Qatar Foundation Annual Research Forum Volume 2012 Issue 1

Automated essay scoring is a research field which is continuously gaining popularity. Grading essays by hand is expensive and time consuming, automated scoring systems can yield fast, effective and affordable solutions that would make it possible to grade essays and other sophisticated testing tools. This study has been conducted on a dataset of thousands of English essay sets belonging to eight different categories provided by the Hewlett Foundation. Each category corresponds to the same question or problem statement. The score of each essay of the training set is provided in this dataset by human raters. Several features have been determined to predict the final grade. First, the number of occurrences of the 100 most frequent words in English is computed in each essay. Then, the list of average scores associated to each compounding word in the training set is determined. From this list several statistical values are considered as separate feature including the minimum, maximum, mean and median values, variance, skewness and kurtosis. These statistical features are also computed for the list of average scores associated to each compounding bigram (sequence of 2 words). Moreover, each word in the essays has been tagged using the NLTK toolkit into its grammatical role (verb, noun, adverb…etc). The number of occurrences of each grammatical role has also been used as a separate feature. All those features have been combined using different classifiers with random forests generally preferred. This system participated in the Automated Essay Scoring Contest sponsored by the Hewlett Foundation. The results have been evaluated using the quadratic weighted kappa error metric, which measures the agreement between the human rater and the automatic rater. This metric typically varies from 0 (only random agreement) to 1 (complete agreement). This method scored 0.76519 and ranked 13th out of 156 teams: http://www.kaggle.com/c/asap-aes/leaderboard. The proposed system combines structural and grammatical features to automatically grade essays and achieves promising performance. There is ongoing work on the extension of the developed method for short essay scoring as well as grading an unseen category of essays.

The accurate design of chemical processes depends on the availability of models to predict the physical properties of the materials being processed. Thermodynamic properties such as enthalpies, entropies, and fugacities are particularly important in this context. Most of the models to evaluate them have adjustable parameters, fitted to give the best possible representation of the experimental data available for a given substance or mixture. Depending on how much information is available, this may entail the use of hundreds or thousands of data points. As several modern thermodynamic models have intricate mathematical expressions, especially equations of state, using so many data points to fit their parameters leads to substantial computational effort. This makes it difficult to run the parameter fitting problem from different initial estimates. The consequence is that this decreases the likelihood of finding the global minimum of the objective function used for parameter fitting. Despite the fact that current desktops and laptops are capable of parallel computations, little has been done to take advantage of their computational power for equation of state parameter fitting. The authors have recently developed procedures to that end, executed in different desktop and laptop computers, which provided speedups compatible with the number of processors available. One of the procedures is based on the conventional, sequential simplex minimization algorithm with a parallel evaluation of the objective function (SSPO approach). The other procedure is based on a modified, parallel version of the simplex minimization algorithm with a sequential evaluation of the objective function (PSSO approach). In this paper, we extend the evaluation of these procedures, executing them in the Suqoor supercomputer of Texas A&M University at Qatar, using single and multiple nodes. Because of numerical algorithm used, speedups in the PSSO approach are limited by the number of parameters to be fitted, which does not happen in the SSPO approach. On the other hand, the PSSO approach often ends at solutions with smaller objective functions, showing a greater tendency to escape local minima.

Extracting named entities is an important step for information extraction from a text, based on a given ontology. Dealing with Arabic language invokes an additional number of challenges compared to English, French and other languages within similar families. The major difficulties involve complex morphological systems, no capitalization, and no standardization of Arabic writing. The Arabic language has a rich and complex morphological landscape due to its highly inflected nature. Usually, any Arabic lemma word can be constructed using different internal structure, prefixes and suffixes. Furthermore, there is no standardization of Arabic writing because of the spelling inconsistency of Arabic words. In this work, we propose an operational hybrid approach combining dictionary-based and rule-based detection for extracting seven categories of named entities which are organization by name, date, interval, price/value, percentage, currency and unit. The dictionary-based approach performs exact or approximate matching of the words with prepared Arabic organization names. In case of non-exact matching with the dictionary words, the approximate matching is an efficient solution for morphological difficulties. Specificities of Arabic language are also processed by rule-based detection, which is based on capturing the entities patterns in terms of regular expressions or patterns provided by experts. We evaluated our Arabic name entity recognition system using financial news articles and we obtained around an 80% of recognition rate.

