Validity and reliability of the Iranian version of the Pediatric Quality of Life Inventory™ 4.0 (PedsQL™) Generic Core Scales in children

<p>Abstract</p> <p>Background</p> <p>This study aimed to investigate the reliability and validity of the Iranian version of the Pediatric Quality of Life Inventory™ 4.0 (PedsQL™ 4.0) Generic Core Scales in children.</p> <p>Methods</p> <p>A standard forward and backward translation procedure was used to translate the US English version of the PedsQL™ 4.0 Generic Core Scales for children into the Iranian language (Persian). The Iranian version of the PedsQL™ 4.0 Generic Core Scales was completed by 503 healthy and 22 chronically ill children aged 8-12 years and their parents. The reliability was evaluated using internal consistency. Known-groups discriminant comparisons were made, and exploratory factor analysis (EFA) and confirmatory factor analysis (CFA) were conducted.</p> <p>Results</p> <p>The internal consistency, as measured by Cronbach's alpha coefficients, exceeded the minimum reliability standard of 0.70. All monotrait-multimethod correlations were higher than multitrait-multimethod correlations. The intraclass correlation coefficients (ICC) between the children self-report and parent proxy-reports showed moderate to high agreement. Exploratory factor analysis extracted six factors from the PedsQL™ 4.0 for both self and proxy reports, accounting for 47.9% and 54.8% of total variance, respectively. The results of the confirmatory factor analysis for 6-factor models for both self-report and proxy-report indicated acceptable fit for the proposed models. Regarding health status, as hypothesized from previous studies, healthy children reported significantly higher health-related quality of life than those with chronic illnesses.</p> <p>Conclusions</p> <p>The findings support the initial reliability and validity of the Iranian version of the PedsQL™ 4.0 as a generic instrument to measure health-related quality of life of children in Iran.</p

The Effectiveness of Matrix Interventions in Reducing the Difficulty in Cognitive Emotion Regulation and Craving in Methamphetamine-Dependent Patients

Background:&nbsp; Craving is a persistent factor in addictive behaviors. The aim of study was to investigate the effectiveness of matrix interventions in reducing the difficulty in cognitive emotion regulation and craving in methamphetamine-dependent patients.Methods: &nbsp;The research method was experimental and the research design was pre and posttest with the control group. The statistical population of the study consisted of all methamphetamine-dependent patients who visited the Golestan hospital of Ahvaz in 2019. Among them, 40 ones were selected by a purposive sampling method and were randomly classified into experimental and control groups (n = 20 per group). The Cognitive Emotion Regulation and Craving Questionnaires were used for data collection. The experimental group received the matrix program (24 fifty-minute sessions), but the control group did not receive any intervention. Data were analyzed by the analysis of covariance (ANCOVA). Significant level was set at 0.05.Results:&nbsp;The results indicated that the matrix program was effective in reducing the difficulty in cognitive emotion regulation (F = 13.483, Pvalue &lt; 0.001). The research results also indicated that the matrix program was effective in reducing craving in methamphetaminedependent patients (F = 60.716, Pvalue &lt; 0.001).Conclusions:&nbsp;According to results, the therapy could be used to reduce the difficulty in cognitive emotion regulation and craving in methamphetamine-dependent patients. Keywords:&nbsp;Matrix model, Cognitive emotion regulation, Craving, Methamphetamine

The Effectiveness of Matrix Interventions in Reducing the Difficulty in Cognitive Emotion Regulation and Craving in Methamphetamine-Dependent Patients

Background:&nbsp; Craving is a persistent factor in addictive behaviors. The aim of study was to investigate the effectiveness of matrix interventions in reducing the difficulty in cognitive emotion regulation and craving in methamphetamine-dependent patients.Methods: &nbsp;The research method was experimental and the research design was pre and posttest with the control group. The statistical population of the study consisted of all methamphetamine-dependent patients who visited the Golestan hospital of Ahvaz in 2019. Among them, 40 ones were selected by a purposive sampling method and were randomly classified into experimental and control groups (n = 20 per group). The Cognitive Emotion Regulation and Craving Questionnaires were used for data collection. The experimental group received the matrix program (24 fifty-minute sessions), but the control group did not receive any intervention. Data were analyzed by the analysis of covariance (ANCOVA). Significant level was set at 0.05.Results:&nbsp;The results indicated that the matrix program was effective in reducing the difficulty in cognitive emotion regulation (F = 13.483, Pvalue &lt; 0.001). The research results also indicated that the matrix program was effective in reducing craving in methamphetaminedependent patients (F = 60.716, Pvalue &lt; 0.001).Conclusions:&nbsp;According to results, the therapy could be used to reduce the difficulty in cognitive emotion regulation and craving in methamphetamine-dependent patients. Keywords:&nbsp;Matrix model, Cognitive emotion regulation, Craving, Methamphetamine

