The rapid and ongoing trend of metropolises transitioning into global cities and world cities is influenced by the increasing political, economic, cultural, and scientific dimensions of the urban political geography. Cities, as dynamic entities, evolve from new cities to mother cities, metropolises, global cities, and ultimately world cities. In this competitive environment, metropolises strive to enhance their level of competitiveness vis-à-vis their counterparts. In this study, the researcher compares the performance of Tehran and Istanbul, two metropolises in the Islamic world, based on the criteria and functions of world cities. The researcher employs an index developed by political geography scholars who specialize in studying world cities. The objective is to determine the ranking of these cities among world cities. To accomplish this, relevant maps have been prepared using ArcMap software, and numerical indicators and criteria have been analyzed in a descriptive-analytical manner. The analysis reveals that Istanbul outperforms Tehran in most indicators and occupies a strong position in the network society and flow space of global cities. This can be attributed to factors such as geopolitical location, an open economic environment, and other indicators. The results of this comparative study can guide city administrators in designing a more suitable future landscape for these cities, ultimately leading to achieving global city status.

The purpose of this study is to present a model of religious tourism based on the art of taziyeh in Kashan based on structural equation modeling, based on a mixed exploratory approach. In the qualitative part, the phenomenological method was used and in the quantitative part, the descriptive method, correlation based on structural equations was used. The statistical population of this research in the qualitative part included professors and experts in tourism and condolence in Kashan, which was obtained after 15 saturated interviews. In order to evaluate the validity of qualitative data, structural, external, descriptive and interpretive validity were used and in order to evaluate the reliability, the three approaches of Rao and Perry (2003) were used. The results of qualitative research were extracted from 11 economic, strategic, development, research, organizational, technical, content, environmental and advertising dimensions. After examining the extraction of components, the quantitative part of the research was examined. Factor and content validity were used to evaluate the validity. The reliability of the questionnaire was used from Cronbach's alpha, which had the necessary reliability. Available was selected. The data obtained from the questionnaires were analyzed at two levels of descriptive and inferential statistics, including structural equations, through SPSS 22, LISREL 18.9 and Warp PLS 5 software. Findings showed that the most impact was economic dimension (impact factor 0.86) followed by cultural dimension with (0.79) and strategic dimension with (0.78).
 

Extreme precipitation events pose a significant and growing threat to society, often leading to floods, landslides, and widespread socio-economic damage. Daily precipitation data collected from 9 rain gauges during 1/1/1991 to 31/12/2023. To identify days associated with heavy precipitation, the 95th-percentile threshold was employed. Days on which the recorded precipitation exceeded the long-term mean of the 95th percentile at more than half stations were classified as heavy-precipitation days for Kurdistan Province. Based on this threshold and criterion, 210 days were selected. Two data arrays with an S-mode structure were constructed for sea-level pressure and 500-hPa geopotential height. Using Principle Component Analysis (PCA) analysis, components explaining more than one percent of the variance were retained as significant modes. For sea-level pressure, nine components were identified, and for the 500-hPa geopotential height, eight components were extracted. Together, these components explained over 92% of the variance in sea-level pressure and more than 95% of the variance in the 500-hPa geopotential height over the study domain. Cluster analysis (CA) performed on the score matrix of the 17 components was then used to identify the prevailing circulation patterns.
 

Characterizing the scaling structure of topography is essential for linking observable land-surface morphology to the processes governing landscape evolution. Conventional geomorphometric approaches often describe terrain using self-affine models parameterized by a single roughness exponent, implicitly assuming statistical homogeneity across scales. Such formulations, however, may not adequately capture landscapes shaped by the coupled action of tectonic deformation, climatic forcing, and nonlinear erosional dynamics. In this study, we evaluate the scale dependence of surface roughness using high-resolution digital elevation data from the Iranian Plateau, a morphotectonically diverse region representative of actively evolving continental domains in West Asia. Fractal analysis based on box-counting yields a dimension of Df≈2.20, while spectral analysis of elevation fields provides a Hurst-type roughness exponent of α≈0.48. The lack of agreement with the canonical self-affine relation Df=3−α indicates that the terrain cannot be characterized by a single scaling descriptor. To investigate this departure, we compute higher-order statistical measures of elevation variability, including generalized structure functions, multiscale height–height correlations, and scale-dependent curvature metrics. These analyses reveal that the scaling exponents vary systematically with statistical moment, demonstrating clear multiscaling and supporting a multifractal description of the surface. The results suggest that topographic organization in tectonically active settings reflects heterogeneous process interactions operating over a hierarchy of spatial scales, rather than uniform rough-surface behavior. This work underscores the limitations of single-exponent geomorphic parameterizations and highlights the value of multifractal diagnostics for quantitative terrain analysis, comparative morphotectonic studies, and improved representation of landscape complexity in Earth-surface models.

