It is well-known that nanofluids differ significantly from traditional heat transfer fluids in terms of their thermal and transfer characteristics. Two of CO2 transfer characteristics, its thermal conductivity and its viscosity, are crucial to improved oil retrieval methods and industries refrigeration. By combining molecular modelling with various machine learning algorithms, this study predicts the conduction characteristics of iron oxide CO2 nanofluids. It is possible to evaluate the accuracy of these transfer parameter estimates by applying machine learning methods such as decision tree, K-nearest neighbors, and linear regression. Predicting these transfer qualities requires knowing the size, fraction of nanoparticle volume, and temperature. To determine the characteristics, molecular dynamics simulations are run using the large-scale atom Vastly equivalent simulant. An inter- and intra-variable Pearson correlation was established to confirm that the input variables were reliant on m and thermal conductivity. The results were finally confirmed by using statistical coefficients of determination. For a variety of temperature ranges, volume fractions, and nanoparticle sizes, the study found that the decision tree model was the best at predicting the transport parameters of nanofluids. It has a 99% success rate.

Data transmission is the process of transmitting digital information from one device to another over a network. With the increasing demand for high-speed data transmission and the proliferation of connected devices, the development of advanced data transmission technologies and networks has become crucial. This paper reviews the most commonly used data transmission technologies and networks, including wired and wireless technologies, and highlights their advantages and limitations.

Multibeam antenna (MBA) systems operating in the millimeter-wave frequency bands have drawn significant attention from researchers and are being effectively studied due to the demanding system needs for the fifth-generation (5G) wireless communication systems and the drastic spectrum scarcity at the existing cellular frequency range. To enable a greater transmission speed, an enhanced signal-to-interference-plus-noise ratio, an enhanced spectral and energy performance, and flexible beam shaping, they serve as the primary antenna technology. They offer tremendous potential for serving as the crucial infrastructure for facilitating beamforming and massive multiple-input multiple-output (MIMO) which uplift the 5G. To achieve this, a novel beamforming design and optimization approach based on the Convex Time Modulated Particle Swarm Optimization (CTM-PSO) algorithm is suggested for 5G communication networks, offering multiple concurrent transceivers to support efficient beamformed interactions whereas a second beam concurrently detects the atmosphere around the base-station. The suggested beamforming method combines the optimization of the transmitter and receiver beamforming weights to increase sensor performance, reduce potential intervention resulting from the communication beams, and ensure the communications link's target beamforming gains. Comprehensive numerical assessments are used to evaluate the effectiveness of the recommended method, showing that it may provide significant improvements over more conventional beamforming methods.