QUALITY OF SERVICE IN IRAN BANKING CASE STUDY MELLI BANKS IN TABRIZ, IRAN

This is a case study of Melli Banks in Tabriz to investigate the Islamic banking view. Today Iran as an Islamic country has an increasing economic growth in different sectors such as banking. This study investigated the quality of Islamic banking system in regard to basic dimensions including empathy, reliability, responsiveness and assurance and indicated the quality of service in Bank Melli Iran has direct connection to customer's satisfaction. Customers satisfaction is strategic factor to win the competition and Islamic banking has considered this issue well , so that to make and preserve its position in this competitive banking market because financial services based on Islamic banking have focused on customer's satisfaction and needs. So the effectiveness of Islamic banking system to enhance the quality of services is acceptable and this higher quality have perceived by the customers of Bank Melli in Iran.  Article visualizations

The influence of (beta) technology intensity and evaluating TCC using aHP model in Iran Tractor Manufacturing Company (ITMCO)

Due to the strategists in business World, the most important strategic consideration in international economic and industrial competition is in managing innovation and technology context. Increasing the competitive advantage of each economic institution needs the increase of technologic content improvement applied in production process of that institution. The present paper aims to study and evaluate the technology intensity and TCC in Iran Tractor Manufacturing Company (ITMCO). According to the results of research work, the authors have offered proposals to improve the technology by introducing the turning points and defects existed in technology parts in the company

THE IMPACT OF CV AND PERSONALITY ANALYSIS ON THE RECRUITMENT PROCESS: THE CASE STUDY OF TURKEY

In the era of economic globalization and knowledge economy, science and technology are not only the focus of competition among countries but also the focus of competition among enterprises. The most significant competition in the science and technology fields is related to human resources. For this reason, the recruitment and retention of the talent suitable for the development of the organization has become the center and origin of sustainable development and competitiveness. The traditional recruitment emphasizes on matching people to positions. An individual's knowledge, skills, and abilities are important to follow and fulfill the needs of a position in the best way. Therefore, the person-position recruitment model had become important for a certain period. However, this approach ignores the effects of individuals and other organizational factors on organizational development. In this approach, the degree of the match between recruited individuals and individual and organizational culture values cannot be estimated, and this leads to some adjustment problems. Therefore, there was a necessity to make some changes in the traditional recruitment model. If there is a new position, it should not only be called to fill it, candidates should be seen as individual psychological entities. In the new period, the focus was on the attitudes, character traits, and even hobbies of the employees. Before the companies decided on a candidate, they began to compare these features with their current teams’ and employees’ features. This approach helps a new employee to adjust himself/herself to the organization more easily and help the company save more time and money.  Article visualizations

Providing a Hybrid Methodology to Solve the Supplier Selection Problems: Application of MCDM Techniques

The emphasis of supply chain management (SCM) is majorly on the relationship between enterprise alliance and core enterprise. One of the main decision-making problems in SCM is choosing strategic partners, which also is the key to a prosperous SCM. In the present study, SCM is investigated using the analytical hierarchy process (AHP) simulation approach o examine the uncertainty involved in AHP and reduce its risk to some extent. Finally, this approach is employed to solve the problem of supplier selection in SCM

Modeling income distribution: An econophysics approach

This study aims to develop appropriate models for income distribution in Iran using the econophysics approach for the 2006–2018 period. For this purpose, the three improved distributions of the Pareto, Lognormal, and Gibbs-Boltzmann distributions are analyzed with the data extracted from the target household income expansion plan of the statistical centers in Iran. The research results indicate that the income distribution in Iran does not follow the Pareto and Lognormal distributions in most of the study years but follows the generalized Gibbs-Boltzmann distribution function in all study years. According to the results, the generalized Gibbs-Boltzmann distribution also properly fits the actual data distribution and could clearly explain the income distribution in Iran. The generalized Gibbs-Boltzmann distribution also fits the actual income data better than both Pareto and Lognormal distributions

Parameter Estimation of the Exponentiated Pareto Distribution Using Ranked Set Sampling and Simple Random Sampling

CC BY 4.0In this paper, we have considered that ranked set sampling is able to estimate the parameters of exponentiated Pareto distribution. The method with which the maximum likelihood estimators for the parameters of exponentiated Pareto distribution is studied is numerical since there is no presence or possibility of a closed-form at the hands of estimators or any other intellectual. The numerical approach is a well-suited one for this study as there has been struggles in achieving it with any other technique. In order to compare the different sampling methods, simulation studies are performed as the main technique. As for the illustrative purposes, analysis of a simulated dataset is desired for the objective of the presentation. The conclusion that we can reach based on these is that the estimators based on the ranked set sample have far better efficiency than the simple random sample at the same sample size

