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This study aims to use frequency response analysis, a powerful tool to detect the location and types of transformer winding faults. Proposing an effective intelligent approach for interpreting the frequency responses is the most crucial problem of this method and has created many challenges.

Heat maps based on appropriate statistical indices have been supplied to depict the variations in the frequency responses associated with each fault type, fault location and fault extent along the windings. Also, after analyzing the results of artificial neural network (ANN) techniques, the generalized regression neural network method is introduced as the most effective solution for the classification of transformer winding faults.

Using a comparative approach, the performance of the used indices and ANN techniques are evaluated. The results showed the proper performance of Lin’s concordance coefficient (LCC) index and the amplitude (Amp) part of the frequency response. The proposed fitting percentage (FP) index can assist the intelligent classifiers in diagnosing the radial deformation (RD) fault with the highest accuracy considering all frequency response components in the classification procedure of winding faults.

Various ANN techniques are used to detect and determine the type of four important faults of transformer winding, i.e. axial displacement, RD, disc space variation and short circuit. Various statistical indices, such as cross-correlation factor, LCC, standard difference area, sum of errors, normalized root-mean-square deviation and FP, are used to extract the features of the frequency responses to consider as the ANN inputs. In addition, different components of the frequency response, such as Amp, argument, real and imaginary parts are examined in this paper. To implement the proposed procedure, step by step, various types of winding faults with different locations and extents are applied on the 20 kV winding of a 1.6 MVA distribution transformer.

Contributions have been made in identifying and diagnosing transformer winding defects through the use of appropriate algorithms for future research.

In Iran the role of entrepreneurs in developing communities is considered to be a special issue by planners and policy‐makers; thus the aim of this paper is to explore and examine some of the main structural, behavioural and environmental barriers faced by entrepreneurs in Iranian public industrial corporations.

A survey (questionnaires and interviews), observation and available documentation formed the main methods (triangulation) for the generation of relevant data. Thirteen public organisations responsible for social affairs in the country's budget document were involved. Using an unlimited sampling formula and a categorised random sampling method 220 organisations were accessed, from which 169 questionnaires were received and analysed.

There is a direct relationship between managerial characteristics and organisational entrepreneurship. Also, there is a significant correlation between employees' characteristics and organisational entrepreneurship in public social cultural organisations. However, a host of constraints such as low income and a lack of research have caused a decrease in organisational entrepreneurship.

The survey is concerned with managers in public organisations. Future studies should include private‐sector organisations, which ought to provide a basis for comparative analysis.

There is a need for sustained structural and managerial reform. To achieve this, realistic human resource development policies should be formulated which lead to changes of attitude and behaviour in managers.

This is a first attempt to study managers and their entrepreneurial tendencies in public sector organisations. It has policy implications for future development of the sector.

The purpose of this paper is to propose a gauge for the convergence of the deterministic particle swarm optimization (PSO) algorithm to obtain an optimum upper bound for PSO algorithm and also developing a precise equation for predicting the rock fragmentation, as important aims in surface mines.

In this study, a database including 80 sets of data was collected from 80 blasting events in Shur river dam region, in Iran. The values of maximum charge per delay (W), burden (B), spacing (S), stemming (ST), powder factor (PF), rock mass rating (RMR) and D80, as a standard for evaluating the fragmentation, were measured. To check the performance of the proposed PSO models, artificial neural network was also developed. Accuracy of the developed models was evaluated using several statistical evaluation criteria, such as variance account for, R-square (R²) and root mean square error.

Finding the upper bounds for the difference between the position and the best position of particles in PSO algorithm and also developing a precise equation for predicting the rock fragmentation, as important aims in surface mines.

For the first time, the convergence of the deterministic PSO is studied in this study without using the stagnation or the weak chaotic assumption. The authors also studied application of PSO inpredicting rock fragmentation.

