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As the main factor for sustainable development of countries, entrepreneurship is a difficult path only chosen by those who have a high level of risk-taking. On this path, entrepreneurship requires an ecosystem that welcomes this type of thinking and eliminates the barriers on the path as much as possible. This ecosystem comprises various components that attempt to pave the way in a private and public manner. The entrepreneurial ecosystem still has many latent aspects after several years. This study aims to provide a big picture of all studies published in the Web of Science database to help future researchers.

In this research, 765 scientific papers published in the database were analyzed using 3 main approaches of network analysis, co-occurrence analysis of keywords and co-citation clustering.

In the end, four major clusters were identified for articles in this field in the clustering section, including the entrepreneurial ecosystem, academic entrepreneurship, innovation ecosystem and institutional entrepreneurship.

This paper used a new approach for reviewing the entrepreneurial ecosystem and made a big picture of all previous research studies. In the end, an unsupervised machine learning approach was used to clustering the research studies and four major clusters were identified.

To avoid wastage of resources of a startup accelerator, this paper aims to present a model for accelerator managers’ decision-making to enter startups into acceleration and initial capitalization using a fuzzy Delphi approach and an affinity diagram is one of the design thinking tools.

The high failure rate of startups has led to a waste of resources and a lot of capital. This failure rate is much higher in the early stages of startups and subsequently higher risk. This is where startup accelerators play a role in supporting startups and provide startups with the capital needed to accelerate. The point to note at this point is that choosing the team with the lowest success potential by the accelerators will eliminate their capital and energy. The purpose of this research is to avoid this wastage.

In this research, using the Fuzzy Delphi method and aggregation of opinions of 5 experts and managers of the acceleration field, additional criteria were eliminated and 35 criteria were considered as final criteria of the evaluation model. In the final stage, a 10-member committee of managers, specialists and faculty members was formed and the criteria were grouped using the affinity diagram method. Finally, the final model was presented considering the components of a business plan.

Using design thinking methods and a combination of that with a fuzzy and quantitative method is one of the contributions of this research. Also, making a model for selection startups in the acceleration stage of fundraising is another value of this research.

Entrepreneurship is the driving force of countries for sustainable economic development. The importance of this issue is to the extent that in recent years, countries have made great efforts to develop their entrepreneurial ecosystem. But the starting point for entrepreneurship is when an opportunity is identified and the entrepreneur rises to use it. Accordingly, opportunity recognition will be the foundation of entrepreneurship and ultimately sustainable development. Given the importance of this topic, this paper attempts to provide a large picture of the studies conducted in this field.

Given the importance of this topic, this paper attempts to provide a large picture of the studies conducted in this field by reviewing 868 articles published on the Web of Science database in the field of opportunity recognition. Accordingly, using statistical descriptions of articles, analyzing the communication network among elements such as authors, countries, institutions, keyword analysis in articles and examining their trends over time, identifying the most important articles using co-citation analysis and finally this macroimage has been mapped, clustered and identified in leading articles in the last decade by co-citation clustering.

The results of the clustering show that the five main clusters of recent decades have included entrepreneurial characteristics and opportunity recognition, macroeconomic opportunity recognition cluster (community and impact on economic development of the country), opportunity recognition process cluster, opportunity recognition cluster in serial and intra-entrepreneurship and opportunity recognition cluster in new venture internationalization.

Using a bibliometric analysis and co-citation analysis in the field of opportunity recognition and making a big picture of studies in this field of study is a contribution that can be used for future studies and researchers and managers in this field.

The purpose of this study/paper is conducting a Systematic mapping review, as a systematic literature review method for reviewing the literature of new product development by textmining and mapping the results of this review.

This research has been conducted with the aim of systematically reviewing the literature on the field of design and development of products based on textual data. This research wants to know, how text data and text mining methods, can use for the design and development of new products.

This review finds out what are the most popular algorithms in this field? What are the most popular areas in using these approaches? What types of data are used in this area? What software is used in this regard? And what are the research gaps in this area?