Our aim is to develop a culturally aware robot capable of communicating with people from different ethnic and cultural backgrounds and performing competently in a multi-lingual, cross-cultural context. Our test bed is a female robot receptionist, named Hala, deployed at the reception area in Carnegie Mellon University in Qatar. Hala answers questions in Arabic and English about people, locations of offices, classrooms and other rooms in the building. She also provides information about the weather, Education City, and her personal life. Our first model, Hala 1.0, was a bilingual robot extending an American model whose personality and utterances conform to the American culture. Three years of interaction logs have shown that 89% of Hala 1.0's interactions were in English. We conjecture that this is due to the robot's poor ability to equally portray both Arabic and American cultures and to its limited Arabic content. In order for us to investigate cultural factors that bear on communication significantly, we developed Hala 2.0 which is also a bilingual robot designed to be an Arab-American robot with more Arabic features in appearance, expression and interaction. The robot's personality is constructed taking into account the socio-cultural context in which its interactions will take place. To achieve bilingualism we had to create symmetry between Arabic and English linguistic content. Since the robot's utterances were developed primarily in English we resorted to translating them into Arabic and adapting them to the constraints of our socio-cultural context. Since Arabic is a highly inflected language, we adopted the plural case in formulating the robot's replies so as to avoid gender bias. To improve query coverage, we added word synonyms, including context-related synonyms (exp:هل تحبين عملك؟/ هل يعجبك عملك؟ ) and different formulations for the same question (exp: do you sleep? / do you go to sleep? and هل تنامين؟/ أتنامين؟). Furthermore, based on three years of recorded query logs, we expanded the range of topics that the robot is knowledgeable about by adding 3000 question/answer sentences to increase the robot's capacity for engaging users. All content and utterances were developed to align with the robot's designed personal traits.

Background and Objective: The Autonomic Nervous System (ANS) regulates physiologic processes autonomously through the sympathetic (SNS) and parasympathetic systems (PNS) with both working in balance e.g. sympathetic input accelerates heart rate and prepares for emergencies while the parasympathetic slows the heart rate and relaxes the body. Stress can lead to imbalances in these two systems which can harm the human body. Persistent imbalances caused by chronic stress may trigger diseases such as hypertension, diabetes, asthma and depression and also lead to social problems. In this paper we discuss the effectiveness of a wearable physiological monitoring device in identifying the response of subjects to stressful and relaxation activities to monitor the long term impact of stress. Methods: To achieve this objective we developed a body sensor network to wirelessly monitor heart rate, respiratory rate and skin conductance. We collected data while subjects performed mental challenges, chosen to measure a range of stress responses interleaved with deep breathing activities, which they also assessed. We examined the data using several measures of heart rate variability--spectral power in the low frequency (HRV-LF) and high frequency range (HRV-HF), mean (AVNN) and standard deviation of successive RR intervals, the portion of RR interval that changes more than 25 msec (pNN25) and the root mean square of successive differences of RR (RMSSD). Respiratory effect was evaluated using normalized respiratory high and low frequency components and their ratio. To assess the impact on the skin conductance, the mean and standard deviation of the slow varying tonic skin conductance level (SCL) and rapidly varying phasic response-skin conductance response (SCR) were computed. Results: An analysis of the computed features indicated that not all features were able to accurately identify the impact of stress on the subjects. The HRV and skin conductance measures were more highly correlated to stress levels with best discrimination obtained using AVNN, RMSSD, PNN25, HRV-HF, SCL mean and SCR standard deviation. Conclusions: This study has shown that it is possible to extract features from physiological signals that can be transformed into meaningful measures of individual stress.

Stereo vision is a common sensing technique for mobile robots and is becoming more broadly used in automotive, industrial, entertainment, and consumer products. The quality of range data from a stereo system is highly dependent on the intrinsic and extrinsic calibration of the sensor head. Unfortunately, for deployed systems, drift in extrinsic calibration is nearly unavoidable. Thermal variation and cycling combined with shock and vibration can cause transitory or permanent changes in extrinsics that are not modeled accurately by a static calibration. As a result the quality of the sensor degrades significantly. We have developed a new approach that provides real-time continuous calibration updates to extrinsic parameters. Our approach optimizes the extrinsic parameters to reduce epipolar errors over one or multiple frames. A Kalman Filter is used to continually refine these parameter estimates and minimize inaccuracies resulting from visual feature noise and spurious feature matches between the left and right images. The extrinsic parameter updates can be used to re-rectify the stereo imagery. Thus, it serves as a pre-processing step for any stereo process ranging, from dense reconstruction to visual odometry. We have validated our system in a range of environments and stereo tasks and demonstrated it at the recent Computer Vision and Pattern Recognition conference. Significant improvements to stereo visual odometry and scene mapping accuracy were achieved for datasets collected using both custom built and commercial stereo heads.

In this paper, we consider a distributed beamforming scheme (DBF) in a spectrum sharing system where multiple secondary users share the spectrum with the licensed primary users under an interference temperature constraint. We assume that DBF is applied at the secondary users. We first consider optimal beamforming and compare it with the user selection scheme in terms of the outage probability and bit-error-rate performance. Since perfect feedback is difficult to obtain, we then investigate a limited feedback DBF scheme and develop an outage probability analysis for a random vector quantization (RVQ) design algorithm. Numerical results are provided to illustrate our mathematical formalism and verify our analysis.

