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    An innovative workflow for selecting appraisal area in low permeability greenfield development under uncertainties

    , Article Journal of Petroleum Science and Engineering ; Volume 206 , 2021 ; 09204105 (ISSN) Motahhari, S. M ; Rafizadeh, M ; Pishvaie, M. R ; Ahmadi, M ; Sharif University of Technology
    Elsevier B.V  2021
    There are uncertainties in both inherent geological properties and IOR/EOR performance parameters of low permeability greenfield reservoirs. Therefore, efforts to reduce uncertainties in the appraisal phase are necessary for the development and production phases. An adequate selection of the appraisal area in the hydrocarbon field is an imperative factor since the results of the appraisal well drilling and IOR/EOR pilot tests will be utilized for the development of the entire field. The major challenge in selecting an appraisal area is the lack of an integrated and systematic approach. In this study, we present a novel systematic and quantitative approach consisting of a better... 

    Private shotgun and sequencing

    , Article 2019 IEEE International Symposium on Information Theory, ISIT 2019, 7 July 2019 through 12 July 2019 ; Volume 2019-July , 2019 , Pages 171-175 ; 21578095 (ISSN); 9781538692912 (ISBN) Gholami, A ; Maddah Ali, M. A ; Abolfazl Motahari, S ; Sharif University of Technology
    Institute of Electrical and Electronics Engineers Inc  2019
    Current techniques in sequencing a genome allow a service provider (e.g. a sequencing company) to have full access to the genome information, and thus the privacy of individuals regarding their lifetime secret is violated. In this paper, we introduce the problem of private DNA sequencing, where the goal is to keep the DNA sequence private to the sequencer. We propose an architecture, where the task of reading fragments of DNA and the task of DNA assembly are separated, the former is done at the sequencer(s), and the later is completed at a local trusted data collector. To satisfy the privacy constraint at the sequencer and reconstruction condition at the data collector, we create an... 

    Experimental and numerical investigations of radial flow compressor component losses

    , Article Journal of Mechanical Science and Technology ; Vol. 28, issue. 6 , 2014 , p. 2189-2196 Mojaddam, M ; Hajilouy-Benisi, A ; Abolfazl Moussavi-Torshizi, S ; Movahhedy, M.R ; Durali, M ; Sharif University of Technology
    This research numerically and experimentally investigates a small turbocharger radial flow compressor with a vane-less diffuser and volute. The geometry of the compressor is obtained via component scanning, through which a 3D model is prepared. The flow inside this model is numerically analyzed by using a Navier-Stokes solver with a shear-stress transport turbulence model. The characteristic curves of the compressor and the contributions of its components to total pressure drop are acquired by measuring the static and total pressures at different cross sections of the compressor. Numerical results are verified with the experimental test results. The model results exhibit good agreement with... 

    Inference of Recombination Rate in Iranian Population Genetics

    , M.Sc. Thesis Sharif University of Technology Ansari, Ehsan (Author) ; Motahari, Abolfazl (Supervisor)
    Population genetics studies the distribution and changes in allele frequencies under the influence of five main evolutionary processes: natural selection, genetic drift, mutation, gene flow, and recombination. Among these, the recombination process can influence a wide range of biological processes by rearranging genes, repairing DNA structure, and participating actively in cell division mechanisms. Recombination has the ability to create genetic diversity through gene rearrangement, which is the main reason for creating diversity and evolution in organisms. Models such as Hill-Robertson have proven the influential role of recombination in accelerating evolutionary mechanisms. Also,... 

    Genome-wide Association Studies: Controlling False Discovery Rate using Knockoffs

    , M.Sc. Thesis Sharif University of Technology Kafi, Mahdi (Author) ; Motahari, Abolfazl (Supervisor)
    In recent years, with the advancement of genetics technologies, many data from this field have been made available to researchers. Therefore, many analytical problems have been defined for these data. Genome-wide association studies, or GWAS for short, is one of these issues that deals with finding genetic positions affecting traits or diseases. Common approaches to this problem either examine genetic variants one by one or fail to consider the specific structure of genetic data. Also, both mentioned approaches do not provide a guarantee to control the rate of false positives. In this thesis, an attempt has been made to propose a method to solve the GWAS problem by using the new statistical... 

