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Abstract Honing the quality of cement plays a pivotal role in the petroleum industry throughout drilling operations, undoubtedly. In cement plants, producing high-quality clinker is essential for improving oil well maintenance, reducing expenditures, enhancing safety, and more, demonstrating its importance in the long term. Despite tight control of clinker operational conditions, cement kiln operators can still produce dusty clinker. This work uniquely establishes the previously unexplored relationship between dusty clinker and the qualitative characteristics of class G oil well cement slurries, offering a practical solution to prevent the production of poor-quality cement in factories. All API tests were done according to API Specification 10 A. Furthermore, a thorough microscopic analysis was conducted on both standard and dusty clinkers to establish a connection between the properties of class G oil well cement slurries and the changes in crystal size observed in the minerals of dusty clinker. According to the studies conducted in this regard, changing the size of the cement particles has a considerable influence on the cement performance. Here, results revealed that increasing the alite (C3S) crystal size to 60–70 microns in dusty clinker led to a roughly 20-minute increase in the average thickening time. In addition, the compressive strength cured at 38 and 60 °C for 8 h, decreased by 1.47 and 2.89 MPa, respectively. More importantly, it was found that the average maximum consistency in dusty clinker oil well cement increased by almost 6.4 Bc over a 15 to 30-minute period.

Abstract Carbon dioxide (CO2) is the main greenhouse gas that drives global warming, climate change, and other environmental issues. CO2 absorption using amine solvents stands out as one of the most well-known industrial technologies of CO2 capture. However, accurate prediction of CO2 absorption in aqueous amine solutions under different operating conditions is crucial for designing an efficient amine scrubbing system in power plants. In this work, CO2 solubility in aqueous piperazine (PZ) solutions was modeled using 517 experimental data points covering a temperature range of 298 to 373 K, PZ concentration of 0.1 to 6.2 mol/L (M), and CO2 partial pressure of 0.03 to 7399 kPa. To this end, four robust machine learning algorithms, including gradient boosting with categorical features support (CatBoost), light gradient boosting machine (LightGBM), extreme gradient boosting (XGBoost), and adaptive boosting decision trees (AdaBoost-DT) were utilized. Among the developed models, the CatBoost model presented the highest accuracy with an overall determination coefficient (R2) of 0.9953 and an average absolute relative error of 2.36%. Sensitivity analysis revealed that CO2 partial pressure had the greatest influence on CO2 absorption in aqueous PZ solutions, followed by PZ concentration and temperature. Moreover, CO2 partial pressure positively influenced CO2 absorption in aqueous PZ solutions, while PZ concentration and temperature exhibited negative effects. Finally, the leverage technique indicated that both the experimental data bank used for modeling and the model’s estimates were statistically acceptable and valid showing only 8 points (∼1.5% of total data) as possible suspected data.

Abstract Magnetic separation is a common procedure for the enrichment of magnetic iron ores. Davis tube (DT) test is a standard laboratory technique used to determine the optimum magnetic recovery of iron ore using wet low-intensity magnetic separators. However, the DT test is time-consuming and labour-intensive. In this study, based on the results of DT-tests, generalized regression (GRNN) and radial basis function (RBF) neural networks were developed to predict iron recovery through the Fe and FeO content of the feed. First, the DT tests were performed on 613 iron ore samples with varying Fe and FeO content. Then, neural networks were used to model the iron recovery from the DT test, using the Fe and FeO content of the feed as input data. The modeling results showed that GRNN is a better model for predicting iron recovery. The main statistical metrics indicated that GRNN has AAPRE, RMSE, and R2 values of 3.929%, 2.804, and 0.976 respectively for a total of 613 data points. Moreover, sensitivity analysis demonstrated that iron recovery is directly influenced by both Fe and FeO contents, with FeO content having a more pronounced effect. Finally, Leverage analysis showed that GRNN is highly reliable for predicting iron recovery, with only 2.77% of data points flagged as suspicious based on outlier estimation.

