High performance self-consolidating concrete HP-SCC is one of the most complex types of concrete which have the capacity to consolidated under its own weight, have excellent homogeneity and high durability. This study aims to focus on the possibility of using industrial by-products like Silica fumes SF in the preparation of HP-SCC enhanced with discrete steel fibers (DSF) and monofilament polypropylene fibers (PPF). From experimental results, it was found that using DSF with volume fraction of 0.50 %; a highly improvements were gained in the mechanical properties of HP-SCC. The compressive strength, splitting tensile strength, flexural strength and elastic modulus improved about 65.7 %, 70.5 %, 41.7 % and 80.3 % at 28 days age, respectively with retaining the main properties of HP-SCC. Furthermore, using PPF with volume fraction up to 0.10 %; an enhancement in the compressive and elastic modulus of HP-SCC about 47.4 % and 31 % was obtained at age of 28 days, respectively. Moreover, using PPF with volume fraction of 0.15 %, the splitting tensile strength and flexural strength of HP-SCC improved about 44.5 % and 20.3 %, respectively.
In this paper, a handwritten digit classification system is proposed based on the Discrete Wavelet Transform and Spike Neural Network. The system consists of three stages. The first stage is for preprocessing the data and the second stage is for feature extraction, which is based on Discrete Wavelet Transform (DWT). The third stage is for classification and is based on a Spiking Neural Network (SNN). To evaluate the system, two standard databases are used: the MADBase database and the MNIST database. The proposed system achieved a high classification accuracy rate with 99.1% for the MADBase database and 99.9% for the MNIST database
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Background: This in vitro study compares a self-etch primer (SEP) to an etch-and-rinse (EaR) for bonding sapphire brackets by evaluation of the enamel etch-pattern, shear bond strength, amount of remnant adhesive and enamel surface damage following thermal and fatigue cyclic loading. Material and Methods: Ceramic (sapphire) brackets were bonded to 80 extracted human premolars using two enamel etching protocols: conventional EaR using 37% phosphoric acid (PA) gel (control), and a SEP (Transbond Plus). Each group was subdivided into two subgroups (n=20 teeth) according to the time of bracket debonding: after 24 h water storage or following 5000 thermo-cycles plus 5000 cycles fatigue loading, to determine the shear bond strength (SBS), adhesiv
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Background This in vitro study compares a self-etch primer (SEP) to an etch-and-rinse (EaR) for bonding sapphire brackets by evaluation of the enamel etch-pattern, shear bond strength, amount of remnant adhesive and enamel surface damage following thermal and fatigue cyclic loading. Material and Methods Ceramic (sapphire) brackets were bonded to 80 extracted human premolars using two enamel etching protocols: conventional EaR using 37% phosphoric acid (PA) gel (control), and a SEP (Transbond Plus). Each group was subdivided into two subgroups (n=20 teeth) according to the time of bracket debonding: after 24 h water storage or following 5000 thermo-cycles plus 5000 cycles fatigue loading, to determine the shear bond strength (SBS), adhesive
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The influence and hazard of fire flame are one of the most important parameters that affecting the durability and strength of structural members. This research studied the influence of fire flame on the behavior of reinforced concrete beams affected by repeated load. Nine self- compacted reinforced concrete beams were castellated, all have the same geometric layout (0.15x0.15x1.00) m, reinforcement details and compressive strength (50 Mpa). To estimate the effect of fire flame disaster, four temperatures were adopted (200, 300, 400 and 500) oC and two method of cooling were used (graduated and sudden). In the first cooling method, graduated, the tested beams were leaved to cool in air while in the second method, sudden, water splash was use
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Researchers need to understand the differences between parametric and nonparametric regression models and how they work with available information about the relationship between response and explanatory variables and the distribution of random errors. This paper proposes a new nonparametric regression function for the kernel and employs it with the Nadaraya-Watson kernel estimator method and the Gaussian kernel function. The proposed kernel function (AMS) is then compared to the Gaussian kernel and the traditional parametric method, the ordinary least squares method (OLS). The objective of this study is to examine the effectiveness of nonparametric regression and identify the best-performing model when employing the Nadaraya-Watson
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