Background & Objectives: The Hajj and minor Muslim pilgrimage of Umrah, present some of the most densely crowded human environments in the world, and thus offer an excellent testbed for the study of dense crowd dynamics. Accurate characterisation of such crowds is crucial to improve simulations that are ubiquitously applied to crowded environments such as train stations, and which require a high degree of detailed parameterisation. Accurate measurements of key crowd parameters can also help to develop better strategies for mitigating disasters such as the tragic stampede of 2006 that killed over 300 pilgrims during the Hajj. With Qatar set to be one of the major cultural centres in the region, e.g. hosting 2022 FIFA World Cup, the proper control and management of large singular events is paramount for safety and Qatar's standing on the international stage. We aim to use the unique video data gathered from Hajj 2011, to assess the dynamics of very dense crowded environments with a particular focus on dangerous crowd instabilities and systemic shocks. Methods: We make use of increasingly complex optical flow algorithms (Horn-Schunck, Lucas-Kanade, TV-L1) to extract the instantaneous velocity field between each pair of frames in the videos. From these velocity vector fields we then construct the pedestrian (Lagrangian) flow field by the use of texture advection techniques that initially seed the flow with particles or random noise. Results: We present results of the above application of optical flow and texture advection methods to the data we collected in a field study during Hajj 2011. Particularly, we aim to illustrate the specific flow patterns that arise in such crowded environments. We also aim to present the preliminary results of a pilot multiple camera stereovision study conducted in the London Central Mosque on a Friday when the mosque was particularly crowded.

Fitting an analytic function to the two-dimensional surface brightness profile of a galaxy provides a powerful method of quantifying its internal structure. The resulting parameters reveal the size, shape and luminosity of the galaxy and its separate structural components (e.g., disk and bulge). Current galaxy fitting softwares packages consider only a single waveband image at a time. However, variations in stellar populations between and within galaxy structures mean that their observed properties depend on wavelength. Correctly studying the physical properties of galaxy components requires that these wavelength variations be accounted for. Multi-wavelength studies are presently possible, but require significant compromises: either the fits to each band must be done independently, or, one band must be favored for determining structural parameters, which are then imposed on fits to the other bands. Both of these approaches waste valuable information, and therefore result in suboptimal decompositions. Our project, 'MegaMorph', is developing a next-generation tool for decomposing galaxies, in terms of both their structures and stellar populations. We aim to present a modified version of the two-dimensional galaxy fitting software, GALFIT, developed as part of our MegaMorph project. These new additions enable a galaxy to be fit using images at many different wavelengths simultaneously. All the available data is therefore used to constrain a wavelength-dependent model of the galaxy, resulting in more robust, physically meaningful, component properties. We verify the advantages of our technique by applying it to a sample of 160 well-studied galaxies at redshifts smaller than 0.025, with ugriz imaging from the Sloan Digital Sky Survey to demonstrate how the resulting decompositions allow us to study the links between stellar population and galaxy structure in detail. Furthermore, we illustrate the advantages of our new method with regard to galaxy surveys, by fitting a sample of ~4000 artificially redshifted images of the galaxies described above. Our technique enables physical parameters of galaxy components to be robustly measured at lower signal-to-noise and resolution than would otherwise be possible. This paves the way for detailed statistical studies of the physical properties of galaxy disks and bulges.

Background & Objectives: Cognitive radios (CRs) strive to utilize the white holes in the radio frequency (RF) spectrum in an opportunistic manner. Because interference is an inherent and a very critical design parameter for all sorts of wireless communication systems, many of the recently emerging wireless technologies prefer smaller size coverage with reduced transmit power in order to decrease interference. Prominent examples of short-range communication systems trying to achieve low interference power levels are CR relays in CR networks and femtocells in next generation wireless networks (NGWNs). It is clear that a comprehensive interference model including mobility is essential especially in elaborating the performance of such short-range communication scenarios. This work focuses on analyzing how interference evolves in time under long and short term fading. Such an analysis is essential, because once the interference behavior is understood, it can be managed in a better way. Also, NGWNs and CRs can be designed in such a way that arduous and expensive planning stage is omitted. This way, deployment costs can be reduced drastically. Methods: It is known that received signal in a general wireless propagation environment includes the effects of both long- and short-term fading. Therefore, a logarithmic transformation reveals the individual impact of each fading phenomenon. The two-dimensional (2D) random walk model is incorporated into the physical layer signal model. Results: The results show that relatively larger displacements in short primary-user-receiver (PU-Rx) and secondary-user-transmitter (SU-Tx) separations lead to drastic power level fluctuations in the observed interference power levels. The impact of path loss is one of the major factors changing the future interference conditions for low-speed mobility scenarios especially within short communication ranges. Conclusions: It is shown that long-term fading plays a crucial role for the temporal evolution of interference for NGWNs and CRs. By using the interference statistics, the design and deployment of future cellular mobile radio systems in Qatar could be optimized. This is crucial, especially for rapidly changing network topographies as is the case with the city of Doha. Even for well-established network topographies, the proposed method provides an analytical way of examining and managing the interference.

