محمد جمشیدی

Although the concept of “unity” has been a contentious and debatable issue from a theoretical perspective,particularly in terms of relevant definitions, undoubtedly any form of “activism” on the international stage necessitates a call for cooperation, alliance and coalition within the foreign policy agenda. This perspective resulted in the formation of a trilateral security Pact among three nations namely Australia,the United Kingdom and the United States in September 2021.This alliance draws inspiration from a long history of shared security, political and civilizational ties among the mentioned states.Simultaneously, it has been heralded by their leaders as an innovative response to the emerging threats in the Asia-pacific region, with a particular focus on challenges posed by China.In this article, employing the qualitative research methodology, specifically the explanatory approach, we aim to address the question: “How has the security alliance inked by the three nations of the USA, the UK and Australia, in the form of the AUKUS Treaty (2021), impacted the security landscape within the Asia-pacific region? “Our hypothesis suggests that, the AUKUS Treaty, in spite of possessing the characteristics of a security alliance,such as maximum involvement of the three Anglo-Saxon Powers in terms of nuclear cooperation, it has the capability to activate and absorb other security alliances in the region as well.However,due to the fact that the Pact has disregarded the logic of non-proliferation and triggered the balance of threats, it has undermined the regulations concerning Nuclear Non-proliferation Treaty and escalated China’s tension-inducing standpoint,which can confront the region with new security crises.

The changes in the middle east region after 2011 and the developments in the Arab Spring prompted Germany to pursue a pragmatic policy in the region along with strengthening the military presence, and no longer insist on democratizination the countries of the middle east region. Accordingly, Germany has tried to increase its influence in the region using various security, political and military tools while maintaining positive relations with the countries of the region. "Stability" is the keyword of Germany's strategy towards middle east. The "Enable and Enhance Initiative" is among the main programs of the federal government to pursue this goal, which tries to prevent the increase of tensions and the growth of terrorism by empowering fragile governments in the region. Therefore, in view of the changes in the German security and military approach after 2011, the aim of this study is to examine and analyze the German strategy in middle east through the examination of existing documents, speeches and policies focusing on understanding its motivations, goals and prospects in the future. So this research can help better understand Germany's role in the middle east region and its impact on regional dynamics and global politics.

With fifteen neighboring countries on its land and water borders, Iran is the second country with the largest number of neighbors in the world after China. However, when considering a broader neighborhood (including secondary neighbors), an additional ten countries can be included, bringing the total count of bordering and non-bordering neighbors to 25. This extensive neighborhood presents both challenges and opportunities for Iran. While it offers potential avenues for cooperation and growth, it also poses threats that require careful consideration. Paying attention to this issue from the perspective of opportunities or challenges shows the necessity of placing it at the top of the country's foreign policy priorities. However, despite the emphasis in foundational documents on the significance of the neighborhood environment in foreign policy, the absence of a strategic approach in this regard over the past four decades has led to the creation of threats rather than opportunities for the national interests of the Islamic Republic of Iran. In response, the 13th Iranian government has critically assessed the historical neglect of neighboring countries in Iran’s foreign policy. As a result, it has introduced the doctrine of ‘neighborhood and integration’ to capitalize on these opportunities and effectively address the associated threats. Based on this, the paper aims to explore the meaning and application of this doctrine. Specifically, it delves into its principles, goals, and strategic approaches in the context of developing relations with neighboring countries.

This study employs a qualitative research method, specifically the ‘descriptive-analytical’ approach. It draws from documentary sources, official statements by foreign policy officials, library research, and online resources. The analysis of the 13th government’s neighborhood doctrine is presented in six sections: 1) Neighborhood and integration in international relations knowledge; 2) Historical gaps in Iran’s foreign policy neighborhood approach; 3) Neighborhood and integration as a focal point in the 13th government’s foreign policy; 4) Principles of neighborhood and integration policy in the 13th government; 5) Strategy for neighborhood and integration policy by the 13th government; and 6) Implementation of the neighborhood and integration doctrine in practice.

Iran, as the second country with the largest number of neighboring countries in the world, possesses significant potential. This potential can be both an opportunity and a challenge and largely depends on the perception of political governance and its orientation. Unfortunately, in Iran’s contemporary history, the challenging and threatening aspects of the neighborhood environment have often prevailed. While the ever-changing geopolitics and shifting borders of Iran’s neighbors contribute to this situation, other critical factors come into play. Notably, past governments have often overlooked the importance and position of the neighborhood environment in foreign relations. Their minimalistic approach and the absence of a well-defined strategic framework have also exacerbated the situation.
In addressing these issues, the 13th government has anchored its foreign policy on the doctrine of neighborhood and integration. Within this framework, fostering relations with neighboring countries and promoting regional integration serve as crucial components of foreign relations and act as a balancing factor in the country’s overall foreign policy. The government extends the concept beyond mere good neighborhood, taking a comprehensive view that encompasses both immediate neighbors and secondary neighbors. This approach is guided by principles such as respect for territorial integrity, non-interference in internal affairs, nurturing an endogenous regional order, cooperative security, and mutual interaction. These principles are deemed essential for establishing strategic and systematic relations with neighboring nations and are seen as prerequisites for successful regional integration.
The 13th government has characterized neighborhood policy as a stable and enduring strategy, rather than a temporary or fleeting tactic. Consequently, its advancement necessitates a well-defined plan and a specific strategic approach. Within this framework, the government acknowledges the region’s realities, particularly significant challenges such as security issues and the limitations of regional cooperation. It firmly believes that the region cannot swiftly extricate itself from these complexities. Instead, the path forward lies in building mutual trust, fostering regional stability, enhancing economic cooperation, and extending coordinated and collective security efforts.

