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Volume 3, Issue 5 (12-2014)
Abstract
Two species of Pratylenchoides recovered from the grasslands in Sabalan region and one species recovered from natural habitats of Tehran are illustrated based on morphological, morphometric and molecular characters. The first species, P. crenicauda is characterized mainly by its lip region with three-four annuli, lateral field with four incisures areolated throughout the length and having rod shaped sperm cells. It is further distinguished by the positions of the pharyngeal glands nuclei. P. magnicauda was found in Tehran and its morphological characters and phylogenetic relations with other species are discussed. The Iranian populations of P. variabilis are characterized by three lip annuli, stylet 20-22 µm long, four and six incisures in lateral field, rounded sperm and one of the pharyngeal glands nuclei located posterior to pharyngo-intestinal valve. The phylogenetic tree inferred from the partial sequences of D2-D3 segment of 28S rDNA revealed the three sequenced species are separate from each other and form a clade with high (1.00) Bayesian posterior probability (BPP) in Bayesian inference (BI) and 86% bootstrap support value (BS) in maximum likelihood (ML) analyses with other two sequenced species of the genus for this genomic region.
Volume 5, Issue 2 (6-2016)
Abstract
Leptonchus granulosus, recovered from Lorestan province, is described and illustrated based on morphological, morphometric and molecular data. The Iranian population of the species is characterized by its body length of 1091 - 1374 mm, cuticle distinctly two layered, outer layer finely annulated, inner layer distinctly annulated, being partly separated from the body and shriveled after fixation, cap-like lip region separated from the rest of body by constriction, distinctly sclerotised walls of prestoma and stoma, delicate needle-like odontostyle with distinct narrow lumen, 8.0-9.5 mm long, slightly arcuate odontophore, 17-21 mm long, with arms slightly thickened at base, small pear-shaped pharyngeal bulb, occupying 16.6-24.3% of pharynx length, simple intestine, very long prerectum (617-663 µm long), its junction with intestine having three distinct guard cells located between anterior ovary and cardia, didelphic female reproductive system, composed of equally sized less developed tracts, but with distinct parts (tubular uterus, simple oviduct and ovary), conoid to hemispheroid tail and absence of males. In comparison with the available reports of the species, no remarkable variation in morphometric data ranges was observed. This is the first representative of the genus for Iran’s nematode fauna found so far. Molecular phylogenetic studies of Iranian population of L. granulosus using 1669 nt partial sequences of 18S rDNA revealed it forming a clade with another isolate of the species in Bayesian inference (BI) with 0.95 Bayesian posterior probability (BPP).
Volume 9, Issue 1 (10-2019)
Abstract
Risk and failure in the supply chain can have a significant and negative effect on the short-term and long-term performance of the participants in the chain. Therefore, this research has an innovative look for a mathematical model for analyzing the interactive risks of the supply chain system using Bayesian belief networks. The study is descriptive in terms of purpose and has descriptive nature. The research community is classified into the two categories of academic experts and operational experts. In this research, information on the drug supply chain of the Imam Reza Hospital of Mashhad was obtained and analyzed using the bayesian belief network modeling process. The findings of this study show that Bayesian belief networks are much better than traditional risk analysis methods, because it can analyze basic risk analysis, including risk ranking and scenario analysis, and other essentials. BBN can also display different uncertainties in the language of probabilities with an appropriate visual form and provide more comprehensive view of the supply chain conditions and its risks
Volume 10, Issue 4 (11-2021)
Abstract
Habrobracon hebetor Say (Hymenoptera: Braconidae) is an ectoparasitoid wasp in the family Braconidae and is widely used in biological pest control. Little information is available on the genetic diversity of geographically isolated populations of H. hebetor. In the present study, we assess the genetic structure and diversity of geographically distinct populations of H. hebetor collected from different regions of Iran. To this end, 19 populations of H. hebetor (Dehloran, Hamadan, Minab, Rudan, Ahvaz, Sari, Semnan, Bandar Lengeh, Haji Abbad, Jiroft, Shiraz, Sarpol-e Zahab, Gorgan, Isfahan, Urmia, Kahurestan, Taziyan, Isin, and Sarkhun) were collected from natural niches. For each population, we sequenced a ~660 base pair fragment of Cytochrome Oxidase subunit I (COI) successfully. Analysis of molecular variance revealed sharp differentiation among H. hebetor populations. Populations from Ahvaz, Dehloran, Jiroft and Minab were the most genetically diverged. A Mantel test showed significant positive correlation between genetic and geographic distances (r = 0.47, P < 0.001). The phylogenetic analysis clustered the populations into two major groups (A and B) (100); the major part was assigned to group A. Group B mainly included the populations from southern Iran. Based on these results, we conclude that H. hebetor in Iran is comprised of many diverse populations. These may be successfully applied in innundative release programs.
Volume 10, Issue 4 (12-2024)
Abstract
Microgastrines are diverse group of endoparasitoid wasps attacking caterpillars (Lepidoptera). Despite their importance in biological control, there is still no consensus concerning the phylogeny relationships among taxa. Although previous phylogenetic analyses have advanced the overall understanding of phylogenetic relationships of Microgastrinae, the small numbers of sampled taxa have led to disagreement in taxonomic assignments. In the present study, we performed a molecular genetic survey using both mitochondrial and nuclear data, increasing the taxons' sampling, to clarify the generic relationships and improve the inferences of the taxonomic status within Microgastrinae. We reconstructed a phylogenomic tree of Microgastrinae with sequences that exist up till now, from fifty-five genera for COI and thirty genera for 28S rDNA, both new and from previous studies. Several species and genera have been sequenced for the first time. In this study, we identified some of the closest phylogenetic relatives of Microgastrinae genera by analyzing DNA sequences from the mitochondrial COI and 28S rDNA. Most clades of the current findings correspond to the latest morphological classification of Microgastrinae. New clades and several well-supported clades, conform to the most previously recorded clades and provide an increased understanding of the Microgastrinae evolution. Based on molecular examination, Pholetesor psedocircumscriptus Abdoli, 2019 is synonymized with Pholetesor circumscriptus (Nees, 1834).
