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| 1 | Detailed investigation of optimized alkali catalyzed transesterification of Jatropha oil for biodiesel production显示文摘The non-edible oils are believed to be one of the major feedstock for the production of biodiesel in future.In the present study,we investigated the production of Jatropha oil methyl esters(JOMEs) via alkali-catalyzed transesterification route.The biophysical characteristics of Jatropha oil were found within the optimal range in accordance with ASTM standards as a substitute diesel fuel.The chemical composition and production yield of as-synthesized biodiesel were confirmed by various analytical techniques such as FT-IR,1H NMR,13 C NMR and gas chromatography coupled with mass spectrometry.A high percentage conversion,~96.09%,of fatty acids into esters was achieved under optimized transesterification conditions with 6 :1 oil to methanol ratio and 0.9 wt% Na OH for 50 min at ~60°C.Moreover,twelve fatty acids methyl esters(FAME) were quantified in the GC/MS analysis and it was interesting to note that the mass fragmentation pattern of saturated,monounsaturated and diunsaturated FAME was comparable with the literature reported values. | Waqas Ahmed Muhammad Faizan Nazar Syed Danish Ali Usman Ali Rana Salah Ud-Din Khan | 2015 | Journal of Energy Chemistry2015，24，3： | 5 |
| 2 | 太赫兹感知通信一体化波形设计与信号处理显示文摘太赫兹波段感知通信一体化技术能够在提高数据传输速率和感知分辨率的同时,有效降低硬件资源和频谱资源的消耗。首先,简要介绍了感知通信一体化及太赫兹通信、感知的现状。然后,分别从感知和通信的角度讨论了一体化波形设计及优化策略,同时分析了两类信号接收机的信号处理算法,并实验展示了一种97GHz基于OFDM信号的一体化系统,对系统的距离、速度和通信等性能进行了测试。最后,总结和展望了太赫兹感知通信一体化的技术难题和未来研究方向。 | 余显斌 吕治东 李涟漪 Nazar Muhammad Idrees 张鹿 | 2022 | 通信学报2022，43，2： | 2 |
| 3 | 脱氮硫杆菌的筛选及其对锶离子的矿化作用显示文摘放射性污染日益严重,其中锶污染作为土壤典型污染之一成为研究热点。土壤中存在着一些矿化菌,能够对锶离子进行矿化固定。本实验对从土壤中分离的3株脱氮硫杆菌的特性及其对Sr^(2+)的矿化行为进行了研究,发现该菌对1.0g/L模拟Sr^(2+)污染的去除率可达80%。扫描电子显微镜(SEM)、能谱分析(EDS)、X射线衍射(XRD)、傅里叶变换红外光谱(FTIR)等结果显示,矿化产物为硫酸锶。可见,利用脱氮硫杆菌治理土壤中Sr^(2+)污染具有可行性,该方法将会有一定应用前景。 | 邬琴琴 代群威 韩林宝 王岩 党政 赵玉连 Nazar Muhammad | 2017 | 核化学与放射化学2017，39，2： | 2 |
| 4 | An Integrated Deep Learning Framework for Fruits Diseases Classification显示文摘:Agriculture has been an important research area in the field of image processing for the last five years.Diseases affect the quality and quantity of fruits,thereby disrupting the economy of a country.Many computerized techniques have been introduced for detecting and recognizing fruit diseases.However,some issues remain to be addressed,such as irrelevant features and the dimensionality of feature vectors,which increase the computational time of the system.Herein,we propose an integrated deep learning framework for classifying fruit diseases.We consider seven types of fruits,i.e.,apple,cherry,blueberry,grapes,peach,citrus,and strawberry.The proposed method comprises several important steps.Initially,data increase is applied,and then two different types of features are extracted.In the first feature type,texture and color features,i.e.,classical features,are extracted.In the second type,deep learning characteristics are extracted using a pretrained model.The pretrained model is reused through transfer learning.Subsequently,both types of features are merged using the maximum mean value of the serial approach.Next,the resulting fused vector is optimized using a harmonic threshold-based genetic algorithm.Finally,the selected features are classified using multiple classifiers.An evaluation is performed on the PlantVillage dataset,and an accuracy of 99%is achieved.A comparison with recent techniques indicate the superiority of the proposed method. | Abdul Majid Muhammad Attique Khan Majed Alhaisoni Muhammad Asfand Eyar Usman Tariq Nazar Hussain Yunyoung Nam Seifedine Kadry | 2022 | Computers, Materials & Continua2022，，4： | 1 |
