1,3,4-oxadiazole-5-thion ring (2) successfully formed at position six of 2-methylphenol and five of their thioalkyl (3a-e). Furthermore 6-(5-(Aryl)-1,3,4-oxadiazol-2-yl)-2-methylphenol (5a-i) were formed at position six by two method. The first method was from cyclization their corresponding hydrazones (4a-e) of 2-hydroxy-3-methylbenzohydrazide (1) using bromine in glacial acetic acid. The second method was from cyclization the hydrazide with aryl carboxylic acid in the presence of phosphorusoxy chloride. The newly synthesized compounds were characterized from their IR, NMR and mass spectra. The antioxidant properties of these compounds were screened by 2,2-Diphenyl-1-picrylhydrazide (DPPH) and ferric reducing antioxidant power (FRAP) assays. Compound (4d) and (5h) exhibited significant antioxidant properties in both assays, compared to ascorbic acid, while compound (4e) exhibited slightly less antioxidant properties than ascorbic acid. Antibacterial activity was tested for the twenty one compounds against eight microorganisms (gram negative and gram positive). Compound (4d) and (5d) exhibited significant antibacterial activities compared to Amoxicillin and Kanamycin as antibiotic standards.
This paper presents a robust algorithm for the assessment of risk priority for medical equipment based on the calculation of static and dynamic risk factors and Kohnen Self Organization Maps (SOM). Four risk parameters have been calculated for 345 medical devices in two general hospitals in Baghdad. Static risk factor components (equipment function and physical risk) and dynamics risk components (maintenance requirements and risk points) have been calculated. These risk components are used as an input to the unsupervised Kohonen self organization maps. The accuracy of the network was found to be equal to 98% for the proposed system. We conclude that the proposed model gives fast and accurate assessment for risk priority and it works as p
... Show MoreIn this paper, we introduce the concept of cubic bipolar-fuzzy ideals with thresholds (α,β),(ω,ϑ) of a semigroup in KU-algebra as a generalization of sets and in short (CBF). Firstly, a (CBF) sub-KU-semigroup with a threshold (α,β),(ω,ϑ) and some results in this notion are achieved. Also, (cubic bipolar fuzzy ideals and cubic bipolar fuzzy k-ideals) with thresholds (α,β),(ω ,ϑ) are defined and some properties of these ideals are given. Relations between a (CBF).sub algebra and-a (CBF) ideal are proved. A few characterizations of a (CBF) k-ideal with thresholds (α, β), (ω,ϑ) are discussed. Finally, we proved that a (CBF) k-ideal and a (CBF) ideal with thresholds (α, β), (ω,ϑ) of a KU-semi group are equivalent relations.
Breast cancer is the most diagnosed form of malignant tumour in Iraqi women. Tamoxifen and trastuzumab are highly effective adjuvant therapy for breast cancer.
This study's objectives were to define the patient's belief in tamoxifen or trastuzumab when used as adjuvant therapy and to determine the variation in belief between the two medications in a sample of Iraqi breast cancer patients.
The cross-section survey was conducted using the BMQ-Specific questionnaire. Ninety-seven participants (sixty-seven tamoxifen, thirty trastuzumab) participated in this study.
The mean of specific-necessity scale for tamoxifen was (3.7) and for trastuzumab (4). The findings showed a high necessity for both medicines, and there wer
... Show MoreThis article aims to determine the time-dependent heat coefficient together with the temperature solution for a type of semi-linear time-fractional inverse source problem by applying a method based on the finite difference scheme and Tikhonov regularization. An unconditionally stable implicit finite difference scheme is used as a direct (forward) solver. While by the MATLAB routine lsqnonlin from the optimization toolbox, the inverse problem is reformulated as nonlinear least square minimization and solved efficiently. Since the problem is generally incorrect or ill-posed that means any error inclusion in the input data will produce a large error in the output data. Therefore, the Tikhonov regularization technique is applie
... Show MoreThe dynamic development of computer and software technology in recent years was accompanied by the expansion and widespread implementation of artificial intelligence (AI) based methods in many aspects of human life. A prominent field where rapid progress was observed are high‐throughput methods in biology that generate big amounts of data that need to be processed and analyzed. Therefore, AI methods are more and more applied in the biomedical field, among others for RNA‐protein binding sites prediction, DNA sequence function prediction, protein‐protein interaction prediction, or biomedical image classification. Stem cells are widely used in biomedical research, e.g., leukemia or other disease studies. Our proposed approach of
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