    Development of Software Based on Statistical Iterative Algorithm in the Reconstruction of Sparse View CT images

    , M.Sc. Thesis Sharif University of Technology Jamaati, Sayna (Author) ; Hosseini, Abolfazl (Supervisor)
    X-ray Computed Tomography (CT) is a widely used medical imaging technique that provides cross-sectional images by measuring the attenuation of X-rays in the body. However, the increasing use of CT has raised concerns about the potential long-term risks associated with radiation exposure. Various approaches have been proposed to reduce radiation dose, and one of the latest methods is sparse-view CT, which has gained popularity due to its lower challenges compared to other techniques. In sparse-view CT, data acquisition is restricted to specific angles, resulting in a significant reduction in radiation dose. However, this approach can introduce streak artifacts in the reconstructed images due... 

    Investigation of Ocular Tumor Dose Enhancement in Proton Therapy in the Presence of Nanoparticles of Different Materials

    , M.Sc. Thesis Sharif University of Technology Alamgir, Jafar (Author) ; Hosseini, Abolfazl (Supervisor) ; Salimi, Ehsan (Supervisor)
    In recent years, the effect of the presence of nanoparticles in the tumor in order to increase the benefit of the treatment in radiation therapy has been the focus of many researchers. Although for photon irradiation, a significant dose increase due to the presence of nanoparticles has been observed, in the case of proton irradiation, due to the different nature of the beam and the lower cross-section of protons with metals compared to photons, scattered and in some cases contradictory findings have been published in the articles, and more studies are needed in this field. Due to laboratory limitations, Monte Carlo simulation is an appropriate tool for simulating difficult real-world... 

    Estimation of Pressure Fluctuation Coefficient in Stilling Basins Using Computational Intelligent Models

    , M.Sc. Thesis Sharif University of Technology Mazandarani, Mahan (Author) ; Shamsai, Abolfazl (Supervisor)
    Hydraulic jump is a significant hydraulic phenomenon that occurs in stilling basins and causes energy dissipation of water flow. Due to the severe pressure fluctuations, cavitation, and fatigue damage to concrete materials, hydraulic jump can cause damage to the stilling basin and its related components. Therefore, studying pressure fluctuations is one of the essential topics in the safe design and operation of stilling basins. Due to the nonlinear relationship between the effective variables in the pressure fluctuation phenomenon, the use of computational intelligent models that can extract the relationship between the effective variables is necessary. In this study, laboratory data... 

    Deep Neural Networks: Tradeoff Between Compression and Communication Rates

    , M.Sc. Thesis Sharif University of Technology Najafiaghdam, Kossar (Author) ; Motahari, Abolfazl (Supervisor)
    In recent years, the use of Deep Neural Networks in solving various problems has grown considerably. Possessing a large number of parameters, these networks have the ability to reconstruct complex functions and relations from large amounts of data and have been able to achieve the best results in a wide range of problems. But using these models comes with its own problems. These networks typically require considerable resources in order to run. This makes it inefficient or impossible to use them in systems with limited processing capabilities, e.g mobile phones. The existing approaches, e.g. the deployment of the model on a powerful server and network compression, have their own drawbacks... 

    Fundamental Bounds for Clustering of Bernoulli Mixture Models

    , M.Sc. Thesis Sharif University of Technology Behjati, Amin (Author) ; Motahari, Abolfazl (Supervisor)
    A random vector with binary components that are independent of each other is referred to as a Bernoulli random vector. A Bernoulli Mixture Model (BMM) is a combination of a finite number of Bernoulli models, where each sample is generated randomly according to one of these models. The important challenge is to estimate the parameters of a Bernoulli Mixture Model or to cluster samples based on their source models. This problem has applications in bioinformatics, image recognition, text classification, social networks, and more. For example, in bioinformatics, it pertains to clustering ethnic groups based on genetic data. Many studies have introduced algorithms for solving this problem without... 

