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The purpose of this paper is to investigate the effects of combined risperidone (RIS) and pivotal response treatment (PRT) in children with autism spectrum disorder (ASD).

In all, 34 children diagnosed with ASD (mean age of 12.36 years) were randomly divided into two groups: an RIS treatment group (n=18) and an RIS plus PRT (n=16). Communication skills were evaluated with the child communication checklist (CCC).

Total score of the CCC was increased in both groups after three months compared with the score prior to treatment. The total score of the CCC was significantly higher in the combined treatment group than in the RIS group.

Treatment with RIS combined with PRT may result in a better outcome in communication skill for children with autism than RIS training alone.

The low resistance against penetration of water, oxygen and the other corrosive ions through the paths of coating is one the most important problems. So, protective properties of coating such as polyester must be promoted. Recently, the use of nanoparticles in the matrix of polymer coating to increase their protection and mechanical properties has been prospering greatly. The purpose of this study is to improve the corrosion resistance of the polyester powder coating with ZnO nanoparticles. The ZnO nanoparticles have been synthesized by hydrothermal method in a microwave. Using polyester – ZnO nanocomposite coating as powder – combining them by ball milling process and coating them by electrostatic process are innovative ideas and have not been used before it.

Polyester powder as the matrix and ZnO nanoparticles as reinforcing were combined in three different weight percentage (0.5, 1, 2 Wt.%), and they formed polymer nanocomposite by ball milling process. Then, the fabricated nanocomposite powder was applied to the surface of carbon steel using an electrostatic device, and then the coatings were cured in the furnace. The morphology of synthesized zinc oxide nanoparticles was investigated by transmission electron microscope. Also, the morphology of polyester powder and fabricated coatings was studied by scanning electron microscope. The effects of zinc oxide nanoparticles on the corrosion resistance of coated samples were studied by electrochemical impedance spectroscopy (EIS) test at various times (1-90 days) of immersion in 3.5 per cent NaCl electrolyte.

Scanning electron microscopy (SEM) results reveal that there are no obvious crack and defects in the nanocomposite coatings. In contrast, the pure polyester coatings having many cracks and pores in their structure. According to the EIS results, the corrosion resistance of nanocomposite coating compared to pure coating is higher. The value obtained from EIS test show that corrosion resistance for coating that contains 1 Wt.% nanoparticle was 32,150,000 (Ωcm²), which was six times bigger than that of pure coating. In addition to providing a barrier against diffusion of electrolyte, ZnO nanoparticles act as a corrosion inhibitor and, thus, increases the corrosion resistance. The corrosion resistance of coating containing 0.5 Wt.% nanoparticles was lower as compared to that of 1 Wt.% nanoparticles. The low content of nanoparticles caused partial covering of the porosity of coating which in turn leads to provide weaker barrier properties. The increase in quantity of nanoparticles from 1 to 2 Wt.% also caused a decrease in corrosion resistance which is attributed to the agglomeration of nanoparticles.

The results of this study indicated the significant effect of ZnO nanoparticles on the protective performance and corrosion resistance of the polyester powder coating. Evaluation of coating surface and interface with SEM technique revealed that nanocomposite coating compared with pure polyester coating provided a coating with lower number of pores and with higher quality. The EIS measurements represented that polymeric coating that contains nanoparticles compared to pure coating provides a better corrosion resistance. In addition to providing a barrier against diffusion of electrolyte, ZnO nanoparticles act as a corrosion inhibitor and thus increase the corrosion resistance. The corrosion resistance of coating containing 0.5 Wt.% nanoparticles was lower as compared to that containing 1Wt.% nanoparticles. The low content of nanoparticles caused partial covering of the porosity of coating which in turn leads to provide weaker barrier properties. The increase in quantity of nanoparticles from 1 to 2 Wt.% also caused a decrease in corrosion resistance which is attributed to the agglomeration of nanoparticles.

Radiometric calibration is a method that estimates the reflection of the target from the measured input radiation. The purpose of this study is to radiometrically calibrate three spectral bands of Sentinel-2A, including green, red and infrared. For this purpose, Landsat-8 OLI data are used. Because they have bands with the same wavelength range and they have the same structure. As a result, Landsat-8 OLI is appropriate for relative radiometric calibration.

The method used in this study is radiometric calibration uncorrected data from a sensor with corrected data from another sensor. Also, another aim of this study is a comparison between radiometric correction data and data that, in addition to radiometric correction, has been sharpened with panchromatic data. In this method, both of them have been used for radiometric calibration. Calibration coefficients have been obtained using the first-order polynomial equation.

This study showed that the corrected data has more valid answers than corrected and sharpened data. This method studied three land-cover types, including soil, water and vegetation, which it obtained the most accurate coefficients of calibration for soil class because R-square in all three bands was above 88%, and the root mean square error in all three bands was below 0.01. In the case of water and vegetation classes, only results of red and infrared bands were suitable.

For validating this method, the radiometric correction module of SNAP software was used. According to the results, the coefficient of radiometric calibration of the Landsat-8 sensor was very close to the coefficients obtained from the corrected data by SNAP.

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 purpose of this study, 3-aminopyridine, 8-aminoquinoline and some new synthesized 2-aminobenzothiazoles were diazotized with nitrosyl sulfuric acid and subsequently coupled with 5-chloro-8-hydroxy quinoline to synthesize the corresponding heteroarylazo dyes 6–13.