This research is aimed at evaluating the usability of notational systems that are used for specifying programs. We provide a conceptual analysis of the development of constraint diagrams (CD) as a diagrammatic notation which developed to support program specification. A detailed analysis of multi-case comparisons of formal languages and graphical systems for expressing logic and program constructs was conducted in order to trace how the development of the notations overcame the limitations of earlier generations of notations. By following the evolution of logic diagrams, we consider why and how they have been successively revised to increase their expressivity or their ease of comprehensibility and use. Visualizations of logic were proposed over the centuries. Leonhard Euler presented Euler diagrams, John Venn generalized Euler diagrams and presented Venn diagrams, and Charles Peirce extended Venn diagrams by increasing their expressiveness and presented Venn-Peirce diagrams. The modifications from Peirce to Shin concentrate on restoring visual clarity, but without loss of expressive power. Based on that, Constraint Diagrams were presented by Stuart Kent for constraint specification, behavioural specification, and relational navigation. We found that the gradual changes in diagrams from Euler Circles through Venn, Peirce, Shin to Constraint Diagrams share three complementary common themes: (1) to increase the expressiveness, (2) to increase the logical power of the formality of the system, and (3) to enhance the visual clarity. Depending on the purpose of designing a diagram, the priority of a theme over other themes is granted. For example, both Venn and Peirce adopted the same kind of solution in order to achieve these improvements: to introduce new syntactic objects, that is, shadings by Venn, and x's, o's, and lines by Peirce. However, on the negative side, these revised systems suffer from a loss of visual clarity, mainly because of the introduction of more arbitrary conventions. The modifications from Peirce to Shin concentrate on restoring visual clarity, but without loss of expressive power. The extension from these diagrams resulted in CD, which allows relational navigation (expressions involving two place predicates), is more expressive than previous diagrams, and has higher visual clarity and logical power.

Under the scenario of an underlay cognitive radio network, we introduce the concept of minimum-selection maximum ratio transmission (MS-MRT). Inspired by the mode of operation of the minimum-selection generalized selection combining (MS-GSC) technique, the main idea behind MS-MRT is to present an adaptive variation of the existing maximum ratio transmission (MRT) technique. While in the MRT scheme, all the transmit antennas are used for transmission, and only a subset of antennas verifying the interference constraint to the primary receiver in MS-MRT are adaptively selected and optimally beamformed in order to meet a given modulation requirement. The main goal of these schemes is to maximize the capacity of the secondary link while satisfying the bit error rate (BER) requirement and a peak interference constraint to the primary link. The performance of the proposed schemes is analyzed in terms of the average spectral efficiency, the average number of antennas used for transmission, the average delay, and the average BER performance. These results are then compared to the existing bandwidth efficient and switching efficient schemes (BES and SES, respectively). The obtained analytical results are then verified with selected numerical examples obtained via Monte-Carlo simulations. We demonstrate through these examples that the proposed schemes improve the spectral and the delay performance of the SES and BES schemes and fit better to delay sensitive applications. The proposed schemes also offer better processing-power consumption than the MRT schemes since a minimum number of antennas is used for communication in the MS-MRT schemes. The MS-MRT techniques represent power and spectral efficient schemes that can be extended to more practical scenarios. As an example, these schemes can be studied in the context of Long term Evolution (LTE) Networks where adaptive modulation, beamforming, and interference management are of the major enabling Techniques.

Background and Objectives: A new generation of software emerged with mobile devices, cloud computing and the web. New usages come with new security threats, and a new generation of malware (malicious software) is emerging as well. Recent security reports show that these malware are on the increase. The goal of this project is to evaluate the risk of exposure to malware in popular app ecosystems such as Apple iOS, Google Android, Facebook, Google App Engine, Mozilla Firefox and Google Chrome. Methods: Eight students from Carnegie Mellon Qatar participated in this project. Each looked at a specific technology (either iOS, Android, Facebook, Google App, Mozilla Firefox or Google Chrome). The researchers learned how to develop applications and, as proof of concept, developed several malware apps that were able to steal user's personal information. One collects logins, passwords and credit card numbers from user's Gmail. One collects user's private information on Facebook and propagates through the victim's friends. One records the "clicking" passwords that users enter on online banking websites. One records keystrokes made on the computer without being detected by existing antivirus. One is an Android app that records people conversations while the phone is on standby mode. Results: Based on these experiments, we were able to assess the risks and analyze the security issues of these popular apps that we use everyday. These preliminary results were presented at 6th INTERPOL's Group meeting-MENA Region conference in Doha (March 22nd). We plan to publish the scientific results during the Fall of 2012. As future work, the security expertise gained during this project will allow us to design new security tools to protect users against these new kinds of malware. Conclusion: Qatar offers many services related to e-government, e-business, e-education and e-health through web portals and mobile applications. Deploying such a global infrastructure requires a strong security assurance. This project contributes to this vision by developing a local expertise on cyber security.

