IDENTIFYING MANAGERS’ MENTAL MODELS OF EXPORT DEVELOPMENT STIMULI IN THE MARKET OF DEVELOPING COUNTRIES

Abstract. Managers form their mental models of a competitive environment, and differences in mentalmodels lead to different managerial decisions and performance levels for the firm. Despite the repeated requests of researchers in the literature, little is known about market actors’ mental models. On the other hand, considering the role of export in the economic growth and development of countries, researchers have begun to analyze emerging аnd developing markets in recent years. Thus, the present study aims to identify the mental model of export stimuli belonging to managers of Iranian firms active in the dairy industry. The contribution of this study is entering the theory of mental models into international marketing studies and explaining perceptual differences based on this theory. To this end, interviews with experts continued until saturation point using theoretical sampling. Based on the data resulting from semi-structured interviews analyzed using thematic analysis in MAXQDA, six mental models were identified among chief managers of 12 dairy firms: these models include government-based, benefit-based, firm-based, brand-based, manager-based, and environment-based. Results showed that government-, benefit-, and brand-based mental models are the most prominent models in this study, respectively. Moreover, the points raised in the government-based mental model refer to potential export stimuli, while the points raised in other mental models point to actual export stimuli.Keywords: Mental model, Export stimuli, Export performance, Developing countrie

A submodular representation for hub networkdesign problems with profits and single assignments

Hub network design problems (HNDPs) lie at the heart of network design planning in transportation and telecommunication systems. They constitute a challenging class of optimization problems that focus on the design of a hub network. In this work, we study a class of HNDPs, named hub network design problems with profits and single assignments, which forces each node to be assigned to exactly one hub facility. We propose three different combinatorial representations for maximizing the total profit defined as the difference between the perceived revenues from routing a set of commodities minus the setup cost for designing a hub network, considering the single allocation assumption. We investigate whether the objective function of each representation satisfies the submodular property or not. One representation satisfies submodularity, and we use it to present an approximation algorithm with polynomial running time. We obtain worst-case bounds on the approximations’ quality and analyze some special cases where these worst-case bounds are sharper

Predicting Candidate Epitopes on Ebola Virus for Possible Vaccine Development

Zaire ebolavirus, a member of family Filoviridae is the cause of hemorrhagic fever. Due to lack of appropriate antiviral or vaccine, this disease is very lethal. In this study, we tried to find epitopes for superficial glycoprotein and nucleoprotein of Zaire ebolavirus (that have high antigenicity for MHC I, II and B cells) by using in silico methods and immunoinformatics approach. By using CTLPred, SYFPEITHI and ProPred web applications for MHC class I and SYFPEITHI and ProPred1 web applications for MHC class II, we had been able to find epitopes (peptides) that have the highest score. Also ElliPro, IgPred and DiscoTope web tools had been performed to predict B cells conformational epitopes. Linear epitope prediction for B cell was performed with six methods from IEDB. All of the results that including candidate epitopes for T cells and B cells were reported. It was expected that these peptides could be stimulated immune response and used for designing the multipeptide vaccine against ZEV but these results should be reliable with experimental analysis

Biodiesel synthesis using clinoptilolite-Fe3O4-based phosphomolybdic acid as a novel magnetic green catalyst from salvia mirzayanii oil via electrolysis method : optimization study by Taguchi method

Abstract: Objective of current study is the synthesis of biodiesel from salvia mirzayanii oil using phosphomolybdic acid (H3PMo12O40, HPA) supported on the clinoptilolite-Fe3O4. The prepared catalyst properties were determined by different analyses including FESEM, EDX, TEM, FTIR, and VSM, and its performance was studied in the process of biodiesel production. Four key factors effects like reaction time, catalyst weight, methanol to oil molar ratio, and temperature were investigated and optimized by the Taguchi method. In this method, the significance of effective factors on the biodiesel yield is controlled by analysis of variance (ANOVA). The highest biodiesel yield was found 80% at operating conditions of 0.5 wt.% HPA/clinoptilolite-Fe3O4 catalyst, methanol/oil ratio of 12:1, and temperature of 75 °C at 8 hours. The GC-MS analysis identified the fatty acid profile of salvia mirzayanii oil and biodiesel, the FTIR spectrum was performed to ensure biodiesel formation from the final product. The H-NMR analysis compared the properties of oil and biodiesel. The physicochemical properties of the produced biodiesel revealed that the biodiesel has the same properties as the diesel..

