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Energy has a strategic role in the social and economic development of countries. Affordability, accessibility and availability of energy sources are the priorities of the governments in energy supply. Therefore, understanding the robustness of energy supply is an important subject of energy researchers and policymakers. This paper aims to analyze the robustness of the electricity system at the national level.

First, the strengths, weaknesses, opportunities and threats analysis is implemented for a selected case study. Then, the expert panel weighed the parameters’ effect on sustainable power generation, the survey is quantified using fuzzy logic. Finally, cross functional analysis is applied to evaluate the influence/dependence of the parameters.

The results show three determinant parameters which have the most influence on the system: fluctuations in oil prices, governmental acts and sanctions against the country. The most dependent parameters, as objectives variables, are the share of renewables and distributed generation (DG), system reliability, power generation diversity and transmission efficiency.

Using future studies methods in the energy level at the nation level has been done for the first time in the current work.

This paper aims to evaluate the prevalence of general obesity (GO) and abdominal obesity (AO) in the north-west of Iran and investigate the association with food choices and socioeconomic status (SES).

In this cross-sectional study, 500 subjects aged ≥ 18 years were studied. Data on their basic characteristics, anthropometric measurements, dietary habits and physical activity were collected. The authors examined the association between GO and AO with SES and food choices using multiple logistic regression analysis.

The prevalence of GO and AO was 26.6 and 43.4%, respectively. A positive association was observed between age and GO (p for trend <0.001) and AO (p for trend 0.005) in both sexes. However, a negative correlation was detected between education and income with GO and AO (p for trend <0.001). Two or more servings of fruit consumption a day were associated with lower odds of obesity. It was observed that the odds of GO and AO decreased by three or more servings of daily fruit. The consumption of dairy products in two or more servings a day led to a reduction in odds of GO and AO. The consumption of five or more servings of legumes, beans and nuts a week was associated with lower odds of GO and AO.

Educational attainment, greater income and a higher intake of some specific food groups were associated with lower odds of obesity in the area. More population-based investigations are required to develop effective preventive strategies to control the status of being overweight and obesity in different regions.

This study aims to minimize the cost and time of blood delivery in the whole blood supply chain network (BSCN) in disaster conditions. In other words, integrating all strategic, tactical and operational decisions of three levels of blood collection, processing and distribution leads to satisfying the demand at the right time.

This paper proposes an integrated BSCN in disaster conditions to consider four categories of facilities, including temporary blood collection centers, field hospitals, main blood processing centers and medical centers, to optimize demand response time appropriately. The proposed model applies the location of all permanent and emergency facilities in three levels: blood collection, processing and distribution. Other essential decisions, including multipurpose facilities, emergency transportation, inventory and allocation, were also used in the model. The LP metric method is applied to solve the proposed bi-objective mathematical model for the BSCN.

The findings show that this model clarifies its efficiency in the total cost and blood delivery time reduction, which results in a low carbon transmission of the blood supply chain.

The researchers proposed an integrated BSCN in disaster conditions to minimize the cost and time of blood delivery. They considered multipurpose capabilities for facilities (e.g. field hospitals are responsible for the three purposes of blood collection, processing and distribution), and so locating permanent and emergency facilities at three levels of blood collection, processing and distribution, support facilities, emergency transportation and traffic on the route with pollution were used to present a new model.

Nowadays, the tourism industry is considered the largest and most diverse industry in the world, which can play a significant role in the economic growth and development of a country as the main source of income and employment. In this regard, the purpose of this study is to focus on evaluating the competitiveness indicators of Shiraz city tourism destination based on a combined model.

For this purpose, a questionnaire with 78 questions was used to evaluate the indicators, in which a total of 1,432 tourists participated, including 927 domestic tourists and 505 international tourists. The collected data were analyzed to determine the hierarchical relationship between the indicators using equation structural modeling.

The research findings indicate that the most effective and influential indicators in the field of tourism competitiveness are natural and handmade resources, and the most influential factors are demand conditions and cultural and natural resources. In this hierarchy, each factor affects its previous level and influences the next level.

This study offers significant potential for uncovering credible and robust approaches to further investigate the contextualization of tourism competitiveness on both national and international scales, thereby generating valuable new insights. By conceptualizing the diverse dimensions of tourism competitiveness and delving into the variations in its impacts across multiple levels, this research not only challenges existing notions but also aids destinations in maintaining and enhancing their market position and share over time. This study offers valuable insights and practical implications for both researchers and practitioners in the field of urban tourism. It enhances the understanding of destination competitiveness, informs policy decisions, facilitates benchmarking and best practices, guides strategic decision-making and promotes sustainable tourism development.

The Russia–Ukraine war has disrupted the wheat supply worldwide. Given that wheat is one of the most important agri-food products in the world, it is necessary to pay attention to the wheat supply chain during the global crises. The use of resilience strategies is one of the solutions to face the supply chain disruptions. In addition, there is a possibility of multiple crises occurring in global societies simultaneously.

In this research, the resilience strategies of backup suppliers (BS) and inventory pre-prepositioning (IP) were discussed in order to cope with the wheat supply chain disruptions. Furthermore, the p-Robust Scenario-based Stochastic Programming (PRSSP) approach was used to optimize the wheat supply chain under conditions of disruptions from two perspectives, feasibility and optimality.

After implementing the problem of a real case in Iran, the results showed that the use of resilience strategy reduced costs by 9.33%. It was also found that if resilience strategies were used, system's flexibility and decision-making power increased. Besides, the results indicated that if resilience strategies were used and another crisis like the COVID-19 pandemic occurred, supply chain costs would increase less than when resilience strategies were not used.

