Recent Submissions

  • Experimental Investigation on The Influence of Liquid Fuels Composition on The Operational Characteristics of The Liquid Fueled Resonant Pulse Combustor

    Qatomah, Mohammad (2018-07)
    In this study, the response of a liquid-fueled resonant pulse combustor to changes in liquid fuel composition was investigated. Experiments were performed with gasoline- ethanol, gasoline-diesel, and gasoline-heptane mixtures selected to produce meaningful variations in the ignition delay time. A review of ignition quality tester (IQT) data provided an expected increase in the overall delay for gasoline-ethanol mixtures with increasing ethanol concentrations, and a decrease for gasoline-diesel mixtures with increasing diesel concentrations in the mixture. By taking the phase of the ion signal as an indicator of heat release timing, the experimental results showed an agreement of gasoline-ethanol cases with the IQT data with a near linear increase with increasing ethanol concentrations. However, for gasoline-diesel, there exit no linear relation with the IQT data. For the case of gasoline-heptane mixtures, the results showed a linear decrease in delay with increasing heptane concentrations. Furthermore, it was shown that small changes in the physical properties of the fuel can significantly in sequence the cold-start operation of the combustor and alter the coupling between the unsteady heat release and resonant acoustic pressure wave during resonant operation. Dynamic combustion chamber pressure, stagnation temperature and pressure are recorded after a fixed warm-up time to characterize the performance and operation of the device. Results are interpreted in the context of fuel sensitivity and performance optimization of a resonant pulse combustor for pressure gain turbine applications.
  • Investigation of the Effect of Operational Parameters on the Fouling Development and Control in an Algal Membrane Photobioreactor for the Treatment of Simulated Secondary Wastewater

    Lamprea Cala, Andres (2018-07)
    The release of water effluents rich in nutrients such as nitrogen and phosphorus without adequate treatment represents environmental and human health concerns. Growing concerns about these impacts have resulted in increasingly stringent water quality regulations that encouraged the adoption of advanced treatment processes. Microalgae-based advanced wastewater treatment has gained momentum owing to its well-known advantages for advanced wastewater treatment, including the recovering of nutrients for the production of fertilizers, biofuels and fine chemical from microalgal biomass. Nevertheless, the progressive membrane fouling and permeate flux declining hamper the large-scale commercialization of membrane photobioreactors (MPBRs) in the wastewater sector. In order to get a further understanding of the fouling mechanisms and antifouling control strategies, this study investigated the effect of the hydraulic retention time on the fouling development, and the effect of different physical fouling control strategies in the fouling mitigation. A synthetic secondary effluent was continuously fed to three MPBRs operated at different HRTs (12, 24 and 36 hours). Different fouling behaviors were found as the HRT changed, which was confirmed by continuously monitoring the transmembrane pressure (TMP) and by measurements in the biomass and its algal organic matter (AOM) properties. Lowering the HRT resulted in higher fouling rates due to changes in the biomass and AOM properties. Higher HRTs led to lower fouling rates and to a lower organic rejection across the membrane. The retention of small-MW organics in SMPBR12h was found to exacerbate the fouling resistance, whereas the accumulation of large-MW biopolymers enhanced the rejection of organics, despite of not imparting significant resistance in SMPBR24h. In order to assess the impact of different physical fouling control strategies, namely relaxation, backwash and air scouring, OCT in-situ monitoring was employed in MPBR12h to provide real-time information of the fouling layer properties (thickness and relative roughness) and its interaction with the membrane surface. Different fouling mechanisms were observed under different fouling control strategies. MPBRRLX and MPBRBW presented similar fouling rates despite of the lower permeate productivities of the latter. The lowest fouling rates were observed in MPBRSC, where stronger interactions between the membrane and small-MW organics and particles was observed.
  • Mechanism Design for Virtual Power Plant with Independent Distributed Generators