Background and Objectives: The target of this work is to construct a mathematical model to help resolve aircraft scheduling over multiple runways. This problem is considered a hard topic in transportation research due to the constantly increasing aviation traffic volume around the world. Surprisingly, although there exists an impressive amount of literature for the landing and arrival cases, there is no proposed exact solution to solve this problem. Therefore, the main contribution of this work is to present exact methods involving exact procedures specifically related to this complex arrival-departure variant. This method will solve the problem optimally based on mixed-integer linear formulation with the constraints of limited time windows and separation constraints. The project was funded by Qatar Foundation. Our objective for the investigation stems from its practical relevance to airports where good scheduling increases the airport capacity, maintains a good level of safety and reduces the controller's workload. Methods: We formulated a basic aircraft sequencing model using a mixed-integer linear formulation. Then, we proposed another model by adding valid inequalities, combining constraints and removing some variables. All these mathematical models are based on linear ordering feature. These models were solved using the mathematical programming language AMPL. Then, we solved them using a professional solver CPLEX. Results: We investigated the efficacy of reformulation arrival-departure scheduling problem over multiple runways. The results show that the solver is very effective in obtaining optimal solutions. In fact, the experimental tests reveal that most of instances are solved to optimality within a short time. In addition, we realized that adding the valid inequality constraint among the model yields less CPU ( Central Processing Unit) time and less number of nods. Conclusion: We proposed linear ordering formulations for the problem of minimizing total weighted tardiness to sequence arrival-departure aircraft over multiple runways. We presented the results of computational study that were carried out on a large variety of random instances and that shows the importance of reformulating the same problem. Interestingly, we observed that the proposed models enable us to optimally solve problems with up 15 aircraft and four runways.

Two significant identities-de Bruijn and Stein-were independently studied in information theory and statistics. De Bruijn identity shows a connection between two fundamental concepts in information theory and signal processing: differential entropy and Fisher information. On the other hand, Stein identity represents a relationship between the expectation of a function and its first-order derivative. Due to their several applications in statistics, information theory, probability theory, and economics, de Bruijn and Stein identities have attracted a lot of interest. In this study, two different extensions of de Bruijn identity and its relationship with Stein identity will be established. In addition, a number of applications using de Bruijn identity and its extensions will be introduced. The main theme of this study is to prove the equivalence between de Bruijn identity and Stein identity, in the sense that each identity can be derived from the other one. In a particular case, not only are de Bruijn and Stein identities equivalent, but they are also equivalent to the heat equation identity, which is another important result in statistics. The second major goal of this study is to extend de Bruijn identity in two distinctive ways. Given an additive non-Gaussian noise channel, the first-order derivative of differential entropy of the output signal is expressed as a function of the posterior mean, and the second-order derivative of differential entropy of the output signal is represented in terms of Fisher information. The third most important result is to introduce practical applications based on the results mentioned above. First, two fundamental bounds-the Bayesian Cramér-Rao lower bound (BCRLB) and the Cramér-Rao lower bound (CRLB)-in statistical signal processing, and a novel lower bound, tighter than BCRLB, are presented. Second, Costa's entropy power inequality is proved in two distinctive ways. Finally, min-max optimal training sequences for channel estimation and synchronization in the presence of unknown noise distribution are designed.

Having multiple users gives rise to multi-user diversity which can be exploited to give good quality-of-service to each user in the network and also increase the overall capacity of the network. In a spectrum-sharing setting, the multi-user diversity can be exploited; however, this is different from the traditional multi-user case because of the interference power constraints imposed on the secondary users. In this work, we consider a multi-user underlay cognitive network, where multiple cognitive users concurrently share the spectrum with a primary network, and a single secondary user is selected for transmission. The channel is assumed to have independent but not identical Nakagami-m fading. Considering an interference power constraint and a maximum transmit power constraint on the secondary user, a power allocation policy is derived based on the peak interference power constraint. For this policy the secondary user transmitter (SU-Tx) requires the instantaneous channel state information (CSI) of the link between the SU-Tx and the primary user receiver (PU-Rx). The advantage of this scheme is that the interference constraint is never violated and there is no loss of performance of the primary network. The user is selected for transmission based on a greedy scheduling scheme where the user with the highest instantaneous signal-to-noise ratio is chosen for transmission. For this user scheduling scheme, we analyze the uplink performance of the multi-user underlay secondary network in terms of outage probability and symbol error rate (SER). Exact closed-form expressions for the outage performance, moment-generating-function and SER performance of a multi-user cognitive network are derived. These expressions are obtained for an independent but non-identical distributed (i.n.i.d) Nakagami-m fading channel which is a more generic fading model and can cover a variety of fading environments including Rayleigh fading. Numerical results based on Monte-Carlo simulations are presented to verify the derived results. It is shown that the SER reduces as the peak interference power constraint is relaxed. Furthermore, as the number of users increases the SER reduces. If the interference power constraint is relaxed the power allocated becomes constant depending on the peak transmit power and thus the SER also becomes constant.

