امید کریمی

Spatial regression models are used to analyze quantitative spatial responses based on linear and non-linear relationships with explanatory variables. Usually, the spatial correlation of responses is modeled with a Gaussian random field based on a multivariate normal distribution. However, in practice, we encounter skewed responses, which are analyzed using skew-normal distributions. Closed skew-normal distribution is one of the extended families of skew-normal distributions, which has similar properties to normal distributions. This article presents a hierarchical Bayesian analysis based on a flexible subclass of closed skew-normal distributions. Given the time-consuming nature of Monte Carlo methods in hierarchical Bayes analysis, we have opted to use the variational Bayes approach to approximate the posterior distribution. This decision was made to expedite the analysis process without compromising the accuracy of our results. Then, the proposed model is implemented and analyzed based on the real earthquake data of Iran.

Investors typically seek to strike the optimal balance between potential returns and associated risks in their trades. Various models have been presented to choose the optimal portfolio using different approaches. one of these methods is based on the statistical distribution of asset return. In these methods, the type of distribution of returns is first identified, and a suitable portfolio selection method is then applied based on this identified distribution type. This study compares the effectiveness of the mean-absolute deviation-entropy model utilizing both Skew-Normal Distribution and Skew-Laplace-Normal Distribution for constructing an optimal portfolio in the Tehran Stock Exchange over 36 months from April 2018 to March 2020.

The data used in this study comprises the monthly returns of 181 companies listed on the Tehran Stock Exchange. These returns were gathered from a statistical population of 338 members utilizing Morgan's table and Cochran’s formula. After fitting density functions for Skew-Normal and Skew-Laplace-Normal distributions to the returns, maximum likelihood estimates were obtained using the Stats package and the optim Function in R software. The reliability of these estimates was then checked using bootstrap sampling with 1,000 repetitions. Subsequently, relationships corresponding to the mathematical expectation of return distribution and the objective function representing the risk of absolute deviation were estimated using numerical methods. Therefore, this paper aimed to propose a multi-objective optimization model, namely a mean-absolute deviation-entropy model for portfolio optimization by using a goal-programming approach based on Skew-Normal Distribution and Skew-Laplace-Normal Distribution. The objective functions of the model were to maximize the mean return, minimize the absolute deviation, and maximize the entropy of the portfolio.

It can be inferred from the observed values ​​of the descriptive statistics of the monthly stock returns corresponding to the stock exchange symbols that some stocks have different skewness and kurtosis values ​​compared to the normal distribution. For example, The symbol "Shepna" exhibits negative skewness, indicating a left-skewed distribution. Similarly, the distribution of the "Basama" symbol exceeds the normal distribution. These instances suggest that the normal distribution is inadequate for describing monthly return distributions. Instead, distributions with parameters should be employed to account for skewness and kurtosis. According to the obtained results, the model utilizing the Skew- Laplace- Normal distribution has a higher performance ratio than the model based on the Skew-Normal distribution.

The reason for this superiority, where the model utilizing the Skew-Laplace-Normal distribution outperforms the model based on the Skew-Normal distribution, is the incorporation of both skewness and kurtosis criteria within the former. Additionally, upon analyzing the descriptive statistics of the symbols, it's evident that the kurtosis of most stock symbols is substantial. Therefore, integrating a combination of higher-order moments (skewness and kurtosis) along with entropy leads to enhanced performance.

The spatial generalized linear mixed models are often used, where the latent variables representing spatial correlations are modeled through a Gaussian random field to model the categorical spatial data. The violation of the Gaussian assumption affects the accuracy of predictions and parameter estimates in these models. In this paper, the spatial generalized linear mixed models are fitted and analyzed by utilizing a stationary skew Gaussian random field and employing an approximate Bayesian approach. The performance of the model and the approximate Bayesian approach is examined through a simulation example, and implementation on an actual data set is presented.

