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Surface morphology and cell viability of samarium(Ⅲ) oxide/chromium(Ⅲ) oxide/graphene oxide/polycaprolactone targeting wound dressing

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Journal of Rare Earths 2024年第3期42卷 555-561,I0005页

作者： muteb H.alshammari Ahmed Onayzan alshammari mohamed Tharwat Elabbasy Rafat Zreiq Faris mohammed Albati m.A.El-morsy A.A.menazea Department of Health Informatics College of Public Health and Health InformaticsUniversity of Ha'ilHa'ilSaudi Arabia Department of Surgery College of MedicineUniversity of Ha'ilHa'ilSaudi Arabia Department of Public Health College of Public Health and HealthInformaticsHalUniversityHa'il2440Saudi Arabi Food Control Department Faculty of Veterinary MedicineZagazig UniversityZagazig44519Egypt Applied Science Research Center Applied Science Private UniversityAmman541350Jordan College of Shariah and Law Ha'il UniversityHa'il2440Saudi Arabia College of Science and Humanities in Al-Kharj Physics DepartmentPlasma Technology and Material Science UnitPrince Sattam Bin Abdulaziz UniversityAl-kharj11942Saudi Arabia Physics Department Faculty of ScienceUniversity of DamittaNew Damietta34517.Egyp Spectroscopy Department Physics Research InstituteNational Research CentreDokki12622GizaEgypt

Combinations of metal and lanthanide oxides have been done through casted films for potential medical applications. In this regard, samarium(Ⅲ) oxide/chromium(Ⅲ) oxide/graphene oxide(GO)/polycaprolactone(PCL) based films nano-composites(NCs) were fabricated, pointing their utilization as a biological scaffold for wound dressing purposes. Also, samarium(Ⅲ) oxide and chromium(Ⅲ) oxide have been merged as promising optical constituents due to their unique optical behavior. The structural and compositional examination of the studied NCs was executed by X-ray diffraction(XRD), Raman, and field emission scanning electron microscopy(FESEm). The Sm_(2)O_(3)/Cr_(2)O_(3)/GO/PCL NC exhibits a surface with a lower roughness degree owing to the presence of GO. Cr_(2)O_(3)shows size reduction upon GO insertion to reach 1.2 μm as the average grain size, whilst Sm_(2)O_(3)records an average grain size of less than 1 μm. As well, the polymeric nano-compositions exhibit variation in contact angle values that hit 29.76°± 3.52°for Sm_(2)O_(3)/PCL, and 48.62°± 1.37°for Sm_(2)O_(3)/Cr_(2)O_(3)/GO/PCL as the second lowest contact angle. The optical behavior contributes to absorption edge relocation along the x-axis from 1.7 eV for pure PCL, to 2.65 eV for Sm_(2)O_(3)/Cr_(2)O_(3)/GO/PCL. Regarding biological responses, the cell exposed to 2.5 μg/m L of Sm_(2)O_(3)/Cr_(2)O_(3)/GO/PCL shows cell viability of 119.31%, while 5 μg/m L hits 99.6%. Additionally, the resulting cell attachment micrographs show layers of fibroblast tissue, besides the proliferation and growth of cultivated cells. Thus, the Sm_(2)O_(3)/Cr_(2)O_(3)/GO/PCL scaffold provides 3D proliferation of fibroblast cells endorsing the wound healing process.

关键词： Wound healing Wound dressing Polycaprolactone Sm_(2)O_(3) Cr_(2)O_(3) Rare earths

On multi-Granulation Rough Sets with Its Applications

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Computers, materials & Continua 2024年第4期79卷 1025-1038页

作者： Radwan Abu-Gdairi R.mareay m.badr Department of mathematics Faculty of ScienceZarqa UniversityZarqa13132Jordan Department of mathematics Faculty of ScienceKafrelsheikh UniversityKafrelsheikh33516Egypt Department of mathematics Faculty of ScienceNew Valley UniversityEl Kharga72713Egypt

Recently,much interest has been given tomulti-granulation rough sets (mGRS), and various types ofmGRSmodelshave been developed from different viewpoints. In this paper, we introduce two techniques for the classificationof mGRS. Firstly, we generate multi-topologies from multi-relations defined in the universe. Hence, a novelapproximation space is established by leveraging the underlying topological structure. The characteristics of thenewly proposed approximation space are *** introduce an algorithmfor the reduction ***, a new approach for the classification ofmGRS based on neighborhood concepts is introduced. Finally, areal-life application from medical records is introduced via our approach to the classification of mGRS.

