Background: Limited data are available on the dimensional stability and surface roughness of ThermoSens, which is a material used in denture processing. This study aimed to measure the vertical teeth changes and surface roughness of ThermoSens dentures prepared using three different investment materials. Materials and methods: For the dimensional changes test, 30 complete maxillary dentures were prepared using different investment methods: group I, dental stone; group II, silicone putty; and group III, a mixture of dental stone and plaster (ratio, 1:1; n = 10 for each group). Four screws were attached to the dentures: two were attached to the buccal surface of the canine and first molar, and the other two were attached in the flange areas o

In order to obtain a mixed model with high significance and accurate alertness, it is necessary to search for the method that performs the task of selecting the most important variables to be included in the model, especially when the data under study suffers from the problem of multicollinearity as well as the problem of high dimensions. The research aims to compare some methods of choosing the explanatory variables and the estimation of the parameters of the regression model, which are Bayesian Ridge Regression (unbiased) and the adaptive Lasso regression model, using simulation. MSE was used to compare the methods.

The aim of the research is to identify the economic and cultural role of contemporary interactive artworks from the public's point of view, with knowledge of the most important obstacles and proposals to improve the level of design and spread of interactive artworks. The descriptive approach was used, and the researcher used a questionnaire tool that was distributed to the audience for the events of Noor Al Riyadh (2021) and the size of the participating sample was (194) individuals from all spectrums of society. It was found through the results of the existence of public approval of the economic role of contemporary interactive artworks, as they contribute to supporting the national economy. It attracts the public to attend various paid

Positron annihilation lifetime has been utilized for the first time to investigate the free - volume hole properties in thermolumenscent dosimeter ( TLD ) as a function of gamma-dosc . The hole volume, free volume fraction determined form orthopsitronium lifetime are found to be ?lamatically increase to large values , and then to minimum values as a function ofgamma-dose . The free - volume holes size is found to be 0.163nm’ and to have maximum of 0.166nm^ at the gamma-dose of 0.1 and 0.8 Gy, respectively-

Echocardiography is a widely used imaging technique to examine various cardiac functions, especially to detect the left ventricular wall motion abnormality. Unfortunately the quality of echocardiograph images and complexities of underlying motion captured, makes it difficult for an in-experienced physicians/ radiologist to describe the motion abnormalities in a crisp way, leading to possible errors in diagnosis. In this study, we present a method to analyze left ventricular wall motion, by using optical flow to estimate velocities of the left ventricular wall segments and find relation between these segments motion. The proposed method will be able to present real clinical help to verify the left ventricular wall motion diagnosis.

In this paper the use of a circular array antenna with adaptive system in conjunction with modified Linearly Constrained Minimum Variance Beam forming (LCMVB) algorithm is proposed to meet the requirement of Angle of Arrival (AOA) estimation in 2-D as well as the Signal to Noise Ratio (SNR) of estimated sources (Three Dimensional 3-D estimation), rather than interference cancelation as it is used for. The proposed system was simulated, tested and compared with the modified Multiple Signal Classification (MUSIC) technique for 2-D estimation. The results show the system has exhibited astonishing results for simultaneously estimating 3-D parameters with accuracy approximately equivalent to the MUSIC technique (for estimating elevation and a

In high-dimensional semiparametric regression, balancing accuracy and interpretability often requires combining dimension reduction with variable selection. This study intro- duces two novel methods for dimension reduction in additive partial linear models: (i) minimum average variance estimation (MAVE) combined with the adaptive least abso- lute shrinkage and selection operator (MAVE-ALASSO) and (ii) MAVE with smoothly clipped absolute deviation (MAVE-SCAD). These methods leverage the flexibility of MAVE for sufficient dimension reduction while incorporating adaptive penalties to en- sure sparse and interpretable models. The performance of both methods is evaluated through simulations using the mean squared error and variable selection cri

This paper is concerned with the numerical solutions of the vorticity transport equation (VTE) in two-dimensional space with homogenous Dirichlet boundary conditions. Namely, for this problem, the Crank-Nicolson finite difference equation is derived.  In addition, the consistency and stability of the Crank-Nicolson method are studied. Moreover, a numerical experiment is considered to study the convergence of the Crank-Nicolson scheme and to visualize the discrete graphs for the vorticity and stream functions. The analytical result shows that the proposed scheme is consistent, whereas the numerical results show that the solutions are stable with small space-steps and at any time levels.

Autism Spectrum Disorder, also known as ASD, is a neurodevelopmental disease that impairs speech, social interaction, and behavior. Machine learning is a field of artificial intelligence that focuses on creating algorithms that can learn patterns and make ASD classification based on input data. The results of using machine learning algorithms to categorize ASD have been inconsistent. More research is needed to improve the accuracy of the classification of ASD. To address this, deep learning such as 1D CNN has been proposed as an alternative for the classification of ASD detection. The proposed techniques are evaluated on publicly available three different ASD datasets (children, Adults, and adolescents). Results strongly suggest that 1D