محمد کاظمی

The present study aims to conceptualize the entrepreneurial ecosystem in Iranian higher education in order to provide a comprehensive and systematic framework in the form of factors affecting higher education entrepreneurship.

This applied research was conducted with a qualitative meta-synthesis method. The population of the study are all internal and external sources related to the topic of entrepreneurial ecosystem in higher education, which are in the period 2015-2024. In this regard, 230 articles were evaluated and finally 40 articles were selected purposefully and analyzed. The validity of the study was confirmed through the Cohen Kappa test.

In this study, the factors affecting the entrepreneurial ecosystem in higher education were obtained through open, axial and selective coding in the form of 109 key concepts, 23 secondary categories and 8 primary categories. The results showed that higher education policy-making, cultural and social factors, human capital, higher education market, support and advocacy, higher education financing, strategic entrepreneurial programs, and communication and interaction factors are among the primary and main categories affecting entrepreneurship in higher education. Finally, an integrated model of the entrepreneurial ecosystem in higher education was developed.

This research provides a deep understanding and a holistic view of the factors affecting entrepreneurship in higher education in the form of a model that can provide new and valuable insights for the development of the higher education entrepreneurial ecosystem that can even be used as a prerequisite for cluster strategies in innovation systems and knowledge-based economies in national competitiveness policies in the field of higher education.

In many organizations, the reason for weak performance is the inappropriate culture and lack of positive psychology issues. The purpose of this study is to identify the possible factors that affect the reduction of positive organizational psychology in the Isfahan Gas Company. In this research the thematic analysis method was used. Data was collected through semi-structured interviews. The interviews lasted from 60 to 90 minutes. The research population consisted of employees of the Isfahan Gas Company, who were selected by a purposive sampling method. After interviewing 24 people, theoretical data saturation was achieved; however, interviews were continued with 5 other people to ensure certainty. Based on this, a total of 29 people were interviewed. Based on the results of the research, four main themes were identified, including factors related to employees; factors related to managers; factors related to the organization and factors related to external to the organization. During the analysis of the results, 46 subthemes were also identified.

Remote sensing has become a valuable tool for acquiring integrated spatial data regarding land cover and land use across various temporal and spatial scales. One of the primary challenges in multi-temporal land cover and land use mapping is the availability and integrity of training data for supervised classification algorithms. Collecting training samples for each class of land cover and land use over different time periods can be time-consuming and, particularly in rapidly changing environments, fieldwork can be challenging. This issue is further exacerbated by the potential spectral and phenological changes in land cover features over time, which can diminish the transferability of training samples. The concept of "migration" or "transfer" of training samples from a reference year to target years has been explored as a means to overcome the limitations of training data. In this context, the use of Google Earth Engine (GEE) has facilitated multi-temporal land cover and land use mapping. GEE's ability to integrate diverse data sources, including Sentinel-2 imagery and a wide range of spectral indices, enables researchers to develop robust and scalable applications for land cover and land use classification. Monitoring changes in land use and cover over time is crucial for understanding and managing the environment. However, when there are limitations in training data for different time periods, this can pose significant challenges. This study presents an innovative approach to classify Sentinel-2 satellite images from different years using a set of reference training samples.

In this study, an innovative application was explored using migrated training samples from a reference year (Sentinel-2 imagery from 2019), along with bands from Sentinel-2 images and spectral indices, to classify land cover and land use in the dynamic and ecologically significant mangrove region (Khoran Protected Area). The use of machine learning algorithms within the Google Earth Engine (GEE) framework was examined to achieve high classification accuracy and monitor land cover changes over time. Satellite images from Sentinel-2 covering the study area for the target years 2022 and 2024, as well as the reference year 2019, were retrieved in GEE. Ground truth data, including the location and classification of various land cover types for the reference year, were collected using the European Space Agency's land use maps. Subsequently, high-quality ground truth data and their corresponding image samples from the reference year (2019) were transferred to the target year images using the Spectral Angle Distance (SAD) algorithm. Classification algorithms such as Random Forest (RF), Gradient Boosting Trees (GBT), and Classification and Regression Trees (CART) were employed to classify the target year images using the variable training samples. The classified images were evaluated using various accuracy metrics, including overall accuracy, Kappa coefficient, producer accuracy, user accuracy, as well as commission and omission errors. Finally, the importance of different spectral bands from Sentinel-2 and spectral indices in the classification process was analyzed to identify the most suitable features for distinguishing various phenomena in the study area.

