mohammadjavad abolhasani

Significant fluctuations in the price of cryptocurrencies has led to the identification of effective factors on their prices, and it is important to understand this in the financial markets. In the present paper, the time series analysis method is used to study the determinants of Bitcoin and Ethereum prices. For this purpose, the stock price index on the New York Stock Exchange, the Standard & Poorchr500 Index, the world price of gold and the exchange rate of the dollar and the euro as economic factors, the difficulty of mining, the number of daily transactions, the daily number of circulating Bitcoin and Ethereum as technical factors, The variables of the number of daily tweets related to Bitcoin or Ethereum and the number of daily searches of Bitcoin or Ethereum in Google have been examined as social factors. The time period for Bitcoin is from 2014 to 2020 and for Ethereum from 2016 to 2020. The research method is applied and the statistical population of the research includes two cryptocurrencies, Bitcoin and Ethereum. The econometric method used to study the effect of high factors on the price of Bitcoin and Ethereum in the short-run and long-run periods is OLS and VECM. Among the factors studied, the world price of gold has the greatest impact on the price of Bitcoin and Ethereum.On the other hand, in the short run, the exchange rate of the dollar and the euro and the world price of gold have a negative relationship with the value of cryptocurrencies.

Empirical evidence shows that the banking crisis is one of the leading causes of economic crises. The occurrence of a banking crisis due to the interconnectedness of the banking network with countrieschr('39') economies makes it very difficult to study and predict them. The research method in this research is applied. The statistical population of the research includes Saderat, Mellat, Tejarat, Eghtesad Novin, Parsian, Pasargad, Sina, Sarmayeh, Karafarin, Hekmat Iranian, Ansar, Shahr, Gardeshgari, Tose Etebari, Saman, Dey is the Khavaremianeh, and Postbank and the research period is from 2007 to 2017. This research has evaluated the banking crisis prediction by simple and dynamic probit panel method in two periods when the first period includes the first and second lags and the second period includes the third and fourth lags. For this purpose, the variables of total stock index, land price index, credit to GDP ratio, leverage, profit, and the capital ratio of each bank, which indicates the state of the countrychr('39')s economy and banks, are used. According to the results, the correct prediction rate in dynamic probit is higher than the simple probit prediction rate, and dynamic probit is more capable of predicting banking crises. On the other hand, the ratio of loans granted by banks to the private sector to GDP and the ratio of leverage have a significant and effective relationship with the probability of a banking crisis in Iran.

Due to the importance of non-performing loan banking in the health of the banking system and the role of banks in financing the country's enterprises in this study was tried to use the study of Abid et al. (2014) in which the effect of economic and banking variables on non-performing loan banking of Tunisia has been studied, To evaluate the effect of macroeconomic variables (GDP, Government dept, exchange rate, and liquidity) and banking (bank size, moral hazard, and bad management) on the volume of non-preforming loans bankings, accepted banks in Tehran stock exchange during the period 1396-1386 to be paid. In this study, the two-step Generalized Method of Moments (GMM) and panel data have been used. Finally, the accuracy of the results was checked by Sargan tests and serial autocorrelation. The statistical population of the study is the banks accepted by tehran stock exchange (Saderat, Mellat, Tejarat, Sina, Sarmayeh, Saman, Parsian, Pasargad, Hekmat Iranian, Ansar, Eghtesad Novin, Dey, Khavaremianeh, Ayandeh, Gardeshgary, Shahr, Karafarin, Post Bank and Tosee Etebari). Macroeconomic variables such as liquidity, government debt and exchange rates have a significant effect on the volume of non-performing loan. The relationship between liquidity and government debt variables and the volume of non-performing loan is positive. The relationship between the exchange rate and the volume of non-performing loan was reported in most negative coefficients. Also, banking variables such as bank size have a negative relationship with the volume of non-performing loan and the relationship between bad management and the volume of non-performing loan is positive.