| 5 | Synthesis of l -methionine stabilized nickel nanowires and their application for catalytic oxidative transfer hydrogenation of isopropanol显示文摘 | Nazar Hussain Kalwar Sirajuddin Syed Tufail H. Sherazi Muhammad Ishaq Abro Zulfiqar Ali Tagar Syeda Sara Hassan Yasmin Junejo Manzoor Iqbal Khattak | 2011 | Applied Catalysis A, General2011，，1： | 1 |
| 6 | Deposition and electrical properties of cadmium telluride thin films by thermal vacuum evaporation technique显示文摘 | Nazar Abbas Shah Manzar Abbas Muhammad Bashir Waqar Ahmad Adil Syed Muhammad Ashraf Atta | 2009 | 材料科学与工程(中英文版)2009，3，10： | 1 |
| 7 | Properties of Certain Subclasses of Analytic Functions Involving q-Poisson Distribution显示文摘By using the basic(or q)-Calculus many subclasses of analytic and univalent functions have been generalized and studied from different viewpoints and perspectives.In this paper,we aim to define certain new subclasses of an analytic function.We then give necessary and sufficient conditions for each of the defined function classes.We also study necessary and sufficient conditions for a function whose coefficients are probabilities of q-Poisson distribution.To validate our results,some known consequences are also given in the form of Remarks and Corollaries. | Bilal Khan Zhi-Guo Liu Nazar Khan Aftab Hussain Nasir Khan Muhammad Tahir | 2022 | Computer Modeling in Engineering & Sciences2022，，6： | 0 |
| 8 | Enhanced urease inhibition activity of Ag nanomaterials capped with N-substituted methyl 5-acetamido-β-resorcylate显示文摘Medically, bacterial ureases are important virulent factors and are used for treatment of peptic ulcers and urinary tones. Reported urease inhibitors are associated with various side effects including antibiotic resistance as a major one. Still there is an urgent need to synthesize new urease inhibitors. In this context we have synthesized new urease inhibitor i.e. AgL that is composed of Ag nanomaterials capped with N-substituted methyl 5-acetamido-β-resorcylate(L). The conjugation of L to silver was confirmed through FTIR, UV–vis and TEM analysis.Bare silver nanomaterials(Ag) were also prepared. The stability of AgL nanostructures was determined against various parameters(temperature, high salt concentration, pH) and found to be stable. The in vitro antimicrobial(antibacterial, antifungal), enzyme inhibition(xanthine oxidase, urease, carbonic anhydrase, α-chymotrypsin,cholinesterase) and antioxidant activities of AgL were investigated and compared with Ag, L and standard drugs.In comparison to other bioactivities, AgL shows statistically enhanced selective enzyme inhibition activity against urease enzyme. Urease inhibition activity of AgL was significantly greater than standard drug(thiourea),L and Ag. On a per weight basis, AgL required about 11–18 times less amount of L for inhibition of urease enzyme. | Syeda Sohaila Naz Muhammad Raza Shah Nazar Ul Islam Syed Sartaj Alam | 2019 | Progress in Natural Science:Materials International2019，29，2： | 0 |
| 9 | Fabrication and Characterization of Crack Free Nano Porous Titanium Dioxide Thin Films by Spray Deposition Technique显示文摘 | Manzar Abbas Nazar Abbas Shah Fazali Subhan Muhammad Irfan | 2010 | 材料科学与工程(中英文版)2010，4，5： | 0 |
| 10 | Multiclass Cucumber Leaf Diseases Recognition Using Best Feature Selection显示文摘Agriculture is an important research area in the field of visual recognition by computers.Plant diseases affect the quality and yields of agriculture.Early-stage identification of crop disease decreases financial losses and positively impacts crop quality.The manual identification of crop diseases,which aremostly visible on leaves,is a very time-consuming and costly process.In this work,we propose a new framework for the recognition of cucumber leaf diseases.The proposed framework is based on deep learning and involves the fusion and selection of the best features.In the feature extraction phase,VGG(Visual Geometry Group)and Inception V3 deep learning models are considered and fine-tuned.Both fine-tuned models