The results of this study indicated that among the classification algorithms, the highest accuracy for overall accuracy and Kappa coefficient in the classified images for 2024 and 2022 was achieved using the Random Forest classification, with accuracies of 0.9104 and 0.8742, and Kappa values of 0.8955 and 0.8570, respectively. Additionally, the findings revealed that the area of mangrove forests decreased from 7530.74 hectares to 6546.51 hectares during the study period, representing a reduction of approximately 984.23 hectares, or about 164 hectares per year.The area of residential and construction zones increased from 72.52 hectares in 2019 to 96.48 hectares in 2024, indicating rapid growth in these land uses over the last two years. The synthesis and summary of various classification methods across different years highlighted the relative importance of bands and indices. Specifically, the EMVI and mNDWI indices demonstrated greater dominance due to their effectiveness in capturing the phenomena of mangrove forests and aquatic areas within the study region. Consequently, for the band combinations aimed at distinguishing various phenomena, the use of green, red, and near-infrared bands, along with the mangrove index and optimized water body index, were identified as the most suitable for the study area. These findings are recommended for similar areas in southern Iran and other mangrove regions. This research employed a simple and effective application by transferring ground truth points from the reference year to their corresponding images as training samples for the target year images, utilizing the Google Earth Engine platform. This approach holds the potential for extension to other regions.

In summary, this study demonstrates the potential of using migrated training samples and advanced machine learning algorithms such as Random Forest, Gradient Boosted Trees, and Classification and Regression Trees, along with spectral indices as auxiliary data, for the accurate classification of multi-temporal satellite imagery. The development of spatial tools, including the online Google Earth Engine (GEE) platform, is essential for the up-to-date management of land uses, particularly in wetland and mangrove areas. In this research, high-quality training samples were successfully migrated from the reference year to the target year, resulting in high classification accuracy using the Random Forest classification algorithm compared to other methods such as boosted regression or regression and classification trees. This method offers a viable solution in multi-temporal land use studies, especially in cases of insufficient or inadequate training samples within the GEE system. For future studies, it is recommended to employ a combination of Euclidean Distance (ED), Spectral Angle Distance (SAD), and K-means clustering for generating variable training samples, and to compare and analyze the classification results obtained through these methods. This approach presents a promising solution for producing up-to-date land use/land cover maps, even in challenging environments with limited training data. The findings of this study can guide future research aimed at monitoring land cover, managing, and effectively protecting valuable natural resources, such as mangrove forests, in other regions.

Atousa cultivar is results of a cross-pollination of Caesar cultivar (as female parent) and promising colone 397009 from the International Potato Center (CIP) (as male parent) and various quantitative and qualitative evaluations during 12 years. To release Atousa variety, single plant selection, visual assessment, preliminary and advanced yield trials, adaptability test, study on resistance to fungi, bacteria and viruses was carried out in different places such as Karaj, Ardabil, Hamadan, Esfahan and Mashhad (Jolgehrokh). Although, Atousa cultivar had superiority to the Agria in terms of yield, but scatter Bi-plot analysis with respect to average yield and relative coefficient of excellence showed that its relative coefficient of excellence was almost the same as that of the Agria. The yield of Atousa cultivar under on-farm studies in Ardabil and Hamadan regions was 25.86% and 2.3% higher than that of Agria (control cultivar), respectively. Atousa cultivar is highly resistant to PVY that is very important and harmful virus around the world. The reaction of Atousa to Alternaria alternata was semi-resistant and it was semi tolerant to Rhizoctonia solani AG-3 and Rhizoctonia solani AG-4. The tuber dry matter of Atousa is 1.4% more than Agria and is excellent for frying and processing purposes. Atousa as a new potato cultivar is suitable for spring cultivation in the cold and moderately cold temperate regions of Iran.

Lighting within educational environments is critical in establishing an optimal atmosphere that supports health, conserves energy, enhances the utility of educational spaces, and fosters effective learning. This study aims to assess the lighting quality in educational institutions, considering international standards and criteria and providing recommendations for improvements. The research focuses specifically on universities located in District 6 of Tehran, including the University of Tehran, Tarbiat Modares University, and Amirkabir University of Technology. To achieve these objectives, a mixed-method research approach has been adopted. This approach encompasses a comprehensive literature review, architectural and electrical documentation analysis, direct observations, and comparative assessments. Additionally, relevant strategies are proposed based on an examination of successful case studies that align with Iran’s lighting methodologies and technologies. A life cycle cost assessment has also been conducted to validate these proposed solutions. The findings indicated that strategies aimed at enhancing the quality of educational lighting include (1) designing lighting systems tailored to various functions while ensuring sufficient illumination levels, (2) emphasizing the integration of daylight considerations alongside the implementation of control systems, (3) designing lighting systems following geometry, spatial configurations, proportions, and furniture arrangements, and (4) ensuring that environments retain adaptability concerning the lighting systems implemented. Replacing fluorescent lamps with LED lighting and installing motion sensors in educational corridors can lead to a significant annual energy consumption reduction of 91.1%. Additionally, integrating daylight with electric lighting and utilizing active control mechanisms could achieve an even greater reduction of 95.3%.