Supply chain contract selection in the healthcare industry: a hybrid mcdm method in uncertainty environment

The aim of this research study is to address a critique of how and when a supply chain contract is selected based on critical success factors (CSFs) utilizing stepwise weight assessment ratio analysis (SWARA) and Evaluation by an Area-based Method of ranking (EAMR). This research study ranked supply chain contracts by the EAMR in uncertainty environments, such as when breaking down the health care industry. This is done by providing a theoretical framework for sustainable entrepreneurship in telecommunications industry, focusing on managerial and operational practices that should be modified, in accordance to a set of CSFs identified from experts in fertility hospital. As a novel strategy, in this research, the initial factors of selecting customized Supply Chain Management (SCM) were extracted via a Delphi method along with the EAMR to symbolize a decision matrix that needs primary weights acquired through the SWARA method by hesitant fuzzy number. CSFs for achieving SCM contract selection in fertility hospitals were found to rely on a tripod based on effectiveness, transparency, and accountability that are embedded within the ambit of managerial and operational practices, such as focusing and reducing cost and based on these factors the best SCM contract must be selected. Besides, the EAMR method has more reliability than other similar MCDM methods such as TOPSIS, MOORA, VIKOR, and so on main contribution of this paper is the combination of SWARA, EAMR, and using hesitant fuzzy set in the EAMR method. Finally, the result indicates that hospitals based on these CSFs must be selected contracts

Application of fuzzy Topsis and Taguchi methods for optimization problems with disruptive risk: A systematic review

Ranking and choosing research projects and analyzing experiments are usually difficult and complex responsibilities for professional research councils at universities and research centers. Its complexity stems from having more than one variable in each project, and the participation of many decision-makers in the ranking process and selection of research projects based on many variables. The fuzzy set theory provides the required flexibility to show the uncertainty about the lack of knowledge, and also it can manage the uncertainty in the real world that the values of criteria are not defined properly. For this purpose, in the environment where the criteria of research projects are vaguely defined, the ranking methods such as Taguchi, which can reduce the number of experiments and making process more efficient, can be used for quality design in designing and processing product. In this work, first of all, the authors review fuzzy TOPSIS technique and the Taguchi method as well; then they approach research efficiency and optimization of the level of effective parameters in an experiment

Nutrient Digestibility, Rumen Fermentation Parameters, and Production Performance in Response to Dietary Grain Source and Oil Supplement of Holstein Dairy Cows