Abstract The adsorption of asphaltene on the rock surface and the changes in its wettability are very relevant issues in flow assurance and oil recovery studies, and for carbonate reservoirs, they are even more important. During microbial enhanced oil recovery (MEOR) processes, wettability alteration is considered a crucial mechanism leading to improved oil recovery. Therefore, it is essential to understand the mechanisms of surface wettability changes by bacteria and biosurfactants and find new and reliable methods to prevent asphaltene adsorption. Hence, the main aim of this research was to investigate the effect of a mixture of thiobacillus thiooxidans and thiobacillus ferooxidans microorganisms with an optimum effective temperature of around 30 °C (referred to as mesophilic bacteria), as well as a mixture of two moderate thermophiles Sulfobacillus thermosulfidooxidans for operating temperatures around 50 °C (referred to as moderately thermophilic bacteria) on the adsorption of asphaltene samples isolated from two different crude oils onto main reservoir minerals (i.e., quartz and dolomite). The results indicated that after two weeks of mineral aging in moderate thermophilic bacteria, the adsorption of asphaltene on both minerals increased between 180 and 290%. Fourier-transform infrared spectroscopy (FTIR) analysis for quartz and dolomite samples demonstrated that after aging in bacterial solution, bonds related to the adsorption of bacterial cells and biosurfactant production appear, which are the main factors of change in wettability. Alteration in wettability towards hydrophilicity expands hydrogen bonds on the surface, thus improving asphaltene adsorption due to polar interaction. Asphaltene 1 changed the contact angle of dolomite from 53.85° to 90.51° and asphaltene 2 from 53.85° to 100.41°. However, both strains of bacteria caused a strong water-wetting effect on the dolomite rock samples. The influence of moderate thermophilic bacteria on surface wettability is more significant than that of mesophilic bacteria, which may be caused by the high protein content of these bacteria, which expands hydrogen bonding with the surface. Adsorption of asphaltenes on dolomite rocks previously aged with bacteria showed that the wetted rock samples retained their water-wet state. This study highlights the dual impact of the used microorganisms. On one hand, they significantly reduce contact angles and shift wettability towards a strongly water-wet condition, a crucial positive factor for MEOR. On the other hand, these microorganisms can elevate the adsorption of asphaltenes on reservoir rock minerals, posing a potential challenge in the form of formation damage, particularly in low-permeability reservoirs.

Abstract Accurate prediction of fuel deposition during crude oil pyrolysis is pivotal for sustaining the combustion front and ensuring the effectiveness of in-situ combustion enhanced oil recovery (ISC EOR). Employing 2071 experimental TGA datasets from 13 diverse crude oil samples extracted from the literature, this study sought to precisely model crude oil pyrolysis. A suite of robust machine learning techniques, encompassing three black-box approaches (Categorical Gradient Boosting—CatBoost, Gaussian Process Regression—GPR, Extreme Gradient Boosting—XGBoost), and a white-box approach (Genetic Programming—GP), was employed to estimate crude oil residue at varying temperature intervals during TGA runs. Notably, the XGBoost model emerged as the most accurate, boasting a mean absolute percentage error (MAPE) of 0.7796% and a determination coefficient (R2) of 0.9999. Subsequently, the GPR, CatBoost, and GP models demonstrated commendable performance. The GP model, while displaying slightly higher error in comparison to the black-box models, yielded acceptable results and proved suitable for swift estimation of crude oil residue during pyrolysis. Furthermore, a sensitivity analysis was conducted to reveal the varying influence of input parameters on residual crude oil during pyrolysis. Among the inputs, temperature and asphaltenes were identified as the most influential factors in the crude oil pyrolysis process. Higher temperatures and oil °API gravity were associated with a negative impact, leading to a decrease in fuel deposition. On the other hand, increased values of asphaltenes, resins, and heating rates showed a positive impact, resulting in an increase in fuel deposition. These findings underscore the importance of precise modeling for fuel deposition during crude oil pyrolysis, offering insights that can significantly benefit ISC EOR practices.

Abstract Interfacial tension (IFT) between surfactants and hydrocarbon is one of the important parameters in petroleum engineering to have a successful enhanced oil recovery (EOR) operation. Measuring IFT in the laboratory is time-consuming and costly. Since, the accurate estimation of IFT is of paramount significance, modeling with advanced intelligent techniques has been used as a proper alternative in recent years. In this study, the IFT values between surfactants and hydrocarbon were predicted using tree-based machine learning algorithms. Decision tree (DT), extra trees (ET), and gradient boosted regression trees (GBRT) were used to predict this parameter. For this purpose, 390 experimental data collected from previous studies were used to implement intelligent models. Temperature, normal alkane molecular weight, surfactant concentration, hydrophilic–lipophilic balance (HLB), and phase inversion temperature (PIT) were selected as inputs of models and independent variables. Also, the IFT between the surfactant solution and normal alkanes was selected as the output of the models and the dependent variable. Moreover, the implemented models were evaluated using statistical analyses and applied graphical methods. The results showed that DT, ET, and GBRT could predict the data with average absolute relative error values of 4.12%, 3.52%, and 2.71%, respectively. The R-squared of all implementation models is higher than 0.98, and for the best model, GBRT, it is 0.9939. Furthermore, sensitivity analysis using the Pearson approach was utilized to detect correlation coefficients of the input parameters. Based on this technique, the results of sensitivity analysis demonstrated that PIT, surfactant concentration, and HLB had the greatest effect on IFT, respectively. Finally, GBRT was statistically credited by the Leverage approach.

