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- 저자 : Mohammad Mehdi Kiani (검색결과 4 건)
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Mohammad Amin Bahrami,Mohammad Mehdi Kiani,Raziye Montazeralfaraj,Hossein Fallah Zadeh,Morteza Mohammad Zadeh 질병관리본부 2016 Osong Public Health and Research Persptectives Vol.7 No.3
Objectives: Organizational learning is defined as creating, absorbing, retaining, transferring, and application of knowledge within an organization. This article aims to examine the mediating role of organizational learning in the relationship of organizational intelligence and organizational agility. Methods: This analytical and cross-sectional study was conducted in 2015 at four teaching hospitals of Yazd city, Iran. A total of 370 administrative and medical staff contributed to the study. We used stratified-random method for sampling. Required data were gathered using three valid questionnaires including Alberkht (2003) organizational intelligence, Neefe (2001) organizational learning, and Sharifi and Zhang (1999) organizational agility questionnaires. Data analysis was done through R and SPSS 18 statistical software. Results: The results showed that organizational learning acts as a mediator in the relationship of organizational intelligence and organizational agility (path coefficient = 0.943). Also, organizational learning has a statistical relationship with organizational agility (path coefficient = 0.382). Conclusion: Our findings suggest that the improvement of organizational learning abilities can affect an organization’s agility which is crucial for its survival.
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Mohammad Mehdi Papari,Masoumeh Kiani,Jalil Moghadasi 한국공업화학회 2011 Journal of Industrial and Engineering Chemistry Vol.17 No.4
The present work evaluates the performance of a molecular-based equation of state in predicting thermodynamic properties of several fluids in a very wide range of temperatures encompassing 100 K < T < 1100 K and pressures ranging from zero to 3200 bar. The theoretical equation of state (EOS)is that of Tao–Mason (TM) which is based on statistical mechanical perturbation theory. The 21 fluids including: argon (Ar), krypton (Kr), xenon (Xe), nitrogen (N_2), oxygen (O_2), carbon dioxide (CO_2),methane (CH_4), ethane (C_2H_6), propane (C_3H_8), normal butane (n-C_4H_(10)), isobutene (i-C_4H_(10)), ethene (C_2H_4), benzene (C_6H_6), toluene (C_7H_8) as well as refrigerants consisting of 1,1,1,2 tetra fluoroethane (R134a), tetrafluoromethane (R14), chlorodifluromethane (R22), 1,1,1-trifluoroethane (R143a), 1,1,1-trifluoro,2,2-dichloroethane (R123), octafluoropropane (R218), and 1,1-difluoroethane (R152a) are selected and compared with literature data. The calculations cover the ranges from the dilute vapor or gas to the highly compressed liquid and supercritical regions. The thermodynamic properties are the vapor and liquid densities, the vapor pressure, the internal energy, the enthalpy, the entropy, the heat capacity at constant pressure and constant volume, and the speed of sound. It was found that the overall agreement with literature in all phases especially the vapor/gas phase is remarkable. Furthermore, the Zeno line regularity can be well represented by the TM EOS. Finally, the TM EOS is further assessed through comparing with the Ihm–Song–Mason (ISM) equation of state. In general, the TM EOS outperforms the ISM equation of state.
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Application of modified Tao-Mason equation of state to refrigerant mixtures
Masoumeh Kiani,Mohammad Mehdi Papari,Zahra Nowruzian,Jalil Moghadasi 한국화학공학회 2015 Korean Journal of Chemical Engineering Vol.32 No.7
In our previous work, we modified the Tao-Mason EOS [1] to predict the volumetric properties of pure refrigerants [2]. In the present study, we have successfully extended the modified Tao-Mason EOS to refrigerant mixtures. The second virial coefficient, B2(T), and the temperature-dependent correction factor α(T) and van der Waals co-volume b(T) were calculated from a two-parameter corresponding-states correlation along with the enthalpy of vaporization and the molar density, both at the normal boiling point. Then the cross parameters B12(T), α12(T), and b12(T), were determined with the help of simple combining rules. The constructed Tao-Mason EOS was employed to predict the densities and vapor pressures of several HFC, hydrocarbons and HFO mixtures. The calculated results were compared with literature data. The overall agreement between our results and literature values is remarkable.
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Using Hybrid Wavelet Approach and Neural Network Algorithm to Forecast Distribution Feeders
Bagheri Mehdi,Zadehbagheri Mahmoud,Kiani Mohammad Javad,Zamani Iman,Nejatian Samad 대한전기학회 2023 Journal of Electrical Engineering & Technology Vol.18 No.3
In this paper, using an algorithm based on the combination of data based on neural network virology and bacterial nutrition algorithm, improves the performance of the neural network prediction method. Also, the selection of two types of downstream and upstream filters in the wavelet transformation increases the predictive efficacy of neurological prediction. Based on the results, the optimized clustered neural network method has a more favorable response than the other methods. By selecting the appropriate filter and multichannel processing method, the maximum error percentage has improved by 15%. However, compared to the neural network prediction method, the proposed method has more computational volume due to the use of wavelet transform and also three times the use of neural prediction. Due to the large number of layers and used neurons, the neural network method has a much higher computational volume than the linear prediction method, where the linear prediction method has a higher error than the proposed method depending on the data used for training.
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