    Kulmukhanova, Alfiya (2018-07)
    We discuss a model of a virtual power plant (VPP) that provides market access to privately-owned distributed generations (DGs). The VPP serves passive loads, processes bids from generators, and trades in the wholesale market. The generators can be renewable or thermal, and they act strategically to maximize their own profit. The VPP establishes the rules of the internal market to minimize the cost of energy and the cost of balancing while ensuring generator participation and load balancing. We derive a heuristic mechanism for internal market and propose a dynamic programming approach for minimizing the VPP cost. We present illustrative simulations for both single and multistage market bidding and then compare the resulting performance to the centralized VPP model, where the DGs are assumed to be owned by the VPP. We show that the proposed design incentivizes the DG agents to behave the same as in the centralized case, but the optimal cost paid by VPP is higher due to the payments to the DG owners.
  • Indoor Localization Using Three dimensional Multi-PDs Receiver Based on RSS

    Liu, Yinghao (2018-07)
    In modern life, there are many applications where positioning plays an important role. People have developed the global positioning system (GPS) to locate world wide position with error in decameter scales, which brings people much convenience. However, the accuracy of GPS is too low for indoor localization. The signals will drop down due to the signal attenuation caused by construction materials. With the well-developed GPS being indispensable for outdoor activities, many researchers have been also devoted to seeking an indoor positioning system to realize indoor localization with acceptable error. Indoor localization can be very useful in different situations, like locating, tracking, navigation and identification. For example, in the mall, locating the exact goods for customers can provide much convenience and benefits. Locating and tracking in the airport can greatly help passengers save their time and energy in reaching the destination. In another general scenario of identification, the population of observed targets is usually larger than just one. Hence, only with small error, indoor localization system (ILS) can be able to identify the targets despite the neighbors. Due to the emerging and urging demands of increasing the accuracy of indoor localization, we propose a novel design of three dimensional (3-D). optical receiver for visible light communication (VLC) indoor positioning system. First, we model the optical wireless channel. Then we utilize modified triangulation method to obtain more robust receiver position by using at least two light-emitting diodes (LEDs) and one receiver consisting of nine photodetectors (PDs). Finally, the improved algorithm is implemented and the results are shown under our three dimensional multiple photodetectors (multi-PDs) structure receiver. In the simulation, we take the parameters of Lambertian radiation pattern, LEDs and PDs as those shown in [1] . To be noticed, our design of multi-PDs receiver is fully expanded into three dimensions compared with the pyramid receiver (PR), which allows indoor positioning with our receiver structure to be more robust to the higher or corner positions. The details will be explained in the following sections. Based on Multiple-Photodiodebased Indoor Positioning algorithm [1], the indoor positioning algorithm is improved by redefining the optimization problem of obtaining the direction from receiver to LED and using weighted triangulation method to locate receiver position. We admit the solution under the redefined problem is not optimal to the actual problem. Yet, our given solution is better to that in [1] due to the existence of noise, which is reasonable and has been verified.
  • Entropy Stability of Finite Difference Schemes for the Compressible Navier-Stokes Equations

    AlSayyari, Mohammed (2018-07)
    In this thesis, we study the entropy stability of the compressible Navier-Stokes model along with a modification of the model. We use the discretization of the inviscid terms with the Ismail-Roe entropy conservative flux. Then, we study entropy stability of the augmentation of viscous, heat and mass diffusion finite difference approximations to the entropy conservative flux. Additionally, we look at different choices of the diffusion coefficient that arise from combining the viscous, heat and mass diffusion terms. Lastly, we present numerical results of the discretizations comparing the effects of the viscous terms on the oscillations near the shock and show that they preserve entropy stability.
  • Towards macroscopic modeling of electro-thermo-mechanical couplings in PEDOT/PSS: Modeling of moisture absorption kinetics