    Fully Automatic Segmentation of Pelvic CT Images based on Deep Learning Methods

    , M.Sc. Thesis Sharif University of Technology Ghaedi, Elnaz (Author) ; Hosseini, Abolfazl (Supervisor)
    The initial step in radiotherapy treatment planning involves delineating the clinical target volumes (CTVs) and organs at risk (OARs). However, manual contouring is a time-consuming, labor-intensive, and subjective process. This study explores the potential of utilizing convolutional neural networks (CNNs) as an automated segmentation tool and an alternative to manual delineation. 218 Computed Tomography (CT) scans were gathered from two local hospitals in Tehran and the Whole Abdominal Organ Dataset (WORD). We employed ResUNet, a variant of UNet, as well as the original UNet from the Medical Open Network for AI (MONAI), an open-source framework for Deep Learning (DL) in healthcare imaging,... 

    Out-of-Distribution Generalization In Pathology Whole Slide Images Classification

    , M.Sc. Thesis Sharif University of Technology Movasaghinia, Mohammad Hossein (Author) ; Motahari, Abolfazl (Supervisor)
    Histopathology images exhibit significant variations across different datasets, which can pose challenges for machine learning models. When a model is trained on a certain domain, its performance may severely deteriorate when applied to unseen domains, magnifying the necessity of out-of-distribution generalization. To address this problem, we present a novel framework that harnesses the strengths of self-supervised training to extract domain-invariant features, and combine it with domain transfer techniques. Our approach is built on the premise that pre-trained vision transformers, highly effective in natural image analysis, can be adapted for histopathology image analysis in a... 

    Modelling and Prediction of Water Quality of Dam Reservoir Using CE-QUAL-W2 Model and Machine Learning Methods (Case Study: Shahr Bijar Dam)

    , M.Sc. Thesis Sharif University of Technology Ebrahimi, Abolfazl (Author) ; Shamsai, Abolfazl (Supervisor) ; Ghaemian, Mohsen (Supervisor)
    Dams are constructed for various purposes, including flood control, drinking water supply, agriculture irrigation, electricity generation, etc. The dam converts the river's natural and dynamic flow into a stagnant artificial lake where humans control the outflow of water, reducing the velocity of water movement and increasing residence time, followed by the entry and accumulation of nutrients in the reservoir, resulting in phenomena such as thermal stratification and eutrophication. Due to the changing climatic conditions of Iran and sudden droughts and wet periods, the need to monitor the water level of dam reservoirs and their water quality to keep them in the best operational condition... 

    Over-parameterized Neural Networks: Convergence Analysis and Generalization Bounds

    , M.Sc. Thesis Sharif University of Technology Tinati, Mohammad (Author) ; Maddah Ali, Mohammad Ali (Supervisor) ; Motahari, Abolfazl (Supervisor)
    Despite its extraordinary empirical achievements, the theoretical foundation of modern Machine Learning, and in particular deep neural networks (DNN), is still a mystery. In this thesis, we have studied the effect of optimization algorithms on the generalization properties for shallow neural networks. Particularly, we have focused on the implicit biases these optimization procedures, specifically dropout, deal with. As an example for this implicit bias, classical results had shown that for linear regression, in the interpolation regime, gradient descent, among all the possible solutions, converges to the minimum L2-norm interpolation. Due to the complex nature of the neural networks... 

    Investigating Sediment Transport in Deep Reservoirs Using the 3D Numerical Model MIKE3

    , M.Sc. Thesis Sharif University of Technology Eydpour, Danial (Author) ; Shamsai, Abolfazl (Supervisor) ; Ghaemial, Mohsen (Supervisor)
    Nowadays, in the world and our country, Iran, sediment transport and the phenomenon of sedimentation are of great importance. The entry of water flow with suspended sediments into the dam reservoir by floods with different return periods during the initial period of dam construction causes a considerable part of the helpful volume of the reservoir to be filled with sediments. As a result, the discharge of these sediments and the methods that exist in this field should be studied so that a large volume of reservoir water is not wasted during the discharge of these sediments. In this research, the opening time and extent of the bottom outlets of Qeshlaq Dam when the flood enters the reservoir... 

