2期35卷 2219-2236页

作者： Dalel Ben Ismail Hela Elmannai Souham Meshoul mohamed saber Naceur LTSIrS Lr20ES06 Institut National des Sciences Appliquées et de Technologie INSATUniversitéde CarthageTunisia Department of Information Technology College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh 11671Saudi Arabia

Imagery assessment is an efﬁcient method for detecting craniofacial anomalies.A cephalometric landmark matching approach may help in orthodontic diagnosis,craniofacial growth assessment and treatment planning.Automatic landmark matching and anomalies detection helps face the manual labelling lim-itations and optimize preoperative planning of maxillofacial surgery.The aim of this study was to develop an accurate Cephalometric Landmark Matching method as well as an automatic system for anatomical anomalies classiﬁcation.First,the Active Appearance Model(AAM)was used for the matching process.This pro-cess was achieved by the Ant Colony Optimization(ACO)algorithm enriched with proximity information.Then,the maxillofacial anomalies were classiﬁed using the Support Vector Machine(SVM).The experiments were conducted on X-ray cephalograms of 400 patients where the ground truth was produced by two experts.The frameworks achieved a landmark matching error(LE)of 0.50±1.04 and a successful landmark matching of 89.47%in the 2 mm and 3 mm range and of 100%in the 4 mm range.The classiﬁcation of anomalies achieved an accuracy of 98.75%.Compared to previous work,the proposed approach is simpler and has a comparable range of acceptable matching cost and anomaly classiﬁcation.results have also shown that it outperformed the K-nearest neigh-bors(KNN)classiﬁer.

关键词： Maxillofacial anomalies cephalometric landmarks similarity chi-square distance quadratic assignment problem ant colony optimization SVM

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Modified Differential Evolution Algorithm for Solving Dynamic Optimization with Existence of Infeasible Environments

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Computers, Materials & Continua 2023年第1期74卷 1-17页

作者： mohamed A.Meselhi saber M.Elsayed Daryl L.Essam ruhul A.Sarker School of Engineering and Information Technology University of New South Wales Canberra Australia

Dynamic constrained optimization is a challenging research topic in which the objective function and/or constraints change over time.In such problems,it is commonly assumed that all problem instances are feasible.In reality some instances can be infeasible due to various practical issues,such as a sudden change in resource requirements or a big change in the availability of resources.Decision-makers have to determine whether a particular instance is feasible or not,as infeasible instances cannot be solved as there are no solutions to implement.In this case,locating the nearest feasible solution would be valuable information for the decision-makers.In this paper,a differential evolution algorithm is proposed for solving dynamic constrained problems that learns from past environments and transfers important knowledge from them to use in solving the current instance and includes a mechanism for suggesting a good feasible solution when an instance is infeasible.To judge the performance of the proposed algorithm,13 well-known dynamic test problems were solved.The results indicate that the proposed algorithm outperforms existing recent algorithms with a margin of 79.40%over all the environments and it can also find a good,but infeasible solution,when an instance is infeasible.

关键词： Dynamic optimization constrained optimization disruption differential evolution

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Transfer Learning for Chest X-rays Diagnosis Using Dipper Throated Algorithm

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Computers, Materials & Continua 2022年第11期73卷 2371-2387页

作者： Hussah Nasser AlEisa El-Sayed M.El-kenawy Amel Ali Alhussan mohamed saber Abdelaziz A.Abdelhamid Doaa Sami Khafaga Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadh11671Saudi Arabia Department of Communications and Electronics Delta Higher Institute of Engineering and TechnologyMansoura35111Egypt Faculty of Artificial Intelligence Delta University for Science and TechnologyMansoura35712Egypt Electronics and Communications Engineering Department Faculty of EngineeringDelta University for Science and TechnologyMansoura35712Egypt Department of Computer Science Faculty of Computer and Information SciencesAin Shams UniversityCairo11566Egypt Department of Computer Science College of Computing and Information TechnologyShaqra University11961Saudi Arabia