Volume 11, Issue 2 (3-2020)
Abstract
An overview of social science history implies numerous methodological controversies about the rationale of historical explanation or causal inference in a particular case with the design of "single case research". In this paper, three methodological criticisms of historical explanations of a particular case were first described. Then, the main answer was the methodologists who defended historical research or internal explanations, who argued that such criticisms were based on a particular logic of causal inference, which governed quantitative or statistical research tradition. the historical explanation and causal inference in a single case uses a different logic called Bayesianism.
Volume 12, Issue 6 (3-2021)
Abstract
Keyword extraction aims to extract words that are able to represent the corpus meaning. Keyword extraction has a crucial role in information retrieval, recommendation systems and corpora classification. In Persian language, keyword extraction is known as hard task due to Persian’s inherent complication. In this research work, we aim to address keyword extraction with a combination of statistical and Machine Learning as a novel approach to this problem. First the required preprocessing is applied to the corpora. Then three statistical methods and Bayesian classifier was utilized to the corpora to extract the keywords pattern. Also, a post processing methods was used to decrease the number of True Positive outputs. It should be pointed out that the built model can extract up to 20 keywords and they will be compared with keywords in the corresponding corpus. The evaluation results indicate that the proposed method, could extract keywords from scientific corpora (Specifically Thesis and Dissertations) with a good accuracy.
1. Introduction
Automated keyword extraction is the process of identifying document terms and phrases that can appropriately represent the subject of our writing. With the proliferation of digital documents today, extracting keywords manually can be impractical. Many applications such as auto-indexing, summarization, auto-classification, and text filtering can benefit from this process since the keywords provide a compact display of the text. Automated keyword generation can be broadly classified into two categories: keyword allocation and keyword extraction.
In keyword allocation, a set of potential keywords is selected from a set of controlled vocabularies, while keyword extraction examines the words in the text. Keyword extraction methods can be broadly classified into four groups: statistical approaches, linguistic approaches, machine learning approaches, and hybrid approaches.
2. Literature Review
working on Persian words is a big challenge for the paucity of sufficient research. The inadequacy of text pre-processing programs has made it more complex than the Latin language. Also, the presence of large dimensions of input data is one of the challenges that has always arisen in such researches and this problem becomes more apparent due to the variety of Persian written forms (Gandomkar, 2017, p. 233:256). In Moin Maedi's article (2015, p. 34:42) A method for extracting keywords in Persian language is presented. This article extracts keywords from each text separately and without seeing another text as training data.
Accordingly, the current paper examines the integration of statistical keyword extraction methods with the Naive Bayes Classifier. Initially, we integrated input texts which are dissertations in Persian by using preprocessing (deletion of stop words, etymology, etc.) methods. Then, using the available statistical features, each word has been given a certain weight. Then, the valuable words of each text were selected and the proposed model was taught using the selected category, then the selected words were processed by the trained model, and at the end, the words extracted from the final model were evaluated using the keywords suggested by the authors themselves. Figure 1 depicts all the steps performed.
4. Results
Literature review shows that this is the first time that these combinations are used to extract Persian keywords, so that unlike other studies, each text is as a sample for category input and words as its properties, however, in this paper the words of each text input are categorized and words are extracted using statistical methods that are considered as features. The choice of keywords by the authors has always been a personal decision and people may not make a single decision to choose a set of words for a single text.
Figure 1
Proposed research framework for keyword extraction
Create unigrams
and bygrams
The current paper attempts to create a model and program with a new approach, due to the small number of input documents, which to extract keywords without dependence on the orientation of dissertations and the meaning of their words and only by using statistical features of words in each text. According to Tables 1 and 2, the developed model is able to extract a maximum of 20 keywords from each dissertation with an overall accuracy of 98.1%, in best condition which that is the use of a maximum frequency feature. The keywords written in each dissertation with 84% and 98% accuracy, correspond to one-word and two-word expressions, respectively.
Table 1
Evaluation criteria for Bayesian outputs in different states of statistical Features
Table 2
Evaluation of post-processing test data for outputs that have been categorized by keyword
.
1. Introduction
Automated keyword extraction is the process of identifying document terms and phrases that can appropriately represent the subject of our writing. With the proliferation of digital documents today, extracting keywords manually can be impractical. Many applications such as auto-indexing, summarization, auto-classification, and text filtering can benefit from this process since the keywords provide a compact display of the text. Automated keyword generation can be broadly classified into two categories: keyword allocation and keyword extraction.
In keyword allocation, a set of potential keywords is selected from a set of controlled vocabularies, while keyword extraction examines the words in the text. Keyword extraction methods can be broadly classified into four groups: statistical approaches, linguistic approaches, machine learning approaches, and hybrid approaches.
2. Literature Review
working on Persian words is a big challenge for the paucity of sufficient research. The inadequacy of text pre-processing programs has made it more complex than the Latin language. Also, the presence of large dimensions of input data is one of the challenges that has always arisen in such researches and this problem becomes more apparent due to the variety of Persian written forms (Gandomkar, 2017, p. 233:256). In Moin Maedi's article (2015, p. 34:42) A method for extracting keywords in Persian language is presented. This article extracts keywords from each text separately and without seeing another text as training data.
In the article by Mohammad Razaghnouri (2017, P. 16:27) using the Word2Vec method and the TIF-IDIF frequency, they created a question and answer system in Persian, which is a new work due to the use of Word2Vec in Persian. However, with size reduction techniques and Word2Vec, this 72% success rate can be enhanced in the future.3. Methodology
Accordingly, the current paper examines the integration of statistical keyword extraction methods with the Naive Bayes Classifier. Initially, we integrated input texts which are dissertations in Persian by using preprocessing (deletion of stop words, etymology, etc.) methods. Then, using the available statistical features, each word has been given a certain weight. Then, the valuable words of each text were selected and the proposed model was taught using the selected category, then the selected words were processed by the trained model, and at the end, the words extracted from the final model were evaluated using the keywords suggested by the authors themselves. Figure 1 depicts all the steps performed.
4. Results
Literature review shows that this is the first time that these combinations are used to extract Persian keywords, so that unlike other studies, each text is as a sample for category input and words as its properties, however, in this paper the words of each text input are categorized and words are extracted using statistical methods that are considered as features. The choice of keywords by the authors has always been a personal decision and people may not make a single decision to choose a set of words for a single text.
