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Non-local orthotropic elastic shell model for vibration analysis of protein microtubules
Muhammad Taj,Afnan Majeed,Muzamal Hussain,Muhammad N. Naeem,Muhammad Safeer,Manzoor Ahmad,Hidayat Ullah Khan,Abdelouahed Tounsi 사단법인 한국계산역학회 2020 Computers and Concrete, An International Journal Vol.25 No.3
Vibrational analysis in microtubules is examined based on the nonlocal theory of elasticity. The complete analytical formulas for wave velocity are obtained and the results reveal that the small scale effects can reduce the frequency, especially for large longitudinal wave-vector and large circumferential wave number. It is seen that the small scale effects are more significant for smaller wave length. The methods and results may also support the design and application of nano devices such as micro sound generator etc. The effects of small scale parameters can increase vibrational frequencies of the protein microtubules and cannot be overlooked in the analysis of vibrating phenomena. The results for different modes with nonlocal effect are checked.
Optimized Deep Learning Techniques for Disease Detection in Rice Crop using Merged Datasets
Muhammad Junaid,Sohail Jabbar,Muhammad Munwar Iqbal,Saqib Majeed,Mubarak Albathan,Qaisar Abbas,Ayyaz Hussain International Journal of Computer ScienceNetwork S 2023 International journal of computer science and netw Vol.23 No.3
Rice is an important food crop for most of the population in the world and it is largely cultivated in Pakistan. It not only fulfills food demand in the country but also contributes to the wealth of Pakistan. But its production can be affected by climate change. The irregularities in the climate can cause several diseases such as brown spots, bacterial blight, tungro and leaf blasts, etc. Detection of these diseases is necessary for suitable treatment. These diseases can be effectively detected using deep learning such as Convolution Neural networks. Due to the small dataset, transfer learning models such as vgg16 model can effectively detect the diseases. In this paper, vgg16, inception and xception models are used. Vgg16, inception and xception models have achieved 99.22%, 88.48% and 93.92% validation accuracies when the epoch value is set to 10. Evaluation of models has also been done using accuracy, recall, precision, and confusion matrix.
Majeed, Anwar P.P. Abdul,Taha, Zahari,Abdullah, Muhammad Amirul,Azmi, Kamil Zakwan Mohd,Zakaria, Muhammad Aizzat Techno-Press 2018 Advances in robotics research Vol.2 No.3
This study evaluates the efficacy of a class robust control scheme namely active force control in performing a joint based trajectory tracking of an upper limb exoskeleton in rehabilitating the elbow joint. The plant of the exoskeleton system is obtained via system identification method whilst the PD gains were tuned heuristically. The estimated inertial parameter that enables the AFC disturbance rejection effect is attained by means of a non-nature based metaheuristic optimisation technique known as simulated Kalman filter (SKF). It was demonstrated from the present investigation that the proposed PDAFC scheme outperformed the classical PD algorithm in tracking the prescribed trajectory both in the presence and without the presence of disturbance attributed by the mannequin limb weights (1 kg and 1.5 kg) that mimics the weight of actual human limb weight. Therefore, it is apparent from the results obtained from the present study that the proposed control scheme, i.e., PDAFC is suitable for the application of exoskeleton for stroke rehabilitation.
Pest Prediction in Rice using IoT and Feed Forward Neural Network
( Muhammad Salman Latif ),( Rafaqat Kazmi ),( Nadia Khan ),( Rizwan Majeed ),( Sunnia Ikram ),( Malik Muhammad Ali-shahid ) 한국인터넷정보학회 2022 KSII Transactions on Internet and Information Syst Vol.16 No.1
Rice is a fundamental staple food commodity all around the world. Globally, it is grown over 167 million hectares and occupies almost 1/5<sup>th</sup> of total cultivated land under cereals. With a total production of 782 million metric tons in 2018. In Pakistan, it is the 2<sup>nd</sup> largest crop being produced and 3<sup>rd</sup> largest food commodity after sugarcane and rice. The stem borers a type of pest in rice and other crops, Scirpophaga incertulas or the yellow stem borer is very serious pest and a major cause of yield loss, more than 90% damage is recorded in Pakistan on rice crop. Yellow stem borer population of rice could be stimulated with various environmental factors which includes relative humidity, light, and environmental temperature. Focus of this study is to find the environmental factors changes i.e., temperature, relative humidity and rainfall that can lead to cause outbreaks of yellow stem borers. this study helps to find out the hot spots of insect pest in rice field with a control of farmer’s palm. Proposed system uses temperature, relative humidity, and rain sensor along with artificial neural network to predict yellow stem borer attack and generate warning to take necessary precautions. result shows 85.6% accuracy and accuracy gradually increased after repeating several training rounds. This system can be good IoT based solution for pest attack prediction which is cost effective and accurate.
