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Islamabad, Pakistan

The Pakistan Institute of Engineering and Applied science , is a public research university located in the Islamabad Capital Territory, near the remote town of Nilore.Founded in 1967 as Reactor School in response to increasing industrialization of Pakistan, the institute started its educational activities with the affiliation of Quaid-e-Azam University, and later became Centre for Nuclear Studies in 1976; the institute gained its new name and started developing charter in nineties. Now, the university has on-campus dorms and houses in an area of 150 acres , which offers academic programs with a strong emphasis on scientific, engineering, and technological education & research.Currently the university is ranked as the top engineering institute of Pakistan by the HEC in "engineering and technology" cateogory, as of 2013. QS World University Rankings ranked the institute top in Pakistan and 106th in Asia. Wikipedia.


Aslam M.S.,Pakistan Institute of Engineering and Applied Sciences
Automatica | Year: 2016

Maximum likelihood methods are significant for parameter estimation and system modeling. This paper derives a maximum likelihood principle based least squares identification algorithm for online secondary path modeling in feed-forward active noise control systems with autoregressive moving average noise. This derivation proves that minimizing the cost function of least squares is equivalent to the maximum of likelihood function. Proposed method requires tuning of only one parameter in comparison with other recognized methods. Simulation tests show that proposed algorithm has better estimation accuracy and noise reduction capability than the current state-of-the-art methods in the presence and absence of disturbance at the error microphone. © 2016 Elsevier Ltd. All rights reserved. Source


Hussain A.,Pakistan Institute of Nuclear Science and Technology | Hussain A.,Pakistan Institute of Engineering and Applied Sciences
Journal of Physical Chemistry C | Year: 2013

Density functional theory calculations within the generalized gradient approximations were employed to examine the molecular oxygen adsorption, decomposition, and CO oxidation upon a number of Cu modified Au surfaces cleavaged in (100) orientation. Amount of Cu was varied on the Au slab to optimize the model, which serves as the best one to investigate the synergic effect between Au and Cu for CO oxidation process. The adsorption energy of O2 varies between -0.61 and -2.20 eV depending on the surface composition. O-O bond activation up to 1.86 Å has been observed for the most favorable configuration. CO adsorbs preferably on top configuration of the Cu-Au catalytic surface with an Eads of -1.12 eV. The atomic O adsorption is strongly site specific, 4-fold hollow being the most favorable. A coincident activation barrier of 0.60 eV has been found employing constraint minimization and cNEB method for CO2 formation. Initially, a stable O-CO complex is formed, which leads to CO2 formation without the expenditure of any significant energy. Both the O-CO complex formation and its conversion to CO2 are thermodynamically favorable processes. The process has been repeated on a pure Cu(100) surface to distinguish the role of Au. CO on a bridge yields the highest adsorption energy -0.97 eV, being slightly less stable on other locations. O2 has strong adsorption on this surface as well, but bond activation and Eads are comparatively less. The minimum energy path follows a similar route to CO2 as on a Au-Cu surface but with higher activation energy and no exothermicity. CO2 created desorbs by surmounting a small barrier through an exothermic process. Comparison suggests that the Cu-modified Au surface is superior and more active than pure Cu toward CO oxidation. Analogous to the Au-Cu bimetallic structure model, a Ag-Cu slab consisting of a top monolayer of Cu and three layers beneath Ag was considered for comparison. A similar pronounced effect regarding O 2 adsorption and an easy CO oxidation reaction was revealed. The results suggest that Cu bearing Au and Ag surfaces appear to be good candidates for low-temperature CO oxidation. © 2013 American Chemical Society. Source


Hayat M.,Pakistan Institute of Engineering and Applied Sciences | Khan A.,Pakistan Institute of Engineering and Applied Sciences
Analytical Biochemistry | Year: 2012