Figure 1
Proposed research framework for keyword extraction
 |
 |
||
Create unigrams
|
The current paper attempts to create a model and program with a new approach, due to the small number of input documents, which to extract keywords without dependence on the orientation of dissertations and the meaning of their words and only by using statistical features of words in each text. According to Tables 1 and 2, the developed model is able to extract a maximum of 20 keywords from each dissertation with an overall accuracy of 98.1%, in best condition which that is the use of a maximum frequency feature. The keywords written in each dissertation with 84% and 98% accuracy, correspond to one-word and two-word expressions, respectively.
Table 1
Evaluation criteria for Bayesian outputs in different states of statistical Features
| Precision | F1-Score | Recall | Accuracy | Statistical Features |
| 0.98 | 0.98 | 0.98 | 97.2% | Tf_Idf, Most Frequent, Tf_Isf |
| 0.99 | 0.99 | 0.982 | 98.1% | Most Frequent |
| 0.99 | 0.94 | 0.91 | 99.8% | Tf_Idf, Tf_Isf |
Table 2
Evaluation of post-processing test data for outputs that have been categorized by keyword
| Number of keywords that selected by writers | Number of words | Precision | F1-Score | Recall | Statistical Features | Step |
| 42 | 210 | 0.2 | 0.323 | 0.84 | Most Frequent | Uni-Grams |
| 34 | 158 | 0.8 | 0.888 | 0.98 | Most Frequent | By-Grams |
.
Dr Seyed Nezamuddin Makiyan, Mojtaba Rostami, Hanieh Ramezani,
Volume 18, Issue 3 (8-2018)
Volume 18, Issue 3 (8-2018)
Abstract
Crime is a phenomenon studied from the perspective of sociology, psychology, law and economics. From the economic point of view, when economies face with economic problems such as inflation, unemployment, poverty, income inequality, and high necessary costs and so on, the expected rise in crimes is inevitable. Generally, robbery has a high share in economic crimes. In this study, an attempt is made to analyze the relationship between income inequality and robbery in Iran within a Bayesian model and Jeffry Prior approach. The period under study is from 1996 to 2012. The educational expenditure and inflation are used as control variables. The results indicate a positive relation between robbery and income inequality. Also, there is a negative relation between educational expenditure and robbery; however, the inflation has no significant effect on the robbery.
Volume 18, Issue 4 (11-2018)
Abstract
In this paper, for the first time using of Bayesian regularized artificial neural network (BRANN) model, which is a novel method of among soft computing (SC) methods (such as fuzzy logic, genetic programming, neural network) to predict the rotational capacity of wide-flange steel beams. Steel is one of the most commonly used materials in construction industries, mainly in steel structures. There are many researches and studies on the behavior of a structural member of steel structure such as beams under different types of loading. The accurate estimation of rotation capacity (plastic rotation capacity) is of significant importance issue for plastic and seismic analysis and design of steel structures especially for high rise building (nonlinear behavior). Similarly, the moment redistribution in a steel structure also depends on the rotation capacity of the section. So the determination and accurate prediction of rotation capacity of steel structures members such as wide flange beams become an important task. Using different methods such as finite element, regression and statistical methods in previous studies has been used in recent years. Therefore, in order to estimate the more accurate value of the rotational capacity of wide flange beams, Artificial neural networks are used with the Bayesian learning process. The Bayesian regularized network assigns a probabilistic nature to the network weights, allowing the network to automatically and optimally penalize excessively complex models. The proposed technique (BRANN) reduces the potential for overfitting and overtraining, improving the prediction quality and generalization of the network. The proposed model (BRANN) is based on experimental data that collected from previous studies. After a comprehensive review of existing literature, 77 data of wide flange beam were selected which had experienced to determined rotation capacity. For this purpose, Half-length of flange, height of web, thickness of flange, thickness of web, length of beam, yield strength of flange and yield strength of web were consider as input parameters (six inputs) while rotation capacity is treated as target of the Bayesian regularized artificial neural network model. The Bayesian regularized artificial neural network is modeled in MATLAB software and applied to predict the rotation capacity. The results of this model were compared with experimental results and other models and equations that presented in the past (including Genetic programming (GP), Li equation and Kemp Equation. An analysis is carried out to check the performance of the proposed BRANN model based on the common criteria such as Mean Absolute Percentage Error (MAPE). The optimal and best model should have the lowest values of MAPE, this parameter is 20.32% for BRANN, 23.49% for a Genetic Programming model that proposed by Cevik, 47/20% for Li’s Equation and 56.98% for Kemp’s equations. The results of Bayesian regularized artificial neural network approach indicate a good agreement between the predicted and measured data. Furthermore, the Bayesian regularized artificial neural network model shows the most optimized results compared to all the previous model and equations. The result indicated that the Bayesian regularized artificial neural network could be used as a powerful tool for engineers and researcher to solve this kind of problems.
Volume 19, Issue 3 (7-2012)
Abstract
Archaeological excavations on the western part of the Central Iranian Plateau, known as the Qazvin Plain provides invaluable information about the sedentary communities from early occupation to the later prehistoric era. Despite the past archeological data, chronological studies mostly rely on the relative use of the Bayesian modeling for stratigraphically-related radiocarbon dates. The current paper provides a new model for excavations and the chronological framework based on new radiocarbon dating of the six key archeological enclosures in the Qazvin plain. A Bayesian analysis of these data is presented on a site-by-site basis to give the best chronologies. Finally, all dates are combined into a single model of the chronology of the Qazvin Plain from the Late Neolithic to the Iron Age. The procedure aims to use the Bayesian model to predict the transition points between the archaeologically-defined periods with the highest possible precision, to redefine the existing chronology for the Qazvin Plain
Volume 19, Issue 4 (3-2016)
Abstract
For years, researchers and managers have been popular with Balanced Scorecard and during the time, several edits have been developed from this base model. “Strategy map” is a topic that have been invented by researchers of the Balanced Scorecard to ease the understanding of issues that related to the strategic objectives set. The organization's strategic plan to achieve organizational goals, there are several ways that researchers have developed models to analyze these pathways. Bayesian networks, probabilistic networks have many applications in various sciences have provided. In this study, path analysis in Bayesian networks has been the strategy map. In this study, a model is presented by which one can achieve the ultimate goals of risk below the risk-based goals (in lower amounts) can be calculated and appropriate solution to mitigate risks identified organizational goals In this study, an investment firm is studied and the model is implemented and the results have been analyzed.
