Fiscal decentralization can effect on the government revenues and expenditures and consequently the budget deficit by influencing the level of corruption. Accordingly, the main purpose of this study is to investigate the long run effect of fiscal decentralization on the budget deficit in Iran through the corruption channel during the period of 1992-2017. For this purpose, the three indicators of fiscal decentralization of revenue, fiscal decentralization of expenditures, fiscal decentralization of autonomy power, corruption index and also some control variables including inflation and the share of oil revenues in GDP have been used. In order to estimate the model, Park co-integration test and Canonical Co-Integration Regression (CCR) approach have been applied. Based on the results from model estimation, all three indicators of fiscal decentralization reduce the budget deficit in the long run through the control of corruption channel. Therefore, providing the necessary conditions for the expansion of fiscal decentralization can help to reduce the budget deficit in Iran by controlling the level of corruption. According to other results, in the long run, inflation and the share of oil revenues in GDP have a positive and negative effect on the budget deficit, respectively.

According to the Kau and Robin (K&R) hypothesis, an increase in the government's power to collect taxes increases the size of government. In this regard, the main objective of this paper is to test this hypothesis for the Iranian economy during the period of 1971-2014. For this purpose, two variables are used as indicators of government's power to collect taxes: rate of female participation in the labor market and self-employment rate. The estimation method is a canonical co-integration regression (CCR). The results indicate no significant impact of the mentioned indicators on the government size. Thus, Kau-Rubin hypothesis is rejected for the Iranian economy. The FMOLS and DOLS estimators reconfirm the results.

Exacerbation of tension and regional rivalry between Iran and Saudi Arabia is important to experimental forecast the situation of future arms race between these two countries. Accordingly, the main objective of this paper is experimental forecast the situation of arms race between Iran and Saudi Arabia during the years of 2018-2040. To this regard, first, a military demand function for both countries of Iran and Saudi Arabia is simulated and by using the data from years of 1963-2017 and the Particle Swarm Optimization (PSO) algorithm, the military expenditures of the countries of Iran and Saudi Arabia are predicted for the period 2018-2040. Then, using these data and using a Richardson Arms Race Model and Canonical Co-integration Regression (CCR), the situation of arms race between Iran and Saudi Arabia is projected at 2040 horizons. According to forecasts, military spending in Iran and Saudi Arabia increase over the years 2040-2018, with a roughly upward slope (positive), which will be more significant for Saudi Arabia. The estimation of the Richardson model system of equations shows that there is an arms race between Iran and Saudi Arabia during the period of 2018-2040, so that, as a result of rising military spending on both sides, Saudi Arabia's response to military spending is much higher than Iran. A review of the situation of arms race also indicates an unstable arms race between Iran and Saudi Arabia during the years of 2018-2040.

Available data indicate that close to two-thirds of the population of the Republic of Benin lacks access to electricity which has dire consequences on the country’s development agenda. Although electricity consumption has been increasing, the supply of electricity is not enough to meet the growing demand in the country. Managing electricity consumption in Benin has therefore become critical owing to the expected rise in the future consumption. This will help ensure future electricity security. Accordingly, this paper examines the determinants of electricity consumption for Benin using annual time series data for the period of 1971-2014. Estimation from the Autoregressive Distributed Lag model, the Fully Modified Ordinary Least Squares and the Canonical Co-integrating Regression reveal that population, urbanization, education and industrialisation positively affect electricity consumption for the country while income negatively reduces it. A further analysis using the rolling regression indicates that the effects of the above variables are time-varying with some social, political and economic changes. Among others, the paper recommends the need to increase awareness on environmental-energy issues; and the need for a policy to make new residential and non residential buildings that would be constructed in the urban centres especially, to be energy efficiently designed.

Information and Communication Technology (ICT) is recognized as a critical driver of economic development in the modern era, significantly enhancing the productivity of production factors. However, the widespread adoption of ICT, particularly in countries reliant on fossil fuels, may contribute to increased greenhouse gas emissions, including carbon dioxide (CO2). So, this study investigates the interconnections among ICT, CO2, renewable energy, and Total Factor Productivity (TFP) in Iran. This descriptive-analytical and applied study used time series data from the World Bank and Iran Statistics Center from 2000 to 2023. This study, the Autoregressive Distributed Lag (ARDL) model to evaluate the long-term and short-term dynamic, unit root tests and diagnostic tests CUSUM and CUSUMQ and Canonical Co-Integrating Regression (CCR) Dynamic least squares (DOLS), and fully modified least squares (FMOLS) have been used to validate the results of ADRL estimates.The results of the ARDL estimation method showed that in the long run, TFP and economic growth on carbon dioxide had a coefficient of 0.07 and 0.14, respectively. Renewable energy consumption with a coefficient of -0.0808 had a significant negative role in reducing carbon dioxide. The coefficient of -0.286 obtained for the ICT variable at 95% indicates a reduction in carbon dioxide in parallel with the development of ICT. CUSUM and CUSUMQ confirmed the stability of the parameters, and CCR, DOLS, and FMOLS regressions confirmed the results of the ADRL model. The study recommends adopting green technologies and effective energy policies to balance productivity gains with environmental protection.