are trained using deep transfer learning.Features are extracted in the later step and fused using a parallel maximum fusion approach.In the later step,best features are selected usingWhale Optimization algorithm.The best-selected features are classified using supervised learning algorithms for the final classification process.The experimental process was conducted on a privately collected dataset that consists of five types of cucumber disease and achieved accuracy of 96.5%.A comparison with recent techniques shows the significance of the proposed method. | Nazar Hussain Muhammad Attique Khan Usman Tariq Seifedine Kadry Muhammad Asfand E.Yar Almetwally M.Mostafa Abeer Ali Alnuaim Shafiq Ahmad | 2022 | Computers, Materials & Continua2022，，2： | 0 |
| 11 | Multiclass Stomach Diseases Classication Using Deep Learning Features Optimization显示文摘In the area of medical image processing,stomach cancer is one of the most important cancers which need to be diagnose at the early stage.In this paper,an optimized deep learning method is presented for multiple stomach disease classication.The proposed method work in few important steps—preprocessing using the fusion of ltering images along with Ant Colony Optimization(ACO),deep transfer learning-based features extraction,optimization of deep extracted features using nature-inspired algorithms,and nally fusion of optimal vectors and classication using Multi-Layered Perceptron Neural Network(MLNN).In the feature extraction step,pretrained Inception V3 is utilized and retrained on selected stomach infection classes using the deep transfer learning step.Later on,the activation function is applied to Global Average Pool(GAP)for feature extraction.However,the extracted features are optimized through two different nature-inspired algorithms—Particle Swarm Optimization(PSO)with dynamic tness function and Crow Search Algorithm(CSA).Hence,both methods’output is fused by a maximal value approach and classied the fused feature vector by MLNN.Two datasets are used to evaluate the proposed method—CUI WahStomach Diseases and Combined dataset and achieved an average accuracy of 99.5%.The comparison with existing techniques,it is shown that the proposed method shows signicant performance. | Muhammad Attique Khan Abdul Majid Nazar Hussain Majed Alhaisoni Yu-Dong Zhang Seifedine Kadry Yunyoung Nam | 2021 | Computers, Materials & Continua2021，，6： | 0 |
| 12 | Improvement in Sensing Accuracy of an OFDM-Based W-Band System显示文摘Orthogonal frequency division multiplexing (OFDM) waveform is promising to converge communications and sensing functionalities for future wireless applications. This paper presents a novel method to improve the OFDM-based sensing accuracy by estimating the delay/Doppler leakages in the channel matrix, which is constructed by the received and the transmitted OFDM symbols. Both simulation and proof-of-concept experiment validate the proposed method for sensing improvement.The experiment uses a heterodyne W-band system at 97 GHz to transmit and receive an OFDM waveform of bandwidth 3.9 GHz.We achieve an improvement in sensing accuracy by an order of magnitude which is significant for OFDM-based converged systems. | Nazar Muhammad Idrees Zijie Lu Mu hammad Saqlain Hongqi Zhang Shiwei Wang Lu Zhang Xianbin Yu | 2022 | Journal of Communications and Information Networks2022，7，1： | 0 |