Volume 19, Issue 6 (11-2017)
Abstract
This study aimed to develop a multi-sector Dynamic Stochastic General Equilibrium (Large DSGE) model for Iran’s economy. In this model, economy was divided into three sectors: Agriculture, non-agriculture, and oil. Imports and exports were also included in the model. In order to adapt the model with Iran’s economic conditions, price stickiness in agriculture and non-agriculture were included. Then, the impact of rising oil prices on agricultural sector was examined. To calculate the required coefficients, 1971-2012 data was gathered and Bayesian method was used. The results showed the negative impacts of rising oil prices on agriculture as well as the negative effects of Dutch Disease.
Volume 21, Issue 1 (1-2019)
Abstract
Gardening products, like apple, are exposed to a variety of risks caused by unfavorable weather conditions. This kind of risk is unavoidable, but manageable. Agricultural insurance is an effective scheme in weather risk management. Nevertheless, current insurance schemes have challenges, such as high transaction costs, and problems caused by asymmetric information, i.e. adverse selection and moral hazard. Therefore, this study aimed to present an appropriate insurance scheme for apple production in Damavand, the so-called “weather-based index insurance”. In this regard, the information on apple yield and weather variables was collected between 1987-2016, from Iranian Agriculture Jihad Organization and the local meteorological station. The dependency structure between apple yield and weather variables was investigated by C-Vine Copula as a joint distribution to compute the expected loss. Then, according to the expected loss, weather-based index insurance premium was measured. The premium amount was equal to Thousand Rials 32,546.11 in the crop year 2016-17, which is different from the current insurance premium. This difference is because of the distinct nature of the two insurance schemes and the imperative and official mode of current insurance scheme.
Volume 21, Issue 1 (3-2021)
Abstract
Compressive strength (CS) and rapid chloride permeability test (RCPT) are the most important tests in the concrete industry. CS is the most significant characteristic of concrete mechanical properties that can show other mechanical properties like the module of elasticity. Chloride penetration could degradation of concrete durability. In the Persian Gulf, chloride penetration is the most dangerous effect on steel rebar corrosion. Therefore, CS and RCPT are related to mechanical and durability properties and should be studied more carefully. In this research, CS and RCPT are predicted using soft computing. For this purpose, Bayesian inference is used for prediction of them. Bayesian inference is a subset of linear regression but unlike conventional regressions that are deterministic, this type of regression is probabilistic. So, in this research is used of probabilistic analysis replaced deterministic analysis. Gene expression programming (GEP) is used for comparison of their results versus Bayesian inference. For research performing, 100 concrete samples containing metakaolin are considered that 75 samples are selected as training, and 25 samples are selected as testing data. seven input data are considered for prediction of CS and RCPT that contains the age of concrete (day), cement (kg/m3), water (kg/m3), metakaolin (kg/m3), fine aggregate (kg/m3), coarse aggregate (kg/m3) and surface resistance (KΩS). Output parameters are CS (MPa) and RCPT (Coulomb) that for predicting them, independent analysis should be performed. Results show that Bayesian inference in CS prediction has an excellent ability that the R2 coefficient for training and testing is 0.96. These values for GEP were 0.93 and 0.96 respectively. Values of root mean square error (RMSE) and mean absolute error (MAE) in Bayesian inference for training are 2.55 and 1.84 MPa respectively. These values for testing are 2.75 and 2.25 MPa. The values of RMSE and MAE for GEP training are 3.46 and 2.60 MPa and for testing these values are 3.43 and 2.65 MPa respectively. A comparison between evaluation parameters (i.e. R2, RMSE, and MAE) showed that Bayesian inference and GEP have excellent accuracy. In Bayesian inference, R2 coefficients for RCPT training and testing are 0.98 and 0.97 respectively. These values for GEP are 0.96 and 0.97 respectively. RMSE and MAE values in Bayesian inference for training are 223.14 and 161.58 Coulomb and these values for testing are 269.56 and 233.25 Coulomb respectively. RMSE and MAE values for GEP in training are 311.73 and 239.34 Coulomb respectively and these values for testing are 306.92 and 252.67 respectively. Results of CS and RCPT are showed that Bayesian inference is a good method for the prediction of concrete properties. On the other side, Bayesian is linear and has a little time consuming compared to nonlinear methods like GEP. In the next part of this study, first-order reliability method (FORM) is used for reliability analysis of CS and RCPT. Reliability index or beta and probability of failure (Pf) are the most important component in FORM analysis that are calculated in each analysis. For this purpose, mean values of input data are selected as inputs in reliability analysis. Results of reliability analysis indicated that when the CS is considered less than 45 MPa, the probability of failure is not considerable. Reliability analysis of RCPT in concrete samples is indicated that the value of 2000 Coulomb is a threshold value for the probability of failure. Therefore, if the RCPT of concrete samples is less than 2000 Coulomb, the probability of permeability is increased.
Volume 21, Issue 7 (12-2019)
Abstract
In this study, a gender analysis of various dimensions that affect the food security status of households in the villages of Kermanshah County was conducted based on a sustainable livelihood framework. The non-experimental research method involved data collection, which was performed to identify causal relationships. There were 25,671 households in the agricultural sector of the villages of Kermanshah County. Among them, 750 people (375 women and 375 men in 375 households) were selected as a proportional sample based on a stratified sampling method. A questionnaire was used for data collection. The validity of the questionnaire was confirmed by expert opinions, and its reliability was confirmed by sequential theta coefficients (0.714-0.838). Structural equation modeling was implemented based on the Multiple-Indicator, Multiple-Cause (MIMIC) Bayesian approach. Then, the structural MIMIC model was presented as the basis for comparison between two groups. The results of the research indicate that men had greater food security than women in terms of food availability, accessibility, and stability in the studied households. Men had more financial capital, social capital, and natural capital than women and were more affected by vulnerability and the transformation of structures and processes. Conversely, women had more human and physical capital and better livelihood strategies than men. Therefore, the economic empowerment of women and the professional training of men in the region should be prioritized to improve food security and development programs. These results can play a decisive role in the continuation or halting of programs for achieving food security and sustainable development.