关键词： multi-granulation rough sets data classifications information systems interior operators closure operators approximation structures

Steady State Analysis of an Isolated Self-Excited Dual Three-Phase Induction Generator for Renewable Energy

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International Journal of modern Nonlinear Theory and Application 2014年第5期3卷 191-198页

作者： mohamed Arbi Khlifi badr m. alshammari College of Engineering Hail University Hail KSA

In this paper modelling and analysis in autonomous mode of dual three-phase induction generator (DTPIG) with a new algorithm have been done. We develop the steady state model of a dual three-phase self-excited induction generator for stand-alone renewable generation dispensing with the segregating real and imaginary components of the complex impedance of the induction generator. The obtained admittance yields the adequate magnetizing reactance and the frequency. These two key parameters are then used to compute the self-excitation process requirements in terms of the prime mover speed, the capacitance and the load impedance on the one hand and to predict the generator steady state performance parameters on the other. Steady state performances and characteristics of different configurations are clearly examined and compared. The analytical results are found to be in good agreement with experimental results.

关键词： Dual Stator Windings Asynchronous Generator Autonomous mode Steady State Analysis Renewable Electric Energy Generation

Therapeutic potential of Calotropis gigantea extract against invasive pulmonary aspergillosis:In vitro and in vivo study

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Asian Pacific Journal of Tropical Biomedicine 2022年第6期12卷 243-252页

作者： Enas m.Ali manal A.Alfwuaires Gehan m.badr Department of Biological Sciences Faculty of ScienceKing Faisal UniversityP.O.Box 380Al-Ahsa 31982Saudi Arabia Department of Botany and microbiology Faculty of ScienceCairo UniversityCairo 12613Egypt Department of Zoology Faculty of ScienceAin Shams UniversityCairo11566Egypt

Objective:To characterize the antifungal activity of methanolic leaf extract of Calotropis gigantea alone or in combination with amphotericin B against invasive pulmonary aspergillosis in ***:GC/mS was used for analysis of active constituents of Calotropis gigantea *** germination assay and broth micro-dilution method were used to determine antifungal potential of Calotropis gigantea/amphotericin B against Aspergillus *** mice were randomly assigned into 5 groups:group 1 was neutropenic(control);group 2 was infected with Aspergillus fumigatus;group 3 was infected with Aspergillus fumigatus,and treated with Calotropis gigantea extract;group 4 was infected with Aspergillus fumigatus and treated with amphotericin B;group 5 was infected with Aspergillus fumigatus and treated with both Calotropis gigantea extract and amphotericin *** lung tissues were histopathologically *** burden and gliotoxin concentration were evaluated in lung ***,superoxide dismutase,and malondialdehyde content were determined in lung ***,tumor necrosis factor-alpha,interleukin-1,and interleukin-17 were also estimated by the sandwich enzyme-linked immuno-sorbent ***:Calotropis gigantea/amphotericin B had a minimum inhibitory concentration and minimum fungicidal concentration of 80 and 160μg/mL,respectively,for Aspergillus ***,Calotropis gigantea/amphotericin B significantly reduced lung fungal burden by 72.95%and inhibited production of gliotoxin in lung tissues from 6320 to 1350μg/g *** gigantea/amphotericin B reduced the oxidative stress of the lung via elevating the activity of antioxidant enzymes and decreasing the levels of lipid *** activity and the production of pro-inflammatory cytokines were also significantly *** electron microscopy revealed deteriorations in the hyphae ultrastructure in Calotropis gigantea/amphotericin B trea

关键词： Aspergillus fumigatus Antifungal Invasive pulmonary aspergillosis Calotropis gigantea Amphotericin B mice

Optimal Fusion-Based Handcrafted with Deep Features for Brain Cancer Classification

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Computers, materials & Continua 2022年第10期73卷 801-815页

