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Adsorptive removal of phosphate by the bimetallic hydroxide nanocomposites embedded in pomegranate peel

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Journal of Environmental Sciences 2020年第5期32卷 189-198页

作者： muhammad Akram Xing Xu Baoyu Gao Qinyan Yue Shang Yanan Rizwan khan muhammad Ali Inam Shandong Key Laboratory of Water Pollution Control and Resource Reuse School of Environmental Science and EngineeringShandong UniversityQingdao 266200China Department of Chemical Engineering Quaid-e-Awam University of EngineeringScience and Technology(QUEST)Naiuabshah 67480SindfiPakistan Institute of Environmental Sciences and Engineering(IESE) School of Civil and Environmental Engineering(SCEE)National University of Sciences and Technology(NUST)H-12 CampusIslamabad 44000Pakistan

This study aimed to fabricate new and effective material for the efficiency of phosphate *** types of adsorbent materials,the zirconium hydroxides embedded in pomegranate peel(Zr/Peel)and zirconium-lanthanum hydroxides embedded in pomegranate peel(Zr-La/Peel)were *** electronic microscopy(SEM),x-ray photoelectron spectroscopy(XPS)and x-ray diffraction(XRD)were evaluated to give insight into the physicochemical properties of these ***-La/Peel exceeded the adsorption efficiency of Zr/Peel adsorbents in batch adsorption experiments at the same pH *** peel as a host can strive to have a strong"shielding effect"to increase the steadiness of the entrenched Zr and La *** and Zr are hydroxide metals that emit many hydrogen ions during the hydrolysis reaction,which contribute to protonation and electrostatic *** highest adsorption capacity of La-Zr/Peel for phosphate was calculated to be40.21 mg/g,and pseudo second-order equation is very well fitted for kinetic *** adsorption efficiency was reduced by an increase of *** the background of coexisting Cl-,little effect on adsorption efficiency was observed,while adsorption capacities were reduced by almost 20-30%with the coexistence of SO42-,NO3-and humic acid(HA).

关键词： Bimetallic hydroxides Pomegranate peel Phosphate Adsorption

Comparative analysis of twelve transfer learning models for the prediction and crack detection in concrete dams,based on borehole images

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Frontiers of Structural and Civil Engineering 2024年第10期18卷 1507-1523页

作者： Umer Sadiq khan muhammad ISHFAQUE Saif Ur Rehman khan Fang Xu Lerui CHEN Yi LEI School of Computer and Information Science Hubei Engineering UniversityXiaogan 432000China Institute for AI Industrial Technology Research Hubei Engineering UniversityXiaogan 432000China College of Water Conservancy and Hydropower Engineering Hohai UniversityNanjing 210098China School of Computer Science and Engineering Central South UniversityChangsha 410083China College of Aviation Zhongyuan University of TechnologyZhengzhou 451191China School of Civil Engineering Central South UniversityChangsha 410083China

Disaster-resilient dams require accurate crack detection,but machine learning methods cannot capture dam structural reaction temporal patterns and *** research uses deep learning,convolutional neural networks,and transfer learning to improve dam crack *** deep-learning models are trained on 192 crack *** research aims to provide up-to-date detecting techniques to solve dam crack *** finding shows that the EfficientNetB0 model performed better than others in classifying borehole concrete crack surface tiles and normal(undamaged)surface tiles with 91%*** study’s pre-trained designs help to identify and to determine the specific locations of cracks.

关键词： concrete dam borehole closed-circuit television deep learning models crack detection water resources management management

Brain Tumor Segmentation in Multimodal MRI Using U-Net Layered Structure

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Computers, Materials & Continua 2023年第3期74卷 5267-5281页

作者： muhammad Javaid Iqbal muhammad Waseem Iqbal muhammad Anwar muhammad Murad khan Abd Jabar Nazimi Mohammad Nazir Ahmad Department of Information Technology The Superior UniversityLahorePakistan Department of Software Engineering The Superior UniversityLahorePakistan Department of Information Sciences Division of Science and TechnologyUniversity of EducationLahorePakistan Department of Computer Science Government College UniversityFaisalabadPakistan Deptment of Oral and Maxillofacial Surgery Faculty of DentistryUniversiti Kebangsaan MalaysiaKuala LumpurMalaysia Institute of IR4.0 Universiti Kebangsaan MalaysiaBangiSelangorMalaysia

