In this work, the modified Lyapunov-Schmidt reduction is used to find a nonlinear Ritz approximation of Fredholm functional defined by the nonhomogeneous Camassa-Holm equation and Benjamin-Bona-Mahony. We introduced the modified Lyapunov-Schmidt reduction for nonhomogeneous problems when the dimension of the null space is equal to two.  The nonlinear Ritz approximation for the nonhomogeneous Camassa-Holm equation has been found as a function of codimension twenty-four.

The effect of mixed corrosion inhibitors in cooling system was evaluated by using carbon steel specimens and weight loss analysis. The carbon steel specimens immersed in mixture of sodium phosphate (Na₂ HPO₄) used as corrosion inhibitor and sodium glocunate (C₆ H₁₁ NaO₇) as a scale dispersant at different concentrations (20,40, 60, 80 ppm) and at different temperature (25,50,75 and 100)ºC for (1-5) days. The corrosion inhibitors efficiency was calculated by using uninhibited and inhibited water to give 98.1%. The result of these investigations indicate that the corrosion rate decreases with the increase the corrosion inhibitors concentration at 80 ppm and at 100ºC for 5 days, (i.e,

In this research, geopolymer mortar had to be designed with 50% to 50% slag and fly ash with and without 1% micro steel fiber at curing temperature of 240℃. The molarity of alkaline solution adjusted with 12 molar sodium hydroxid to sodium silicate was 2 to 1, reaspectivly. The heat of curing increased the geopolymerization proceses of geoplymer mortar, which led to increasing strength, giving the best result and early curing age. The heat was applied for two days by four hours each day. It was discovered in the impact test that the value first crack of each mix was somewhat similar, but the failure increased 72%  for the mixture that did not contain fiber. For the energy observation results it was shown that the mixt

With the continuous downscaling of semiconductor processes, the growing power density and thermal issues in multicore processors become more and more challenging, thus reliable dynamic thermal management (DTM) is required to prevent severe challenges in system performance. The accuracy of the thermal profile, delivered to the DTM manager, plays a critical role in the efficiency and reliability of DTM, different sources of noise and variations in deep submicron (DSM) technologies severely affecting the thermal data that can lead to significant degradation of DTM performance. In this article, we propose a novel fault-tolerance scheme exploiting approximate computing to mitigate the DSM effects on DTM efficiency. Approximate computing in hardw

The injection of Low Salinity Water (LSWI) as an Enhanced Oil Recovery (EOR) method has recently attracted a lot of attention. Extensive research has been conducted to investigate and identify the positive effects of LSWI on oil recovery. In order to demonstrate the impact of introducing low salinity water into a reservoir, simulations on the ECLIPSE 100 simulator are being done in this work. To simulate an actual reservoir, an easy static model was made. In order to replicate the effects of injecting low salinity water and normal salinity, or seawater, the reservoir is three-phase with oil, gas, and water. It has one injector and one producer. Five cases were suggested to investigate the effect of low salinity water injection with differen

This work focuses on the use of biologically produced activated carbon for improving the physi-co-chemical properties of water samples obtained from the Tigris River. An eco-friendly and low-cost activated carbon was prepared from the Alhagi plant using potassium hydroxide (KOH) as an impregnation agent. The prepared activated carbon was characterised using Fourier-transform infrared spectroscopy to determine the functional groups that exist on the raw material (Alhagi plant) and Alhagi activated carbon (AAC). Scanning electron microscope–energy-dispersive X-ray spectroscope was also used to investigate the surface shape and the elements that compose the powder. Brunauer–Emmett–Teller surface area analysis was used to evaluate the spe

The use of real-time machine learning to optimize passport control procedures at airports can greatly improve both the efficiency and security of the processes. To automate and optimize these procedures, AI algorithms such as character recognition, facial recognition, predictive algorithms and automatic data processing can be implemented. The proposed method is to use the R-CNN object detection model to detect passport objects in real-time images collected by passport control cameras. This paper describes the step-by-step process of the proposed approach, which includes pre-processing, training and testing the R-CNN model, integrating it into the passport control system, and evaluating its accuracy and speed for efficient passenger flow