The body freedom flutter phenomenon is one of the aeroelastic instabilities that occurs due to the coupling of the aeroelastic bending mode of the wing with the short-period mode in the flight dynamics of the aircraft. By using the aeroservoelastic model and applying closed loop control, this phenomenon can be suppressed in the operating conditions of the aircraft and the velocity of this event can be increased. The simplest model aircraft capable of displaying this instability includes the flexible wing and the planar flight dynamics model. For this purpose, the wing structure is modeled using the Euler-Bernoulli beam and, the theory of minimum variable State is used to model unstable aerodynamics to make the conditions suitable for modeling the system in State space. In the control section, the elevator is used as the control surface and LQR theory with Kalman filter is used to body freedom flutter suppression. Finally, the effect of adding a closed loop control to increase the body freedom flutter velocity and the limitations of this work are studied.

The main objective of this paper is to compare the accuracy of ARIMA, GARCH, ARIMA-GARCH and State space models in estimating and forecasting the Tehran Exchange Stock Index (TEPIX). For this purpose, daily data from 21st of January 2011 to 19th February 2014 (corresponding to Iranian calendar: from first of Bahman 1389 to 30th of Bahman 1392) as out -of- sample and daily data from 20th of February 2014 to 19th May 2015 (corresponding to Iranian calendar: from first of Esfand 1392 to 30th of Ordibehesht 1393) as in- sample have been used. On the other hand, for more consideration and for boosting the accuracy of the mentioned models’ forecasting the TEPIX in the long run, simulation has been done using the Monte Carlo Method for two periods of time. These include the medium run and the short run, using out- of- sample and using in- sample for a general comparison. Next, the accuracy of the forecasts have been evaluated by calculating the Root Mean Square Errors (RMSE). According to the out- of- sample, the results indicate that the GARCH model has more accuracy of forecasting rather than the other models in the three periods of time (long run, medium run and short run), and compared with in-sample, the ARIMA model is a more sufficient model.

Objective: After beginning of stock markets liberalization in mid-1980, integration of these markets into global markets is increasing. The countries are attempting to attract international investors to divide their portfolio risk, increase liquidity level and promote informational transparency and hence improve stock market efficiency. This study investigates the effects of financial liberalization on stock market informational efficiency in developing economies, during 2000-2016. Methods: with regarding to informational efficiency level in economies with developing characteristics is time-varying because of benefit from their investor experiences and increase of informational access and quality. In order to, we applied a State space model with GARCH effects to determine the degree of informational efficiency during time for any selected countries. In continue, we use panel GMM model that allow measuring and assessing the impact of financial liberalization on the informational efficiency. Results: The results of State space model realized the weak efficiency hypothesis in selected countries. In other words, the past returns do not contribute much to anticipate future returns, but it affected by macroeconomic, external shocks and political events. Also, the results of GMM model indicate that financial liberalization significantly (of course tiny) improves the degree of informational efficiency. Likewise, we found that information variable (turnover) and macroeconomic variables (inflation rate and exchange rate volatilities) would strengthen significantly informational efficiency in developing countries. Conclusion: This research contributes to improving informational efficiency in developing economies, this is necessary to implement financial liberalization process as well as improving macroeconomic circumstance and declining exchange rate volatilities.

Distributed Denial of Service (DDoS) attacks are among the primary concerns in internet security today. Machine learning can be exploited to detect such attacks. In this paper, a multi-layer perceptron model is proposed and implemented using deep machine learning to distinguish between malicious and normal traffic based on their behavioral patterns. The proposed model is trained and tested using the CICDDoS2019 dataset. To remove irrelevant and redundant data from the dataset and increase learning accuracy, feature selection is used to select and extract the most effective features that allow us to detect these attacks. Moreover, we use the grid search algorithm to acquire optimum values of the model’s hyperparameters among the parameters’ space. In addition, the sensitivity of accuracy of the model to variations of an input parameter is analyzed. Finally, the effectiveness of the presented model is validated in comparison with some State-of-the-art works.