    Zhanshayeva, Lyazzat (2018-07)
    Organic conducting polymer, poly(3,4-ethylene dioxythiophene)-poly(styrene sulfonate) (PEDOT:PSS), is widely recognized for its electro-actuation mechanism and is used in flexible electronics. Its high potential as actuator is based on a strong coupling between chemical, mechanical and electrical properties which directly depends on external stimuli. There is no model today to describe the interplay between moisture absorption, mechanical expansion and electrical stimulus. Elucidating the role of each component in the effective actuation properties is needed to further optimize and tailor such materials. The objective of this thesis is to develop a macroscopic model to describe water sorption kinetics of the PEDOT:PSS film. We used gravimetric analysis of pure PEDOT:PSS film of three different thicknesses to investigate absorption kinetics over a broad range of temperatures and relative humidity. Our results revealed that the moisture uptake of PEDOT:PSS film does not follow Fickian diffusion law due to the retained amount of water after desorption process. We used an existing diffusionreaction model to describe this behavior, and COMSOL Multiphysics and MATLAB software programs to implement it. We observed that the generic model we used in our work could predict polymer behavior with 95% accuracy. However, our model was not able to properly represent the data at very high relative humidity at low temperature, which was attributed to the excessive swelling of the film. Also, we examined a relation between the moisture content of PEDOT:PSS and its mechanical strain and electrical conductivity. The results presented here are the first step towards a general multiphysics electro-thermo-mechanical description of PEDOT:PSS based actuators.
  • Design of a Hydraulic Variable Compression Ratio Piston for a Heavy Duty Internal Combustion Engine

    Al Mudraa, Sultan (2018-07)
    A High percentage of fuel consumption worldwide is in internal combustion engines which has led environmental organizations and authorities to put further pressure on the engine industries to reduce CO2 emissions and enhance engine efficiency. However, historically, the effect of the compression ratio on increasing thermal efficiency of the engine is well known, hence; numerous technical solutions have been proposed to implement a variable compression ratio concept. A new first-class engineering solution to use a hydraulic piston was initially patented by BICERA (British Internal Combustion Engine Research Association) , then improved by Continental and Daimler Benz. A Hydraulic variable compression ratio piston is a hydraulically actuated piston that provides a practical method of obtaining a variable compression ratio piston. In this literature, a hydraulic variable compression ratio piston for a Volvo D13 diesel engine was designed, analyzed, modeled and discussed. This analysis was accomplished by first performing kinematic and dynamic analyses for the piston motion and acceleration based on the crank-slider mechanism. Following this the oil flow characteristics were defined in every mechanical element transferring the oil in its journey from the engine pump to the piston. Moreover, two different designs were proposed in an attempt to predict the compression ratio by modeling the hydraulic, dynamic and engine execution simultaneously. Additionally, stress on the piston was analyzed using Finite Element Analysis (FEA) to assure piston sustainment and rigidity against the harsh combustion chamber environment. In conclusion, the best design was successfully selected and finalized to reach a wide compression ratio range under a boosted inlet pressure based on the selected design, dimensions, check valves and relief valves.
  • Synthesis and Application of PN3P Cobalt Pincer Complex for Selective Hydrogenation of Nitriles to Secondary Imines and α -Alkylation of Nitriles with Alcohols

    Al Dakhil, Abdullah (2018-07)
    Pincer complexes moieties have attracted much attention in the past years. They have been proved that they are highly active catalysts in many different known transition metal-catalyzed organic reaction and some unpredictable organic transformation. In this thesis, we will use PN3P Cobalt pincer complex in two different applications. The first application is the unpresented Cobalt-catalyzed hydrogenation of nitriles to secondary imines. The selective hydrogenation of nitriles into secondary imines is a very challenging task and the catalysts play a very important role in the reaction and the selectivity. Herein in the thesis, we report the first selective hydrogenation of nitriles to secondary imines catalyzed by a well-defined and accessible PN3P cobalt pincer complex. Our results show different selectivity compared with the known PNP cobalt catalytic system during the nitriles hydrogenation. A set of aliphatic and aromatic nitriles are hydrogenated to the secondary imine under relatively mild conditions. The second application is the alkylation of nitriles with alcohols using PN3P cobalt pincer complex. The alcohol is being used here as alkylating agent in state of using toxic alkyl halides or excess amount of base to avoid any salt waste. The cobalt pincer complex work as catalyst for transformation that undergoes alkylation via hydrogen transfer pathways. The beauty of this reaction that it is delver water as the only byproduct. A different nitriles and alcohol are tolerated in this reaction.
  • Host-Guest Chemistry of Inorganic Porous Platforms