Al land use changes pose a most serious challenge to agricultural production, resulting in the loss of fertile soil and food security. The present study was conducted to investigate the challenges associated with fertile agricultural lands in Iran as an attempt to examine and identify the different aspects of the threats agricultural lands are faced with. As a second step, the laws and regulations governing land use change in different countries and in Iran were reviewed and the general policies related to the protection of agricultural lands were examined.  The review of land use laws in Iran showed that, despite their abundance, they have suffered from poor law enforcement as a result of both social and historical reasons as well as a public negligence of and non-adherence to the laws. Evaluations of temporal changes in the growth and development of metropolises as provincial centers revealed that they had greatly expanded beyond their original limits to annex fertile agricultural lands in the suburbs to these large cities. The five-year statistics of the requests submitted for land use change in each province also revealed landlords’ increasing demands submitted for land use change motivated by more profits out of their property, which result in loss of fertile soil, decreased agricultural production, and endangered food security.

In all of battles and wars, especially wars of will and determination, early understanding the enemy's intentions, plans and maps is an important factor in victory. Knowing the general plan or understanding the intentions of the enemy requires model and framework; In this article, by descriptive and analytical method and using documentary and library sources, the Sentos model is presented to understand the strategic intentions of the United States against Iran. Almost after Gary Hamel, most of the literature for understanding strategic intentions, especially with adverserial approach, belongs to Eugene Sentos. The components of the Santos intentions model to terms of comprehensiveness and depth are superior than other models in, especially for strategic levels. The components of her model include "Iran's fundamental beliefs and assumptions about itself", "Iran's beliefs about the United States", "Iran's basic goals toward the United States", and "Iran's basic actions toward the United States". His model is more valuable than his findings about Iran. Understanding strategic intentions have shifted from person-centered to intent fusion systems, this systems connect to a variety of behavioral sensors in the air (satellite, aircraft, and UAV), land, sea, and political, Social, economic, industrial and infrastructure networks and this systems with the help of knowledge bases, artificial intelligence and software systems are on the verge of accessing big data, meta-analyzes and social knowledge are more real. The IRI and its institutions must logically and systematically design models and processes for understanding the strategic intentions of the enemies in their structure and contexts.

Imbalanced data remains a widespread and significant challenge, particularly impacting machine learning algorithms. Therefore, addressing imbalanced data classification has emerged as a crucial research area within the field of data mining. This issue, often characterized by a limited number of instances in one class and a substantial number in other classes, poses substantial hurdles for machine learning algorithms. Consequently, data mining experts and machine learning professionals are actively working on refining methods and models for classifying imbalanced data with the aim of improving the accuracy of such classifications. The principal objective of this study is to precisely detect and categorize samples from the minority class, ultimately enhancing the precision of soil class classification. This research is conducted in a specific region, encompassing the southwestern territories of Zanjan province.

To achieve this objective, a total of 148 soil profiles were excavated using a regular grid pattern with an average spacing of 500 meters (and in some locations, up to 700 meters based on expert recommendations). After the samples were air-dried, they were transported to the laboratory. Physical and chemical analyses were conducted on all collected samples, including assessments of soil texture, soil pH, calcium carbonate equivalent, cation exchange capacity, electrical conductivity, organic carbon content, and gypsum content. Subsequently, the soil samples were meticulously classified and described up to the family level, following the comprehensive standards of the soil classification system. The most appropriate covariates were selected among 57 covariates including geomorphological and geological maps, digital elevation model (DEM), and data from Landsat 8 satellite images, using principal component analysis (PCA) and expert knowledge approaches for predicting soil classes selected. Saga-GIS and ENVI software were used to extract environmental covariates. Modeling of the soil-landscape relationship was performed using three algorithms, namely multinomial logistic regression (MNLR), random forest (RF), boosted regression tree (BRT) and ensemble model (after data balancing) in “R studio” software. To check the accuracy of the used model, the data was randomly divided into training and validation data. 80% of the data (118 profiles) were used for model training and 20% (30 profiles) were used as validation data for evaluation.

The results of the selection of covariates showed that 10 information covariates of geomorphological maps, geological information and features extracted from the digital elevation model (DEM), including Analytical hill shading (AHS), sunrise, valley depth (VD), LS Factor, Channel network distance (CND), Topographic wetness index (TWI) and Multi-resolution ridge top flatness (MRRTF) were selected as input variables. Based on the results of profile analysis, the soils of the region at the subgroup level were categorized into five classes, with imbalanced distribution, including Typic Calcixerepts, Typic Haploxerepts, Gypsic Haploxerepts, Typic Xerorthents, and Lithic Xerorthents. The results of evaluation metrics such as overall accuracy and Kappa index were 65% and 0.32 for the RF algorithm, %60 and 0.35 for the boosted regression tree algorithm, 65% and 0.41 for the MNLR algorithm and after balancing the data with the ensemble model approach, it was 70% and 0.62 respectively. The results of two statistics of user’s accuracy and producer’s accuracy showed that among individual models, the multinomial logistic regression model has higher accuracy in predicting soil classes. Although the ensemble model has succeeded in predicting the soil minority classes well, due to the fact that the two weaker models of the RF and BRT are involved in the modeling, It showed lower values compared to the individual multinomial logistic regression model, in predicting some classes of the majority of soil, especially the two classes of Typic Haploxerepts and Typic Xerorthents.

