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Static and adaptive feedback control for synchronization of different chaotic oscillators with mutually Lipschitz nonlinearities

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Chinese Physics B 2014年第11期23卷 223-231页

作者： muhammad Riaz muhammad Rehan Keum-Shik Hong muhammad ashraf Haroon Ur Rasheed Department of Electrical Engineering Pakistan Institute of Engineering and Applied Sciences (PIEAS) Islamabad Pakistan Department of Electronics Engineering Mohammad Ali Jinnah University Department of Cogno-Mechatronics Engineering and School of Mechanical Engineering Pusan National University 2 Busandaehak-ro Geumjeong-gu Busan 609-735 Republic of Korea Department of Electronics Engineering Mohammad Ali Jinnah University Islamabad Pakistan

This paper addresses the control law design for synchronization of two different chaotic oscillators with mutually Lipschitz nonlinearities. For analysis of the properties of two different nonlinearities, an advanced mutually Lipschitz condition is proposed. This mutually Lipschitz condition is more general than the traditional Lipschitz condition. Unlike the latter, it can be used for the design of a feedback controller for synchronization of chaotic oscillators of different dynamics. It is shown that any two different Lipschitz nonlinearities always satisfy the mutually Lipschitz condition. Applying the mutually Lipschitz condition, a quadratic Lyapunov function and uniformly ultimately bounded stability, easily designable and implementable robust control strategies utilizing algebraic Riccati equation and linear matrix inequalities, are derived for synchronization of two distinct chaotic oscillators. Furthermore, a novel adaptive control scheme for mutually Lipschitz chaotic systems is established by addressing the issue of adaptive cancellation of unknown mismatch between the dynamics of different chaotic systems. The proposed control technique is numerically tested for synchronization of two different chaotic Chua＇s circuits and for obtaining identical behavior between the modified Chua＇s circuit and the R6ssler system.

关键词： control theory and feedback synchronization mutually Lipschitz nonlinearity adaptive controlsystem

Perspectives of Using L-Tryptophan for Improving Productivity of Agricultural Crops: A Review

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Pedosphere 2018年第1期28卷 16-34页

作者： Ayesha MUSTAFA muhammad IMRAN muhammad ashraf Khalid MAHMOOD Soil and Environmental Sciences Division Nuclear Institute for Agriculture and Biology (NIAB)

Plant growth regulators are biologically active signaling molecules that regulate a number of plant physiological processes. Auxin(indole-3-acetic acid) is an important plant growth regulator and is synthesized within plant tissues through L-tryptophan(L-TRP)-dependent and-independent pathways. It has been found that plants respond to exogenously applied L-TRP due to insufficient endogenous auxin biosynthesis. The exogenous application of L-TRP is highly significant for normal plant growth and development.L-tryptophan is applied through foliar spray, seed priming, and soil application. Soil-applied L-TRP is either directly taken up by plants or metabolized to auxin by soil microbiota and then absorbed by plant roots. Similarly, foliar spray and seed priming with L-TRP stimulates auxin synthesis within plants and improves the growth and productivity of agricultural crops. Furthermore, L-TRP contains approximately 14% nitrogen(N) in its composition, which is released upon its metabolism within a plant or in the rhizosphere and plays a role in enhancing crop productivity. This review deals with assessing crop responses under the exogenous application of L-TRP in normal and stressed environments, mode of action of L-TRP, advantages of using L-TRP over other auxin precursors, and role of the simultaneous use of L-TRP and auxin-producing microbes in improving the productivity of agricultural crops. To the best of our knowledge, this is the first review reporting the importance of the use of L-TRP in agriculture.

关键词： auxin biosynthesis auxin precursors auxin-producing microbes crop productivity indole-3-acetic acid plant growth-promoting rhizobacteria plant response to abiotic stresses

A Hybrid Quantum Inspired Particle Swarm Optimization and Least Square Framework for Real-time Harmonic Estimation

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Journal of Modern Power Systems and Clean Energy 2021年第6期9卷 1548-1556页

作者： Abu Bakar Waqas muhammad Mansoor ashraf Yasir Saifullah Department of Communication Science and Engineering School of Information Science and TechnologyFudan University Electrical Engineering Department University of Engineering and Technology