    Discrimination of Neutron and Gamma Spectrum Using a Method Based on Digital Filters

    , M.Sc. Thesis Sharif University of Technology Valipour, Mahdi (Author) ; Hosseini, Abolfazl (Supervisor)
    Since the beginning of discussion on neutron-gamma discrimination, different methods were suggested for this purpose. In early 2000s, Analogue methods were mainly used to discriminate the spectrum in mixed fields which mostly depends on electronic modules used in the lab setup; Nowadays, with improvements in digital pulse processing techniques, analyzing mixed neutron-gamma fields for pulse discrimination is also available.In this research, the recent MC developed toolkit called GEANT4 is used to simulate the 3-inch BC501A scintillation detector and 500 mCi Am-Be source (that emits neutron and gamma at the same time). For the next step, an experiment with same source and detector was done... 

    Presentation and Modeling of a New Nanocomposite Shield Against Gamma Radiation Based on Simulation and Computational Tools

    , M.Sc. Thesis Sharif University of Technology Arvaneh, Ali (Author) ; Hosseini, Abolfazl (Supervisor)
    In this study, using the MCNPX computer code based on the Monte Carlo method, the properties of protection against gamma-rays of the glass system with the combination of (55-x)Bi2O3-15Pb3O4-20Al2O3-10ZnO-xTiO2 with certain concentrations (x= 0, 5, 10, 15, 20, 25, 30 and 35 mol percent) and in nano and micro dimensions by calculating several parameters related to photon attenuation such as half value layer (HVL), Tenth value layer (TVL), mean free range (MFP), mass attenuation coefficient (m), linear attenuation coefficient (l), effective atomic number (Zeff) and buildup factor (BF) for We investigated different energy levels in the range of 1500-100 keV. To verify the simulation results,... 

    Dose Reduction Via Development of a Novel Image Reconstruction Method for Few-View Computed Tomography

    , Ph.D. Dissertation Sharif University of Technology Khodajou Chokami, Hamid Reza (Author) ; Hosseini, Abolfazl (Supervisor) ; Ay, Mohammad Reza (Supervisor)
    Sparse-view computed tomography (CT) is recently proposed as a promising method to speed up data acquisition and alleviate the issue of CT high-dose delivery to patients. However, traditional reconstruction algorithms are time-consuming and suffer from image degradation when faced with sparse-view data. To address this problem, we propose two new frameworks based on deep learning (DL) that can quickly produce high-quality CT images from sparsely sampled projections and is able for clinical use. Our first DL-based proposed model is based on the convolution, and residual neural networks in a parallel manner, named the parallel residual neural network (PARS-Net). Besides, our proposed PARS-Net... 

    Inferring the Demographic History of Iranian Populations from Whole Exome Sequencing Data

    , M.Sc. Thesis Sharif University of Technology Heidari, Jalal (Author) ; Motahari, Abolfazl (Supervisor) ; Khalaj, Babak (Supervisor)
    One of the cornerstones of population genetics is finding ancestral relations between people of a population. Mutations and recombinations are two major signals that can be exploited to infer the population ancestral relationships. Next Generation Sequencing (NGS) has paved the way for achieving this endeavor by providing massive data including single nucleotide polymorphisms, insertions, and deletions as well as structural variations between individuals. In this thesis, the goal is to infer the ancestral structure of a given gene from thousands of NGS datasets. We also attempt to decode times of important events including mutations and recombinations. To this end, we first split a gene into... 

    Inferring Relation between World and Iranian Populations from Microarray Data

    , M.Sc. Thesis Sharif University of Technology Saberi, Sasan (Author) ; Hossein Khalaj, Babak (Supervisor) ; Motahhari, Abolfazl (Supervisor)
    One of the branches of genetic studies is population genetics. Each population has its own characteristics due to its evolutionary history, cultural characteristics and geography, which distinguish it from other populations. Scientific and technological advances in recent decades have led to the production of new generation sequencing machines and the creation of large genetic data. These data contain important genetic information and answers to many questions about the origin of humans, the history of populations and their evolutionary process. More and better understanding of the human genome and the distance between populations can help to better understand biological mechanisms and deal...