Introduction: Juvenile Myoclonic Epilepsy (JME) is a generalized epileptic syndrome. Age of onset is usually between 12 to 18 years. JME consists of myoclonic jerks, generalized tonic-clonic seizures (GTCs) and typical absence attacks. EEG shows characteristic changes in JME. LONG TERM video-electroencephalography monitoring (VEM) is a helpful diagnostic procedure in the diagnosis of patient with unclear history or EEG findings. In the current study, we aimed to evaluate the role of VEM in diagnosis of refractory epileptic patients.Materials and Methods: This study is retrospective and descriptive on patients of Epilepsy Monitoring Unit of Razavi Hospital, Mashhad, Iran between March 2011 and March 2012. Telephone interview was scheduled 6-18 months after discharge to evaluate results of VEM on the frequency of seizures, the therapeutic regimes and patients’ quality of life.Results: 24 cases with diagnosis of JME were chosen among 250 patients who were admitted with refractory epilepsy. Fourteen of them were female. The average age of patients was 24 years old and the average duration of the seizure attacks was 12.97 years. The mean frequency of GTCs was 2.76 attacks per month and after VEM and proper treatment, it decreased to 0.27 attacks per month.Conclusion: VEM is a helpful diagnostic procedure for evaluating of refractory JME epileptic patients.

One of the important issues in the production of concrete is environmental variability that affects the concrete and elements constructed from this material. Due to the importance of different characteristics of concrete in short- and LONG-TERM conditions, this article aims to investigate the influence of initial temperature conditions of self-compacting concrete on its LONG-TERM characteristics and deTERMine the initial optimal temperature of concrete. For these purposes, several experiments under three temperature conditions of 5, 20, and 40 degree-of-Celsius were conducted to evaluate the rheology and mechanical properties of fresh concrete in the range of 7 and 28 days. In these experiments, alternative mineral admixtures of cement such as micro-silica, fly ash, and zeolite were used to build the concrete specimens. Results demonstrate that the mechanical properties of the specimens increase with increasing initial temperature in the short TERM, while such properties significantly decrease in the LONG TERM compared to the specimens constructed under lower initial temperature. Moreover, the fluidity of self-compacting concrete increases with increasing initial temperature.

Corona Virus (COVID-19) is a major problem among people, and it causes suffering worldwide. Yet, the traditional prediction models are not yet suitably efficient in catching the fundamental expertise as they cannot visualize the difficulty in the health's representation problem areas. This paper states prediction mechanism that uses a model of deep learning called LONG Short-TERM MEMORY (LSTM). We have carried this model out on corona virus dataset that obtained from the records of infections, deaths, and recovery cases across the world. Furthermore, producing a dataset which includes features of geographic regions (temperature and humidity) that have experienced severe virus outbreaks, risk factors, spatio-temporal analysis, and social behavior of people, a predictive model can be developed for areas where the virus is likely to spread. However, the outcomes of this study are justifiable to alert the authorities and the people to take precautions.

The results of various studies on the efficiency of some stock exchanges show that these markets are inefficient even in weak form. Therefore, considering the inefficiencies of these markets, one can conclude that by trading on the basis of a set of information, economic profit can be obtained. It can also be said that inefficiency is a sign that models can be designed to generate unusual profits. Many studies designed to evaluate the LONG-TERM MEMORY and prediction of future models is conducted. In this research, we tried to use an ARFIMA method and a periodogram analysis method (which is a time series analysis method) from another angle to study LONG-TERM MEMORY as well as prediction model design. The main objective of this research is the analysis of time series of total، financial and industry index on a daily basis from 27 to 2015/07/15 has been collected. The results show that the index of Tehran Stock Exchange has a MEMORY with the LONG-TERM، also the periodogram analysis method is the best method for the prediction of the stock exchange and in the end it can be stated that the ulterior period (repeatableperiod) is exsintence in the index exchange data.