Sharif Digital Repository

In this study, a numerical finite difference model of self-boring pressuremeter test (SBPM) is performed. Limit pressure is believed to be a key parameter for estimation of soil parameters from pressuremeter tests, however; self-boring pressuremeter tests are practically conducted up to 10-15% strains, so determination of limit pressure usually needs extrapolation. For an alternative solution, it is recommended to consider cavity pressure corresponding to 10% strain (P10) for interpretation of soil parameters instead of limit pressure; therefore, more than 5000 numerical analyses of SBPM are carried out to correlate cavity pressure corresponding to 10% strain (P10) to sand parameters with... 

The paper presents a numerical finite difference model of self-boring pressuremeter test (SBPM) using FLAC software. Different cavity expansion theories in sand have been compared to the results of numerical analyses carried out in this study. Limit pressure is believed to be used as a key parameter for the estimation of soil parameters from pressuremeter tests. In practice, SBPM tests are conducted up to 10–15% cavity strains, and the strain level associated with the limit pressure state is not reached. Therefore, determination of limit pressure usually needs extrapolation. In this paper, the authors suggest to use cavity pressure at 10% strain (P10) for the interpretation of in situ... 

Impure rhenium and molybdenum bearing solution was passed through a column of Varian strong base anionic resin for selective adsorption of rhenium and molybdenum ions from the solution. Elution of molybdenum and rhenium was carried out using 2 N NaOH and 0.2 N NH4SCN, respectively. The effect of resin bed height on breakthrough time was investigated. Experimental and theoretical curves were compared with good agreement. The results showed that the behavior pattern of these curves was repeatable and almost constant. © 2006 Elsevier B.V. All rights reserved  

Nowadays, content distribution is of high attention in peer to peer information systems. There are two main problems that could be mentioned in this context. The first problem is how to disseminate fragments of information efficiently and the next is to avoid missing same rare fragments towards end of download. For overcoming these problems, a new mechanism is presented in this paper which uses gossip algorithms on basis of social networks. Our mechanism maintains simplicity of gossip and has low overhead. This mechanism includes two phases for managing traffic and solving bottleneck problem: one for spreading rumors inside the social network and finding network of interests and the other... 

Using solid-state power amplifiers for next generation of weather radars becomes feasible by pulse compression techniques. In this study a 1.5 kW solid-state power amplifier (transmitter) for C-band weather radars is designed and fabricated by GaN high electron mobility transistor (HEMT) technology. An experimental setup based on heterodyne receiver with 16-bit digitiser is developed to investigate the behavior of the power amplifier under different cooling methods and back-off points. Several measurements with shaped LFM pulse show an approximately identical pulse to pulse (P2P) stability for 3 dB compression, P1dB and 2 dB back-off points while the best sidelobe level (SLL) is achieved for... 

A two-level Sparse Topical Coding (STC) topic model is proposed in this paper for analyzing video sequences of traffic surveillance containing hierarchical patterns accompanied by complicated motions and co-occurrences. In order to automatically cluster optical flow features into motion patterns, a first level STC model is used. Next, the second level STC model is applied for clustering motion patterns into traffic phases. The effectiveness of the suggested method is proved by experiments on a traffic dataset in the real world. Our simulations show that the proposed two-level STC is able to extract the motion patterns and traffic phases accurately, leading to realistic describing the traffic... 

Functional magnetic resonance imaging (fMRI) is an imaging tool that is used to analyze the brain’s functions. Brain functional connectivity analysis based on fMRI signals often calculated correlations among time series in different areas of the brain. For FC analysis most prior research works generate the brain graphs based on linear correlations, however, the nonlinear behavior of the brain can lower the accuracy of such graphs. Usually, the Pearson correlation coefficient is used which has limitations in revealing nonlinear relationships. One of the proper methods for nonlinear analysis is the Kernel trick. This method maps the data into a high dimensional space and calculates the linear... 

Alzheimer's Disease (AD) begins several years before the symptoms develop. It starts with Mild Cognitive Impairment (MCI) which can be separated into Early MCI and Late MCI (EMCI and LMCI). Functional connectivity analysis and classification are done among the different stages of illness with Functional Magnetic Resonance Imaging (fMRI). In this study, in addition to the four stages including healthy, EMCI, LMCI, and AD, the patients have been tracked for a year. Indeed, the classification has been done among 7 groups to analyze the functional connectivity changes in one year in different stages. After generating the functional connectivity graphs for eliminating the weak links, three... 

Purpose: Alzheimer's disease (AD) starts years before its signs and symptoms including the dementia become apparent. Diagnosis of the AD in the early stages is important to reduce the speed of brain decline. Aim of the study: Identifying the alterations in the functional connectivity of the brain during the disease stages is among the main important issues in this regard. Therefore, in this study, the changes in the functional connectivity during the AD stages were analyzed. Materials and methods: By employing the functional magnetic resonance imaging (fMRI) data and graph theory, weighted undirected graphs of the whole-brain and default mode network (DMN) network were investigated... 

Functional magnetic resonance imaging (fMRI) is a non-invasive method that helps to analyze brain function based on BOLD signal fluctuations. Functional Connectivity (FC) catches the transient relationship between various brain regions usually measured by correlation analysis. The elements of the correlation matrix are between -1 to 1. Some of them are very small values usually related to weak and spurious correlations due to noises and artifacts. They can not be concluded as real strong correlations between brain regions and their existence could make a misconception and leads to fake results. It is crucial to make a conclusion based on reliable and informative correlations. In order to... 