    Alsufyani, Maryam (2018-07)
    Complexes made by hosts that completely surround their guests provide a mean to stabilize reactive chemical intermediates, transfer biologically active cargo to a diseased cell, and construct molecular scale devices. By the virtue of inorganic host‐guest self‐assembly, the nucleation processes in the cavity of a {P8W48}‐archetype phosphotungstate has afforded a nanoscale 16‐GaIII‐32‐oxo cluster that contain the largest number of GaIII ions yet found in polyoxometalate chemistry. Catalytic activity via thus “Metal-Oxo Cluster within Cluster” Assembly has been preliminarily investigated. Besides, the hybrid aggregates composed of the inorganic {P8W48} and orgainc cyclic moiety has been studied.
  • Airborne Prokaryote and Virus abundance over the Red Sea

    Yahya, Razan (2018-07)
    Aeolian dust exerts a notable influence on atmospheric and oceanic conditions and human health, particularly in arid and semi-arid regions like Saudi Arabia. Dust is often characterized by its mineral and chemical composition, but there is a microbiological component of natural aerosols which has received comparatively little attention. Moreover, the amount of materials suspended in the atmosphere is highly variable from day to day. Thus, knowing the loads of dust and suspended microbes and its variability over the year is essential to understand the possible effects of dust on the Red Sea ecosystem. Here, we present the first estimates of dust and microbial loads at a coastal side on the Red Sea over a two-year period supplemented with information from dust samples collected along the Red Sea in offshore water and their variability. Weekly average dust loads ranged from 4.63 to 646.11 μg m-3, while the abundance of airborne prokaryotic cells and viral particles ranged from 31,457 to 608,333 cells m-3 and from 69,615.5 to 3,104,758 particles m-3, respectively. These are the first estimates of airborne microbial abundance that we are aware of in this region. The large number of dust particles and suspended microbes found in the air indicates that airborne microbes may have a large impact on our health and that of the Red Sea ecosystem.
  • Numerical Investigation of Shock Bubble Interaction using Wavelet Adaptive Multi-Resolution Method

    Dhopeshwar, Rahul (2018-07)
    When a shock interacts with a bubble having a different density than the environment or medium, the interaction causes compression and deformation of the bubble and generation of a vortex pair. Later, secondary vortices appear causing enhanced mixing. The enhanced mixing induced by the shock bubble interactions is particularly of interest in supersonic combustion and detonation. The Wavelet Adaptive Multi-resolution Representation (WAMR) method is particularly suitable for challenging continuum physics problems like shock bubble interaction, which has strong multi-scale character. This method provides an efficient strategy to create a dynamically adaptive spatial grid and to obtain a verified solution. Since the wavelet amplitude provides a first-hand estimate of the local error at each point, the method is able to efficiently capture a wide spectrum of spatial scales by dynamically changing the adaptive grid. Highly resolved computations are done only in the regions where abrupt transition occurs. In this work a detailed investigation of Shock Bubble Interaction (SBI) is carried out using shocks having Mach numbers from 1.2 to 3 for helium, nitrogen and krypton bubbles. Simulations carried out using WAMR method were used to analyze the effects of Mach number and density contrast on the shape, location and velocity of the bubble as well as vorticity and pressure in the flow field.
  • Fabrication and Characterization of Geometrically Confined Fe3Sn2 Skyrmion-based Devices

