Pakistan Institute of Engineering and Applied Sciences
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.

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Agency: GTR | Branch: EPSRC | Program: | Phase: Training Grant | Award Amount: 4.34M | Year: 2014

This world-leading Centre for Doctoral Training in Bioenergy will focus on delivering the people to realise the potential of biomass to provide secure, affordable and sustainable low carbon energy in the UK and internationally. Sustainably-sourced bioenergy has the potential to make a major contribution to low carbon pathways in the UK and globally, contributing to the UKs goal of reducing its greenhouse gas emissions by 80% by 2050 and the international mitigation target of a maximum 2 degrees Celsius temperature rise. Bioenergy can make a significant contribution to all three energy sectors: electricity, heat and transport, but faces challenges concerning technical performance, cost effectiveness, ensuring that it is sustainably produced and does not adversely impact food security and biodiversity. Bioenergy can also contribute to social and economic development in developing countries, by providing access to modern energy services and creating job opportunities both directly and in the broader economy. Many of the challenges associated with realising the potential of bioenergy have engineering and physical sciences at their core, but transcend traditional discipline boundaries within and beyond engineering. This requires an effective whole systems research training response and given the depth and breadth of the bioenergy challenge, only a CDT will deliver the necessary level of integration. Thus, the graduates from the CDT in Bioenergy will be equipped with the tools and skills to make intelligent and informed, responsible choices about the implementation of bioenergy, and the growing range of social and economic concerns. There is projected to be a large absorptive capacity for trained individuals in bioenergy, far exceeding current supply. A recent report concerning UK job creation in bioenergy sectors concluded that there may be somewhere in the region of 35-50,000 UK jobs in bioenergy by 2020 (NNFCC report for DECC, 2012). This concerned job creation in electricity production, heat, and anaerobic digestion (AD) applications of biomass. The majority of jobs are expected to be technical, primarily in the engineering and construction sectors during the building and operation of new bioenergy facilities. To help develop and realise the potential of this sector, the CDT will build strategically on our research foundation to deliver world-class doctoral training, based around key areas: [1] Feedstocks, pre-processing and safety; [2] Conversion; [3] Utilisation, emissions and impact; [4] Sustainability and Whole systems. Theme 1 will link feedstocks to conversion options, and Themes 2 and 3 include the core underpinning science and engineering research, together with innovation and application. Theme 4 will underpin this with a thorough understanding of the whole energy system including sustainability, social, economic public and political issues, drawing on world-leading research centres at Leeds. The unique training provision proposed, together with the multidisciplinary supervisory team will ensure that students are equipped to become future leaders, and responsible innovators in the bioenergy sector.

News Article | December 2, 2015

As former chairman of Pakistan's Higher Education Commission and former coordinator-general of the Organisation of Islamic Cooperation's science and technology body COMSTECH, I suggest that some universities in the Muslim world are not in such dire need of revitalization as Nidhal Guessoum and Athar Osama imply (Nature 526, 634–636; 2015). At least 3 such institutions are ranked in the world's top 250 — the University of Malaya in Kuala Lumpur, and King Fahd University and King Saud University, both in Saudi Arabia (see In 2013 and 2014, the Middle East Technical University, Istanbul Technical University and Bilkent University in Turkey were ranked in the top 400 globally (see Pakistan's National University of Sciences and Technology and the Pakistan Institute of Engineering and Applied Sciences were ranked in the top 200 Asian universities in 2014 (see The King Abdullah University of Science and Technology in Saudi Arabia and the Masdar Institute in Abu Dhabi are rising stars. According to 2014 data on scientific publications, Iran ranks 16th in the world, Turkey is 19th and Malaysia is 23rd — on a par with Switzerland, Taiwan and some Scandinavian countries, and ahead of South Africa (see Furthermore, the requirements of the United Arab Emirates' Commission of Academic Accreditation (CAA) are more stringent than those of the US Accreditation Board for Engineering and Technology (ABET), for instance. Whereas the CAA requires faculty members to have the highest degree in their field (such as a PhD), ABET requires only appropriate qualifications. The CAA also requires universities to have accredited PhD programmes in addition to accredited bachelor's and master's degrees.

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.

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.

