The study of the future of the international system currently appears, according to scientific data and existing facts in light of the emergence of international actors from non-states and international informal institutions, to be heading towards a non-polarity system and this trend is fueled by many variables to reduce polarity, and it is expected in the future that the international system will turn into a non-polarity.

Breast cancer is highlighted in recent research as one of the most prevalent types of cancer. Timely identification is essential for enhancing patient results and decreasing fatality rates. Utilizing computer-assisted detection and diagnosis early on may greatly improve the chances of recovery by accurately predicting outcomes and developing suitable treatment plans. Grading breast cancer properly, especially evaluating nuclear atypia, is difficult owing to faults and inconsistencies in slide preparation and the intricate nature of tissue patterns. This work explores the capability of deep learning to extract characteristics from histopathology photos of breast cancer. The research introduces a new method called SMOTE-based Convolut

OpenStreetMap (OSM) represents the most common example of online volunteered mapping applications. Most of these platforms are open source spatial data collected by non-experts volunteers using different data collection methods. OSM project aims to provide a free digital map for all the world. The heterogeneity in data collection methods made OSM project databases accuracy is unreliable and must be dealt with caution for any engineering application. This study aims to assess the horizontal positional accuracy of three spatial data sources are OSM road network database, high-resolution Satellite Image (SI), and high-resolution Aerial Photo (AP) of Baghdad city with respect to an analogue formal road network dataset obtain

This study aims to determine the reasons for the increase in the frequency of sand and dust storms in the Middle East and to identify their sources and mitigate them. A set of climatic data from 60 years (1960–2022) was analyzed. Sand storms in Iraq are a silty sand mature arkose composed of 72.7% sand, 25.1% silt, and 2.19% clay; the clay fraction in dust storms constitutes 70%, with a small amount of silt (20.6%) and sand (9.4%). Dust and sand storms (%) are composed of quartz (49.2, 67.1), feldspar (4.9, 20.9), calcite (38, 5), gypsum (4.8, 0.4), dolomite (0.8, 1.0), and heavy minerals (3.2, 6.6). Increasing temperatures in Iraq, by an average of 2 °C for sixty years, have contributed to an increase in the number of dust storm

This study aimed to conduct a comparative analysis of seven calliphorid species of third- instar larvae that were collected from central Iraq. The purpose was to morphologically characterize these species and develop an identification key to differentiate them from closely related species. Scanning Electron Microscope (SEM) graphical images and high-resolution traditional optical microscopes were used to analyze the morphological characteristics of the species Calliphora vomitoria (Linnaeus, 1758), C. vicina Robineau-Desvoidy, 1830, Chrysomya albiceps (Widemann, 1819), Ch. rufifacies (Macquart, 1844), Ch. megacephala (Fabricius, 1794), Lucilia cuprina (Wiedemann, 1830), and L. sericata (Meigen, 1826). An identification key was const

    The fauna of bees (Hymenoptera, Apoidea) from different regions of Iraq is surveyed in this study; there were 16 species, 13 genera that belong to four families which are collected in this investigation.

    Also, all the species that are recorded for Iraq in previous investigations are revised; totally there are 110 species, 32 genera belonging to  five families: Apidae, Andernidae, Colletidae, Halictidae and Megachilidae were listed.

This work includes a detailed description of the Leucostoma nigricorpuris sp. nov. from
Iraq. Locality, host plants and data of collection were given.

Background/Objectives: The purpose of this study was to classify Alzheimer’s disease (AD) patients from Normal Control (NC) patients using Magnetic Resonance Imaging (MRI). Methods/Statistical analysis: The performance evolution is carried out for 346 MR images from Alzheimer's Neuroimaging Initiative (ADNI) dataset. The classifier Deep Belief Network (DBN) is used for the function of classification. The network is trained using a sample training set, and the weights produced are then used to check the system's recognition capability. Findings: As a result, this paper presented a novel method of automated classification system for AD determination. The suggested method offers good performance of the experiments carried out show that the