This valve is intended for use in valves for steering movement, using the qualities of the Magneto-rheological (MR) fluid to regulate the fluid, direct contact without the utilization of moving parts like a spool, a connection between electric flux, and fluid power was made, The simulation was done to employ the" finite element method of magnetism (FEMM)" to arrive at the best design. This software is used for magnetic resonance valve finite element analysis. The valve's best performance was obtained by using a closed directional control valve in the normal state normally closed (NC) MR valve, with simulation results revealing the optimum magnetic flux density in the absence of a current and the shedding condition, as well as the optimum

In this research a study of the effect of quality, sequential and directional layers for three types of fibers are:(Kevlar fibers-49 woven roving and E- glass fiber woven roving and random) on the fatigue property using epoxy as matrix. The test specimens were prepared by hand lay-up method the epoxy resin used as a matrix type (Quick mast 105) in prepared material composit . Sinusoidal wave which is formed of variable stress amplitudes at 15 Hz cycles was employed in the fatigue test ( 10 mm )and (15mm) value 0f deflection arrival to numbers of cycle failure limit, by rotary bending method by ( S-N) curves this curves has been determined ( life , limit and fa

The massive growth of the automotive industry and the development of vehicles use lead to produce a huge amount of waste tire rubber. Rubber tires are non-biodegradable, resulting in environmental problems such as fire risks. In this search, the flexural behavior of steel fiber reinforced self-compacting concrete (SFRSCC) beams containing different percentages and sizes of waste tire rubbers were studied and compared them with the flexural behavior of SCC and SFRSCC. Micro steel fiber (straight type) with aspect ratio 65 was used in mixes. The replacement of coarse and fine aggregate was 20% and 10% with chip and crumb rubber. Also, the replacement of limestone dust and silica fume was 50%, 25%, and 12% with ground rubbe

Lead-free 0.88(Na0.5Bi0.5)TiO3–0.084(K0.5Bi0.5)TiO3–0.036BaTiO3 (BNT–BKT–BT) piezoelectric ceramics were prepared using the conventional mixed-oxide method with a sintering temperature range of 1120–1200 °C. The effect of the sintering temperature on the crystal structure, microstructure, and densification, as well as the dielectrics, piezoelectrics, and the pyroelectric properties of BNT–BKT–BT ceramics were investigated. Scanning electron microscopy and X-ray diffraction were used to study the microstructures of the sintered samples. The results showed that the increase in sintering temperature was very effective in improving both the density and electrical properties. However, the samples deteriorated when the sintering te

The purpose of this paper is to find the best multiplier approximation of unbounded functions in    –space by using some discrete linear positive operators. Also we will estimate the degree of the best multiplier approximation in term of modulus of continuity and the averaged modulus.

In recent years, Bitcoin has become the most widely used blockchain platform in business and finance. The goal of this work is to find a viable prediction model that incorporates and perhaps improves on a combination of available models. Among the techniques utilized in this paper are exponential smoothing, ARIMA, artificial neural networks (ANNs) models, and prediction combination models. The study's most obvious discovery is that artificial intelligence models improve the results of compound prediction models. The second key discovery was that a strong combination forecasting model that responds to the multiple fluctuations that occur in the bitcoin time series and Error improvement should be used. Based on the results, the prediction acc

. In recent years, Bitcoin has become the most widely used blockchain platform in business and finance. The goal of this work is to find a viable prediction model that incorporates and perhaps improves on a combination of available models. Among the techniques utilized in this paper are exponential smoothing, ARIMA, artificial neural networks (ANNs) models, and prediction combination models. The study's most obvious discovery is that artificial intelligence models improve the results of compound prediction models. The second key discovery was that a strong combination forecasting model that responds to the multiple fluctuations that occur in the bitcoin time series and Error improvement should be used. Based on the results, the prediction a