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Salvia ceratophylla L. from South of Jordan: new insights on chemical composition and biological activities

Natural Products and Bioprospecting 2020年第5期10卷 307-316页

作者： Mohammad Sanad Abu-Darwish Célia Cabral Zulfigar Ali Mei Wang shabana IKhan Melissa RJacob Surendra KJain Babu LTekwani Fazila Zulfiqar Ikhlas AKhan Hatem Taifour Lígia Salgueiro Thomas Efferth Department of Basic and Applied Sciences Al-Balqa Applied UniversityAl-Salt 19117Jordan National Center for Natural Products Research School of PharmacyUniversity of MississippiUniversityMS 38677USA Coimbra Institute for Clinical and Biomedical Research(iCBR) Faculty of MedicineUniversity of Coimbra3000-548 CoimbraPortugal Center for Innovative Biomedicine and Biotechnology(CIBB) University of Coimbra3000-548 CoimbraPortugal Centre for Functional Ecology Department of Life SciencesUniversity of CoimbraCalçada Martim de Freitas3000-456 CoimbraPortugal School of Geosciences University of Edinburgh219 Crew BuildingKing’s BuildingsEdinburgh EH93FFUK CIEPQPF/Faculty of Pharmacy University of CoimbraHealth Sciences CampusAzinhaga de S.Comba3000-548 CoimbraPortugal Department of Pharmaceutical Biology Institute of Pharmaceutical and Biocmedical SciencesJohannes Gutenberg UniversityStaudinger Weg 555128 MainzGermany

In Jordan,Salvia ceratophylla L.is traditionally used in the treatment of cancer,microbial infections,and urinary disorders.This study aimed:(1)to chemically characterize S.ceratophylla essential oil(EO)from South Jordan,by gas chromatography(GC)and gas chromatography-mass spectrometry(GC-MS);and(2)to evaluate in vitro the cytotoxic,anti-inflammatory,and antiprotozoal activities of the EO,it’s predominant components,and the hexane(A),ethyl acetate(B),methanol(C)and crude-methanol extracts(D).The analysis revealed that the EO has 71 compounds,with linalool(54.8%)as main constitu-ent.Only the hexane extract(A)showed some cytotoxic activity against SK-MEL,KB,BT-549,SK-OV-3,LLC-PK1 and VERO cells lines with IC50 between 60 and>100μg/mL.The EO inhibited NO production(IC5090μg/mL)and NF-κB activity(IC5038μg/mL).The extracts A,B,and D inhibited NO production and NF-κB activity with IC50 between 32 and 150μg/mL.Linalool considerably inhibited NO production(IC5018μg/mL).The extracts tested did not exhibit antileish-manial activity.Regarding antitrypanosomal activity,the EO exhibited significant results with IC502.65μg/mL.In conclu-sion,Jordan S.ceratophylla EO represents a rich source of linalool and bears a promising therapeutic potential for further antitrypanosomal drug development.

关键词： Lamiaceae Essential oil Chemical composition Cytotoxicity Anti-inflammatory activity Neglected diseases

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Nominal geometry and force measures for solids and fluids

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International Journal of Mechanical System Dynamics 2022年第3期2卷 249-252页

作者： Ahmed A.shabana Department of Mechanical and Industrial Engineering University of Illinois at ChicagoChicagoIllinoisUSA

Understanding solid‐and fluid‐inertia forces and their coupling with the gravity potential in complex motion scenarios is necessary for evaluating system stability and identifying root causes of system failure and accidents.Because solids and fluids have an infinite number of degrees of freedom and distributed inertia and elasticity,having meaningful qualitative and quantitative nominal measures of the kinematics and forces will contribute to a better understanding of the system dynamics.This paper proposes developing new continuum‐based nominal measures for the characterization of the oscillations and forces.By using a material‐point approach,these new nominal measures,which have their roots in the continuum‐mechanics partial‐differential equations of equilibrium and Frenet geometry,are independent of the formulation or generalized coordinates used to develop the dynamic equations of motion.The paper proposes a data‐driven‐science approach to define a nominal continuum space‐curve geometry with nominal curvature and torsion;a nominal instantaneous motion plane(IMP),which contains the resultant of all forces including the inertia forces;and a nominal instantaneous zero‐force axis(IZFA)along which the resultant of all forces vanishes.While using the material‐point approach eliminates the need for introducing moment equations associated with orientation coordinates,the IMP and IZFA concepts can be used to define the instantaneous axis of significant moment components,which can lead to accidents such as in the case of vehicle rollovers.