Volume 22, Issue 4 (6-2020)
Abstract
To extend the genetic base of Iranian tomato germplasm, 93 landraces were collected from the northwest of Iran and East Anatolian of Turkey, along with three commercial cultivars, and their genetic structure were studied using 39 SSR primers. Thirty-five polymorphic SSR loci generated a total of 118 alleles in the studied germplasm. Number of alleles per locus and effective number of alleles averaged 3.37 and 2.47, respectively. Expected heterozygosity of SSRs varied from 0.227 (TMS24) to 0.773 (LEta016), averaged 0.558. The mean number of alleles per genomic-SSRs (3.61) was more than that of EST-SSRs (2.66). Cluster analysis using Neighbour Joining (NJ) method placed 96 tomato genotypes in eight groups. Little congruence was found between NJ dendrogram and geographical distances. Genetic structure analysis of the germplasm using Bayesian method revealed two sub-populations and separated cherry tomatoes from the other landraces and commercial cultivars. Out of the 21 morphological characters, significant (P≤ 0.05) marker-trait associations were found for 18 characters. Each of SSR loci TC11, TC948, and Tom236-237 was associated with three characters. The genetic variability, structure, and markers associated with the studied traits in the current study can be used for planning tomato breeding programs and future studies.
Dr Leila Torki, Baran Mazaheri,
Volume 22, Issue 4 (12-2022)
Volume 22, Issue 4 (12-2022)
Abstract
Aim and Introduction
Financial sanctions have long been a powerful tool for countries to achieve their political goals and secure their interests. Countries usually apply economic sanctions when they intend to force the target country to change certain policies that are not acceptable to the sending countries. The impact of financial sanctions may be far beyond the scope of a country's economy, so that in addition to affect the economy, it can also have a negative effect on the politics, culture, and social welfare of the target country. Iran has always been under the pressure of many sanctions. Therefore, due to the many sanctions that have been imposed on Iran over the years, the concern of many economists has always been how these sanctions affect Iran's economy. The economic and legal dimensions of sanctions as well as their diversity make it difficult to evaluate the implications related to sanctions on macroeconomic variables.
By examining the studies conducted in the field of financial sanctions and their effects on economic variables, it was found that most of these studies had investigated the effect of sanctions on two or more macro-economic variables, However, in the present study, the most important macroeconomic variables are included in the model and analyzed. Another innovation that distinguishes this research from other studies is the research method used in this research, which has not been used in Iran for the subject under study.
First, the optimal interval of the model is determined using the Hannan-Quinn statistic, then the Bayesian vector regression model is estimated using the optimal interval, and then the effect of financial sanctions on the variables of the model is investigated. In order to create a comparative framework, the results of the Bayesian VAR model are analyzed, and the results of both BVAR and VAR models are compared. It should be noted that Eviews 12 and 16, Excel and Matlab 2021 softwares were are used to estimate the model and analyze the results and form the instantaneous response function.
Findings
After estimating the Bayesian vector auto-regression model with the SSVS prior, the results of the instantaneous response functions are as follows:
The effect of the shock on the variable of fixed investments is negative and decreasing. The effect of the shock on the price index variable of consumer goods and services is positive and increasing. The effect of the shock on the export variable is negative and decreasing. The effect of the shock on the import variable is negative and decreasing. The effect of the shock on the GDP variable is negative and decreasing. The effect of the shock on the variable of overdue loans to the private sector is positive and increasing. The shock effect in the monetary base variable is negative and increasing. The effect of the shock on the country's external debt variable is negative and increasing. The effect of the shock on the variable of the currency market pressure index is negative and increasing.
After estimating the vector auto-regression model, the results of the instantaneous response functions are as follows:
The effect of the shock on the variable of fixed investments is negative and increasing. The effect of the shock in the price index variable of consumer goods and services is negative and increasing. The effect of the shock on the export variable cannot be investigated. The effect of the shock on the import variable cannot be investigated. The effect of the shock on the GDP variable is negative and variable. The effect of the shock on the variable of overdue loans to the private sector is negative and variable. The effect of the shock on the monetary base variable is negative and variable. The effect of the shock on the country's external debt variable is negative and increasing. The effect of the shock on the variable of the currency market pressure index is positive and variable.
As it is clear from the results, the information obtained from the auto-regression vector model is very inaccurate and with high variance, and the reason for this is, as previously stated, the existence of many parameters and the reduction of the degree of freedom of the model, which causes the accuracy to decrease. The estimate as well as the dispersion function becomes instantaneous. But Bayesian models solve this problem by shrinking the model and increase the estimation accuracy. As it is clear from the instantaneous response functions obtained by this method, the graphs have less dispersion and are much closer to the middle line, and also by examining the results, it can be said that the results are consistent with experimental studies and predictions taken is closer.
Discussion and Conclusion
The lack of appropriate quantitative indicators has caused most of the studies related to the investigation of the effects of sanctions to be focused on the explanation of the channels of the impact of the sanctions on the economic environment. Sanctions affect various economic sectors such as trade, investment, employment and economic growth regardless of success or failure in achieving the ultimate goal. Therefore, for accurate policies in these areas, it is necessary to evaluate the exact amount of the effects of sanctions on these sectors based on quantitative models, along with the influence channels.
According to the results of the auto-regression Bayesian vector model with SSVS prior, financial sanctions have a negative effect on the GDP and cause it to decrease. With the decrease in the productive capacity of the economy, fixed investments also decrease. A decrease in economic growth causes a recession. A decrease in private consumption, private investment, and a decrease in economic growth can greatly strengthen the recessionary conditions, therefore, it is recommended that the government, while managing the budget, avoid excessive reductions in construction costs, so that by strengthening the effective demand in the economy, it can bring it out of stagnation.
