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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.

A better understanding of the characteristics and capabilities of women entrepreneurs can significantly improve their chances of success. Therefore, three studies were conducted for this exploratory paper. We have discovered the characteristics of entrepreneurial intelligence among female entrepreneurs through semi-structured interviews based on conventional content analysis. According to the second study, qualitative meta-synthesis was utilized to identify characteristics of women's entrepreneurial intelligence at the international level. As a third study, we examined the evolutionary relationships of entrepreneurs' intelligence components following the discovery and creation of opportunities.

The present paper was based on three studies. In the first study, 15 female entrepreneurs were interviewed using purposive sampling in the Guilan province of Iran to identify the characteristics of entrepreneurial intelligence at the national level. An inductive content analysis was performed on the data collected through interviews. Using Shannon entropy and qualitative validation, their validity was assessed. In the second study, using a qualitative meta-synthesis, the characteristics of women's entrepreneurial intelligence were identified. Then the results of these two studies were compared with each other. In the third study, according to the results obtained from the first and second studies, the emergence, priority and evolution of entrepreneurial intelligence components in two approaches to discovering and creating entrepreneurial opportunities were determined. For this purpose, interviews were conducted with 12 selected experts using the purposeful sampling method using the fuzzy total interpretive structural modeling (TISM) method.

In the first research, this article identified the components of entrepreneurial intelligence of women entrepreneurs in six categories: entrepreneurial insights, cognitive intelligence, social intelligence, intuitive intelligence, presumptuous intelligence and provocative intelligence. In the second study, the components of entrepreneurial intelligence were compared according to the study at the national level and international literature. Finally, in the third study, the evolution of the components of entrepreneurial intelligence was determined. In the first level, social intelligence, presumptuous intelligence and provocative intelligence are formed first and social intelligence and provocative intelligence have an interactive relationship. In the second level, entrepreneurial insight and cognitive intelligence appear, which, in addition to their interactive relationship, take precedence over the entrepreneur's intuitive intelligence in discovering entrepreneurial opportunities. With the evolution of the components of entrepreneurial intelligence in the opportunity creation approach, it is clear that intuitive intelligence is formed first at the first level and takes precedence. At the second level, there is cognitive intelligence is created. At the third level, motivational intelligence and finally, at the last level, entrepreneurial insight, social intelligence and bold intelligence.

This study has the potential to discover credible and robust approaches for further examining the contextualization of women's entrepreneurial intelligence at both national and international levels, thereby advancing new insights. By conceptualizing various components of entrepreneurial intelligence for the first time and exploring how contextual factors differ across nations and internationally for women's entrepreneurship, this paper challenges the assumption that the characteristics of women's entrepreneurial intelligence are uniform worldwide. It also depicts the evolution of the components of entrepreneurial intelligence.

Recognizing women’s potential and directing their talents to realize these potentials can be of great benefit. Accordingly, this paper aims to identify the characteristics of entrepreneurial intelligence in female entrepreneurs, drawing on a national-level study and the international literature on this topic.

The present paper conducted two studies. First, 15 female entrepreneurs in the Guilan province of Iran, who were selected using purposive sampling, were interviewed to identify the characteristics of entrepreneurial intelligence nationally. The data gathered by interviews were analyzed using inductive content analysis. Then, their validity was tested using qualitative validation and analyzed using Shannon entropy. In the second study, the characteristics of female entrepreneurial intelligence were identified through a qualitative metasynthesis. The results of the two studies were compared together.

This categorized entrepreneurial intelligence into six categories, namely, entrepreneurial insights, cognitive intelligence, social intelligence, intuitive intelligence, presumptuous intelligence and provocative intelligence. Ultimately the characteristics of women’s entrepreneurial intelligence in each category were compared according to the national-level study and the international literature.

This study has the potential to discover credible and robust approaches for further examining the contextualization of women’s entrepreneurial intelligence at both national and international levels, thereby advancing new insights. By conceptualizing various dimensions of entrepreneurial intelligence for the first time and exploring how contextual factors differ across nations and internationally for women’s entrepreneurship, this paper challenges the assumption that the characteristics of women’s entrepreneurial intelligence are uniform across the world.

Cost overruns have been identified as the most significant challenge for construction sector stakeholders. Like many developing countries, the Iranian construction industry suffers from poor cost performance. So, the current research aims to investigate the causes of cost overrun in Iranian construction projects. This paper also reviews the findings of similar studies conducted in several Asian countries.

