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Art as a therapy to relieve symptoms of Attention Deficit/Hyperactivity Disorder (ADHD), in children
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The aim of the present study is to evaluate the effectiveness of using Art as therapy to reduce the symptoms of Attention Deficit Hyper Activity Disorder (ADHD), in primary school children.
A clinical approach was used to test the validity of the hypothesis of our study, conducted on two second and fourth-year primary school pupils from Algiers, aged 7 and 9 years respectively.
In addition to the clinical observation and interview, we made use of the "Conners" scale for a (pre and post intervention) ADHD assessment, consisting of a combination of Art media in the form of mosaic works on purposely prepared panels. After 10 therapy sessions, results revealed the effectiveness of Art therapy in reducing ADHD in primary education children.

Once the diagnosis of ADHD symptoms in the two study subjects, aged 7 and 9 years, was confirmed through the "Conners" ADHD assessment and by carrying out a clinical interview with the parents as well as an in-depth analysis of subjects’ clinical observation and interview, a treatment using Art as therapy was implemented and found to be very effective in reducing the symptoms of ADHD.

In order to attract children’s attention, this type of therapy uses purposely designed graphics, since the assortment of colors made at their disposition prompted them to willingly engage in the activity and cooperate with the specialist implementing the treatment program.
The child begins to ‘own’ the work of Art, absorbing himself with it until it was finished, all along stimulated by positive reinforcement. The piece of mosaic art has become the child’s focal point onto which he projects his frustrations and internal conflicts and discharges his emotions.
Therefore, a child with ADHD can be helped settle down into calmer behaviours, by using Art media to decrease his hyperactivity. The more in-school training workshops and homework, the further this type of therapy would prove itself to be more effective compared to the other treatments used to treat this category of children.

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Publication Date
Fri Dec 01 2023
Journal Name
Russian Journal Of Bioorganic Chemistry
A Brief Review on Schiff Base, Synthesis, and Their Antimicrobial Activities
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Publication Date
Tue Jul 26 2022
Journal Name
International Journal Of Pharmacy Practice
What might COVID-19 patients experience after recovery? A comprehensive review
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Abstract<sec> <title>Objectives

The objective of this review was to describe the COVID-19 complications after recovery.

Methods

The researchers systematically reviewed studies that reported post-COVID-19 complications from three databases: PubMed, Google Scholar and the World Health Organization (WHO) COVID-19 database. The search was conducted between 21 November 2020 and 14 January 2021. Inclusion criteria were articles written in English, with primary data, reporting complications of COVID-19 after full

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Publication Date
Thu Feb 15 2024
Journal Name
Journal Of Theoretical And Applied Information Technology
CHOOSING THE RIGHT CHAOTIC MAP FOR IMAGE ENCRYPTION: A DETAILED EXAMINATION
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This article investigates how an appropriate chaotic map (Logistic, Tent, Henon, Sine...) should be selected taking into consideration its advantages and disadvantages in regard to a picture encipherment. Does the selection of an appropriate map depend on the image properties? The proposed system shows relevant properties of the image influence in the evaluation process of the selected chaotic map. The first chapter discusses the main principles of chaos theory, its applicability to image encryption including various sorts of chaotic maps and their math. Also this research explores the factors that determine security and efficiency of such a map. Hence the approach presents practical standpoint to the extent that certain chaos maps will bec

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Publication Date
Mon Jan 01 2024
Journal Name
Aip Conference Proceedings
A multivariate Bayesian model using Gibbs sampler with real data application
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In many scientific fields, Bayesian models are commonly used in recent research. This research presents a new Bayesian model for estimating parameters and forecasting using the Gibbs sampler algorithm. Posterior distributions are generated using the inverse gamma distribution and the multivariate normal distribution as prior distributions. The new method was used to investigate and summaries Bayesian statistics' posterior distribution. The theory and derivation of the posterior distribution are explained in detail in this paper. The proposed approach is applied to three simulation datasets of 100, 300, and 500 sample sizes. Also, the procedure was extended to the real dataset called the rock intensity dataset. The actual dataset is collecte

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Publication Date
Tue Feb 01 2022
Journal Name
Journal Of Engineering
Self-Repairing Technique Based on Microcapsules for Cementitious Composites- A Review
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Self-repairing technology based on micro-capsules is an efficient solution for repairing cracked cementitious composites. Self-repairing based on microcapsules begins with the occurrence of cracks and develops by releasing self-repairing factors in the cracks located in concrete. Based on previous comprehensive studies, this paper provides an overview of various repairing factors and investigative methodologies. There has recently been a lack of consensus on the most efficient criteria for assessing self-repairing based on microcapsules and the smart solutions for improving capsule survival ratios during mixing. The most commonly utilized self-repairing efficiency assessment indicators are mechanical resistance and durab

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Publication Date
Thu Dec 16 2021
Journal Name
Translational Vision Science &amp; Technology
A Hybrid Deep Learning Construct for Detecting Keratoconus From Corneal Maps
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Publication Date
Fri Sep 16 2022
Journal Name
Sustainability
Intellectual Capital History and Trends: A Bibliometric Analysis Using Scopus Database
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This article aims to provide a bibliometric analysis of intellectual capital research published in the Scopus database from 1956 to 2020 to trace the development of scientific activities that can pave the way for future studies by shedding light on the gaps in the field. The analysis focuses on 638 intellectual capital-related papers published in the Scopus database over 60 years, drawing upon a bibliometric analysis using VOSviewer. This paper highlights the mainstream of the current research in the intellectual capital field, based on the Scopus database, by presenting a detailed bibliometric analysis of the trend and development of intellectual capital research in the past six decades, including journals, authors, countries, inst

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Publication Date
Mon Jun 26 2023
Journal Name
Asia-pacific Journal Of Chemical Engineering
Sustainable environment through using porous materials: A review on wastewater treatment
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Abstract<p>Porous materials play an important role in creating a sustainable environment by improving wastewater treatment's efficacy. Porous materials, including adsorbents or ion exchangers, catalysts, metal–organic frameworks, composites, carbon materials, and membranes, have widespread applications in treating wastewater and air pollution. This review examines recent developments in porous materials, focusing on their effectiveness for different wastewater pollutants. Specifically, they can treat a wide range of water contaminants, and many remove over 95% of targeted contaminants. Recent advancements include a wider range of adsorption options, heterogeneous catalysis, a new UV/H<sub>2</sub>O<j></j></p> ... Show More
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Publication Date
Sat Jan 01 2022
Journal Name
Materials Today: Proceedings
Energy management and storage systems on electric vehicles: A comprehensive review
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Publication Date
Tue Dec 05 2023
Journal Name
Baghdad Science Journal
AlexNet-Based Feature Extraction for Cassava Classification: A Machine Learning Approach
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Cassava, a significant crop in Africa, Asia, and South America, is a staple food for millions. However, classifying cassava species using conventional color, texture, and shape features is inefficient, as cassava leaves exhibit similarities across different types, including toxic and non-toxic varieties. This research aims to overcome the limitations of traditional classification methods by employing deep learning techniques with pre-trained AlexNet as the feature extractor to accurately classify four types of cassava: Gajah, Manggu, Kapok, and Beracun. The dataset was collected from local farms in Lamongan Indonesia. To collect images with agricultural research experts, the dataset consists of 1,400 images, and each type of cassava has

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