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Stock markets are highly sensitive to foreign and domestic events. Stock exchange markets react promptly to news and are known as an indicator of good and bad trading conditions. Terrorist attacks leave adverse effects on the economy and cause stock price volatility and, consequently, stock return volatility. Therefore, this paper aims to analyze the spatial effects of terrorism on stock market returns in the Middle East from 2008 to 2019.

This paper uses analytical research design and estimates spatial model. Before estimating the spatial model, the spillover effects were confirmed for the spatial autoregressive model using Moran’s diagnostic test for spatial dependence, Geary’s C test and Akaike statistic.

The results of this study on spatial panel data and based on spatial autoregressive estimator indicated that terrorism and associated neighborhood effects had a negative impact on stock returns in Middle East countries. Also, the corruption index and oil price negatively affected stock market return in these countries, while the democracy index had a positive effect on stock market returns. According to the results, to achieve a high and stable stock market return, it is recommended that high-level consultation is pursued with leaders of involved countries to reduce the devastating effects of terrorist activities, increase political and economic stability, attract stockholders to stock markets and spend corresponding incomes developing the infrastructures in this sector in countries of this region.

Most of the studies have investigated the impact of terrorism on the stock market returns at the national or provincial level. However, the effect of terrorism on the stock market index in the tense region of the Middle East, which is the center of terrorist attacks in the world, has not been dealt with by considering the spatial econometric effects. Thus, to the best of the authors’ knowledge, this research is a first attempt to study the impact of terrorism on the stock market returns in Middle East countries using the spatial econometric approach.

The study aims to examine the impact of cultural dependency stemming from exchange rate fluctuations (specifically the US dollar) on herding behavior in the housing market across 31 provinces of Iran from Q2 2011 to Q1 2022, using a spatial econometrics approach. After confirming the presence of spatial effects, the Dynamic Spatial Durbin Panel Model with Generalized Common Effects (SDM-DPD(GCE)) was selected from various spatial models for these provinces.

The study examines the impact of cultural dependency stemming from exchange rate fluctuations (specifically the US dollar) on herding behavior in the housing market across 31 provinces of Iran from Q2 2011 to Q1 2022, using a spatial econometrics approach. After confirming the presence of spatial effects, the Dynamic Spatial Durbin Panel Model with Generalized Common Effects (SDM-DPD(GCE)) was selected from various spatial models for these provinces.

The model estimation results indicate that fluctuations in the free market exchange rate of the dollar significantly and positively impact the housing market in both target and neighboring regions, fostering herding behavior characterized by cultural dependency within the specified timeframe. Additionally, the study found that variables such as the inflation rate, population density index and the logarithm of stock market trading volume have significant and positive impacts on the housing market. Conversely, the variable representing the logarithm of the distance from the provincial capital, Tehran, significantly and negatively impacts the housing market across Iranian provinces.

Given that housing is a fundamental need for households, the dramatic price increases in this sector (for instance, a more than 42-fold increase from 2011–2021) have significantly impacted the welfare of Iranian families. Currently, considering the average housing price in Tehran is around 50 million Tomans, and the average income of worker and employee groups is 8 million Tomans (as of 2021), the time required to purchase a 100-square-meter house, even with a 30% savings rate and stable housing prices, is approximately 180 years. Moreover, the share of housing and rent expenses in household budgets now constitutes about 70%. The speculative behavior in this market is so acute that, despite 25 million of Iran’s 87 million population being homeless or renting, over 2.5 million vacant homes (12% of the total housing stock) are not used. Therefore, various financial behaviors and decisions affect Iran’s housing market. Herd behavior is triggered by the signal of national currency devaluation (with currency exchange rates increasing more than 26-fold between 2011 and 2021) and transactions at higher prices in certain areas (particularly in northern Tehran) (Statistical Center of Iran, 2023). Given the origins of housing price surges, a price increase in one area quickly spreads to other regions, resulting in herd behavior in those areas (spillover effect). Consequently, housing market spikes in Iran tend to follow episodes of currency devaluation. Therefore, considering the presented discussions, one might question whether factors other than economic ones (such as herd behavior influenced by dependence culture) play a role in the rising housing prices. Or, if behavioral factors were indeed contributing to the increase in housing prices, what could be the cause of this herd movement? Has the exchange rate, particularly fluctuations in the free market dollar rate, triggered herd behavior in the housing market across Iran’s provinces? Or has the proximity and neighborhood effect been influential in the increase or decrease in housing prices in the market?

The primary aim of this study is to provide actionable guidance for augmenting profitability in photovoltaic power plant investments within Iran’s solar energy sector. By emphasizing prudent capital management and strategic investment decisions, our research seeks to assist emerging businesses in attaining sustained success in this domain.

This study presents a comprehensive approach to refined decision-making in Iran’s solar energy sector. Our methodology integrates the best-worst method, ArcGIS software for site selection, and the TOPSIS method for decision-making, aiming to enhance precision and reliability.

