New complexes of M(II) with mixed ligand of 5-Chlorosalicylic acid (CSA) C7H5ClO3 as primary ligand and L- Valine (L-Val) C5H11NO2 as a secondary ligand were prepared and characterized by elemental analysis (C.H.N), UV., FT-IR, magnetic susceptibility, μeff (B.M) as well as the conductivity measurements (Λm ). In the complexes, the 5-chlorosalicylic acid is bidentate in all complexes coordinating through –OH- and –COO- groups; also L-Valine behaves as a bidentate ligand in all complexes through –NH2 and –COO- groups. These five mixed ligand complexes formulated as Na3[M(CSA)2(L-Val)]. The proposed molecular structure for all complexes is octahedral geometries. The synthesis complexes were tested in vitro for against four bacteria

The effect of 532nm Diode Pumped Solid State (DPSS) laser at power density of 5.234 W/cm2 on the growth of Gram-negative Pseudomonas aeruginosa and Gram-positive Staphylococcus aureus was evaluated. These bacteria were isolated from samples taken from burn and infected wound areas of 55 patients admitted to the burn-wound unit in Al-Kindy teaching hospital in Baghdad during the period from October 2012 to March 2013. Each isolate was identified using microscopic, cultural and biochemical methods. A standard bacterial suspension was prepared for each isolate. Serial dilutions were then prepared and a dilution of 10-5 was selected. Irradiation experiments included four groups: (L-P-) bacterial suspension in saline solution, (L-P+) bacteria

In this research an Artificial Neural Network (ANN) technique was applied for the prediction of Ryznar Index (RI) of the flowing water from WTPs in Al-Karakh side (left side) in Baghdad city for year 2013. Three models (ANN1, ANN2 and ANN3) have been developed and tested using data from Baghdad Mayoralty (Amanat Baghdad) including drinking water quality for the period 2004 to 2013. The results indicate that it is quite possible to use an artificial neural networks in predicting the stability index (RI) with a good degree of accuracy. Where ANN 2 model could be used to predict RI for the effluents from Al-Karakh, Al-Qadisiya and Al-Karama WTPs as the highest correlation coefficient were obtained 92.4, 82.9 and 79.1% respe

Essential approaches involving photons are among the most common uses of parallel optical computation due to their recent invention, ease of production, and low cost. As a result, most researchers have concentrated their efforts on it. The Basic Arithmetic Unit BAU is built using a three-step approach that uses optical gates with three states to configure the circuitry for addition, subtraction, and multiplication. This is a new optical computing method based on the usage of a radix of (2): a binary number with a signed-digit (BSD) system that includes the numbers -1, 0, and 1. Light with horizontal polarization (LHP) (↔), light with no intensity (LNI) (⥀), and light with vertical polarization (LVP) (↨) is represen

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Conditional logistic regression is often used to study the relationship between event outcomes and specific prognostic factors in order to application of logistic regression and utilizing its predictive capabilities into environmental studies. This research seeks to demonstrate a novel approach of implementing conditional logistic regression in environmental research through inference methods predicated on longitudinal data. Thus, statistical analysis of longitudinal data requires methods that can properly take into account the interdependence within-subjects for the response measurements. If this correlation ignored then inferences such as statistical tests and confidence intervals can be invalid largely.

Lung cancer is one of the most serious and prevalent diseases, causing many deaths each year. Though CT scan images are mostly used in the diagnosis of cancer, the assessment of scans is an error-prone and time-consuming task. Machine learning and AI-based models can identify and classify types of lung cancer quite accurately, which helps in the early-stage detection of lung cancer that can increase the survival rate. In this paper, Convolutional Neural Network is used to classify Adenocarcinoma, squamous cell carcinoma and normal case CT scan images from the Chest CT Scan Images Dataset using different combinations of hidden layers and parameters in CNN models. The proposed model was trained on 1000 CT Scan Images of cancerous and non-c

Feed Forward Back Propagation artificial neural network (ANN) model utilizing the MATLAB Neural Network Toolbox is designed for the prediction of surface roughness of Duplex Stainless Steel during orthogonal turning with uncoated carbide insert tool. Turning experiments were performed at various process conditions (feed rate, cutting speed, and cutting depth). Utilizing the Taguchi experimental design method, an optimum ANN architecture with the Levenberg-Marquardt training algorithm was obtained. Parametric research was performed with the optimized ANN architecture to report the impact of every turning parameter on the roughness of the surface. The results suggested that machining at a cutting speed of 355 rpm with a feed rate of 0.07 m

This study investigated the structural behavior of a beam–slab member fabricated using a steel C-Purlins beam carrying a profile steel sheet slab covered by a dry board sheet filled with recycled aggregate concrete, called a CBPDS member. This concept was developed to reduce the cost and self-weight of the composite beam–slab system; it replaces the hot-rolled steel I-beam with a steel C-Purlins section, which is easier to fabricate and weighs less. For this purpose, six full-scale CBPDS specimens were tested under four-point static bending. This study investigated the effect of using double C-Purlins beams face-to-face as connected or separated sections and the effect of using concrete material that contains different recycled