作者： mahmoud Ragab Sultanah m.alshammari Amer H.Asseri Waleed K.Almutiry Information Technology Department Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Center for Artificial Intelligence in Precision medicines King Abdulaziz UniversityJeddah21589Saudi Arabia mathematics Department Faculty of ScienceAl-Azhar UniversityNaser City11884CairoEgypt Computer Science Department Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Biochemistry Department Faculty of ScienceKing Abdulaziz UniversityJeddah21589Saudi Arabia Department of mathematics College of Science and Arts in Ar RassQassim UniversityBuryadah52571Saudi Arabia

Brain cancer detection and classification is done utilizing distinct medical imaging modalities like computed tomography(CT),or magnetic resonance imaging(mRI).An automated brain cancer classification using computer aided diagnosis(CAD)models can be designed to assist *** the recent advancement in computer vision(CV)and deep learning(DL)models,it is possible to automatically detect the tumor from images using a computer-aided *** study focuses on the design of automated Henry Gas Solubility Optimization with Fusion of Handcrafted and Deep Features(HGSO-FHDF)technique for brain cancer *** proposed HGSO-FHDF technique aims for detecting and classifying different stages of brain *** proposed HGSO-FHDF technique involves Gabor filtering(GF)technique for removing the noise and enhancing the quality of mRI *** addition,Tsallis entropy based image segmentation approach is applied to determine injured brain regions in the mRI ***,a fusion of handcrafted with deep features using Residual Network(ResNet)is utilized as feature ***,HGSO algorithm with kernel extreme learning machine(KELm)model was utilized for identifying the presence of brain *** examining the enhanced brain tumor classification performance,a comprehensive set of simulations take place on the BRATS 2015 dataset.

关键词： Brain cancer medical imaging deep learning fusion model metaheuristics feature extraction handcrafted features

A Template matching Based Feature Extraction for Activity Recognition

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Computers, materials & Continua 2022年第7期72卷 611-634页

作者： muhammad Hameed Siddiqi Helal alshammari Amjad Ali madallah Alruwaili Yousef Alhwaiti Saad Alanazi m.m.Kamruzzaman College of Computer and Information Sciences Jouf UniversitySakakaAljouf2014Kingdom of Saudi Arabia Department of Computer Science COMSATS University IslamabadLahore CampusPakistan

Human activity recognition(HAR)can play a vital role in the monitoring of human activities,particularly for healthcare conscious *** accuracy of HAR systems is completely reliant on the extraction of prominent *** methods find it very challenging to extract optimal features due to the dynamic nature of activities,thereby reducing recognition *** this paper,we propose a robust feature extraction method for HAR systems based on template ***,in this method,we want to associate a template of an activity frame or sub-frame comprising the corresponding *** this regard,the template is placed on the frame pixels to calculate the equivalent number of pixels in the template correspondent those in the *** process is replicated for the whole frame,and the pixel is directed to the optimum *** best count is estimated to be the pixel where the silhouette(provided via the template)presented inside the *** this way,the feature vector is *** feature vector generation,the hiddenmarkovmodel(Hmm)has been utilized to label the incoming *** utilized different publicly available standard datasets for *** proposed method achieved the best accuracy against existing state-of-the-art systems.

关键词： Activity recognition feature extraction template matching video surveillance

Optimal Logistics Activities Based Deep Learning Enabled Traffic Flow Prediction model

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Computers, materials & Continua 2022年第12期73卷 5269-5282页

作者： Basim Aljabhan mahmoud Ragab Sultanah m.alshammari Abdullah S.Al-malaise Al-Ghamdi Ports and maritime Transportation Department Faculty of Maritime StudiesKing Abdulaziz UniversityJeddah21589Saudi Arabia Information Technology Department Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Department of mathematics Faculty of ScienceAl-Azhar UniversityNaser City11884CairoEgypt Center of Excellence in Smart Environment Research King Abdulaziz UniversityJeddah21589Saudi Arabia Department of Computer Science Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Information Systems Department Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia S 7Information Systems Department HECI SchoolDar Alhekma UniversityJeddahSaudi Arabia