Housing is a crucial domain for testing human interactions and serves as a physical framework where social, cultural, and economic resources intertwine. In order to achieve social progress, the realm of housing encompasses not only the residential units themselves but also their surrounding environments. The focus on housing quality indicators is essential for attaining sustainable housing, which in turn lays the foundation for sustainable urban development. However, the increasing demand for housing and the prevailing quantitative approach have led to the neglect of the fundamental issue of housing quality in its true essence. Insufficient access to suitable housing can contribute to or worsen various psychological and social challenges. Hence, this research aims to identify the main indicators and components of housing quality and prioritize them, assuming equal weighting of their influence. The research methodology employed in this study is practical in its objective and descriptive-analytical in its approach. To achieve the intended objective, the Analytic Network Process (ANP) model is utilized to determine the weight and prioritize each indicator and component of housing quality. Subsequently, after evaluating the indicators using the ANP model, the results indicate that among the examined indicators, housing facilities and amenities with  0.318, housing security with 0.218, housing comfort with 0.145, compatibility with complementary uses with 0.142, housing durability with 0.109, and housing climate adaptation with 0.065 have been assigned the highest weights, respectively.

Due to the significance of forests in both the natural and human environment, this study aims to investigate the impact of meteorological drought on oak forest dieback in Ilam province. Specifically, the study seeks to determine the relationship between Zagros Forest drought and droughts in this particular region. The analysis utilizes the Standard Precipitation Index (SPI) to identify the frequency of droughts during different time periods. The results indicate that the years 2007, 2008, 2011, 2015, and 2016 experienced the highest occurrence of droughts. Additionally, remote sensing data from MODIS images were employed to examine the trend in tree greenness (NDVI) from 2000 to 2016. The analysis reveals a significant correlation (R2 = 0.9999) between the greenness trend and the drought index (SPI). Moreover, a land survey of oak drying points and simulation using Landsat satellite images, with a 15×15 pixel output from GIS software, indicate that approximately 17,894 hectares of forests in the region experienced drying and destruction between 2000 and 2016. By combining the oak forest drying layer with the output layers derived from drought zoning, visual indicators were created, and statistical analysis was conducted for three 5-year time series. The results demonstrate a correlation coefficient of 96.6% and an explanation coefficient of R2 = 0.985 for the 2002-2006 time series, a correlation coefficient of 95.4% and an explanation coefficient of R2 = 0.980 for the 2007-2011 time series, and a correlation coefficient of 98.8% and an explanation coefficient of R2 = 0.995 for the 2012-2016 time series. These findings illustrate the influence of drought and its variations in terms of intensity and duration on oak forests in the Zagros region of Ilam. Based on the study results, it is predicted that if the drought persists with the same trend, approximately 1,118.4 hectares of oak forests in Ilam province will dry up and be destroyed annually.

To conduct this research, data on monthly synoptic and hydrometric precipitation observations from the National Meteorological Organization and the Ministry of Energy were obtained for a 30-year period (1976-2005). To assess future changes in rainfall, historical data from the period (1976-2005) and simulated climate data from the period (2021-2050) using two models (CM3 and CSIRO-Mk3.6) from the CMIP5 series were used. These simulations were based on four scenarios (RCP2.6, RCP4.5, RCP6, and RCP8.5) with a spatial resolution of 0.5 x 0.5 using the BCSD method. A mean-based (MB) strategy was employed to correct any bias in the model outputs.  The results of the AOGCM models indicated that the CSIRO-Mk3.6 model had a lower error coefficient than the GFDL-CM3 model when simulating precipitation in the Large Karoun case. The average future rainfall (2021-2050) across the entire basin, compared to the average observed rainfall during the statistical period of 1976-2005, exhibited a significant decrease in both the amount and extent of precipitation in both basins for all models and scenarios. In the Great Karoun Basin, heavy rains were consistently concentrated east of the basin across all scenarios and models, with the central foothills experiencing the highest rainfall and the southwest and southeast regions receiving the lowest amounts.  The findings of this study estimate rainfall to range between 83-116 mm, with the highest rainfall expected in the Greater Karoun Basin under the rcp4.5 and rcp2.6 scenarios for both models.

Entrepreneurship, a burgeoning force in today's economy, plays a pivotal role in rural development by fostering progress, poverty alleviation, and job creation. However, rural areas often grapple with a scarcity of financial capital essential for business initiation or expansion. To address this, governments have introduced micro-rural credits as self-employment loans to stimulate rural employment and entrepreneurship. This study aims to evaluate the impact of government micro-credits on rural entrepreneurship development in Radkan. Adopting an applied, developmental, and descriptive-analytical approach, data was collected through documentary, library, and field research. The study population comprised 27 entrepreneurs, with a sample of 244 individuals from 13 Radkan villages selected using the Cochran formula with a 0.06 error level. Their opinions on research indicators related to the topic were assessed. Findings reveal that micro-government credits have had a negligible impact on rural entrepreneurship in the study area. Most sample entrepreneurs supplemented bank loans with personal funds. Furthermore, insufficient loan amounts, limited credit accessibility, absent insurance support, lengthy loan processing times, and unfavorable repayment terms resulted in low satisfaction levels among entrepreneurs (average 2.32) and rural residents (average 1.91).