Groundwater, as one of the vital resources for supplying drinking water and agriculture, is significantly affected by climate change and human activities. Rising temperatures, changing precipitation patterns, and alterations in vegetation lead to changes in the chemical composition of water, which can have serious implications for the provision of drinking water and agricultural needs. The quality of groundwater, like that of surface water, is constantly changing, and in arid and semi-arid plains, identifying and analyzing the factors influencing groundwater quality is crucial for managing various quality zones. Researchers in this field have acknowledged that soil characteristics and land surface can directly impact groundwater quality, and proper soil management can contribute to improving water quality. Therefore, this study examines the impact of climatic variables and land surface characteristics on groundwater quality in the Kahorestan plain of Hormozgan Province, aiming to identify quality changes in water during irrigation and non-irrigation seasons. It is noteworthy that previous research primarily estimated the Water Quality Index (WQI) using interpolation algorithms within Geographic Information Systems (GIS) and delineated different groundwater quality levels. In the present study, in addition to investigating the significant relationships between climatic variables and land surface characteristics with groundwater quality, the spatial distribution of these variables within the study area is analyzed, and modeling is conducted using Geographically Weighted Regression (GWR) and Ordinary Least Squares (OLS) methods based on the influential independent variables.

The study area in the present research is the Kahorestan plain, covering an area of 273.09 square kilometers, which is one of the rare plains in the western part of Hormozgan Province. This study was conducted based on chemical data from 15 semi-deep wells in the Kahorestan plain during irrigation and non-irrigation periods in the years 2009 and 2018. The independent variables of the study included the Normalized Difference Salinity Index (NDSI), cumulative precipitation (Pr), Normalized Difference Vegetation Index (NDVI), soil water deficit (DEF), potential evapotranspiration (PET), land surface temperature (LST), Palmer Drought Severity Index (PDSI), and actual evapotranspiration (AET), which were extracted as monthly raster satellite images from the Google Earth Engine (GEE). The dependent variable of the study was the Groundwater Quality Index (WQI), which was modeled within a Geographic Information System (GIS). To assess the significance of the relationships between these variables at different time periods, the Chi-square test was employed. Subsequently, Geographically Weighted Regression (GWR) and Ordinary Least Squares (OLS) techniques were utilized to model the spatial variations in groundwater quality. To compare the validity or efficiency of the multiple regression models, criteria such as the coefficient of determination, root mean square error, and Akaike Information Criterion were used. Additionally, it is noted that the Local Moran's I statistic was employed to investigate the spatial distribution of groundwater quality zones during these time periods.

The chi-square test indicated a significant relationship between climatic variables and land surface characteristics with groundwater quality. Spatial autocorrelation analysis revealed that the spatial distribution of groundwater quality in the Khorasan plain is random. Among the two methods, GWR (Geographically Weighted Regression) and OLS (Ordinary Least Squares), the GWR technique provided better results with a root mean square error of 0.14, a residual sum of squares of 11.3, a coefficient of determination (R²) of 0.82, and an Akaike Information Criterion (AIC) of -570.19, compared to the OLS method. Additionally, NDVI (Normalized Difference Vegetation Index) and NDSI (Normalized Difference Snow Index) showed a coefficient of determination (R²) of 0.47 in the first time period (4/2009), with the NDSI variable showing an R² of 0.63 in the second time period (11/2009). In the third time period (5/2018), both NDVI and NDSI had an R² of 0.65, and in the fourth time period (12/2018), the two variables NDVI and NDSI again showed the highest impact among climatic variables and land surface characteristics on groundwater quality, with an R² of 0.55. The groundwater quality classes of the Kahorestan plain only included two categories: very poor and unsuitable for agricultural use. In the first-time frame, the "very poor" quality class of groundwater in the Khorasan plain covered an area of 2.44 Km2. In the second time frame, the only groundwater quality class in the Khorasan plain during that irrigation season was "unsuitable for agricultural use," with an area of 275.2 Km2. In the third time frame, May 2018, the "very poor" quality class of surface groundwater accounted for 14.91 Km2 of the plain's area. In the fourth time frame, December 2018, the area classified as "very poor" quality was estimated to be 8.51 Km2.

