Various of 2,5- disubstituted 1,3,4-oxadiazole (Schiff base, ?- lactam and azo) were synthesized from 2,5-di (4,4?-amino-1,3,4-oxadiazole which usequently synth-esized from mixture of 4- amino benzoic acid and hydrazine arch of polyphosphorus acid. The synthesized compounds were cherecterized by using some spectral data (UV, FT-IR , and 1H-NMR)

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

Four new copolymers were synthesized from reaction of bis acid monomer 3-((4-carboxyphenyl) diazenyl)-5-chloro-2-hydroxybenzoic acid with five diacidhydrazide in presence of poly phosphoric acid. The resulted monomers and copolymers have been characterized by FT-IR, 1H-NMR, 13C-NMR spectroscopy as well as EIMs technique. The number averages of molecular weights of the copolymers are between 4822 and 9144, and their polydispersity indexes are between 1.02 and 2.15. All the copolymers show good thermal stability with the temperatures higher than 305.86 C when losing 10% weight under nitrogen. The cyclic voltammetry (CV) measurement and the electrochemical band gaps (Eg) of these copolymers are found below 2.00 ev.

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

Zirconia ceramic restoration (ZCR) has a higher fracture incidence rate than metal ceramic restoration. Different surface treatments were used to improve fracture performance of ZCR such as grit blasting (GB) by aluminium oxide powder. This type of surface treatment generate residual stresses on veneering ceramic causing crack initiation and ending with a fracture. In order to overcome the stress generated by GB, zirconia surface coating is used as a surface treatment to improve fracture resistance and to accommodate stresses along the ZCR layers. Fifty zirconia ceramic crowns were fabricated and divided according to the type of surface treatment into three groups; the first group is (ZG), involving 20 cores were coated with a mixture of pa

Social media is known as detectors platform that are used to measure the activities of the users in the real world. However, the huge and unfiltered feed of messages posted on social media trigger social warnings, particularly when these messages contain hate speech towards specific individual or community. The negative effect of these messages on individuals or the society at large is of great concern to governments and non-governmental organizations. Word clouds provide a simple and efficient means of visually transferring the most common words from text documents. This research aims to develop a word cloud model based on hateful words on online social media environment such as Google News. Several steps are involved including data acq

Background: Eucalyptus extracts and derivatives are natural substances with potent antimicrobial properties. This study investigated the in- vitro effects of non-nutritive sweeteners on the antifungal activity of alcoholic and aqueous Eucalyptus extracts against Candida albicans, a common oral pathogen. Materials and Method: Ten isolates of Candida albicans were isolated from dental students’ salivary samples. The alcoholic and aqueous extracts were prepared from fresh Eucalyptus leaves using maceration. The sensitivity of Candida albicans isolates to various concentrations of Eucalyptus extracts ranging from 50 to 250 (mg/mL) was evaluated via agar well diffusion method, while the agar streaking method  was used to assess the minimum

This effort is related to describe and assess the performance of the Iraqi cement sample planned for oil well-cementing jobs in Iraq. In this paper, major cementing properties which are thickening time, compressive strength, and free water in addition to the rheological properties and filtration of cement slurry underneath definite circumstances are experimentally tested. The consequences point to that the Iraqi cement after special additives encounter the requests of the API standards and can consequently is used in cementing jobs for oil wells. At this research, there is a comparative investigation established on experimental work on the effectiveness of some additives that considered as waste materials which are silica fume, bauxite,