Abstract Asphaltene precipitation and its adsorption on different surfaces are challenging topics in the upstream and downstream of the oil industries and the environment. In this research, the phenomenon of asphaltenes adsorption in the presence and absence of water on the surface of magnetite, hematite, calcite, and dolomite nanoparticles (NPs) was investigated. Five asphaltenes of different origins, four NPs as adsorbents and Persian Gulf water were used for three-phase (asphaltene/toluene solution + NPs + water) experiments. Characterization of asphaltenes and NPs was performed using Fourier transform infrared spectroscopic (FTIR), dynamic light scattering (DLS), elemental analysis, and field emission scanning electron microscopy (FESEM). Adsorption experiments were performed in two- (asphaltene/toluene solution + NPs) and three-phase systems. The results showed that the most effective parameters for asphaltene adsorption onto these NPs are the asphaltene composition, namely nitrogen content, and the aromaticity of asphaltenes. The significant effects of these parameters were also confirmed by the relevancy factor function as a sensitivity analysis. In the competition of asphaltene adsorption capacity by NPs, iron oxide NPs had the highest adsorption (Magnetite NPs > Hematite NPs > Calcite NPs > Dolomite NPs). From the results of the experiments in the presence of water phase, it could be pointed out that the asphaltenes adsorption onto the NPs was accompanied by a decrease compared to the experiments in the absence of water. The modeling also showed that physical adsorption has a significant contribution to the asphaltenes adsorption on the surface of iron oxides and lime NPs. The results of this research can assist in a better understanding of the asphaltene adsorption phenomenon and the role of iron oxide and lime NPs in solving this problem.

Asphaltene precipitation/deposition is one of the main problems in petroleum industry at various stages. In this study, various controlling factors on the adsorption of different types of asphaltene on the surface of dolomite of different size, as one of the most abundant minerals in the reservoir is investigated. First, asphaltenes and adsorbents (dolomite) properties were analyzed via several analytical techniques. Then, adsorption of four asphaltene samples of various origins and chemical composition on dolomite at micron and nano size were carried out in both static and dynamic modes and results were examined following the adsorption process. Based on the static tests, the adsorption amount of asphaltenes on nano- and micro-dolomite was measured 3.52–11 mg/m2 and 0.82–2.74 mg/m2, respectively. The results revealed that the most significant parameters that would affect the asphaltene adsorption on dolomite as an adsorbent are the total nitrogen and sulfur (N + S) content and the aromaticity of the asphaltenes. Moreover, as the flow rate was increased three times in the dynamic experiments, a decrease of 15–25% in asphaltene adsorption was observed, which was attributed to physisorption of asphaltene on dolomite or due to higher flow velocities. Collectively, the findings of this study can enable us to better understand the mechanisms of asphaltene adsorption/precipitation on dolomite and lead to better management of oil production from dolomitic reservoirs.

Abstract Knowledge of the solubilities of hydrocarbon components of natural gas in pure water and aqueous electrolyte solutions is important in terms of engineering designs and environmental aspects. In the current work, six machine-learning algorithms, namely Random Forest, Extra Tree, adaptive boosting support vector regression (AdaBoost-SVR), Decision Tree, group method of data handling (GMDH), and genetic programming (GP) were proposed for estimating the solubility of pure and mixture of methane, ethane, propane, and n-butane gases in pure water and aqueous electrolyte systems. To this end, a huge database of hydrocarbon gases solubility (1836 experimental data points) was prepared over extensive ranges of operating temperature (273–637 K) and pressure (0.051–113.27 MPa). Two different approaches including eight and five inputs were adopted for modeling. Moreover, three famous equations of state (EOSs), namely Peng-Robinson (PR), Valderrama modification of the Patel–Teja (VPT), and Soave–Redlich–Kwong (SRK) were used in comparison with machine-learning models. The AdaBoost-SVR models developed with eight and five inputs outperform the other models proposed in this study, EOSs, and available intelligence models in predicting the solubility of mixtures or/and pure hydrocarbon gases in pure water and aqueous electrolyte systems up to high-pressure and high-temperature conditions having average absolute relative error values of 10.65% and 12.02%, respectively, along with determination coefficient of 0.9999. Among the EOSs, VPT, SRK, and PR were ranked in terms of good predictions, respectively. Also, the two mathematical correlations developed with GP and GMDH had satisfactory results and can provide accurate and quick estimates. According to sensitivity analysis, the temperature and pressure had the greatest effect on hydrocarbon gases’ solubility. Additionally, increasing the ionic strength of the solution and the pseudo-critical temperature of the gas mixture decreases the solubilities of hydrocarbon gases in aqueous electrolyte systems. Eventually, the Leverage approach has revealed the validity of the hydrocarbon solubility databank and the high credit of the AdaBoost-SVR models in estimating the solubilities of hydrocarbon gases in aqueous solutions.