Tahir M.,Pakistan Institute of Engineering and Applied Sciences | Khan A.,Pakistan Institute of Engineering and Applied Sciences | Majid A.,Pakistan Institute of Engineering and Applied Sciences
Bioinformatics | Year: 2012

Motivation: Subcellular localization of proteins is one of the most significant characteristics of living cells. Prediction of protein subcellular locations is crucial to the understanding of various protein functions. Therefore, an accurate, computationally efficient and reliable prediction system is required.Results: In this article, the predictions of various Support Vector Machine (SVM) models have been combined through majority voting. The proposed ensemble SVM-SubLoc has achieved the highest success rates of 99.7% using hybrid features of Haralick textures and local binary patterns (HarLBP), 99.4% using hybrid features of Haralick textures and Local Ternary Patterns (HarLTP). In addition, SVM-SubLoc has yielded 99.0% accuracy using only local ternary patterns (LTPs) based features. The dimensionality of HarLBP feature vector is 581 compared with 78 and 52 for HarLTP and LTPs, respectively. Hence, SVM-SubLoc in conjunction with LTPs is fast, sufficiently accurate and simple predictive system. The proposed SVM-SubLoc approach thus provides superior prediction performance using the reduced feature space compared with existing approaches. © The Author 2011. Published by Oxford University Press. All rights reserved.

Islam A.,Pakistan Institute of Engineering and Applied Sciences | Yasin T.,Pakistan Institute of Engineering and Applied Sciences
Carbohydrate Polymers | Year: 2012

The blends of chitosan (CS) and poly(vinyl alcohol) (PVA) react with tetraethoxy silane (TEOS) to give network structure and their selective crosslinking give hydrogel properties. The swelling properties against different media are changed by varying the amount of PVA in CS/PVA blend. The degree of swelling in water is decreased with increasing amount of PVA. The most significant behavior of these blends is their response against pH, exhibiting low swelling in acidic and basic pH conditions and maximum swelling at neutral pH. This unique behavior along with the biocompatibility of the components made them suitable for oral delivery of enzymes and medicines. Dexamethasone has been selected as a model drug. The released profile of dexamethasone loaded CS/PVA complex showed 9.37% of drug release over a period of 2 h in simulated gastric fluid and its transfer to simulated intestinal fluid showed consistent release of remaining drug up to 7 h. © 2012 Elsevier Ltd. All rights reserved.

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.

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.

Hayat M.,Pakistan Institute of Engineering and Applied Sciences | Khan A.,Pakistan Institute of Engineering and Applied Sciences
Journal of Theoretical Biology | Year: 2011

Membrane proteins are vital type of proteins that serve as channels, receptors, and energy transducers in a cell. Prediction of membrane protein types is an important research area in bioinformatics. Knowledge of membrane protein types provides some valuable information for predicting novel example of the membrane protein types. However, classification of membrane protein types can be both time consuming and susceptible to errors due to the inherent similarity of membrane protein types. In this paper, neural networks based membrane protein type prediction system is proposed. Composite protein sequence representation (CPSR) is used to extract the features of a protein sequence, which includes seven feature sets; amino acid composition, sequence length, 2 gram exchange group frequency, hydrophobic group, electronic group, sum of hydrophobicity, and R-group. Principal component analysis is then employed to reduce the dimensionality of the feature vector. The probabilistic neural network (PNN), generalized regression neural network, and support vector machine (SVM) are used as classifiers. A high success rate of 86.01% is obtained using SVM for the jackknife test. In case of independent dataset test, PNN yields the highest accuracy of 95.73%. These classifiers exhibit improved performance using other performance measures such as sensitivity, specificity, Mathew's correlation coefficient, and F-measure. The experimental results show that the prediction performance of the proposed scheme for classifying membrane protein types is the best reported, so far. This performance improvement may largely be credited to the learning capabilities of neural networks and the composite feature extraction strategy, which exploits seven different properties of protein sequences. The proposed Mem-Predictor can be accessed at © 2010 Elsevier Ltd.

Hayat M.,Pakistan Institute of Engineering and Applied Sciences | Khan A.,Pakistan Institute of Engineering and Applied Sciences
Journal of Theoretical Biology | Year: 2012

About 50% of available drugs are targeted against membrane proteins. Knowledge of membrane protein's structure and function has great importance in biological and pharmacological research. Therefore, an automated method is exceedingly advantageous, which can help in identifying the new membrane protein types based on their primary sequence. In this paper, we tackle the interesting problem of classifying membrane protein types using their sequence information. We consider both evolutionary and physicochemical features and provide them to our classification system based on support vector machine (SVM) with error correction code. We employ a powerful sequence encoding scheme by fusing position specific scoring matrix and split amino acid composition to effectively discriminate membrane protein types. Linear, polynomial, and RBF based- SVM with Bose, Chaudhuri, Hocquenghem coding are trained and tested. The highest success rate of 91.1% and 93.4% on two datasets is obtained by RBF- SVM using leave-one-out cross-validation. Thus, our proposed approach is an effective tool for the discrimination of membrane protein types and might be helpful to researchers/academicians working in the field of Drug Discovery, Cell Biology, and Bioinformatics. The web server for the proposed MemHyb-SVM is accessible at © 2011 Elsevier Ltd.

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