The adhesion strength between Polyethylene (PE) film and Aluminum surface by using the adhesive material (Cyanoacrylate) has been studied. Aluminum (Al) was used as a substrate, and polyethylene (PE) was used as a film adhered to the Al surface. Standard specimens were prepared to use in the peeling test in dry condition, other specimens were immersed in water for 12 days at room temperature. the results for the specimens in the dry condition had shown that high value in the peel force and the peel energy, the peel force was 0.38*103 N/m and the peel energy was 0.605*103 N/m, peeling the film from Al surface leaves a residual of the adhesive material on both adherend, the failure for this specimen were combination of adhesive and cohesive f

Encasing glass fiber reinforced polymer (GFRP) beam with reinforced concrete (RC) improves stability, prevents buckling of the web, and enhances the fire resistance efficiency. This paper provides experimental and numerical investigations on the flexural performance of RC specimens composite with encased pultruded GFRP I-sections. The effect of using shear studs to improve the composite interaction between the GFRP beam and concrete was explored. Three specimens were tested under three-point loading. The deformations, strains in the GFRP beams, and slippages between the GFRP beams and concrete were recorded. The embedded GFRP beam enhanced the peak loads by 65% and 51% for the composite specimens with and without shear connectors,

Concrete columns with hollow-core sections find widespread application owing to their excellent structural efficiency and efficient material utilization. However, corrosion poses a challenge in concrete buildings with steel reinforcement. This paper explores the possibility of using glass fiber-reinforced polymer (GFRP) reinforcement as a non-corrosive and economically viable substitute for steel reinforcement in short square hollow concrete columns. Twelve hollow short columns were meticulously prepared in the laboratory experiments and subjected to pure axial compressive loads until failure. All columns featured a hollow square section with exterior dimensions of (180 × 180) mm and 900 mm height. The columns were categorized into

Aims: This study was conducted to assess the effect of the addition of yttrium oxide (Y2O3) nanoparticles on the tensile bond strength, tear strength, shore A hardness, and surface roughness of soft-denture lining material. Materials and Methods: Y2O3 NPs with 1.5 and 2 wt.% were added into acrylic-based heat-cured soft-denture liner. A total of 120 specimens were prepared and divided into four groups according to the test to be performed (tensile bond strength, tear strength, surface hardness, and surface roughness). Results: There was a highly significant increase in tensile bond strength between the soft liner and the acrylic denture base, tear strength, and hardness at both concentrations as compared to the control group, whereas ther

Aim: To evaluate the effect of ultrasonic agitation for retrograde biodceramic root repair, MTA and biodentine filling materials on push-out bond strength to dentine walls. Materials and Methods: Ninety extracted human teeth with single straight roots were selected randomly. After disinfection and cleaning, the coronal portions were sectioned to standardize the root canal length at 15mm. following root canal shaping, obturation and apical roots resection, retrograde cavities were prepared. Teeth were categorized depending on the filling material used into three groups, 30 teeth each. Group A filled with bioceramic root repair material, B with MTA and C with Biodentine material. These groups were divided in to three subgroup (n= 10). Subgrou

Codes of red, green, and blue data (RGB) extracted from a lab-fabricated colorimeter device were used to build a proposed classifier with the objective of classifying colors of objects based on defined categories of fundamental colors. Primary, secondary, and tertiary colors namely red, green, orange, yellow, pink, purple, blue, brown, grey, white, and black, were employed in machine learning (ML) by applying an artificial neural network (ANN) algorithm using Python. The classifier, which was based on the ANN algorithm, required a definition of the mentioned eleven colors in the form of RGB codes in order to acquire the capability of classification. The software's capacity to forecast the color of the code that belongs to an ob

Codes of red, green, and blue data (RGB) extracted from a lab-fabricated colorimeter device were used to build a proposed classifier with the objective of classifying colors of objects based on defined categories of fundamental colors. Primary, secondary, and tertiary colors namely red, green, orange, yellow, pink, purple, blue, brown, grey, white, and black, were employed in machine learning (ML) by applying an artificial neural network (ANN) algorithm using Python. The classifier, which was based on the ANN algorithm, required a definition of the mentioned eleven colors in the form of RGB codes in order to acquire the capability of classification. The software's capacity to forecast the color of the code that belongs to an object under de