In summary, the results have demonstrated that when learning algorithms are individually applied, they do not exhibit high accuracy in spatially predicting soil classes. However, when these algorithms are amalgamated into an ensemble model, they exhibit remarkable accuracy in spatial soil class prediction, outperforming individual models in terms of performance and accuracy. Moreover, the ensemble model substantially enhances prediction accuracy and reduces the occurrence of misclassifications, especially at the subgroup level. While each specific model excels in predicting a particular soil classification, the cumulative ensemble models consistently outperform individual models in terms of overall performance and accuracy, underscoring the effectiveness of ensemble modeling in improving spatial soil classification.

After the Russian invasion of Ukraine in February 2022, the United States of America tried to be at the forefront of the fight against Russian expansionism in Europe by creating an international diplomatic coalition against Moscow on the one hand and equipping and strengthening the Ukrainian military forces on the other hand. The question raised in this article is what strategy and goals does the United States pursue by accepting the leadership of the fight against Russia's presence in Ukraine? A review of the existing literature shows that American thinkers and policymakers are satisfied with generalizing about a series of principles of the Neoliberal system such as the "defense of democracy", "defense of liberal order" and "human rights" and have avoided explicitly stating the goals and interests of that country. However, the findings of this research, which were obtained by using the descriptive-analytical method of the available data, show that due to the focus of America's strategic policy on East Asia and China and the lack of strategic importance of Ukraine in the Western-oriented international order, Washington will soon sacrifice Ukraine and force it to accept some of Russia's demands.

The purpose of this study was to investigate the role of managers’ ethical leadership in teachers' job performance with the mediating role of professional commitment. This study used quantitative descriptive-correlational method with structural equations modeling. The study population comprised all elementary school teachers in Tuyserkan (n=332), who were selected by census because the statistical population was small. Data were collected through the Ethical Leadership Questionnaire of Kalshovenet al. (2011), Clikeman & Hening's Professional Commitment (2000), and Patterson (1996) Job Performance Questionnaire. Cronbach's alpha coefficient and confirmatory factor analysis were used to determine the reliability of the instruments. Data were analyzed using Pearson’s correlation test and structural equation modeling technique in LISREL10.30 software. Findings showed that managers' ethical leadership and teachers' professional commitment have a significant effect on job performance (0.05). Ethical leadership mediated by professional commitment also affects teachers' job performance (0.05).

The association of leptin with psoriasis is currently controversial. The purpose of this study was to evaluate serum leptin levels in patients with psoriasis and its relationship with the severity of the disease.

In this case-control study, serum leptin levels were measured in 40 patients with psoriasis and 40 age- and gender-matched healthy controls by sandwich enzyme-linked immunosorbent assay method. The severity of psoriasis was assessed by the Psoriasis Area and Severity Index (PASI). Data were analyzed using SPSS software version 16.

Of 40 patients, 35 (87.5%) had plaque psoriasis, 3 (7.5%) had palmo-plantar, and one of the remaining two patients had erythrodermic psoriasis while the other one had a pustular type. The mean disease severity based on PASI was 10.2 with a range of 0.8 to 39.9. The most common sites of involvement were generalized (27.5%), upper and lower extremities together (20.0%), lower extremities alone (15%), and trunk and lower extremities (15%) in descending order. Leptin was higher in the patient group than the control group (19.2±16.3 ng/ml versus 15.5±16.1 ng/ml) (P=0.227). The correlation between leptin and body mass index was positive and significant in both groups. No significant relationship was found between the severity and duration of disease and leptin.

The findings of this study showed that similar to most studies, serum leptin levels were higher in patients with psoriasis than in healthy subjects, and in obese people, this difference was greater than in normal-weight individuals

The prevalence of kidney disease (KD) as a global health burden is increasing rapidly and is known as a health problem that imposes a large economic cost on health systems. The present study was conducted with the aim of investigating the 30-year trend of the burden of KD in Iran from 1990 to 2019. This is a secondary data analysis study that provided information on the trend of KD in Iran during the years 1990 to 2019. The data related to the indicators of the burden of KD in Iran, the Eastern Mediterranean region and the global average were extracted from the Global Burden of Diseases (GBD) report. The results of the study have been reported descriptively and in the form the trend of disease burden in a 30-year period using three indices, including DALY, YLL and YLD. The highest burden of disease caused by chronic kidney diseases in the last 30 years in Iran was related to people over 50 years old, and the corresponding DALY index was lower than the average of the region and the world. Moreover, the highest burden of disease caused by acute glomerulonephritis in the last 30 years was related to the age group over 70 years old, and in general, the DALY index for this disease in Iran has exceeded the average of the region and the world. The results of the study showed that the burden of KD is increasing over time, especially in older age and due to acute glomerulonephritis. Therefore, it is necessary to develop and implement effective preventive and treatment measures with the aim of reducing the incidence of KD and also to slow down the progress of the disease.

