Hawalli, Kuwait

Gulf University for Science & Technology is the first private university established in Kuwait. It has a dual-enrollment agreement with the University of Missouri–St. Louis. Wikipedia.

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Simon D.,Cleveland State University | Omran M.G.H.,Gulf University for Science and Technology
Information Sciences | Year: 2014

Biogeography-based optimization (BBO) is an evolutionary optimization algorithm that uses migration to share information among candidate solutions. One limitation of BBO is that it changes only one independent variable at a time in each candidate solution. In this paper, a linearized version of BBO, called LBBO, is proposed to reduce rotational variance. The proposed method is combined with periodic re-initialization and local search operators to obtain an algorithm for global optimization in a continuous search space. Experiments have been conducted on 45 benchmarks from the 2005 and 2011 Congress on Evolutionary Computation, and LBBO performance is compared with the results published in those conferences. The results show that LBBO provides competitive performance with state-of-the-art evolutionary algorithms. In particular, LBBO performs particularly well for certain types of multimodal problems, including high-dimensional real-world problems. Also, LBBO is insensitive to whether or not the solution lies on the search domain boundary, in a wide or narrow basin, and within or outside the initialization domain. © 2014 Elsevier Inc. All rights reserved.

Adjerid S.,Virginia Polytechnic Institute and State University | Temimi H.,Gulf University for Science and Technology
Computer Methods in Applied Mechanics and Engineering | Year: 2011

We present a new discontinuous Galerkin method for solving the second-order wave equation using the standard continuous finite element method in space and a discontinuous method in time directly applied to second-order ode systems. We prove several optimal a priori error estimates in space-time norms for this new method and show that it can be more efficient than existing methods. We also write the leading term of the local discretization error in terms of Lobatto polynomials in space and Jacobi polynomials in time which leads to superconvergence points on each space-time cell. We discuss how to apply our results to construct efficient and asymptotically exact a posteriori estimates for space-time discretization errors. Numerical results are in agreement with theory. © 2010 Elsevier B.V.

Kisswani K.M.,Gulf University for Science and Technology | Nusair S.A.,Gulf University for Science and Technology
Energy Economics | Year: 2013

Examining stationarity is of particular importance and represents the first step in empirical time-series research. Non-stationarity invalidates many of the results obtained from standard techniques and, therefore, requires special treatment. Because oil prices play an important role in affecting economic variables, this paper examines the stationarity of real oil prices (Brent, Dubai, WTI and the World) over the period 1973:2-2011:2. Real oil prices are expressed in the currencies of seven Asian countries (Indonesia, Japan, Korea, Malaysia, the Philippines, Singapore and Thailand) and in the U.S. dollar. While using linear unit root tests without structural breaks shows no evidence of stationarity, allowing for breaks shows very limited evidence of stationarity. We argue that these results are attributed to the presence of nonlinearities in the behavior of oil prices. Testing for nonlinearity shows significant evidence of nonlinearity in all the cases with evidence of exponential smooth transition autoregression (ESTAR) nonlinearity-type in most cases. Applying unit root tests that account for two types of nonlinearities (smooth transition and nonlinear deterministic trends) reveals evidence of stationarity in all the cases. © 2012 Elsevier B.V.

Kisswani K.M.,Gulf University for Science and Technology
Energy Policy | Year: 2016

In this paper I use quarterly and monthly data from 1994 to 2014 to test if OPEC acts as a cartel, and therefore, it affects oil prices through members' coordination. I use Engle and Granger two-step approach, Johansen cointegration test and Autoregressive Distributed Lag (ARDL) bounds testing approach of cointegration to examine the long-run relation between OPEC production and each member's production as an evidence of coordination. Besides, I apply Granger causality and Toda and Yamamoto tests to check the direction of causality between the OPEC production and oil prices (U.K. Brent and Dubai Fateh). The findings show no evidence of cointegration between the production of the members and that of OPEC, indicating no cartel behavior exists. Moreover, the results show that OPEC production does not cause oil prices; rather it is the other way around. © 2016 Elsevier Ltd

Nusair S.A.,Gulf University for Science and Technology
Energy Policy | Year: 2016

This paper examines the effects of oil price shocks on the real GDP of the Gulf Cooperation Council (GCC) countries. The empirical method used is the nonlinear cointegrating autoregressive distributed lag (NARDL) model of Shin et al. (2013) in which short-run and long-run nonlinearities are introduced via positive and negative partial sum decompositions of the explanatory variable(s). The results suggest evidence of asymmetries in all the cases. We find significant positive oil price changes in all the cases with the expected positive sign, implying that increases in oil price lead to increases in real GDP. Conversely, negative oil price changes are significant for only Kuwait and Qatar with the expected positive sign, suggesting that decreases in oil price lead to decreases in their real GDP. Further analysis implemented using panel data shows that positive oil prices changes increase real GDP and negative changes decrease real GDP. Overall, the results suggest that positive oil price changes have a considerably larger impact on real GDP than negative changes. © 2016 Elsevier Ltd.

