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| 1 | Stability analysis of different cotton genotypes under normal and water-deficit conditions显示文摘Cotton plant observes significant reduction in seed cotton yield when subjected to water stress.To find out genotypes having better drought tolerance,seven genotypes of Gossypium hirsutum L.were tested under two moisture levels,i.e.,normal and water deficit stress conditions at five locations of Punjab,Pakistan(Faisalabad,Sahiwal,Vehari,Rahim Yar Khan,and Bahawalpur)in 2013 and 2014.Genotype×environment interaction(GEI)was studied using the genotype main effects and genotype by environment interaction(GGE)biplot and additive main effect and multiplicative interaction analysis.The genotypes G3(7001/11)and G6(FH-942)were stable under normal condition,while under drought condition,the stable genotype was G5(FH-326)when analysed using additive main effects and multiplicative interaction(AMMI)biplot scheme.While GGE biplot analysis on the basis of best performance revealed that under normal condition the genotypes,G1(L-13/10)and G2(FH-2056/10),carrying the common position in biplot.Whereas,under water deficit stress condition,G5 was the best adaptive genotype at all five locations.In the same way,ranking of genotypes showed that the G5 was the ideal genotype under both conditions.So,it is concluded that the genotype G5(FH-326)was found best for water deficit stress condition and can be cultivated under water scarce areas of Punjab. | Muhammad Riaz Jehanzeb Farooq Saghir Ahmed Muhammad Amin Waqas Shafqat Chattha Maria Ayoub Riaz Ahmed Kainth | 2019 | Journal of Integrative Agriculture2019，18，6： | 5 |
| 2 | Surfactants as additives for NOx reduction during SNCR process with urea solutionas reducing agent 显示文摘 | Muhammad Ayoub Muhanamad Faisal Irfan Kyung - Seun Yoo | 2011 | Energy Conversion and Management2011，，52： | 1 |
| 3 | Surfactants as additives for NOx reduction during SNCR process with urea solution as reducing agent 显示文摘 | Muhammad Ayoub Muhammad Faisal Irfan Kyung-Seun Yoo | 2011 | Energy Conversion and Management2011，，10： | 1 |
| 4 | Surfactants as additives for NO x reduction during SNCR process with urea solution as reducing agent显示文摘 | Muhammad Ayoub Muhammad Faisal Irfan Kyung-Seun Yoo | 2011 | Energy Conversion and Management2011，，10： | 1 |
| 5 | Genesis of Manganese Deposits in the Ali Khanzai Block of the Zhob Ophiolite,Pakistan:Inferences from Geochemistry and Mineralogy显示文摘The Zhob ophiolite comprises the Naweoba, Omzha and Ali Khanzai blocks, which are surrounded by the sediments of the Alozai Group and Loralai Formation. The Ali Khanzai Block contains metamorphic, ultramafic, gabbroic, volcanic and volcaniclastic rocks with associated chert. The Zhob manganese deposits found in the Ali Khanzai Block, occur in banded, lenticular and massive forms within red to brown coloured metachert. Braunite and pyrolusite are the main constituent manganese-bearing minerals with minor hausmannite, hematite and barite while quartz is the major gangue mineral with some carbonate minerals. Geochemical evidence from the major oxides indicates that the manganese mineralization and associated metachert at Zhob were formed by hydrothermal activity with little contribution from contemporaneous volcanic materials and this is confirmed by high Fe/Mn and low Co/Zn ratios and trace element patterns. These deposits formed along with seafloor spreading centres and were later obducted as part of Ali Khanzai Block of Zhob ophiolite. | Muhammad Ayoub Khan Muhammad Ishaq Kakar Thomas Ulrich Liaqat Ali Andrew CKerr Khalid Mahmood Rehanul Haq Siddiqui | 2020 | Journal of Earth Science2020，31，5： | 0 |
| 6 | Ensemble Deep Learning Framework for Situational Aspects-Based Annotation and Classification of International Student’s Tweets during COVID-19显示文摘As the COVID-19 pandemic swept the globe,social media plat-forms became an essential source of information and communication for many.International students,particularly,turned to Twitter to express their struggles and hardships during this difficult time.To better understand the sentiments and experiences of these international students,we developed the Situational Aspect-Based Annotation and Classification(SABAC)text mining framework.This framework uses a three-layer approach,combining baseline Deep Learning(DL)models with Machine Learning(ML)models as meta-classifiers to accurately predict the sentiments and aspects expressed in tweets from our collected Student-COVID-19 dataset.Using the pro-posed aspect2class annotation algorithm,we labeled bulk unlabeled tweets according to their contained aspect terms.However,we also recognized the challenges of reducing data’s high dimensionality and sparsity to improve performance and annotation on unlabeled datasets.To address this issue,we proposed the Volatile Stopwords Filtering(VSF)technique to reduce sparsity and enhance classifier performance.The resulting Student-COVID Twitter dataset achieved a sophisticated accuracy of 93.21%when using the random forest as a meta-classifier.Through testing on three benchmark datasets,we found that the SABAC ensemble framework performed exceptionally well.Our findings showed that international students during the pandemic faced various issues,including stress,uncertainty,health concerns,financial stress,and difficulties with online classes and returning to school.By analyzing and summarizing these annotated tweets,decision-makers can better understand and address the real-time problems international students face during the ongoing pandemic. | Shabir Hussain Muhammad Ayoub Yang Yu Junaid Abdul Wahid Akmal Khan Dietmar P.F.Moller Hou Weiyan | 2023 | Computers, Materials & Continua2023，，6： | 0 |
| 7 | Classification and Categorization of COVID-19 Outbreak in Pakistan显示文摘Coronavirus is a potentially fatal disease that normally occurs in mammals and birds.Generally,in humans,the virus spreads through aerial droplets of any type of fluid secreted from the body of an infected person.Coronavirus is a family of viruses that is more lethal than other unpremeditated viruses.In December 2019,a new variant,i.e.,a novel coronavirus(COVID-19)developed in Wuhan province,China.Since January 23,2020,the number of infected individuals has increased rapidly,affecting the health and economies of many countries,including Pakistan.The objective of this research is to provide a system to classify and categorize the COVID-19 outbreak in Pakistan based on the data collected every day from different regions of Pakistan.This research also compares the performance of machine learning classifiers(i.e.,Decision Tree(DT),Naive Bayes(NB),Support Vector Machine,and Logistic Regression)on the COVID-19 dataset collected in Pakistan.According to the experimental results,DT and NB classifiers outperformed the other classifiers.In addition,the classified data is categorized by implementing a Bayesian Regularization Artificial Neural Network(BRANN)classifier.The results demonstrate that the BRANN classifier outperforms state-of-the-art classifiers. | Amber Ayoub Kainaat Mahboob Abdul Rehman Javed Muhammad Rizwan Thippa Reddy Gadekallu Mustufa Haider Abidi Mohammed Alkahtani | 2021 | Computers, Materials & Continua2021，，10： | 0 |
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