Presently, we are faced with a demographic crisis to the extent that within the next forty years, we will witness a demographic catastrophe, transforming our young country into an elderly one. One of the strategies to increase society's inclination towards childbearing is establishing legal frameworks and incentives, which have been demonstrated in recent years through a proposal presented to the Islamic Consultative Assembly under the title "Population and Youth Plan." This plan contains 15 incentives for increasing childbirth and is currently under review in the parliament. One crucial aspect of evaluating this plan is the lawmakers' confidence in its success in achieving the desired objectives, namely increasing the desire for childbearing in society. The present study aims to investigate this matter through conducting a social survey. The target population comprises all adults aged 18 to 45, approximately 35 million individuals, according to the population pyramid announced by the Statistical Center of Iran in the 2016 census. The sampling method was stratified proportional, and the sample size was determined to be 384 individuals based on Cochran's formula with a 5% error margin. The research findings indicate that the most significant motivating factor for increasing childbirth, from society's perspective, is adequate support for mothers and children and ensuring a promising future for themselves and their children. The 15 incentives devised in this regard are expected to be more successful

Gaussian random field is usually used to model Gaussian spatial data. In practice, we may encounter non-Gaussian data that are skewed. One solution to model skew spatial data is to use a skew random field. Recently, many skew random fields have been proposed to model this type of data, some of which have problems such as complexity, non-identifiability, and non-stationarity. In this article, a flexible class of closed skew-normal distribution is introduced to construct valid stationary random fields, and some important properties of this class such as identifiability and closedness under marginalization and conditioning are examined. The reasons for developing valid spatial models based on these skew random fields are also explained. Additionally, the identifiability of the spatial correlation model based on empirical variogram is investigated in a simulation study with the stationary skew random field as a competing model. Furthermore, spatial predictions using a likelihood approach are presented on these skew random fields and a simulation study is performed to evaluate the likelihood estimation of their parameters.

Spatial generalized linear mixed model is commonly used to model Non-Gaussian data and the spatial correlation of the data is modelled by latent variables. In this paper, latent variables are modeled using a stationary skew Gaussian random field and a new algorithm based on composite marginal likelihood is presented. The performance of this stationary random field in the model and the proposed algorithm is implemented in a simulation example.

 Count data over time are observed in many application areas. Many researchers use time series patterns to analyze this data. In this paper, the poisson count time series linear models and negative binomials on this type of data with the explanatory variables are studied. The Likelihood analysis and the evaluation of count time series model based on generalized linear models are presented and investigated. Also, the data of the number of addicts referred to the addiction treatment centers in Semnan county are analyzed based on the proposed models and methods. According to the results of the model selection criteria, the negative binomial count time series model is a good fit for this data and based on it, a monthly forecast for the year 2019 was made.

Spatial generalized linear mixed models are used commonly for modeling discrete spatial responses. In this models the spatial correlation of the data is considered as spatial latent variables. For simplicity, it is usually assumed in these models that spatial latent variables are normally distributed. An incorrect normality assumption may leads to inaccurate results and is therefore erroneous. In this paper we model the spaial latent variables in a general random field, namely the closed skew Gaussian random field which is more flexible and includes the Gaussian random field. We propose a new algorithm for maximum likelihood estimates of the parameters. A key ingredient in our algorithm is using a Hamiltonian Monte Carlo version of the EM algorithm. The performance of the proposed model and algorithm is presented through a simulation study.

The Gaussian random field is commonly used to analyze spatial data. One of the important features of this random field is having essential properties of the normal distribution family, such as closure under linear transformations, marginalization and conditioning, which makes the marginal consistency condition of the Kolmogorov extension theorem. Similarly, the skew-Gaussian random field is used to model skewed spatial data. Although the skew-normal distribution has many of the properties of the normal distribution, in some definitions of the skew-Gaussian random field, the marginal consistency property is not satisfied. This paper introduces a stationery skew-Gaussian random field, and its marginal consistency property is investigated. Then, the spatial correlation model of this skew random field is analyzed using an empirical variogram. Also, the likelihood analysis of the introduced random field parameters is expressed with a simulation study, and at the end, a discussion and conclusion are presented.