The brain tumour is the mass where some tissues become old or damaged,but they do not die or not leave their *** brain tumour masses occur due to malignant *** tissues must die so that new tissues are allowed to be born and take their *** segmentation is a complex and time-taking problem due to the tumour’s size,shape,and appearance *** finding such masses in the brain by analyzing Magnetic Resonance Images(MRI)is a crucial task for experts and *** could not work for large volume images simultaneously,and many errors occurred due to overwhelming image *** main objective of this research study is the segmentation of tumors in brain MRI images with the help of digital image processing and deep learning *** research study proposed an automatic model for tumor segmentation in MRI *** proposed model has a few significant steps,which first apply the pre-processing method for the whole dataset to convert Neuroimaging Informatics Technology Initiative(NIFTI)volumes into the 3D NumPy *** the second step,the proposed model adopts U-Net deep learning segmentation algorithm with an improved layered structure and sets the updated *** the third step,the proposed model uses state-of-the-art Medical Image Computing and Computer-Assisted Intervention(MICCAI)BRATS 2018 dataset withMRI modalities such as T1,T1Gd,T2,and Fluidattenuated inversion recovery(FLAIR).Tumour types in MRI images are classified according to the tumour *** of these masses carried by state-of-the-art approaches such that the first is enhancing tumour(label 4),edema(label 2),necrotic and non-enhancing tumour core(label 1),and the remaining region is label 0 such that edema(whole tumour),necrosis and *** proposed model is evaluated and gets the Dice Coefficient(DSC)value for High-grade glioma(HGG)volumes for their test set-a,test set-b,and test set-c 0.9795, 0.9855 and 0.9793, respectively.

关键词： Brain tumour segmentation magnetic resonance images modalities dice coefficient low-grade glioma U-Net

Synergistic antibacterial effect of honey and Herba Ocimi Basilici against some bacterial pathogens

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Journal of Traditional Chinese Medicine 2013年第6期33卷 810-814页

作者： Ali Talha Khalil Imran khan Kafeel Ahmad Yusra Ali khan Momin khan muhammad Jaseem khan Department of Biotechnology Quaid-i-Azam University Centre of Biotechnology and Microbiology University of Peshawar Institute of Paramedical Sciences Khyber Medical University

OBJECTIVE： To evaluate the antibacterial activity of the combination of different honey brands and methanolic fraction of Herba Ocimi Basilici using agar well diffusion assay. METHODS： The antibacterial activity was deter- mined against thirteen pathogenic bacterial clini- cal isolates including six gram negative （Klebsiella pneumonia, Pseudomonas aeroginosa, Escherichia coli, Salmonella typhi, Salmonella typhimirium, Xanthomonas campestris） and six gram positive strains （Enterococcus faecalis faecalis, Bacillus subti- lis, Staphylococcus aureus, Clostridium perfringens type C, Clostridium perfringens type D, CIostridium chauvoei）. Agar well diffusion method was used while zones of inhibition were measured with verni- er scale. RESULTS： At higher concentration, all the honey brands showed good to significant activity. Thehighest activity was observed for Hamdard brand honey （27.60±0.40） against Enterococcus faecalis. CONCLUSION： These results revealed that combi- nations of plant extracts of Herba Ocimi Basilici with honey can be used for the development of potent and novel antibacterial agents.

关键词： Anti-infective agents Honey Gram-negative bacteria Gram-positive bacteria Well dif-fusion assay

Genome-wide association analysis for stripe rust resistance in spring wheat(Triticum aestivum L.) germplasm

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Journal of Integrative Agriculture 2020年第8期19卷 2035-2043页

作者： Sher muhammad muhammad SAJJAD Sultan Habibullah khan muhammad SHAHID muhammad ZUBAIR Faisal Saeed AWAN Azeem iqbal khan muhammad Salman MUBARAK Ayesha TAHIR muhammad UMER Rumana KEYANI muhammad InamAFZAL Irfan MANZOOR Javed Iqbal WATTOO Aziz-ur REHMAN Department of Bioinformatics and Biotechnology GC UniversityFaisalabad 38090Pakistan Department of Biosciences COMSATS University Islamabad(CUI)Islamabad 45550Pakistan Centre of Agricultural Biochemistry and Biotechnology(CABB) University of AgricultureFaisalabad 38000Pakistan Department of Plant Breeding and Genetics University of AgricultureFaisalabad 38000Pakistan Department of Biotechnology Faculty of Life SciencesUniversity of Central PunjabLahore 54000Pakistan Wheat Research Institute Ayub Agricultural Research Institute(AARI)Faisalabad 38950Pakistan

