Alarm systems are essential in safe operation of industrial plants. Since many process variables are interacting with each other, so in this paper, an approximate method is introduced to design and analysis of a multivariate alarm system. In this method, the alarm system is designed base on joint indices. The Joint FAR and Joint MAR are defined for a m-variable alarm system thanks to multivariate Markov scheme. In proposed method, the alarm joint indices are defined by solving a Linear Programing (LP) optimization problem. By defining joint indices, tuning of the alarm parameters (like, threshold and etc. ) can be done by these indices instead of correlation analysis. In this paper, penalty scenario and Genetic algorithm are used for alarm generation, and parameter optimization in Tennessee Eastman (TE) Process. The results of proposed method are compared with other methods.

Landsat data for 1992, 2000, and 2013 land use changes for Ekbatan Dam watershed was simulated through CA-Markov” model. Two classification methods were initially used, viz. the maximum likelihood (MAL) and support vector machine (SVM). Although both methods showed high overall accuracy and Kappa coefficient, visually MAL failed in separating land uses, particularly built up and dry lands. Therefore, the results of SVM were used for Markov Chain Model and “CA” filter to predict land use map for 2034. In order to assess the ability of “CA Markov” model, simulation for 2013 was performed. Results showed that simulated map was in agreement with the existing map for2013 at 84% level. The land use map prediction showed that built up area of 0.8298 km2 in 2013 will increase to 1.02113 km2 in 2034. In contrast, irrigated agriculture will decrease from 17.33 km2 to 17.16 km2, and rain fed agriculture from 45.07 km2 to 44.49 km2. Results of this research proved the application of “CA Markov” model in simulating the land use changes.

Considering the importance of implementing monetary policy in any economy, its impact on the main economic variables, including production, is very crucial. This paper examines and analyzes the effects of positive and negative monetary shocks on production in Iran using the MS-DSGE model during the period of 1979-2004. The results of this study indicate that positive and negative monetary policies in the recession periods, as well as boom periods, have asymmetric effects on the growth of domestic production. The effect of positive and negative monetary shocks on production is higher during the recession than during the boom. Generally, the monetary shocks are more effective during the boom than the recession. Accordingly, it is suggested that to achieve the economic goals policymakers should implement their policies during the economic recessions.

According to theoretical literature, accurate prediction in the digital currency market plays a fundamental role in risk management, portfolio optimization . It assists investors in making more informed decisions, managing risk, and maximizing returns. Based on the importance of the ever-growing cryptocurrencies Bitcoin and Ethereum, this research has predicted their performance and yield fluctuations using a combination of Hidden Markov Models and the Garch-Markov method, based on daily data. Based on the prediction results, the accuracy of Ethereum’s performance prediction using the Hidden Markov Model method with the DAP = 65.38%, and with the MAPE = 1.76%. Therefore, using this method has led to a 25% improvement in trend prediction accuracy. However, the absolute prediction accuracy for values has slightly worsened compared to the simple Markov model. For the cryptocurrency Bitcoin, the prediction results indicate that the model’s accuracy based on the DAP = 76.92%, and based on the MAPE = 1.43%. After employing the GARCH-Markov method, the model’s performance in predicting trends has improved by 5%, but the absolute value index has reached 55%.