关键词： nominal geometry and force measures instantaneous motion plane instantaneous zero-force axis Frenet geometry nominal continuum mechanics measures

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Fuzzy User Access Trust Model for Cloud Access Control

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Computer Systems Science & Engineering 2023年第1期44卷 113-128页

作者： Aakib Jawed Khan shabana Mehfuz Department of Electrical Engineering Jamia Millia IslamiaNew Delhi110025India

Cloud computing belongs to a set of policies,protocols,technologies through which one can access shared resources such as storage,applications,net-works,and services at relatively low cost.Despite the tremendous advantages of cloud computing,one big threat which must be taken care of is data security in the cloud.There are a dozen of threats that we are being exposed to while avail-ing cloud services.Insufficient identity and access management,insecure inter-faces and Applications interfaces(APIs),hijacking,advanced persistent threats,data threats,and many more are certain security issues with the cloud platform.APIs and service providers face a huge challenge to ensure the security and integ-rity of both network and data.To overcome these challenges access control mechanisms are employed.Traditional access control mechanisms fail to monitor the user operations on the cloud platform and are prone to attacks like IP spoofing and other attacks that impact the integrity of the data.For ensuring data integrity on cloud platforms,access control mechanisms should go beyond authentication,identification,and authorization.Thus,in this work,a trust-based access control mechanism is proposed that analyzes the data of the user behavior,network beha-vior,demand behavior,and security behavior for computing trust value before granting user access.The method that computes thefinal trust value makes use of the fuzzy logic algorithm.The trust value-based policies are defined for the access control mechanism and based on the trust value outcome the access control is granted or denied.

关键词： Cloud architecture fuzzy logic trust-based access mechanism

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Rotation-based finite elements:reference-configuration geometry and motion description

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Acta Mechanica Sinica 2021年第1期37卷 105-126,I0004页

作者： Ahmed A.shabana Lingmin Xu Department of Mechanical and Industrial Engineering University of Illinois At ChicagoChicagoIL60607USA Faculty of Mechanical Engineering and Automation Zhejiang Sci-Tech UniversityHangzhou310018China

Infinitesimal-rotation finite elements allow creating a linear problem that can be exploited to systematically reduce the number of coordinates and obtain efficient solutions for a wide range of applications,including those governed by nonlinear equations.This paper discusses the limitations of conventional infinitesimal-rotation finite elements(FE)in capturing correctly the initial stress-free reference-configuration geometry,and explains the effect of these limitations on the definition of the inertia used in the motion description.An alternative to conventional infinitesimal-rotation finite elements is a new class of elements that allow developing inertia expressions written explicitly in terms of constant coefficients that define accurately the reference-configuration geometry.It is shown that using a geometrically inconsistent(GI)approach that introduces the infinitesimal-rotation coordinates from the outset to replace the interpolation-polynomial coefficients is the main source of the failure to capture correctly the reference-configuration geometry.On the other hand,by using a geometrically consistent(GC)approach that employs the position gradients of the absolute nodal coordinate formulation(ANCF)to define the infinitesimal-rotation coordinates,the reference-configuration geometry can be preserved.Two simple examples of straight and tapered beams are used to demonstrate the basic differences between the two fundamentally different approaches used to introduce the infinitesimal-rotation coordinates.The analysis presented in this study sheds light on the differences between the incremental co-rotational solution procedure,widely used in computational structural mechanics,and the non-incremental floating frame of reference formulation(FFR),widely used in multibody system(MBS)dynamics.

关键词： Reference-configuration geometry Infinitesimal-rotation finite elements Rigid-body inertia Floating frame of reference Absolute nodal coordinate formulation Co-rotational procedure

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An Optimized Approach to Vehicle-Type Classification Using a Convolutional Neural Network

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Computers, Materials & Continua 2021年第12期69卷 3321-3335页

作者： shabana Habib Noreen Fayyaz Khan Department of Information Technology College of ComputerQassim UniversityBuraidah51452Saudi Arabia Department of Computer Science Islamia College UniversityPeshawarPakistan

There are numerous application areas of computing similarity between process models.It includes finding similar models from a repository,controlling redundancy of process models,and finding corresponding activities between a pair of process models.The similarity between two process models is computed based on their similarity between labels,structures,and execution behaviors.Several attempts have been made to develop similarity techniques between activity labels,as well as their execution behavior.However,a notable problem with the process model similarity is that two process models can also be similar if there is a structural variation between them.However,neither a benchmark dataset exists for the structural similarity between process models nor there exist an effective technique to compute structural similarity.To that end,we have developed a large collection of process models in which structural changes are handcrafted while preserving the semantics of the models.Furthermore,we have used a machine learning-based approach to compute the similarity between a pair of process models having structural and label differences.Finally,we have evaluated the proposed approach using our generated collection of process models.

