The  insect is diagnosed and named by the National Center of Biotechnology Information (NCBI), USA as the Mint leaf Beetle Chrysolina herbacea alnadawi (Duftschmid, 1825), (Coleoptera: Chrysomelidae). The diagnosis was performed depending on the DNA analysis by 73% similarity with Chrysolina herbacea (Duftschmid, 1825) sequence, In the present study. It is recorded as a new insect pest on mint plant Mentha  puleguim (L,1753) (Lamiaceae). DNA analysis confirmend that it is recorded for the first time in Iraq and the Arab world as well as the Middle East. Those insects were observed initially during August 2017 in residential gardens of Al-Bonooq district in Baghdad / Iraq.

Assume that G is a finite group and X = tG where t is non-identity element with t3 = 1. The simple graph with node set being X such that a, b ∈ X, are adjacent if ab-1 is an involution element, is called the A4-graph, and designated by A4(G, X). In this article, the construction of A4(G, X) is analyzed for G is the twisted group of Lie type 3D4(3).

The monogenean Gyrodactylus bychowskianus Bogolepova, 1950 is recorded in the present study for the first time in Iraq from the gills of the cyprinid fish Arabibarbus grypus (Heckel, 1843); which was collected from the Tigris River at Al-Taji Beach north of Baghdad Province during the period from July until November 2018.

The occurrence of two species of the genus Myxobolus Bütschli, 1882 (Myxozoa: Myxosporea) for the first time in Iraq from freshwater fishes.

Objective: To measure the serum levels of Fetuin-A, ischemia-modified albumin (IMA), and ferritin in hospitalized patients with severe COVID-19in Baghdad, Iraq. Moreover, to determine these biomarkers' cut-off valuesthat differentiate between severely ill patients and control subjects. Methods: This case-control study was done from 15 September to the end of December 2021 and involved a review of the files and collectionof blood samples from patients (n=45, group1) hospitalized in COVID-19 treatment centersbecause of severe symptoms compared tohealthy subjects as controls (n=44, group2). Results: Fetuin-A serum levels were not statistically different between patients and controls. In contrast, IMA and ferritin levels were significan

The research aims to employ one of the most important strategies for recovery from the crisis of the Covid-19 pandemic, which ravaged the economies of the entire world and its various sectors, including the banking sector, through financial technology that is based on digital transformation to achieve financial sustainability and the creation of innovative financial value chains in light of the decline in the banking sector as a result of The negative effects of the Covid-19 pandemic, be guided by the relevant international accounting standards to control the risks associated with financial technology. To recover from the Covid-19 crisis, the research came out with a set of recommendations, most notably financial technology from

Nowadays, information systems constitute a crucial part of organizations; by losing security, these organizations will lose plenty of competitive advantages as well. The core point of information security (InfoSecu) is risk management. There are a great deal of research works and standards in security risk management (ISRM) including NIST 800-30 and ISO/IEC 27005. However, only few works of research focus on InfoSecu risk reduction, while the standards explain general principles and guidelines. They do not provide any implementation details regarding ISRM; as such reducing the InfoSecu risks in uncertain environments is painstaking. Thus, this paper applied a genetic algorithm (GA) for InfoSecu risk reduction in uncertainty. Finally, the ef

Botnet detection develops a challenging problem in numerous fields such as order, cybersecurity, law, finance, healthcare, and so on. The botnet signifies the group of co-operated Internet connected devices controlled by cyber criminals for starting co-ordinated attacks and applying various malicious events. While the botnet is seamlessly dynamic with developing counter-measures projected by both network and host-based detection techniques, the convention techniques are failed to attain sufficient safety to botnet threats. Thus, machine learning approaches are established for detecting and classifying botnets for cybersecurity. This article presents a novel dragonfly algorithm with multi-class support vector machines enabled botnet

Consider a simple graph   on vertices and edges together with a total  labeling . Then ρ is called total edge irregular labeling if there exists a one-to-one correspondence, say  defined by  for all  where  Also, the value  is said to be the edge weight of . The total edge irregularity strength of the graph G is indicated by  and is the least  for which G admits   edge irregular h-labeling.  In this article,   for some common graph families are examined. In addition, an open problem is solved affirmatively.