Environmental pollution by cadmium (Cd) is increasing. The entry of this heavy toxic metal into the food chain is a serious danger to human and animal populations. This study aims to evaluate the effect of turmeric (Curcuma longa) powder (TP) containing 2.96% of total curcuminoides (TCM) in modulating the adverse impacts of Cd in Japanese quail. 180 22-day-old male Japanese quails were randomly divided into six groups for 23 days and fed a basal diet (BD) supplemented with 3 gr/kg TP (containing 88.18 mg of TCM), 5 mg/kg TP (containing 148 mg of TCM), 100 mg/kg Cd, 3 mg/kg TP plus 100 mg/kg Cd, and 5 gr/kg TP and 100 mg/kg Cd. The addition of TP to the Cd-contaminated diet reduced serum levels of pro-inflammatory cytokines TNF-αand IL-6, improved total antioxidant capacity, increased SOD and CAT activity, as well as decreasing MDA concentration. The TP supplementation significantly (p<0.05) reduced the harmful effects of Cd on body weight loss, relative liver weight gain, and total protein concentration, besides increasing serum levels of ALT, AST, and ALP enzymes. The findings of this study suggest that TP may reduce the toxic effects of Cd in Japanese quail by improving antioxidant status and reducing the activity of pro-inflammatory cytokines. The addition of 5 g/kg TP to the Cd-contaminated diet results in more antioxidant and anti-inflammatory properties than 3 g/kg TP.

One of the major challenges in camel husbandry is the lack of records and registration. The camel breeding system in the country is Extensive and dependent on rangelands and the long distances and high dispersal of herds have added to the problems of recording and the establishment of net. On the other hand, recording, especially in weights of camels, is very difficult and it is necessary to use statistical models and estimation of body weight by body measurements. Networking to communicate camel breeders with each other and education and research centers can provide camel productivity in the country. therefore, it is necessary to use ICT in camel husbandry. In camel breeder assisted system, by communicating through the SARBANYAR app, in addition to communicating and providing skill training, camel breeder knowledge in the mobile platform will be enhanced. The camel herd is also tracked online using the GPS hardware and this APP.

Ictiophytosis is one of the most important freshwater fish diseases. In this study, the therapeutic effect of aqueous extract of garlic in treatment of Goldfish was investigated. A group of 200 apparently healthy goldfish with weigh 15g were randomly divided into five treatments group with three replications. Extract obtained from garlic was obtained by direct and indirect methods at 177 and 570 ppm concentrations. In long-term bath was tested on fish infected with parasites. During the experiment, which continued until the fish's body was completely cleaned, the parasites were counted once every 24 hours with a wet mount method. The results showed that the extract had a significant effect on the number of parasites and disease treatment in different groups. In other words, in cure during the number of visible parasites on the fish's body controls was significantly (p < 0.05) was observed in the parasites on the fish groups were further tested. The extract obtained from the Buchmann’s methods on both the concentration of the extract obtained by Penea’s Method was more effective (p < 0.05) and this can be an effective herbal remedy used to treat disease in Gold Fish.

