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This paper is concerned with an analysis of the electrical conductivity of graphene/cement composites by means of DC (direct current) and AC (alternating current) techniques. Moreover, the micrograph and element composition of composites have been characterized through SEM (scanning electron microscopy) and EDS (energy-dispersive spectrometers) techniques, respectively. Results revealed that a percolation transition region Φ2–Φ1 (Φ2 and Φ1 values are determined as 0.8% and 1.8%, respectively) can be observed in the S-shaped curve. In addition, the logistic model has been recommended to characterize the relationship between the conductivity and the graphene concentration, which ranged from 0.001% to 2.5%. The micrographs obtained by SEM technique clearly indicate a complete conductive network as well as agglomeration of graphene slices when the graphene content reaches the threshold value. Furthermore, graphene slices can be distinguished from the cement hydration products by means of the analysis of element composition obtained through the EDS technique. It is promising to apply the graphene/cement composites as intelligent materials. [ABSTRACT FROM AUTHOR]

A design scheme of multi-element sensor which included electrical resistivity probes, multiple Cl− selective electrodes, and a steel corrosion monitoring system was proposed in this work. Embedding this multi-element sensor in concrete enables the real-time and non-destructive monitoring of internal electrical resistivity, free Cl− (Clf) contents in the concrete pore solution at different depths, and steel corrosion parameters. Based on the monitoring data obtained by the multi-element sensor, the freezing-thawing (F-T) damage degree, the Clf diffusion coefficient, the quantitative relation between F-T damage degree and Clf diffusion coefficient, the initiation period of steel corrosion, and the critical content related to steel corrosion are determined. To conclude, the multi-element sensor provides key durability parameters for the establishment of the Clf diffusion model, the assessment of health condition, and the prediction of service life of concrete under the coexistence of the F-T cycle and Cl−. [ABSTRACT FROM AUTHOR]

A reconfigurable intelligent reflective surface (RIS)-assisted cognitive radio (CR) multiple-input multiple-output (MIMO) secure communication system is considered. In the presence of an eavesdropper, a primary base station (PBS) and a cognitive base station (CBS) equipped with multiple antennas communicate to a primary user (PU) and a secondary user (SU), respectively. In order to maximize the achievable secrecy rate of the system, the secrecy rate maximization problem is first transformed into a secure energy efficiency (SEE) problem using an objective function. Then, the security energy efficiency of the system is maximized by jointly optimizing the transmit beam formation of the base station and the reflected beam formation of the smart reflecting surface under the conditions that the total transmitted power constraint and the interference power constraint are satisfied. To address the difficulty in solving the resulting optimization problem, we apply an algorithm based on alternating optimization and semidefinite relaxation, as well as Dinklbach's algorithm, to solve the problem. Simulation results show that this method can significantly improve the safety energy efficiency of the system. [ABSTRACT FROM AUTHOR]

Future wireless communication systems require higher performance requirements. Based on this, we study the combinatorial optimization problem of power allocation and dynamic user pairing in a downlink multicarrier non-orthogonal multiple-access (NOMA) system scenario, aiming at maximizing the user sum rate of the overall system. Due to the complex coupling of variables, it is difficult and time-consuming to obtain an optimal solution, making engineering impractical. To circumvent the difficulties and obtain a sub-optimal solution, we decompose this optimization problem into two sub-problems. First, a closed-form expression for the optimal power allocation scheme is obtained for a given subchannel allocation. Then, we provide the optimal user-pairing scheme using the actor–critic (AC) algorithm. As a promising approach to solving the exhaustive problem, deep-reinforcement learning (DRL) possesses higher learning ability and better self-adaptive capability than traditional optimization methods. Simulation results have demonstrated that our method has significant advantages over traditional methods and other deep-learning algorithms, and effectively improves the communication performance of NOMA transmission to some extent. [ABSTRACT FROM AUTHOR]

Temperature distribution in concrete is significant to the concrete structure's macro properties and different factors affect the heat transfer in concrete, and therefore influence the temperature distribution. This work established a three-dimensional transient heat transfer model coupled with various environmental factors, using the finite element method for calculating the results and real-measured data for testing accuracy. In addition, a sensitivity evaluation of various factors was conducted. Due to various environmental factors, the results revealed that the prediction of temperature distribution in concrete by the three-dimensional model had great accuracy with an error of less than 4%. A particular hysteresis effect of temperature response in the concrete existed. Considering heat transfer in different spatial directions, the model can predict the temperature change of each spatial point instead of the spatial surface in different depths, proving the shortcomings of a one-dimensional heat transfer model. A greater solar radiation intensity caused a more significant temperature difference on the concrete surface: the surface temperature difference in July was twice as significant as that in December. Wind speed had a cooling effect on the concrete surface, and stronger wind speed accompanied with a stronger cooling effect made the surface temperature closer to the ambient temperature. Material properties had different effects on the temperature distribution of the surface part and internal part: the specific heat capacity determined the speed of the outer layer temperature change while the thermal conductivity determined the speed of the inner layer temperature change. [ABSTRACT FROM AUTHOR]