The structures of dyes were characterized by mass, Fourier transform infra red, ¹H proton nuclear magnetic resonance and ultra violet-visible spectroscopic techniques. Absorption spectra of the dyes were measured in acetic acid, ethanol, chloroform, acetonitrile, dimethyl formamide and dimethyl sulfoxide and correlated with the nature of the solvents and substituents. The effects of varying pH on the absorption wavelengths of the azo dyes were also studied. In addition, the acidity constants (pKa) of the dyes were determined using the spectrophotometric method in an ethanol-water mixture (80:20, v/v) at 20–23°C. Besides, density functional theory (DFT) calculations were carried out to compare the energies of proposed azo and hydrazone tautomers of the dyes.

The results showed that the withdrawing chloro groups on the diazo moiety have significant influence (red shift) on the electron absorption spectra of these dyes. In addition, introducing electron withdrawing chloro groups into the benzothiazoles moiety increased the acidic character of dyes.

The synthesized 7-hetroarylazo-5-chloro-8-hydroxy quinoline dyes are new members in the 8-hydroxyquinoline azo dyes family, where very few details regarding the synthesis of such dyes are reported before in the literature. They are unique in terms of synthesis, spectral properties and DFT calculations.

The purpose of this paper is to review studies on mathematical optimization of the sustainable gasoline supply chain to help decision-makers understand the current situation, the exact dimensions of the problem and the models provided in the literature. So, a more realistic mathematical optimization model can be achieved by fully covering all dimensions of the supply chain of this product.

To evaluate and comprehend the mathematical optimization of the sustainable gasoline supply chain research area, a systematic literature review is undertaken that covers material collection, descriptive analysis, content analysis and material evaluation steps. Finally, based on this process, 69 related articles were carefully investigated.

The results of the systematic literature review show the main areas of the published papers on mathematical optimization of sustainable gasoline supply chain problems and the gaps for future research in this field presented based on them.

This approach is subject to limitations because the protocol of the systematic review of the research literature only included searching for the considered combination of keywords in the Scopus and ProQuest databases. Furthermore, the protocol used in this paper restricts documents to English.

The results have significant implications for both academicians and practitioners in this field. It can be useful for academics to comprehend the gaps and future trends in this field. Also, for practitioners, it can be useful to identify and understand the parts of the mathematical optimization model, which can help them model this problem effectively and efficiently.

No systematic literature review has been done in this field by considering gasoline to the best of the authors’ knowledge and delivers new facts for the future development of this field.

This study aims to compare many artificial neural network (ANN) methods to find out which method is better for the prediction of Covid19 number of cases in N steps ahead of the current time. Therefore, the authors can be more ready for similar issues in the future.

The authors are going to use many ANNs in this study including, five different long short-term memory (LSTM) methods, polynomial regression (from degree 2 to 5) and online dynamic unsupervised feedforward neural network (ODUFFNN). The authors are going to use these networks over a data set of Covid19 number of cases gathered by World Health Organization. After 1,000 epochs for each network, the authors are going to calculate the accuracy of each network, to be able to compare these networks by their performance and choose the best method for the prediction of Covid19.

The authors concluded that for most of the cases LSTM could predict Covid19 cases with an accuracy of more than 85% after LSTM networks ODUFFNN had medium accuracy of 45% but this network is highly flexible and fast computing. The authors concluded that polynomial regression cant is a good method for the specific purpose.

Considering the fact that Covid19 is a new global issue, less studies have been conducted with a comparative approach toward the prediction of Covid19 using ANN methods to introduce the best model of the prediction of this virus.

The purpose of this study was to develop a model for the digital transformation of university libraries based on meta-synthesis.

The approach of this research was qualitative and used the Sandelowski and Barroso’s (2007) seven-step meta-synthesis method to systematically review the literature. The statistical population of the research includes all the scientific publications related to the foundations and dimensions of the digital transformation of university libraries, counting the scientific publications retrieved from six scientific databases in the period from 2004 to 2022, and was based on the critical appraisal skills programme and a screening process.

Based on the retrieved publications, 32 documents were selected for further review and analysis. First, a separate code was considered for all the factors extracted from the selected documents, and then, considering the concept of each of the codes, they were categorized into a similar concept. In this way, the research concepts were determined. Based on the analysis done with the help of the content analysis method, there are a total of seven main categories and 24 concepts, including digital culture (including: digital attitude, user-oriented, agility, participation and cooperation, creativity and innovation and learning digital skills of employees), digital librarian (containing: individual competence, knowledge competence, communication competence and skill competence), digital manager (comprising: individual competence, knowledge competence, skill competence), digital services and resources (including, optimally reorganizing library resources, providing digital services to users and providing digital services to the mother organization), digital technologies (containing: digital infrastructure, technological readiness and using digital technologies), support and cooperation of upstream institutions and the mother organization (dealing with: providing human resources, providing technological resources and equipment and making macro policies) and work process and development of digital strategies (comprising: digitalization of processes and development of library digital strategies).

Digital transformation is a complex and multi-faceted process, yet it is an indispensable endeavour for university libraries, and managers and librarians cannot be expected to be familiar with these concepts without training or study and then be able to move in the right direction towards the digital transformation of libraries. To the best of the authors’ knowledge, this is the first study to develop a model for the digital transformation of university libraries. The results of this research indicate the effective factors and requirements needed for the digital transformation of university libraries and address the importance of understanding the concepts for managers and librarians.

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.