Stripe rust is a continuous threat to wheat crop all over the *** causes considerable yield losses in wheat crop every *** deployment of adult plant resistance(APR)genes in newly developing wheat cultivars is the most judicious strategy to combat this ***,we dissected the genetics underpinning stripe rust resistance in Pakistani wheat *** association panel of 94 spring wheat genotypes was phenotyped for two years to score the infestation of stripe rust on each accession and was scanned with 203 polymorphic *** on D’measure,linkage disequilibrium(LD)exhibited between loci distant up to 45 c ***-trait associations(MTAs)were determined using mixed linear model(MLM).Total 31 quantitative trait loci(QTLs)were observed on all 21 wheat *** QTLs were newly discovered as well as 19 QTLs and 35 previously reported Yr genes were validated in Pakistani wheat *** major QTLs were ***.2 AL and ***.3 BS(PVE,11.9%).Dissection of genes from the newly observed QTLs can provide new APR genes to improve genetic resources for APR resistance in wheat crop.

关键词： wheat Puccinia striiformis LD GWAS MTA PCoA

Real-Time Violent Action Recognition Using Key Frames Extraction and Deep Learning

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Computers, Materials & Continua 2021年第11期69卷 2217-2230页

作者： Muzamil Ahmed muhammad Ramzan Hikmat Ullah khan Saqib Iqbal muhammad Attique khan Jung-In Choi Yunyoung Nam Seifedine Kadry Deparment of Computer Science and Information Technology The University of LahoreSargodha CampusSargodha40100Pakistan Department of Computer Science COMSATS University IslamabadWah CampusWah Cantt47040Pakistan School of System and Technology University of Management and TechnologyLahore54782Pakistan Department of Computer Science and Information Technology University of SargodhaSargodha40100Pakistan College of Engineering Al Ain UniversityAl AinUnited Arab Emirates Department of Computer Science HITEC University TaxilaTaxilaPakistan Applied Artificial Intelligence Ajou UniversitySuwonKorea Department of Computer Science and Engineering Soonchunhyang UniversityAsanKorea Department of Mathematics and Computer Science Faculty of ScienceBeirut Arab UniversityLebanon

Violence recognition is crucial because of its applications in activities related to security and law *** semi-automated systems have issues such as tedious manual surveillances,which causes human errors and makes these systems less *** approaches have been proposed using trajectory-based,non-object-centric,and deep-learning-based *** studies have shown that deep learning techniques attain higher accuracy and lower error rates than those of other ***,the their performance must be *** study explores the state-of-the-art deep learning architecture of convolutional neural networks(CNNs)and inception V4 to detect and recognize violence using video *** the proposed framework,the keyframe extraction technique eliminates duplicate consecutive *** keyframing phase reduces the training data size and hence decreases the computational cost by avoiding duplicate *** feature selection and classification tasks,the applied sequential CNN uses one kernel size,whereas the inception v4 CNN uses multiple kernels for different layers of the *** empirical analysis,four widely used standard datasets are used with diverse *** results confirm that the proposed approach attains 98%accuracy,reduces the computational cost,and outperforms the existing techniques of violence detection and recognition.

关键词： Violence detection violence recognition deep learning convolutional neural network inception v4 keyframe extraction

A Deep Learning Approach for Prediction of Protein Secondary Structure

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Computers, Materials & Continua 2022年第8期72卷 3705-3718页

作者： muhammad Zubair muhammad Kashif Hanif Eatedal Alabdulkreem Yazeed Ghadi muhammad Irfan khan muhammad Umer Sarwar Ayesha Hanif Department of Computer Science Government College UniversityFaisalabadPakistan Department of Computer Science College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadh 11671Saudi Arabia Department of Software Engineering/Computer Science Al Ain UniversityAbu DhabiUnited Arab Emirates

The secondary structure of a protein is critical for establishing a link between the protein primary and tertiary *** this reason,it is important to design methods for accurate protein secondary structure *** of the existing computational techniques for protein structural and functional prediction are based onmachine learning with *** deep learning architectures have already been applied to tackle protein secondary structure prediction *** this study,deep learning based models,i.e.,convolutional neural network and long short-term memory for protein secondary structure prediction were *** input to proposed models is amino acid sequences which were derived from CulledPDB *** tuning with cross validation was employed to attain best parameters for the proposed *** proposed models enables effective processing of amino acids and attain approximately 87.05%and 87.47%Q3 accuracy of protein secondary structure prediction for convolutional neural network and long short-term memory models,respectively.

