We have performed first-principles calculations to obtain magnetic moment, magnetocrystalline anisotropy energy (MAE), i.e., the magnetic crystalline anisotropy constant (K), and the Curie temperature (T_c) of low temperature phase (LTP) MnBi and also estimated the maximum energy product (BH)_max at elevated temperatures. The full-potential linearized augmented plane wave (FPLAPW) method, based on density functional theory (DFT) within the local spin density approximation (LSDA), was used to calculate the electronic structure of LPM MnBi. The T_c was calculated by the mean field theory. The calculated magnetic moment, MAE, and T_c are 3.63 μ_B/f.u. (formula unit) (79 emu/g or 714 emu/cm³), −0.163 meV/u.c. (or K = −0.275 × 10⁶ J/m³) and 711 K, respectively. The (BH)_max at the elevated temperatures was estimated by combining experimental coercivity (H_ci) and the temperature dependence of magnetization (M_s(T)). The (BH)_max is 17.7 MGOe at 300 K, which is in good agreement with the experimental result for directionally-solidified LTP MnBi (17 MGOe). In addition, a study of electron density maps and the lattice constant c/a ratio dependence of the magnetic moment suggested that doping of a third element into interstitial sites of LTP MnBi can increase the M_s.

5G-based millimeter Waves (mmWave) systems have the prospective of enabling >1Gbps communications in the Intelligent Transportation Systems (ITS). ITS relies on vehicle-to-everything (V2X) communications to share information among vehicles. However, the V2X Communications via existing technologies such as DSRC, 3G, 4G and LTE, are not able to achieve such a high data rate. Although 5G-based mmWave can support ultra-low-delay V2X transmissions, it comes with beam alignment difficulties as well as the routing stability issues due to rapid mobility of vehicles. The dynamic vehicle traffic causes frequent beam misalignment which tends to degrade the quality-of-service (QoS) performance. In this paper, we first propose a 3D-based position detection scheme for beam alignment/selection purpose. Then a group-based routing algorithm is performed to select a secure path for achieving trustworthy data transmissions. The road traffic is automatically segmented to divide the vehicles into different groups, and each group head is selected and members are added. Group members are authenticated by the group head via elliptic curve algorithms. Huffman coding is performed to compress the data and encrypt the binary files. This proposed novel intelligent beam control and secure stable routing scheme have been verified in simulations to demonstrate much better performance than existing schemes.

First-principles calculations of fundamental magnetic properties were performed for ordered MnBi, MnBi-Co, and MnBi-Co-Fe alloys to evaluate maximum energy product (BH)max. Full potential linear-augmented plane wave (FLAPW) and linear-muffin-tin-orbital (LMTO) calculations using density functional theory (DFT) within the local spin density approximation (LSDA) were used and found to give a reasonable description of saturation magnetization (Ms), effective anisotropy constant (Keff), and Curie temperature (Tc) for NiAs-structured MnBi crystal. We found that upon addition of Co, the Ms and Keff increased, while Tc reduced. The magnetic anisotropy changed from weak anisotropy easy plane for MnBi to the strong easy axis anisotropy for MnBi-Co and MnBi-Co-Fe.

Hybrid electric vehicles can achieve better fuel economy than conventional vehicles by utilizing multiple power sources. While these power sources have been controlled by rule-based or optimization-based control algorithms, recent studies have shown that machine learning-based control algorithms such as online Deep Reinforcement Learning (DRL) can effectively control the power sources as well. However, the optimization and training processes for the online DRL-based powertrain control strategy can be very time and resource intensive. In this paper, a new offline–online hybrid DRL strategy is presented where offline vehicle data are exploited to build an initial model and an online learning algorithm explores a new control policy to further improve the fuel economy. In this manner, it is expected that the agent can learn an environment consisting of the vehicle dynamics in a given driving condition more quickly compared to the online algorithms, which learn the optimal control policy by interacting with the vehicle model from zero initial knowledge. By incorporating a priori offline knowledge, the simulation results show that the proposed approach not only accelerates the learning process and makes the learning process more stable, but also leads to a better fuel economy compared to online only learning algorithms.

We report a simple wireless power charging (WPC) antenna system (50 mm × 40 mm × 0.1 mm) for use in simulating power transfer efficiency (η) with and without ferrite and a metallic object (battery case), thereby, identifying suitable magnetic parameters and ferrite sheet thickness. The simulation results suggest that magnetic loss tangent (tan δμ) and permeability (μ′) need to be less than 0.05 and higher than 125 at 13.56 MHz, respectively, to achieve at least 75% of the transfer efficiency (ηmax) of the WPC antenna without ferrite and the metallic object. Accordingly, we have fabricated NixZn0.85-xCu0.15Fe2O4 (x = 0.32 - 0.38) spinel ferrites using a one-step sintering process and obtained relatively high μ′ of 169 and low tan δμ of 0.1 with a nickel (Ni) concentration of 0.38. This magnetic loss is still too high to achieve 75% of the ηmax. In order to further reduce the magnetic loss, a two-step sintering process was used. The twostep sintered Ni0.38Zn0.47Cu0.15Fe2O4 shows μ′ of 132 and tan δμ of 0.03 at the 13.56 MHz, respectively. Therefore, this ferrite meets criteria identified by the transfer efficiency simulation and is a good candidate for 13.56 MHz wireless power transfer charging antenna system. In addition, our designed WPC system is capable of evaluating other magnetic materials that are suitable for WPC antenna applications.