Coping strategies in children of parents deceased from cancer and children of parents healed from cancer

This study aimed to compare coping strategies in children of parents deceased from cancer and children of parents healed from cancer in the city of Shiraz, Iran. One-hundred and fifteen people [58 children of parents healed from cancer and 57 children of parents deceased from cancer] were recruited in this study via a convenience sampling method. Coping Inventory for Stressful Situations was used to measure different types of coping strategies [task-oriented coping strategy, emotion-oriented coping strategy, and avoidance coping strategy]. The results showed that the children of parents healed from cancer used task-oriented coping strategy significantly more than children of parents deceased from cancer. Moreover, the results showed that the use of emotion-oriented coping strategy in children of parents deceased from cancer was significantly more than children of parents healed from cancer. No significant difference was observed between the two groups in the use of avoidance coping. This study highlights the importance of coping strategies in families with a cancer parent which demands the importance of teaching appropriate coping strategies in order to reduce the adverse consequence of cancer in the family

Photothermal conversion efficiency of nanofluids: An experimental and numerical study

This work investigated experimentally the photothermal conversion efficiency (PTE) of gold nanofluids in a cylindrical tube under natural solar irradiation conditions, which was also compared with a developed 3-D numerical model. The PTE of gold nanofluids was found to be much higher than that of pure water, and increased non-linearly with the nanoparticle concentration, reaching 76.0% at a concentration of 5.8 ppm. Significant non-uniform temperature distribution was identified both experimentally and numerically, and a large uncertainty can be produced in the PTE calculation by using only one-point temperature measurement. A mathematical model was also established to calculate the solar absorption efficiency without knowing the temperature field within the nanofluids, which can be used to predict the theoretical PTE for nanofluids based on their optical properties only

Replication of TCF7L2 rs7903146 association with type 2 diabetes in an Iranian population

The transcription factor 7-like 2 gene (TCF7L2) rs7903146 T allele is constantly associated with Type 2 diabetes in various populations and ethnic groups. Nevertheless, this has not been observed in two studies involving Arab populations. The aim of the present study was to investigate the association between TCF7L2 rs7903146 in an Iranian population. Type 2 diabetes patients (N = 258) and normal healthy control subjects (N = 168) from the same area, were examined. The ARMS- PCR (Amplification Refractory Mutation System) technique, subsequently validated by direct sequencing, was used for genotyping. Allele and genotype frequencies were significantly different between patients and controls TT vs. CT + CC [p 0.0081 OR 3.4 95%CI (1.27-11.9)] and T vs. C allele [p 0.02 OR 1.4 95%CI (1.03-1.9)]. Our data thus confirm the association between the rs7903146 T allele and T2D in an Iranian population, contrary to previous reports in Arab populations. This can possibly be attributed to differences in ethnic background or the effects of environmental factors

Prevalence and Correlates of Psychiatric Disorders in a National Survey of Iranian Children and Adolescents

Objective: Considering the impact of rapid sociocultural, political, and economical changes on societies and families, population-based surveys of mental disorders in different communities are needed to describe the magnitude of mental health problems and their disabling effects at the individual, familial, and societal levels. Method: A population-based cross sectional survey (IRCAP project) of 30 532 children and adolescents between 6 and 18 years was conducted in all provinces of Iran using a multistage cluster sampling method. Data were collected by 250 clinical psychologists trained to use the validated Persian version of the semi-structured diagnostic interview Kiddie-Schedule for Affective Disorders and Schizophrenia-PL (K-SADS-PL). Results: In this national epidemiological survey, 6209 out of 30 532 (22.31%) were diagnosed with at least one psychiatric disorder. The anxiety disorders (14.13%) and behavioral disorders (8.3%) had the highest prevalence, while eating disorders (0.13%) and psychotic symptoms (0.26%) had the lowest. The prevalence of psychiatric disorders was significantly lower in girls (OR = 0.85; 95% CI: 0.80-0.90), in those living in the rural area (OR = 0.80; 95% CI: 0.73-0.87), in those aged 15-18 years (OR = 0.92; 95% CI: 0.86-0.99), as well as that was significantly higher in those who had a parent suffering from mental disorders (OR = 1.96; 95% CI: 1.63-2.36 for mother and OR = 1.33; 95% CI: 1.07-1.66 for father) or physical illness (OR = 1.26; 95% CI: 1.17-1.35 for mother and OR = 1.19; 95% CI: 1.10-1.28 for father). Conclusion: About one fifth of Iranian children and adolescents suffer from at least one psychiatric disorder. Therefore, we should give a greater priority to promoting mental health and public health, provide more accessible services and trainings, and reduce barriers to accessing existing services