Abstract Determining the solubility of non-hydrocarbon gases such as carbon dioxide (CO2) and nitrogen (N2) in water and brine is one of the most controversial challenges in the oil and chemical industries. Although many researches have been conducted on solubility of gases in brine and water, very few researches investigated the solubility of power plant flue gases (CO2–N2 mixtures) in aqueous solutions. In this study, using six intelligent models, including Random Forest, Decision Tree (DT), Gradient Boosting-Decision Tree (GB-DT), Adaptive Boosting-Decision Tree (AdaBoost-DT), Adaptive Boosting-Support Vector Regression (AdaBoost-SVR), and Gradient Boosting-Support Vector Regression (GB-SVR), the solubility of CO2–N2 mixtures in water and brine solutions was predicted, and the results were compared with four equations of state (EOSs), including Peng–Robinson (PR), Soave–Redlich–Kwong (SRK), Valderrama–Patel–Teja (VPT), and Perturbed-Chain Statistical Associating Fluid Theory (PC-SAFT). The results indicate that the Random Forest model with an average absolute percent relative error (AAPRE) value of 2.8% has the best predictions. The GB-SVR and DT models also have good precision with AAPRE values of 6.43% and 7.41%, respectively. For solubility of CO2 present in gaseous mixtures in aqueous systems, the PC-SAFT model, and for solubility of N2, the VPT EOS had the best results among the EOSs. Also, the sensitivity analysis of input parameters showed that increasing the mole percent of CO2 in gaseous phase, temperature, pressure, and decreasing the ionic strength increase the solubility of CO2–N2 mixture in water and brine solutions. Another significant issue is that increasing the salinity of brine also has a subtractive effect on the solubility of CO2–N2 mixture. Finally, the Leverage method proved that the actual data are of excellent quality and the Random Forest approach is quite reliable for determining the solubility of the CO2–N2 gas mixtures in aqueous systems.

Abstract Accurate prediction of the solubility of gases in hydrocarbons is a crucial factor in designing enhanced oil recovery (EOR) operations by gas injection as well as separation, and chemical reaction processes in a petroleum refinery. In this work, nitrogen (N2) solubility in normal alkanes as the major constituents of crude oil was modeled using five representative machine learning (ML) models namely gradient boosting with categorical features support (CatBoost), random forest, light gradient boosting machine (LightGBM), k-nearest neighbors (k-NN), and extreme gradient boosting (XGBoost). A large solubility databank containing 1982 data points was utilized to establish the models for predicting N2 solubility in normal alkanes as a function of pressure, temperature, and molecular weight of normal alkanes over broad ranges of operating pressure (0.0212–69.12 MPa) and temperature (91–703 K). The molecular weight range of normal alkanes was from 16 to 507 g/mol. Also, five equations of state (EOSs) including Redlich–Kwong (RK), Soave–Redlich–Kwong (SRK), Zudkevitch–Joffe (ZJ), Peng–Robinson (PR), and perturbed-chain statistical associating fluid theory (PC-SAFT) were used comparatively with the ML models to estimate N2 solubility in normal alkanes. Results revealed that the CatBoost model is the most precise model in this work with a root mean square error of 0.0147 and coefficient of determination of 0.9943. ZJ EOS also provided the best estimates for the N2 solubility in normal alkanes among the EOSs. Lastly, the results of relevancy factor analysis indicated that pressure has the greatest influence on N2 solubility in normal alkanes and the N2 solubility increases with increasing the molecular weight of normal alkanes.

Abstract Due to industrial development, designing and optimal operation of processes in chemical and petroleum processing plants require accurate estimation of the hydrogen solubility in various hydrocarbons. Equations of state (EOSs) are limited in accurately predicting hydrogen solubility, especially at high-pressure or/and high-temperature conditions, which may lead to energy waste and a potential safety hazard in plants. In this paper, five robust machine learning models including extreme gradient boosting (XGBoost), adaptive boosting support vector regression (AdaBoost-SVR), gradient boosting with categorical features support (CatBoost), light gradient boosting machine (LightGBM), and multi-layer perceptron (MLP) optimized by Levenberg–Marquardt (LM) algorithm were implemented for estimating the hydrogen solubility in hydrocarbons. To this end, a databank including 919 experimental data points of hydrogen solubility in 26 various hydrocarbons was gathered from 48 different systems in a broad range of operating temperatures (213–623 K) and pressures (0.1–25.5 MPa). The hydrocarbons are from six different families including alkane, alkene, cycloalkane, aromatic, polycyclic aromatic, and terpene. The carbon number of hydrocarbons is ranging from 4 to 46 corresponding to a molecular weight range of 58.12–647.2 g/mol. Molecular weight, critical pressure, and critical temperature of solvents along with pressure and temperature operating conditions were selected as input parameters to the models. The XGBoost model best fits all the experimental solubility data with a root mean square error (RMSE) of 0.0007 and an average absolute percent relative error (AAPRE) of 1.81%. Also, the proposed models for estimating the solubility of hydrogen in hydrocarbons were compared with five EOSs including Soave–Redlich–Kwong (SRK), Peng–Robinson (PR), Redlich–Kwong (RK), Zudkevitch–Joffe (ZJ), and perturbed-chain statistical associating fluid theory (PC-SAFT). The XGBoost model introduced in this study is a promising model that can be applied as an efficient estimator for hydrogen solubility in various hydrocarbons and is capable of being utilized in the chemical and petroleum industries.

