Characterization is commonly known in stylistics to be the cognitive process in the readers' minds when comprehending a fictional character in a literary work .In one approach, it is assumed that characters are the outcome of the interaction between the words in the text on the one hand and the contents of our heads on the other. This paper is an attempt to understand how characterization is achieved by applying Culpeper’s (2001) model which seems to be to present a method of analysis that is more objective and more systematic in analyzing characters. Two characters are selected for discussion; Ralph and Jack from Golding’s (1954) Lord of the Flies. The novel talks about the corruption of human beings and the capacity of evil th

The study involved 45 male and 45 females of diabetic patients type- ?? aged from 40-69years , and with the same numbers of males and females for control , all the patients and controls were without any periodontal diseases and without any systemic disease. Diabetic patients were divided in to three groups according to the degree of periodontitis , and the inflamed gingiva of all groups of diabetic patients were treated with the dried fruits powder (crude) of medicinal plants Quercus robur , Thuja occidenalis , Terminalia chebula, Anethum graveolens , respectively and mixture. Some immunological and antimicrobial factors (IgA, Lactoferrin , Lysozyme ) , were detected in serum and saliva of diabetic patients and the control

This study included a survey and review of the scientific names of the marsh insects (aquatic and surrounding it) for the purpose of unifying and updating the database.
The survey reveals 109 species under 77 genera that belong to 32 families and 7 orders as follow: Coleoptera (44 species), Diptera (7 species) Ephemeroptera (2 species), Hemiptera (14 species), Hymenoptera (11 species), Lepidoptera (2 species) and Odonata with 29 species.
Information of specimens' collection for each species, synonyms and geographical distribution were provided.

This study included a survey and review of the scientific names of the marsh insects (aquatic and surrounding it) for the purpose of unifying and updating the database. The survey reveals 109 species under 77 genera that belong to 32 families and 7 orders as follow: Coleoptera (44 species), Diptera (7 species) Ephemeroptera (2 species), Hemiptera (14 species), Hymenoptera (11 species), Lepidoptera (2 species) and Odonata with 29 species. Information of specimens' collection for each species, synonyms and geographical distribution were provided.

This paper work new and unprecedented definitions of sets, which we have named supra fan, supra. delta fan, supra. semi delta fan sets, which are generated by three sets of specific type of supra open sets, it was utilized supra open, supra delta open, supra. semi delta open sets with special conditions. It is highlighted many details of these new types of fan sets, their axis, blades and their annular sets using tables. Attention is given to the interior and the closure of these three types in supra topological spaces. The research was further enriched numerous and diverse examples. Subsequently, the focus shifted to supra. semi delta fan sets to prove lemma and theorem.

Due to the easily access to the satellite images, Google Earth (GE) images have become more popular than other online virtual globes. However, the popularity of GE is not an indication of its accuracy. A considerable amount of literature has been published on evaluating the positional accuracy of GE data; however there are few studies which have investigated the subject of improving the GE accuracy. In this paper, a practical method for enhancing the horizontal positional accuracy of GE is suggested by establishing ten reference points, in University of Baghdad main campus, using different Global Navigation Satellite System (GNSS) observation techniques: Rapid Static, Post-Processing Kinematic, and Network. Then, the GE image for the study

Attention-Deficit Hyperactivity Disorder (ADHD), a neurodevelopmental disorder affecting millions of people globally, is defined by symptoms of hyperactivity, impulsivity, and inattention that can significantly affect an individual's daily life. The diagnostic process for ADHD is complex, requiring a combination of clinical assessments and subjective evaluations. However, recent advances in artificial intelligence (AI) techniques have shown promise in predicting ADHD and providing an early diagnosis. In this study, we will explore the application of two AI techniques, K-Nearest Neighbors (KNN) and Adaptive Boosting (AdaBoost), in predicting ADHD using the Python programming language. The classification accuracies obtained w