| 13 | Classification of Positive COVID-19 CT Scans Using Deep Learning显示文摘In medical imaging,computer vision researchers are faced with a variety of features for verifying the authenticity of classifiers for an accurate diagnosis.In response to the coronavirus 2019(COVID-19)pandemic,new testing procedures,medical treatments,and vaccines are being developed rapidly.One potential diagnostic tool is a reverse-transcription polymerase chain reaction(RT-PCR).RT-PCR,typically a time-consuming process,was less sensitive to COVID-19 recognition in the disease’s early stages.Here we introduce an optimized deep learning(DL)scheme to distinguish COVID-19-infected patients from normal patients according to computed tomography(CT)scans.In the proposed method,contrast enhancement is used to improve the quality of the original images.A pretrained DenseNet-201 DL model is then trained using transfer learning.Two fully connected layers and an average pool are used for feature extraction.The extracted deep features are then optimized with a Firefly algorithm to select the most optimal learning features.Fusing the selected features is important to improving the accuracy of the approach;however,it directly affects the computational cost of the technique.In the proposed method,a new parallel high index technique is used to fuse two optimal vectors;the outcome is then passed on to an extreme learning machine for final classification.Experiments were conducted on a collected database of patients using a 70:30 training:Testing ratio.Our results indicated an average classification accuracy of 94.76%with the proposed approach.A comparison of the outcomes to several other DL models demonstrated the effectiveness of our DL method for classifying COVID-19 based on CT scans. | Muhammad Attique Khan Nazar Hussain Abdul Majid Majed Alhaisoni Syed Ahmad Chan Bukhari Seifedine Kadry Yunyoung Nam Yu-Dong Zhang | 2021 | Computers, Materials & Continua2021，，3： | 0 |
| 14 | Effects of lead(Pb)-induced oxidative stress on morphological and physio-biochemical properties of rice显示文摘In rice,high concentration of lead(Pb)can cause phyto-toxicity affecting several physiological functions.Cultivation of rice varieties that are resistant to Pb-induced oxidative stress is an important management strategy in Pb-contaminated soils.In the current study,we evaluated four different rice cultivars for their response to Pb-induced stress.Three japonica type cultivars X-Jigna,Ediget,and Furat,and one Indica type cultivar Amber 33 were grown in soil containing different Pb concentrations(0 mM,0.6 mM,and 1.2 mM).The soil was treated with 0 mM or 0.6 mM or 1.2 mM Pb solution one month prior to rice seedling transplantation.Thereafter,four-week-old rice seedlings were transplanted into the treated soil and their responses were observed until maturity.The data revealed that a highest concentration of Pb(1.2 mM)induced significant reduction in agronomic traits such as plant height,number of tillers per plant,number of panicles per plant,and number of spikelets per panicle in all the rice cultivars.However,least reduction in the agronomic traits was observed in X-Jigna,whereas the highest reduction in the agronomic traits was observed in Ediget.Antioxidant activity of catalase(CAT),peroxidase(POD),polyphenol oxidase(PPO),and superoxide dismutase(SOD),was evaluated along with the accumulation of superoxide ions(O2.-),protein,proline,chlorophyll,sucrose,glucose,and fructose contents in all the rice cultivars.A significant increase in antioxidant activity and in the accumulation of proline and sucrose contents with the least reduction in the chlorophyll and protein contents was observed in X-Jigna suggesting that X-Jigna is the most tolerant among all the rice cultivars tested against Pb-stress.On the other hand,non-significant and slightly significant increase in the antioxidant activity,less accumulation of proline and sucrose contents,and higher reduction in the chlorophyll and protein contents was observed in Ediget,which further suggest that Ediget is the most susceptible rice cultivar to Pb-stress.In addition,the other rice cultivars Furat and Amber 33,were found to be moderately tolerant to Pb-induced oxidative stress.In summary,our results suggest that tolerance to Pb-induced oxidative stress would be a result of a synergetic action of both enzymatic and non-enzymatic antioxidant systems,leading to a balanced redox status in rice. | MURTAZA KHAN IBA NAZAR IBRAHIM AL AZZAWI MUHAMMAD IMRAN ADIL HUSSAIN BONG-GYU MUN ANJALI PANDE BYUNG-WOOK YUN | 2021 | BIOCELL2021，45，5： | 0 |