On the other hand, financial sanctions reduce the country's exports and imports and increase the country's foreign debt. Therefore, it is suggested that the import of luxury goods, which have a high value, should be put on the agenda in the conditions of prohibited sanctions and self-sufficiency in the production of some imported products. Besides, increasing the diversification of export goods can partially compensate for the decrease in exports. In this case, the policy of supporting domestically produced goods and export-oriented goods is recommended.
Since financial sanctions increase the pressure index of the currency market, it is suggested to prevent the entry of luxury goods and to put autarky in the production of these goods. In this regard, the creation of knowledge-based companies and the creation of career guidance and specialized employment offices in universities and the policies of training human resources in the specialties needed by society should be included in the goals of the country's vision.
Volume 24, Issue 4 (10-2024)
Abstract
Structures, including concrete bridges, may be exposed to gradual damage during operation due to environmental conditions such as corrosion, which will reduce their useful life. Knowing the amount of remaining useful life of the structures makes it possible to improve, strengthen or rebuild them at the right time. To determine the remaining useful life of a structure, there are three common methods under the titles of data-driven method, failure physics method and combined method. In this article, the combined method of determining the remaining useful life of structures has been studied. The purpose of this research is to propose a suitable method for predicting the remaining useful life of a bridge structure with a reinforced concrete deck under chloride ion corrosion using a Bayesian network. The remaining useful life of reinforced concrete parts under chloride attack includes two parts of the time related to the initial stage of corrosion and the time related to the release of chlorine ions. To determine the remaining useful life part related to the initial stage, various researches have been done and the American ACI365 committee has proposed a software called Life-365 for this purpose. There is no comprehensive research to determine the second part of the remaining life, which is related to the release stage. Based on the prepared Bayesian network and the formula obtained in this research, the remaining life of the chloride diffusion stage in concrete was estimated to be 9.116 years in the best conditions and 2.73 years in the worst conditions. Meanwhile, the number suggested by the ACI365 committee, in practical work, is usually equal to 6 years for the release stage. This issue clarifies the need for more research in this regard. In this article, using the data available in past researches and reproducing the data and using the Bayesian network, relationships are presented to determine the useful life of the bridge structure in both the initial and release stages.Based on the proposed method, using the Bayesian network, relationships can be obtained for each of the two parts of the remaining useful life of the structure under chloride corrosion, i.e., the corrosion initiation stage and the chloride release stage, in terms of factors affecting the remaining useful life in a specific project. . In these networks, the effect of various factors can be considered, which is one of the advantages of the proposed method.The remaining useful life has an inverse relationship with temperature. When the average temperature increases by 20 degrees, the remaining useful life decreases by an average of 30%.With the help of the proposed relationships, a parametric study was conducted to investigate the effect of different conditions of using pozzolanic compounds on the remaining life of the structure. In this regard, 17 states of different pozzolanic compounds with different concentrations were considered and the average remaining useful life due to different states was calculated. The average life obtained compared to the case where no pozzolan is used in concrete showed a 38% increase in life. In order to evaluate the results of the proposed relationships, the problem of determining the remaining useful life for a numerical model of a concrete bridge and several marine structures located in the Persian Gulf was investigated. The results of this research show that by using the proposed relationships, it is possible to improve the accuracy of estimating the remaining useful life of bridges with concrete decks exposed to chloride ion penetration, relying on the data obtained from the field inspections of the structure.
Mr Yahya Mohaghegh, Dr Hashem Zare, Dr Mehrzad Ebrahimi,
Volume 24, Issue 4 (12-2024)
Volume 24, Issue 4 (12-2024)
Abstract
Aim and Introduction
The world has suffered severe environmental degradation in the last two decades. The effects of ecological deviations and environmental destruction are alarming and cause concern for stakeholders and environmentalists. These problems have led to environmental disasters such as extreme weather changes and rising sea levels. For this reason, countries are trying to address environmental crises and economic growth at the same time. Basically, it is believed that the destruction of ecosystems in many countries is the result of human actions, including rapid industrialization, population growth, expansion of economic activities, urbanization, and widespread consumption of fossil fuels. Undoubtedly, one of the main factors of climate change and environmental degradation is the emission of carbon dioxide (CO2) caused by economic activities. The relationship between carbon dioxide emissions and economic growth has attracted the attention of researchers and policymakers to reduce carbon dioxide emissions without affecting economic growth. Achieving this goal may be difficult, but studies have included various factors in the growth-carbon dioxide relationship, such as different energy sources, technological innovations, population, financial development, urbanization, and trade openness. In addition to the factors mentioned above, there is a need to assess other relevant factors such as human capital for effective policy development. Despite the extensive role of human capital in promoting energy efficiency and economic growth, by skilled workers in the production process and the preference of educated households to use home appliances with high energy efficiency, few researchers have included this factor in the existing literature. Education is considered as one of the most important human capital investments and plays a vital role in the process of economic growth. Education, at the micro level, affects the future income flow of people, and at the macro level, it is thought that a workforce with better education increases the stock of human capital in the economy. Increased human capital in turn can have positive externalities, such as lower crime or improved health outcomes, which are socially desirable and likely to have positive effects on productivity. In fact, it is the existence of such positive externalities that provides economic justification for governments to support education. It is believed that public spending on education and health will lead to improvement of human capital, reduction of poverty, economic growth and development, and reduction of pollution.
Theoretical studies emphasize different mechanisms through which education affects economic growth. (1) Education increases the human capital of the labor force, thereby increasing labor productivity and shifting growth toward a higher equilibrium level of output. (2) In endogenous growth theories, education increases the economy's capacity to innovate new technologies, products, and processes, thereby promoting growth.
In the environmental field, improving human capital can reduce the use of fossil fuel in the production process. The motive of this research is to understand the effect of public education expenditure shocks on economic growth and environmental quality in Iran.
Methodology
To achieve the goal of the research, a dynamic stochastic general equilibrium (DSGE) model and Bayesian approach have been used. In this regard, observable variables, gross domestic product, private consumption, investment, government expenditure and the gross growth rate of money have been used in the period of 2004 to 2021.