The Role of Stylistic Approach in Teaching Contemporary Adabi TextsThe Case of Elaik ya Valadi of Saad-e-Sabbah

The Role of Stylistic Approach in Teaching Contemporary Adabi Texts The Case of Elaik ya Valadi of Saad-e-Sabbah Nouroddin Parvin * Jahangir Amiri** Literary texts play a vital role in teaching Arabic language and literature since they are a tool for the growth of students’ literary taste and the improvement of their aural and written competences. Moreover, they increase the students' ability in understanding, analysis and review of literature. One way for realizing this role is the application of appropriate methods in teaching literature. Accordingly, the stylistic approach is one of the new methods of literary criticism and is based on analysis and recognition of literary works. It is one of the advanced methods of teaching Arabic texts and recognition of important characteristics of Arabic texts. This method does not suffer from the defects and deficiencies of the other methods which are applied for teaching Arabic texts. In this research, we will analyze the "eleik Ya Valadi" elegy of Saad e Sabbah, one of the pioneers of contemporary Arabic Rassa poetry at the morphological and syntactic levels with discretional-analytical methods and in stylistic context to analyze the role of stylistics in teaching Arabic texts. One of the most important results of this research is that it is one of the best methods in education of literacy, because of the fact that it considers the consistency and coordination between method of teaching and literacy, and also increases the students’ motivation for understanding and communication. Keywords: Contemporary literacy, teaching literacy, stylistics, Saad e sabba

An Intelligent Network Traffic Prediction Model Based on Ensemble Learning for Vehicular Ad-Hoc Networks