As the results indicated, groundwater quality during irrigation seasons (months 11 and 12 of the years 2009 and 2018) decreases compared to non-irrigation seasons (months 4 and 5 of the years 2009 and 2018), with the quality levels of groundwater also diminishing during irrigation periods. The findings revealed that increased vegetation cover and reduced soil salinity are key factors in improving groundwater quality in this region. A decrease in vegetation cover and an increase in soil salinity during irrigation seasons lead to a reduction in the extent and quality of good and moderate quality zones. Given the superiority of the GWR model over OLS, this model can serve as a useful tool in managing the quality zones of groundwater resources and analyzing changes in water quality. Ultimately, this research emphasizes that attention to climatic variables and land surface characteristics is essential for the optimal management of groundwater resources. This study not only utilized various interpolation methods for the WQI based on the chemical data from observation wells within a Geographic Information System (GIS), but also highlighted the prediction of different groundwater quality zones using Geographically Weighted Regression and Ordinary Least Squares techniques, considering the impact of various climatic variables and land surface characteristics. Additionally, obtaining independent variables at a monthly scale across different time periods of irrigation and non-irrigation seasons from the online Google Earth Engine (GEE) was another focus of the current study regarding the examination of groundwater quality changes.

Papaya (Carica papaya) is a tropical tree that leaves, fruits, seeds and roots has many medicinal and edible uses. Choosing the appropriate planting medium is one of the important ways to improve seedling growth of fruit trees. By improving the physical and chemical properties, it causes better absorption of nutrients and improves plant growth. Also, aerial root pruning to produce seedlings with branched and strong root systems is one of the effective factors in growing plants.

To study the effect of different culture media and aerial root pruning on the growth of Papaya (Carica papaya), an experiment was conducted in the form of a completely randomized statistical design with three replications and two factors of planting media (cocopeat 100%, peat mass 100%, and cocopeat 70% + perlite 30%, peat moss 70% + perlite 30%) and performing or not performing aerial root pruning was implemented in the greenhouse. The seeds were sown in the culture trays containing the desired planting beds, and horizontal cuts were made in the walls of the culture tray cavities to perform aerial pruning of the roots in the desired treatments. Sampling was done 50 days after sowing the seeds and some morphologic, physiologic and biochemical traits include fresh and dry weight of seedlings, root length, seedling height, internode length, node number, leaf area, total chlorophyll, peroxidase, catalase and superoxide dismutase activity were analyzed.

The results showed that the treatments had a significant effect on the assessed traits so that the highest fresh and dry weight of seedlings (1.69, 0.66 g, respectively), root length (17.66 cm), seedling height (7.92 cm), internode length (8.61 mm) and node number (4.67) were observed in 70% peat moss + 30% perlite and no aerial root pruning. The maximum leaf area (9.14 cm3) and total chlorophyll (1.69 mg g-1 FW) content were obtained in the 100% peat moss planting bed treatment and no aerial root pruning and the 70% peat moss + 30% perlite treatment and root aerial pruning. The highest activity of peroxidase (37.60 U enzyme-1 FW), catalase (7.60 U enzyme-1 FW) and superoxide dismutase (16.08 U enzyme-1 FW) enzymes was also in 100% peat moss treatment and root aerial pruning.

In general, the results of this research showed that the combination of two organic and inorganic substrates, peat mass 70% and perlite 30%, showed the best effect on vegetative characteristics and total chlorophyll content in papaya. Also, the highest activity of peroxidase, catalase and superoxide dismutase enzymes was observed in 100% peat moss bed. Root aerial pruning also decreased the evaluated growth characteristics and increased the total chlorophyll content and activity of peroxidase, catalase and superoxide dismutase enzymes. According to the results, different culture media had different physical and chemical characteristics that can affect plant growth. In this research it is recommended to sowing papaya seeds in 70% peat moss and 30% perlite and not to perform aerial root pruning to produce this product.

The increasing human needs and changes in climate patterns have led to the construction of water storage structures to meet the water demand in many regions worldwide, including Iran. Consequently, the hydrological regimes of rivers in various parts of Iran, due to human activities such as dam construction, have undergone alterations in recent decades. Understanding the effects of dams on river hydrological regimes is essential for river flow management and the preservation of river ecosystems. In this regard, the present study assesses the changes in the health of the Urmia's Shahrchai River flow during three periods: pre-dam construction, dam construction, and dam operation, from 1951 to 2017, through calculating different flow health related indices. The results indicate that the deviation of all hydrological health sub-indices in the post-dam construction period is higher than the pre-dam construction period. Moreover, the deviation of flow regime sub-indicators was predominantly low to moderate until 1998 and varied from low to very high after 1998. Additionally, the highest deviation of studied flow health-related indices occurred during the reference and dam operation periods for flood occurrence and minimum monthly flow, respectively, while during the dam construction period, it pertained to minimum monthly flow. Furthermore, the findings suggest that the hydrologic health of Shahrchai River flow decreased by approximately 16% and 45% during the dam construction/operation periods, respectively, compared to the reference period. The results of this study can be utilized in the understanding of flow alteration and the sustainable regulation of the Shahrchai River flow regime.