The power quality is becoming an extensively addressing aspect of the power system because of the sensitive operation of the smart grid, awareness of power quality, and the equipment of modern power systems. In this paper, we have conceived a new hybrid Quantum inspired particle swarm optimization and least square(QPSO-LS) framework for real-time estimation of harmonics presented in time-varying noisy power signals. The technique has strong, robust, and reliable search capability with powerful convergence properties. The proposed approach is applied to various test systems at different signal to noise ratio(SNR) levels in the presence of uniform and Gaussian noise. The results are presented in terms of precision, computation time, and convergence characteristics. The computation time decreases by 3-5 times as compared to the existing algorithms. The technique is further authenticated by estimating harmonics of real-time current or voltage waveforms, obtained from light emitting diode(LED) lamp and axial flux permanent magnet synchronous generator(AFPMSG). The results demonstrate the superiority of QPSO-LS over other methods such as LS-based genetic algorithm(GA), particle swarm optimization(PSO), bacterial foraging optimization(BFO), artificial bee colony(ABC), and biogeography based optimization with recursive LS(BBO-RLS) algorithms, in terms of providing satisfactory solutions with a significant amount of robustness and computation efficiency.

关键词： Harmonic estimation power quality particle swarm optimization(PSO) least square(LS) smart grid

Novel Decision Making Methodology under Pythagorean Probabilistic Hesitant Fuzzy Einstein Aggregation Information

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Computer Modeling in Engineering & Sciences 2023年第8期136卷 1785-1811页

作者： Shahzaib ashraf Bushra Batool muhammad Naeem Institute of Mathematics Khwaja Fareed University of Engineering&Information TechnologyRahim Yar Khan64200Pakistan Department of Mathematics University of SargodhaSargodha40100Pakistan Department of Mathematics Deanship of Applied SciencesUmm Al-Qura UniversityMakkah24382Saudi Arabia

This research proposes multicriteria decision-making(MCDM)-based real-time Mesenchymal stem cells(MSC)transfusion *** testing phase of the methodology denotes the ability to stick to plastic surfaces,the upregulation and downregulation of certain surface protein markers,and lastly,the ability to differentiate into various cell ***,two scenarios of an enhanced dataset based on a medical perspective were created in the development phase to produce varying levels of ***,for real-timemonitoring ofCOVID-19 patients with different emergency levels(i.e.,mild,moderate,severe,and critical),an automated triage algorithmbased on a formal medical guideline is proposed,taking into account the improvement and deterioration procedures fromone level to the *** this strategy,Einstein aggregation information under the Pythagorean probabilistic hesitant fuzzy environment(PyPHFE)is *** operations on PyPHFE such as Einstein sum,product,scalar multiplication,and their properties are ***,several Pythagorean probabilistic hesitant fuzzy Einstein aggregation operators,namely the Pythagorean probabilistic hesitant fuzzy weighted average(PyPHFWA)operator,Pythagorean probabilistic hesitant fuzzy Einstein weighted geometric(PyPHFEWG)operator,Pythagorean probabilistic hesitant fuzzy Einstein ordered weighted average(PyPHFEOWA)operator,Pythagorean probabilistic hesitant fuzzy Einstein ordered weighted geometric(PyPHFEOWG)operator,Pythagorean probabilistic hesitant fuzzy Einstein hybrid average(PyPHFEHA)operator and Pythagorean probabilistic hesitant fuzzy Einstein hybrid geometric(PyPHFEHG)operator are *** the above-mentioned operators are helpful in design the algorithm to tackle uncertainty in decision making *** last,a numerical case study of decision making is presented to demonstrate the applicability and validity of the proposed ***,the comparison of the existing and the proposed technique is

关键词： Pythagorean probabilistic hesitant fuzzy set aggregation operators decision making

Does Seed Priming Induce Changes in the Levels of Some Endogenous Plant Hormones in Hexaploid Wheat Plants Under Salt Stress？

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Journal of Integrative Plant Biology 2006年第2期48卷 181-189页

作者： muhammad Iqbal muhammad ashraf Amer Jamil Shafiq-ur-Rehman Department of Botany University of Agriculture Faisalabad 38040 Pakistan Department of Biochemistry University of Agriculture Faisalabad 38040 Pakistan Faculty of Biological Resources Science College of Agriculture Chonbuk National University Chonju 561-756 Republic of Korea