Objective: There has been little effort to conduct systematic reviews or meta-analyses of the available literature to find global prevalence rates of conduct disorder and analyze the sources of heterogeneity. Method: We searched multiple databases, including Web of Science, PubMed, Scopus, and Google Scholar to identify cross-sectional studies with random or nonrandom sampling to assess the global prevalence of conduct disorder in children and adolescents aged under 18 in the general or school-based populations. Quality assessment and data extraction were independently carried out by two authors. Subgroup analysis was used to find the potential sources of heterogeneity. Results: We reached 50 studies, incorporating 186,056 children and adolescents from 35 countries. The total prevalence of conduct disorder was 8% (CI: 7-9%; I2: 99.77%), including 7% in females (CI: 4-9%; I2: 99.56%) and 11% in males (CI: 7-15%; I2: 99.74%). The results of subgroup analysis showed that total heterogeneity could be explained by measurement tools. When diagnostic interviews such as the Kiddie Schedule for Affective Disorders and Schizophrenia for School-Age Children—Present and Lifetime Version (K-SADS-PL) and Development and Well-Being Assessment (DAWBA) were employed, the pooled prevalence rates for conduct disorder were 0.4% and 0.7%, respectively, and heterogeneity decreased. However, the use of the screening tools such as the Strengths and Difficulties Questionnaire (SDQ)-parent or teacher report and SDQ-self report increased the pooled prevalence of conduct disorder to 10% and 16% respectively. Conclusion: The prevalence of conduct disorder in the epidemiological studies should be estimated by employing the diagnostic interviews to reach accurately assessments.

Today, airplanes have become indispensable parts of our lives. They are controlled by the pilots trained in aviation field. But the question is whether physiology of human being is capable of adjusting his visual ability and brain capacity to process the images and information necessary to survive in the air. In this paper, we have tried to address some of the weaknesses of human visual system from a physiological and psychological point of view, and ultimately, to find a solution to reduce visual illusion. Studying various scientific resources and interviews with experts involved in the field of air accidents; visual illusion in the statistical population of the pilots who have experienced an air accident, was investigated. Final analysis of the results of this research indicated that to reduce the risk of visual illusion, a good deal of knowledge about the physiology of human visual system as well as the related training systems designed to reduce the cognitive illusions is required on the part of the air crew, so that they can adjust themselves to the flight unconventional environment and hopefully the air accidents caused by the visual illusions are minimized.

Objective: In Western countries, the prevalence of childhood trauma (CT) ranges from 15 to 25%. CT might be indirectly associated with lower parental socioeconomic status and educational attainments. The aims of this cross-sectional study were fourfold: to assess prevalence of CT in a large sample of Iranian children; to compare the Iranian prevalence rates with those of Western countries; to explore gender-specific patterns; and to explore possible socioeconomic predictors. Method: The sample comprised 608 children (mean age 11.49 years, 51.5% females). All completed the Farsi version of the Trauma Symptoms Checklist for Children. Additionally, parents reported on their current employment status and highest educational level. Results: Trauma symptoms were reported by 20 of 295 boys and 23 of 313 girls. The overall prevalence was 7.1%. Child-reported trauma symptoms were not associated with parents’ socioeconomic status or highest educational level. Compared to prevalence findings from U.S. national surveys (ranging from 15-25% of children and adolescents), the prevalence among 11- and 12-year-olds in the present study was considerably lower. Conclusions: The overall prevalence of reported trauma symptoms among a large sample of Iranian children was unrelated to parents’ socioeconomic status, and was lower than that reported in U.S. surveys.

Background and Objective: Conduct disorder is characterized with aggressive behaviors, deceitfulness or theft, destruction of property and serious violations of rules, prior to age 18 years. Attachment to God is a relationship with God that reveals aspects of individual thought. Secure attachment is associated with an increased ability to forgive. Various studies indicated the association between insecure attachment and delinquency and criminal behavior. The aim of this study was to evaluate the attachment to God and forgiveness in adolescents with conduct disorder at Tehran reformatory.Materials and Methods: This study is a cross-sectional study. The attachment to God and Transgression-Related Interpersonal Motivations Scale--12-Item Form (TRIM-12), were completed by 60 adolescents between 14 -18 years old with conduct disorder, with or without substance abuse disorders, and ADHD, at Tehran reformatory. Descriptive statistics and linear regression methods was used to analyze the data in SPSS-16.Results: The results showed that anxiety and avoidant attachments to God and avoidance and revenge motivations in adolescents with conduct disorder are high. A history of addiction, criminality, and mental disorders among family members predicted increasing avoidant attachment to God among this group of adolescents in the univariate model. Also, parental divorce and attention deficit-hyperactivity variables predicted increased revenge motivation in the univariate model, and unemployed father predicted avoidance motivation, in the multivariate model.Conclusion: There is a defect in the ability to forgive in adolescents with insecure attachment and conduct disorder, there are basic requirements for the design of interventions and spiritual treatment programs specifically for this group of adolescents.