Findings
The results indicate that an increase in public education expenses by one standard deviation increases the marginal efficiency of private education expenses. Because private and public education expenses complement each other and entered the model in the form of a Cobb-Douglas function. Therefore, education hours, investment in education and subsequently, human capital increased. The increase in human capital led to an increase in production, economic growth and a decrease in inflation. The decrease in inflation led to an increase in the real wages of the workforce and finally the desire of the household to increase the supply of labor. Consumption increased in response to an increase in real wages. Also, the behavior of investment is very similar to the behavior of consumption and production, but its changes are more intense than other expenses. Therefore, it can be said that the shock of public education expenses has affected the performance of the economy like an expansionary impulse and has improved the economic conditions. In the environmental sector, with the increase in human capital, economic growth has been improved and this has led to an increase in air pollution and a decrease in environmental quality.
Discussion and Conclusion
In justifying these results, we can point to the weak institutional quality in Iran, that despite the huge amount of human capital investment, there is no necessary effectiveness in the environmental sector, and this is a confirmation that Iran is in the early stages of economic development. As a policy recommendation, in addition to paying attention to education and its role in the development of human capital as a long-term investment, improving the quality of institutions should be considered
The world has suffered severe environmental degradation in the last two decades. The effects of ecological deviations and environmental destruction are alarming and cause concern for stakeholders and environmentalists. These problems have led to environmental disasters such as extreme weather changes and rising sea levels. For this reason, countries are trying to address environmental crises and economic growth at the same time. Basically, it is believed that the destruction of ecosystems in many countries is the result of human actions, including rapid industrialization, population growth, expansion of economic activities, urbanization, and widespread consumption of fossil fuels. Undoubtedly, one of the main factors of climate change and environmental degradation is the emission of carbon dioxide (CO2) caused by economic activities. The relationship between carbon dioxide emissions and economic growth has attracted the attention of researchers and policymakers to reduce carbon dioxide emissions without affecting economic growth. Achieving this goal may be difficult, but studies have included various factors in the growth-carbon dioxide relationship, such as different energy sources, technological innovations, population, financial development, urbanization, and trade openness. In addition to the factors mentioned above, there is a need to assess other relevant factors such as human capital for effective policy development. Despite the extensive role of human capital in promoting energy efficiency and economic growth, by skilled workers in the production process and the preference of educated households to use home appliances with high energy efficiency, few researchers have included this factor in the existing literature. Education is considered as one of the most important human capital investments and plays a vital role in the process of economic growth. Education, at the micro level, affects the future income flow of people, and at the macro level, it is thought that a workforce with better education increases the stock of human capital in the economy. Increased human capital in turn can have positive externalities, such as lower crime or improved health outcomes, which are socially desirable and likely to have positive effects on productivity. In fact, it is the existence of such positive externalities that provides economic justification for governments to support education. It is believed that public spending on education and health will lead to improvement of human capital, reduction of poverty, economic growth and development, and reduction of pollution.
Theoretical studies emphasize different mechanisms through which education affects economic growth. (1) Education increases the human capital of the labor force, thereby increasing labor productivity and shifting growth toward a higher equilibrium level of output. (2) In endogenous growth theories, education increases the economy's capacity to innovate new technologies, products, and processes, thereby promoting growth.
In the environmental field, improving human capital can reduce the use of fossil fuel in the production process. The motive of this research is to understand the effect of public education expenditure shocks on economic growth and environmental quality in Iran.
Methodology
To achieve the goal of the research, a dynamic stochastic general equilibrium (DSGE) model and Bayesian approach have been used. In this regard, observable variables, gross domestic product, private consumption, investment, government expenditure and the gross growth rate of money have been used in the period of 2004 to 2021.
Findings
The results indicate that an increase in public education expenses by one standard deviation increases the marginal efficiency of private education expenses. Because private and public education expenses complement each other and entered the model in the form of a Cobb-Douglas function. Therefore, education hours, investment in education and subsequently, human capital increased. The increase in human capital led to an increase in production, economic growth and a decrease in inflation. The decrease in inflation led to an increase in the real wages of the workforce and finally the desire of the household to increase the supply of labor. Consumption increased in response to an increase in real wages. Also, the behavior of investment is very similar to the behavior of consumption and production, but its changes are more intense than other expenses. Therefore, it can be said that the shock of public education expenses has affected the performance of the economy like an expansionary impulse and has improved the economic conditions. In the environmental sector, with the increase in human capital, economic growth has been improved and this has led to an increase in air pollution and a decrease in environmental quality.
Discussion and Conclusion
In justifying these results, we can point to the weak institutional quality in Iran, that despite the huge amount of human capital investment, there is no necessary effectiveness in the environmental sector, and this is a confirmation that Iran is in the early stages of economic development. As a policy recommendation, in addition to paying attention to education and its role in the development of human capital as a long-term investment, improving the quality of institutions should be considered
Mr Mousa Maghsoudi, Dr Mansour Zarra-Nezhad, Dr Masoud Khodapanah,
Volume 25, Issue 1 (3-2025)
Volume 25, Issue 1 (3-2025)
Abstract
Aim and Introduction
Studies and contributions of structural vector autoexplanatory models using Bayesian and classical techniques have provided evidence that shocks to the marginal efficiency of investment are the main drivers of economic volatility in US postwar data. However, dynamic stochastic general equilibrium models attempt to explain the movement of consumption with production following a marginal efficiency of investment (MEI) shock. Indeed, the decline in consumption after a positive MEI shock contradicts empirically identified business cycles. This issue is referred to as the consumption puzzle. In other words, consumption usually decreases after a positive investment shock in the model. Therefore, the usual DSGE models do not produce the observed co-movement between macroeconomic variables in response to the marginal efficiency of investment shock. From an empirical perspective, consumption, investment, working hours and production all move together. This lack of coordination of consumption in response to investment shocks is problematic as an important source of business cycles.
A review of empirical studies indicates that investment shocks and consumption puzzle have received limited attention. In this regard, the main aim and innovation of the current study is to set up a dynamic stochastic general equilibrium (DSGE) model and use the Bayesian approach for Iran in order to bridge this study gap as much as possible.
The marginal efficiency of investment shock is a source of exogenous changes in the efficiency with which the final good can be converted into physical capital and thus into future capital input. This change may be due to technological factors specific to the production of investment goods. On the other hand, exogenous changes in efficiency can result from disturbances in the process of converting these investment goods into productive capital.