This paper includes a literature review and a quantitative method with a questionnaire survey. The review is limited to the studies investigating the causes of cost overrun in an Asian country in the last decade. Moreover, the current research was conducted through a questionnaire prepared based on 43 common factors identified through the literature review. The required data were gathered from the representatives involved in handling construction activities in Iran. The collected data were analyzed based on relative importance index (RII), using the SPSS software package.

The results showed that poor site management, improper planning, fluctuation of prices of materials, lack of experience, and poor economic condition are the critical reasons for cost overrun in Iranian construction projects. The findings also indicated that among the studies conducted in Asian countries, the first three factors have the highest frequency.

This paper highlighted most studies concerning the cost overrun factors in construction projects in different Asian countries in recent years. Up-to-date knowledge helps to understand the complexity of this field in various periods and therefore minimizes the risk of cost overrun. This research can also be used as a benchmark for further studies to clarify similar issues in other developing countries.

Despite long decades of research studies in the field of urban neighborhood resilience, there are still some unknown dimensions. In this regard, the study aims to develop a new framework for assessment of physical and social resilience at neighborhood level against earthquake.

Accordingly, first all indicators affecting earthquake resilience at neighborhood level have been determined. Subsequently, they have been weighted and prioritized, using analytical hierarchy process technique. One of the neighborhoods of Tehran city in Iran was selected for the case study. Then, four criteria of “robustness,” “redundancy,” “rapidity” and “resourcefulness” were considered as the basis for assessing neighborhood resilience.

It is shown that besides four well-known criteria, four other criteria including “adaptability,” “regularity,” “density” and “efficiency” should also be considered to assess earthquake resilience at neighborhood level. Therefore, a new framework is developed and formulated at this level. Paying attention to all eight criteria, the appropriate interventions to improve resiliency can be addressed.

This study proposes several practical approaches toward the more resilient neighborhoods against earthquakes. By considering the complexity and dynamic characteristics of earthquake resilience, it recommends the implementation of all resilience phases at every neighborhood to increase resilience.

This study proposes a framework in which in addition to physical and tangible indicators of resilience, non-physical and intangible ones are presented. Also, it is shown that besides well-known criteria, other criteria should also be considered using practical and community-based approaches.

In the past three decades, remote sensing-based models for estimating crop yield have addressed critical problems of general food security, as the unavailability of grains such as rice creates serious worldwide food insecurity problems. The main purpose of this study was to compare the potential of time-series Landsat-8 and Sentinel-2 data to predict rice yield several weeks before harvest on a regional scale.

To this end, the sum of normalized difference vegetation index (NDVI)-based models created the best agreement with actual yield data at the golden time window of six weeks before harvest when rice grains were in milky and mature growth stages. The application of nine other vegetation indicators was also investigated in the golden time window in comparison to NDVI.

The findings of this study demonstrate the viability of identifying locations with poor and superior performance in terms of production management approaches through a rapid and economical solution for early rice grain yield assessment. Results indicated that while some of those, such as enhanced vegetation index (EVI) and optimized soil adjusted vegetation index, were able to estimate rice yield with high accuracy, NDVI is still the best indicator to predict rice yield before harvest. However, experiments can be conducted in different regions in future studies to evaluate the generalizability of the approach.

To achieve this objective, the authors considered the following purposes: using Sentinel-2 time-series data, determining the appropriate growth stage for estimating rice yield and evaluating different vegetation indices for estimating rice yield.

Urmia lake water has impressively decreased recently and seriously endangered the lives of the inhabitants. In this paper, the effects of various factors on the reduction of the lake water are investigated and appropriate scenarios are proposed for future improvement. Due to the significant impact of agricultural issues on this crisis, this paper has focused specifically on agriculture. So, this paper aims to forecast and improve the lake water level.

In this paper, a system dynamics (SD) model, which is capable to consider various parameters and variables affecting the lake water level within nonlinear and dynamic relations, is developed.

To show the effectiveness of SD model, real data are used to run the model and the results show that the actual behavior of the lake water is reproduced with high validation (around 98.28 per cent). Also, five different scenarios are proposed to increase lake water volume. The hybrid Scenario 5 (which combines three other scenarios including increasing irrigation efficiency in the agricultural sector, changing cultivation pattern of agricultural products and returning some dams’ water that are consumed in the agricultural sector into the lake) is chosen as the most effective scenario for increasing lake volume about 15 billion m³.