The Effect of Grapex on Wounds Healing in Patients with Scleroderma: A Randomized Controlled Clinical Trial

Background and Aim: Scleroderma (SC) is a connective tissue disease, characterized by diffuse microangiopathy and excessive production of collagen. The current study aimed to investigate the effectiveness of Grapex extract in improving the wound of patients with scleroderma. Methods: This randomized controlled double-blind clinical trial was performed from 2018 to 2019 on patients with scleroderma referred to Golestan Hospital in Ahvaz, Iran. Forty patients with active SC were selected and randomly divided into two groups. Patients applied the ointment twice a day for 4 weeks on the surface of their wounds. After four weeks of using the cream, the rate of wound healing was determined by clinical examination of the wounds. Results: 6 people were excluded from the study due to the lack of referral and final analyzes were performed on 34 patients (16 patients in the control group and 18 patients in the case group). The results of this study showed that there was a significant difference between the two groups in terms of response to treatment (p <0.0001). At the end of the fourth week, 88.89% of the patients in the case group (16 of the 18 patients) achieved complete healing of the wounds in comparison with 18.75% of the control group (3 of the 16 patients). Neither the control group nor the case group had a significant association between response to treatment with age and gender of patients, type of scleroderma, duration of illness, and symptoms. Conclusion: This study showed the effectiveness of Grapex cream ointment in healing scleroderma wounds. Therefore, Grapex cream is an effective, inexpensive, safe, and available medicine that can be used to accelerate wound healing in patients with scleroderma. *Corresponding Authors: Elham Rajaei, Email: [email protected] Please cite this article as: Hemmati AA, Deris Zayeri Z, Rajaei E, Ghanavati M. The Effect of Grapex on Wounds Healing in Patients with Scleroderma: A Randomized Controlled Clinical Trial. Arch Med Lab Sci. 2021;7:1-10 (e1). https://doi.org/10.22037/amls.v7.3105

Determining Induction Conditions for Expression of Truncated Diphtheria Toxin and Pseudomonas Exotoxin A in E. coli BL21

Background: Targeted cancer therapies have played a great role in the treatment of malignant tumors, in the recent years. Among these therapies, targeted toxin therapies such as immunotoxins, has improved the patient’s survival rate by minimizing the adverse effect on normal tissues, whereas delivering a high dose of tumoricidal agent for eradicating the cancer tissue. Immunological proteins such as antibodies are conjugated to plant toxins or bacterial toxins such as Diphtheria toxin (DT) and Pseudomonas exotoxin A (PE) . In this case optimizing and expressing Diphtheria toxin and Pseudomonas exotoxin A which their binding domains are eliminated play a crucial role in producing the desired immunotoxins.Materials and Methods: We expressed the truncated DT and PE toxin in a genetically modified E.coli strain BL21 (DE3). For this reason we eliminated the binding domain sequences of these toxins and expressed these proteins in an expression vector pET28a with the kanamycin resistant gene for selection. The optimization of Diphtheria toxin and Pseudomonas exotoxin A expression was due to different IPTG concentration, induction and sonication time. Results: We observed that the optimal protein expression of the Diphtheria toxin was gained in 4 hours of 0.4 mM IPTG concentration at 25˚C on the other hand the optimization of Pseudomonas exotoxin A protein occurred in 4 hours of 0.5 mM IPTG concentration at 25 ˚C.Conclusion: Our study also showed lower IPTG concentrations could result in higher protein expression. By optimizing this procedure, we facilitate the protein production which could lead to acceleration of the drug development