Most children and elderly people worldwide die from pneumonia,which is a contagious illness that causes lung ulcers.For diagnosing pneumonia from chest X-ray images,many deep learning models have been put forth.The goal of this research is to develop an effective and strong approach for detecting and categorizing pneumonia cases.By varying the deep learning approach,three pre-trained models,GoogLeNet,resNet18,and DenseNet121,are employed in this research to extract the main features of pneumonia and normal cases.In addition,the binary dipper throated optimization(DTO)algorithm is utilized to select the most significant features,which are then fed to the K-nearest neighbor(KNN)classifier for getting the final classification decision.To guarantee the best performance of KNN,its main parameter(K)is optimized using the continuous DTO algorithm.To test the proposed approach,six evaluation metrics were employed namely,positive and negative predictive values,accuracy,specificity,sensitivity,and F1-score.Moreover,the proposed approach is compared with other traditional approaches,and the findings confirmed the superiority of the proposed approach in terms of all the evaluation metrics.The minimum accuracy achieved by the proposed approach is(98.5%),and the maximum accuracy is(99.8%)when different test cases are included in the evaluation experiments.

关键词： Metaheuristic pneumonia dipper throated optimization KNN

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Secondary antiviral metabolites from fungi with special reference to coronaviruses

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BIOCELL 2022年第8期46卷 1979-1988页

作者： mohamed SALEM MOHAMMAD EL-METWALLY WESAMELDIN saber SALLY NEGM ATTALLA EL-KOTT YASSEr MAZrOUA ABEEr MAKHLOUF MAHMOUD MOUSTAFA Department of Chemistry Faculty of Science and ArtKing Khalid UniversityMohailAssirSaudi Arabia Department of Chemistry Faculty of ScienceAl-Azhar UniversityCairoEgypt Botany and Microbiology Department Faculty of ScienceDamanhour UniversityEl-BeheraEgypt Microbial Activity Unit Department of MicrobiologySoilsWater and Environment Research InstituteAgricultural Research CenterGizaEgypt Life Sciences Department College of Science and ArtKing Khalid UniversityMohailAseerSaudi Arabia Unit of Food Bacteriology Central Laboratory of Food HygieneMinistry of HealthSharkiaEgypt Department of Biology College of ScienceKing Khalid UniversityAbhaSaudi Arabia Zoology Department Faculty of ScienceDamanhour UniversityEl-BeheraEgypt Department of Business Administration Community CollegeKing Khalid UniversityAbhaSaudi Arabia Department of Agricultural Economics Faculty of AgricultureTanta UniversityTantaEgypt Department of Botany Faculty of AgricultureMenoufia UniversitySheben El-KomMenoufiaEgypt Botany and Microbiology Department Faculty of ScienceSouth Valley UniversityQenaEgypt

Profound inspection of the life forms on the earth teaches how to be the complexity of interrelationships among the various systems.Because of the emergence of novel viruses all the time and the inadequate of vaccines and antivirals,viral contagions are amongst the most causative diseases affecting people worldwide.Fungi exemplify a massive source of bioactive molecules as,many fungal secondary metabolities like Oxoglyantrypine,Carneic acid F,Scedapin C,Asteltoxin E,Phomanolide,Norquinadoline A and Quinadoline B have antiviral activity.This review deals with how secondary metabolites of fungi can help in the war against viruses in general and especially Coronaviruses moreover several pieces of literature pointed out that many clusters of fungi in different biotopes are waiting to be exploited.