Our research has identified ten promising regions suitable for photovoltaic power plant installations in Iran. Leveraging the TOPSIS method, we have made optimal selections among these alternatives. Furthermore, our exhaustive cost analysis, incorporating factors like land prices, system maintenance, revenue estimation, and various financial scenarios, has yielded insights into project cost-effectiveness.

By filling a notable gap in the literature regarding optimal site selection and investment strategies for photovoltaic power plants in Iran, our research contributes to the sustainable development of solar energy infrastructure. Through a thorough literature review and the development of a novel methodology, we offer valuable guidance for businesses and investors seeking success in Iran’s solar energy sector. Our study represents a significant advancement by introducing a novel methodology that integrates the best-worst method, ArcGIS software, and the TOPSIS method for site selection and investment analysis. These findings furnish valuable guidance for businesses seeking success in the solar energy sector, thereby contributing to the sustainable development of renewable energy infrastructure in Iran and beyond.

Recent studies have shown that hyperuricemia is a predictor of non-communicable disease and an increment of mortality rate. Also, elevated serum uric acid may be associated with obesity in the adult population. This study aims to evaluate the association between serum uric acid levels with metabolic parameters and risk of obesity in the Iranian population.

The cross-sectional study was done on 550 participants, who were referred to a hospital for elective angiography in Rasht, Iran; anthropometric indices (waist circumference (WC) and body mass index (BMI)) and hematological factors were measured using the standard approaches. Based to the angiography results, the severity of atherosclerosis was defined.

The mean (SD) concentration of serum uric acid for all participants was 5.15 (1.37) mg/dl. Individuals who were at the highest tertile had higher mean (SD) of weight (p = 0.004), creatinine and blood urea nitrogen (p < 0.001) lower fasting blood sugar (FBS) (p = 0.000) and HbA1c (p = 0.016), and they were mostly men compared with those in the lowest tertile. After adjusting for confounders, FBS (ß = –0.145, p = 0.001) and HbA1c (%) (ß = –0.130, p = 0.019) had inverse and weight (ß = 0.156, p = 0.001) had direct association with serum uric acid. After adjustment for additionally potential confounders subjects in the highest tertile of serum uric acid had 92 per cent higher chance of obesity compared with subjects in the lowest tertile (OR 1.92; 95 per cent CI 1.13, 3.23).

The present study has concluded that increase serum uric acid related to high risk of obesity and low mean of FBS and HbA1c.

To design, control and evaluate photovoltaic (PV) systems, an accurate model is required. Accuracy of PV models depends on model parameters. This study aims to use a new algorithm called improved social spider algorithm (ISSA) to detect model parameters.

To improve performance of social spider algorithm (SSA), an elimination period is added. In addition, at the beginning of each period, a certain number of the worst solutions are replaced by new solutions in the search space. This allows the particles to find new paths to get the best solution.

In this paper, ISSA is used to estimate parameters of single-diode and double-diode models. In addition, effect of irradiation and temperature on I–V curves of PV modules is studied. For this purpose, two different modules called multi-crystalline (KC200GT) module and polycrystalline (SW255) are used. It should be noted that to challenge the performance of the proposed algorithm, it has been used to identify the parameters of a type of widely used module of fuel cell called proton exchange membrane fuel cell. Finally, comparing and analyzing of ISSA results with other similar methods shows the superiority of the presented method.

Changes in the spider’s movement process in the SSA toward the desired response have improved the algorithm’s performance. Higher accuracy and convergence rate, skipping local minimums, global search ability and search in a limited space can be mentioned as some advantages of this modified method compared to classic SSA.

The purpose of this study is to empirically investigate the role of dynamic capabilities, specifically the sequence of sensing, seizing, and transforming capabilities, in highly uncertain, emerging technology environments. Focusing on the extended reality industry, the study aims to understand the antecedents to these dynamic capabilities, their sequential nature, and their subsequent impact on innovation and company performance.

Based on a survey of 130 German companies in the extended reality sector, we built a structural equation model that explores the relationship between dynamic capabilities, their antecedents, and their effect on innovation and company performance.

The analysis suggests that sensing capabilities positively influence seizing and transforming capabilities, while seizing directly contributes to transforming. Transforming capabilities are linked to improved innovation performance, which in turn boosts company performance. Organizational ambidexterity, market orientation, and technology orientation are found to be crucial antecedents, accounting for 33.1% of the variance in sensing capabilities.

This research illuminates the interdependence of dynamic capabilities in highly uncertain business environments, such as emerging technology markets. It contributes original insights by elucidating the sequential nature of dynamic capabilities and identifying their vital antecedents. It also enlarges the understanding of how dynamic capabilities impact firms’ innovation performance.

This article addresses the need for a comprehensive model for structural crack detection in the context of structural health monitoring. The main innovation of this research is the introduction of a dynamic attention-based transformer model that significantly enhances the accuracy and efficiency of detecting and localizing cracks in structures. This study seeks to overcome previous limitations and contribute to advancements in structural health monitoring practices.