The main contributions of this paper are systemic view to the whole problem, paying attention to the agriculture subject as one of the most important issues, considering many critical variables (e.g. evaporation, salinity and precipitation) and providing improvement policies along with assessing the effects of them.

The purpose of this qualitative study is to investigate how young female customers establish psychological relationships with small- to medium-scale retail stores over time forming purchase intentions, actual purchase patterns and repurchase behaviour. Role of various customer typologies was also considered.

A case study approach was implemented to collect and analyse data, where data was collected from 20 young female customers and ten clothing retailers using purposive sampling via semi-structured interviews. Interviews with customers were conducted in a place of their choice such as in a coffee shop, whereas data from retailers were collected in the retail stores. Both online and offline retail patronage was considered to incorporate the growing tendency towards online shopping. Results were analysed using thematic analysis.

This study managed to reveal a number of interesting findings on how female customers form and develop psychological relationships with clothing retailers over time that ultimately builds customer loyalty. Customer behaviour in pre-purchase, purchase and re-purchase stages can significantly vary according to their individual perceptions, whereas they have a few favourite clothing brands that they frequently shop for. Preference for online shopping was found to be minimal, most of them enjoying in store experiences. Further, word of mouth and unique designs emerged as key contributors in establishing retail brand loyalty.

This paper provides better insights for clothing retailers and industry practitioners in understanding how customer perceptions affect clothing purchase decisions.

This paper contributes to the retail literature by emphasizing on various elements that should be amalgamated through proper synthesis to serve customers. The research is unique as it analyses customer behaviour using a recreational activity model as opposed to marketing models to demonstrate how customers develop relationships with retail brands overtime.

The purpose of this study is to investigate and analysis of energy consumption for this industry. The core part of any energy management system (EnMS) in industry is to perfectly monitor the energy consumption of significant users and to continuously improve the energy performance. In petrochemical plants, production deals with energy-intensive processes, and measuring energy performance for recognition and assessment of potentials for saving is critical.

The required data are exploited for the period of March 2011-August 2016 (data set: 2,012 days). Multivariate linear regression (MLR) and multi-layer perceptron artificial neural network (ANN) methods are separately used to anticipate the energy consumption. The baseline will be assumed as a reference to be compared with the actual data to estimate the real saving values. Finally, cumulative summations (CUSUM) are proposed and applied as an effective indicator for measurement of energy performance in an LDPE.

In this study, two statistical methods of MLR and ANN were used to design and develop a comprehensive energy baseline representing the predicted amounts of energy consumption based on the recognized drivers. Although both models imply robust outcomes, when the relative errors are taken into account, performance of ANN models appears fairly superior compared to the MLR model.

It is highly suggested to the ISO technical committee dealing with energy management standards, to consider the proposed model for baseline development in the future version of the standard ISO 50006 as the supplementary extension for the ISO 50001 for measuring energy performance using EnB and EnPI. As for future studies, the research can be extended to investigate the uncertainty and the model could also become completed applying more advanced ANNs such as recurrent neural networks.

This paper aims to overcome some of the practical difficulties in assistive control of exoskeletons by developing a new assistive algorithm, called output feedback assistive control (OFAC) method. This method does not require feedbacks from force, electromyography (EMG) or acceleration signals or even their estimated values.

The presented controller uses feedbacks from position and velocity of the output link of series elastic actuators (SEAs) to increase the apparent integral admittance of the assisted systems. Optimal controller coefficients are obtained by maximizing the assistance ratio subjected to constraints of stability, coupled stability and a newly defined comfort measure.

The results confirm the effectiveness of using the inherent properties of SEAs for removing the need for extra controversial sensors in assistive control of 1 degree of freedom (1-DOF) SEA powered exoskeletons. The results also clearly indicate the successful performance of the OFAC method in reducing the external forces required for moving the assisted systems.

As the provided experiments indicate, the proposed method can be easily applied to single DOF compliantly actuated exoskeletons to provide a more reliable assistance with lower costs. This is achieved by removing the need for extra controversial sensors.

This paper proposes a novel assistive controller for SEA-powered exoskeletons with a simple model-free structure and independent of any information about interaction forces and future paths of the system. It also removes the requirement for the extra sensors and transforms the assistive control of the compliantly actuated systems into a simpler problem of position control of the SEA motor.