Background: Curve of Spee (CS) is an anteroposterior anatomical curve established by the occlusal alignment of the teeth viewed in the sagittal plane. This occlusal curvature has clinical importance in orthodontics and other fields of dentistry. This study aimed to evaluate the relationship between the CS and dentofacial morphology of different skeletal patterns in both genders. Materials and Methods: Eighty six Iraqi Arab subjects (44females,42 males ) their age ranged from 17 -30 years, classified into: Skeletal I with normal occlusion(15 females and 15 males), skeletal II with CI II div 1 malocclusion (15 females and 15 males) and skeletal III with CI III malocclusion (14 females and 12 males). Forty one variables measured using direct dental cast measurements , dental cast photographs and cephalometric radiographs with the aid of AutoCAD program version 15 (2006). Results: No significant differences in the CS depth between males and females or between right and left sides in both arches of different skeletal patterns. No significant differences in the maxillary CS among the 3 skeletal patterns, the mandibular CS in CI II div 1 malocclusion was larger than normal occlusion and CI III malocclusion. Maxillary CS significantly correlated to arch length, inter canine distance and inter second premolar distance in normal occlusion and overbite in Cl III malocclusion. Mandibular CS significantly correlated with overbite and overjet in Cl II div 1 and Cl III malocclusions. Conclusions: CS was not influence by sides and gender in both arches of different skeletal patterns. CS was concave in the mandibular arch with the maximum concavity at the mesio-buccal cusp tip of the mandibular first molar and convex in the maxillary arch with the maximum convexity at the buccal cusp tip of the maxillary second premolar, in different skeletal patterns. Key words: Curve of Spee, arch length, overbite, overjet, dentofacial morphology.
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In this paper two main stages for image classification has been presented. Training stage consists of collecting images of interest, and apply BOVW on these images (features extraction and description using SIFT, and vocabulary generation), while testing stage classifies a new unlabeled image using nearest neighbor classification method for features descriptor. Supervised bag of visual words gives good result that are present clearly in the experimental part where unlabeled images are classified although small number of images are used in the training process.
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Biosignal analysis is one of the most important topics that researchers have tried to develop during the last century to understand numerous human diseases. Electroencephalograms (EEGs) are one of the techniques which provides an electrical representation of biosignals that reflect changes in the activity of the human brain. Monitoring the levels of anesthesia is a very important subject, which has been proposed to avoid both patient awareness caused by inadequate dosage of anesthetic drugs and excessive use of anesthesia during surgery. This article reviews the bases of these techniques and their development within the last decades and provides a synopsis of the relevant methodologies and algorithms that are used to analyze EEG sig
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Asphaltene is one of the fractions of the crude oil which is soluble in aromatics such as benzene or toluene and insoluble in alkane such as n-heptane, n-pentane or petroleum ether (mixture of alkane compounds). Asphaltene precipitation is one of the most common problems that sometimes occurs in both oil recovery and refinery processes as a result of changing in pressure, oil composition, or temperature. Therefore the stability of asphaltene in the crude oil must be studied to show the tendency of it for precipitating asphaltene to prevent it (Asphaltene precipitation and deposition problem) and eliminate the burden of high treatment costs.
In the present study, saturate, aromatic, resin and asphaltene (SAR
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This article deals with the approximate algorithm for two dimensional multi-space fractional bioheat equations (M-SFBHE). The application of the collection method will be expanding for presenting a numerical technique for solving M-SFBHE based on “shifted Jacobi-Gauss-Labatto polynomials” (SJ-GL-Ps) in the matrix form. The Caputo formula has been utilized to approximate the fractional derivative and to demonstrate its usefulness and accuracy, the proposed methodology was applied in two examples. The numerical results revealed that the used approach is very effective and gives high accuracy and good convergence.
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Within the framework of big data, energy issues are highly significant. Despite the significance of energy, theoretical studies focusing primarily on the issue of energy within big data analytics in relation to computational intelligent algorithms are scarce. The purpose of this study is to explore the theoretical aspects of energy issues in big data analytics in relation to computational intelligent algorithms since this is critical in exploring the emperica aspects of big data. In this chapter, we present a theoretical study of energy issues related to applications of computational intelligent algorithms in big data analytics. This work highlights that big data analytics using computational intelligent algorithms generates a very high amo
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Many oil and gas processes, including oil recovery, oil transportation, and petroleum processing, are negatively impacted by the precipitation and deposition of asphaltene. Screening methods for determining the stability of asphaltenes in crude oil have been developed due to the high cost of remediating asphaltene deposition in crude oil production and processing. The colloidal instability index, the Asphaltene-resin ratio, the De Boer plot, and the modified colloidal instability index were used to predict the stability of asphaltene in crude oil in this study. The screening approaches were investigated in detail, as done for the experimental results obtained from them. The factors regulating the asphaltene precipitation are different fr
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