关键词： Machine learning intelligent data management similarities of process models structural metrics dataset graph edit distance process matching artificial intelligence

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Diagnosis of Autism Spectrum Disorder by Imperialistic Competitive Algorithm and Logistic Regression Classifier

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Computers, Materials & Continua 2023年第11期77卷 1515-1534页

作者： shabana R.Ziyad Liyakathunisa Eman Aljohani I.A.Saeed Department of Computer Science College of Computer Engineering and SciencesPrince Sattam bin Abdulaziz UniversityAl Kharj16274Saudi Arabia Department of Computer Science College of Computer Science and EngineeringTaibah UniversityMadinah41411Saudi Arabia Department of Information Systems College of Computer Engineering and SciencesPrince Sattam bin Abdulaziz UniversityAl Kharj16274Saudi Arabia

Autism spectrum disorder(ASD),classified as a developmental disability,is now more common in children than ever.A drastic increase in the rate of autism spectrum disorder in children worldwide demands early detection of autism in children.Parents can seek professional help for a better prognosis of the child’s therapy when ASD is diagnosed under five years.This research study aims to develop an automated tool for diagnosing autism in children.The computer-aided diagnosis tool for ASD detection is designed and developed by a novel methodology that includes data acquisition,feature selection,and classification phases.The most deterministic features are selected from the self-acquired dataset by novel feature selection methods before classification.The Imperialistic competitive algorithm(ICA)based on empires conquering colonies performs feature selection in this study.The performance of Logistic Regression(LR),Decision tree,K-Nearest Neighbor(KNN),and Random Forest(RF)classifiers are experimentally studied in this research work.The experimental results prove that the Logistic regression classifier exhibits the highest accuracy for the self-acquired dataset.The ASD detection is evaluated experimentally with the Least Absolute Shrinkage and Selection Operator(LASSO)feature selection method and different classifiers.The Exploratory Data Analysis(EDA)phase has uncovered crucial facts about the data,like the correlation of the features in the dataset with the class variable.

关键词： Autism spectrum disorder feature selection imperialist competitive algorithm LASSO logistic regression random forest

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Learning Patterns from COVID-19 Instances

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Computer Systems Science & Engineering 2022年第2期40卷 765-777页

作者： Rehan Ullah Khan Waleed Albattah Suliman Aladhadh shabana Habib Department of Information Technology College of ComputerQassim UniversityBuraydahSaudi Arabia

Coronavirus disease,which resulted from the SARS-CoV-2 virus,has spread worldwide since early 2020 and has been declared a pandemic by the World Health Organization(WHO).Coronavirus disease is also termed COVID-19.It affects the human respiratory system and thus can be traced and tracked from the Chest X-Ray images.Therefore,Chest X-Ray alone may play a vital role in identifying COVID-19 cases.In this paper,we propose a Machine Learning(ML)approach that utilizes the X-Ray images to classify the healthy and affected patients based on the patterns found in these images.The article also explores traditional,and Deep Learning(DL)approaches for COVID-19 patterns from Chest X-Ray images to predict,analyze,and further understand this virus.The experimental evaluation of the proposed approach achieves 97.5% detection performance using the DL model for COVID-19 versus normal cases.In contrast,for COVID-19 versus Pneumonia Virus scenario,we achieve 94.5% accurate detections.Our extensive evaluation in the experimental section guides and helps in the selection of an appropriate model for similar tasks.Thus,the approach can be used for medical usages and is particularly pertinent in detecting COVID-19 positive patients using X-Ray images alone.