    GONG, CHEN (2018-06-27)
    Skyrmion is a topologically protected nanometer-sized spin configuration, which makes it a promising candidate for future memory devices. All skyrmion applications are based on the formation and manipulation of spin textures in nanostructured elements. Therefore, fabrication of geometrically confined skyrmion-based nanodevices is an essential step in the investigation of skyrmion properties. In this study, my research mainly focuses on the fabrication of high-quality Fe3Sn2 nanostripes with different geometric parameters for Lorentz transmission electron microscopy (LTEM) by a focused ion beam (FIB) system. The observation of the skyrmions using LTEM was mainly performed by Dr. Qiang Zhang, although I have deeply involved the discussion on new samples to be fabricated based on the results obtained from LTEM and also performed some LTEM experiments. To investigate the formation process and thermal stability of skyrmions in a geometrically confined environment, I have fabricated more than fifty high-quality nanostripes with a width of 265-4,000 nm. Studying with LTEM, a distinct evolutionary path of stripe-skyrmion transformation is observed after gradually increasing the magnetic field (out-of-plane direction) and the critical magnetic field of skyrmion is found to decrease with an increasing strength of confinements. Moreover, a series of racetrack devices with controlled thicknesses (125-404 nm) is fabricated to study the effect of thickness in skyrmion formation. Overall, in order to obtain less damaged, flat skyrmion-based devices by FIB system, experimental parameters are optimized and fabrication skills are improved. This method develops the possible application of centrosymmetric frustrated magnet Fe3Sn2 in skyrmion-based racetrack devices.
  • Monitoring the effects of offshore aquaculture on water quality in the Red Sea

    Dunne, Aislinn (2018-06)
    The Saudi Arabian government has announced an economic development plan (Vision 2030) to invest in a range of industries across the Kingdom, one of which is the development of aquaculture. In the face of a likely increase in Red Sea fish farming, we investigated the impacts of offshore fish farms on the coastal water quality of the Red Sea by a) measuring the environmental impacts of an operational Red Sea fish farm, and b) testing whether an existing aquaculture modeling software can be used as a meaningful planning tool in the development of Red Sea aquaculture. Water quality parameters such as dissolved oxygen, nutrients, particulate matter, chlorophyll, ammonium, and bacterial abundance were measured seasonally over the course of a year around an offshore fish farm along the south-central coast of Saudi Arabia to determine the impacts of fish farm effluent on the surrounding waters. Bacteria, phosphate, inorganic nitrogen, and suspended particulate matter showed patterns of enrichment close to the fish farm. Additionally, dissolved oxygen has slightly lower concentrations close to and down current from the fish farms. Benthic sediments from a nearby coral reef were also assessed for organic enrichment, but concentrations of total organic carbon and total nitrogen were not significantly different from those at an offshore reef. The data from these sampling efforts were then used as input parameters for an aquaculture modeling software (, however many of the input parameters required to run the model were unavailable and meaningful conclusions could not be drawn from the results. Through field studies and modeling, we assessed the current impact of a Red Sea fish farm on water quality with the goal of predicting the potential impacts of future offshore aquaculture development in Saudi Arabia.
  • A Study of Recurrent and Convolutional Neural Networks in the Native Language Identification Task

    Werfelmann, Robert (2018-05-24)
    Native Language Identification (NLI) is the task of predicting the native language of an author from their text written in a second language. The idea is to find writing habits that transfer from an author’s native language to their second language. Many approaches to this task have been studied, from simple word frequency analysis, to analyzing grammatical and spelling mistakes to find patterns and traits that are common between different authors of the same native language. This can be a very complex task, depending on the native language and the proficiency of the author’s second language. The most common approach that has seen very good results is based on the usage of n-gram features of words and characters. In this thesis, we attempt to extract lexical, grammatical, and semantic features from the sentences of non-native English essays using neural networks. The training and testing data was obtained from a large corpus of publicly available essays written by authors of several countries around the world. The neural network models consisted of Long Short-Term Memory and Convolutional networks using the sentences of each document as the input. Additional statistical features were generated from the text to complement the predictions of the neural networks, which were then used as feature inputs to a Support Vector Machine, making the final prediction. Results show that Long Short-Term Memory neural network can improve performance over a naive bag of words approach, but with a much smaller feature set. With more fine-tuning of neural network hyperparameters, these results will likely improve significantly.
  • Neural Inductive Matrix Completion for Predicting Disease-Gene Associations