A recent randomized clinical trial showed buspirone efficacy in the treatment of attention-deficit/hyperactivity disorder (ADHD) in children. However, results from a recent multi-site controlled clinical trial of transdermal buspirone failed to separate it from placebo in a large sample of children with ADHD. Therefore, due to these inconsistent findings, this study was designed to assess the efficacy of buspirone in the treatment of children with ADHD compared to methylphenidate in a double blind randomized clinical trial. Forty outpatients with a DSM-IV-TR diagnosis of ADHD were study population of this trial. Subjects were recruited from an outpatient child and adolescent clinic for a 6 week double blind, randomized clinical trial. All study subjects were randomly assigned to receive treatment using tablet of buspirone at a dose of 20-30 mg/day depending on weight (20 mg/day for < 30kg and 30 mg/day for > 30kg) (group 1) or methylphenidate at a dose of 20-30 mg/day depending on weight (20 mg/day for < 30kg and 30 mg/day for > 30kg (group 2) for a 6 week double blind, randomized clinical trial. The principal measure of outcome was the Teacher and Parent ADHD Rating Scale IV. Patients were assessed at baseline and at 21 and 42 days after the medication started. Significant differences were observed between the two groups on the Parent and Teacher Rating Scale scores. The changes at the endpoint compared to baseline were: -8.95±8.73 (mean±SD) and -15.60±7.81 (mean±SD) for buspirone and methyphenidate, for Parent ADHD Rating Scale. The changes at the endpoint compared to baseline were: -9.80 ±7.06 (mean±SD) and -22.40±9.90 (mean±SD) for buspirone and methyphenidate, respectively for Teacher ADHD Rating Scale. The difference between the buspirone and methylphenidate groups in the frequency of side effects was not significant except for decreased appetite, headache and insomnia that were observed more frequently in the methylphenidate group. The results of this study suggest that administration of buspirone was less effective than methylphenidate in the treatment of ADHD.

"n Objective: "n "nThis study surveys children and their parents' perceptions, and their treatment preference of significant/bothersome symptoms in children with Tourette syndrome. "nMethods: "nThirty five children and adolescents who referred to an out-patient clinic of a Child Psychiatry Clinic were selected as subjects for this study. The children and their parents were interviewed about their perception of significant/bothersome symptoms of motor tics, vocal tics, learning difficulties, attention deficit disorder, hyperactivity, obsessions, compulsions, and rage attacks. "nResults: "nAbout two thirds of the subjects had symptom of rage. Inattentiveness and hyperactivity were observed in more than half of the children. There was a statistically significant difference between parents and their children in frequency of motor tics and rage attacks . Children reported the necessity for controlling and management of these symptoms less than their parents. "nDiscussion: "nThe rates of motor, vocal tics and rage attacks in the Iranian sample are similar to other studies. Rage attack is one of the most common significant/bothersome symptoms reported that should be treated. While motor tics were not rated among the most common features that should be treated in a study in Canada, it was the most common significant/bothersome symptom in Iran. Parents perceive motor tics and rage attacks as more significant/bothersome symptoms compared to children.

"nObjective: The aim of the present study is to perform a systematic review of studies that investigated the prevalence of any kind of mental disorders in high school students in Iran. "nMethod: A broad search was conducted in MEDLINE/PubMed, ISI web of Science, PsychINFO, CINAHL, EMBASE, and three Iranian databases, including IranPsych, IranMedex, and Scientific Information Database (SID). To cover studies not published, we did a hand searching of all theses, reports and congresses' abstract booklets which were available in IranPsych. Then, we included the original studies which reported the prevalence of any kind of mental disorders in high school students; data extraction was performed with two researchers for each document. "nResults: Sixteen studies were finally included, representing 19 estimates of mental disorders in high school students, using diagnostic or screening instruments. The prevalence rate of any mental disorder reported by two studies using diagnostic instruments was equal to 16.6% and 4.34%. The median of prevalence rates of mental disorders reported in studies using screening instruments was 34.4%. There was a significant heterogeneity between the studies. "nConclusion: Prevalence rates of mental disorders were reported in a wide range in high school students of Iran. More studies with improved quality are needed in this field.