With the advent of ultra high-throughput DNA sequencing technologies used in Next-Generation Sequencing (NGS) machines, we are facing a daunting new era in petabyte scale bioinformatics data. The enormous amounts of data produced by NGS machines lead to storage, scalability, and performance challenges. At the same time, cloud computing architectures are rapidly emerging as robust and economical solutions to high performance computing of all kinds. To date, these architectures have had limited impact on the sequence alignment problem, whereby sequence reads must be compared to a reference genome. In this research, we present a methodology for efficient transformation of one of the recently developed NGS alignment tools, SHRiMP, into the cloud environment based on the MapReduce programming model. Critical to the function and performance of our methodology is the implementation of several techniques and mechanisms for facilitating the task of porting the SHRiMP sequence alignment tool into the cloud. These techniques and mechanisms allow the "cloudified" SHRiMP to run as a black box within the MapReduce model, without the need for building new parallel algorithms or recoding this tool from scratch. The approach is based on the MapReduce parallel programming model, its open source implementation Hadoop, and its underlying distributed file system (HDFS). The deployment of the developed methodology utilizes the cloud infrastructure installed at Qatar University. Experimental results demonstrate that multiplexing large-scale SHRiMP sequence alignment jobs in parallel using the MapReduce framework dramatically improves the performance when the user utilizes the resources provided by the cloud. In conclusion, using cloud computing for NGS data analysis is a viable and efficient alternative to analyzing data on in-house compute clusters. The efficiency and flexibility of the cloud computing environments and the MapReduce programming model provide a powerful version of the SHRiMP sequence alignment tool with a considerable boost. Using this methodology, ordinary biologists can perform the computationally demanding sequence alignment tasks without the need to delve deep into server and database management, without the complexities and hassles of running jobs on grids and clusters, and without the need to modify the existing code in order to adapt it for parallel processing.

Background and Objectives: In this work, we explore the implementation of the binary consensus algorithm in wireless sensor networks. Binary consensus is used to allow a collection of distributed entities to reach consensus regarding the answer to a binary question. Existing work on the algorithm focuses on simulation of the algorithm under the assumption of a fully connected network topology and unlimited messaging capabilities. In this new work, we adapt the algorithm to function in wireless sensor networks where the topology might not be fully connected and the number of messages sent should be minimized in order to save power. Methods: We are deploying and testing our implementation in a hardware embedded systems, mainly IRIS Motes. Our implementation of the binary consensus algorithm is written in NesC and runs on the Tiny Operating System (TinyOS). The implementation was tested on 11 sensor nodes, with current plans to test it on far more. Results: To support our hardware implementation results, a simulation using the Tiny Operating System SIMulator (TOSSIM) was done. Our results in hardware implementation and simulation are consistent with the original binary consensus algorithm. Conclusion: In this work, we adapted the binary consensus algorithm for use in wireless sensor networks. Our implementation for a small number of IRIS motes shows correct results consistent with those of simulation. In future work we will evaluate the effectiveness of the algorithm when scaled to hundreds of sensor motes.

Water-related problems are increasingly recognized as one of the most immediate and serious environmental threats to mankind. In particular, all the GCC countries being located in an arid region, suffer from lack of natural freshwater resources. Groundwater is the major source of water for irrigation in these countries. The groundwater aquifers contain either fresh or brackish waters. In countries like Kuwait and Qatar the groundwater available is mostly brackish. Agricultural development has put great pressure on groundwater resources and resulted in varying degrees of depletion and contamination as the demand for water has been increasing due to population growth and economic development. Over-pumping of groundwater has compounded water quality degradation caused by salts and other pollutants. In addition, saltwater intrusion is caused by over-abstraction of coastal aquifers. Meanwhile, the GCC countries are also facing changes in climatic conditions, such as rainfall patterns which affect the water cycle and limit natural groundwater recharge. The states of Kuwait and Qatar share almost similar problems and adopt the same approach in the management of their water resources under a severe stress of absence of natural freshwater resources. In these countries, wastewater collection serves almost all the population and tertiary wastewater treatment has been the common practice. Treated wastewater reuse is considered with proper attention to sanitation, public health and environmental protection. This paper will present a detailed evaluation of groundwater recharge using tertiary-treated or advanced (reverse osmosis) -treated wastewater. Recent advances, challenges, and future arrangements are discussed. A case study in Kuwait and an ongoing study in Qatar will be presented which includes advanced wastewater treatment comprising ultrafiltration and reverse osmosis systems, followed by the artificial recharge of the treated water into a groundwater lens. A simulation model is developed based on hydrogeological studies in which the augmentation of groundwater resources would provide water storage, and prevent depletion and deterioration of the groundwater. Hence, long-term sustainable groundwater management could be achieved.