关键词： Coronavirus COVID-19 machine learning deep learning convolutional neural network

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Quercetin modulates iron homeostasis and iNOS expression of splenic macrophages in a rat model of iron deficiency anemia

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Chinese Journal of Natural Medicines 2018年第8期16卷 580-589页

作者： Maryam Mazhar Nurul Kabir shabana U Simjee Dr. Panjwani Center for Molecular Medicine and Drug Research International Centerjbr Chemical and Biological SciencesUniversity of Karachi Karachi 75270 Pakistan H.E.J. Research Institute. for Chemistry International CenterJor Chemical and Biological Sciences University of Karachi Karachi 75270 Pakistan

Iron deficiency anemia is one of the most common micronutrient deficient conditions around the globe with various consequences,including the weakened immune system.Quercetin is widely distributed bioflavonoid;it has been debated for its dual roles in iron regulation.Quercetin-iron interaction in the body is a complex mechanism which has not been completely understood.The present study aimed to investigate the effect of quercetin on iron supplementation in iron deficiency anemia and on i NOS expression in splenic macrophages.The rat model of iron deficiency anemia was induced by feeding low iron diet to weanling rats for 20 days.The animals were then administered with ferrous sulfate,quercetin,and their combination for 30 days.Blood parameters,histopathological analysis,iron storage,CD68,i NOS and SLC40 expression in rat spleen were investigated.Our results showed that quercetin regulated iron absorption,despite SLC40 down-expression,indicating possible alternate route of iron transport,and that quercetin modulated i NOS production in splenic macrophages..

关键词： Iron deficiency anemia Inducible nitric oxide synthase Macrophages Spleen Quercetin

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An Ingenious IoT Based Crop Prediction System Using ML and EL

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Computers, Materials & Continua 2024年第4期79卷 183-199页

作者： shabana Ramzan Yazeed Yasin Ghadi Hanan Aljuaid Aqsa Mahmood Basharat Ali Department of Computer Science&IT Government Sadiq CollegeWomen UniversityBahawalpur63100Pakistan Department of Computer Science/Software Engineering Al Ain UniversityAl Ain64141UAE Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman University(PNU)Riyadh11671Saudi Arabia Agronomic Research Station Bahawalpur63100Pakistan

Traditional farming procedures are time-consuming and expensive as based on manual labor. Farmers haveno proper knowledge to select which crop is suitable to grow according to the environmental factors and soilcharacteristics. This is the main reason for the low yield of crops and the economic crisis in the agricultural sectorof the different countries. The use of modern technologies such as the Internet of Things (IoT), machine learning,and ensemble learning can facilitate farmers to observe different factors such as soil electrical conductivity (EC),and environmental factors like temperature to improve crop yield. These parameters play a vital role in suggestinga suitable crop to cope the food scarcity. This paper proposes a systemcomprised of twomodules, first module usesstatic data and the second module takes hybrid data collection (IoT-based real-time data and manual data) withmachine learning and ensemble learning algorithms to suggest the suitable crop in the farm to maximize the yield.Python is used to train the model that predicts the crop. This system proposed an intelligent and low-cost solutionfor the farmers to process the data and predict the suitable crop.We implemented the proposed system in the field.The efficiency and accuracy of the proposed system are confirmed by the generated results to predict the crop.

关键词： Machine learning Internet of Things sensors ensemble learning

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Predicting the Need for ICU Admission in COVID-19 Patients Using XGBoost

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Computers, Materials & Continua 2021年第11期69卷 2077-2092页

作者： Mohamed Ezz Murtada K.Elbashir Hosameldeen shabana College of Computer and Information Sciences Jouf UniversitySakakaKSA Faculty of Engineering Al Azhar UniversityCairoEgypt Faculty of Mathematical and Computer Sciences University of GeziraWad MadaniSudan Faculty of Medicine Al Azhar UniversityCairoEgypt College of Medicine Shaqra UniversityKSA

It is important to determine early on which patients require ICU admissions in managing COVID-19 especially when medical resources are limited.Delay in ICU admissions is associated with negative outcomes such as mortality and cost.Therefore,early identification of patients with a high risk of respiratory failure can prevent complications,enhance risk stratification,and improve the outcomes of severely-ill hospitalized patients.In this paper,we develop a model that uses the characteristics and information collected at the time of patients’admissions and during their early period of hospitalization to accurately predict whether they will need ICU admissions.We use the data explained and organized in a window-based manner by the Sírio-Libanês hospital team(published on Kaggle).Preprocessing is applied,including imputation,cleaning,and feature selection.In the cleaning process,we remove zero-variance,redundant,and/or highly correlated(measured by the Pearson correlation coefficient)features.We use Extreme Gradient Boosting(XGBoost)with early stopping as a predictor in our developed model.We run the experiment in four stages starting from the features of Window 1 in Stage 1 and then incrementally add the features of Windows 2–4 in Stages 2–4 respectively.We achieve AUCs of 0.73,0.92,0.95,and 0.97 in those four stages.

关键词： COVID-19 ICU admission XGBoost

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