Introduction High-producing dairy cows require large amounts of concentrates that are rich in energy and crude protein to meet their nutrient requirements. Cereal grains and oil supplements are commonly used for increasing energy density of diets fed to high-producing dairy cows. Dietary grain source (barley vs. corn) and oil supplement (soybean- vs. fish oil) resulted in varied dry matter intake and milk production responses in different research studies based on effects on nutrient digestibility and rumen fermentation characteristics. Therefore, the main purpose of this study was to determine the effects of, and interactions between, grain source and oil supplement on the feed intake, rumen fermentation characteristics, nutrient digestibility and lactational performance of Holstein cows. Materials and Methods Eight lactating multiparous Holstein cows (parity = 3.3 ± 1.3 and days in milk = 77 ± 22.1; mean ± SD), were used in a replicated 4 × 4 Latin square design with 25-d periods. Each experimental period consisted of an 18-d diet adaptation period and a 7-d collection period. Cows within a square were assigned randomly to dietary treatments. Cows were blocked into 2 squares of 4 cows each based upon milk production, and days in milk, and within blocks were assigned to 1 of the 4 experimental diets with a 2 × 2 factorial arrangement: 1) BF = barley-based diet supplemented with fish oil at 2% of dietary DM, 2) BS = barley-based diet supplemented with soybean oil at 2% of dietary DM, 3) CF = corn-based diet supplemented with fish oil at 2% of dietary DM, and 4) CS = corn-based diet supplemented with soybean oil at 2% of dietary DM. The TMR amounts offered and refused were measured daily for each cow and DMI determined daily for each cow. Cows were milked three times daily at 0200, 1000, and 1800 h in a herringbone milking parlor. Milk yield for all cows was recorded and sampled at each milking during the last 7 d of each period. Milk samples were composited in proportion to milk yield, preserved with potassium dichromate, stored at 4°C, and analyzed for fat, protein, lactose, and total solids using an infrared analyzer (MilkoScan 134 BN; Foss Electric, Hillerød, Denmark). At the end of each experimental period, rumen samples were obtained at 4 h after the morning feeding using the stomach tube technique. Rumen pH was determined immediately after the samples were collected using a mobile pH meter (HI 8314 membrane pH meter, Hanna Instruments, Villafranca, Italy). Rumen fluid samples were acidified by sulfuric acid and analyzed for volatile fatty acid by gas chromatography (model no. CP-9002 Vulcanusweg 259 a.m., Chrompack, Delft, the Netherlands). Two fecal grab samples per cow were taken from the rectum twice daily across day 19 to 23 of each period and frozen at −20°C until analyzed. Acid detergent insoluble ash was used as an internal marker to determine apparent total-tract nutrient digestibility. Data were composited within period and analyzed with the MIXED MODEL procedure of SAS (SAS Institute, 2003) to account for effects of square, period within square, cow within square, treatments (grain source and oil supplement), and the interaction between grain source (barley vs. corn) and oil supplement (fish oil vs. soybean oil). Results and Discussion Apparent total-tract digestibility of dry matter (P = 0.05) and ether extract (P < 0.01) were greater in the corn- vs. barley-based diets. Fish oil tended (P = 0.07) to decrease and decreased (P = 0.03) apparent total-tract digestibility of non-fibrous carbohydrate and ether extract as compared to soybean oil, respectively. An interaction of main treatment effects tended to occur for molar concentration of propionate (P = 0.09). Barley-based diets increased molar concentration of propionate compared to corn-based diets for cows fed soybean oil, but not for cows fed fish oil. Dry matter intake tended (P = 0.09) to be greater for barley- vs. corn-based diets, but was reduced for the fish oil compared to soybean oil supplemented diets (P < 0.01). Grain source did not affect milk yield or milk composition. Compared to soybean oil, fish oil negatively affected milk yield and milk composition. Feed efficiency remained unchanged among treatments. Conclusion Results indicated that grain source and oil supplement do not interact to affect productive performance of lactating cows. Due to lowering DM intake, feeding fish- vs. soybean-oil, but not changing diets fermentability, did not influence production performance of lactating cows

Design and implementation of a hybrid MLP-GSA model with multilayer perceptron-gravitational search algorithm for monthly lake water level forecasting

Lakes are primitive water holding geographic structures containing most the fresh water on the Earth’s surface, but the recent trends show that climate change can potentially lead to a significant aberration in the Lake water level and its overall pristine state, and therefore, could also threaten the source of freshwater. The ability to forecast the lake water is a paramount decision-making and risk-reduction task, and this is required to retain the sustainability of the natural environment, and to reduce the risk to the local and global food chain, recreation activities, agriculture and ecosystems. In this study, we have designed and evaluated a new hybrid forecasting model, integrating the gravitational search algorithm (GSA), as a heuristic optimization tool, with the Multilayer Perceptron (MLP-GSA) algorithm to forecast water level in Winnipesaukee and Cypress Lakes in the United States of America. The performance of the resulting hybrid MLP-GSA model is benchmarked and compared with the traditional MLP trained with Levenberg–Marquadt back propagation learning algorithm, two other intelligent hybrid models (MLP-PSO and MLP-FFA) and also two stochastic models namely, ARMA and ARIMA models. In this case study, the monthly time scale water level data from 1938 to 2005 and 1942 to 2011 for the Lakes Winnipesaukee and Cypress, respectively, were applied to train and evaluate the MLP-GSA model. The best input combinations of the standalone (MLP) and the hybrid MLP-GSA forecasting models were determined by sensitivity analysis of historical water level training data for 1-month lead forecasting. The hybrid MLP-GSA model was evaluated independently with statistical score metrics: coefficient of correlation, coefficient of efficiency, the root mean square and relative root mean square errors, and the Bayesian Information Criterion. The results showed that the hybrid MLP–GSA4 and MLP-GSA5 model (where the ‘4 and 5 months’ of lagged input combinations of Lake water level data were utilized as the model inputs) performed more accurately than the ARIMA, ARMA, MLP4, MLP-PSO4 and MLPFFA4 models for the Cypress Lake and ARIMA, ARMA, MLP5, MLP-PSO5 and MLP-FFA5 models for the Winnipesaukee lake, respectively. This study ascertained the robustness of hybrid MLP-GSA over ARMA, ARIMA, MLP, MLP-PSO and MLP-FFA for the forecasting of Lake water level. The high efficacy of the hybrid MLP-GSA model over the other applied models, indicate significant implications of its use in water resources management, decision-making tasks, irrigation management, management of hydrologic structures and sustainable use of water for agriculture and other necessities