Determining The Minimum Distance Between Centers of Two Parallel Tunnels to Apply The Law of Super Position in Order to Calculate Subsidence by Using The Software FLAC 3D

Due to the development of cities as well as rapid population growth, urban traffic is increasing nowadays. Hence, to improve traffic flow, underground structures such as metro, especially in metropolises, are inevitable. This paper is a research on the twin tunnels Of Isfahan's metro between Shariaty station and Azadi station from the North towards the South. In this study, simultaneous drilling of subway's twin tunnels is simulated by means of Finite Difference Method (FDM) and FLAC 3D software. Moreover, the lowest distance between two tunnels is determined in a way that the Law of Super Position could be utilized to manually calculate the amount of surface subsidence, resulted by drilling two tunnels, by employing the results of the analysis of single tunnels without using simultaneous examination and simulation. In this paper, this distance is called "effective distance". For this purpose, first, the optimum dimensions of the model is chosen and then, five models with optimum dimensions will be analyzed separately, each of which in three steps. The results of analyses shows that the proportions (L/D) greater than or equal 2.80, the Law of Super Position can be applied for prediction of surface subsidence, caused by twin tunnels' constructio

Management of Ocular Graft-Versus-Host Disease: A Brief Review

Graft-versus-host disease (GVHD) remains a major complication following hematopoietic stem cell transplantation (HSCT). Ocular GVHD develops in a substantial number of patients following HSCT and 60 % to 90 % of patients with systemic GVHD experience the ocular complications to some extents. In this brief review we will discuss the conventional and updated novel therapies in the management of patients suffering from ocular GVHD.Keywords: Eye; Dry Eye; Graft versus Host Disease; Treatment

Master of Science

thesisThis research studies the passive dynamics of an under-actuated trotting quadruped. The goal of this project is to perform three-dimensional (3D) dynamic simulations of a trotting quadruped robot to find proper leg configurations and stiffness range, in order to achieve stable trotting gait. First, a 3D simulation framework that includes all the six degrees of freedom of the body is introduced. Directionally compliant legs together with different leg configurations are employed to achieve passive stability. Compliant legs passively support the body during stance phase and during flight phase a motor is used to retract the legs. Leg configurations in the robot's sagittal and frontal plane are introduced. Numerical experiments are conducted to search the design space of the leg, focusing on increasing the passive stability of the robot. Increased stability is defined as decreased pitching, rolling, and yawing motion of the robot. The results indicate that optimized leg parameters can guarantee passive stable trotting with reduced roll, pitch, and yaw. Studies suggest that a quadruped robot with compliant legs is dynamically stable while trotting. Results indicate that the robot based on a biological model (i.e., caudal inclination of humeri and cranial inclination of femora) has the best performance. Stiff springs at hips and shoulders, soft spring at knees and elbows, and stiff springs at ankles and wrists are recommended. The results of this project provide a conceptual framework for understanding the movements of a trotting quadruped

Multiple feature-enhanced SAR imaging using sparsity in combined dictionaries

Nonquadratic regularization-based image formation is a recently proposed framework for feature-enhanced radar imaging. Specific image formation techniques in this framework have so far focused on enhancing one type of feature, such as strong point scatterers, or smooth regions. However, many scenes contain a number of such feature types. We develop an image formation technique that simultaneously enhances multiple types of features by posing the problem as one of sparse representation based on combined dictionaries. This method is developed based on the sparse representation of the magnitude of the scattered complex-valued field, composed of appropriate dictionaries associated with different types of features. The multiple feature-enhanced reconstructed image is then obtained through a joint optimization problem over the combined representation of the magnitude and the phase of the underlying field reflectivities

Sparse signal representation for complex-valued imaging

We propose a sparse signal representation-based method for complex-valued imaging. Many coherent imaging systems such as synthetic aperture radar (SAR) have an inherent random phase, complex-valued nature. On the other hand sparse signal representation, which has mostly been exploited in real-valued problems, has many capabilities such as superresolution and feature enhancement for various reconstruction and recognition tasks. For complex-valued problems, the key challenge is how to choose the dictionary and the representation scheme for effective sparse representation. We propose a mathematical framework and an associated optimization algorithm for a sparse signal representation-based imaging method that can deal with these issues. Simulation results show that this method offers improved results compared to existing powerful imaging techniques

