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Empirical Scaling Relations for the Photospheric Magnetic Elements of the Flaring and Non-Flaring Active Regions

Moradhaseli M. A., Javaherian M., Fathalian N., Safari H.

Acta Astronomica

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2021

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Vol. 71, No. 2

163--188

EN

Here, we analyze magnetic elements of the solar active regions (ARs) observed in the line-of-sight magnetograms (the 6173 Å FeI line) recorded with the Solar Dynamics Observatory (SDO)/Hel\-ioseismic and Magnetic Imager (HMI). The Yet Another Feature Tracking Algorithm (YAFTA}) was employed to analyze the statistical properties of these features (e.g., filling factor, magnetic flux, and lifetime). Magnetic features were extracted from the areas of 180o×180o inside the flaring AR (NOAA 12443) for November 3-5, 2015 and non-flaring AR (NOAA 12446) for November 4-6, 2015. The mean filling factor of polarities was found to be about 0.49 for the flaring AR, while this value was 0.08 for the non-flaring AR. Time series of the filling factors of the negative and positive polarities for the flaring AR showed anti-correlated behavior (with the Pearson value of -0.80). However, there was a strong positive correlation (with the Pearson value of 0.95) for the non-flaring AR. A power-law function was fitted to the frequency distributions of flux (F), size (S), and lifetime ($T$). Power exponents of the distributions of flux, size, and lifetime for the flaring AR were found to be -2.36±0.27, -3.11±0.17, and -1.70±0.29, respectively, while for the non-flaring AR: -2.53±0.20, -3.42±0.21, and -1.61±0.19, respectively. The code detected a magnetic element with the maximum flux of 23.54×1020 Mx. The maximum size of detected patches was found to be about 300 Mm2. The most long-lived patch in the flaring AR belonged to an element with a lifetime of 2208 min. We showed that S, F, and T for patches in the flaring AR follow empirical scaling relations: S∼F0.66±0.01, F∼T0.48±0.04, and S∼T0.32±0.02, respectively. For patches in the non-flaring AR, we obtained S∼F0.64±0.02, F∼T0.37±0.06, and S∼T0.23±0.03, respectively. The comparisons indicated that correlations between parameters of F and T, and also, S and T for the flaring AR, are larger than those of the non-flaring AR. The scaling law relation between the flux growth rate of positive polarities and their size indicates a strong correlation of more than 0.7 in both ARs.

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Ranking new comers to work based on commitment propensity through hybrid method of AHP and TOPSIS

Safari H., Cabrita M. R., Hesan M., Maleki M., Mirzaeirabore F.

Management and Production Engineering Review

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2018

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Vol. 9, No. 4

86--95

EN

This research provides a tool to select and prioritize new comers to work based on their preentry organizational commitment propensity through examining links between the big five personality factors: extroversion, agreeableness, conscientiousness, neuroticism, openness; and three component model of organizational commitment: affective commitment, continues commitment, normative commitment. Findings show that extroversion and openness respectively have positive and negative effects on all three components of organizational commitment. Results gained by Structured Equation Modelling (SEM) indicate neuroticism is negatively related to affective and continues commitment and positively to conscientiousness effects on continues commitment. In the second part of the study, the received results are applied to extract the general equations that enables to estimate new comer’s pre-entry organizational commitment and to rank them using TOPSIS and AHP. The AHP is used to determine the relative weights of commitment criteria and TOPSIS is employed for the final ranking of new comers based on these criteria’s.

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Multidimensional personnel selection through combination of TOPSIS and Hungary assignment algorithm

Safari H., Cruz-Machado V., Sarraf A. Z., Maleki M.

Management and Production Engineering Review

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2014

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Vol. 5, No. 1

42--50

EN

This paper proposes an effective solution based on combined TOPSIS and Hungary assignment approach to help companies that need to assign personnel to different departments. An extension of TOPSIS (technique for order performance by similarity to ideal solution) combined by Hungary assignment algorithm is represented for this purpose. According to decrease resistance of employee opposite of recruitment of new employee, Decision criteria are obtained from the nominal group technique (NGT) and managers of each departments has been involved in decision making process. In the presented solution, managers of four departments have been involved in evaluating four candidates for their department and data is analyzed by TOPSIS and at the end, an effective fit between personnel and their corresponding department is presented.