The purpose of the present study is to investigate primary psychometric properties of the Persian version of children's depression inventory. 407 students, 234 boys and 173 girls, aged between 12 and 14 served as the subjects of this study. They were selected through multi-stage random cluster sampling.The sampling group completed Children Depression Inventory, children Depression Scale and Beck Depression Inventory. Results show the total CDI Test-retest reliability and internal consistency to be 0.82 and 0.83 respectively. Also, the correlation coefficients between CDI and Children Depression Scale and Beck Depression Inventory were respectively 0.79 and 0.87 (p<0.01), which indicate a satisfactory convergent validity for CDI. Confirmatory factor analysis results show five factor model of CDI fit in the Iranian sample. MANOVA results indicate that on average the total score of depression for girls was significantly higher than the one for boys. Furthermore, the total average depression for subjects aged 14 was significantly higher than the one for 12 year olds.

The aim of present research was to investigate psychometric properties (reliability, validity and factor analysis) of children depression inventory (Kovacs, 1981) in boy and girl students of junior high schools in Ahvaz. 399 boy and girl students were randomly selected by multi-stage sampling. The results of factor analysis extracted six factors. Among those, the greatest proportion of variance accounted for the first factor. In addition, the test-retest reliability coefficients for total inventory were obtained 0.81, split-half of 0.84, and internal consistency of 0.87. Also correlation coefficient between child depression inventory and short form children depression inventory for total sample were 0.36. For girls was 0.54, and for boys was 0.23. Multivariate Analysis of Variance indicated that there aren't significant differences between boys and girls, of Arabic and Persian language and course of study students in terms depression, but there is significant interaction between sex and course of study.

Capacitive deionization (CDI) is an emerging energy efficient, low-pressure and low-cost intensive desalination process that has recently attracted experts’ attention. The process is to explain that ions (cations and anions) can be separated by a pure electrostatic force imposed by a small bias potential. Even at a rather low voltage of 1. 2 V, desalinated water can be produced. The process can be well operational by a professional cell design. Although various processes have been manufactured before, in this study, membrane was removed and a new unit was designed and manufactured (Using CFD Simulation). In this case, the combination of activated carbon powder (with an effective surface area of 2600 m2 per gram), carbon black, and polyvinyl alcohol with a ratio of 35/35/30 coated on carbon paper as electrode materials was considered for tests. The weight was 1. 41 grams for each material, and the thickness was 0. 44 mm. CDI system was tested, and the results of charge-discharge cycles, cyclic voltammetry, and impedance spectroscopy were evaluated. It can be implied that there is no need for a strong pump and, also, pressure drop can be reduced due to such a noticeable space between two electrodes. Preliminary experimental results showed high specific capacitance (2. 1 Farad) and ultra-high salt adsorption capacity, compared with similar cases.

In our previous work, the effect of use of a water soluble CDI/NHS system as nontoxic cross-linking agent on fabrication of gelatin nanoparticles was investigated. In this research, the effect of variation in some synthetic parameters of gelatin nanoparticles cross-linked by CDI/NHS system such as type of gelatin and formulation of crosslinking agent on their size and distribution was examined. The conventional two step desolvation method was used for preparation of gelatin nanoparticles. The morphology, mean size and size distribution of the formed nanoparticles were evaluated and compared with each other. In addition, intrinsic viscosities of all the nanoparticles were measured and compared under different conditions. The results showed that the presence of more NHS and absence of NHS catalyst in CDI/NHS system lead to the large particle size and broad size distribution of nanoparticles that were attributed to the fast and slow cross-linking rate, respectively.