This study aimed to estimate the spatial distribution of soil properties including field capacity (FC), permanent wilting point (PWP) and  total available water (TAW) using ordinary kriging (OK) and random forest (RF) in agricultural lands of the southern Sistan Plain, covering an area of approximtely 147000 hectars. FC, PWP, and TAW and soil texture components were measured for a total of 200 surface soil samples (0-30 cm). Performance evaluation of the two methods based on the percentage of normalized root mean square error (nRMSE) revealed that the conventional OK with 5% less error in estimating FC, 3% less error in estimating PWP, and 5% less error in estimating TAW performed slightly better than RF. Comparing Bias values showed that OK underestimates both FC and PWP and overestimates TAW, while RF overestimates all three parameters. Spatial distribution maps of FC, PWP, and TAW produced by OK model showed that the highest amount of FC (23%) and TAW (14.4%) were in the west and northeast of the region, which had heavier texture and lower altitude from the sea level. In the southern and southeastern regions, which have lighter soil texture, the amount of available water was less compared to the western and northeastern regions. In the RF model, the most important variable extracted from satellite images was Digital Elevation Model  (DEM), and all three features had higher values in areas where DEM was lower. It seems that the flatness of the study area and the inadequacy of auxiliary variables caused the lower accuracy of the RF method.

Digital soil mapping (DSM), which uses machine learning (ML) algorithms and environmental covariates, is used worldwide for predicting soil properties and classes, due to being time-effective and cost-saving. This research was conducted to compare ML models and their efficiency in predicting the spatial distribution of soil subgroups in part of Zanjan Province. For this purpose, sampling was carried out through a regular pattern with 500 meters intervals, and 148 soil profile samples were randomly collected and classified. The soils of the region at the subgroup level were categorized in five classes, including Typic Calcixerepts, Typic Haploxerepts, Gypsic Haploxerepts, Typic Xerorthents, and Lithic Xerorthents. Environmental covariates included geomorphological and geological maps, digital elevation model (DEM), and remote sensing (RS), selected by principal component analysis (PCA) and expert knowledge approaches. Fifty-seven environmental variables were extracted as representatives of soil forming factors. Modeling of the soil-landscape relationship was performed using three algorithms, namely, multinomial logistic regression (MNLR), random forest (RF), and boosted regression tree (BRT) in Rstudio software. The results of evaluation metrics such as overall accuracy and Kappa index were 65% and 0.32 for the RF algorithm, 60% and 0.35 for the BRT, and 65% and 0.41 for the MNLR. Referring to the importance of environmental variables, results showed that the four factors of valley depth, LS-Factor, channel network distance, and multiple resolution ridge top flatness index (MRRTF) were the most important variables in MNLR algorithm. Also, the results of two statistics of user’s accuracy (UA) and producer’s accuracy (PA) showed that the MNLR model performed better in the spatial prediction of soil at subgroup level. Soil classes with higher frequency had higher accuracy. The results of the prediction accuracy of soil classes showed that the model prediction is more accurate for the more frequent types in the observations.

Condyloma acuminata is treated in different ways, each of which may have advantages and disadvantages. The present study aimed to evaluate the effective factors in response to cryotherapy treatment in women with condyloma acuminata.

This case-control study was conducted on 200 women with condyloma acuminata who were candidates for cryotherapy and were referred to Farshchian Hospital (Sinai) in 2021-2022. Before starting cryotherapy, a biopsy sample was taken from the lesions to perform a polymerase chain reaction (PCR) and determine the genotype of the human papillomavirus. Based on the extracted subtype, the lesions were classified into three high-risk, medium- and low-risk groups. Cryotherapy was performed for each lesion once every two weeks and up to six sessions at most. Patients were examined one month after the last cryotherapy session in terms of recovery with no recovery. Non-response to treatment was considered the case group, and response was regarded as the control group.

In terms of genotype, 43% were high risk, 24.5% were intermediate, and 32.5% were low risk. The frequency of response to treatment was 37%. A response to cryotherapy of condyloma acuminata in the anogenital area of participating showed a significant correlation with age (P=0.008), lesion size (P=0.001), duration of disease (P=0.001), genotype (P=0.001), and lesion location (P =0.014).

Response to cryotherapy treatment in anogenital condyloma acuminata warts is relatively low. Lesions in the vaginal area, younger age, the smaller size of lesions, and shorter duration of infection had a better prognosis after cryotherapy

