Sharif Digital Repository

According to the increasing amount of data exchanged among data centers, the need for speeding up and bandwidth and reduced power consumption has been increased. The information show that about 77% of the data is moved into the data centers. On the other hand, 10% of data center’s power consumption is used to data transmission. Improving the interconnection network of data centers can play an important role in reducing power consumption and speeding up. In recent years, optical interconnects have gained attention as a promising solution. Nevertheless, offering an all-optical and efficient architecture is an important issue. In this study, we intend to provide a multi-stage, all-optical... 

Today with the deployment of cloud computing and web applications; We need to have powerful datacenters with provisioning high bandwidth. Current data centers with electronic network interconnects, using excessive power to provisioning requisite bandwidth. Nevertheless, interconnecting networks in data centers isn’t in maximum efficiency and many components of them aren't used efficiently. So it is necessary to use an optical network with dynamic provisioning variable bandwidth and energy management. In this approach, our proposed architecture is designing topology with adopting dynamically for energy management in optical interconnect networks in data centers. To achieve this we can study... 

This paper decomposes the saving rate into cohort, age and year effects. This objective is accomplished by constructing a pseudo-panel that amalgamates nineteen consecutive, cross-sectional Household Income and Expenditure Surveys from 1991 to 2009. To construct year-of-birth cohorts with consistent composition over time, analyses are limited to those born between 1926 and 1975. Results reveal that those belonging to younger cohorts save a higher fraction of their income relative to their predecessors at the same age. Moreover, the increase in the saving rate can not be explained by the significant decline in household size or rise in educational attainment among younger cohorts. Closer... 

Fine-grained image classification is image classification where the considered classes are all sub-classes of a certain, more general class. In this setting of the problem, the classes are visually very similar to each other, such that an unskilled human cannot discriminate between them. In this case, proposed methods for the ordinary image classification problem do not obtain good classification accuracy. So proposing new methods for solving this problem is necessary. In this thesis two new methods, based on recent advances in deep learning are proposed for solving the fine-grained image classification problem. First by improving several parts of one of the recent proposed methods for this... 

This investigation is intended to assess the behavior of SCBFs in the presence of typical brace members, which buckles in compression, and BRBs joining by yielding elements. While braces play a role in providing stiffness for structure, central yielding elements will act as energy-absorbing fuses. In this frame type, energy dissipation will mainly be undertaken by BRBs and yielding elements. The mentioned yielding elements do this by initiating plastic hinges in each corner of the central frame. As it is clear, designing a structure that includes the stated lateral-load-resisting system necessitates seismic performance parameters to be evaluated. Hence, by pursuing the FEMA P695 methodology,... 

This study estimates a hedonic model for the price of housing in the city of Tehran. Using 1340 observations from the eighth region of the city as a sample and applying geographically weighted regression, the study shows that hedonic price function regardless of space variation in house price, leads to spatial autocorrelation in the error term of the model. Thus geographically weighted regression method can deal with this problem and is able to explain more than 76% of the variation in housing prices. Additionally, almost all three structural (floor area, kind of housing appearance and type of houses), environmental (distance from nearest hospital, park, school, health club and mosque) and... 

The seasonal fluctuations in economics variables relate to the different behavior of economic agents across different seasons. In past, seasonality has been viewed as a redundant feature that needs to be removed from data before economic analysis. From 1988, modeling seasonality has become the major concern of many economists; moreover, it was seen that many economic analysis and forecasts could be flawed if seasonality is ignored. In the present research, periodic times series approach is used for the first time in modeling the seasonality feature of the housing market. Regarding the importance of the housing sector in economy from micro and macroeconomic points of view, using a more... 