Objective: This study aimed to describe the characteristics of Iran’s child and adolescent mental health research published from 1973 to 2002. Method: The articles were drawn from IranPsych, which is a national database of published research in mental health and related fields. This database gathers scientific papers on psychiatry, psychology, and neuroscience published in both national and international journals. Bibliometric data, general scientific areas of research, specific subject topics and research design, were extracted from aticles. Egypt was selected as one of the Middle East countries that has some similarities with Iran. Iranian and Egyptian child and adolescent mental health research articles indexed in ISI 1994 to 2004 also were compared. Results: From a total of 883 articles, 9% appeared in international journals. There was a marked increase in the number of publications over time, especially in the last 5 years. Psychology and clinical research constituted 70%, and 32% of articles, respectively; followed by epidemiology 11%, health service research 2% and neuroscience 0.7%. During the first five years of the mentioned period Iran fell behind Egypt in terms of international child and adolescent mental health research articles; however, in the last five years, Iranian articles numbered 2.75 times the Egyptian’s. Conclusion: This study showed that it is feasible to outline the status of research activity of a country by using databases that cover publications and would yield several remarkable findings that could be used as a basis for policy making and strategy development in child and adolescent mental health research.

Objective:To study the prevalence and demographic characteristics of mood disorders among Iranian adults. Method: In this cross-sectional population-based epidemiological study (age > 18) in Iran, 25180 individuals were selected through a randomized cluster sampling method for a diagnosis using the Schedule for Affective Disorders and Schizophrenia (SADS). They were then interviewed at home by 250 trained clinical psychologists. Results: The estimated lifetime prevalence of Major Depressive Disorder (MDD) and Minor Depressive Disorder (mDD) were 3.1% and 0.3% respectively. Also, the estimated lifetime prevalence of Bipolar Mood disorder (BMD) type I and type II were 0.1% and 0.7% respectively. The current prevalence of MDD, mDD, BMD-I, and BMD-II were 1.8%, 0.2%, 0.04%, and 0.3% respectively. Mood disorders were associated with female gender, lower education, being married, being middle-aged, living in cities, and not being a homemaker. Conclusion: The prevalence of mood disorders was lower among Iranian adults than reported in Western studies, and a number of demographic associations differed from those reported in Western studies. Important cultural differences in the nature or manifestation of depression are implied by these results.

Objective: We aimed to conduct a review to investigate the prevalence of anxiety disorders among Iranian children and adolescents.We systematically reviewed the literature up to June 2014. Method: We searched three Persian databases (Magiran, IranMedex and SID) and three English databases: PubMed, Scopus and PsycINFO. All original studies that investigated the current prevalence of anxiety in a sample of Iranian children and adolescents were entered into the study. All studies conducted on special samples or in special settings were excluded. By searching English databases, we obtained 124 original studies. After removing duplicate papers, 120 articles remained. In the next step, we screened the articles based on their title. In sum, 95 Persian and English articles had relevant titles. After screening based on the abstract and full text, 26 studies remained. After screening based on the full text, all selected studies were qualitatively assessed by two evaluators separately. Result: Twenty five studies were eligible and reported different types of anxiety disorders (i.e., generalized anxiety, separation anxiety, obsessive-compulsive disorder, phobias and panic disorder). The samples varied from 81 to 2996 among studies and their age range was 5 to 18 years. These studies were conducted in different cities of Iran. SCL-90 is a frequently used questionnaire. All anxiety disorders were mostly investigated with the prevalence rates ranging from 6.8% in Saravan to 85% in Bandar Abbas. OCD was the second common study with prevalence rates ranging from 1% in Tabriz to 11.9% in Gorgan. Conclusion: Our findings revealed considerable amount of anxiety disorder among Iranian children and adolescents. Given the fact that anxiety disorder has negative effects on the well-being and function of individuals and can lead to severe problems, this disorder should be considered in mental health programs designed for children and adolescents.

Olanzapine is the frequently prescribed drug in children and adolescents with bipolar disorder, but unfortunately it has metabolic side-effects. On the other hand, in a number of melatonin studies on sleep cycle, regulation of metabolic abnormalities has been reported. Therefore, we aimed to study effects of melatonin in reducing metabolic side-effects of olanzapine in 11-17 year-old patients with bipolar disorder. Seventy-seven 11-17 year-old outpatients entered into the study after their initial diagnosis of bipolar mood disorder by a psychiatrist. After assessing inclusion and exclusion criteria, 48 patients consented to participate in the study. Of this number, 24 patients were allocated to olanzapine, lithium carbonate, and melatonin and 24 patients were allocated to olanzapine, lithium carbonate, and placebo. Young mania rating scale was performed at baseline. Before treatment initiation and at sixth and twelfth weeks after treatment, Lipid profile, Fasting Blood Sugar (FBS), Systolic Blood Pressure (SBP) and Diastolic Blood Pressure (DBP) were measured. ANOVA with repeated measure and independent sample t-test were used for data analysis. Nineteen patients in each group completed the study and yielded data for analysis. ANOVA with repeated measure showed that FBS and Triglyceride (TG) (especially in boys) demonstrated greater increase in the placebo group compared to the melatonin group but the differences were not statistically significant. Melatonin significantly inhibited the rise in Total Cholesterol levels compared to placebo (P=0.032). Mean SBP rose more slowly in the melatonin group (1.05mmHg) compared to placebo (6.36 mmHg) (P=0.023). The trends in DBP did not show any significant pattern. Administration of melatonin along with olanzapine and lithium carbonate could significantly inhibit the rise in cholesterol level and SBP compared to placebo. The effect of melatonin on TG was more obvious in boys. Melatonin was more effective in prevention of SBP rise.