Despite the great use of digital soil maps, the problems of imbalance in classification disrupt the classification performance of many machine learning algorithms, and for this reason, it has attracted the attention of many researchers. Therefore, the aim of this research is to improve the classification of unbalanced soil data using resampling pretreatment technique in three forecasting models including Random forest (RF), Boosted regression trees (BRT) and Multinomial logistic regression (MNLR) in a part of the lands of Zanjan province in Iran.Sampling was done based on a regular grid pattern with 500 meters intervals, and 148 soil surfaces were randomly studied and classified. The region's soils at the subgroup level were in five classes with imbalanced distribution, including Typic Calcixerepts, Typic Haploxerepts, Gypsic Haploxerepts, Typic Xerorthents, and Lithic Xerorthents. Environmental covariates included geomorphological and geological maps, digital elevation model (DEM), and remote sensing (RS), selected by principal component analysis (PCA) and expert knowledge methods AND a number of environmental variables including geomorphological map information, Geological information and features extracted from the DEM were selected as the most effective environmental variables for predicting soil classes and as input to the model. Extraction of environmental covariates was done in ENVI and SAGA_GIS software and modeling of soil-landscape relationship was done using the aforementioned algorithms in Rstudio software. The resampling technique was applied to the minority and majority soil classes prior to modeling.The results showed that using original data that have imbalanced classes for mapping resulted in loss of the minority classes and relatively low Kappa agreement values and overall accuracy for RF (ovrall=65%, k=0.32) and BRT models (ovrall=60%, k=0.35). However, after resampling the data, two overall accuracy and Kappa coefficient statistics increased in all models. In addition, the BRT model provided an acceptable estimate by maintaining the minority classes and the Kappa coefficient of 0.64 and the overall accuracy of 75% in the spatial prediction of soil subgroups. The producer accuracy (PA) and user accuracy (UA) results showed that the two classes of Gypsic Haploxerepts and Lithic Xerorthents, which were excluded when training using imbalanced datasets in RF and BRT algorithms, showed significant improvement after balancing the data. Results show that they were well predicted in RF algorithm (UA =100%, 78%) and BRT algorithm (UA= 60% and 70%) using treated data. Also, these minority classes showed Producer accuracy in RF algorithm (PA= 75%, 88%) and BRT algorithm (PA=100%, 78%) in compared to zero accuracy when training using imbalanced data. On the other hand, the validation results of the MNLR algorithm showed that despite maintaining the minority classes after balancing the data, the minority classes were predicted with less accuracy. Results showed that modeling using imbalanced distribution of class observation caused uncertain maps with minority classes being lost and relatively poor accuracies. After data treatment, with over- and under-sampling, all models showed significant improvement in maintaining the minority classes, in evaluations. Data resampling technique can be a useful solution for dealing with imbalanced class observations to produce more certain digital soil maps.

Despite the great use of digital soil maps, the problems of imbalance in classification disrupt the classification performance of many machine learning algorithms, and for this reason, it has attracted the attention of many researchers. Therefore, the aim of this research is to improve the classification of unbalanced soil data using resampling pretreatment technique in three forecasting models including Random forest (RF), Boosted regression trees (BRT) and Multinomial logistic regression (MNLR) in a part of the lands of Zanjan province in Iran.Sampling was done based on a regular grid pattern with 500 meters intervals, and 148 soil surfaces were randomly studied and classified. The region's soils at the subgroup level were in five classes with imbalanced distribution, including Typic Calcixerepts, Typic Haploxerepts, Gypsic Haploxerepts, Typic Xerorthents, and Lithic Xerorthents. Environmental covariates included geomorphological and geological maps, digital elevation model (DEM), and remote sensing (RS), selected by principal component analysis (PCA) and expert knowledge methods AND a number of environmental variables including geomorphological map information, Geological information and features extracted from the DEM were selected as the most effective environmental variables for predicting soil classes and as input to the model. Extraction of environmental covariates was done in ENVI and SAGA_GIS software and modeling of soil-landscape relationship was done using the aforementioned algorithms in Rstudio software. The resampling technique was applied to the minority and majority soil classes prior to modeling.The results showed that using original data that have imbalanced classes for mapping resulted in loss of the minority classes and relatively low Kappa agreement values and overall accuracy for RF (ovrall=65%, k=0.32) and BRT models (ovrall=60%, k=0.35). However, after resampling the data, two overall accuracy and Kappa coefficient statistics increased in all models. In addition, the BRT model provided an acceptable estimate by maintaining the minority classes and the Kappa coefficient of 0.64 and the overall accuracy of 75% in the spatial prediction of soil subgroups. The producer accuracy (PA) and user accuracy (UA) results showed that the two classes of Gypsic Haploxerepts and Lithic Xerorthents, which were excluded when training using imbalanced datasets in RF and BRT algorithms, showed significant improvement after balancing the data. Results show that they were well predicted in RF algorithm (UA =100%, 78%) and BRT algorithm (UA= 60% and 70%) using treated data. Also, these minority classes showed Producer accuracy in RF algorithm (PA= 75%, 88%) and BRT algorithm (PA=100%, 78%) in compared to zero accuracy when training using imbalanced data. On the other hand, the validation results of the MNLR algorithm showed that despite maintaining the minority classes after balancing the data, the minority classes were predicted with less accuracy. Results showed that modeling using imbalanced distribution of class observation caused uncertain maps with minority classes being lost and relatively poor accuracies. After data treatment, with over- and under-sampling, all models showed significant improvement in maintaining the minority classes, in evaluations. Data resampling technique can be a useful solution for dealing with imbalanced class observations to produce more certain digital soil maps.

Mycosis fungoides is a neoplastic skin disorder whose underlying cause is not yet fully understood. There are hypotheses about the possible effect of viral agents on the development of mycosis fungoids that have not yet been proven. The present study aimed to evaluate the lymphocyte variant in peripheral blood smear of patients with mycosis fungoides fungoids to determine the possible role of viral agents in the development of mycosis fungoids.

In this case-control study, 55 patients with a definite diagnosis of mycosis fungoides based on pathology, clinical and immunological findings (ISCL) were selected by convenience and consecutive sampling method from those referring to referred to Farshchian Hospital (Sina) in Hamadan in 2020 and compared with 55 healthy individuals regarding variant lymphocytes percent of peripheral blood smear.