In this study, we estimated the parameters using the State space model described in ARIMA form. We’ve also used the Monte Carlo Method for simulating the process in 10000 reputations. Then the estimated parameters and the Monte Carlo simulation method are used to forecast TEPIX index, including 739 observations as an in-sample data from 21th of January 2011 to 19th February 2014 and 59 observations from 20th February 2014 to 21th May 2014 as an out of sample data. Furthermore, For more investigation we’ve considered different horizons of forecasting, short-term (equal to 1 week), mid-term (equal to 1 month) and long term (equal to 3 month). The results showed that Tehran stock market data has enough efficiency to forecast them, and showed that the State space in Form ARIMA model and the Monte Carlo simulation method can be used as a predictive algorithm for TEPIX index and other indices with similar nature.

This paper is investigating the mixture autoregressive model with constant mixing weights in State space form and generalization to ARMA mixture model. Using a sequential Monte Carlo method, the forecasting, filtering and smoothing distributions are approximated and parameters f the model is estimated via the EM algorithm. The results show the dimension of parameter vector in State space representation reduces. The results of the simulation study show that the proposed filtering algorithm has a steady State close to the real values of the State vector. Moreover, according to simulation results, the mean vectors of filtering and smoothing distribution converges to State vector quickly.

A B S T R A C TToday, all officials involved with the challenges of city sustainability have accepted the idea of a smarter city, using more technologies, creating better living conditions and protecting the environment for a better quality of life. The central part of the metropolis of Tabriz is based on Region 8 and part of Region 10, a historical-cultural area with an area of 286 hectares and a population of about 40 thousand people in 8 neighbourhoods. This research was compiled to evaluate and explain the regeneration of the central part of Tabriz city with the approach of smart growth and in line with the sustainable development of problematic neighbourhoods in the 8th district of Tabriz metropolis. The present review is methodologically practical, to describe and analyze the goals of regeneration based on library studies. The statistical population of the research includes experts and people living in the central context. In the first part, the Delphi method was used by asking experts. In the second part, the structural equation method of SPSS and AMOS software is used. According to the results of using the scales of design, pedestrian-oriented, intergenerational justice in the actual situation and 3 indicators of management and productivity, stable and value-creating jobs are in the potential situation. Also, two environmental indicators with the most direct and indirect effects and a cultural index have been decisive in the regeneration with the smart growth approach in the central part of Tabriz city. Therefore, in the regeneration of the central part, the studied indicators should be used simultaneously and integrated into the reconstruction projects of the central fabric of the city to lead to smart sustainabilityExtended AbstractIntroductionSmart growth is considered a lever for revitalizing neighborhoods and urban centers. In a general scenario of smart growth, knowing the time and resources of investment provides a new and sustainable life in the city center and worn-out and old textures. Moreover, finally, it will improve the quality of urban neighborhoods, especially worn-out ones, and revive them. Tabriz metropolis, one of the most important and largest cities in the country, with a long history of settlement, especially the central and old texture, due to developments in information and communication technology, extensive environmental, social, and economic changes in terms of time requirements, the traditional texture of roads, Deterioration of residential units, lack of equipment, infrastructure and urban services and environmental problems have caused the failure and physically turned into inefficient and problematic textures. Urban planning and development science must adopt a new decision and approach to face the existing and complex issues of worn-out texture. The main problem of the research is to evaluate and explain the regeneration of the central texture and strengthen them in line with the sustainable development of the problematic neighborhoods of Tabriz metropolis according to the solutions and principles of smart urban growth. Therefore, the main goal of this research is to answer the following question:- What are the effective indicators and components in the regeneration of the central texture of Tabriz metropolis with a smart growth approach? MethodologyThis research is applied and fundamental in terms of purpose and descriptive-analytical. The collection methods are based on documentary and library studies, surveys, and interviews. In the first part, qualitative data was prepared with an open questionnaire and through interviews, as well as the review of documents and quantitative data used in this research numerically and through the weighting of Delphi questionnaires based on the consensus index model to determine importance, certainty, and priority. The panel members were 19 people (including 5 university faculty members, 7 field research experts, 4 executive experts, and 3 graduate students) selected based on the non-probability (targeted) method. In the second part, Cronbach's