Background: Orthodontic mini-implants are increasingly used in orthodontics and the bone density is a very important factor in stabilization and success of mini-implant. The aim of this study was to observe the relationship among maximum bite force (MBF); body mass index (BMI); face width, height and type; and bone density in an attempt to predict bone density from these variables to eliminate the need for CT scan which have a highly hazard on patient. Materials and Methods: Computed tomographic (CT) images were obtained for 70 patients (24 males and 46 females) with age range 18-30 years. The maxillary and mandibular buccal cortical and cancellous bone densities were measured between 2nd premolar and 1st molar at two levels from the alveolar crest (3 and 6 mm). Face height and width were measured from CT. Clinically; Maximum bite force was measured on first molar region unilaterally by a digital device. The sample was divided into two groups according to the body mass index into; normal and overweight. Results: The results obtained showed that there were no statistical significant differences in MBF or bone density in both genders. Only the cortical bone density in maxilla in overweight group tended to be higher than normal BMI group. The face width and height correlated significantly negatively with MBF which correlated significantly positively with cortical bone density. Conclusions: It was concluded that a prediction of cortical bone density of preselected areas can be made from maximum bite force, body mass index and inter-zygomatic width.
In this article, the research presents a general overview of deep learning-based AVSS (audio-visual source separation) systems. AVSS has achieved exceptional results in a number of areas, including decreasing noise levels, boosting speech recognition, and improving audio quality. The advantages and disadvantages of each deep learning model are discussed throughout the research as it reviews various current experiments on AVSS. The TCD TIMIT dataset (which contains top-notch audio and video recordings created especially for speech recognition tasks) and the Voxceleb dataset (a sizable collection of brief audio-visual clips with human speech) are just a couple of the useful datasets summarized in the paper that can be used to test A
... Show MoreThis study was focused on biotreatment of soil which polluted by petroleum compounds (Diesel) which caused serious environmental problems. One of the most effective and promising ways to treat diesel-contaminated soil is bioremediation. It is a choice that offers the potential to destroy harmful pollutants using biological activity. Four bacterial strains were isolated from diesel contaminated soil samples. The isolates were identified by the Vitek 2 system, as Sphingomonas paucimobilis, Pentoae species, Staphylococcus aureus, and Enterobacter cloacae. The potential of biological surfactant production was tested using the Sigma 703D stand-alone tensiometer showed that these isolates are biological surfactant producers. The bet
... Show MoreThe current research includes the adsorption of Rhodmine-B Dye on the surface of Citrus Leaves using the technique of UV. Vis spectrophotometer to determine data of quantitative adsorption at various contact time, ionic strength, PH and temperature conditions. As a function of temperatures 25,35,45,55 0C, the dsorption phenomenon was examined, and the results showed that Rhodamine-B adsorption Citrus leaves rose with increasing temperatures on the surface (endothermic process). Using various NaCl solution concentrations, the effect of ionic strength on adsorption has also been studied. Increasing the importance of ionic strength has been shown to improve the amount of adsorption of Rhodamine-B on citrus leaves at constant temp
... Show MoreFacial emotion recognition finds many real applications in the daily life like human robot interaction, eLearning, healthcare, customer services etc. The task of facial emotion recognition is not easy due to the difficulty in determining the effective feature set that can recognize the emotion conveyed within the facial expression accurately. Graph mining techniques are exploited in this paper to solve facial emotion recognition problem. After determining positions of facial landmarks in face region, twelve different graphs are constructed using four facial components to serve as a source for sub-graphs mining stage using gSpan algorithm. In each group, the discriminative set of sub-graphs are selected and fed to Deep Belief Network (DBN) f
... Show MoreThere are two main categories of force control schemes: hybrid position-force control and impedance control. However, the former does not take into account the dynamic interaction between the robot’s end effector and the environment. In contrast, impedance control includes regulation and stabilization of robot motion by creating a mathematical relationship between the interaction forces and the reference trajectories. It involves an energetic pair of a flow and an effort, instead of controlling a single position or a force. A mass-spring-damper impedance filter is generally used for safe interaction purposes. Tuning the parameters of the impedance filter is important and, if an unsuitable strategy is used, this can lead to unstabl
... Show MoreThe thermal distribution in the diseased tissue treated by different methods faces the problem of an uncontrollable defused heat. In the present article, we use a plasmonic bowtie nanoantenna working in the near infrared region to enhance the temperature confinement in the tissue. The Computer Simulation Technology Studio Suite package version 2019 was used to execute the design of both plasmonic nanoantenna and the tissue. Gold nanostructure and silicon carbide dioxide are the components the plasmonic nanoantenna in the bowtie shape. The results showed that the distance between the tumor tissue and the antenna is important to determine the intensity field where the maximum field is 5.9*107 V/m at a distance of 100 nm. The maximum
... Show MoreFace recognition is required in various applications, and major progress has been witnessed in this area. Many face recognition algorithms have been proposed thus far; however, achieving high recognition accuracy and low execution time remains a challenge. In this work, a new scheme for face recognition is presented using hybrid orthogonal polynomials to extract features. The embedded image kernel technique is used to decrease the complexity of feature extraction, then a support vector machine is adopted to classify these features. Moreover, a fast-overlapping block processing algorithm for feature extraction is used to reduce the computation time. Extensive evaluation of the proposed method was carried out on two different face ima
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