Patients with mycosis fungoides and the control group were matched in terms of age and gender. In the study of peripheral blood smear in patients with mycosis fungoides and the control group, the mean percentage of eosinophils was 5.35±5.11 and 3.00±2.01 (P=0.043), monocytes were 8.27±4.08 and 3.17±1.57 (P<0.001), neutrophils were 53.38±12.97 and 61.64±13.00 (P=0.001), lymphocytes were 31.67±12.57 and 32.95±12.60 (P=0.597), variant lymphocytes was 24.69±15.58 and 3.60±4.66 (P<0.001), and band cells were 1.75±0.96 and 2.25±0.71 (P=0.368) respectively.

In patients with mycosis fungoides, the percentage of variant lymphocytes was significantly higher than that in the control group, suggesting the role of infectious agents, especially EBV & CMV, in this disease.

The purpose of this study was the effect of mobile learning on improving the performance of Allameh Tabatabai University staff. The research method is quantitative and quasi-experimental with a pretest-posttest design with a control group. The statistical population of this study was the staff of Allameh Tabatabai University that 54 university staff (28 in the control group and 26 in the experimental group) were selected by convenience sampling method. Which were randomly assigned to the experimental and control groups. First, the pre-test was performed on the members of the experimental and control groups, and then the training package prepared on the subject of time management on mobile learning was provided to the experimental group. The conventional method was taught at the university for 4 weeks. At the end of the post-test was performed on both groups. The training package including the four dimensions of time management in the form of text, PowerPoint and short educational videos (micro learning) along with educational goals and end-of-course evaluation was provided to the staff of Allameh Tabatabai University. After training by the training package and conducting pre-test and post-test to evaluate time management knowledge, a staff performance questionnaire has been reviewed to assess the amount of adequate time utilization. This questionnaire had validity, reliability and scoring. Therefore, the mobile learning training package has increased the knowledge of time management of Allameh Tabatabai University staff and also due to the high

For the proper use of water resources and prevention of soil degradation, it is necessary to select the best irrigation system. The aim of this study was to map the land suitability evaluation for different irrigation methods (surface and drip) in 46000 ha of Sistan plain including Hirmand, Hamon, Nimroz, Zahak, and Zabol counties. For this purpose, 600 pedons with an approximate distance of 700 to 800 m were excavated, described, and sampled. In this study, land suitability evaluation for different irrigation methods based on soil properties and topography was done using the parametric method. Then, land suitability maps for surface and drip irrigation methods were prepared using the Inverse Distance Weighting (IDW) method. The results showed that in the studied area, the conditions for drip irrigation are better than the surface irrigation method. However, in many studied areas, there are restrictions for drip irrigation, and for proper and efficient use of this irrigation system, the restrictions must be removed. Moreover, the results confirmed that in all studied areas the most important limitations are salinity/alkalinity and limitations due to physical soil properties. The silty clay and silty loam textural were the most limitation classes. Due to the fact that changing the physical properties is not easily possible, it seems that soil remediation based on salinity and alkalinity can help to remove or reduce the limitations.

In March 2021 during the visit of Chinese Foreign Minister Wang Yi to Tehran and after several months of controversies, Wang and his Iranian then-counterpart Mohammad Javad Zarif finally signed a 25-year bilateral strategic partnership agreement. The agreement promises to expand Iran’s relations with the world's second-largest economy.  It seems that the current developments in the field of international politics and the intensification of competition between China and the United States on the one hand and the experience of the US withdrawal from the Joint Comprehensive Plan of Action and the failure of the European parties to adhere to their commitments, on the other hand, had clearly heighten Iran’s eastward foreign policy. In this regard, one of the questions that arises is about the existing strategic relations between China and Saudi Arabia and China's approach toward the geopolitical competition between Riyadh and Tehran. This article tries to explain China's foreign policy in the Persian Gulf region based on economic diplomacy and examines China’s relationship with the two main powers in the Persian Gulf, namely, Iran and Saudi Arabia. Resorting to the strategic hedging strategy, Beijing tries to avoid entering into geopolitical dualities and guarantee its strategic interests by pursuing strategic relations with both great regional powers

Soil organic carbon (OC) is one of the most important soil properties, especially from an environmental point of view. For this reason, OC modeling and estimating has been highly considered. In modeling, application of pedotransfer functions to estimate soil properties from the other ones have an important place in soil science. Unfortunately, not much attention has been paid to the valuable data that are obtained with the least cost and time in the soil profile description. The aim of this study was to determine the importance of data that obtained from soil profile description to estimate the soil organic carbon in Dehgolan region in Kordestan Province. For this purpose, 30 pedons were excavated and described. Soil samples were collected from different horizons and soil properties such as texture, pH, EC, CCE and gypsum were determined. Modeling was performed in three scenario including laboratory data, data of soil profile description and application of laboratory and soil profile description data simultaneously. The results showed that based on laboratory data, soil organic carbon has a significant relationship with silt and CCE properties with a coefficient of determination about 25% (R2 = 0.25); While, the two soil profile description data of soil color (chroma) and genetic horizon with coefficients of determination about 65% (R2 = 0.65). With compilation of laboratory and soil profile description data the coefficient of determination was also obtained 65%. This level of accuracy clearly shows the value and importance of data related to the soil profile description data.

