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Nowadays, engineering, procurement and construction (EPC) contracts are being widely used to perform industrial and infrastructure projects because of several reasons like high speed of implementation. However, these contracts are always accompanied by high risks and uncertainties. Thus, selection of the right EPC contractor has significant importance. This paper aims to present a fuzzy multi-criteria decision-making (MCDM) model for EPC contractor prequalification.

First, the EPC contractor prequalification criteria are defined by using literature review and interviewing experts. Second, the weights of criteria are determined by interviewing experts. Then, each EPC contractor is evaluated in each criterion. Finally, fuzzy weighted average (FWA) approach is employed to select the right contractor among potential EPC contractors.

The proposed model is prepared as an applicable model for clients to select the right EPC contractors among contractors who want to conduct the project.

As a lack of applicable model does exist to assign the prequalification of EPC contractors, this study is one of the first research studies which proposed a fuzzy MCDM model for evaluation of EPC contractors. To cope with the uncertainty of the prequalification problem, fuzzy logic has been used. Using fuzzy sets leads to reaching more reliable results. Also, a real case study is provided to explain the proposed model.

Water and wastewater (WW) projects are gaining attention in Iran because of shortages of water resources. However, these projects are lengthy and they are accompanied by numerous risks, such as lack of sufficient financial resources. Public–private partnerships (PPPs) are taken into account as a constructive approach to deal with the problem of insufficient government funds and they are increasingly being implemented to construct the required infrastructures in different countries. Although WW projects in PPPs can reduce the government’s debt, investors are still uncertain about this approach. Hence, this study aims to identify and evaluate the risk of all parties involved in WW-PPP projects, from the viewpoint of investor.

First, the risk factors which are involved in WW projects are identified by interviewing experts and reviewing the literature by means of fault tree analysis (FTA) tool. Second, the probability and effects of the risky factors which are related to specific event are evaluated and analyzed by hybridization of interval fuzzy Type-2 sets (IT2FS) and risk score formulation. Finally, some solutions are proposed to deal with the most challenging risks.

Six gate events, namely, risks which are related to investors such as investor’s consultant-related risks, risky conditions from engineering, procurement, and construction (EPC) contractors’ point of view, risk factors which public sector takes into account, public sector’s consultant-related risks which public sector’s consultant consider challenging and external factors were defined according to the literature. From FTA tool and by interviewing the experts, 94 basic events were identified. Finally, from hybridization of IT2FSs and risk score formulation, top five risks are determined as “Difficulty of injecting financial resources into the project,” “Fluctuation in inflation rate,” “Poor decision-making process” in public sector, “Difficulty of importing the equipment which are required for the project (such as pumps, grain catchers, garbage collectors, etc.) from other countries” and “Impact of risky conditions in other projects on operation of PPP project.”

In the absence of a constructive approach for risk identification and a reliable model for evaluating the identified risks in PPP projects, this research project is one of the first research studies which used FTA for identifying risks and hybridization of IT2FSs and risk score formulation for evaluating the risks.

This paper aims to examine the integration of lateral transshipment and road vulnerability into the humanitarian relief chain in light of affected area priority to address equitable distribution and assess the impact of various parameters on the total average inflated distance traveled per relief item.

After identifying comprehensive critical criteria and subcriteria, a hybrid multi-criteria decision-making framework was applied to obtain the demand points’ weight and ranking in a real-life earthquake scenario. Direct shipment and lateral transshipment models were then presented and compared. The developed mathematical models are formulated as mixed-integer programming models, considering facility location, inventory prepositioning, road vulnerability and quantity of lateral transshipment.

The study found that the use of prioritization criteria and subcriteria, in conjunction with lateral transshipment and road vulnerability, resulted in a more equitable distribution of relief items by reducing the total average inflated distance traveled per relief item.

To the best of the authors’ knowledge, this study is one of the first research on equity in humanitarian response through prioritization of demand points. It also bridges the gap between two areas that are typically treated separately: multi-criteria decision-making and humanitarian logistics.

This is the first scholarly work in Shiraz focused on the equitable distribution system by prioritization of demand points and assigning relief items to them after the occurrence of a medium-scale earthquake scenario considering lateral transshipment in the upper echelon.

The paper clarifies how to prioritize demand points to promote equity in humanitarian logistics when the authors have faced multiple factors (i.e. location of relief distribution centers, inventory level, distance, lateral transshipment and road vulnerability) simultaneously.

Hepatitis C is one of the major health issues in both developed and developing countries. Hepatitis C virus (HCV) infection is more common in prisoners than in the general population. The purpose of this study was to determine the prevalence of HCV and its associated risk factors in Iranian male prisoners in Tehran.

