فهرست مطالب امیرحسین رحمانی

The epidemic of COVID19 in Iran has wide-ranging effects. The impact of the disease on medical education led to the closure of universities and the restriction of treatment to emergency interventions. With the closure of university classes, online and distance education became more widespread. This study examines the quantity and quality and effectiveness of online training in the postgraduate course of plastic surgery.

The present study is a descriptive-analytical cross-sectional study that was approved by the Medical Ethics Committee. In this study, the satisfaction of residents and professors of the Department of Reconstructive and Plastic Surgery in terms of quantity and quality of virtual education classes was examined. For this purpose, a questionnaire and quantitative information about class time and participation rate based on available documents and class videos were used.Data analysis was performed using SPSS software version 22. The frequency of qualitative variables was measured as percentage and ratio and the frequency of quantitative variables was measured by calculating the mean and standard deviation.

The results of the study in ten domains include: the use of teaching aids in the virtual education process, the possibility of participants' access to educational resources, review of interaction and mutual participation, satisfaction with class time, access to the class for participants, evaluation methods and tests Virtual access to out-of-class teachers, satisfaction with the teaching method, satisfaction with the content presented in the class and general satisfaction with the virtual education course were evaluated and showed that in all cases virtual education with a statistically significant difference accepted by group members Is located.

Given corona disease persists and similar diseases may occur in the future, and given that our study recommends the appropriateness of e-learning, it is better to have this method taught and used on a larger scale.

Hydroxyapatite (HA) is one of the effective and efficient adsorbents of strontium ions in aqueous solutions. Considering this fact, the current study put its main focus on investigating the structure of hydroxyapatite after strontium adsorption to better understand the mechanism of this process. Nanostructured hydroxyapatite was prepared using a wet chemical method used for the adsorption of strontium ions with various initial concentrations after calcination. The structure of hydroxyapatite was well characterized after adsorption based on X-Ray Diffraction (XRD), Scanning Electron Microscopy (SEM), Energy Dispersive X-Ray Spectroscopy (EDS), and Fourier Transform Infrared Spectrometer (FTIR) analyses. The micro-strain of the structure was also investigated based on fitting the XRD patterns by Voigt function and Williamson-Hall analysis. The crystallite size and degree of anisotropy in the presence of Sr were measured. Interpreting the obtained data and comparing it with different existing adsorption mechanisms suggested dissolution-precipitation as an effective mechanism for strontium adsorption by hydroxyapatite. The SEM images and EDS results also confirmed this mechanism.

In industrial environments, a large amount of data is generated which in turn stores the database and data from all relevant areas such as planning, process design, materials, assembly, production, quality, process control, scheduling, error detection, shutdown, relationship management. Collects with the customer, etc. Data mining has become the tool used to gain knowledge of the industrial process of iron and steel making. Due to the rapid growth of data mining, various industries have been using data mining technology to search for hidden patterns that may be more relevant to the new windshield system, which will introduce new models to improve production quality, optimal cost of productivity and maintenance, and so on. Continuous improvement of the entire steel production process due to the avoidance of quality deficiencies and associated production improvement is an essential task of the steel producer. Therefore, the zero defect strategy is popular today and several quality assurance techniques are used to maintain it. This article attempts to identify the effective state-of-the-art sensors in the system using data mining and then to obtain a suitable model using a support vector machine to predict the system status in this article, the accuracy of more than 95% of the error states is detected.

Photovoltaic-wind hybrid systems are a particular type of energy source that can be used as a source of distributed generation to reduce power losses. There are two major limitations in designing these systems: availability of the electricity generated and the cost of the equipment. The purpose of this study is to determine the capacity and optimum design of the combined systems with energy sources and different strategies, as well as increase the amount of available index and reduce costs. In this design, the battery is used as a hybrid system backup to reduce the volatility of renewable energy sources. In this research, using a distributed generation source such as wind turbine, photovoltaic and battery, an intelligent optimization method based on multi-objective particle swarm optimization (MOPSO) algorithm is used. The used method is simulated in MATLAB and implemented on a 27.6 kW system installed on Texas A&M University campus and the outcomes have been evaluated. By comparing the results of the suggested method with multi-objective genetic algorithm, it is observed that multi-objective particle swarm optimization algorithm provides better responses.

Nowadays, phasor measurement units are used in power networks for different purposes, such as Linearization of state estimation equations and speed improvement of controlling and protecting systems. Optimization of position and number of these equipments is considerably important because of their high costs. Effective parameters on position and number of PMUs optimization are: Network topology, zero injection buses and commonly used measurement devices in power networks. In this paper an objective function based on integer linear programming (ILP) is presented to determine the number and optimized position of PMUs, in order to complete observability of power systems. The effect of zero injection buses and prevalent measurement devices on objective function in power networks is evaluated. Simulation results are presented for the IEEE 9, 14, 30, 39, 57 and 118 buses systems. The optimization method is numerically solved using MATLAB software environment and formation of parameters of objective function in IEEE 14 buses network has been studied.