The health sector will face a shortage of manpower during crises. The sustainability and  retention of human resources during these conditions are vital. The purpose of this study was to explain possible policies and strategies to strengthen health workers during the crisis and prevent them from leaving the organizations and hospitals.

This was a scoping review research to find strategies for retention of human resources during epidemics. English and Persian databases were searched and relevant studies were selected and reviewed. The key pieces of information obtained were then categorized. Finally, suggested efficient policies and strategies were summarized and reported.

A total of 168 different articles were collected. After excluding irrelevant studies, 21 articles were selected and fully reviewed. The results were classified into six main categories of policy-making as well as strategies to implement them.

Considering the epidemic situations, in order to strengthen and support human resources in times of crisis, it is recommended that policymakers and decision-makers of the health sector focus their attention on six major topics including education reform, financial support, psychological support, social and welfare support, professional support, and development of infrastructures in deprived and crisis-stricken areas. The important point about suggested strategies is that they need to be in accordance with the ongoing situation of their target societies, as we know that underdeveloped, developing and developed countries have a different set of circumstances and therefore require different priorities and policies.

Distinctly recognized as one of, if not the most, influential factors in shaping societies, immigrants and their movement create a dynamic shift in the fabric of not only their country of origin but their host country as well. This transformational phenomenon influences every aspect of individual and societal life from culture and religion to politics and even way of thinking; to the point where it is impossible to distinguish a clear ‘before and after’ while studying the advancement of a country. Historically, the United States of America has been heralded as a country of immigrants, and a great ‘melting pot’. Americans take pride in their multinational ancestry and immigrant forefathers who helped build and shape their unique nation. Without a doubt, immigrants are significant factors in the American success story and its countless scientific and technological advances since WW II. This country has been constantly growing and evolving in a manner which is considered an advantageous asset in its quest for superior international status. All things considered, it is near impossible to scrutinize U.S. immigration policies without a better understanding of the general context in which they are formulated and implemented. One of the main goals is to address the domestic and international factors that have shaped key U.S. immigration laws from 1917 to 2016. In this paper, several key factors influencing U.S. immigration policymaking will be explored, including legislation, economics, and culture. The main research questions to be examined are: 1.To what extent have international factors affected the U.S. legislation regarding the legal and illegal immigration? 2. How has U.S. immigration laws, regulations, and policies influenced its foreign politics, domestic socioeconomic conditions and cultural values? With the help of a neoclassical theoretical framework, the causes and consequences of changes in its immigration policies are analyzed during the 1917-2016 time period. In the research hypothesis, the authors argue that the roots of the evolution of US immigration policies can be traced back to the domestic socio-economic and cultural factors, but American foreign  policy and its standing in the international system have also influenced its immigration laws and policies. The findings in this article illustrate that United States immigration policies have been evolving since its inception; and its historical progression clearly demonstrates that American lawmakers have sought to take advantage of laws and regulations that were tailored to the specific circumstances in any given time period. Legislators throughout the decades under investigation have attempted to bring about changes in various substantive areas of the country by changing immigration policies in order to use them as effective instruments of foreign and domestic policies. These changes in immigration laws and policies have affected a variety of key domains including the demographic, economic, cultural characteristics as well as political and security environment of the country. A review of the international developments that took place in the background of the enactment of these laws show that, although the international environment has been somewhat a contributing variable, the transformation of immigration laws has been related to domestic variables more strongly than previously speculated. Actually, the American people themselves have been the main driving force behind the changing nature of these laws. The nationally-rooted laws have revolutionized American society in such a way that their impact is evident in every domain of the society. With the growth and maturity of the fledgling country, its immigration system has also evolved from an affiliated, subordinate  unit to an independent institution within the federal government. The significance of this organization can be clearly seen in the aftermath of the 9/11 events, when the security situation inside the country shifted from a relatively-closed state to a completely-closed state. However, one cannot ignore the role played by the international environment as an effective factor in enabling and facilitating the changes in the U.S. immigration system over the years. Today, the US immigration system is arguably a prominent factor in shaping many aspects of the country’s development. Therefore, it is an important subject to be addressed by dedicating academic time and attention to analyze the causes and consequence of the immigration laws for the U.S. domestic and foreign policy priorities, and for the international community as a whole. In reference to the question of the factors influencing American immigration policy, it is noteworthy to mention that over time, the immigration system has moved from an externally- to internally- influenced system, taking into consideration that the international environment has always played a key role in policymaking. Furthermore, it is argued that that United States is an evolving state that constantly adapts to its domestic conditions. Although foreign policy considerations have always played an indisputable role, over time as the system has evolved, the United States domestic security priorities have become increasingly significant factors in immigration policymaking. Moreover, contrary to popular belief and the nostalgic romanticism of this ‘melting pot’, revolutionary changes in immigration policy have invariably led to newly constructed barriers. Moreover, despite the fact that the legacy of this nation is built on the efforts of immigrants, the new potential migrants are in fact perpetually prevented open access and must overcome countless hurdles to gain entry into the country. These restrictions will remain in effect for the time being, but the striking projected demographic condition shows that in the near future, immigrants and their children will make up the majority of the American population. In turn, this will inevitably affect all aspects of policy-making, and there will undoubtedly be a significant shift in the immigration system.

