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This work aims to focus on the study of natural convection heat transfer characteristics in a vertical wall embedded in a non-Darcy porous medium filled with nanofluids.

The governed partial differential equations are transformed into ordinary differential equations which are obtained by similarity solution and then have been solved through homotopy analysis method.

The results show that Cu-water as nanofluid enhances the local Nusselt number more than other nanofluids.

Nanoparticles and nanofluid enhance the local Nusselt number.

The purpose of this study is to estimate inputs (outputs) and flexible measures when outputs (inputs) are changed provided that the relative efficiency values remain without change.

A novel inverse data envelopment analysis (DEA) approach with flexible measures is proposed in this research to assess inputs (outputs) and flexible measures when outputs (inputs) are perturbed on condition that the relative efficiency scores remain unchanged. Furthermore, flexible inverse DEA approaches proposed in this study are used for a numerical example from the literature and an application of Iranian banking industry to clarify and validate them.

The findings show that including flexible measures into the investigation effects on the changes of performance measures estimated and leads to more reasonable achievements.

The traditional inverse DEA models usually investigate the changes of some determinate input-output factors for the changes of other given input-output indicators assuming that the efficiency values are preserved. However, there are situations that the changes of performance measures should be tackled while some measures, called flexible measures, can play either input or output roles. Accordingly, inverse DEA optimization models with flexible measures are rendered in this paper to address these issues.

Assessing inputs and outputs is a significant aspect of taking decisions while there are complex and multistage processes in many examinations. Due to the presence of interval performance measures in various real-world studies, the purpose of this study is to address the changes of interval inputs of two-stage processes for the perturbations of interval outputs of two-stage systems, given that the overall efficiency scores are maintained.

Actually, an interval inverse two-stage data envelopment analysis (DEA) model is proposed to plan resources. To illustrate, an interval two-stage network DEA model with external interval inputs and outputs and also its inverse problem are suggested to estimate the upper and lower bounds of the entire efficiency and the stages efficiency along with the variations of interval inputs.

An example from the literature and a real case study of the banking industry are applied to demonstrate the introduced approach. The results show the proposed approach is suitable to estimate the resources of two-stage systems when interval measures are presented.

To the best of the authors’ knowledge, there is no study to estimate the fluctuation of imprecise inputs related to network structures for the changes of imprecise outputs while the interval efficiency of network processes is maintained. Accordingly, this paper considers the resource planning problem when there are imprecise and interval measures in two-stage networks.

Measuring healthcare service quality provides an objective guide for managers and policy makers to improve their services and patient satisfaction. Consequently, the purpose of this paper is to measure service quality provided to surgical and medical inpatients at Kerman Medical Sciences University (KUMS) in 2015.

A descriptive-analytic study, using a cross-sectional method in the KUMS training hospitals, was implemented between October 2 and March 15, 2015. Using stratified random sampling, 268 patients were selected. Data were collected using an importance-performance analysis (IPA) questionnaire, which measures current performance and determines each item’s importance from the patients’ perspectives. These data indicate overall satisfaction and appropriate practical strategies for managers to plan accordingly.

Findings revealed a significant gap between service importance and performance. From the patients’ viewpoint, tangibility was the highest priority (mean=3.54), while reliability was given the highest performance (mean=3.02). The least important and lowest performance level was social accountability (mean=1.91 and 1.98, respectively).

Healthcare managers should focus on patient viewpoints and apply patient comments to solve problems, improve service quality and patient satisfaction.

The authors applied an IPA questionnaire to measure service quality provided to surgical and medical ward patients. This method identifies and corrects service quality shortcomings and improving service recipient perceptions.

The Health Sector Evolution Plan (HSEP) is the most recent reform in Iran’s health care system that was launched in May 2014 in all university-affiliated hospitals to reduce health care expenditure for patients, while improving the efficiency and quality of hospital services. The purpose of this paper is to evaluate the impact of the HSEP on the performance of 15 hospitals affiliated with Kermanshah University of Medical Sciences (KUMS), located in the western region of Iran.