The contribution of this review is creating a macro and comprehensive map for research in this field of study from various aspects and identifying the pros and cons of this field of study by systematic mapping review.

With the rapid advancement of technology, companies use new technologies to produce their products and services to maintain a competitive advantage. As companies alone cannot research and develop their technologies, they should use knowledge sources outside the organization that may exist throughout the world; hence, organizations need technology transfer. Because the success rate of technology transfer projects is low, the need to accurately assess and investigate the critical success factors of technology transfer projects is felt. In this regard, this study aims to identify and prioritize the critical success factors in technology transfer projects.

In this research, 56 critical success factor (CSF) were extracted from the context of the articles and were adjusted using experts’ opinions in different phases, as well as the fuzzy-Delphi approach. Finally, 15 factors were categorized in the form of steps of the technology transfer model: STAGE-GATE. In the next step, the set of criteria needed to prioritize CFSs was extracted from the literature and finalized with the help of the experts. Then, how each of the CSF influences the identified criteria was scored according to the organization’s export opinions. Finally, the priority of each key success factor was calculated using the additive ratio assessment (ARAS) method.

The results obtained for prioritization of the critical success factors show that experience in technology transfer in the transferee company, the existence of experienced technology transfer managers, sufficient organizational infrastructure and documenting project problems, achievements and experiences are four critical success factors of the technology transfer projects. Considering the long-term and short-term specific goals of the technology transfer process and the choice of technology in line with the company’s commercial strategy are also the critical success factors with the next priorities.

The combination of ARAS and step-wise weight assessment ratio analysis methods for identifying and prioritizing managerial decisions in the high-tech industries is a value of this research. Also, a combination of novel multi-attribute decision-making methods by the older framework of new product development is another contribution of this research.

Dual-thrust hybrid unmanned aerial vehicle (UAV) technology offers a highly robust and efficient system that incorporates the take-off and landing capabilities of rotary-wing aircraft with the endurance capacities of fixed-wing aircraft. The purpose of this study is to model and control a hybrid UAV in three distinct flight modes: rotary-wing, fixed-wing and over-actuated model.

Model predictive control (MPC) along with linear models are applied to design controllers for the rotary-wing or vertical take-off and transition to the fixed-wing flight. The MPC algorithm is implemented with two approaches, first in its usual form and then in a new form with the help of tracking error variables as state variables.

Because the tracking error variables are more compatible with the cost function used in MPC, the results improve significantly. This is especially important for a safe and stable transition from rotary-wing to fixed-wing flight, which should be done quickly. The authors also propose a control allocation strategy with MPC algorithm to exploit the thrust and control inputs of both rotary-wing and fixed-wing systems for the transition phase. As the control system is over-actuated, the proposed algorithm distributes the control signal among the actuators better than the MPC alone. The numerical results show that the flight trajectory is also improved.

The research background is reviewed in the introduction section. The other sections are originally developed in this paper to the best of the authors’ knowledge.

The study aims to determine the relationship between emotional maturity and domestic violence in infertile women.

The cross-sectional study was conducted on 184 infertile women. Demographic questionnaire, Emotional Maturity Scale and domestic violence questionnaire were used to collect data. The Chi-square test and multivariate logistic regression were used to analyze the data.

More than 50% of the women in the study experienced domestic violence, and about the same percentage of them had unstable emotional maturity. The total score of domestic violence and its types were significantly related to the women’s emotional maturity (P < 0.001). In multivariate analysis, the significant relationship was found between domestic violence with Spouse's education level (primary/secondary) [OR = 0.25 (0.09–0.66)] and emotional maturity (unstable) [OR = 3.59 (1.83–7.02)].

The overall prevalence of infertility in Iran was 7% in 2021. Furthermore, although infertility is a problem among couples, most of its social burden lies with women. In a number of developing countries, as well as in Iran, childbearing is perceived as a social value for married women. Therefore, infertile women are at risk for depression, anxiety, low self-esteem, dissatisfaction and reduced quality of life.

