In this research a local adsorbent was prepared from waste tires using two-step pyrolysis method. In the carbonization process, nitrogen gas flow rate was 0.2L/min at carbonization temperature of 500ºC for 1h. The char products were then preceded to the activation process at 850°C under carbon dioxide (CO2) activation flow rate of 0.6L/min for 3h. The activation method produced local adsorbent material with a surface area and total pore volume as high as 118.59m2 /g and 0.1467cm3/g, respectively. The produced . local adsorbent (activated carbon) was used for adsorption of lead from aqueous solution. The continuous fixed bed column experiments were conducted. The adsorption capacity performance of prepared activated carbons in this work

For the first time in Iraq, the crustacean Ergasilus ogawai Kabata,

1992 was recorded from the gills of Silurus triostegus, Mastacembelus mastacembelus, Mystus pelusius and Acanthopagrus latus out of 12 fish species caught from Garmat Ali river north of Basrah city during the period from September 1999 till August 2000. The percentage incidence of infestations of these four fish species were 98.9%, 100%,

49.6% and 71.4% while the intensity of infestations were 417, 81.8,

3.4 and 2, respectively. No significant differences in infestations of

male and female hosts  with this crustacean were detected.

    Nine fish specimens of Thryssa setirostris (Broussonet, 1782) were collected from the Arabian Gulf, during the period from July 2015 to April 2016. Because of the scarcity of this fish and overlapping and ambiguous of its taxonomic characters with other Thryssa spp., a detailed taxonomic study was conducted. The present study includes the most important meristic and morphometric characteristics.

    The mean of the total length of the specimens was 149.67 mm; dorsal fin consists of 12 rays, anal fin with 34-37 rays and pectoral fin with 12-13 rays; Gill rakers were 4 upper,1 medial an.10 lower. The most important character that isolates T. setiristis from

There are a few studies that discuss the medical causes for diabetic foot (DF) ulcerations in Iraq, one of them in Wasit province. The aim of our study was to analyze the medical, therapeutic, and patient risk factors for developing DF ulcerations among diabetic patients in Baghdad, Iraq.

In this paper, a compression system with high synthetic architect is introduced, it is based on wavelet transform, polynomial representation and quadtree coding. The bio-orthogonal (tap 9/7) wavelet transform is used to decompose the image signal, and 2D polynomial representation is utilized to prune the existing high scale variation of image signal. Quantization with quadtree coding are followed by shift coding are applied to compress the detail band and the residue part of approximation subband. The test results indicate that the introduced system is simple and fast and it leads to better compression gain in comparison with the case of using first order polynomial approximation.

Objective: Per-implantitis is one of the implant treatment complications. Dentists have failed to restore damaged periodontium by using conventional therapies. Tissue engineering (stem cells, scaffold and growth factors) aims to reconstruct natural tissues. The paper aimed to isolate both periodontal ligament stem cells (PDLSCs) and bone marrow mesenchymal stem cells (BMMSCs) and use them in a co-culture method to create three-layered cell sheets for reconstructing natural periodontal ligament (PDL) tissue. Materials and methods: BMMSCs were isolated from rabbit tibia and femur, and PDLSC culture was established from the lower right incisor. The cells were co-cultured to induce BMMSC differentiation into PDL cells. Cell morphology, stem cel

During COVID-19, wearing a mask was globally mandated in various workplaces, departments, and offices. New deep learning convolutional neural network (CNN) based classifications were proposed to increase the validation accuracy of face mask detection. This work introduces a face mask model that is able to recognize whether a person is wearing mask or not. The proposed model has two stages to detect and recognize the face mask; at the first stage, the Haar cascade detector is used to detect the face, while at the second stage, the proposed CNN model is used as a classification model that is built from scratch. The experiment was applied on masked faces (MAFA) dataset with images of 160x160 pixels size and RGB color. The model achieve

During COVID-19, wearing a mask was globally mandated in various workplaces, departments, and offices. New deep learning convolutional neural network (CNN) based classifications were proposed to increase the validation accuracy of face mask detection. This work introduces a face mask model that is able to recognize whether a person is wearing mask or not. The proposed model has two stages to detect and recognize the face mask; at the first stage, the Haar cascade detector is used to detect the face, while at the second stage, the proposed CNN model is used as a classification model that is built from scratch. The experiment was applied on masked faces (MAFA) dataset with images of 160x160 pixels size and RGB color. The model achieve