In a first ever joint venture initiative, Qatar Petroleum has joined forces with Total in an effort to improve acid stimulation programs. Acid stimulation in carbonates can greatly increase well productivity. Near-wellbore impairment or formation damage is typically analysed by a term called skin factor. It is this 'skin' that is removed during an acidizing operation in a well. Typically, reducing the skin factor by a factor of 5 can increase a well's productivity by up to 50 percent (Furui et al. 2003). Acid stimulations performed in Qatar on 23 offshore wells in 2008-2009, increased oil production by 100 percent while at the same time reducing the water cut by 10 percent. In this joint venture project conducted by researchers and engineers from Total and Qatar Petroleum, the study is divided into three phases which also includes knowledge transfer and training. Phase 1 consists of core-flooding under reservoir conditions using standard acid recipes on outcrop and field cores. In Phase 2, improved or novel acidizing systems will be tested using a dual core setup, allowing the study of acid diversion from high permeability zones to low permeability zones. The objective here is not only to improve acidizing efficiency but also to mitigate the water production from heavily watered-out zones. Modeling activities will be undertaken to design acid stimulation treatments using results from the laboratory experiments. Phase 3 involves knowledge sharing and training on mud cake removal treatments. Mud cake is the damage caused to the near-wellbore, i.e., the interface between the reservoir matrix and the well, during the drilling of open hole wells. The knowledge gained will be implemented in both onshore and offshore fields as part of acid stimulation field trials.

Background: The coral ecosystem in Qatar is very important as it provides a foundation habitat for many aquatic species. An extensive two-year field study was conducted to evaluate the effectiveness of pulse amplitude modulation (PAM) fluorometry in monitoring the health of sensitive ecosystems such as coral reefs along the coast of Qatar. The study demonstrated that PAM fluorometry can provide reliable and objective information on coral health in advance of visual signs of stress. The scope has now been expanded to include laboratory-based research. Objectives: The objectives of this research are: a) to establish a viable laboratory-based Qatari coral (Acropora sp.) culture system and b) to utilize laboratory-based imaging-PAM fluorometry to compile baseline data, and gain an understanding of environmental parameters that affect the health of the Qatari coral. Methods: Laboratory studies were initiated in December 2011; Acropora samples were collected from mother colonies in Umm Al-Arshan (north of Qatar); the 'nubbins' were cultured in pre-acclimatized laboratory aquaria. Imaging-PAM fluorometer was used to measure photosynthetic processes that were correlated to laboratory culture conditions. A wide range of water quality parameters have been measured, including: temperature, salinity, ammonia, nitrate, nitrite, phosphate, calcium and pH. Results: This research showed that it is possible to successfully culture Acropora coral; the initial colonies have grown to the point that several subsequent colonies have been produced to initiate laboratory assay development. The results of the imaging-PAM also show good correlation with the data obtained using the instrument used in the field. Conclusion: This study demonstrated for the first time the successful culture of Qatari Acropora in a laboratory setting in Qatar. The imaging-PAM fluorometer was also used to obtain detailed visual images of photosynthesis processes. Future studies include Acroproa eco-toxicological experiments to study contaminants that could affect the health of the corals around the Qatari coastal area.