Due to the political, economic and infrastructure barriers and risks that international entrepreneurs (IEs) face when researching an emerging economy's agrifood sector, this research aims to identify the major barriers, analyse their relationships, quantify their importance, classify and rank them. Thus, the IEs will gain a better understanding and vision of their decision-making processes in this era.

To do this, the authors first created a list of barriers to entry for IEs into Iran's rising economy's agrifood industry. Following that, a multi-layer decision-making approach was developed and implemented to accomplish the research objectives. The first stage utilized a hybrid of interpretive structural modelling (ISM) and cross-impact matrix multiplication applied to classification (MICMAC) to depict the level-based conceptual model and classification of the IEs’ obstacles to entry into the agrifood sector. Following that, a hybrid decision-making trial and evaluation laboratory (DEMATEL), and analytic network process (ANP) called DANP was utilized to present a causal relationship between the barriers, identify their causes and effects, and also quantify the relevance of each barrier.

After employing the multi-layer decision-making approach, the results demonstrated that fundamental limitations, including infrastructure and technology limitations, are the most critical barriers alongside policy factors encompassing governmental support and access to global or regional economy/market. According to the results, innovation and economic sustainability of the agrifood supply chain also matter. All of these critical barriers are intertwined and should be planned and solved simultaneously. Furthermore, based on DANP results, the sustainability pillars (economy, environment, society), besides the low efficiency of the agrifood sector in Iran, should be investigated further for future policy makings.

A hybrid multi-layer decision-making approach has been used for analysing the barriers of investment in the agrifood sector of the emerging economy of Iran for the international entrepreneurs. Moreover, the authors provide implications and insights for IEs and officials for decision-making in the future.

This study investigates consumers' willingness to adopt genetically modified foods (GM) and the role of innovation and behavioral determinants.

A new integrated model that expands the Health Belief Model (HBM) into the Innovation Diffusion Theory (IDT) was developed and examined. A total of 241 consumers were selected from the capital of Iran, Tehran, through a multistage random sampling. The data were analyzed using SEM-PLS modeling.

The extended HBM model was able to predict about 84% of the variance changes in consumers' willingness to adopt GM foods. The effects of net benefit and perceived compatibility on the willingness were also significant. In addition, the mediating effect of compatibility on the relationship between net benefit/self-efficacy and willingness to adopt GM foods was also significant.

This study contributes to the literature by developing the HBM into the IDT to assess consumers' willingness to adopt GM foods.

The purpose of this study was to assess the self-perceived need, seeking and use of dental care and its main determinants in Kurdistan province, Iran.

A total of 1,056 adults in Kurdistan province were included in this cross-sectional study. Multistage sampling approach was used to select the samples. Data on sociodemographic characteristics, economic status, self-perceived need, seeking and use of dental care were collected using a self-administrated questionnaire. Multivariate logistic regression model was used to assess factors affecting the use of dental care. In addition, socioeconomic inequality relating to dental care needs and use of dental care were examined using concentration curve and concentration index.

In this study, unmet dental care need was 62.7%. There was a perceived need for dental care among 13.7% (n = 145) of the participants in the past month, with only 39.3% (n = 57) seeking the care. The most important reasons for unmet dental care need were “Could not afford the cost” and “Insurance did not cover the costs.” Multivariate logistic regression showed that supplementary insurance status and household economic status were identified as main determinants affecting dental care-seeking behavior. The result of concentration index revealed that seeking dental care was more concentrated among the rich, whereas the perceived dental care need is more prevalent among the poor.

This study demonstrated that the prevalence of unmet dental care needs is high in the study setting. Also, financial barrier was identified as the main determinant of unmet dental care needs.

Students’ academic achievement is a multifaceted phenomenon. While depression can suppress academic performance, academic satisfaction can promote it. This study aims to investigate the relationship between depression and academic satisfaction among students studying at Shahroud University of Medical Sciences.