关键词： COVID-19 SArS-CoV-2 Antibiotics Virus Fungi

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Dipper Throated Algorithm for Feature Selection and Classification in Electrocardiogram

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Computer Systems Science & Engineering 2023年第5期45卷 1469-1482页

作者： Doaa Sami Khafaga Amel Ali Alhussan Abdelaziz A.Abdelhamid Abdelhameed Ibrahim mohamed saber El-Sayed M.El-kenawy Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Science Faculty of Computer and Information SciencesAin Shams University11566CairoEgypt Department of Computer Science College of Computing and Information TechnologyShaqra University11961Saudi Arabia Computer Engineering and Control Systems Department Faculty of EngineeringMansoura UniversityMansoura35516Egypt Electronics and Communications Engineering Department Faculty of EngineeringDelta University for Science and TechnologyMansouraEgypt Department of Communications and Electronics Delta Higher Institute of Engineering and TechnologyMansoura35111Egypt Faculty of Artificial Intelligence Delta University for Science and TechnologyMansoura35712Egypt

Arrhythmia has been classified using a variety of methods.Because of the dynamic nature of electrocardiogram(ECG)data,traditional handcrafted approaches are difficult to execute,making the machine learning(ML)solutions more appealing.Patients with cardiac arrhythmias can benefit from competent monitoring to save their lives.Cardiac arrhythmia classification and prediction have greatly improved in recent years.Arrhythmias are a category of conditions in which the heart's electrical activity is abnormally rapid or sluggish.Every year,it is one of the main reasons of mortality for both men and women,worldwide.For the classification of arrhythmias,this work proposes a novel technique based on optimized feature selection and optimized K-nearest neighbors(KNN)classifier.The proposed method makes advantage of the UCI repository,which has a 279-attribute high-dimensional cardiac arrhythmia dataset.The proposed approach is based on dividing cardiac arrhythmia patients into 16 groups based on the electrocardiography dataset’s features.The purpose is to design an efficient intelligent system employing the dipper throated optimization method to categorize cardiac arrhythmia patients.This method of comprehensive arrhythmia classification outperforms earlier methods presented in the literature.The achieved classification accuracy using the proposed approach is 99.8%.

关键词： Feature selection electrocardiogram metaheuristics dipper throated algorithm

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Contribution of the FPGAs for Complex Control Algorithms: Sensorless DTFC with an EKF of an Induction Motor

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International Journal of Automation and computing 2019年第2期16卷 226-237页

作者： saber Krim Soufien Gdaim Abdellatif Mtibaa mohamed Faouzi Mimouni Laboratory of Electronics and Microelectronics EE Faculty of SciencesUniversity of Monastir research Unit "Industrial Systems Study and renewable Energy" University of Monastir Polytechnic School of Engineering (PSE) University of Sousse National Engineering School University of Monastir

In a conventional direct torque control(CDTC) of the induction motor drive, the electromagnetic torque and the stator flux are characterized by high ripples. In order to reduce the undesired ripples, several methods are used in the literature. Nevertheless,these methods increase the algorithm complexity and dependency on the machine parameters such as the space vector modulation(SVM). The fuzzy logic control method is utilized in this work to decrease these ripples. Moreover, to eliminate the mechanical sensor the extended kalman filter(EKF) is used, in order to reduce the cost of the system and the rate of maintenance. Furthermore, in the domain of controlling the real-time induction motor drives, two principal digital devices are used such as the hardware(FPGA) and the digital signal processing(DSP). The latter is a software solution featured by a sequential processing that increases the execution time. However, the FPGA is featured by a high processing speed because of its parallel processing. Therefore, using the FPGA it is possible to implement complex algorithms with low execution time and to enhance the control bandwidth. The large bandwidth is the key issue to increase the system performances. This paper presents the interest of utilizing the FPGAs to implement complex control algorithms of electrical systems in real time. The suggested sensorless direct torque control using the fuzzy logic(DTFC) of an induction motor is successfully designed and implemented on an FPGA Virtex 5 using xilinx system generator. The simulation and implementation results show proposed approach s performances in terms of ripples, stator current harmonic waves, execution time, and short design time.

关键词： Direct torque control fuzzy logic control (FLC) extended Kalman filter Xilinx system generator (XSG) field programmable gate array (FPGA)

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