关键词： Convolutional neural network machine learning protein secondary structure deep learning long short-term memory protein secondary structure prediction

Photokilling of waterborne-resistant pathogenic bacteria using cobalt-doped zinc oxide doped on reduced graphene oxide nanoparticles

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Journal of Innovative Optical Health Sciences 2022年第4期15卷 1-10页

作者： Ameer muhammad khan Laiba Pervez Jonathan Celli Mutiullah Khattak Maqdad Ullah ZiaUllah Shah muhammad Mustafa khan muhammad Nadeem Sulaiman Faisal Akhtar Nadhman Department of Biotechnology Institute of Integrative Biosciences CECOS University Hayatabad Phase VI PeshawarPakistan Department of Physics University of Massachusetts BostonMA 02125USA Department of Microbiology Institute of Pathology and Diagnostic Medicine Khyber Medical University Hayatabad PeshawarKPPakistan Department of Pharmacy Institute of Integrative Biosciences CECOS University Hayatabad Phase VIPeshawarPakistan

This study is aimed at the chemical synthesis of light-activated cobalt-doped zinc oxide and its further doping on reduced graphene oxide(RGO)and assessment of its antibacterial activity on antibiotic-resistant waterborne pathogens including Enterococcus faecalis,Staphylococcus aureus,Klebsiella pneumonia,and Pseudomonas *** synthesized nanoparticles were characterized via UV–vis spectroscopy,scanning electron microscopy(SEM),and energy-dispersive X-ray spectroscopy(EDS).The minimal inhibitory concentration(MIC)of nanoparticles portrayed a significant killing of both Gram-positive and Gram-negative *** synthesized nanoparticles were further found as active killers of bacteria in drinking ***,these nanoparticles were found photothermally active alongside ROS *** photokilling activity makes them ideal replacement candidates for traditional water filters.

关键词： MDR Bacteria graphene oxide doping photokilling ZnO.

Robust Length of Stay Prediction Model for Indoor Patients

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Computers, Materials & Continua 2022年第3期70卷 5519-5536页

作者： Ayesha Siddiqa Syed Abbas Zilqurnain Naqvi muhammad Ahsan Allah Ditta Hani Alquhayz M.A.khan muhammad Adnan khan Department of Mechatronics and Control Engineering University of Engineering and TechnologyLahore54000Pakistan Department of Information Sciences Division of Science and TechnologyUniversity of EducationLahore54000Pakistan Department of Computer Science and Information College of Science in ZulfiMajmaah UniversityAl-Majmaah11952Saudi Arabia Riphah School of Computing&Innovation Faculty of ComputingRiphah International UniversityLahore CampusLahore54000Pakistan Pattern Recognition and Machine Learning Lab Department of SoftwareGachon UniversitySeongnam13557Korea

Due to unforeseen climate change,complicated chronic diseases,and mutation of viruses’hospital administration’s top challenge is to know about the Length of stay(LOS)of different diseased patients in the *** management does not exactly know when the existing patient leaves the hospital;this information could be crucial for hospital *** could allow them to take more patients for *** a result,hospitals face many problems managing available resources and new patients in getting entries for their prompt ***,a robust model needs to be designed to help hospital administration predict patients’LOS to resolve these *** this purpose,a very large-sized data(more than 2.3 million patients’data)related to New-York Hospitals patients and containing information about a wide range of diseases including Bone-Marrow,Tuberculosis,Intestinal Transplant,Mental illness,Leukaemia,Spinal cord injury,Trauma,Rehabilitation,Kidney and Alcoholic Patients,HIV Patients,Malignant Breast disorder,Asthma,Respiratory distress syndrome,*** been analyzed to predict the *** selected six Machine learning(ML)models named:Multiple linear regression(MLR),Lasso regression(LR),Ridge regression(RR),Decision tree regression(DTR),Extreme gradient boosting regression(XGBR),and Random Forest regression(RFR).The selected models’predictive performance was checked using R square andMean square error(MSE)as the performance evaluation *** results revealed the superior predictive performance of the RFRmodel,both in terms of RS score(92%)and MSE score(5),among all selected *** Exploratory data analysis(EDA),we conclude that maximumstay was between 0 to 5 days with the meantime of each patient 5.3 days and more than 50 years old patients spent more days in the *** on the average LOS,results revealed that the patients with diagnoses related to birth complications spent more days in the hospital than other *** finding coul

关键词： Length of stay machine learning robust model random forest regression