In this cross-sectional study, the authors investigated the frequency and risk factors of hepatitis C infection among male prisoners in the Great Tehran Prison. Information on risk factors including the length of imprisonment, previous history of imprisonment, history of drug injection, history of tattooing, history of piercing, history of high-risk sex and family history of hepatitis C were extracted from patients’ records. To evaluate HCV status, blood samples were collected and tested.

In this study, 179 participants were included. Nine participants (5.0%, 95% CI, 2.3-9.3) were positive for hepatitis C. HCV infection was not significantly associated with age, marital status, education, previous history of imprisonment, length of imprisonment, piercing and high-risk sex; however, there was a significant association between a history of tattooing and a history of injecting drug use and Hepatitis C.

The prevalence of hepatitis C among male prisoners in Great Tehran Prison was 5% in this study, similar to recent studies on prisoners in Tehran. A history of drug injections as well as tattooing were the most important risk factors for hepatitis C in male prisoners.

The number of natural and man-made disasters is remarkable and threatened human lives at the time of occurrence and also after that. Therefore, an efficient response following a disaster can eliminate or mitigate the adverse effects. This paper aims to help address those challenges related to humanitarian logistics by considering disaster network design under uncertainty and the management of emergency relief volunteers simultaneously.

In this paper, a robust fuzzy stochastic programming model is proposed for designing a relief commodity supply chain network in a disaster by considering emergency relief volunteers. To demonstrate the practicality of the proposed model, a case study is presented for the 22 districts of Tehran and solved by an exact method.

The results indicate that there are many parameters affecting the design of a relief commodity supply chain network in a disaster, and also many parameters should be controlled so that, the catastrophe is largely prevented and the lives of many people can be saved by sending the relief commodity on time.

This model helps decision-makers and authorities to explore optimal location and allocation decisions without using complex optimization algorithms.

To the best of the authors’ knowledge, employee workforce management models have not received adequate attention despite their role in relief and recovery efforts. Hence, the proposed model focuses on the problem of managing employees and designing a disaster logistics network simultaneously. The robust fuzzy stochastic programming method is applied for the first time for controlling the uncertainties in the design of humanitarian relief supply chains.

The purpose of this paper is to propose three imperialist competitive algorithm (ICA)-based models for predicting the blast-induced ground vibrations in Shur River dam region, Iran.

For this aim, 76 data sets were used to establish the ICA-linear, ICA-power and ICA-quadratic models. For comparison aims, artificial neural network and empirical models were also developed. Burden to spacing ratio, distance between shot points and installed seismograph, stemming, powder factor and max charge per delay were used as the models’ input, and the peak particle velocity (PPV) parameter was used as the models’ output.

After modeling, the various statistical evaluation criteria such as coefficient of determination (R2) were applied to choose the most precise model in predicting the PPV. The results indicate the ICA-based models proposed in the present study were more acceptable and reliable than the artificial neural network and empirical models. Moreover, ICA linear model with the R2 of 0.939 was the most precise model for predicting the PPV in the present study.

In the present paper, the authors have proposed three novel prediction methods based on ICA to predict the PPV. In the next step, we compared the performance of the proposed ICA-based models with the artificial neural network and empirical models. The results indicated that the ICA-based models proposed in the present paper were superior in terms of high accuracy and have the capacity to generalize.

A set of routers that are connected over communication channels can from network-on-chip (NoC). High performance, scalability, modularity and the ability to parallel the structure of the communications are some of its advantages. Because of the growing number of cores of NoC, their arrangement has got more valuable. The mapping action is done based on assigning different functional units to different nodes on the NoC, and the way it is done contains a significant effect on implementation and network power utilization. The NoC mapping issue is one of the NP-hard problems. Therefore, for achieving optimal or near-optimal answers, meta-heuristic algorithms are the perfect choices. The purpose of this paper is to design a novel procedure for mapping process cores for reducing communication delays and cost parameters. A multi-objective particle swarm optimization algorithm standing on crowding distance (MOPSO-CD) has been used for this purpose.

In the proposed approach, in which the two-dimensional mesh topology has been used as base construction, the mapping operation is divided into two stages as follows: allocating the tasks to suitable cores of intellectual property; and plotting the map of these cores in a specific tile on the platform of NoC.

The proposed method has dramatically improved the related problems and limitations of meta-heuristic algorithms. This algorithm performs better than the particle swarm optimization (PSO) and genetic algorithm in convergence to the Pareto, producing a proficiently divided collection of solving ways and the computational time. The results of the simulation also show that the delay parameter of the proposed method is 1.1 per cent better than the genetic algorithm and 0.5 per cent better than the PSO algorithm. Also, in the communication cost parameter, the proposed method has 2.7 per cent better action than a genetic algorithm and 0.16 per cent better action than the PSO algorithm.

