The phytoremediation technique has become very efficient for treating soil contaminated with heavy metals. In this study, a pot experiment was conducted where the Dodonaea plant (known as hops) was grown, and soil previously contaminated with metals (Zn, Ni, Cd) was added at concentrations 100, 50, 0 mg·kg-1 for Ni and Zn, and at concentrations of 0, 5, 10 mg·kg-1 for cadmium. Irrigation was done within the limits of the field capacity of the soil. Cadmium, nickel and zinc was estimated in the soil to find out the capacity of plants to the absorption of heavy and contaminated metals by using bioconcentration factors (BCFs), bioaccumulation coefficient (BAC) and translocation factor (TF). Additionally, BCF values of both Ni and Zn were l

Background: The COVID-19 infection is a more recent pandemic disease all over the world and studying the pulmonary findings on survivors of this disease has lately commenced.

Objective: We aimed to estimate the cumulative percentage of whole radiological resolution after 3 months from recovery and to define the residual chest CT findings and exploring the relevant affecting factors.

Subjects and Methods: Patients who had been previously diagnosed with COVID-19 pneumonia confirmed by RT-PCR test and had radiological evidence of pulmonary involvement by Chest CT during the acute illness were included in the present study. The radiol

     Lately, a growing interest has been emerging in age estimation from face images because of the wide range of potential implementations in law enforcement, security control, and human computer interactions. Nevertheless, in spite of the advances in age estimation, it is still a challenging issue. This is due to the fact that face aging process is not only set by distinct elements, such as genetic factors, but by extrinsic factors, such as lifestyle, expressions, and environment as well. This paper applied machine learning technique to intelligent age estimation from facial images using J48 classifier on FG_NET dataset. The proposed work consists of three phases; the first phase is image preprocessing which include

Background: Metal ions can be released from metallic orthodontic appliances due to corrosion in the oral cavity; prophylactic mouthwashes may have an effect on ion release from orthodontic wires. Materials and Methods: Thirty six orthodontic sets of half maxillary fixed appliance with 2 types of arch wires SS and NiTi(Morelli) were constructed and immersed in 2 types of mouthwashes; Claradone (non-fluoridated) and Silver Care (fluoridated) for 28 days at 37°C, then the released Ni and Cr ionswere measured using atomic absorption spectrophotometer and compared statistically. Results: Ni ion release was higher from NiTi wire group than SS wire group for both mouthwashes and also was higher for Silver Care group than for Claradone group.

Background: Toxoplasmosis is a common zoonotic disease affecting one-third of the world population with potentially serious health implications.

Aim of the study: To find the prevalence and risk factors of Toxoplasma gondii infection among females in Al-Anbar, Iraq. 

Methodology: Over an eight-month period from (1^st November 2021 to 30^th June 2022), 200 females visiting private veterinary clinics in Al-Anbar (92 owned cats) participated in this prospective study. Age and residency were recorded. Blood samples were collected from all females and subjected first to the Toxoplasma Latex Agglutination test (TLAT). Samples of cat feces were collec

     In this research, we obtained nanoparticles (NPs) using a type of microwave (cold plasma) and water extract from Origanum vulgare with the addition of silver nitrate (AgNO3) solution. Silver nanoparticles are characterized by high production techniques. Experimental factors such as morphology, structure, optical properties and active compounds of the nanoparticle extract were studied through specific tests such as morphology, UV spectroscopy, FE-SEM, FT-IR, and Atomic Force Microscopy (AFM). Plants were used in manufacturing the nanoparticles, as plants are well known for their accessibility and applicability in different pathological areas. The current research provides information on the Green Union System of N

Its well known that understanding human facial expressions is a key component in understanding emotions and finds broad applications in the field of human-computer interaction (HCI), has been a long-standing issue. In this paper, we shed light on the utilisation of a deep convolutional neural network (DCNN) for facial emotion recognition from videos using the TensorFlow machine-learning library from Google. This work was applied to ten emotions from the Amsterdam Dynamic Facial Expression Set-Bath Intensity Variations (ADFES-BIV) dataset and tested using two datasets.

Background Type two diabetes (T2DM) is characterized by insufficient insulin production and secretion. Additionally, the body develops insulin resistance which affects 90–95% of diabetics. Complex cytokines, receptors, genetic pathways, and the immune system are involved in T2DM. Interleukin-18 (IL-18) is one of the inflammatory cytokines associated with Type 2 diabetes. Environmental and genetic variables, including genetic polymorphisms, can increase T2DM risk and its consequences. Single nucleotide gene polymorphisms (SNPs) are important risk factors for diabetes that can be used to find the disease early and treat it better. Objective This study aimed to determine the levels of IL-18 in the serum of Iraqi patients with Type 2 diabetes

this research is to identify the level of information awareness of the chemistry students in their fourth year studying at Ibn Al-Haytham Education College of pure sciences at the University of Baghdad. The research sample consisted of (107) male and female students out of the total number of (153) students studying during the (2017-2018) academic year, The sample therefore represents 71% of the total students. The research methodology used consisted of two parts. The first part is concerned with measuring information awareness using a multiple choice type of test related to (40) issues. The students were required to select the between (5) alternative answers for each issue. The objectives of the test and the issues used are to measure the

The identification of a bed’s lithology is fundamental to all reservoir characterization because the physical and chemical properties of the rock that holds hydrocarbons and/or water affect the response of every tool used to measure formation properties. The main purpose of this study is to evaluate reservoir properties and lithological identification of Nahr Umr Formation in Luhais well -12 southern Iraq. The available well logs such as (sonic, density, neutron, gamma ray, SP, and resistivity logs) are digitized using the Didger software. The petrophysical parameters such as porosity, water saturation, hydrocarbon saturation, bulk water volume, etc. were computed and interpreted using Techlog software. The lithology prediction of Nahr