The most important causes of water shortage in today's world are population growth, reduced rainfall, land use change and urban development. Water is essential for many aspects of economic and social development, agricultural, urban and industrial uses and is an important component for the environment. In other words, water is considered as one of the most valuable natural resources and the most important issue and challenge in this century. Increased water demand due to population growth, development of agricultural and horticultural lands and in general the quantitative and qualitative limitations of harvestable water resources as one of the most important challenges in water resources management and planning, led to the combined use of surface water resources and Groundwater seems to be essential as the two main sources of water supply. Integrated management of surface and groundwater resources is the combined exploitation of two sources in which surface and groundwater resources are used together. The main goal of integrated management is to achieve sustainable development. Therefore, the principles of integrated management are based on the combination of supply management with demand management, which also considers environmental, social and economic aspects. Considering that most of the water needs in our country are met through aquifers, so knowing the status of aquifers can help in proper management of water resources in the region. One of the solutions to deal with the issue of over-extraction from groundwater resources is the use of integrated surface and groundwater management. Due to the decrease in precipitation, especially in recent decades, the reduction of surface and groundwater resources and the uncontrolled abstraction of aquifers and the increase in demand have resulted frome this. In such situations, the need for accurate management of water resources is strongly felt, and the tools of this management and careful planning are the extraction of relationships between factors such as rainfall, groundwater level, consumption, and so on. Marand plain is one of the forbidden plains of East Azerbaijan province, which lacks permanent surface water resources. Therefore, excessive exploitation of groundwater resources has caused a sharp drop in water levels in the region. Therefore, the study of surface and groundwater in this area in order to achieve a proper management approach seemed necessary. In this research, the consumptions of Marand plain were evaluated by using the simulation model of WEAP and GMS. The calibration and validation of these two models respectively were done based on a ten-year-period of 2002-2011 and a of 3-year period of 2012-2014. Then, different managerial scenarios were considered providing the drinking water in Marand. The scenarios were defined through limitations in using wells, increase the efficiency of irrigation in agriculture section, construct a sewage system, and two combinations of the above mentioned scenarios, in order to reduce the water demand and improve the condition of aquifers. Predictions were made for a 23-year-period according to these scenarios and its effects on the studied water sources. Results indicated that simultaneously utilizing different methods of water management is better than using a single method, which leads to the improvement and revival of aquifers and reduction of water extraction from different sources. The reliability index of providing water in the compound method, for different sections of urban drinking water, rural, agriculture, and industry was calculated at 100, 100, 64.7 and 74.6, respectively. By the compound scenario, the water level would have 16 meter reduction, while the aquifer could be improved with the yearly recharge of 0.29 million square meters and have a relative balance between withdrawals and recharges of the aquifer.

In spatial generalized linear mixed models, spatial correlation is assumed by adding normal latent variables to the model. In these models because of the non-Gaussian spatial response and the presence of latent variables the likelihood function cannot usually be given in a closed form, thus the maximum likelihood approach is very challenging. The main purpose of this paper is to introduce two new algorithms for the maximum likelihood estimations of parameters and to compare them in terms of speed and accuracy with existing algorithms. The presented algorithms are applied to a simulation study and their performance are compared.

Spatial count data is usually found in most sciences such as environmental science, meteorology, geology and medicine. Spatial generalized linear models based on poisson (poisson-lognormal spatial model) and binomial (binomial-logitnormal spatial model) distributions are often used to analyze discrete count data in which spatial correlation is observed. The likelihood function of these models is complex as analytic and so computation.  The Bayesian approach using Monte Carlo Markov chain algorithms can be a solution to fit these models, although there are usually problems with low sample acceptance rates and long runtime to implement the algorithms. An appropriate solution is to use the Hamilton (hybrid) Monte Carlo algorithm  in The Bayesian approach. In this paper, the new Hamilton (hybrid) Monte Carlo method for Bayesian analysis of spatial count models on air pollution data in Tehran is studied. Also, the two common Monte Carlo algorithms such as the Markov chain (Gibbs and Metropolis-Hastings) and Langevin-Hastings are used to apply  the complete Bayesian approach on the data modeling.  Finally, an appropriate approach to data analysis and forecasting in all points of the city is introduced with the diagnostic criteria.