Membrane proteins are a major class of proteins and encoded by approximately 20% to 30% of genes in most organisms. In this work, a two-layer novel membrane protein prediction system, called Mem-PHybrid, is proposed. It is able to first identify the protein query as a membrane or nonmembrane protein. In the second level, it further identifies the type of membrane protein. The proposed Mem-PHybrid prediction system is based on hybrid features, whereby a fusion of both the physicochemical and split amino acid composition-based features is performed. This enables the proposed Mem-PHybrid to exploit the discrimination capabilities of both types of feature extraction strategy. In addition, minimum redundancy and maximum relevance has also been applied to reduce the dimensionality of a feature vector. We employ random forest, evidence-theoretic K-nearest neighbor, and support vector machine (SVM) as classifiers and analyze their performance on two datasets. SVM using hybrid features yields the highest accuracy of 89.6% and 97.3% on dataset1 and 91.5% and 95.5% on dataset2 for jackknife and independent dataset tests, respectively. The enhanced prediction performance of Mem-PHybrid is largely attributed to the exploitation of the discrimination power of the hybrid features and of the learning capability of SVM. Mem-PHybrid is accessible at http://www.111.68.99. 218/Mem-PHybrid. © 2012 Elsevier Inc. All rights reserved. Source


Zia-ur-Rehman,Pakistan Institute of Engineering and Applied Sciences | Khan A.,Pakistan Institute of Engineering and Applied Sciences
Protein and Peptide Letters | Year: 2012

G-protein coupled receptor (GPCR) is a membrane protein family, which serves as an interface between cell and the outside world. They are involved in various physiological processes and are the targets of more than 50% of the marketed drugs. The function of GPCRs can be known by conducting Biological experiments. However, the rapid increase of GPCR sequences entering into databanks, it is very time consuming and expensive to determine their function based only on experimental techniques. Hence, the computational prediction of GPCRs is very much demanding for both pharmaceutical and educational research. Feature extraction of GPCRs in the proposed research is performed using three techniques i.e. Pseudo amino acid composition, Wavelet based multi-scale energy and Evolutionary information based feature extraction by utilizing the position specific scoring matrices. For classification purpose, a majority voting based ensemble method is used; whose weights are optimized using genetic algorithm. Four classifiers are used in the ensemble i.e. Nearest Neighbor, Probabilistic Neural Network, Support Vector Machine and Grey Incidence Degree. The performance of the proposed method is assessed using Jackknife test for a number of datasets. First, the individual performances of classifiers are assessed for each dataset using Jackknife test. After that, the performance for each dataset is improved by using weighted ensemble classification. The weights of ensemble are optimized using various runs of Genetic Algorithm. We have compared our method with various other methods. The significance in performance of the proposed method depicts it to be useful for GPCRs classification. © 2012 Bentham Science Publishers. Source


Hayat M.,Pakistan Institute of Engineering and Applied Sciences | Khan A.,Pakistan Institute of Engineering and Applied Sciences
Protein and Peptide Letters | Year: 2012

Outer membrane proteins (OMPs) play important roles in cell biology. In addition, OMPs are targeted by multiple drugs. The identification of OMPs from genomic sequences and successful prediction of their secondary and tertiary structures is a challenging task due to short membrane-spanning regions with high variation in properties. Therefore, an effective and accurate silico method for discrimination of OMPs from their primary sequences is needed. In this paper, we have analyzed the performance of various machine learning mechanisms for discriminating OMPs such as: Genetic Programming, K-nearest Neighbor, and Fuzzy K-nearest Neighbor (Fuzzy K-NN) in conjunction with discrete methods such as: Amino acid composition, Amphiphilic Pseudo amino acid composition, Split amino acid composition (SAAC), and hybrid versions of these methods. The performance of the classifiers is evaluated by two datasets using 5-fold crossvalidation. After the simulation, we have observed that Fuzzy K-NN using SAAC based-features makes it quite effective in discriminating OMPs. Fuzzy K-NN achieves the highest success rates of 99.00% accuracy for discriminating OMPs from non-OMPs and 98.77% and 98.28% accuracies from α-helix membrane and globular proteins, respectively on dataset1. While on dataset2, Fuzzy K-NN achieves 99.55%, 99.90%, and 99.81% accuracies for discriminating OMPs from non-OMPs, α-helix membrane, and globular proteins, respectively. It is observed that the classification performance of our proposed method is satisfactory and is better than the existing methods. Thus, it might be an effective tool for high throughput innovation of OMPs. © 2012 Bentham Science Publishers. Source

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