Present study seeks to investigate the existing canonical correlation among the effective factors including X variables that consist of environmental factors, interorganizational factors and the creation factors of the perceptual value; on the components of competitiveness, as well as Y variables consisting of enterprise performance against the market, the customers and the internal performance of the enterprise. The research also aims to study the co-changeability between the two variable sets so that the proper samples of relationship between sets of canonical variables on the competitiveness components may simultaneously be determined in the first rate. Furthermore, the possibility of applying the co-efficiency of the canonical correlation for presenting the method of predicting the shift of position of competitiveness is investigated. For this purpose, a survey research was conducted at the insurance companies in the city of Tehran using a questionnaire distributed randomly among members of the experts of essential information insurance. Data from 138 returned questionnaires was then analyzed using SAS 9.1, Statistica 8, and Lisrel 8.5sofwares. Results from canonical correlation analysis indicated that there is a proper linear mixture between the variables of canonical effective factors on competitiveness and its components. According to the findings, with the increase of the factors of creation of perceptual value, the factors of internal organization, and the factors of environment, the level of the performance of the enterprise in market and the internal performance of the enterprise are also increased. In addition, the factors of the creation of perceptual value and the performance of the enterprise with the customers, in comparison with the other canonical variables have a substantial role in the creation of the first co-efficiency of canonical correlation.

This study investigates non-canonical syntax in the Persian language from a non-hierarchical perspective, emphasizing the significant role of information-structural triggers, including topic and focus, in shaping the non-canonical order of left-dislocated possessive constructions. We apply Optimality Theory (Prince & Smolensky, 1993; 2004) to account for the grammatical and pragmatic properties of these constructions. Additionally, we utilize Syntax First Alignment (Newson, 2004; 2010) to explore the constraints and discourse-pragmatic reasons for left-dislocated possessive constructions, particularly those related to left dislocation in Persian.While left dislocation may resemble preposing, it differs in that a co-referential pronoun occupies the canonical position of the marked constituent (Schiffrin & Hamilton, 2008). Dabir-Moghaddam (2005) explains that left dislocation involves moving an item to the sentence-initial position, with a resumptive pronoun taking its original place.

Gas sensors are essential for protecting humans against hazardous gases such as flammable and toxic gases. Unlike traditional gas sensors, MXenes gas sensors act at room temperature, Which is a good advantage. In this work Reactive molecular dynamics simulation calculations were run through the canonical ensemble calculation method with the Nosé Hoover thermostat temperature controller at T=298 K and the dynamic condition of the simulation was based on the popular Verlet algorithm with a time step of  0.01 fs and conducted MD simulation for 0.3 ns.  the ReaxFF force field allowed atoms to break and form bonds with other atoms during the simulation, So it is a powerful force field. Adsorption of the CO and CO2 pollution gas molecules by Ti2C monolayer and nanotube was investigated and adsorption weight percentages were reported. Results show that Ti2C monolayer, and nanotube, have more adsorbed weight percentage against CO2 pollution gas molecules than CO one, and two gases have more affinity to the Ti2C monolayer than Ti2C nanotube.

Water is essential for all forms of life and its pollution is generally considered more critical than soil or air. Distinguishing and correlating among physical and chemical parameters and finding source of water contamination are major issues in monitoring water quality. Application of statistical methods in water quality monitoring can be useful to achieve this goal. Heavy metals are important water pollutants especially due to their bioaccumulation nature and toxicity. Anthropogenic and natural sources are the main entrance ways of heavy metals into water.
Water quality assessment with multivariate statistical methods is the main objective of this study. Statistical Principal Component Analysis (PCA) method was used to determine the major water quality parameters and Canonical Correlation Analysis (CCA) was employed to find the relationship between water quality parameters and heavy metal contents. Water samples from twenty three wells in Ramiyan district (Golestan province) were collected in 2012. Water quality parameters, such as; temperature (T), electrical conductivity (EC), pH, Ammonia (NH3), Fluoride (F-), Sulfate (SO42-), Nitrate (NO3-), Nitrite (NO2-), Phosphate (PO43-), Bicarbonate (HCO3-) and heavy metal contents including Zn, Cd, Pb, Cu, Ni and Co were used in this research. Heavy metal contents in water samples were determined by voltammetry method in Environmental Science Research Laboratory of University of Zanjan. Temperature, electrical conductivity in the samples was measured by a portable device in sampling sites.
Principle Component Analysis identified six factors explaining 79.67% of total variance affecting water quality in the studied area. CCA also identified three canonic classes with correlation coefficients; 0.973, 0.795 and 0.624, respectively, suggesting that predictor variables (EC, HCO3-, NO2-, NO3-, pH & PO43-) and response variables (Zn, Co, Cd & Ni), were highly scored among all parameters in water quality assessment.
The results of canonical correlation analysis also show a significant correlation between the two categories of variables. The studied metals can be selected based on the main human activities in the area (agricultural activities in this region). The information obtained can be used to improve water quality monitoring and management program. By measuring relevant parameters, there is no need to measure all the physical and chemical properties of water, reducing considerable coasts of water analysis.

Osteoblasts arise from multipotent mesenchymal stem cells (MSCs) present in the bone marrow stroma and undergo further differentiation to osteocytes or bone cells. Many factors such as proteins present in the Wnt signaling pathway affect osteoblast differentiation. ROR2 is an orphan tyrosine kinase receptor that acts as a co-receptor in the non-canonical Wnt signaling pathway. However, ROR2 has been shown to be regulated by both canonical and non-canonical Wnt signaling pathways. ROR2 expression increases during differentiation of MSCs to osteoblasts and then decreases as cells differentiate to osteocytes. On the other hand, research has shown that ROR2 changes MSC fate towards osteoblasts by inducing osteogenic transcription factor OSTERIX. Here we speculated whether ROR2 gene expression regulation during osteoblastogenesis is epigenetically determined. MSCs from bone marrow were isolated, expanded and characterized in vitro according to standard procedures. ROR2 promoter methylation status was determined using methylation specific PCR in a multipotent state and during differentiation to osteoblasts. We determined that the demethylation process in ROR2 promoter occurs during the differentiation process. The process of demethylation begins at day 8 and continues until 21 days of differentiation. This result is in concordance with previous works on the role of ROR2 on osteoblast differentiation, which have shown an upregulation of ROR2 expression during this process.