Traffic flow prediction becomes an essential process for intelligent transportation systems(ITS).Though traffic sensor devices are manually controllable,traffic flow data with distinct length,uneven sampling,and missing data finds challenging for effective *** traffic data has been considerably increased in recent times which cannot be handled by traditional mathematical *** recent developments of statistic and deep learning(DL)models pave a way for the effectual design of traffic flow prediction(TFP)*** this view,this study designs optimal attentionbased deep learning with statistical analysis for TFP(OADLSA-TFP)*** presentedOADLSA-TFP model intends to effectually forecast the level of traffic in the *** attain this,the OADLSA-TFP model employs attention-based bidirectional long short-term memory(ABLSTm)model for predicting traffic *** order to enhance the performance of the ABLSTm model,the hyperparameter optimization process is performed using artificial fish swarm algorithm(AFSA).A wide-ranging experimental analysis is carried out on benchmark dataset and the obtained values reported the enhancements of the OADLSA-TFP model over the recent approaches mean square error(mSE),root mean square error(RmSE),and mean absolute percentage error(mAPE)of 120.342%,10.970%,and 8.146%respectively.

关键词： Traffic flow prediction deep learning artificial fish swarm algorithm mass gatherings statistical analysis logistics

Efficient Resource Allocation in Fog Computing Using QTCS model

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Computers, materials & Continua 2022年第2期70卷 2225-2239页

作者： m.Iyapparaja Naif Khalaf alshammari m.Sathish Kumar S.Siva Rama Krishnan Chiranji Lal Chowdhary School of Information Technology and Engineering Vellore Institute of TechnologyVellore632014India mechanical Engineering University of HailSaudi Arabia

Infrastructure of fog is a complex system due to the large number of heterogeneous resources that need to be *** embedded devices deployed with the Internet of Things(IoT)technology have increased since the past few years,and these devices generate huge amount of *** devices in IoT can be remotely connected and might be placed in different locations which add to the network *** time applications require high bandwidth with reduced latency to ensure Quality of Service(QoS).To achieve this,fog computing plays a vital role in processing the request locally with the nearest available resources by reduced *** of themajor issues to focus on in a fog service is managing and allocating *** theory is one of the most popular mechanisms for task *** this work,an efficient model is designed to improve QoS with the efficacy of resource allocation based on a Queuing Theory based Cuckoo Search(QTCS)model which will optimize the overall resource management process.

关键词： Queuing theory Cuckoo search QoS resource allocation energy efficiency

Evaluation of Landsat 8 image pansharpening in estimating soil organic matter using multiple linear regression and artificial neural networks

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Geo-Spatial Information Science 2022年第3期25卷 353-364页

作者： Abdelkrim Bouasria Khalid Ibno Namr Abdelmejid Rahimi El mostafa Ettachfini badr Rerhou Faculty of Sciences Chouaib Doukkali UniversityEl JadidaMorocco Agricultural Development Department Doukkala Regional Agricultural Development OfficeEl JadidaMorocco Soil Science Department Hassan II Agronomic and Veterinary InstituteRabatMorocco

In agricultural systems,the regular monitoring of Soil Organic matter(SOm)dynamics is *** task is costly and time-consuming when using the conventional method,especially in a very fragmented area and with intensive agricultural activity,such as the area of Sidi *** study area is located in the Doukkala irrigated perimeter in *** data can provide an alternative and fill this gap at a low *** to predict SOm from a satellite image,whether linear or nonlinear,have shown considerable *** study aims to compare SOm prediction using multiple Linear Regression(mLR)and Artificial Neural Networks(ANN).A total of 368 points were collected at a depth of 0-30 cm and analyzed in the *** image at 15 m resolution(mSPAN)was produced from a 30 m resolution(mS)Landsat-8 image using image pansharpening processing and panchromatic band(15 m).The results obtained show that the mLR models predicted the SOm with(training/validation)R^(2)values of 0.62/0.63 and 0.64/0.65 and RmSE values of 0.23/0.22 and 0.22/0.21 for the mS and mSPAN images,*** contrast,the ANN models predicted SOm with R2 values of 0.65/0.66 and 0.69/0.71 and RmSE values of 0.22/0.10 and 0.21/0.18 for the mS and mSPAN images,*** pansharpening improved the prediction accuracy by 2.60%and 4.30%and reduced the estimation error by 0.80%and 1.30%for the mLR and ANN models,respectively.

关键词： Digital soil mapping soil organic matter remote sensing multiple linear regression artificial neural networks irrigated area Doukkala morocco