In neoclassical models, after a positive MEI shock, households trading in
financial markets increase their investment and reduce consumption. In fact, an intertemporal substitution effect occurs between the current consumption and investment, which creates a negative wealth effect and, therefore, creates the so-called consumption puzzle. The mechanism behind the puzzle was first described by Barrow and King (1984). The idea is that if an efficient equilibrium exists, the marginal rate of substitution between consumption and leisure should equal the marginal product of labor. This condition implies that with exogenous shocks that only indirectly affect marginal production labor, as MEI shocks actually do, consumption and labor hours move in opposite directions. Therefore, although MEI shocks account for up to 60% of the variance in output and working hours, the argument that investment shocks are one of the most important drivers of macroeconomic fluctuations is challenging.
Methodology
The core of current research model is derived from the studies of Rohe (2012) and by expanding it, the marginal efficiency of investment shock and the consumption puzzle have been modeled for Iran.
To estimate the model parameters, the Bayesian method, and the Random Walk Metropolis-Hastings algorithm were used. The data of the model’s observable variables include seasonal adjusted data, gross domestic production, private consumption, private investment, government expenditure, and inflation rate (gross) from 2004 to 2022, which underwent a de-trending procedure using the Hodrick-Prescott filter.
Findings
The marginal efficiency of investment shock leads to an increase in the rate of return on capital and investment. Consumption behavior is similar to investment behavior but with less volatility. Due to the increase in the demand side of the economy, inflation will increase and the real exchange rate will decrease. In response to the increase in the demand side, production, wage rates and employment increase. It should be noted that the contractionary monetary policy has led to a reduction in the fluctuations of macroeconomic variables, yet the dynamic of the variables has not changed.
Discussion and Conclusion
In justifying these results, the average mark-up equation of the economy can be used:
Mu p , t = MPN t W t P t
where,Mu p , t MPN t W t P t
The equilibrium conditions of the labor market can also be introduced as follows:
1 Mu p , t MPN t N t = MRS t C t , N t
Despite nominal price stickiness, firms are not able to increase their prices in response to the increase in demand caused by the investment boom resulted from shocks. Therefore, the average mark-up of the economy decreases and effectively shifts the labor demand curve upwards. In this situation, consumption and working hours increase. In other words, in spite of inseparable preferences and in the conditions that the increase of working hours has a positive effect on the marginal utility of consumption (the complementarity of working hours and consumption), the co-movement of investment, production, working hours and consumption can be justified and the puzzle of consumption does not occur
Studies and contributions of structural vector autoexplanatory models using Bayesian and classical techniques have provided evidence that shocks to the marginal efficiency of investment are the main drivers of economic volatility in US postwar data. However, dynamic stochastic general equilibrium models attempt to explain the movement of consumption with production following a marginal efficiency of investment (MEI) shock. Indeed, the decline in consumption after a positive MEI shock contradicts empirically identified business cycles. This issue is referred to as the consumption puzzle. In other words, consumption usually decreases after a positive investment shock in the model. Therefore, the usual DSGE models do not produce the observed co-movement between macroeconomic variables in response to the marginal efficiency of investment shock. From an empirical perspective, consumption, investment, working hours and production all move together. This lack of coordination of consumption in response to investment shocks is problematic as an important source of business cycles.
A review of empirical studies indicates that investment shocks and consumption puzzle have received limited attention. In this regard, the main aim and innovation of the current study is to set up a dynamic stochastic general equilibrium (DSGE) model and use the Bayesian approach for Iran in order to bridge this study gap as much as possible.
The marginal efficiency of investment shock is a source of exogenous changes in the efficiency with which the final good can be converted into physical capital and thus into future capital input. This change may be due to technological factors specific to the production of investment goods. On the other hand, exogenous changes in efficiency can result from disturbances in the process of converting these investment goods into productive capital.
In neoclassical models, after a positive MEI shock, households trading in
financial markets increase their investment and reduce consumption. In fact, an intertemporal substitution effect occurs between the current consumption and investment, which creates a negative wealth effect and, therefore, creates the so-called consumption puzzle. The mechanism behind the puzzle was first described by Barrow and King (1984). The idea is that if an efficient equilibrium exists, the marginal rate of substitution between consumption and leisure should equal the marginal product of labor. This condition implies that with exogenous shocks that only indirectly affect marginal production labor, as MEI shocks actually do, consumption and labor hours move in opposite directions. Therefore, although MEI shocks account for up to 60% of the variance in output and working hours, the argument that investment shocks are one of the most important drivers of macroeconomic fluctuations is challenging.
Methodology
The core of current research model is derived from the studies of Rohe (2012) and by expanding it, the marginal efficiency of investment shock and the consumption puzzle have been modeled for Iran.
To estimate the model parameters, the Bayesian method, and the Random Walk Metropolis-Hastings algorithm were used. The data of the model’s observable variables include seasonal adjusted data, gross domestic production, private consumption, private investment, government expenditure, and inflation rate (gross) from 2004 to 2022, which underwent a de-trending procedure using the Hodrick-Prescott filter.
Findings
The marginal efficiency of investment shock leads to an increase in the rate of return on capital and investment. Consumption behavior is similar to investment behavior but with less volatility. Due to the increase in the demand side of the economy, inflation will increase and the real exchange rate will decrease. In response to the increase in the demand side, production, wage rates and employment increase. It should be noted that the contractionary monetary policy has led to a reduction in the fluctuations of macroeconomic variables, yet the dynamic of the variables has not changed.
Discussion and Conclusion
In justifying these results, the average mark-up equation of the economy can be used:
where,
The equilibrium conditions of the labor market can also be introduced as follows:
Despite nominal price stickiness, firms are not able to increase their prices in response to the increase in demand caused by the investment boom resulted from shocks. Therefore, the average mark-up of the economy decreases and effectively shifts the labor demand curve upwards. In this situation, consumption and working hours increase. In other words, in spite of inseparable preferences and in the conditions that the increase of working hours has a positive effect on the marginal utility of consumption (the complementarity of working hours and consumption), the co-movement of investment, production, working hours and consumption can be justified and the puzzle of consumption does not occur
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