Today, self-injury behavior has a significant prevalence among teenagers, and appropriate skills and interventions are necessary to prevent this destructive behavior. The purpose of this study was to determine the effectiveness of dialectical behavior therapy on social cognition and adaptive behaviors in students with self-harm. The method of this research was semi-experimental and pre-test-post-test with a control group. The statistical population of this research included all the male students with self-injurious behavior who referred to the Babol education counseling center in the academic year of 1400-1401. The research sample included 30 people who were selected by available sampling method and were randomly divided into two experimental groups (15 people) and control (15 people) by lottery. The subjects responded to the questionnaires of Gratz's intentional self-harm (2001), Nejati et al.'s social cognition (2017), child and adolescent social/adaptive functioning, Price et al.'s (2003) before and after the intervention. Dialectical behavior therapy was implemented in 12 sessions of 90 minutes and twice a week in the experimental group and during this period the control group did not receive any type of treatment. Data analysis was done using mean, standard deviation and covariance analysis by SPSS software version 24. The results showed that the effectiveness of dialectical behavior therapy had an effect on social cognition and adaptive behaviors in students with self-harm (P<0.01). Based on the findings, dialectical behavior therapy was effective in increasing social cognition and adaptive behaviors in students with self-harm; therefore, dialectical behavior therapy can be suggested as a suitable intervention to reduce problems in students with self-harm.

The construction industry is responsible for approximately 34% of the final energy consumption and generates nearly 24% of total greenhouse gas emissions. Integrating renewable energy technologies, such as photovoltaic panels and solar water heaters, offers several advantages, including long-term energy savings and enhanced living and working conditions. However, the adoption of these technologies is hindered by several behavioral barriers. Therefore, this study aims to investigate the purchase of photovoltaic panels and solar water heaters, the related barriers, and solutions based on the categorization of building energy consumers. The data was collected in Tehran in 2022 from stores located between 1st Sadeghiyeh Square and Poonak Square using the Davidson et al. questionnaire. Sixty-one sellers responded to the questionnaire, and all items were measured on a 7-point Likert scale. A Scree plot and rotated component matrix resulted in identifying the main barriers that reflect critical perspectives in this field, including the lack of education, the lack of motivation, fear/resistance to change, the lack of confidence, and the lack of information. The study results demonstrate that enhancing occupant awareness about the barriers to energy-efficient buildings and solutions to them through education programs, providing relevant information, and government support can promote the purchase of photovoltaic panels and solar water heaters.

The effort of this article is to solve one of the main problems in the field of banking, which is closely related to the field of information technology. The combination of the management discussion of this topic with the field of information technology will be one of the important topics in the field of information technology management. The main goal of this article is the clustering of bank customers.At first, all customer characteristics were extracted from the bank's database, which was randomly extracted for 900,000 customers, which will be provided as input to the proposed method of this article. All the characteristics of these customers were extracted and 10 characteristics (except four characteristics of the LRFM method) were listed using the opinions of experts. The proposed method should be able to choose among these 10 features for clustering customers, which results in more resolution in clustering. Due to the high number of cases of this problem, it is not possible to do it manually, and the proposed method tries to provide a separate model for clustering for the customers of each bank by examining different cases. Also, the problem of choosing the right value for the number of clusters in the K-means method is solved by the method proposed in this article. The results show that it is better than the basic RFM and LRFM methods.Keywords: relationship management with bank customers, clustering, RFM model, LRFM model, particle swarm algorithm, K-means method.

Flooding is one of the environmental hazards that causes a lot of repeated damage to water and soil resources in different regions of Iran and around the world. Check dams as a mechanical practice have been built in many watershed areas of the country to control peak discharge and prevent soil erosion. Therefore, it is essential to examine the role of these mechanical practices in flood control at the level of watersheds. The current study aims to investigate the effect of the construction of three proposed structures (two masonry check dams and one embankment dam), with spillway heights of 9, 6, and 8 meters, on flood characteristics in Kander Abdolreza watershed, Fars province, Iran. In other words, this study examines whether the construction of these three consecutive structures fulfills the goal of the operation, which is flood control through a significant reduction in peak discharge and flood volume in the study area. Therefore, the most important variables in this study is the flood volume and the volume of reservoirs proposed for flood control. The amount of flood volume is extracted from the flood hydrograph, and the volume of reservoirs is obtained using a contour map and storage-height curves. It is obvious that if the volume of the reservoir is more than the volume of the flood, all the flood will be attenuated by the structure and the runoff would not pass through the spillway of the dam. 