RÉSUMÉ: Les réseaux véhiculaires Ad Hoc « Vehicular Ad-hoc networks » (VANET) constituent une partie importante du système de transport intelligent (STI). Les VANET ont le potentiel d’améliorer la sécurité routière et la gestion du trafic en fournissant des applications de sécurité ou autre. Cependant, l’évolution des VANET vers l’Internet des Véhicules (IoV) pose certains défis dans les services VANET. Les applications VANET utilisent des communications dédiées à courte portée (DSRC) pour la communication entre les véhicules et les unités routières, connues sous le nom de communications basiques, comme les communications véhicule-à-véhicule (V2V) et véhicule-à-unité-routière (V2R). De plus, le développement de VANET vers IoV apporte de nouvelles exigences telles que véhicule-à-tout (V2X), soit une variété de modes de communications entre les noeuds du réseau comme le véhicule-à-véhicule (V2V), véhicule-à-infrastructure (V2I), infrastructure-à-infrastructure (I2I) et de véhicule-àpiéton (V2P). Le défi principal des services VANET est lié à la large quantité de données générées par les usagers, entraînant un trafic sur le réseau et, par conséquent, la réduction de la qualité de service (QoS) pour les services VANET. Dans ce cas, lorsqu’il s’agit d’applications de sécurité, cela peut coûter une vie humaine. Les techniques d’Intelligence Artificielle (IA) sont des solutions prometteuses pour résoudre les problèmes de trafic réseau dans les VANET. À cette fin, la prévision du trafic réseau est une tâche difficile qui peut aider les opérateurs de réseau à éviter les congestions et la réduction de la qualité de service dans les services de réseau véhiculaire. Ce domaine a attiré de nombreux chercheurs pour étudier la conception d’une solution d’IA pour les approches de prédiction du trafic réseau. Cependant, concevoir une méthode d’IA optimale capable d’obtenir une prédiction plus précise et stable reste un défi. Étant donné que chaque technique d’IA a ses propres limites et problèmes, si nous ne pouvons pas compter sur un seul modèle ML, cela imposera plus de problèmes. De plus, ces algorithmes doivent s’adapter aux nouvelles exigences de VANET comme la communication V2X. Par conséquent, il reste encore beaucoup de recherche inaboutie dans ce domaine. Dans cette thèse, la première méthode proposée pour la prédiction du trafic dans VANET considère un apprentissage automatique (ML) basé sur les ensembles comme une sous-catégorie de méthodes d’IA. Ainsi, un modèle doté de l’IA peut obtenir de meilleures performances et une prédiction plus précise et plus stable qu’un modèle ML unique. Le problème de prédiction est défini comme un problème de classification. Un ensemble de données VANET réelles est utilisé comme entrée pour le modèle Ensemble Learning (EL). De plus, en ce qui concerne l’importance de la qualité des données d’entrée, des méthodes de sélection de caractéristiques, notamment Boruta et LightGBM, sont utilisées pour extraire les attributs les plus utiles du V2V et du V2R en tant qu’ensemble de données fusionnées. La méthode proposée nommée STK-EBM est basée sur la stratégie d’empilement d’apprentissage d’ensembles qui comprend deux couches : la couche de base et la méta-couche. Dans la couche de base, l’algorithme Random Forest (RF), les modèles K-Nearest Neighbor (KNN) et XGBoost sont intégrés et sélectionnés en fonction de leur efficacité pour notre cible qui est la prévision du trafic dans le réseau. Les résultats de prédiction des modèles de couche de base sont agrégés par une régression logistique (LR) optimisée. L’analyse comparative des résultats de modèles ML uniques bien connus est effectuée dans le but d’indiquer pleinement l’avantage du modèle proposé qui apporte précision et stabilité de mode dans les résultats d’évaluation. En ce qui concerne le VANET avec communication V2X, le modèle d’ensemble de vote souple est proposé dans notre deuxième modèle. Données simulées extraites des simulateurs Simulation of Urban Mobility (SUMO) et OMNet++. Les classes de trafic et de non-trafic sont définies à l’aide du Packer Delivery Ratio (PDR) qui est une métrique importante dans la prévision du trafic étudiée. Il convient de noter que la réalisation de la meilleure stratégie appropriée pour intégrer les modèles d’IA et fournir des résultats de performance améliorés et équilibrés est un défi. Étant donné que la quantité de données collectées est presque doublée dans le deuxième modèle par rapport à la première méthode proposée, nous devons appliquer une stratégie simple qui fournit non seulement une performance améliorée par rapport aux modèles ML simples, mais fournit également une méthode qui n’augmente pas la complexité du modèle qui a entraîné plus de ressources de calcul et de consommation de temps. Le résultat montre que le modèle proposé est plus précis et stable avec moins de temps d’exécution que le modèle individuel. Cependant, les stratégies EL ont leurs propres coûts et avantages. Dans notre cas, la méthode STK-EBM atteint une meilleure stabilité que le deuxième modèle proposé au prix d’apporter plus de complexité. Dans le troisième modèle de prédiction du trafic réseau, nous considérons différents problèmes pour obtenir un modèle EL plus généralisable, simple et précis. Les données VANET du monde réel avec communication de base et les données simulées de communication avancée sont toutes deux prises en compte dans la troisième méthode. Les données V2X extraites de l’architecture conçue sont l’intégration des technologies DSRC et cellulaires pour répondre à la fois à la couverture de communication à courte et longue portée. De plus, les ensembles de données considérés sont différents en taille et en fonctionnalités. De cette façon, la méthode peut montrer si elle est plus bénéfique et applicable pour la mise en oeuvre réelle des applicavii tions VANET. Le réseau de neurones artificiels (ANN) est le modèle choisi pour la prévision du trafic dans la littérature. Cependant, les limites et les défis du modèle peuvent être résolus par un ensemble d’ANN et de Swarm Intelligence (SI). De cette manière, un modèle de prédiction intelligent efficace pouvant