In this research, gelatin nanoparticles were synthesized via inverse miniemulsion method by employing a mixture of a water soluble carbodiimide (CDI) and N-hydroxysuccinimide (NHS) as a non-toxic cross-linking system. The gelatin nanoparticles were characterized for their size and size distribution, morphology and stability and were compared with those of nanoparticles cross-linked by glutaraldehyde (GA) as the most commonly utilized cross-linking agent. The results showed the formation of more homogeneous nanoparticles with smaller size when CDI/NHS used as cross-linking agent under the same synthesis condition. Moreover, dilute solution viscosimetry experiments confirmed the stability of the nanoparticles under various physicochemical conditions. The differences in the characteristics of CDI/NHS and GA cross-linked nanoparticles were ascribed to the different nature of network formation using the two cross-linking agents. Generally, these results suggested CDI/NHS cross-linked gelatin nanoprticles as an interesting candidate for drug delivery application.

The present research is a case study of gender differences of language usage in a pair of non-identical twins (a boy [Arsham] and a girl [Arshida]), performed on their using in Farsi Lexicon. The descriptive-analytic research is based on Mac-Arthur-Bates’s Communicative Development Inventories (CDI), according to which the twin’s lexicon has been divided into 22 categories, to which two extra categories are added. The language corpus is the twin’s recorded daily speech including 2535 words, categorized based on the framework. The frequency of each lexical group is evaluated throughout the research, together with the analysis of data accomplished by dependent T-test. The T-test results illustrated that there is a significant difference (p<0.05) between the average of the twin’s usage only within the groups of 6, 10 and 16 as well as on their score of total scale (diagram 1 and table 26).This is while their average is near to each other in all other groups. Arsham’s average is higher than Arshida’s in any case of significant difference, showing his better doing in some groups. In the groups number 2 and 13, their doing and average is the same. Other similarities and differences in their lexicon can be summarized as follows: 1) Verbs in their lexicon have the highest frequency, 2) words have the least frequency, 3) Broadness and variety in the boy’s lexicon is more than the girl’s (of the whole corpus, 56.25% is devoted to the boy and 43.74% to the girl).

Introduction Drought is one of the greatest challenges of our time due to the dangers it poses to the world. In arid and semi-arid regions, it is necessary to continuously monitor agricultural systems that face water shortages and frequent droughts. Therefore, it is necessary to have large-scale information about agricultural systems and land use for managing and making decisions for the sustainability of food security. Continuous monitoring of drought requires a large amount of information to be processed with great speed and accuracy. Due to the complexity and impact of various factors on drought, in recent years, the methods of combining several factors to create a comprehensive drought index have received much attention. Machine learning and deep learning methods can provide a more accurate and efficient tool to predict droughts and be used in drought risk management. The review of sources shows that until now no studies have been conducted in the field of drought monitoring using deep learning approach and satellite images in the catchment area of Lake Urmia in Iran. A large part of its economic activities is dedicated to agriculture. The increase in temperature, the increase in evaporation-transpiration and the excessive use of water resources for agriculture have caused an upward trend in the frequency of droughts in this basin during consecutive years, one of the harmful effects of which is a significant decrease in the lake level. Therefore, for drought management in this basin, it is very important to identify drought behavior so It is very important to determine appropriate and reliable indicators to measure and predict the effects of droughts. According to the investigations, it was observed that most of the studies in the field of drought in this basin have been carried out from the meteorological point of view, or by individual plant indicators, so in this study, using the approach of principal component analysis, we tried to provide a composite drought index for drought modeling and forecasting.   Materials and Methods In this research, satellite images and deep learning and machine learning methods have been used to predict the Combined Drought Index. For this purpose, satellite images were first obtained for the study area and pre-processing was done on the data. Then, all the data were converted to a scale with a spatial resolution of 500 meters, and the VCI index was calculated using NDVI data, the TCI index using the land surface temperature product, and the CWSI index using the Modis evapotranspiration product, and finally, CDI drought index was calculated using principal component analysis method. Then the correlation between CDI data and other meteorological variables including evapotranspiration, potential evapotranspiration, land surface temperature during the day, and land surface temperature at night was calculated. Finally, the CDI index is modeled using deep learning and machine learning methods.   Results and Discussion This study modeled the Combined Drought Index based on a different combination of input variables and deep learning and machine learning methods. Examining the results showed that the variables of the normalized difference vegetation index, the land surface temperature during the day and at night, evapotranspiration, and potential evapotranspiration were the most influential parameters for modeling the CDI index, and all four methods with acceptable accuracy and error have been able to model the combined drought index. The CART model with a correlation coefficient of 0.96, RMSE equal to 0.029, and Nash Sutcliffe coefficient of 0.92 was chosen as the best model among the methods.   Conclusion In this research, different combinations of input variables extracted from satellite image products were evaluated in the form of 6 independent scenarios to predict the Combined Drought Index. By examining the evaluation parameters including correlation coefficient, Nash Sutcliffe coefficient, and root mean square error, it was found that all four methods can estimate the combined drought index with acceptable accuracy and error. Among all the methods, the CART method performed better (R=0.96 and RMSE=0.029) than the other methods for predicting the time series of the Combined Drought Index. On the other hand, the SVM method has been able to model the combined drought index with acceptable accuracy (R=0.94 and RMSE=0.034). However, contrary to expectations, two deep learning methods were able to model the combined drought index with less accuracy than machine learning methods. In general, by examining the results, it was found that with the method presented in this research, it is possible to accurately predict the CDI combined drought index time series and predict drought in different periods of plant growth, and use its results for regional drought management and policies, especially in Basins without statistics.