As yet, the MOPSO-CD algorithm has not been used for solving the task mapping issue in the NoC.

It can be said that the most important function of the health system is to provide health-care services. This directly affects society’s health and justice. To the best of the authors’ knowledge, this study is the first study that has meta-analyzed the percentage of health services utilization among Iranians to answer this question “How much is the percentage of health-care services utilization in Iran?”

This study is a systematic review and meta-analysis conducted in 2020. The literature review was done by searching the databases of Google Scholar, Scopus, PubMed, Web of Science, Science Direct, SID, Magiran and Irandoc from January 2010 to August 2020. Pooled utilization percentage (UP) was estimated by STATA 15 through random-effects meta-analysis with 95% confidence interval. The I² statistic was used to investigate the possibility of heterogeneity of articles (I² ≥ 50% indicate heterogeneity). A forest plot was applied to report the results. A funnel plot exploited due to publication bias assessment.

Approximately 22% of the reviewed articles have been done at the national level of Iran. Most studies have examined the utilization of outpatient and inpatient services. Based on the random effects method, the pooled value of UP was obtained at 47.82%, (95% CI: 39.33–56.31). Also, the pooled percentage of utilization was statistically significant (Test of UP = 0, Z = 456.44, p = 0.000).

Policymakers can improve the level of utilization from health services by expanding universal coverage, expanding insurance coverage and removing direct and indirect barriers.

To the best of the authors’ knowledge, this is the first systematic review and meta-analysis on health services utilization in Iran that completely considers the utilization rate in all health services and states the practical solutions to cope with utilization deficiencies and barriers.

The purpose of this paper is to investigate the possible relationship between novice vs experienced EFLs teachers’ Big Five personality traits, ambiguity tolerance and risk taking. To this purpose, 30 teachers of TEFL courses were randomly selected, and three instruments of NEO Five-Factor Inventory, Ambiguity Tolerance Scale and Risk-taking Propensity Measure were employed to measure their Big Five personality traits, their ambiguity tolerance and risk taking, respectively.

The study was a quantitative ex post facto study. The first phase of the study was to investigate the relationship among variables of the study. On the other hand, the second phase of the study examined the impact of experience of teachers on their risk taking and ambiguity tolerance.

The results showed that the more experienced the teachers are, the less risk they take and the more ambiguity tolerant they are. On the other hand, the less experienced the teachers are, the more risk they will take and the less they can tolerate ambiguity. The findings of this research can have useful implications for teacher training programs as well as teaching practices.

This study can add to the circle of knowledge and enhance theoretical assumptions of the field. Moreover, considering the Iranian context, a few studies have focused on the importance of uncovering relationship between five big personality traits and teachers’ personality factors. Therefore, this study is an attempt to investigate the relationship between the Big Five personality traits of teachers and their ambiguity tolerance and risk taking.

This study aims to numerically investigate the heat transfer and laminar forced and two-phase flow of Water/Cu nanofluid in a rectangular microchannel with oblique ribs with angle of attacks equal to 0-45°. This simulation was conducted in the range of Reynolds numbers of 5-120 in volume fractions of 0, 2 and 4 per cent of solid nanoparticles in three-dimensional space.

This study investigates the effect of the changes of angle of attack of rectangular rib on heat transfer and hydrodynamics of two-phase flow. This study was done in three-dimensional space and simulation was done with finite volume method. SIMPLEC algorithm and second-order discretization of equations were used to increase the accuracy of results. The usage of nanofluid, application of rips with different angles of attacks and using the two-phase mixture method is the distinction of this paper compared with other studies.

The results of this research revealed that the changing angle of attack of ribs is an effective factor in heat transfer enhancement. On the other hand, the existence of rib on the internal surfaces of a microchannel increases friction coefficient. By increasing the volume fraction of nanoparticles, due to the augmentation of fluid density and viscosity, the pressure drop increases significantly. For all of the angle of attacks studied in this paper, the maximum rate of performance evaluation criterion has been obtained in Reynolds number of 30 and the minimum amount of performance evaluation criterion was been obtained in Reynolds numbers of 5 and 120.

Many studies have been done in the field of heat transfer in ribbed microchannel. In this paper, the laminar flow in the ribbed microchannel Water/Cu nanofluid in a rectangular microchannel by using two-phase mixture method is numerically investigated with different volume fractions (0-4 per cent), Reynolds numbers (5-120) and angle of attacks of rectangular rib in the indented microchannel (0-45°).