The purpose of this study was to use the Multi-Temporal Sentinel-2 images and phenological index in separating and determining the cultivated area of the agricultural land in the Bastam region. To this end, the agricultural crops of the region were identified according to their types and phenological periods comprising apricot, grape, wheat, and forage corn. Three classifiers including support vector machine, maximum likelihood, and minimum distance models and field observations (points and boundaries provided by GPS) were used in order to compile a land use prediction map. Comparison of the accuracy of the three models showed that the support vector machine had the best performance, with overall accuracy and kappa coefficient of 0.86 and 0.82, respectively. The minimum distance model had the lowest classification performance with overall accuracy and kappa coefficient of 0.69 and 0.61, respectively. According to the model of support vector machine, the highest area (3423 hectares) was obtained for wheat, and the lowest was predicted for forage corn (738 hectares). Finally, the results showed that multi-temporal images and the phenological index had an acceptable capability for separation of the crops, prediction of their areas, and making suitable agricultural land use maps for the study area.

The BCC model is studied in this paper in an uncertain environment where uncertain inputs and outputs were belief degree-based uncertainty‚ useful for the cases for which no historical information of an uncertain event is available. As the solution method‚ the uncertain BCC model was converted to a crisp form using two approaches, separately. Finally, an applied example regarding the Iranian Banking system is presented to document the proposed models.

Choosing the best land suitability method to determine land suitability classes has always been questioned and researched. Many land suitability surveys are conducted using four conventional methods, including: 1-simple limitation, 2- number and intensity of limitations, 3- Khiddir (square root) and 4- storie. In the parametric approach, the land index is calculated using the Khiddir method or the Storie method, and then based on this index, the land suitability classes are determined. The land index is in two forms: the uncorrected land index and the corrected land index. Numerous studies on land suitability evaluation have shown that the results of different land suitability methods are very different. However, the results of many studies suggest that the two methods of simple limitation and Khiddir are more consistent. Since these researches have been for different products and different regions, it has not been possible to evaluate the degree of adaptation of different methods without considering the type of product and its location. On the other hand, the numerical value of the adaptation or conformity of these methods with each other has not been determined. So, regardless of the type of product and location, a basic question that arises is how well do the different land suitability methods fit together, and are the degree of adaptation different for various land suitability classes? Current research seeks to answer this question.

In this study, land suitability classes were determined using numerical simulation, without considering any product and location. In numerical simulation, each of the eight factors involved in determining the suitability classes accounted for a random number from zero to 100, and then based on these simulated numbers the land suitability classes were determined with the four conventional methods and in two forms of the uncorrected land index and the corrected land index. The simulation process for each proportional class, including classes S1(suitable), S2(Moderately suitable), S3(Marginally suitable), N1 (temporary unsuitable), and N2 (permanently unsuitable), was performed one million times. So a total of five million simulations were done. In the next step, the degree of adaptation for each class and each method was calculated based on the Overall Accuracy(OA) formula.

The results showed that the numerical value of the degree of adaptation of different land suitability methods in various classes are very different; Overall, however, the least and most adaptation between the different methods occurs for Class N1 and N2, respectively. After class N2, there is the greatest adaptation between the different methods in class S1. For classes S2 and S3, the degree of adaptation between different methods does not follow a specific trend. Among the various methods, in the case of using the uncorrected index, the simple limitation method was more consistent with the Khiddir method. However, using the corrected land index, the greatest correlation was observed between the simple limitation method and the parametric approach (both Khiddir and Storie method) and in this case there was not much difference between the results of Khiddir method and Storie method. Therefore, the corrected index is superior to the uncorrected index.

Based on the findings of this study, it can be said that the results obtained from the uncorrected land indices may be far from reality, and this issue is much more severe for the Storie method. The use of the uncorrected index causes a large difference between the parametric approach with the two methods of simple limitation and the number and intensity of limitations. Therefore, it is necessary to use corrected indices to determine the suitability classes so that the results of different methods are as close as possible.

There are innumerable challenges for digital soil mapping in the world full of change. One of these challenges is the sampling method which plays an important role in providing appropriate information for digital soil mapping and increasing its efficiency. Sampling method efficiency, with considering the number of samples, space changes, and cost, is a way to identify a set of scattered sampling locations in geographical space that have good location coverage of features. A good space coverage of features ensures accurate estimation of regression parameters and it makes effective spatial interpolation. In soil evaluation, the number of samples collected is limited by time and cost. Also, lack of roads, dense vegetation, and rugged terrain are caused more restrictions when visiting the area. These deficiencies lead to the use of stronger sampling methods. Methods that can provide a good description of space changes of soil and its features to reduce the number of samples, time, and cost are needed. So that it supports the final quality of the maps. Here are checked several methods of statistical sampling and geometric that optimize the geometric sampling pattern in geographical space. The strengths and weaknesses these methods considering spatial coverage, simplicity, accuracy, and efficiency briefly expressed. The results showed in terms of accuracy and efficiency; classified random sampling has the highest accuracy and has been widely used. In terms of spatial coverage; spatial coverage sampling is the best method. Due to the simplicity in the design and implementation stages; Simple random sampling, network sampling, and spatial sampling are the simplest sampling methods. Among the sampling methods studied, the Latin conditional sampling method is the most common method. It is widely used and recommended, and stratified random sampling and spatial sampling are the most efficient methods that optimize the sampling pattern in geographical space.