In this study, the HEC-HMS model was used to simulate the rainfall-runoff process and the reservoir effect of each of the three proposed consecutive check dams on flood characteristics. The study area, Kander Abdul Reza watershed, is located about 27 kilometers southeast of Lamerd city, Fars province, Iran. According to the division of watersheds in the country, it is part of the Kol-Mehran basin which is a subbasin of the major basin of the Persian Gulf and Oman Sea. In this regard, after preparing the required data, including properties related to topography, rainfall, soil hydrologic group, and designed structures, the peak discharge and volume of flood under different return periods were estimated using the HEC-HMS model. Ghahreman-Abkhezr method was also used to estimate the intensity of rainstorms. The characteristics of the flood in the downstream were investigated in the absence of three proposed structures (two masonry check dams and one embankment dam) and in the next step, the effect of these reservoirs/dams on the hydrograph of the flood was analyzed. Due to the fact that the proposed structures are located upstream of the residential area and the highway, the basis of the design of the structures was the flood discharge with a return period of 100 years. Finally, using the obtained results, the percentage of reduction in the peak discharge of the flood under the influence of the construction of each of the reservoirs/dams was determined and compared.

The outcomes of SCS unit hydrograph for Kander Abdolreza watershed showed that the flood peak discharge in the return period of 2, 5, 10, 25, 50, and 100 years is equal to 7, 32, 46, 72, 91, and 116 m3.s-1, respectively; so that the time base of the hydrograph varies from 6.5 hours in the 2-year return period to nearly 9 hours in the 100-year return period. Meanwhile, the volume of runoff varies from 1.73 m3 in the return period of 2 years to 1053 m3 in the return period of 100 years. The results also showed that if the three proposed three consecutive structures were built, with a total reservoir volume of 614367 m3, the peak flood discharge would fall from 116 m3.s-1 to 32 m3.s-1 over a 100-year return period. In addition, the outcomes of the simulation of the rainfall-runoff process in the study watershed indicated that the reduction in the peak discharge and volume of the flood after the implementation of the proposed structural measures for the 50-year return period was 84% and 73%, respectively; and for the 100-year return period it was reduced by 72% and 57%, respectively. Thus, according to the results of the simulation of the effect of the designed structures, it can be claimed that the proposed structures for the study watershed would have the ability to mitigate and control floods.

The implementation of the proposed structural measures for the Kander Abdolreza watershed, including two masonry check dams and one embankment dam, in addition to reducing the peak discharge and the volume of flood flows, can stabilize the longitudinal profile of the stream channels, which is useful for the conservation of soil and water resources in the study area. At different return periods (2, 5, 10, 25, 50, and 100 years), the three proposed structures have the required capacity to store runoff and reduce the peak discharge in the study watershed; however, as the return period increases, the role of these reservoirs/dams in controlling the flood and reducing the peak flow decreases. Moreover, the effect of the proposed consecutive structures system on peak flow reduction is greater than the effect of these structures on flood volume reduction.In general, to increase the effectiveness of the watershed studies plans, it is recomended to include the study of the impact of the implementation of structural measures on the characteristics of flood flows in the service description worksheet of these projects so that the decision to carry out or not to carry out for structural operations in each basin or sub-basin is made based on the simulation results and with more detailed information. In addition, to better evaluate the proposed structural measures for flood control, it is suggested to prioritize the installation of hydrometric stations in ungauged watersheds that are prone to flood generation.

Machine learning is a new artificial intelligence method that seeks to write a program with the best performance by using learning experience. Machine learning models with different algorithms can be predictive or descriptive or have both properties and be used in different fields. On the other hand, for better management of flood risk reduction, it is necessary to know the effective factors in each region and flood sensitivity analysis. Since so far, few researchers have analyzed the threshold of influence of variables affecting the occurrence of floods using machine learning methods, the current research is new in this respect. Based on this, the current research has been conducted to identify the threshold of variables affecting the zoning of flooded areas using machine learning and remote sensing data in the Karun Basin area. The results can be put on the agenda of the relevant managers in identifying the influence limits of different variables on the occurrence of floods and the management of flood-sensitive areas by relying on the effective limits of the variables in the study area.

Landsat OLI 8 images on April 8, 2019 were used to identify flooded areas. In this regard, to identify groundwater, the corresponding image of the previous year of the region was used to separate and identify groundwater zones. Then, the remaining pixels of the study area as whole samples and flooded areas were entered into the modelling process as target samples. Therefore, flooded areas with a code of one and other areas with a code of zero entered the modelling process as dependent variables. Also, the variables that were entered as independent variables in the machine learning process include actual evaporation and transpiration, land use, soil density mass, soil clay percentage, soil water deficit, DEM, NDVI, land cover index, Palmer drought severity index, potential evaporation and transpiration, precipitation. cumulative, soil sand percentage, soil texture, soil moisture, minimum and maximum temperature. Next, by entering these variables and performing the machine learning process, the models were evaluated and TreeNet was selected as the best model. Then the threshold of each of the studied components on flood zones was obtained from machine learning. Also, in the present study, learning and test data were used in a ratio of 70% to 30% and completely randomly. It is worth noting that the number of 200 trees with at least six nodes was set for modelling.