In order to assess whether salt tolerance could be Improved In spring wheat （Triticum aestivum L.）, the present study was performed by soaking the seeds of two cultlvars, namely MH-97 （salt sensitive） and Inqlab-91 （salt tolerant）, for 12 h In distilled water or 100 mol/m^3 CaCl2, KCI, or NaCI. Primed seeds from each treatment group and non-primed seeds were sown In a field In which NaCI salinity of 15 dS/m was developed. Priming of seeds with CaCl2, followed by priming with KCI and NaCI, was found to be effective In alleviating the adverse effects of salt stress on both wheat cultivars In terms of shoot fresh and dry weights and grain yield. Priming with CaCl2 alleviated the adverse effects of salt stress on hormonal balance In plants of both cultlvars. In MH-97 plants, CaCl2 pretreatment considerably reduced leaf absclslc acid （ABA） concentrations and Increased leaf free salicylic acid （SA） concentrations under both saline and non-saline conditions. In contrast, In the Inqlab-91 plant, CaCl2 Increased free Indoleacetic acid （IAA） and indolebutyrlc acid （IBA） content. However, priming of seeds with CaCl2 did not alter free polyamlne levels in either cultlvar, although spermldlne levels were considerably lower In plants raised from seeds treated with CaCl2 for both cultlvars under saline conditions. Priming with KCI Increased growth In Inqlab-91 plants, but not In MH-97 plants, under saline conditions. The salinity Induced reducUon In auxins （IAA and IBA） was alleviated by NaCI priming In both cultlvars under saline conditions. However, NaCI Increased leaf free ABA content and lowered leaf SA and putresclne levels In Inqlab-91 plants under saline conditions. In conclusion, although all three priming agents （I.e. CaCl2, KCI, and NaCI） were effective In alleviating the adverse effects of salt stress on wheat plants, their effects on altering the levels of different plant hormones were different In the two cuItlvars.

关键词： halopriming inorganic salts plant growth substances salinity salt tolerance.

Snowmelt runoff prediction under changing climate in the Himalayan cryosphere: A case of Gilgit River Basin

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Geoscience Frontiers 2017年第5期8卷 941-949页

作者： muhammad Adnan Ghulam Nabi muhammad Saleem Poomee Arshad ashraf Climate Change Alternate Energy and Water Resources Institute (CAEWRI) National Agricultural Research Center Park Road Chakshahzad Islamabad Pakistan Center of Excellence in Water Resources (CEWR) Lahore Pakistan

There are serious concerns of rise in temperatures over snowy and glacierized Himalayan region that may eventually affect future river flows of Indus river system. It is therefore necessary to predict snow and glacier melt runoff to manage future water resource of Upper Indus Basin(UIB). The snowmelt runoff model(SRM) coupled with MODIS remote sensing data was employed in this study to predict daily discharges of Gilgit River in the Karakoram Range. The SRM was calibrated successfully and then simulation was made over four years i.e. 2007, 2008, 2009 and 2010 achieving coefficient of model efficiency of 0.96, 0.86, 0.9 and 0.94 respectively. The scenarios of precipitation and mean temperature developed from regional climate model PRECIS were used in SRM model to predict future flows of Gilgit River. The increase of 3 C in mean annual temperature by the end of 21 th century may result in increase of 35-40% in Gilgit River flows. The expected increase in the surface runoff from the snow and glacier melt demands better water conservation and management for irrigation and hydel-power generation in the Indus basin in future.

关键词： Snowmelt runoff model Climate change Gilgit River Himalayan region

Neural stem cell-based in vitro bioassay for the assessment of neurotoxic potential of water samples

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Journal of Environmental Sciences 2021年第3期33卷 72-86页

作者： muhammad irfan Masood Natalie Tamara Hauke muhammad Jawad Nasim muhammad Sarfraz Mahrukh Naseem Karl Herbert Schäfer Division of Bioorganic Chemistry School of PharmacySaarland UniversityD-66123 SaarbrückenGermany Applied Life Sciences University of Applied Sciences Kaiserslautern66482 ZweibrückenGermany ENS Group University of Applied Sciences Kaiserslautern66482 ZweibrückenGermany Abwasserverband Bergstrasse Altau 1069469 WeinheimGermany Institute of Pharmaceutical Sciences University of Veterinary and Animal Sciences54000 LahorePakistan Department of Zoology University of BalochistanQuetta 87550Pakistan

Intensive agriculture activities,industrialization and growing numbers of wastewater treatment plants along river banks collectively contribute to the elevated levels of neurotoxic pollutants in natural water reservoirs across *** established an in vitro bioassay based upon neural stem cells isolated from the subventricular zone of the postnatal mouse to evaluate the neurotoxic potential of raw wastewater,treated sewage effluent,groundwater and drinking *** toxic potential of water samples was evaluated employing viability,proliferation,differentiation and migration *** found that raw wastewater could reduce the viability and proliferation of neural stem cells,and decreased the neuronal and astrocyte differentiation,neuronal neurite growth,astrocyte growth and cell *** sewage water also showed inhibitory effects on cell proliferation and *** results indicated that relatively high concentrations of nitrogenous substances,pesticides,mercuric compounds,bisphenol-A,and phthalates,along with some other pollutants in raw wastewater and treated sewage water,might be the reason for the neuroinhibitory effects of these water *** model successfully predicted the neurotoxicity of water samples collected from different sources and also revealed that the incomplete removal of contaminants from wastewater can be problematic for the developing nervous *** presented data also provides strong evidence that more effective treatments should be used to minimize the contamination of water before release into major water bodies which may be considered as water reservoirs for human usage in the future.