The Pabon Lasso model was used to measure the performance of hospitals before and after the implementation of the HSEP in 2013-2014 and 2015-2016, respectively. Three indicators of average length of stay (ALoS), bed occupancy rate (BOR) and bed turnover rate (BTR) were analyzed by the Pabon Lasso model.

The results showed that the average ALoS, BTR and BOR before the introduction of the HSEP were 2.59 days, 92 times and 57 percent, respectively, and the corresponding figures for these indicators after the implementation of the HSEP were 2.61 days, 98.9 times and 59.9 percent. The results indicated that before the introduction of the HESP, 40 percent of hospitals were in zone 1 (poor performance: low BTR and BOR and high ALoS), 27 percent in zone 2, 20 percent in zone 3 (good performance: high BTR and BOR and low ALoS) and 13 percent in zone 4. After the HSEP, the proportion of hospitals in zones 1-4 was 33, 27, 20 and 20 percent, respectively.

This study is the first to use the Pabon Lasso model technique to evaluate the impact of the HSEP on hospitals affiliated with KUMS.

Neuromarketing, which is an interdisciplinary area, concentrates on evaluating consumers’ cognitive and emotional reactions to different marketing stimuli. In spite of advantages, neuromarketing still requires development and lacks a strong theoretical framework. Techniques that are used in neuromarketing studies have different superiorities and limitations, and thus, there is a need for the evaluation of the relevance of these techniques. The purpose of this study is to introduce a novel integrated approach for the neuromarketing research area.

The proposed approach combines 2-tuple linguistic representation model and data envelopment analysis to obtain the most efficient neuromarketing technique. It is apt to handle information provided by using both linguistic and numerical scales with multiple information sources. Furthermore, it allows managers to deal with heterogeneous information, without loss of information.

The proposed approach indicates that functional magnetic resonance imaging (fMRI) is the best performing neuromarketing technology. Recently, fMRI has been widely used in neuromarketing research. In spite of its high cost, its main superiorities are improved spatial and temporal resolutions. On the other hand, transcranial magnetic stimulation (TMS) and positron emission tomography (PET) are ranked at the bottom because of their poor resolutions and lower willingness of participants.

This paper proposes a common weight data envelopment analysis (DEA)-based decision model to cope with heterogeneous information collected by the experts to determine the best performing neuromarketing technology. The decision procedure enables the decision-makers to handle the problems of loss of information and multi-granularity by using the fusion of 2-tuple linguistic representation model and fuzzy information. Moreover, a DEA-based common weight model does not require subjective experts’ opinions to weight the evaluation criteria.

This study aims to focus on the supply chain (SC) cost drivers of healthcare industries in the USA, as SC costs have increased 40% over the last decade. The second-most significant expense, the SC, accounts for 38% of total expenses in a typical hospital, while most other industries can operate within 10% of their operating cost. This makes healthcare centers supply-chain-sensitive organizations with limited facilities for high-quality healthcare services. As the cost drivers of healthcare SC are almost unknown to managers, their jobs become more complex.

Guided by pragmatism and positivism paradigms, a cross-sectional study has been designed using quantitative and deductive approaches. Both primary and secondary data were used. Primary data were collected from health centers across the country, and secondary data were from healthcare-related databases. This study examined the attributes that explain the most significant variation in each contributing factor. With multiple regression analysis for predicting cost and Student's t-tests for the significance of contributing factors, the authors of this study examined different theories, including the market-based view and five-forces, network and transaction cost analysis.

This study revealed that supply, materials and services represent the most significant expenses in primary care. Supply-chain cost breakdown results in four critical factors: facility, inventory, information and transportation.

This study examined the data from primary and secondary care institutions. Tertiary and quaternary care systems were not included. Although tertiary and quaternary care systems represent a small portion of the healthcare system, future research should address the supply chain costs of highly specialized organizations.

This study suggests methods that can help to improve supply chain operations in healthcare organizations worldwide.

This study presents an empirically proven methodology for testing the statistical significance of the primary factors contributing to healthcare supply chain costs. The results of this study may lead to positive policy changes to improve healthcare organizations' efficiency and increase access to high-quality healthcare.