Different components have certain thresholds at the beginning of land flooding so regarding vegetation as the most important effective factor in flood zoning, it shows that the lack of vegetation causes flooding, and the higher the level of vegetation, the more it prevents flooding. Also, the cumulative precipitation threshold for flooding the studied area was 15 mm of rainfall, and less than that, the incoming rainfall did not pose a risk of flooding the studied lands. The amount of 15.5 mm of rainfall was the turning point and the threshold of the beginning of the flooding in the study area. Regarding the soil moisture deficiency index, it shows that the threshold of flooding based on this index was 144, in other words when the soil moisture profile is more than the mentioned value, the incoming precipitation must compensate for the soil moisture deficiency, and as a result, floods will be prevented. On the other hand, most flooding conditions have existed at a height of 16 m, and as the height increases, the risk of flooding the studied area decreases, so that there is a failure at a height of 19 m, and when the height reaches 22.5 m, the risk Flooding disappears, and at a height higher than 26 m, flooding is restrained and will reach a steady state. The reason for this can be the plainness of the studied area and the widening of the flood zone in the plain.

The results showed that the components of the vegetation cover index, cumulative precipitation, soil water deficit, Palmer drought index, height, and surface soil moisture respectively had the greatest effect on the flooding of the studied area. Also, in the studied area, the effect of soil sand percentage, soil clay percentage, soil density, potential evaporation and transpiration, slope direction, maximum daily temperature, and soil texture on flood zoning was insignificant. The evaluation of the efficiency of the model with the indicators of ROC, specificity, sensitivity, and overall accuracy is 0.95, 91.2, 90.43, and 91.12, respectively, which indicates accuracy. The results of flood zoning with the ground reality indicated R2 and MAE equal to 72.8% and 0.27%, which confirms the accuracy of the zoning results with the ground reality relatively well. The analysis of the results shows that there will be an increased risk of flooding in the wetland and swamp areas due to the high humidity and water level. The results of the present research can be used by planners and managers of natural hazards to reduce floods.

Housing has other objectives such as providing multiple services and providing the needs of people living in that place. When the decision to determine a specified location for the residence, it is important to provide an acceptable level of satisfaction. People are satisfied with their residence time that they meet their needs. People are the most important factors in architecture; therefore designers and architects need to try and work towards providing and promoting the level of satisfaction in residential complexes. In order to investigate the promotion of satisfaction and its influential factors in high - rise residential complexes , support spaces are considered. The research method is descriptive - analytical and based on the applied goal. The method of data collection was library method and for main data of research , field method was considered. The data collection tool is edinterviews with residents of residential complex in chamran campus using questionnaires. The collected data were analyzed by using spss25 software. The results showed that the first priority of supporting space related to service spaces such as the existence of lift and …, after that the existence of garbage dump in the complex, then existence of safe spaces with safety and perfect security for children and existence of shops in order to meet the needs of collection for residents were important.

It is necessary to predict the influencing parameters on homogeneous watersheds for the future in order to make decision and correct management. In this research, 19 bioclimatic variables extracted from the ESM4-GFDL general circulation model based on ssp126 scenario and then were clustered in two statistical periods, including the years average of 1981-2010 and the average of 2041-2070, and also homogeneous watersheds were identified for two statistical periods. Also, the intensity of utility in homogeneous watersheds was investigated. The results showed that the homogeneous watersheds located in cluster 4, will be involved with the highest changes in increasing temperature and decreasing precipitation and it will cover about 16% of the area of Chaharmahal va Bakhtiari province. On the other hand, in all homogeneous watersheds, the seasonal changes in precipitation and average air temperatures of the wettest quarter have an increasing trend, which shows the disproportionate temporal distribution of precipitation will be occurred, then the problems lead to destructive floods, the reduction of plants and agricultural productions and destruction of native habitats of the regions. In this regard, Kohrang city has been studied as the most critical city and has the least desirable area, 23.38 and 22.35 in two periods, respectively.