Objective Early language skills predict the child’, s future language skills and literacy. So, screening and assessment of speech and language at an early age are important. One cost-effective way of assessing a child’, s communication is through parents reporting tools. MacArthur-Bates Communicative Development Inventories (CDIs) are the most widely used forms by professionals in more than 70 live languages worldwide to screen children’, s communication skills. The main purpose of this study was to provide the Persian version of the third form of CDI (CDI-III) and determine its psychometric properties, including face and content validity and internal consistency in 30 to 37 months old Iranian Persian-speaking children. Materials & Methods In a cross-sectional methodological study of instrument validation, a Persian-adapted form of CDI-III was developed. At first, the English form was adapted to Persian, and a Delphi method was used to provide the initial list of the items. The face and content validity rates were examined by asking the opinion of ten Persian-speaking speech and language pathologists with at least three years of clinical experience in the field of child speech/language development and disorders. The final form was compiled based on the results of the content validity ratio (CVR) and content validity index (CVI) of the items. The final form consisted of three sections, including vocabulary, sentences, and use of sentences. The parents of 356 Persian-speaking children aged 30 to 37 months recruited by multi-stage cluster random sampling from health centers in Isfahan City were asked to complete the form. The Kuder-Richardson coefficient assessed internal consistency, and the Spearman coefficient examined correlation. Results The items with CVIs≥, 0. 7 and CVRs≥, 0. 6 remained, and other items were reviewed or removed based on the suggestions of panelists and developers. The final form included 100 words, 22 pairs of sentences, and 16 questions about using sentences. A total of 356 children (Mean±, SD age: 34. 03±, 2. 12 months) were included in the study. According to the English CDI-III guidelines, the data were divided into four age groups with two-month intervals: 30-31, 32-33, 34-35, and 36-37. The internal consistency of the whole form was 0. 97, and internal consistency values of vocabulary, sentences, and sentence usage were calculated as 0. 98, 0. 88, and 0. 88, respectively. The Spearman correlation values between the scores of the form sections and each section with age indicated a significant positive correlation between all sections of the form. Also, the significant positive correlation between age and all three parts showed that the number of expressive words, the grammatical complexity of sentences, and the proper use of sentences increase with age. Conclusion The Persian CDI-III form possesses a good face and content validity and internal consistency to be used as a valid tool for screening language competence of 30-37 months old Persian-speaking children. The results of the mean and standard deviation of the current study can be used as a comparison for screening children suspected of being language deficient.