This cross-sectional study was carried out on 312 undergraduate students of Shahroud University of Medical Sciences. Data collection tools included demographic data form, University Student Depression Inventory and academic satisfaction scale. Data were collected by a simple random sampling method and self-reporting by the participants. Data were analyzed using descriptive and inferential statistics (multivariate multiple regression analysis and multivariate linear regression).

The participants’ mean depression and academic satisfaction scores were 71.92 ± 22.94 and 53.70 ± 9.69, respectively. In addition, the depression score was significantly and inversely correlated with students' academic satisfaction (r = −0.122, p-value = 0.031). Moreover, there was a significant correlation between students’ depression with marital status, level of the semester, interest in the field of study and study topic.

This study emphasizes improving education, spiritual and social support and strengthens strategies to deal with depression and medical science students’ related factors.

Students of medical sciences are exposed to depression during their college years, which is related to their academic satisfaction.

During the Covid-19 period, when human beings are socially isolated, telework is a viable solution to safeguard employees' health. Because many employees have never experienced such a working system and organizations have not planned for it before the pandemic, imposing employees to telework has adversely affected their productivity and efficiency. This study aims to identify factors affecting individuals' tendency toward teleworking during the pandemic, which can lead to practical solutions for the post-pandemic era.

Through the use of technology acceptance models, a conceptual model was designed. Data used to assess the model were cross-sectional and derived from 229 questionnaires filled out by employees in Tehran. The AMOS24 software processed the corresponding structural equation model.

The results from the cross-sectional data indicated that attitude toward telework and perceived behavioral control over the system were significantly correlated directly with the intention to telework, while perceived usefulness and perceived ease of use of telework were correlated indirectly. Therefore, the integrated model predicts behavioral intentions better than single models performed separately.

Psychological and mental health research describing adoption intentions of telework, particularly those focusing on employees, is still lacking. To the best of the authors' knowledge, this is the first study in this regard that has used a conceptual model derived from two technology acceptance models during the Covid-19 outbreak. An era in which the extent of the pandemic has forced employees to experience such working systems and thus the importance and practicality of teleworking have been more evident to nearly every individual.

This paper aims to study the application of high-tolerance and flexible indigenous bacteria and fungi, along with the co-metabolism in recycled paper and cardboard mill (RPCM) wastewater treatment (WWT).

The molecular characterization of isolated indigenous bacteria and fungi was performed by 16S rRNA and 18S rRNA gene sequencing, respectively. Glucose was used as a cometabolic substrate to enhance the bioremediation process.

The highest removal efficiency was achieved for both chemical oxygen demand (COD) and color [78% COD and 45% color removal by Pseudomonas aeruginosa RW-2 (MZ603673), as well as approximately 70% COD and 48% color removal by Geotrichum candidum RW-4 (ON024394)]. The corresponding percentages were higher in comparison with the efficiency obtained from the oxidation ditch unit in the full-scale RPCM WWT plant.

Indigenous P. aeruginosa RW-2 and G. candidum RW-4 demonstrated effective capability in RPCM WWT despite the highly toxic and low biodegradable nature, especially with the assistance of glucose.

Religious and peculiar beliefs are two of the factors affecting consumer behavior and may differentially affect individuals and societies. Therefore, this study aims to investigate them from the study of Iranian customers’ behavior.

These beliefs are sensitive issues, hence, to investigate them, the grounded theory method through 15 in-depth interviews was applied.

The results indicated that evil-eye effect, as a peculiar belief, affected the behavior of luxury cars’ consumers in the Iranian society while religion had no such effect on the consumers’ behavior, in spite of religious notions about luxury. Furthermore, the findings revealed that consumers may legitimize their inconsistent behaviors through a variety of tactics such as different interpretations of religious notions or dissembling their superstitious beliefs.

The present study will contribute to the literature on religion and customer behavior through taking advantage of the application of a qualitative research design. Besides, the originality of the study might be in the application of various tactics by the customers to legitimize their behaviors inconsistent with religion.