In recent years, the field of research around the congestion problem of 4G and 5G networks has grown, especially those based on artificial intelligence (AI). Although 4G with LTE is seen as a mature technology, there is a continuous improvement in the infrastructure that led to the emergence of 5G networks. As a result of the large services provided in industries, Internet of Things (IoT) applications and smart cities, which have a large amount of exchanged data, a large number of connected devices per area, and high data rates, have brought their own problems and challenges, especially the problem of congestion. In this context, artificial intelligence (AI) models can be considered as one of the main techniques that can be used to solve network congestion problems. Since AI technologies are able to extract relevant features from data and deal with huge amounts of data, the integration of communication networks with AI to solve the congestion problem appears promising, and the research requires exploration. This paper provides a review of how AI technologies can be used to solve the congestion problem in 4G and 5G networks. We examined previous studies addressing the problem of congestion in networks, such as congestion prediction, congestion control, congestion avoidance, and TCP development for congestion control. Finally, we discuss the future vision of using AI technologies in 4G and 5G networks to solve congestion problems and identify research issues that need further study.
This article conclude a theoretical study for the possibility to produce additional electric power from Iraqi steam power plants by cutting – off high-pressure feed water heaters . Three separated steam power plants which Dura , south –Baghdad and Nasria were studied . The investigation showed the possibity of increasing the electric power from 10 to 15% for Dura and Nasria , whereas 6% for south – Baghdad . According to the nowadays of operation to Iraqi steam power plants , the results showed that by cutting–off high pressure feed water heaters we can generate additional electric power about 250 MW during 3-4 hrs. daily. In addition, the fuel consumption can be reduced in comparison with diesel generat
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In this work, an experimental investigation has been done for heat transfer by natural-convection through a horizontal concentric annulus with porous media effects. The porous structure in gap spacing consists of a glass balls and replaced by plastic (PVC) balls with different sizes. The outer surface of outer tube is isothermally cooled while the outer surface of inner tube is heated with constant heat flux condition. The inner tube is heated with different supplied electrical power levels. Four different radius ratios of annulus are used. The effects of porous media material, particles size and annulus radius ratio on heat dissipation in terms of average Nusselt number have been analyzed. |
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Fluidization process is widely used by a great assortment of industries worldwide and represents a trillion dollar industry [6]. They are currently used in separation, classification, drying and mixing of particles, chemical reactions and regeneration processes; one of these processes is the mass transfer from an immersed surface to a gas fluidized bed
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Carbon dioxide (CO2) capture and storage is a critical issue for mitigating climate change. Porous aromatic Schiff base complexes have emerged as a promising class of materials for CO2 capture due to their high surface area, porosity, and stability. In this study, we investigate the potential of Schiff base complexes as an effective media for CO2 storage. We review the synthesis and characterization of porous aromatic Schiff bases materials complexes and examine their CO2 sorption properties. We find that Schiff base complexes exhibit high CO2 adsorption capacity and selectivity, making them a promising candidate for use in carbon capture applications. Moreover, we investigate the effect of various parameters such as temperature, and pressu
... Show MoreIn this article, a numerical method integrated with statistical data simulation technique is introduced to solve a nonlinear system of ordinary differential equations with multiple random variable coefficients. The utilization of Monte Carlo simulation with central divided difference formula of finite difference (FD) method is repeated n times to simulate values of the variable coefficients as random sampling instead being limited as real values with respect to time. The mean of the n final solutions via this integrated technique, named in short as mean Monte Carlo finite difference (MMCFD) method, represents the final solution of the system. This method is proposed for the first time to calculate the numerical solution obtained fo
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