This study found that half of the surveyed women experienced domestic violence. Women’s emotional immaturity, as well as their spouses’ lower education level and type of occupation, were highly correlated with women’s experience of domestic violence. To help prevent domestic violence among couples, there should be couple training classes to improve women’s emotional maturity.”

To quantitatively measure the subtle and general symptoms of autism spectrum disorder (ASD), one of the instruments that have been designed and used is the social responsiveness scale (SRS). The purpose of this study is to translate the second edition of the SRS into Persian and to examine its psychometric properties in school and preschool children with ASD.

The present study is a methodological study of the psychometric type; the study population consisted of school and preschool children from 3 to 12 years of age with ASD and an intelligence quotient of approximately 70. In total, 10 professors and occupational therapists with research and clinical experience in the field of children, were selected for the translation and content validation stages by expert sampling. To determine the validity of the instrument, the content validity index (CVI) and the content validity ratio (CVR) were used; in addition, to determine the reliability, the internal consistency determination method with Cronbach’s alpha number report and inter-rater reliability method within-class correlation coefficient report were used.

The calculated CVI and CVR of the instrument for all instrument items were 0.82 and 0.86, respectively. The intra-class correlation coefficient performed by different raters was 0.80 and Cronbach’s alpha for all items was 0.93.

The reliability and validity of the Persian version of the social responsiveness scale-second edition are appropriate and acceptable so that this instrument can be used to assess the social performance skills of 3–12-years-old children with ASD for research and clinical study.

The purpose of this paper is to describe a novel method to compromise between planned (regulated) maintenance and outage initiation and unplanned (unregulated) maintenance and to find an economic model using which one can perform maintenance adequately and in the most optimal state.

In this paper, a system consisting of similar components is considered, and the role of each component in the system is explained. Then, the cost pertaining to failure in each asset is determined. Costs such as energy not supplied, penalties, human resources to resolve the defect and replacing assets are taken into account. Finally, a new comprehensive objective is proposed, and optimization is performed for a sample system.

In this paper, some graphs have been plotted from which plenty of information may be extracted. This is mentioned in the Conclusion.

In this paper, some graphs have been plotted from which plenty of information may be extracted. This is mentioned in the Conclusion.

This paper aims to propose a new deep learning technique to detect the type of material to improve automated construction quality monitoring.

A new data augmentation approach that has improved the model robustness against different illumination conditions and overfitting is proposed. This study uses data augmentation at test time and adds outlier samples to training set to prevent over-fitted network training. For data augmentation at test time, five segments are extracted from each sample image and fed to the network. For these images, the network outputting average values is used as the final prediction. Then, the proposed approach is evaluated on multiple deep networks used as material classifiers. The fully connected layers are removed from the end of the networks, and only convolutional layers are retained.

The proposed method is evaluated on recognizing 11 types of building materials which include 1,231 images taken from several construction sites. Each image resolution is 4,000 × 3,000. The images are captured with different illumination and camera positions. Different illumination conditions lead to trained networks that are more robust against various environmental conditions. Using VGG16 model, an accuracy of 97.35% is achieved outperforming existing approaches.

It is believed that the proposed method presents a new and robust tool for detecting and classifying different material types. The automated detection of material will aid to monitor the quality and see whether the right type of material has been used in the project based on contract specifications. In addition, the proposed model can be used as a guideline for performing quality control (QC) in construction projects based on project quality plan. It can also be used as an input for automated progress monitoring because the material type detection will provide a critical input for object detection.

Several studies have been conducted to perform quality management, but there are some issues that need to be addressed. In most previous studies, a very limited number of material types were examined. In addition, although some studies have reported high accuracy to detect material types (Bunrit et al., 2020), their accuracy is dramatically reduced when they are used to detect materials with similar texture and color. In this research, the authors propose a new method to solve the mentioned shortcomings.