Inflation in Iran: An Empirical Assessment of the Key Determinants

Purpose: To study the key determinants of chronically high inflation in Iran.Design/Methodology/Approach: Relying on annual data from 1978 to 2019, we employ an Auto-Regressive Distributed Lag Model (ARDL) and Error Correction Model (ECM) to study the inflationary effects of monetary and fiscal policies as well as exchange rate swings and sanctions intensification.Findings: We find that increase in money supply, depreciation of nominal exchange rate, increase in fiscal deficit, and intensification of sanctions are among the key drivers of inflation in Iran. Their impact is profound in the long run, but in the short run only money supply and currency depreciation are significant. Also, when exploring the inflation in different components of Consumer Price Index (CPI), we find robust long- and short-run effects from money supply and exchange rate, while the effects of fiscal deficit and sanctions vary across different components.Originality/Value: We contribute to the literature by setting apart the long- vs. short-run effects of key variables on inflation in Iran. We also employ improved measures of fiscal deficit and sanctions that are shown to be of significance in the long run. Lastly, we go beyond the aggregate index and examine the variations in different CPI components.Yes© Journal of Economic Studies, Emerald Publishing Limited. DOI: 10.1108/JES-07-2022-0370. This author accepted manuscript (AAM) is provided for your own personal use only. It may not be used for resale, reprinting, systematic distribution, emailing, or for any other commercial purpose without the permission of the publisher

Poly (Vinylidene Fluride) Membrane Preparation and Characterization: Effects of Mixed Solvents and PEG Molecular Weight

In this study, polyvinylidene fluoride (PVDF) ultrafiltration membranes were prepared via immersion precipitation method using a mixture of two solvents triethyl phosphate (TEP) and dimethylacetamide (DMAc), which had different affinities with the nonsolvent (water). Properties of the prepared membranes were characterized using scanning electron microscope (SEM) and contact angle and membrane porosity measurements. The prepared membranes were further investigated in terms of pure water flux and BSA rejection in cross flow filtration experiments. The results showed that by using a mixture of DMAc and TEP as solvent and changing the mixed solvent composition, membranes with different morphologies from sponge-like to macrovoid containing were obtained. Maximum flux of the prepared membranes with different solvent mixing ratios was obtained for the one with 60%wt TEP in the casting solution of PVDF/TEP-DMAc/ PEG which equals to 76.8 lm-2h-1. The effect of addition of polyethylene glycol with different molecular weight on morphology and performance of the membranes has also been discussed. </span

Modeling hydrogen solubility in hydrocarbons using extreme gradient boosting and equations of state

Due to industrial development, designing and optimal operation of processes in chemical and petroleum processing plants require accurate estimation of the hydrogen solubility in various hydrocarbons. Equations of state (EOSs) are limited in accurately predicting hydrogen solubility, especially at high-pressure or/and high-temperature conditions, which may lead to energy waste and a potential safety hazard in plants. In this paper, five robust machine learning models including extreme gradient boosting (XGBoost), adaptive boosting support vector regression (AdaBoost-SVR), gradient boosting with categorical features support (CatBoost), light gradient boosting machine (LightGBM), and multi-layer perceptron (MLP) optimized by Levenberg–Marquardt (LM) algorithm were implemented for estimating the hydrogen solubility in hydrocarbons. To this end, a databank including 919 experimental data points of hydrogen solubility in 26 various hydrocarbons was gathered from 48 different systems in a broad range of operating temperatures (213–623 K) and pressures (0.1–25.5 MPa). The hydrocarbons are from six different families including alkane, alkene, cycloalkane, aromatic, polycyclic aromatic, and terpene. The carbon number of hydrocarbons is ranging from 4 to 46 corresponding to a molecular weight range of 58.12–647.2 g/mol. Molecular weight, critical pressure, and critical temperature of solvents along with pressure and temperature operating conditions were selected as input parameters to the models. The XGBoost model best fits all the experimental solubility data with a root mean square error (RMSE) of 0.0007 and an average absolute percent relative error (AAPRE) of 1.81%. Also, the proposed models for estimating the solubility of hydrogen in hydrocarbons were compared with five EOSs including Soave–Redlich–Kwong (SRK), Peng–Robinson (PR), Redlich–Kwong (RK), Zudkevitch–Joffe (ZJ), and perturbed-chain statistical associating fluid theory (PC-SAFT). The XGBoost model introduced in this study is a promising model that can be applied as an efficient estimator for hydrogen solubility in various hydrocarbons and is capable of being utilized in the chemical and petroleum industries