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您的检索式：作者名="Muhammad Imran Asghar"
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| 1 | Interactive effect of shade and PEG-induced osmotic stress on physiological responses of soybean seedlings显示文摘Intensively farmed crops used to experience numerous environmental stresses.Among these,shade and drought significantly influence the morpho-physiological and biochemical attributes of plants.However,the interactive effect of shade and drought on the growth and development of soybean under dense cropping systems has not been reported yet.This study investigated the interactive effect of PEG-induced osmotic stress and shade on soybean seedlings.The soybean cultivar viz.,C-103 was subjected to PEG-induced osmotic stress from polyethylene glycol 6000(PEG-6000)under shading and non-shading conditions.PEG-induced osmotic stress significantly reduced the relative water contents,morphological parameters,carbohydrates and chlorophyll contents under both light environments.A significant increase was observed in osmoprotectants,reactive oxygen species and antioxidant enzymes in soybean seedlings.Henceforth,the findings revealed that,seedlings grown under non-shading conditions produced more malondialdehyde and hydrogen peroxide contents as compared to the shade-treated plants when subjected to PEG-induced osmotic stress.Likewise,the shaded plants accumulated more sugars and proline than non-shaded ones under drought stress.Moreover,it was found that nonshaded grown plants were more sensitive to PEG-induced osmotic stress than those exposed to shading conditions,which suggested that shade could boost the protective mechanisms against osmotic stress or at least would not exaggerate the adverse effects of PEG-induced osmotic stress in soybean seedlings. | Muhammad Ahsan ASGHAR JIANG Heng-ke SHUI Zhao-wei CAO Xi-yu HUANG Xi-yu Shakeel IMRAN Bushra AHMAD ZHANG Hao YANG Yue-ning SHANG Jing YANG Hui YU Liang LIU Chun-yan YANG Wen-yu SUN Xin DU Jun-bo | 2021 | Journal of Integrative Agriculture2021，20，9： | 3 |
| 2 | Novel LaFe_(2)O_(4)spinel structure with a large oxygen reduction response towards protonic ceramic fuel cell cathode显示文摘Highly active and stable electrocatalysts are mandatory for developing high-performance and longlasting fuel cells.The current study demonstrates a high oxygen reduction reaction(ORR)electrocatalytic activity of a novel spinel-structured LaFe_(2)O_(4)via a self-doping strategy.The LaFe_(2)O_(4)demonstrates excellent ORR activity in a protonic ceramic fuel cell(PCFC)at temperature range of 350-500℃.The high ORR activity of LaFe_(2)O_(4)is mainly attributed to the facile release of oxide and proton ions,and improved synergistic incorporation abilities associated with interplay of multivalent Fe^(3+)/Fe^(2+)and La^(3+)ions.Using LaFe_(2)O_(4)as cathode over proton conducting BaZr_(0.4)Ce_(0.4)Y_(0.2)O_(3)(BZCY)electrolyte,the fuel cell has delivered a high-power density of 806 mW/cm^(2)operating at 500℃.Different spectroscopic and calculations methods such as UV-visible,Raman,X-ray photoelectron spectroscopy and density functional theory(DFT)calculations were performed to screen the potential application of LaFe_(2)O_(4)as cathode.This study would help in developing functional cobalt-free ORR electrocatalysts for low temperature-PCFCs(LT-PCFCs)and solid oxide fuel cells(SOFCs)applications. | Jinping Wang Yuzheng Lu Naveed Mushtaq M.A.K Yousaf Shah Sajid Rauf Peter D.Lund Muhammad Imran Asghar | 2023 | Journal of Rare Earths2023，41，3： | 1 |
| 3 | Green Synthesis of Silver Nanoparticles: Structural Features and In Vivo and In Vitro Therapeutic Effects against Helicobacter pylori Induced Gastritis显示文摘 | Muhammad Amin Sadaf Hameed Asghar Ali Farooq Anwar Shaukat Ali Shahid Imran Shakir Aqdas Yaqoob Sara Hasan Safyan Akram Khan Sajjad-ur-Rahman Imre Sovago | 2014 | Bioinorganic Chemistry and Applications2014，，： | 1 |
| 4 | Performance Evaluation of Supervised Machine Learning Techniques for Efficient Detection of Emotions from Online Content显示文摘Emotion detection from the text is a challenging problem in the text analytics.The opinion mining experts are focusing on the development of emotion detection applications as they have received considerable attention of online community including users and business organization for collecting and interpreting public emotions.However,most of the existing works on emotion detection used less efficient machine learning classifiers with limited datasets,resulting in performance degradation.To overcome this issue,this work aims at the evaluation of the performance of different machine learning classifiers on a benchmark emotion dataset.The experimental results show the performance of different machine learning classifiers in terms of different evaluation metrics like precision,recall ad f-measure.Finally,a classifier with the best performance is recommended for the emotion classification. | Muhammad Zubair Asghar Fazli Subhan Muhammad Imran Fazal Masud Kundi Adil Khan Shahboddin Shamshirband Amir Mosavi Peter Csiba Annamaria RVarkonyi Koczy | 2020 | Computers, Materials & Continua2020，，6： | 0 |
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