Time series of asset returns display specific regularities such as bell-shaped distribution, leptokurticity and volatility clustering. Economists have made continuous efforts to develop models that explain these patterns and can be used to predict the return and the risk of holding an asset. These efforts can be classified into 3 eras: Bachelier Era (1900-1960) in which the random walk model was developed for speculative prices. Mandelbrot Era (1960-1980) in which the normality assumption was replaced with the Pareto-Levy family of distributions which are flexible enough to justify leptokurticity and infinite variance. And finally, the Dynamic Volatility era in which the focus was on... 

In order to measure poverty, researchers used to consider only the income and the financial aspect of poverty; however, further research has revealed that income in itself cannot be a proper and comprehensive index for measuring poverty, and other factors such as education, health, occupation, personal characteristics, cultural activities, political activities, etc. are also significant in determining the poverty index. Based on the information acquired from "household expenditure-income survey", this study tries to take into account all the factors affecting the poverty index, in four dimensions of economic well-being, capability, political participation and cultural participation. Poverty... 

Lobe blowers are positive displacement machines (PDM) that are widely used in various industries. The efficiency of lobe blowers depends on the clearance between fixed and moving parts. If the clearance between the parts increases, backflow occurs, which means that the flow returns from the high-pressure zone of the outlet to the low-pressure zone of the inlet, and this makes the blower not have the initial efficiency and work with a lower capacity than its design. This research is carried out with the aim of checking the feasibility of backflow detection using two condition monitoring methods, including ultrasonic and thermography techniques. The sample of this research includes seven... 

Methane conversion into higher hydrocarbons has been investigated by non-thermal plasma. The most important advantages of utilizing this type of plasma are atmospheric pressure and ambient temperature of the operating system. In order to prepare non-thermal plasma, a spark discharge reactor has been used with the main prospect of acetylene production. Other components such as hydrogen, ethylene and ethane are also formed in the process as by-products. By optimizing the affecting parameters on methane conversion and products selectivity, maximum methane conversion of 83.2% is obtained. Acetylene yield varies between 25%-63% with input gas flow rate, voltage, frequency and discharge volume.... 

Any reduction in permeability around the well is referred to as formation damage. After drilling operations are completed, various types of tests and logging are conducted. If formation damage is detected, it is typically remedied through acidizing. In carbonate reservoirs, acid is used to dissolve the rock, creating wormholes that bypass the damaged zone. However, it is important to note that in carbonate reservoirs, even in the absence of formation damage, acidizing can increase the productivity index (PI) by up to 50%. A thorough understanding of the mineralogy is necessary for effective acidizing. Generally, chemical analysis requires consideration of parameters such as acid additives,... 

The prediction of Remaining Useful Life (RUL) has become a critical tool in enhancing the availability of rotating machinery. By knowing the remaining time before a bearing failure occurs during operation, significant reductions in unplanned production downtimes and associated costs can be achieved. In this study, by analyzing the time signals of vibration and ultrasonic data related to the bearing of a 4.1 MW electromotor from installation to replacement, and by investigating the behavior trends and correlation between extracted features, RMS has been ultimately identified as the health indicator in both vibration and ultrasonic techniques for monitoring the bearing’s condition. First,... 

In recent decades, many empirical issues have been accomplished about poverty respect to various dimensions of this phenomenon in micro level. At the start, monetary aspect of poverty was measured in uni-dimensional form but more researches result that poverty may be as a result of education level, health condition and other household characteristics; so multidimensional poverty indices were defined.The study employs the fuzzy-Set theoretic framework to compare levels of deprivation like income, housing, acquisition of durable goods, health condition, social security and education in Iran over time (2004, 2006) using micro data  

Bearings play a critical role in the functionality of rotating equipment across various industries, accounting for approximately 50% of equipment failures due to bearing malfunctions. Accurate life prediction of bearings is essential not only for preventing unexpected breakdowns and subsequent damage but also for minimizing unnecessary replacements of functional bearings, which can lead to increased operational costs. With the rise of artificial intelligence, numerous predictive models have been developed; however, many of these require extensive datasets, which are often unavailable in industrial settings. Data collection is typically irregular, based on the sensitivity of the equipment,...