Functional magnetic resonance imaging (fMRI) is an imaging tool that is used to analyze the brain’s functions. Brain functional connectivity analysis based on fMRI signals often calculated correlations among time series in different areas of the brain. For FC analysis most prior research works generate the brain graphs based on linear correlations, however, the nonlinear behavior of the brain can lower the accuracy of such graphs. Usually, the Pearson correlation coefficient is used which has limitations in revealing nonlinear relationships. One of the proper methods for nonlinear analysis is the Kernel trick. This method maps the data into a high dimensional space and calculates the linear... 

Functional magnetic resonance imaging (fMRI) is a non-invasive method that helps to analyze brain function based on BOLD signal fluctuations. Functional Connectivity (FC) catches the transient relationship between various brain regions usually measured by correlation analysis. The elements of the correlation matrix are between -1 to 1. Some of them are very small values usually related to weak and spurious correlations due to noises and artifacts. They can not be concluded as real strong correlations between brain regions and their existence could make a misconception and leads to fake results. It is crucial to make a conclusion based on reliable and informative correlations. In order to... 

Functional magnetic resonance imaging (fMRI) is an imaging tool that is used to analyze the brain’s functions. Brain functional connectivity analysis based on fMRI signals often calculated correlations among time series in different areas of the brain. For FC analysis most prior research works generate the brain graphs based on linear correlations, however, the nonlinear behavior of the brain can lower the accuracy of such graphs. Usually, the Pearson correlation coefficient is used which has limitations in revealing nonlinear relationships. One of the proper methods for nonlinear analysis is the Kernel trick. This method maps the data into a high dimensional space and calculates the linear... 

Purpose: Alzheimer's disease (AD) starts years before its signs and symptoms including the dementia become apparent. Diagnosis of the AD in the early stages is important to reduce the speed of brain decline. Aim of the study: Identifying the alterations in the functional connectivity of the brain during the disease stages is among the main important issues in this regard. Therefore, in this study, the changes in the functional connectivity during the AD stages were analyzed. Materials and methods: By employing the functional magnetic resonance imaging (fMRI) data and graph theory, weighted undirected graphs of the whole-brain and default mode network (DMN) network were investigated... 

Background: Functional Magnetic Resonance Imaging (fMRI) is a non-invasive neuroimaging tool, used in brain function research and is also a low-frequency signal, showing brain activation by means of Oxygen consumption. Objective: One of the reliable methods in brain functional connectivity analysis is the correlation method. In correlation analysis, the relationship between two time-series has been investigated. In fMRI analysis, the Pearson correlation is used while there are other methods. This study aims to investigate the different correlation methods in functional connectivity analysis. Material and Methods: In this analytical research, based on fMRI signals of Alzheimer’s Disease (AD)... 

Laminated composite beams with non-uniform thickness are being used as primary structural elements in a wide range of advanced engineering applications. Tapered composite structures, formed by terminating some of the plies, create geometry and material discontinuities that act as sources for delamination initiation and propagation. Any small damage or delamination in these structures can progress rapidly without any visible external signs. Due to this reason early detection of damage in these systems during their service life is receiving increasing attention. The presence of a crack in a component or structure leads to changes in its global dynamic characteristics results in decreases in... 

This paper deals with the problem of vibration based health monitoring (VHM) in multi-stable asymmetrical laminated composite structures. Multi-stable asymmetrical laminates can be snapped between two or more geometries. The ability of snapping between two or more geometries in multistable composite structures makes them vulnerable to delaminate. Because of these reason early detection of damage in these systems during their service life is receiving increasing attention. In the present work, an efficient and accurate finite element model (FEM) based on layer-wise theory by considering the electro-mechanical coupling effect has been developed to investigate the actuation and /or sensing... 

The paper revisits the performance evaluation of caching in a Named Data Networking (NDN) router where the content store (CS) is supplemented by a pending interest table (PIT) which aggregates requests for a given content that arrive within the download delay. We extend prior work on caching with non-zero download delay by proposing a novel mathematical framework that is applicable to general traffic models and alternative cache insertion policies. Specifically we consider the impact of time locality in demand due to finite content lifetimes and we evaluate the use of an LRU filter to improve CS hit rate performance. The analysis is used to demonstrate that the impact of the PIT on upstream... 

While the cost of the access network could be considerably reduced by the use of caching, this is not currently happening because content providers (CPs), who alone have the detailed demand data required for optimal content placement, have no natural incentive to use them to minimize access network operator (ANO) expenditure. We argue that ANOs should therefore provide such an incentive in the form of direct subsidies paid to the CPs in proportion to the realized savings. We apply coalition game theory to design the required subsidy framework and propose a distributed algorithm, based on Lagrangian decomposition, allowing ANOs and CPs to collectively realize the optimal memory for bandwidth... 

The paper revisits the performance evaluation of caching in a Named Data Networking (NDN) router where the content store (CS) is supplemented by a pending interest table (PIT). The PIT aggregates requests for a given content that arrive within the download delay and thus brings an additional reduction in upstream bandwidth usage beyond that due to CS hits. We extend prior work on caching with non-zero download delay (non-ZDD) by proposing a novel mathematical framework that is more easily applicable to general traffic models and by considering alternative cache insertion policies. Specifically we evaluate the use of an LRU filter to improve CS hit rate performance in this non-ZDD context. We...