The composition of the clay taken from the lake of Hamrin, Iraq was studied with the determination of the concentrations of the major oxides and the minor oxides in it.   Kaolinite was the most dominant clay in the samples, while quartz was the most abundant non-clay mineral. 

Some physico-chemical parameters in lake's water were determined such as oxygen, a chlorosity, nutrient salts,  pH, total alkalinity, total nitrogen, total phosphorous and silicate.

The effect of increased wastewater and human activities affected the composition of water and lead to a decrease in the productivity of the lake which was reflected by the relatively low pH values (average 8.0) and alkalinity in water as a result of t

This work presents a five-period chaotic system called the Duffing system, in which the effect of changing the initial conditions and system parameters d, g and w, on the behavior of the chaotic system, is studied. This work provides a complete analysis of system properties such as time series, attractors, and Fast Fourier Transformation Spectrum (FFT). The system shows periodic behavior when the initial conditions xi and yi equal 0.8 and 0, respectively, then the system becomes quasi-chaotic when the initial conditions xi and yi equal 0 and 0, and when the system parameters d, g and w equal 0.02, 8 and 0.09. Finally, the system exhibits hyperchaotic behavior at the first two conditions, 0 and 0, and the bandwidth of the chaotic

 Accuracy in multiple objects segmentation using geometric deformable models sometimes is not achieved for reasons relating to a number of parameters. In this research, we will study the effect of changing the parameters values on the work of the geometric deformable model and define their efficient values, as well as finding out the relations that link these parameters with each other, by depending on different case studies including multiple objects different in spacing, colors, and illumination. For specific ranges of parameters values the segmentation results are found good, where the success of the work of geometric deformable models has been limited within certain limits to the values of these parameters.

Background: Pregnancy is considered a major risk factor for development and progression of periodontal disease. There are hormonal changes in both estrogen and progesterone hormones in addition to bacterial effect and poor oral hygiene that will enhance development of periodontal disease in pregnant women. Materials and methods: Seventy subjects were enrolled in the study, the subjects with an age range (20-35) years old without any history of systemic disease. The subjects were divided into 20 non-pregnant women they represent the control group (G I), 30 pregnant women with gingivitis (GII) and 20 pregnant women with periodontitis (GIII).All periodontal parameters (plaque index, gingival index, bleeding on probing, probing pocket depth an

Modern emerged technologies impose development and fabrication of miniatur-ized parts and devices in the micro- and nano-scale. Producing micro- and nano-featured structures requires nonconventional machining processes where con-ventional machining processes such as grinding, milling and eroding have failed. New emerging processes, such laser machining processes, are still fraught with almost invincible processes. Micro-/nano-machining are the pro-cesses of producing parts, microsystems or features at a scale of a few microm-eters and less than one hundred nanometers, respectively. Precise cutting and clean material removal accompanied with a negligible heat affected zone (HAZ), which are usually the characteristics of laser ablation, have

SAGD is a thermal recovery process in which steam oil ratio, SOR, is a key parameter that can affect the economic outcome of the process. Reservoirs with underlying or overlying lean bitumen present challenges for SAGD as they can act as a heat sink. Water has higher heat capacity than the bitumen and thus requires more steam to heat up the reservoir leading to higher SOR. The potential outcome of operating SAGD in these conditions may be lower bitumen rate and higher steam injection rate, both of which affect plant throughput and thus the economic matrix of SAGD. This paper looks at the performance of SAGD process in the presence of top lean bitumen. It will examine the theoretical CSOR that is needed to produce the bitumen with different

Groupwise non-rigid image alignment is a difficult non-linear optimization problem involving many parameters and often large datasets. Previous methods have explored various metrics and optimization strategies. Good results have been previously achieved with simple metrics, requiring complex optimization, often with many unintuitive parameters that require careful tuning for each dataset. In this chapter, the problem is restructured to use a simpler, iterative optimization algorithm, with very few free parameters. The warps are refined using an iterative Levenberg-Marquardt minimization to the mean, based on updating the locations of a small number of points and incorporating a stiffness constraint. This optimization approach is eff

        In this study multi objective optimization is utilized to optimize a turning operation to reveal the appropriate level of process features. The goal of this work is to evaluate the optimal combination of cutting parameters like feed, spindle speed, inclination angle and workpiece material to have a best surface quality Taguchi technique L9 mixed orthogonal array, has been adopted to optimize the roughness of surface. Three rods of length around (200 mm) for the three metals are used for this work. Each rod is divided into three parts with 50 mm length. For brass the optimum parametric mix for minimum Ra is A1, B1 and C3, i.e., at tool inclination angle (5), feedrate of 0.01, spindle speed of 120

Abstract

 This study investigated the optimization of wear behavior of AISI 4340 steel based on the Taguchi method under various testing conditions. In this paper, a neural network and the Taguchi design method have been implemented for minimizing the wear rate in 4340 steel. A back-propagation neural network (BPNN) was developed to predict the wear rate. In the development of a predictive model, wear parameters like sliding speed, applying load and sliding distance were considered as the input model variables of the AISI 4340 steel. An analysis of variance (ANOVA) was used to determine the significant parameter affecting the wear rate. Finally, the Taguchi approach was applied to determine