Two of the important issues in the construction of structures located in seismic coastal areas are the study of the potential of liquefaction phenomenon in saturated sandy soils and seismic structure-foundation-soil interaction (SSFSI). Control of structure damage on the liquefiable soil and large deformations of soil due to seismic loading and, also, the other responses such as: the accelerations at top of the structure/foundation and excess pore water pressure related of this phenomenon are very important. The phenomenon of liquefaction happens due to the occurrence of an earthquake and due to the lack of sufficient opportunity for drainage of excess pore water pressure. One of the effective and useful mitigation methods to control of the liquefaction phenomenon is the usage of dense granular column (DGC) in appropriate dimensions and distances on the ground susceptible to liquefaction. The role of the DGC in controlling the liquefaction phenomenon are follow as: firstly, the DGC is made of materials that are more permeable to sandy soils and cause the excess pore water pressure to be dissipate faster, and secondly, DGC increases the stiffness of the system, which proportionally reduces the excess water pore pressure due to the dilative behavior. To reduce the risks of liquefaction, it is necessary to fully understand its consequences. These consequences depend on permanent soil displacements, structural performance, structural characteristics, foundation and structure dimensions, soil conditions of the structure site, type of loading and earthquake intensity. The presence of the structure and impact of SSFSI affects the intensity of liquefaction and static and dynamic stresses in the soil. Most prior studies (e.g., physical, numerical, or analytical models), ignore the existence of the structure or consider the effect of soil-structure interaction (SSI) on the liquefiable soil layer as an equivalent model. Hitherto, the essence and extend of these interactions are not sufficiently understood. These methods can not properly assess the damage caused by liquefaction; Therefore, these methods cannot be used in the design of structures resistant to liquefaction. Therefore, to accurately study the effect of liquefaction, a method and model is needed that can fully consider the soil, foundation, and structure so that it can be used to correctly estimate the amount of subsidence and displacement of the structure. In this study, to evaluate the seismicity and the mitigation effect of DGCs, using OpenSees finite element software, modeling of DGCs and surrounding soils without structure and models with 5, 10 and 15 storey structures were performed. The three-dimensional soil and DGCs modeled in the software are placed under different earthquakes and the effects of structural layers on the lateral displacement, excess pore water pressure, response of acceleration spectrum, drift and shear force of stories are investigated. The liquefiable soil is modelled through the pressure-dependent multi yield surface soil constitutive law (PDMY02) applied in OpenSees. The results of this study are shown the positive effect of DGC on the reduction of lateral displacement components of soil and structure, foundation subsidence and excess water pore pressure. Also, the presence of the structure on the soil and the increase of its floors have increased the mentioned components except for lateral displacement of soil.

Mohammad Kazemi  *PhD of Architecture, Shahid Beheshti University, Tehran, IranAbstractMuseum designing is a three-dimensional and hybrid process. It interconnects various disciplines such as architecture, graphic design, interior design, audio-visual graphics, and print graphics. Museum’s transformation during the 1930s is the main reason that drives storytelling to be adopted in the daily display routine of museums. Storytelling communicates meanings and mediates human knowledge of the world, humans, and architecture. Verbal storytelling, written storytelling, visual storytelling, and spatial storytelling help all age groups to communicate with the collections of the museums. In this paper, we investigate the role of the different kinds of storytelling in the museum's design process. We use an original survey of meaning and interpretation drawn from the fields of philosophy, linguistics, hermeneutics, humanistic geography, narrative theory, psychology, architectural theory and museology. In addition, we use researchers' personal perceptions and experiences. This study uses two case studies (Australian National Maritime Museum and Hong Kong Museum of History) to demonstrate how museums are currently utilizing storytelling and show the fundamental tools for its success. The results of this paper indicate that our understanding of master narratives needs more time and focus. The master narrative has to be followed and leaves little space for unrestricted meaning-making. Furthermore, in the non-linear storytelling, each thematic display can establish a separate identity. Then, museum architecture suggests a delightful environment for communicating meanings by self-learning and organized embodied experience of an entire space.

Today, changes in the volume and type of customer demand are a serious and signicant problem for manufacturing companies. To address this problem, new production systems, including the dynamic cellular manufacturing system, have provided some solutions. In this system, the layout of machines can be changed from one period to another according to changes in demand. On the other hand, in the problem of scheduling parts in the cellular manufacturing system, the relocation of machines is usually done between two periods. Still, no time is considered for this relocation, and it is necessary to consider this time to determine the completion time of parts exactly. This paper introduces an innovative mathematical model to address the scheduling challenges in a cellular manufacturing system with continuous periods. The proposed model allows for dynamic machine relocation and layout changes within each period while taking into account the associated time and cost factors involved in the movement process. The possibility of machine relocation during the period can increase the system's dynamics. In the proposed model, cell formation coincides with scheduling. Other features of the model include alternative processing routes and the existence of identical versions of a machine. The objective of the proposed model is to minimize the total costs of completion time, machine relocation, and intracellular and intercellular material handling. The objective of the proposed model is to minimize the total costs of completion time, machine relocation, and intracellular and intercellular material handling. Validation of the proposed model is performed in ve steps. The results of examining the features of the proposed model show that the model can eectively reduce completion time and other costs. Finally, to solve the model in larger sizes, two meta-heuristic algorithms of simulated annealing (SA) and genetic algorithm (GA) have been designed, and the obtained results have been compared with the results of CPLEX solver.