| 15 | Performance Analysis of Magnetic Nanoparticles during Targeted Drug Delivery:Application of OHAM显示文摘In recent years,the emergence of nanotechnology experienced incredible development in the field of medical sciences.During the past decade,investigating the characteristics of nanoparticles during fluid flow has been one of the intriguing issues.Nanoparticle distribution and uniformity have emerged as substantial criteria in both medical and engineering applications.Adverse effects of chemotherapy on healthy tissues are known to be a significant concern during cancer therapy.A novel treatment method of magnetic drug targeting(MDT)has emerged as a promising topical cancer treatment along with some attractive advantages of improving efficacy,fewer side effects,and reduce drug dose.During magnetic drug targeting,the appropriate movement of nanoparticles(magnetic)as carriers is essential for the therapeutic process in the blood clot removal,infection treatment,and tumor cell treatment.In this study,we have numerically investigated the behavior of an unsteady blood flowinfused with magnetic nanoparticles during MDT under the influence of a uniform external magnetic field in a microtube.An optimal homotopy asymptotic method(OHAM)is employed to compute the governing equation for unsteady electromagnetohydrodynamics flow.The influence of Hartmann number(Ha),particle mass parameter(G),particle concentration parameter(R),and electro-osmotic parameter(k)is investigated on the velocity of magnetic nanoparticles and blood flow.Results obtained show that the electro-osmotic parameter,along with Hartmann’s number,dramatically affects the velocity of magnetic nanoparticles,blood flow velocity,and flow rate.Moreover,results also reveal that at a higher Hartman number,homogeneity in nanoparticles distribution improved considerably.The particle concentration andmass parameters effectively influence the capturing effect on nanoparticles in the blood flow using a micro-tube for magnetic drug targeting.Lastly,investigation also indicates that the OHAM analysis is efficient and quick to handle the system of nonlinear equations. | Muhammad Zafar Muhammad Saif Ullah Tareq Manzoor Muddassir Ali Kashif Nazar Shaukat Iqbal HabibUllah Manzoor Rizwan Haider Woo Young Kim | 2022 | Computer Modeling in Engineering & Sciences2022，，2： | 0 |
| 16 | Classication of COVID-19 CT Scans via Extreme Learning Machin显示文摘Here,we use multi-type feature fusion and selection to predict COVID-19 infections on chest computed tomography(CT)scans.The scheme operates in four steps.Initially,we prepared a database containing COVID-19 pneumonia and normal CT scans.These images were retrieved from the Radiopaedia COVID-19 website.The images were divided into training and test sets in a ratio of 70:30.Then,multiple features were extracted from the training data.We used canonical correlation analysis to fuse the features into single vectors;this enhanced the predictive capacity.We next implemented a genetic algorithm(GA)in which an Extreme Learning Machine(ELM)served to assess GA tness.Based on the ELM losses,the most discriminatory features were selected and saved as an ELM Model.Test images were sent to the model,and the best-selected features compared to those of the trained model to allow nal predictions.Validation employed the collected chest CT scans.The best predictive accuracy of the ELM classier was 93.9%;the scheme was effective. | Muhammad Attique Khan Abdul Majid Tallha Akram Nazar Hussain Yunyoung Nam Seifedine Kadry Shui-Hua Wang Majed Alhaisoni | 2021 | Computers, Materials & Continua2021，，7： | 0 |
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