Background: The daily cleaning routine of the silicone maxillofacial prostheses by the patient may cause some alteration in the materials properties. The purpose of the present study was to investigate the effect of different disinfection procedures on some properties of silicon dioxide reinforced Cosmesil M511 HTV maxillofacial silicone. Materials and Methods: One hundred and sixty (160) specimens were prepared by mixing 5% SiO2 nano particles and 0.5% intrinsic cream color into the silicone polymer according to manufacturer's instructions. Specimens were divided into 4 groups according to the performed test (tear strength, surface hardness, surface roughness and color) with 40 specimens each. Each group was further subdivided according to

Some esters were prepared from reaction of different molecular weight of PVA with some acid chloride (prepared by reaction of acid with thionyl chloride or phosphorous pentachloride)in the presence of pyridine. The thermal and reological properties were studied. The increasing Of bulky groups decreasing stability of the thermal and reological properties.

In this paper, mesoscale modeling is performed to simulate and understand fracture behavior of two concrete composites: cement and asphalt concrete using disk-shaped compact tension (DCT) tests. Mesoscale models are used as alternative to macroscale models to obtain better realistic behavior of composite and heterogeneous materials such as cement and asphalt concrete. In mesoscale models, aggregate and matrix are represented as distinct materials and each material has its characteristic properties. Disk-shaped compact tension test is used to obtain tensile strength and fracture energy of materials. This test can be used as a better alternative to other tests such as three points bending tests because it is more convenient for both field and

Bearing capacity of soil is an important factor in designing shallow foundations. It is directly related to foundation dimensions and consequently its performance. The calculations for obtaining the bearing capacity of a soil needs many varying parameters, for example soil type, depth of foundation, unit weight of soil, etc. which makes these calculation very variable–parameter dependent. This paper presents the results of comparison between the theoretical equation stated by Terzaghi and the Artificial Neural Networks (ANN) technique to estimate the ultimate bearing capacity of the strip shallow footing on sandy soils. The results show a very good agreement between the theoretical solution and the ANN technique. Results revealed that us

Objective: Evaluate the effects of different storage periods on flexural strength (FS) and degree of conversion (DC) of Bis-Acryl composite and Urethane dimethacrylate provisional restorative materials. Material and Methods: A total of 60 specimens were prepared from four temporary crown materials commercially available and assigned to four tested groups (n = 15 for each group): Prevision Temp, B&E CROWN, Primma Art, and Charm Temp groups. The specimens were stored in artificial saliva, and the FS was tested after 24 h, 7 d, and 14 d. A standard three-point bending test was conducted using a universal testing machine. Additionally, the DC was determined using a Fourier transform infrared spectroscopy (FTIR) device. The data were analyzed st

In most manufacturing processes, and in spite of statistical control, several process capability indices refer to non conformance of the true mean (µc ) from the target mean ( µT ), and the variation is also high. In this paper, data have been analyzed and studied for a blow molded plastic product (Zahi Bottle) (ZB). WinQSB software was used to facilitate the statistical process control, and process capability analysis and some of capability indices. The relationship between different process capability indices and the true mean of the process were represented, and then with the standard deviation (σ ), of achievement of process capability value that can reduce the standard deviation value and improve production out of theoretical con

Deep learning has recently received a lot of attention as a feasible solution to a variety of artificial intelligence difficulties. Convolutional neural networks (CNNs) outperform other deep learning architectures in the application of object identification and recognition when compared to other machine learning methods. Speech recognition, pattern analysis, and image identification, all benefit from deep neural networks. When performing image operations on noisy images, such as fog removal or low light enhancement, image processing methods such as filtering or image enhancement are required. The study shows the effect of using Multi-scale deep learning Context Aggregation Network CAN on Bilateral Filtering Approximation (BFA) for d

The unstable and uncertain nature of natural rubber prices makes them highly volatile and prone to outliers, which can have a significant impact on both modeling and forecasting. To tackle this issue, the author recommends a hybrid model that combines the autoregressive (AR) and Generalized Autoregressive Conditional Heteroscedasticity (GARCH) models. The model utilizes the Huber weighting function to ensure the forecast value of rubber prices remains sustainable even in the presence of outliers. The study aims to develop a sustainable model and forecast daily prices for a 12-day period by analyzing 2683 daily price data from Standard Malaysian Rubber Grade 20 (SMR 20) in Malaysia. The analysis incorporates two dispersion measurements (I