In the present work, we reported the performance of the gas sensor based on polyaniline-polyvinyl alcohol (PANI-PVA) thin-film to develop a usable sensor. The PANI-PVA were doped with camphorsulphonic acid (CSA), naphtalenesulfonic acid (NSA), dodecyl benzene sulfonic acid (DBSA) and p-toluene sulfonic acid (PTSA). CSA doped PANI nanocomposite sensors were fabricated on glass substrates by dripping and their gas sensing characteristics for ammonia (NH₃) were investigated at room temperature. PANI was prepared by the dispersion polymerization method. An appropriate amount of PANI and acid were mixed in a mortar and pestle. The mixture was dissolved in 100 mL of water, stirring at room temperature for 3 hours. The blend solution was then used to cast films on glass slides. PANI-PVA blend films are characterized for surface as well as structural morphology SEM and XRD. The morphological analysis shows nanoparticle formation of different shapes depending upon the dopant types. The XRD pictures show some shorts of crystallinity in the blend films. The FTIR spectra show chemical crosslinking between the polymers. The thermal study reveals three steps of degradation of the polymer blends. The electrical properties studies are conducted by in-plane I-V characteristics, and four probe conductivity. We used our blend as an ammonia gas sensor. Among all four sensors the blend film doped with DBSA had good sensitivity and reversibility. This might be because of its enhanced surface morphology that facilitates good adsorption and desorption of ammonia gas on the surface and high conductivity. In this study, ammonia gas sensors based on PANI-PVA composite films were prepared by a solution casting method. The composite films have been characterized by XRD, FTIR and SEM measurements. The SEM images have shown that PANI-PVA film has a different morphology based on the types of doping acids. The film presents significant resistivity upon exposure to ammonia gas at room temperature. It was found that these sensors are sensitive, stable, fast in response and easy to regenerate at room temperature. The advantages of this composite sensor compared to the pure PANI sensor are its fast regeneration associated with improved mechanical properties and chemical stability.

Qatar is the world leader in fuel production from gas-to-liquid (GTL) technology and home of the largest GTL plant in the world (Pearl GTL, a joint development by Qatar Petroleum and Shell). In the GTL process natural gas is converted into liquid fuels and waxes. Fischer-Tropsch synthesis (FTS) is the key part of that process. FTS is a heterogeneously catalyzed reaction in which a mixture of CO and H₂ is converted into a wide range of hydrocarbon products. Advanced design and optimization of large scale FTS reactors requires a detailed knowledge of reaction chemistry. Kinetic models used for this application need to be robust, physically reasonable and fundamental. This study will present one such a model. Experiments were conducted in a 1-L slurry reactor over 25% Co/0.48% Re/Al₂O₃ catalyst. A broad range of operating conditions was achieved (i.e., temperatures of 478, 493 and 503 K, pressures 1.5 and 2.5 MPa, H2/CO feed ratio 1.4 and 2.1 and gas space velocities of 1.0-22.5 NL/g-cat/h). Rate laws for the kinetic model have been derived using the CO-insertion mechanism and chain-length-dependent 1-olefin desorption concept. The model accounts for the formation of n-paraffins and 1-olefins. CO hydrogenation and insertion of CO into the growing chain are considered to be rate determining, as well as the chain termination steps. Non-isothermal model parameters are estimated by minimization of a multi-response objective function. A global minimum is obtained with the hybrid genetic algorithm and a total of 696 experimental responses used in the estimation. Estimated model parameters are meaningful, considering physicochemical tests and statistical tests. They are also in a good agreement with previously reported values for activation energies. The model fit is in good agreement with experimental data and the mean absolute relative residual (MARR) was 24%. The model also provides a good prediction of CO and H₂ rates of consumption, with a MARR of 17.7 and 16.1%, respectively. The main advantage of the proposed model is its ability to explain and predict the main features of GTL product distribution in a physically meaningful and fundamental way over a wide range of industrially relevant process conditions.

With sponsorship from Qatar Science and Technology Park to support Qatar Airways' vision as a world leader in alternative fuels, our research team started work in this field in 2009 as part of a unique academia-industry collaboration model. The undergraduate student researchers are funded by Qatar National Research Fund and play a major role in this project, participating in all its experimental, computational, and theoretical phases. Phase I of this work covers the development of correlations between the Gas-to-Liquid (GTL) synthetic jet fuels' building blocks (paraffinic hydrocarbons) and their physical properties (i.e. density, viscosity, flash point, freezing point, heat content, etc.). The objective of this phase was to identify optimum fuel characteristics and to meet aviation industry standards (e.g. ASTM D1655 & D7566). In Phase II, the experimental data were analyzed using sophisticated statistical techniques (i.e. Artificial Neural Network) to accurately describe the (non-)linear trends for all properties. In Phase III, we investigated the role of aromatics in improving certain properties of GTL jet fuels, such as density and elastomer compatibility (which is essential for fuel tank sealing). Analogous to the investigations conducted in Phase I, visualization models were developed to identify the optimum GTL jet fuel composition formulated by normal-, iso-, cyclic-paraffins and mono-aromatics. Currently, we are working on Phase IV which involves expanding our model to include new additives and component families in order to optimize the blending strategy for Qatar's GTL products and to increase their market value. The success in this direction could provide cheaper and more environmentally friendly synthetic jet fuels derived from natural gas, compared to the current oil-derived Jet A-1 fuels. In addition to the technical results, our fuel characterization lab acts as a training ground for young and talented scientists in order to develop their technical and soft skills. Students get the opportunity to work in a professional environment with strict safety and quality regulations on par with industrial standards, to report scientific data and to draw conclusions from this information in order to make decisions on the next course of research activities.