Sharif Digital Repository

The interstellar medium is the matter and radiation that exists in the space between the stars of a galaxy and is the most important part of a galaxy, as the densest regions of this medium are where stars form, which are the dominant sources of energy. The components of the interstellar medium include: interstellar gas, interstellar dust, cosmic rays, electromagnetic radiation, interstellar magnetic field, gravitational field, and dark matter particles. Interstellar matter is found over a wide range of temperatures and densities, and therefore different phases can be considered for interstellar matter, including: coronal gas (106 K), H II gas
(104 K), hot H I (5000 K), cold H I (100 K),... 

Quality of Experience (QoE) indicates the overall quality of one service such as Voice over IP (VoIP) from users' point of view by considering several systems, human, and contextual factors. QoE measurement and prediction are more challenging than Quality of Service (QoS) which is only related to network parameters. There exist various objective and subjective methods for QoE prediction. This research investigates various features affecting QoE by proposing a comprehensive subjective evaluation by employing a large number of users. We show that many unconsidered factors including speaker specifications and signal properties, such as signal-to-noise ratio (SNR), can affect QoE so that the SNR... 

One of the most simple questions of particle physics, which is still unanswered, is whether neutrino and antineutrino are the same particles or not. The answer to this question has many implications for the physics beyond the Standard Model, supersymmetric theories and unified theories.
In this thesis we first introduce Majorana fermion, the best candidate for which is neutrino. Then its implications for going beyond the standard Model have been discussed. At present time, the most important experiment which can prove this guess is neutrinoless double beta decay which has been studied in details in this thesis. The study of the theoretical and experimental aspects of neutrinoless double... 

Asset pricing models are one of the most important tools in investment management and portfolio allocation. Multi-factor asset pricing models try to explain the relationship between risk and expected return through a limited number of macroeconomic, fundamental or statistical factors. Usually, these models are suggested based on the US market and its intrinsic qualities. Although, there are some similarities between the Iranian and the US market, these economies are structurally different and therefore, proposing a proper multi-factor model based on the Iranian market’s fundamental characteristics will greatly improve the understanding of risk-return relationship in this market. In this... 

In this research, we propose a dynamic econometric microstructure model of trading, and we investigate how the dynamics of trades and trade composition interact with the evolution of market liquidity. We estimate a bivariate generalized autoregressive intensity process for the arrival rates of informed and uninformed trades for fifty actively traded stocks in Tehran Stock Exchange over one year of transaction data. Our results show that both informed and uninformed trades are highly persistent, but that the uninformed arrival forecasts respond negatively to past forecasts of the informed intensity. Our estimation generates daily conditional arrival rates of informed and uninformed trades,... 

The purpose of this study is to investigate the effect of Google search volume of trading tickers listed on the Tehran Stock Exchange, on volatility, abnormal returns, and trading volume. For this purpose, data related to the search volume as well as trading data for 22 trading tickers in the list of the 50 largest stock market companies from 2016 to 2020, has been extracted. After collecting data, the required calculations to estimate volatility and abnormal returns, as well as standardizing search volume and trading volume, are performed. Then the desired variables for all 22 selected tickers are placed in a data panel. Then, using the two main research models, descriptive and predictive... 

Asset pricing models are one of the most important tools in investment management and portfolio allocation. Multi-factor asset pricing models try to explain the relationship between risk and expected return through a limited number of macroeconomic, fundamental or statistical factors. Usually, these models are suggested based on the US market and its intrinsic qualities. Although, there are some similarities between the Iranian and the US market, these economies are structurally different and therefore, proposing a proper multi-factor model based on the Iranian market’s fundamental characteristics will greatly improve the understanding of risk-return relationship in this market. In this... 

This research investigates the impact of public attention, measured using Google Trends data, on stock transactions in the Tehran Stock Exchange, with a distinction between retail and institutional investors. The primary objective of the study was to assess the relationship between public attention and the number, volume, and value of buy and sell transactions for these two groups of investors. Weekly data on Google search trends and trading variables for 486 stock symbols were analyzed. The findings revealed that public attention, particularly among retail investors, has a significant impact on stock transactions. This effect peaks with a one-week lag, supporting the hypothesis, consistent... 

Condition monitoring of machinery is of significant economic importance to mitigate production losses resulting from downtimes. Unforeseen failure of roller element bearings is the most common issue observed in industrial units. However, detecting and tracking the progression of these failures through machine vibration monitoring and predicting the deterioration of these rotating components are viable solutions. Numerous studies have focused on using laboratory accelerated life test data for fault detection and remaining useful life prediction of these components. While online monitoring of all equipment in the industry may not be feasible, and conditions in the field differ from laboratory... 

Intelligent predictive maintenance is recognized as a cornerstone of Industry 4.0, where intelligent software is employed for the early detection of faults and the prevention of unexpected failures. Recent research indicates that the integration of multi-sensor data for fault diagnosis of gearboxes and bearings, using artificial intelligence models, has been successful. However, conventional methods face several challenges. These include an over-reliance on the signal characteristics of a single sensor and the impracticality of applying intelligent learning methods, particularly deep learning, despite their high potential, due to the unavailability of sufficiently large and diverse... 

In this study formation of a single bubble on fluidized bed distributor is investigated. Digital image processing and pressure sensors are used to analyze bubble characteristics. Effects of density and the size of particles on bubble diameter, formation time, separation and passing velocity was studied in different flow rates of nozzle. Bubble diameter when is separated from nozzle was greater for lower density particles which have the same size. The ratio of diameter formation was approximately equal to inverse of densities ratio. Bubble formation time is decreased by increasing the flow rate of nozzle and the formation time is approached to a specified value. In addition, it was observed...