Spatial data analysis is performed in order to remove the skewness, a measure of the asymmetry of the probablitiy distribution. It also improve the normality, a key concept of statistics from the concept of normal distribution “bell shape”, of the properties like improving the normality porosity, permeability and saturation which can be are visualized by using histograms. Three steps of spatial analysis are involved here; exploratory data analysis, variogram analysis and finally distributing the properties by using geostatistical algorithms for the properties. Mishrif Formation (unit MB1) in Nasiriya Oil Field was chosen to analyze and model the data for the first eight wells. The field is an anticline structure with northwest- south

Due  to  the  importance  of  Laser  usage  in  the  field  of  skin diseases  Like  removing.  the cicatrix  (acne  causes).  skin  lines,  cone spots,  two laser systems  have underwent a study and  evaluation for their use in treatingthe skin diseases.

The   research  deals  with  desigrrating  an  optical  system  .The system is for concentrating  the laser beam C02 with a wave length of

10.6   micrometer   Descriptions   of  1he  system  and   value   of   the

perfonnance  

لا يزال المهتمون بلعبة كرة السلة يبحثون عن إيجاد الوسائل الأكثر أهمية وصولاً إلى ما تطمح إليه الدول لتحقيق افضل المستويات في نواحي اللعبة كافة من خلال التغلب على المعوقات التي تحول دون تقدمها إلى الأمام بالدراسة والبحث. ومن هذا المنطلق انصب البحث في ضرورة معالجة القصور الناتج عن عدم وجود المعايير ذات العلاقة باختبارات قدرات اللاعبين وعلى وفق مراكز اللعب ولا سيما المهارية الهجومية مما شكل ذلك ضعفاً في أعداد و

أن لعبة كرة السلة في تقدم وتطور مستمر حالها في ذلك حال الألعاب الرياضية الأخرى حيث أن الفضل في ذلك يعود إلى المعرفة بالعلوم المختلفة وكذلك البحث العلمي من اجل تحقيق نتائج متقدمة تنشدها معظم الدول ومنها العراق على الصعيد المحلي والعربي والدولي. ومما لاشك فيه أن متطلبات تطور مستوى نتائج القدرات البدنية في أيُة لعبة مرتبطة بالبرمجة والتخطيط الصحيح للمناهج التدريبية إذ تقودنا هذه الحقيقة إلى إجراء المزيد من الا

Permeability estimation is a vital step in reservoir engineering due to its effect on reservoir's characterization, planning for perforations, and economic efficiency of the reservoirs. The core and well-logging data are the main sources of permeability measuring and calculating respectively. There are multiple methods to predict permeability such as classic, empirical, and geostatistical methods. In this research, two statistical approaches have been applied and compared for permeability prediction: Multiple Linear Regression and Random Forest, given the (M) reservoir interval in the (BH) Oil Field in the northern part of Iraq. The dataset was separated into two subsets: Training and Testing in order to cross-validate the accuracy

abstract

the research discussed a stage of strategic management. The strategic of the evaluation of the proposed strategy through feedback is to ensure that it is implemented with the least possible variation. The research aims at evaluation  a proposed strategy for the Ministry of Planning for the years 2018-2022 in line with the orientations of the state, taking into account the surrounding environmental conditions. It relies on scientific bases and steps to formulate the strategy The extent of the strategy suitability was tested through a set of statistical means and its objectivity was verified through a survey of a number of specialized experts who were selected in accordance with the principle

Keys for 22 species representing ten genera Thripidae collection carried out during 1999-2001 in different localities in the middle of Iraq. Of them four species are described as new to science, Frankliniella megacephala sp. nov; Retithrips bagdadensis sp. nov; Chirothrips imperatus sp. nov; Taeniothrips tigridis sp. nov; Another thirteen species are recorded for the first time in Iraq; Thrips meridionalis (Pri.); Microcephalothrips abdominils (Crawford); Scolothrips pallidus (Beach); Scritothrips mangiferae Pri.; Frankliniella tritici Bagnall; Frankliniella schultzie Trybom; Frankliniella unicolor Morgan; Retithrips aegypticus Mar

Identifying people by their ear has recently received import attention in the literature. The accurate segmentation of the ear region is vital in order to make successful person identification decisions. This paper presents an effective approach for ear region segmentation from color ear images. Firstly, the RGB color model was converted to the HSV color model. Secondly, thresholding was utilized to segment the ear region. Finally, the morphological operations were applied to remove small islands and fill the gaps. The proposed method was tested on a database which consisted of 105 ear images taken from the right sides of 105 subjects. The experimental results of the proposed approach on a variety of ear images revealed that this approac

The recent emergence of sophisticated Large Language Models (LLMs) such as GPT-4, Bard, and Bing has revolutionized the domain of scientific inquiry, particularly in the realm of large pre-trained vision-language models. This pivotal transformation is driving new frontiers in various fields, including image processing and digital media verification. In the heart of this evolution, our research focuses on the rapidly growing area of image authenticity verification, a field gaining immense relevance in the digital era. The study is specifically geared towards addressing the emerging challenge of distinguishing between authentic images and deep fakes – a task that has become critically important in a world increasingly reliant on digital med

With the rapid development of smart devices, people's lives have become easier, especially for visually disabled or special-needs people. The new achievements in the fields of machine learning and deep learning let people identify and recognise the surrounding environment. In this study, the efficiency and high performance of deep learning architecture are used to build an image classification system in both indoor and outdoor environments. The proposed methodology starts with collecting two datasets (indoor and outdoor) from different separate datasets. In the second step, the collected dataset is split into training, validation, and test sets. The pre-trained GoogleNet and MobileNet-V2 models are trained using the indoor and outdoor se