Evaluating the asymmetric effects of nuclear energy on carbon emissions in Pakistan
Muhammad Tariq Majeed,Ilhan Ozturk,Isma Samreen,Tania Luni 한국원자력학회 2022 Nuclear Engineering and Technology Vol.54 No.5
Achieving sustainable development requires an increasing share of green technologies. World energydemand is expected to rise significantly especially in developing economies. The increasing energy demands will be entertained with conventional energy sources at the cost of higher emissions unless ecofriendly technologies are used. This study examines the asymmetric effects of nuclear energy on carbonemissions for Pakistan from 1974 to 2019. Augmented Dickey-Fuller (ADF) and Phillips Perron (PP) unitroot tests suggest that variables are integrated of order one and bound test of Autoregressive DistributedLag (ARDL) and nonlinear ARDL confirm a long-run relationship among selected variables. The ARDL,Fully Modified Ordinary Least Squares (FMOLS), and Dynamic Ordinary Least Squares (DOLS) resultsshow that the coefficient of nuclear energy has a negative and significant impact on emissions in bothshort and long run. Further, the NARDL finding shows that there exists an asymmetric long-run association between nuclear energy and CO2 emissions. The vector error correction method (VECM) resultsindicate that there exists a bidirectional causal relationship between nuclear energy and carbon emissions in both the short and long run. Additionally, the impact of nuclear energy on ecological footprinthas been examined and our findings remain robust.
( Muhammad Tariq Majeed ) 세종대학교 경제통합연구소 2014 Journal of Economic Integration Vol.29 No.4
Using a panel data set for 146 countries over the period 1984~2007, this study contributes in the area of trade-corruption linkages by highlighting the non-monotonic relationship between trade and corruption and significance of complementary policy reforms in shaping the link. Findings of the study suggest that trade increases corruption in a linear specification while its effect on corruption decreasing in a non-linear specification. The analysis exhibits that this non-linear nature of the relationship is worth noting and help answering the question why the literature on the relationship between trade and corruption is not conclusive. Furthermore, we make argument and find empirical support to our proposition that this is not just openness to trade that can reduce corruption but there are complimentary policy reforms that cause a decline in corruption. Findings of the study are robust to alternative specifications, econometric techniques, control of nonlinearity, control of interactive effects, and exclusion of outliers.
MAJEED, Muhammad Kashif,JUN, Ji Cheng,ZIA-UR-REHMAN, Muhammad,MOHSIN, Muhammad,RAFIQ, Muhammad Zeeshan Korea Distribution Science Association 2020 The Journal of Asian Finance, Economics and Busine Vol.7 No.4
The main objective of this research is to investigate the impact of board size and board composition on financial performance of banks. The sample of this study consists on two countries listed bank sector Pakistan and China. The annul data is used from 2009-2018 to find the objective of this study. The Panel regression model is used to check the relationship between dependent and independent variables. Return on Asset and Return on Equity is used as performance checker dependent variables. The results of this study confirm board size coefficient value positive for ROA and negative for ROE but shows insignificant behavior for Pakistani banking sector while in Chinese banking sector the coefficient value of board size positively for ROA and ROE at 10% level. The board composition coefficient shows the negatively significant with ROA but insignificantly related to ROE for Pakistani banking sector. However, in Chinese banking sector the coefficient value of board composition is insignificant for both ROA and ROE. This study is helpful for banks, management of banks, policy makers, researcher as well as Government.