alpha method was exerted to measure the reliability of the questions, which was obtained as 0.89. In order to evaluate the validity of the questionnaire, the Lawshe method was exploited. The statistical population of the second part includes District 8 of Tabriz, which is the central texture of the city and is equal to 34,231 people. According to Cochran's formula, the sample size was equal to 379 people. Results and discussionIn the next step, path analysis was performed using AMOS 23 software to compare the integrated model with the hypotheses. The results of the path analysis, while verifying the structures, show that the value of the index of the critical ratio to the degree of freedom (CMIN/DF) is equal to 4.01, which is an optimal value for the model fit. Also, the goodness of fit index (GFI) is 0.952, which indicates the acceptability of this amount for the optimal fit of the model. The value of the root means the square error of estimation (RMSEA) is 0.065, which is acceptable because it is smaller than 0.08 and indicates the confirmation of the research model. Also, the Tucker-Lewis index (TLI) is 0.917, the comparative fit index (CFI) is 0.92, and the normalized parsimonious fit index (PNFI) is 0.882, which all indicate that the fit and verification of the research model are desirable.Therefore, none of the objects are removed and remain in the model. Therefore, it is confirmed that the factor structure of regeneration indicators with the smart growth approach has reliability and construct validity. Also, the second-order factor analysis shows that the factor loadings of all regeneration indicators with the smart growth approach are greater than 0.4 and are significant at the 95% probability level. According to the results, the environmental dimension with a factor loading of 0.94 has the most significant effect on urban regeneration with the smart growth approach in the central texture of Tabriz city. After that, quality of life with a factor loading of 0.92, social dimension with a factor loading of 0.88, economic dimension with a factor loading of 0.83, physical dimension with a factor loading of 0.81, transportation dimension with a factor loading of 0.74 and cultural dimension in order with a factor loading of 0.72, are in the following ranks.According to the final structural model results, the environmental index has the highest value, with a direct effect of 0.594 and an indirect effect of 0.275. Moreover, after that, respectively, the quality of life index with a direct effect of 0.513 and an indirect effect of 0.236, a social index with a direct effect of 0.476 and an indirect effect of 0.217, an economic index with a direct effect of 0.451 and an indirect effect of 0.222, Physical with the direct effect of 0.435 and indirect effect of 0.205, mobility index with the direct effect of 0.373 and indirect effect of 0.182 and cultural index with the effect of direct values of 0.295 and indirect effect of 0.153 are influential in regeneration with smart growth approach in the central part of Tabriz city. ConclusionIn this research, seven main indicators related to the subject were investigated. The results showed that the environmental index with the most significant direct and indirect effect is the focus of the evaluation. After that, dimensions and indicators of quality of life, social index, economic index, physical index, mobility index, and cultural index have been influential. According to the results, the urban environment index was analyzed as the most important factor at a high level, and this means that for urban regeneration with a smart growth approach, improving the environmental and physical conditions in the worn-out and central texture of Tabriz city in the current situation is a priority. It is first. This is the most well-known part of regeneration with a smart growth approach. The analysis results show that this is not the case only in the case of transportation infrastructure. However, more comprehensive urban infrastructure such as the urban landscape, open spaces, green and parks, etc. are also crucial in improving the physical conditions of the infrastructure. Also, this study's results show that the economic sector's importance is as important as improving the physical conditions of the infrastructure. In addition, in the worn-out texture of District 8 of Tabriz city, the sociocultural sector is essential, and the revival of historical and cultural resources is considered one of the urban regeneration options. Funding There is no funding support. Authors’ Contribution All of the authors approved the content of the manuscript and agreed on all aspects of the work.  Conflict of Interest Authors declared no conflict of interest. AcknowledgmentsWe are grateful to all the scientific.

Many economists believe that the main channel that inflation makes its harmful impacts on economy is the inflation uncertainty. Inflation uncertainty by its ex-ante and ex-post effects influences the economic agent's real activities and hence causes serious costs on economy.Thus, determining the relationship between inflation and inflation uncertainty can help the policy makers to make appropriate decisions and prevent economy from bearing such costs.In this study our main goals were: first, measuring the inflation uncertainty, second determining the relationship between inflation and inflation uncertainty both in short and long run.The results show that the relationship between these variables in short run is positive. But in the long run there is no relationship. Also in the short run, negative inflation shocks have less effect on inflation uncertainty, comparing with positive ones.