    Hou, Siqing (2018-05-21)
    In silico prioritization of undiscovered associations can help find causal genes of newly discovered diseases. Some existing methods are based on known associations, and side information of diseases and genes. We exploit the possibility of using a neural network model, Neural inductive matrix completion (NIMC), in disease-gene prediction. Comparing to the state-of-the-art inductive matrix completion method, using neural networks allows us to learn latent features from non-linear functions of input features. Previous methods use disease features only from mining text. Comparing to text mining, disease ontology is a more informative way of discovering correlation of dis- eases, from which we can calculate the similarities between diseases and help increase the performance of predicting disease-gene associations. We compare the proposed method with other state-of-the-art methods for pre- dicting associated genes for diseases from the Online Mendelian Inheritance in Man (OMIM) database. Results show that both new features and the proposed NIMC model can improve the chance of recovering an unknown associated gene in the top 100 predicted genes. Best results are obtained by using both the new features and the new model. Results also show the proposed method does better in predicting associated genes for newly discovered diseases.
  • Large-scale Comparative Study of Hi-C-based Chromatin 3D Structure Modeling Methods

    Wang, Cheng (2018-05-17)
    Chromatin is a complex polymer molecule in eukaryotic cells, primarily consisting of DNA and histones. Many works have shown that the 3D folding of chromatin structure plays an important role in DNA expression. The recently proposed Chro- mosome Conformation Capture technologies, especially the Hi-C assays, provide us an opportunity to study how the 3D structures of the chromatin are organized. Based on the data from Hi-C experiments, many chromatin 3D structure modeling methods have been proposed. However, there is limited ground truth to validate these methods and no robust chromatin structure alignment algorithms to evaluate the performance of these methods. In our work, we first made a thorough literature review of 25 publicly available population Hi-C-based chromatin 3D structure modeling methods. Furthermore, to evaluate and to compare the performance of these methods, we proposed a novel data simulation method, which combined the population Hi-C data and single-cell Hi-C data without ad hoc parameters. Also, we designed a global and a local alignment algorithms to measure the similarity between the templates and the chromatin struc- tures predicted by different modeling methods. Finally, the results from large-scale comparative tests indicated that our alignment algorithms significantly outperform the algorithms in literature.
  • Efficiency-limiting processes in OPV bulk heterojunctions of GeNIDTBT and IDT-based acceptors

    Al-Saggaf, Sarah M. (2018-05-16)
    The successful realization of highly efficient bulk heterojunction OPV devices requires the development of organic donor and acceptor materials with tailored properties. Recently, non-fullerene acceptors (NFAs) have emerged as an alternative to the ubiquitously used fullerene derivatives. NFAs showed a rapid increase in efficiencies, now exceeding a PCE of 13%. In my thesis research, I used two small molecule IDT-based acceptors, namely O-IDTBR and O-IDTBCN, in combination with a wide bandgap donor polymer, GeNIDT-BT, as active material in BHJ solar cells and investigated their photophysical characteristics. The polymer combined with O-IDTBR as acceptor achieved a power conversion efficiency of only 2%, which is significantly lower than that obtained for the system of GeNIDT-BT: O-IDTBCN (5.3%). Using nano- to microsecond transient absorption spectroscopy, I investigated both systems and demonstrated that GeNIDT-BT:O-IDTBR exhibits more geminate recombination of interfacial charge-transfer states, leading to lower short circuit currents. Using time-delayed collection field experiments, I studied the field dependence of charge generation and its impact on the device fill factor. Overall, my results provide a qualitative understanding of the efficiency-limiting processes in both systems and their impact on device performance.
  • Enhancing Network Data Obliviousness in Trusted Execution Environment-based Stream Processing Systems