être appliqué aux données de communication de base et avancées est proposé. Les résultats obtenus indiquent que la méthode eSwaNN-NTP peut atteindre plus de précision, de stabilité et moins de temps que les simples ANN et DNN dans les deux ensembles de données. Enfin, les modèles proposés appliqués pourraient améliorer les performances du réseau de manière efficace. ABSTRACT: Vehicular Ad-hoc networks (VANETs) consider an important part of the Intelligent Transportation System (ITS). VANETs have the potential to improve road safety and traffic management by providing safety and non-safety applications. However, the evolution of VANETs towards the Internet of Vehicle (IoV), bring some challenges in VANET services. VANET applications employ Dedicated Short-Range Communications (DSRC) for communication between vehicles and roadside units which are known as basic communications including Vehicle-to-Vehicle(V2V) and Vehicle-to-Roadside Unit (V2R). Moreover, the development of VANET to IoV brings new requirements such as Vehicle-to-everything (V2X), which means a variety type of communications among road entities consisting of Vehicle-to-Vehicle (V2V), Vehicle-to-Infrastructure (V2I), Infrastructure-to-Infrastructure (I2I)and Vehicle-to- Pedestrian (V2P). The important challenge in VANET services is related to the enormous data generated by vehicular road users and resulting in traffic in the network and in turn the reduction of Quality of Service (QoS) for VANET services. In this case, when it comes to safety applications it may cost human life. Artificial Intelligent (AI) techniques are promising solutions to address network traffic issues in VANETs. Toward this end, network traffic prediction is a challenging task that can help network operators to avoid traffic in the network and reduction of QoS in vehicular network services. This domain has attracted many researchers to investigate providing an AI solution for network traffic prediction approaches. However, finding an optimal AI method that can achieve more accurate and stable prediction still is a challenge. Since each AI techniques have its own limitations and problems, if we cannot rely on only one ML model will impose more problems, and it needed to adapt to the new requirements of VANET which is V2X communication as well. Therefore, there are a lot of rooms that still need to be considered in this domain. In this dissertation, the first proposed method for traffic prediction in VANET consider an ensemble-based Machine Learning (ML) as a subset of AI methods. In this way, AI empowered model can achieve better performance, and more accurate and stable prediction than a single ML model. The prediction problem is defined as a classification problem. A real-world VANET dataset is used as input for the Ensemble Learning (EL) model. Moreover, regarding the importance of the quality of input data, feature selection methods including Boruta and LightGBM are employed to extract the most effective attributes of the V2V and V2R as the merged dataset. The proposed method named (STK-EBM) is based on the stacking strategy of ensemble learning that includes two layers: the base layer and the meta layer. In the base layer Random Forest(RF), K-Nearest NeighboR (KNN) and XGBoost models are integrated and are selected based on their effectiveness for our target which is traffic prediction in the network. The prediction results from the base layer models are aggregated by an optimized Logistic Regression (LR). The comparative analysis of the results of well-known single ML models is performed with the aim of full indication of the advantage of the proposed model that brings mode accuracy and stability in the evaluation results. Regarding the VANET with V2X communication, the soft voting ensemble model is proposed in our second model. Simulated data extracted from Simulation of Urban Mobility (SUMO) and OMNet++ simulators. The traffic and non-traffic class are defined using Packer Delivery Ratio (PDR) which is an important metric in traffic prediction studied. It should be noted that the realization of the best appropriate strategy to integrate AI models and provide improved and balanced performance results is a challenge. Since the amount of collected data is almost doubled in the second model compared to the first proposed method, we need to apply a simple strategy that not only provides an enhanced performance than single ML models but also provides a method that does not increase the complexity of the model that resulted in more computation resources and time consumption. The result shows that the proposed model is more accurate, and stable with less execution time than the individual model. However, EL strategies bring their own cost and benefits. In our case, the STK-EBM method achieves better stability than the second proposed model at the cost of bringing more complexity. In the third network traffic prediction model, we consider different problems to achieve a more generalizable, simple, and accurate EL model. Real-world VANET data with basic communication and Simulated data from advanced communication are both considered in the third method. The V2X data extracted from the designed architecture is the integration of the DSRC and Cellular-based technologies to address both short-range and long-range communication coverage. In addition, the considered datasets are different in size and features. In this way, the method can show if it is more beneficial and applicable for real-world implementation of VANET applications. The Artificial Neural Network (ANN) is the well-chosen model for traffic prediction in the literature. However, the limitation and challenges of the model can be addressed by an ensemble of ANN and Swarm Intelligence (SI). In this way, an efficient intelligent prediction model that can be applied to both basic and advanced communication data is proposed. The obtained results indicate that the eSwaNN-NTP method can achieve more accuracy, stability, and less time consumption than simple ANN and DNN in both datasets. Finally, the applied proposed models could improve the network performance in an efficient way