Mostafa M.M.,Gulf University for Science and Technology
Expert Systems with Applications | Year: 2011

Software piracy represents a major damage to the moral fabric associated with the respect of intellectual property. The rate of software piracy appears to be increasing globally, suggesting that additional research that uses new approaches is necessary to evaluate the problem. The study remedies previous econometric and methodological shortcomings by applying Bayesian, robust and evolutionary computation robust regression algorithms to formally test empirical literature on software piracy. To gain further insights into software piracy at the global level, the study also uses five neuro-computational intelligence methodologies: multi-layer perceptron neural network (MLP), probabilistic neural network (PNN), radial basis function neural network (RBF), generalized regression neural network (GRNN) and Kohonen's self-organizing maps (SOM) to classify, predict and cluster software piracy rates among 102 nations. At the empirical level, this research shows that software piracy is significantly affected by the wealth of nation as measured by gross domestic product (GDP), the nation's expenditure on research and development and the nation's judicial efficiency. At the methodological level, this research shows that neuro-computational models outperform traditional statistical techniques such as regression analysis, discriminant analysis and cluster analysis in predicting, classifying and clustering software piracy rates due to their robustness and flexibility of modeling algorithms. © 2011 Elsevier Ltd. All rights reserved.

Mostafa M.M.,Gulf University for Science and Technology
Expert Systems with Applications | Year: 2011

This study uses self-organizing maps (SOM) to examine the effect of various psychographic and cognitive factors on organ donation in Egypt. SOM is a machine learning method that can be used to explore patterns in large and complex datasets for linear and nonlinear patterns. The results show that major variables affecting organ donation are related to perceived benefits/risks of organ donation, organ donation knowledge, attitudes toward organ donation, and intention to donate organs. The study also shows that SOM models are capable of improving clustering quality while extracting valuable information from multidimensional data. © 2010 Elsevier Ltd. All rights reserved.

Mostafa M.M.,Gulf University for Science and Technology
Expert Systems with Applications | Year: 2012

Using functional magnetic resonance imaging (fMRI), this study aimed at investigating the neural mechanisms associated with human and non-human sounds' perception in advertising. The study employed a block design paradigm in which participants heard human versus non-human sounds in different sets of advertisements. The results showed that, compared to nonhuman sounds, human sounds elicited greater activation in several areas in or around the primary auditory cortex (t > 5.16, p < 0.001). This result suggests that different types of sounds are processed in different functional brain pathways. The existence of voice-selective areas in the brain lends strong support to the face perception neurocognitive model which proposes that visual, affective and linguistic information are processed in different cortical regions in the brain. © 2012 Elsevier Ltd. All rights reserved.

Mostafa M.M.,Gulf University for Science and Technology
International Journal of Sustainable Development and World Ecology | Year: 2012

The aim of this paper is to empirically evaluate whether the process of globalisation, through which countries become increasingly interconnected, is related to pro-environmental intentions. Due to the hierarchical nature of the data, the study uses a multilevel modelling approach to cross-culturally test the impact of globalisation on pro-environmental intentions. Using an updated indicator of globalisation, the results from 25 nations show that economic, social and political openness are not related to pro-environmental intentions, as measured by willingness to sacrifice to protect the environment. This result implies that concern for the environment is a global phenomenon and not unique to the wealthy and more globalised nations. The findings of this paper highlight the importance of simultaneously assessing individual- and contextual-level variables in determining pro-environmental intentions across nations. © 2012 Copyright Taylor and Francis Group, LLC.

Mostafa M.M.,Gulf University for Science and Technology | El-Masry A.A.,University of Plymouth
International Journal of Information Management | Year: 2013

This study uses data mining techniques to examine the effect of various demographic, cognitive and psychographic factors on Egyptian citizens' use of e-government services. Data mining uses a broad family of computationally intensive methods that include decision trees, neural networks, rule induction, machine learning and graphic visualization. Three artificial neural network models (multi-layer perceptron neural network [MLP], probabilistic neural network [PNN] and self-organizing maps neural network [SOM]) and three machine learning techniques (classification and regression trees [CART], multivariate adaptive regression splines [MARS], and support vector machines [SVM]) are compared to a standard statistical method (linear discriminant analysis [LDA]). The variable sets considered are sex, age, educational level, e-government services perceived usefulness, ease of use, compatibility, subjective norms, trust, civic mindedness, and attitudes. The study shows how it is possible to identify various dimensions of e-government services usage behavior by uncovering complex patterns in the dataset, and also shows the classification abilities of data mining techniques. © 2013 Elsevier B.V.

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