The performance of broiler chickens is directly influenced by temperature changes and the occurrence of heat stress, whether it is cold or hot. The present study aims to assess the cooling and heating degree days in different stages of broiler chicken production in Khorasan Razavi province. To achieve this objective, daily average temperature data were collected from 13 synoptic stations during the statistical period of 1988-2018. Cooling and heating degree days were calculated for each week of production using specific thresholds, and their spatial distribution was analyzed. Furthermore, the relationship between cooling and heating degree days and geographical features was evaluated. The findings indicate a decrease in cooling degree days and an increase in heating degree days as latitude increases. The northern and western regions of the province exhibited a greater need for heating throughout all stages of broiler chicken production, whereas the eastern and southern regions had higher cooling requirements at different production stages. Overall, the northeastern, southern, and western marginal areas of the province, including Khaf, Gonabad, Kashmar, Sarakhs, and Sabzevar, exhibited the lowest cooling and heating needs during the 6-week period of broiler chicken production, making them suitable climates for this economic activity.

When empires and great nations decline, there are many interrelated reasons involved in. Empires are considered to be powerful entities in the world, and via historical surveys, one can identify several important empires that have existed in different eras. Geopolitical knowledge and its constituent concepts (power, politics, and space) are closely associated with empires and can be explored from this perspective. The aim of this research is to conduct a geopolitical investigation of the collapse of empires. The research deal with the geopolitical factors contributing to the collapse of empires such as Persia, Rome, Byzantium, Ottoman, Britain, Russia, Spain, and Arab countries, using a descriptive-analytical method and field research. The statistical population consists of 50 specialists and experts in the field of geopolitical studies. The Dimtel method has been utilized to test the conceptual model of the research. The findings demonstrate that variables such as the incompetence and capriciousness of rulers, the collapse of the domestic economy, the extravagance and corruption of the governing class, oppression and slavery, and internal rebellions have a high level of interaction and a strong systemic connection with other variables. In other words, when a country faces these challenges in terms of its leadership and governance, it becomes unable to effectively address other problems caused by these factors, leading to a crisis and the eventual collapse of the empire.

Industrialization and modernization have had a significant impact on various aspects of society including architecture, urban planning, environment, climate, and geography. This impact is evident in phenomena such as the construction of high rise buildings, climate inversion, and environmental degradation. Many of the problems observed in recent decades can be attributed to population growth, migration, and changing needs of citizens. As a result, city managers and architects face significant challenges. The main objective of this article is to conduct a theoretical study on the use of residential towers, with a particular focus on sustainable social development and its implications for environmental protection in Tehran. This research adopts a descriptive-analytical approach and has both applied and theoretical purposes. It is also part of a survey that employs both quantitative and qualitative research methods. The sample size for this study was 384 participants, and data were collected using questionnaires. The data analysis was conducted using SPSS software. The most significant finding of this research is that population growth has fueled the construction of tall buildings, which in turn has contributed to issues such as excessive consumption of natural resources and fossil fuels, destruction of natural habitats, decline in social interactions, climate change, increased traffic congestion, and heightened air pollution. These issues have become a complex and seemingly insurmountable crisis.

The current research is based on the process of algorithmic design and simulation of village boundaries using the Grasshopper plugin in the Rhino software. The proposed algorithm utilizes the image simulator component and its importer in the Grasshopper plugin to input two-dimensional images taken from samples into this plugin. Before entering the data, the input images are converted to black and white to allow the system to distinguish between the background texture and the subject of analysis. Additionally, the proposed algorithm identifies the village boundary by comparing the subject and the text in the pixels of the two-dimensional image. By calculating the internal area of the identified boundary, the algorithm estimates the proportion of textures relative to the background. The main objective of this research is to examine the impact of green and dry textures as natural factors on the density of residential texture in terraced villages in Iran, across various climates. In addition to identifying the density of village textures, emphasizing the comparison of parameters will provide new insights into the texture of terraced villages. The simulation analysis tool, known as the density measurement algorithm, is developed based on aerial maps created by the researchers for this study. By using inverted aerial maps, this algorithm can determine the density of natural texture in the formation of villages and residential texture. This capability reduces the need for physical presence and enhances the accuracy of outputs in analyzing village texture. Furthermore, the identification of parameters will provide the next generation of rural housing with the density pattern of previous generations.