Poverty Decomposition provides useful information about the factors affecting poverty and helps the politicians to choose suitable poverty reduction policies. In this context, sectoral Decomposition (Ravallion-Huppi, 1991) and growth– equality Decomposition (Datte-Ravallion, 1992) are the most widely used methods for poverty Decomposition. But the ambiguous elements (such as residual and interaction terms) existing in these methods resulted in developing a new Decomposition method by Fujii (2014). His Decomposition method is residual-free and has some desirable properties including time-reversion consistency, and sub-period additivity. In the present study, following Fujii (2014) and using Iran’s rural and urban household expenditure and income data, the poverty is decomposed into six components: population shift (PS), within-region redistribution (WR), between-region redistribution (BR), nominal growth (NG), inflation (IF), and methodological change (MC). The results show that population shift (PS), within-region redistribution (WR) and inflation components explain the highest portion of the poverty changes in the urban and rural areas. Based on the results, the pro-poor growth policies and immigration-reducing policies are recommended for reducing rural poverty, while the growth-oriented policies with redistribution are recommended for decreasing urban areas. In all periods, inflation is the main poverty-increasing factor in both urban and rural areas; therefore, controlling inflation can reduce poverty rates.

One of the methods widely used by researchers to analyze the risk of maintenance operations is Failure Modes and Effects Analysis (FMEA). However, the conventional approach of the FMEA faces serious drawbacks to rank equipment and failure Modes. The purpose of this paper is to Dynamically rank equipment in an Interval-Valued Intuitionistic Fuzzy environment to identify critical equipment, So that the main drawbacks of the conventional FMEA are eliminated. To this end, we present the Interval-Valued Intuitionistic Fuzzy condition based Dynamic weighing method (IVIF-CBDW). In following, by improving the interval-valued intuitionistic fuzzy power weight Heronian aggregation (IVIFPWHA) operator and improving the approach of multi attributive border approximation area comparison (MABAC) method, these two improved methods by Interval-Valued Intuitionistic Fuzzy condition based Dynamic weighing method (IVIF-CBDW) have been merged and a robust FMEA model has been proposed in which the main drawbacks of the conventional FMEA have been eliminated. In order to prove its applicability, this model was used in a case study to rank the equipment of a HL5000 crane barge. Finally, the results are compared with the traditional FMEA methods. It is indicated that the proposed model is much more flexible and provides more rational results.

Simulation and numerical analysis of physical phenomena, especially for the unsteady problems due to the dependency of the numerical algorithms on the computer hardware and the large number of computational nodes, are the most important problems. For these reasons, the number of computations and computational costs increase. The order reduction method is the one that has been widely used in recent years to reduce computational time. In this way, by reducing the constraints of the system without changing the inherent features of the problem, the computational efficiency will dramatically increase. In this study, using the basic concepts of Dynamical systems, the thermal diffusion problem is investigated using the Dynamic Modes Decomposition method. Then, a reduced order model is established for the related governing equation of this phenomenon. Accordingly, based on the projection of the governing equation in the vector space of Modes, by using Dynamic Modes, a reduced order model is obtained with respect to the properties of Dynamic Modes. The obtained model to simulate the time evolution and parametric variations can be properly replaced with the original equation and predict the behavior of the system with very good accuracy. A comparison between the results of the present reduced order models and the simulations of the exact solution shows high computation accuracy.

Since the most important issue in the production of digital currencies is energy consumption, the ‎use of illegal electricity in mining farms has become very popular. Illegal mining is particularly ‎important in countries such as Iran where the price of electrical energy is extremely low. This issue ‎has caused numerous problems such as frequent blackouts, large losses for industries and even ‎daily power cuts in several large cities. Previous machine learning approaches for miner detection ‎are mostly supervised methods which rely on labeled data. Due to the fact that the number of ‎labeled data is very limited in reality, we propose unsupervised methods in this paper. A real data ‎set from Markazi Province Distribution Company in Iran has been employed to produce the results. ‎The classification process consists of two stages: in the first stage, Dynamic Mode Decomposition ‎‎(DMD) has been used to extract new features which compose the set of features along with certain ‎factors from the Advanced Metering Infrastructure (AMI). These features are selected for 58 ‎subscribers with positive and negative labels. In the second stage, a number of unsupervised models ‎are built from the results of the first stage. The highest accuracy of classification obtained is 74% ‎from unsupervised algorithms and 85% for supervised algorithms, which is very significant ‎considering the fact that unsupervised algorithms do not need labeled data‎.‎

An intelligent strategy for the protection of AC microgrids is presented in this paper. This method was halving to an initial signal processing step and a machine learning-based forecasting step. The initial stage investigates currents and voltages with a window-based approach based on the Dynamic Decomposition method (DDM) and then involves the norms of the signals to the resultant DDM data. The results of the currents and voltages norms are applied as features for a topology data analysis algorithm for fault type classifying in the AC microgrid for fault location purposes. The Algorithm was tested on a microgrid that operates with precision equal to 100% in fault classification and a mean error lower than 20 m when forecasting the fault location. The proposed method robustly operates in sampling frequency, fault resistance variation, and noisy and high impedance fault conditions.

Bimorph piezoelectric cantilevered (BPC) actuators have recently received a great deal of attention in a variety of microelectromechanical systems (MEMS) applications. Dynamic modeling of such actuators needs to be improved in order to enhance the control performance. Previous works have usually taken transverse vibration into account without considering longitudinal vibration. This paper presents a comprehensive modeling for a set of transverse and longitudinal vibration equations for piezoelectric cantilevered actuators. In addition, Dynamic behavior and exact nonminimum phase region along BPC is derived by analyzing first three vibrational Modes. A simulation study is propounded to better analyze the system Dynamic behavior. Finally, an experimental setup is developed to verify the proposed Dynamic model. The modal frequency response of the system for the first three Modes, obtained from the proposed model, is compared with those obtained from the experiment and a good consistency between them confirms the validity of the proposed Dynamic model.

Due to the lack of measurements in many regions, wave characteristics are estimated using different methods. Wave climate hindcasting/forecasting is mostly conducted by numerical models or empirical methods. Until now, different empirical methods have been developed for wave hindcasting. However, with the development of high speed processors, several sophisticated numerical models have been developed for wave prediction. These models are mostly phase-averaged spectral wave models developed in three generations. In the last two decades, third generation wave models have been used widely in academic and practical projects. In this regard, Port and Maritime Organization has produced his own model, PMO Dynamic. This model has been developed as a part of first three phases of Monitoring and Modeling of Study of Iranian Coasts project. PMO Dynamic package is a software available for engineering purposes. It has several modules that have been developed for different objectives. Wave model is the module which is used for the generation and transformation of wind waves in coastal areas. In this paper, in order to test the PMO Dynamic model capabilities, it has been applied for the prediction of wave parameters in Bushehr Bay and the results have been compared with MIKE21 SW model and measured data.