According to the average annual rainfall of 250 mm in Iran, this country among the can be specified among the top arid countries. Increasing population and consequently increase in water demand have threatened the water balance during recent decades. Withdrawal of water from the aquifer in some regions has led to a quick drop in groundwater levels. This fact resulted in several problems such as: drying of wells, reducing the flow of rivers and canals, and also land subsidence. Ground water recharge is an important process in the hydrological balance of water. It is essential for regions where the proactively conservation of groundwater and surface water is strongly required. This study proposed a methodology to delineate as well as to identify favorable artificial recharge sites, using integrated remote sensing (RS), geographical information system (GIS) and multi-criteria decision making (MCDM) techniques for augmenting groundwater resources. Studied area was located in the Maran plain of the Northwest of Tabriz, Iran. This plain has been suffered from water shortage problems since the last decade. Regarding that most of the water demand in Iran is supplied by aquifers, awareness of the condition of aquifers could help manage groundwater sources. Remote Sensing (RS) and Geographical Information System (GIS) play a crucial role in analyzing groundwater. In this study, using GIS and RS and multi-criteria decision-making models, areas with the potential of containing groundwater were identified in Tasuj plain. Analytical Hierarchical Process (AHP) was used as a MCDM technique to normalize the weights of various thematic layers and their classes for delineating the groundwater potential and recharge zone maps. In the current study, areas were classified due to of potential of containing groundwater, in four classes as good, middle, poor and very poor. According to the results of AHP and ANP models, areas of alluvial fans were the best parts, due to having appropriate thickness of alluvium, transmissivity, hydraulic conductivity, water level depth, and vegetation coverage. Suitable areas are located in the northern and eastern parts of the studied plain, according the models estimations. While in the southern parts in which thickness of the alluvium was the lowest, salt marsh has been created. By the results of ANP, percentage of the plain area which belongs to each classification of groundwater potential were estimated as 33.7%, 29.41%, 25.91%,11.58%, respectively for good, middle, poor and very poor. Therefore the summation area of good and middle classes were estimated as 62.48% of the total plain area. This part contained 91% of total wells. The parameter of wells density was calculated in each class and then the results by AHP and ANP were compared. The density in the middle class by AHP and ANP model was 0.648 and 0.458. The greater amount of wells density revealed the higher groundwater potential. It was considered that the best model was that in which the density of wells in the poor and very poor classes was the less. Hence, ANP model had the better results than AHP. Field data from piezometric wells, as well as distribution of wells confirmed these results. By the results of the current study for determination of suitable areas for groundwater artificial recharge, both models of AHP and ANP revealed that the most appropriate area in Tasuj plain was the alluvial fan and low slope areas. Susceptible zones of groundwater storage places were classified in the four classes as good, middle, poor and very poor. The results of the two models were compared with the amount of density of wells. So, the ANP model showed the more acceptable results than the AHP. The detected areas in the current study were in northen parts and eastern. These parts contained gardens and appropriate vegetation coverage, hydraulic conductivity, transmissivity, alluvial thickness, groundwater depth and slope that confirmed the acceptable estimation of the models. Also, the results of this study confirmed the previous studies in the region which indicated that the most appropriate places for artificial recharge were in low-altitude areas and coarse-grained alluvial sediments. The map of groundwater high-potential zone was finally validated using the groundwater depth data from 25 wells. Consequently, the results of this study by ANP could be used in efficiently manage and artificial charge of groundwater in Tasuj plain, so as to ensure sustainable utilization of scarce groundwater resources.

Iranian cyclamen (Cyclamen persicum Mill.) is the most important and valuable pot species of cyclamens because having variant colors. In order to increase thepropagation rapidity of this plant, seeds as explants werecultured in MS medium along with a combination of 0,0.5, 1, 1.5 and 2 mg/l concentrations of both plantgrowth regulators NAA and BA. Results showed that themost leaf number (6.20 per each plant) and the highestgermination percentage (83.50%) were obtained inexplants cultured in medium containing 1 mg/l BAtogether with 1 mg/l NAA. The most leaf surface (16.70mm) and maximum root number (6.15 per each plant)were observed in treatment of 1.5 mg/l BA together with1.5 mg/l NAA. Leaf and root length traits were alsocalculated. Correlation analysis of measured traits witheach other showed that the most correlation wasobserved between leaf number and root number