    Alsibyani, Hassan M. (2018-05-15)
    Cloud computing usage is increasing and a common concern is the privacy and security of the data and computation. Third party cloud environments are not considered fit for processing private information because the data will be revealed to the cloud provider. However, Trusted Execution Environments (TEEs), such as Intel SGX, provide a way for applications to run privately and securely on untrusted platforms. Nonetheless, using a TEE by itself for stream processing systems is not sufficient since network communication patterns may leak properties of the data under processing. This work addresses leaky topology structures and suggests mitigation techniques for each of these. We create specific metrics to evaluate leaks occurring from the network patterns; the metrics measure information leaked when the stream processing system is running. We consider routing techniques for inter-stage communication in a streaming application to mitigate this data leakage. We consider a dynamic policy to change the mitigation technique depending on how much information is currently leaking. Additionally, we consider techniques to hide irregularities resulting from a filtering stage in a topology. We also consider leakages resulting from applications containing cycles. For each of the techniques, we explore their effectiveness in terms of the advantage they provide in overcoming the network leakage. The techniques are tested partly using simulations and some were implemented in a prototype SGX-based stream processing system.
  • Local Likelihood Approach for High-Dimensional Peaks-Over-Threshold Inference

    Baki, Zhuldyzay (2018-05-14)
    Global warming is affecting the Earth climate year by year, the biggest difference being observable in increasing temperatures in the World Ocean. Following the long- term global ocean warming trend, average sea surface temperatures across the global tropics and subtropics have increased by 0.4–1◦C in the last 40 years. These rates become even higher in semi-enclosed southern seas, such as the Red Sea, threaten- ing the survival of thermal-sensitive species. As average sea surface temperatures are projected to continue to rise, careful study of future developments of extreme temper- atures is paramount for the sustainability of marine ecosystem and biodiversity. In this thesis, we use Extreme-Value Theory to study sea surface temperature extremes from a gridded dataset comprising 16703 locations over the Red Sea. The data were provided by Operational SST and Sea Ice Analysis (OSTIA), a satellite-based data system designed for numerical weather prediction. After pre-processing the data to account for seasonality and global trends, we analyze the marginal distribution of ex- tremes, defined as observations exceeding a high spatially varying threshold, using the Generalized Pareto distribution. This model allows us to extrapolate beyond the ob- served data to compute the 100-year return levels over the entire Red Sea, confirming the increasing trend of extreme temperatures. To understand the dynamics govern- ing the dependence of extreme temperatures in the Red Sea, we propose a flexible local approach based on R-Pareto processes, which extend the univariate Generalized Pareto distribution to the spatial setting. Assuming that the sea surface temperature varies smoothly over space, we perform inference based on the gradient score method over small regional neighborhoods, in which the data are assumed to be stationary in space. This approach allows us to capture spatial non-stationarity, and to reduce the overall computational cost by taking advantage of distributed computing resources. Our results reveal an interesting extremal spatial dependence structure: in particular, from our estimated model, we conclude that significant extremal dependence prevails for distances up to about 2500 km, which roughly corresponds to the Red Sea length.
  • Ontology Design Patterns for Combining Pathology and Anatomy: Application to Study Aging and Longevity in Inbred Mouse Strains

    Alghamdi, Sarah M. (2018-05-13)
    In biomedical research, ontologies are widely used to represent knowledge as well as to annotate datasets. Many of the existing ontologies cover a single type of phenomena, such as a process, cell type, gene, pathological entity or anatomical structure. Consequently, there is a requirement to use multiple ontologies to fully characterize the observations in the datasets. Although this allows precise annotation of different aspects of a given dataset, it limits our ability to use the ontologies in data analysis, as the ontologies are usually disconnected and their combinations cannot be exploited. Motivated by this, here we present novel ontology design methods for combining pathology and anatomy concepts. To this end, we use a dataset of mouse models which has been characterized through two ontologies: one of them is the mouse pathology ontology (MPATH) covering pathological lesions while the other is the mouse anatomy ontology (MA) covering the anatomical site of the lesions. We propose four novel ontology design patterns for combining these ontologies, and use these patterns to generate four ontologies in a data-driven way. To evaluate the generated ontologies, we utilize these in ontology-based data analysis, including ontology enrichment analysis and computation of semantic similarity. We demonstrate that there are significant differences between the four ontologies in different analysis approaches. In addition, when using semantic similarity to confirm the hypothesis that genetically identical mice should develop more similar diseases, the generated combined ontologies lead to significantly better analysis results compared to using each ontology individually. Our results reveal that using ontology design patterns to combine different facets characterizing a dataset can improve established analysis methods.

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