A recent randomized clinical trial showed buspirone efficacy in the treatment of attention-deficit/hyperactivity disorder (ADHD) in children. However, results from a recent multi-site controlled clinical trial of transdermal buspirone failed to separate it from placebo in a large sample of children with ADHD. Therefore, due to these inconsistent findings, this study was designed to assess the efficacy of buspirone in the treatment of children with ADHD compared to methylphenidate in a double blind randomized clinical trial. Forty outpatients with a DSM-IV-TR diagnosis of ADHD were study population of this trial. Subjects were recruited from an outpatient child and adolescent clinic for a 6 week double blind, randomized clinical trial. All study subjects were randomly assigned to receive treatment using tablet of buspirone at a dose of 20-30 mg/day depending on weight (20 mg/day for < 30kg and 30 mg/day for > 30kg) (group 1) or methylphenidate at a dose of 20-30 mg/day depending on weight (20 mg/day for < 30kg and 30 mg/day for > 30kg (group 2) for a 6 week double blind, randomized clinical trial. The principal measure of outcome was the Teacher and Parent ADHD Rating Scale IV. Patients were assessed at baseline and at 21 and 42 days after the medication started. Significant differences were observed between the two groups on the Parent and Teacher Rating Scale scores. The changes at the endpoint compared to baseline were: -8.95±8.73 (mean±SD) and -15.60±7.81 (mean±SD) for buspirone and methyphenidate, for Parent ADHD Rating Scale. The changes at the endpoint compared to baseline were: -9.80 ±7.06 (mean±SD) and -22.40±9.90 (mean±SD) for buspirone and methyphenidate, respectively for Teacher ADHD Rating Scale. The difference between the buspirone and methylphenidate groups in the frequency of side effects was not significant except for decreased appetite, headache and insomnia that were observed more frequently in the methylphenidate group. The results of this study suggest that administration of buspirone was less effective than methylphenidate in the treatment of ADHD.

Background: SNAP-IV rating scale developed by Swanson, Nolan and Pelham to diagnose Attention Deficit Hyperactivity Disorder (ADHD). The aim of this study was determination of psychometrics specifications of this scale. Methods: This was a methodological, applied and validity assessment study. One thousand students at 7 to 12 years of age in primary school in Tehran city were selected by cluster sampling. Then the students' teachers were asked to complete rating scale to consider behavior of their student. Thirty staff members of sample group were retested with SNAP-IV and was interviewed with DSM-IV. Data were analyzed by factor analysis, Pearson correlation coefficient, Kolmogrov-Smirnov and Behrens-Fisher t tests. Findings: Criterion validity was 55%. Factor analysis was detected 2 factors that explain 71% of the total variance. Reliability coefficient by test-retest was 88%, internal consistency coefficient was 95%, and Split-half coefficient was 73%. Cut-off point in scale and subscale was 2.08, 2.10, and 2.37 respectively. Conclusion: The SNAP-IV rating scales have fit psychometrics characters. Therefore, it is useable in screen study and therapeutic conditioning. Key words: Rating scale, Attention deficit-hyperactivity disorder, Reliability, Validity, Cut-off point.

Objective: This study surveys children and their parent's perceptions, and their treatment preference of significant/bothersome symptoms in children with Tourette syndrome. Methods: Thirty five children and adolescents who referred to an out-patient clinic of a Child Psychiatry Clinic were selected as subjects for this study. The children and their parents were interviewed about their perception of significant/bothersome symptoms of motor tics, vocal tics, learning difficulties, attention deficit disorder, hyperactivity, obsessions, compulsions, and rage attacks. Results: About two thirds of the subjects had symptom of rage. Inattentiveness and hyperactivity were observed in more than half of the children. There was a statistically significant difference between parents and their children in frequency of motor tics and rage attacks. Children reported the necessity for controlling and management of these symptoms less than their parents. Discussion: The rates of motor, vocal tics and rage attacks in the Iranian sample are similar to other studies. Rage attack is one of the most common significant/bothersome symptoms reported that should be treated. While motor tics were not rated among the most common features that should be treated in a study in Canada, it was the most common significant/bothersome symptom in Iran. Parents perceive motor tics and rage attacks as more significant/bothersome symptoms compared to children.