Over the past decades, the world economy has seen the loss of millions of well-paying manufacturing jobs. This trend has been observed in recent years in the Middle East. Some jobs in the Middle East have been lost forever due to the advent of technology and labor savings. At the same time, many low-wage jobs have been created in the service and retail sectors of their economies. The growth of technology reduces the demand for low-skilled workers and increases the demand for skilled workers, and as a result, the wage gap between the two groups increases. It is noteworthy that technology also affects income distribution by affecting macroeconomic variables such as growth, employment, income, and wage levels. In recent years, technological advances in the Middle East have intensified, and it is important to examine the impact of employment in industry on income inequality.This article uses panel data from Iran, Iraq, Saudi Arabia, Bahrain, Turkey, Jordan, Kuwait, Lebanon, Oman, Syria, the UAE and Egypt from 2000 to 2018 to examine the relationship between industrial employment and inequality. In this study, the variables of Gini coefficient, industrial employment, GDP per capita, the ratio of trade to GDP and the ratio of government expenditure to GDP have been used and econometric method is generalized method of moments two-step (GMM). The analysis shows that industrial employment is negatively related to income inequality, while other variables have little effect on income inequality.

Nowadays cancer detection in preliminary stages is one of the most important problems for physicians. One of the new imaging techniques, which have special capability in the detection of small size lesions, is optoacoustic imaging. In this paper have introduced this technique and have simulated optoacoustic wave generation in spherical tumors of breast.

In this research for the generation of acoustic pressure as a result of laser energy reception in an absorbing sphere, a transfer functions in backward mode has been introduced. Then using this model effect of changes in laser pulse width, dimensions and depth of tumor and absorption coefficient of tumor and media on properties of generated ultrasound have been surveyed. All of simulations have been accomplished in MATLAB7 software platform.

investigation of pulse width effect on generated optoacoustic wave illustrated that increase of laser pulse duration causes to increase in ultrasound amplitude. Investigation of tumor absorption coefficient as a function of tumor type showed that Increase of absorption coefficient of tumor causes to increase in ultrasound amplitude, but more increase in absorption coefficient causes to saturation in ultrasound amplitude and in high absorption coefficient relation between generated ultrasound amplitude and absorption coefficient because of order two.

For designing a laser- ultrasound system with maximum efficiency, we can choose optimum system using this model and simulation results with testing of various parameters of available laser systems. In addition, we can use obtained results for tumor specification in clinical applications of this technique.

Image guided liver surgery based on intra-operative ultrasound images has received much attention in recent years. Using an efficient point-based registration method to improve both the accuracy and computational time for registration of pre-deformation CT liver images to post-deformation Ultrasound images is of great concern during surgical procedure. Although, Iterative Closest Point (ICP) algorithm is widely used in surface-based registration, its performance is strongly dependent on existence of noise and initial alignment. The registration technique based on the Unscented Kalman Filter (UKF) proposed recently can be a solution to overcome to noise and outliers on an incremental registration basis but it suffers from computational complexity. To overcome the limitations of ICP and UKF algorithms we proposed an incremental two-stage registration algorithm based on the combination of ICP and UKF algorithm to update the registration process based on arrival of intra-operative images. The two-stage algorithm is examined on phantom data sets. The results of phantom study confirm that the two-stage algorithm outperforms the accuracy of ICP and UKF by 23% and 13%, respectively and reduces the running time of UKF by 60%.

Studying the behavior of a society of neurons, extracting the communication mechanisms of brain with other tissues, finding treatment for some nervous system diseases and designing neuroprosthetic devices, require an algorithm to sort neuralspikes automatically. However, sorting neural spikes is a challenging task because of the low signal to noise ratio (SNR) of the spikes. The main purpose of this study was to design an automatic algorithm for classifying neuronal spikes that are emitted from a specific region of the nervous system. The spike sorting process usually consists of three stages: detection, feature extraction and sorting. We initially used signal statistics to detect neural spikes. Then, we chose a limited number of typical spikes as features and finally used them to train a radial basis function (RBF) neural network to sort the spikes. In most spike sorting devices, these signals are not linearly discriminative. In order to solve this problem, the aforesaid RBF neural network was used. After the learning process, our proposed algorithm classified any arbitrary spike. The obtained results showed that even though the proposed Radial Basis Spike Sorter (RBSS) reached to the same error as the previous methods, however, the computational costs were much lower compared to other algorithms. Moreover, the competitive points of the proposed algorithm were its good speed and low computational complexity. Regarding the results of this study, the proposed algorithm seems to serve the purpose of procedures that require real-time processing and spike sorting.