Multiple feature-enhanced synthetic aperture radar imaging

Non-quadratic regularization based image formation is a recently proposed framework for feature-enhanced radar imaging. Specific image formation techniques in this framework have so far focused on enhancing one type of feature, such as strong point scatterers, or smooth regions. However, many scenes contain a number of such features. We develop an image formation technique that simultaneously enhances multiple types of features by posing the problem as one of sparse signal representation based on overcomplete dictionaries. Due to the complex-valued nature of the reflectivities in SAR, our new approach is designed to sparsely represent the magnitude of the complex-valued scattered field in terms of multiple features, which turns the image reconstruction problem into a joint optimization problem over the representation of the magnitude and the phase of the underlying field reflectivities. We formulate the mathematical framework needed for this method and propose an iterative solution for the corresponding joint optimization problem. We demonstrate the effectiveness of this approach on various SAR images

Sparse representation-based SAR imaging

There is increasing interest in using synthetic aperture radar (SAR) images in automated target recognition and decision-making tasks. The success of such tasks depends on how well the reconstructed SAR images exhibit certain features of the underlying scene. Based on the observation that typical underlying scenes usually exhibit sparsity in terms of such features, we develop an image formation method which formulates the SAR imaging problem as a sparse signal representation problem. Sparse signal representation, which has mostly been exploited in real-valued problems, has many capabilities such as superresolution and feature enhancement for various reconstruction and recognition tasks. However, for problems of complex-valued nature, such as SAR, a key challenge is how to choose the dictionary and the representation scheme for effective sparse representation. Since we are usually interested in features of the magnitude of the SAR reflectivity field, our new approach is designed to sparsely represent the magnitude of the complex-valued scattered field. This turns the image reconstruction problem into a joint optimization problem over the representation of magnitude and phase of the underlying field reflectivities. We develop the mathematical framework for this method and propose an iterative solution for the corresponding joint optimization problem. Our experimental results demonstrate the superiority of this method over previous approaches in terms of both producing high quality SAR images as well as exhibiting robustness to uncertain or limited data

Sparse representation-based synthetic aperture radar imaging

There is increasing interest in using synthetic aperture radar (SAR) images in automated target recognition and decision-making tasks. The success of such tasks depends on how well the reconstructed SAR images exhibit certain features of the underlying scene. Based on the observation that typical underlying scenes usually exhibit sparsity in terms of such features, we develop an image formation method which formulates the SAR imaging problem as a sparse signal representation problem. Sparse signal representation, which has mostly been exploited in real-valued problems, has many capabilities such as superresolution and feature enhancement for various reconstruction and recognition tasks. However, for problems of complex-valued nature, such as SAR, a key challenge is how to choose the dictionary and the representation scheme for effective sparse representation. Since we are usually interested in features of the magnitude of the SAR reflectivity field, our new approach is designed to sparsely represent the magnitude of the complex-valued scattered field. This turns the image reconstruction problem into a joint optimization problem over the representation of magnitude and phase of the underlying field reflectivities. We develop the mathematical framework for this method and propose an iterative solution for the corresponding joint optimization problem. Our experimental results demonstrate the superiority of this method over previous approaches in terms of both producing high quality SAR images as well as exhibiting robustness to uncertain or limited data

A study on relationship between tail risk on earning management in Iranian banking industry

Risk management plays an important role in banking industry and there are literally many investigations to reduce any risk components in this industry. In this paper, we present a study on relationship between tail risk on earning management in Iranian banking industry. In this survey, we use two series of data. The first set is associated with yearly information of 19 different banks over the period 2005-2011 and it contains 114 observations. The second set of data includes weekly historical data of eight banks over the same period 2005-2011. In this survey, there are four objectives to be investigated. The first hypothesis considers the effects of seven independent variables on loan loss allowance as a fraction of total loans. The second model is associated with the effects of two independent variables on realized gains and losses on securities. The third objective is to study the effects of different independent variables with various interruptions on return of banking sectors. Finally, the last model investigates the effects of revenue management on tail risk. The result of this survey indicates that there is no relationship between tail risk and earning management