In this study, the design of the wheat supply chain was discussed according to the wheat supply disruptions due to the Russia–Ukraine war and its implementation on a real case. In the following, various resilience strategies were used to cope with the wheat supply chain disruptions. Finally, the effect of the COVID-19 pandemic on the wheat supply chain in the conditions of disruptions caused by the Russia–Ukraine war was investigated.

Children and adolescents with autism spectrum disorder need special attention for their mental and physical health and education. This study aims to investigate the effects of the COVID-19 pandemic on them.

This descriptive cross-sectional study was conducted on data collected from an online survey. Respondents were the parents or caregivers of autistic children aged 3–18. It measured opinions about the favorable and unfavorable behavioral changes related to their children during the COVID-19 lockdowns.

In total, 131 respondents participated in the study. The mean age of their autistic children was 11.45 ± 4.26 years, of whom 78% were boys. Improved good behaviors during lockdowns included interaction with parents and siblings, eating healthy foods, quality of sleep, eye contact, pointing and asking for something, imitating sounds and phrases, sharing interests, reaction to emotions and empathy with family members, but sleep hours decreased.

Lockdown provided opportunities for examining the behaviors of children with autism taking into account its favorable and unfavorable effects, to improve communicating and behaving with these children.

In recent decades, the student population in dormitories has increased notably, primarily attributed to the growing number of international students. Dormitories serve as pivotal environments for student development. The coordination and compatibility among students can significantly influence their overall success. This study aims to introduce an innovative method for roommate selection and room allocation within dormitory settings.

In this study, initially, using multi-attribute decision-making methods including the Bayesian best-worst method and weighted aggregated sum product assessment, the incompatibility rate among pairs of students is calculated. Subsequently, using a linear mathematical model, roommates are selected and allocated to dormitory rooms pursuing the twin objectives of minimizing the total incompatibility rate and costs. Finally, the grasshopper optimization algorithm is applied to solve large-sized instances.

The results demonstrate the effectiveness of the proposed method in comparison to two common alternatives, i.e. random allocation and preference-based allocation. Moreover, the proposed method’s applicability extends beyond its current context, making it suitable for addressing various matching problems, including crew pairing and classmate pairing.

This novel method for roommate selection and room allocation enhances decision-making for optimal dormitory arrangements. Inspired by a real-world problem faced by the authors, this study strives to offer a robust solution to this problem.

The purpose of this paper is to identify the level of natural gas consumption that can be adjudged as capable of improving the growth of the Nigerian economy, to investigate whether natural gas consumption is at optimal level in Nigeria and to examine the nature and rate to which natural gas consumption affects economic growth in Nigeria at low and high regimes.

The tool used to achieve the objectives of the paper is the smooth transition regression (STR) model.

The findings of the paper are that the relationship between natural gas consumption and economic growth in Nigeria is asymmetric, where the natural gas consumption threshold value in the country is 9085.36 standard cubic meters, whereas the level of its consumption in the country is below the optimal level. Further, in both low and high regimes, natural gas consumption has been found to have a positive and significant impact on economic growth in Nigeria.

The policy implication of the paper is that natural gas consumption in Nigeria should not be less than 9085.36 standard cubic meters and the country should intensify efforts to increase the level of natural gas consumption, as it is below the optimal level and its consumption bolsters the growth of Nigerian economy.

What is new in this paper is its ability to use the STR model. To the best of the authors’ knowledge, such methodology has not been adopted before in such a relation.

Investigating the import of pharmaceutical products, as one of the essential goods of the country to improve health and medical conditions, is important in accelerating the economic growth and enhancing the welfare of the community. This paper aims to estimate the income and price elasticity of pharmaceutical import demand in Iran.

In this research, the function of Iran's pharmaceutical import demand was estimated and calculated using the co-integration method and error correction techniques with the seasonal data of 2005–2016 to identify factors related to import, as well as relevant price, and income elasticity.

The research results showed that the price elasticity of Iran's long-term pharmaceutical import demand was 0.04; that is, the rate of pharmaceutical import demanded by Iran was not sensitive to change in relative price. Additionally, the long-term income elasticity of Iran's pharmaceutical demand was 0.69, meaning that an increase in Iran's income did not have a highly positive impact on the volume of Iran's pharmaceutical import.

Estimating the function of the demand for pharmaceutical import may pave the way to adopt appropriate economic policies. Furthermore, estimating this function and calculating income and price elasticity is a step toward minimizing government expenditures and can be a great contribution in designing trade policies.

Unfortunately, the occurrence of natural disasters is inevitable all over the world. Hence, this paper aims to analyze a scenario-based collaborative problem in a relief supply chain (RSC), where nongovernmental organizations can participate in relief activities with governmental organizations. This study focuses on location-allocation, inventory management and distribution planning under uncertain demand, budget, transportation and holding costs where government and private distribution centers receive relief items from suppliers then send them to affected areas. The performance of the proposed model is surveyed in a real case study in Dorud.

This paper develops a nonlinear mixed-integer programming model that seeks to maximize the coverage of demand points and minimize operating costs and traveled distance. The linear programming-metric technique and grasshopper optimization algorithm are applied to survey the model's applicability and efficiency.

This study compares noncollaborative and collaborative cases in terms of the number of applied distribution centers and RSC's goals, then demonstrates that the collaborative model not only improves the coverage of demand points but also minimizes cost and traveled distance. In fact, the presented approach helps governments efficiently surmount problems created after a disaster, notwithstanding existing uncertainties, by determining a strategic plan for collaboration with nongovernmental organizations for relief activities.

Relief strategies considered in previous research have not been sufficiently examined from the perspective of collaboration of governmental and nongovernmental organizations and provided an approach to develop the coverage of affected areas and reducing costs and traveled distance despite various uncertainties. Hence, the authors aim to manage RSCs better by offering a mathematical model whose performance has been proved in a real case study.