The research focuses on three primary computer vision tasks: classification, object detection and semantic segmentation applied to crack detection in concrete, brick and asphalt structures. The proposed approach employs transformer encoders with dynamic attention mechanisms to assess the severity and extent of damage accurately.

In this study, we propose a dynamic attention-based transformer model for structural crack detection, achieving a remarkable accuracy of 99.38% and an impressive F1 score. Our method demonstrates superior performance compared to existing techniques, such as the fusion features-based broad learning system and deep convolutional neural networks, while also significantly reducing execution time, highlighting its efficiency and potential for practical applications in structural health monitoring.

This research introduces a novel framework for crack detection, leveraging recent advancements in deep learning technology, with significant implications for the field of civil engineering and maintenance.

The purpose of this paper is to provide insight into how branding is used in an innovative manner, when looking to generate equity and strengthen stakeholder relations – by a global car manufacturing company, governed by Iranian Islamic law. Following this, suggested areas of discussion are offered, in the interests of stimulating new thinking and further knowledge – surrounding marketing and branding in a Muslim context; Iranian culture; and the differences between Arab and Persian‐centric approaches, towards an adherence to Islamic ethic.

Inductive reasoning and syllogisms, as a basis for conceptual metaphor theory and critical discourse analysis. Evidence was gathered via in‐depth interviews, personal observations and experiences; as a result of meetings and the in‐house training of senior decision makers, over a three‐year period. This is further supported by anecdotal evidence; systematic literature reviews; and existing empirical data.

In the face of strict regulations from the Iranian Government, Iranian religious clergy and wider socio‐political factors, the Saipa Group has developed an innovative approach to generate commercial gains and brand equity. This has been achieved through a brand‐extension strategy, which seeks to engage a wide spectrum of stakeholders, through activities which permeate their working and social lives. From this, a platform is created which enables Saipa to market their core offering of vehicles.

Currently, there exists little academic material examining marketing theory, within the context of organisations seeking to comply with Islamic law and Muslim customs. Furthermore, analysis which is reflective of examining the nuances among different Muslim nations remains scarce. To this end, the following case attempts to highlight and address some of these issues.

This article introduces SigBERT, a novel approach that fine-tunes bidirectional encoder representations from transformers (BERT) for the purpose of distinguishing between intact and impaired structures by analyzing vibration signals. Structural health monitoring (SHM) systems are crucial for identifying and locating damage in civil engineering structures. The proposed method aims to improve upon existing methods in terms of cost-effectiveness, accuracy and operational reliability.

SigBERT employs a fine-tuning process on the BERT model, leveraging its capabilities to effectively analyze time-series data from vibration signals to detect structural damage. This study compares SigBERT's performance with baseline models to demonstrate its superior accuracy and efficiency.

The experimental results, obtained through the Qatar University grandstand simulator, show that SigBERT outperforms existing models in terms of damage detection accuracy. The method is capable of handling environmental fluctuations and offers high reliability for non-destructive monitoring of structural health. The study mentions the quantifiable results of the study, such as achieving a 99% accuracy rate and an F-1 score of 0.99, to underline the effectiveness of the proposed model.

SigBERT presents a significant advancement in SHM by integrating deep learning with a robust transformer model. The method offers improved performance in both computational efficiency and diagnostic accuracy, making it suitable for real-world operational environments.

In recent decades, managerial desire to use internal audit services to promote well-controlled organizational processes has significantly increased in the public sector. Yet, there is rare information on how internal audits may effectively be implemented in the public sector. The present research aims to design a comprehensive internal audit effectiveness model for the public sector.

This research is an applied mixed-methods (qualitative–quantitative) and descriptive survey study. The research population in both qualitative and quantitative sections included all internal and external audit managers and staff of Iraqi public organizations. The study sample of the qualitative section included 28 audit managers and staff selected using purposive sampling, and the study sample of the quantitative section included 399 individuals selected using convenience sampling. The data collection instruments in the qualitative and quantitative sections included semi-structured interviews and questionnaires, respectively. Data analysis was carried out using thematic analysis in Atlas.ti software in the qualitative section. Quantitative analysis and model validation were also performed using path analysis and structural equation modeling in Smart-PLS software in the quantitative section.

The results of the qualitative section led to the identification of 38 effectiveness factors among seven main categories, which were finally presented in the form of a comprehensive model. The main categories included the communication between internal and external auditors, granting independence and authority to internal auditors, providing specialized human resources, providing technological resources, developing management support, strengthening organizational culture and developing audit plans. In the qualitative section, the model was validated and confirmed. Quantitative results further showed that the compilation of audit plans and the development of management support were the most important factors.

The present study contributes to the literature by providing a comprehensive framework on internal audit with an emphasis on resource-based theory and reducing the research gap in the field of agents influencing internal audit efficacy in the public sector of developing countries. Further, the study provides insights into an under-studied developing country, Iraq.