关键词： Neural stem cells Subventricular zone Neurotoxicity Bioassay Treated sewage effluents Pesticides

Synthesis and Single Crystal X-ray Studies of 3-(3,5-Bis(trifluoromethyl)phenyl) Quinoline and 3-(4-Fluoro-3-methylphenyl) Quinoline

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Chinese Journal of Structural Chemistry 2014年第1期33卷 90-96页

作者： Malik Aman Ullah muhammad Adeel muhammad Nawaz Tahir muhammad Raza Shah irfan Ullah Department of Chemistry Gomal University Department of Physics University of Sargodha ICCBS HEJ Research Institute of ChemistryUniversity of Karachi

The title compounds 3-（3,5-bis（trifluoromethyl）phenyl）quinoline （1） and 3-（4- fluoro-3-methylphenyl）quinoline （2） were synthesized through Suzuki-Miyaura Cross coupling reaction of 3-bromoquinoloine with aryl boronic acids. The title compounds were characterized by single-crystal X-ray diffraction, 1H NMR, 13C NMR, El-MS, elemental analysis and IR. The crystals of 3-（3,5-bis（trifluoromethyl）phenyl）quinoline （C17H9F6N, Mr = 341.25） belongs to the monoclinic system, space group P21n, a = 12.3072（13）, b = 4.9378（6）, c = 24.493（2） A, V= 1473.1（3） A3, Z = 4, Dc = 1.539 Mg m-3, 2 - 0.71073A, μ = 0.144 mm^-1, F（000） = 688, the final R = 0.0715 and wR = 0.1873 for 1875 obserwed reflections with I 〉 2σ（I） and the crystal of 3-（4-fluoro-3- methylphenyl）quinoline （C16H12FN, Mr= 237.27） belongs to the orthorhombic system, space group Pca21, a = 23.794（2）, b = 3.9094（3）, c = 25.669（2） A, V = 2387.7（4） A3, Z = 8, D, = 1.320 Mg m-3, 2 = 0.71073 A, μ = 0.088 mm-1, F（000） = 992, the final R = 0.0534 and wR = 0.1188 for 2270 observed reflections with I 〉 2σ（I）.

关键词： 3-bromoquinoline crystal structure Pd(PPh3)3 boronic acid suzuki coupling

Data Information integrated Neural Network (DINN) algorithm for modelling and interpretation performance analysis for energy systems

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Energy and AI 2024年第2期16卷 306-315页

作者： Waqar muhammad ashraf Vivek Dua The Sargent Centre for Process Systems Engineering Department of Chemical EngineeringUniversity College LondonTorrington PlaceLondon WC1E 7JEUK

Developing a well-predictive machine learning model that also offers improved interpretability is a key challenge to widen the application of artificial intelligence in various application domains. In this work, we present a Data Information integrated Neural Network (DINN) algorithm that incorporates the correlation information present in the dataset for the model development. The predictive performance of DINN is also compared with a standard artificial neural network (ANN) model. The DINN algorithm is applied on two case studies of energy systems namely energy efficiency cooling (ENC) & energy efficiency heating (ENH) of the buildings, and power generation from a 365 MW capacity industrial gas turbine. For ENC, DINN presents lower mean RMSE for testing datasets (RMSE_test = 1.23 %) in comparison with the ANN model (RMSE_test = 1.41 %). Similarly, DINN models have presented better predictive performance to model the output variables of the two case studies. The input perturbation analysis following the Gaussian distribution for noise generation reveals the order of significance of the variables, as made by DINN, can be better explained by the domain knowledge of the power generation operation of the gas turbine. This research work demonstrates the potential advantage to integrate the information present in the data for the well-predictive model development complemented with improved interpretation performance thereby opening avenues for industry-wide inclusion and other potential applications of machine learning.

关键词： Explainable AI Model interpretation Scientific machine learning Artificial neural network Loss function