Electronic and optical properties of 5-AVA-functionalized BN nanoclusters: A DFT study

We carried out detailed density functional theory (DFT) and time-dependent density functional theory (TD-DFT) calculations upon 5-aminolevulinic acid-functionalized B12N12 and B16N16 nanoclusters with the B3LYP, B3PW91, and PBE methods using the 6-311+G∗∗ basis set. The calculated adsorption energies of 5-aminolevulinic acid with the BN nanoclusters were evaluated at T = 298.15 and 311.15 K in the gaseous and aqueous environments with the B3LYP, B3PW91, and PBE methods. Our results showed that the adsorption of the 5-AVA molecule (NH2 group) with B12N12 is more favorable than-with the B16N16 nanocluster in the gas and solvent phases. It is anticipated that a 5-aminolevulinic acid (5-AVA) drug incorporating BN clusters could find application in drug delivery systems and in biomedical devices. © The Royal Society of Chemistry and the Centre National de la Recherche Scientifique 2016

Effective squirmer models for self-phoretic chemically active spherical colloids

Various aspects of self-motility of chemically active colloids in Newtonian fluids can be captured by simple models for their chemical activity plus a phoretic slip hydrodynamic boundary condition on their surface. For particles of simple shapes (e.g., spheres) -- as employed in many experimental studies -- which move at very low Reynolds numbers in an unbounded fluid, such models of chemically active particles effectively map onto the well studied so-called hydrodynamic squirmers [S. Michelin and E. Lauga, J. Fluid Mech. \textbf{747}, 572 (2014)]. Accordingly, intuitively appealing analogies of "pusher/puller/neutral" squirmers arise naturally. Within the framework of self-diffusiophoresis we illustrate the above mentioned mapping and the corresponding flows in an unbounded fluid for a number of choices of the activity function (i.e., the spatial distribution and the type of chemical reactions across the surface of the particle). We use the central collision of two active particles as a simple, paradigmatic case for demonstrating that in the presence of other particles or boundaries the behavior of chemically active colloids may be \textit{qualitatively} different, even in the far field, from the one exhibited by the corresponding "effective squirmer", obtained from the mapping in an unbounded fluid. This emphasizes that understanding the collective behavior and the dynamics under geometrical confinement of chemically active particles necessarily requires to explicitly account for the dependence of the hydrodynamic interactions on the distribution of chemical species resulting from the activity of the particles.Comment: 26 pages, 11 figure

AI-based Navigation and Communication Control for a Team of UAVs with Reconfigurable Intelligent Surfaces Supporting Mobile Internet of Vehicles

Unmanned aerial vehicles (UAVs) are employed in wireless communication networks (WCNs) to improve coverage and quality. The applications of UAVs become problematic in the millimeters wave fifth-generation (5G) and beyond in the optical 6G WCNs because of two reasons: 1) higher path loss which means UAVs should fly at lower altitudes to be closer to the user's equipment; 2) complexities associated with a multi-input multi-output antenna to be incorporated in the UAV as an active aerial base station. We propose equipping UAVs with a (passive) reconfigurable intelligent surface (RIS) to resolve the issues with UAV-enabled wireless communication in 5G/6G. In this paper, the trajectory planning of the RIS-equipped UAV (RISeUAV) that renders aerial LoS service (ALoSS) is elaborated. The ALoSS facilitates vehicle-to-vehicle (V2V) and vehicle-to-everything (V2X) communication in obstructed dense urban environments for Internet-of-vehicles. (IoVs). To handle the nonconvexity and computation hardness of the optimization problem we use AI-based deep reinforcement learning to effectively solve the optimality and time complexity issues. Numerical simulation results assess the efficacy of the proposed method

SLAPS: Simultaneous Localization and Phase Shift for a RIS-equipped UAV in 5G/6G Wireless Communication Networks

Unmanned aerial vehicles (UAVs) are utilized to improve the performance of wireless communication networks (WCNs). In 5G/6G WCNs, where massive muti-input multi-output (mMIMO) base stations (BSs) are operated for beamforming to address fast fading, shadowing, and blockage issues of millimeter waves (mmWave) and quasi-optic signals, the application of UAVs as active mMIMO transceivers is questionable. This is due to the prohibitive complexity of the required overhead baseband processor. Reconfigurable intelligent surface (RIS) is a complementary technology to mMIMO BSs to address the energy inefficiency and complexity of 5G/6G WCNs. Equipping UAVs with RISs, comprising passive elements, allows UAVs to remain promising gadgets for improving coverage and blockage issues in 5G/6G by reflecting in the sky and providing aerial line-of-sight (ALoS) service. Particularly, RIS-equipped UAVs (RISeUAVs) can be beneficial for ALoS vehicle-to-vehicle (V2V) communication of autonomous intelligent vehicles. However, channel estimation is prohibitive in a highly dynamic environment. In this light, accurate localization makes it feasible to use geometry information for phase shift and passive beam-steering. Also, accurate localization is required for crash avoidance and safe navigation in dense urban canyons. We propose the simultaneous localization and phase shift (SLAPS) method as a mmWave-localization technique for RISeUAVs. Simulation results prove the effectiveness of the method

Microgrid operation improvement by adaptive virtual impedance

© The Institution of Engineering and Technology 2018.. Microgrids (MGs) are regarded as the best solution for optimal integration of the renewable energy sources into power systems. However, novel control strategies should be developed because of the distinct inherent feature of MG components in comparison to conventional power systems. Although the droop-based control method is adopted in the MG to share power among distributed generation units, its dependency to grid parameters makes its implementation not as convenient as that in conventional power systems. Virtual impedance has been proposed as the complementary part of droop control in MGs. In this study, adaptive virtual impedance is designed considering its effects on the system performance in the MG including: (i) decoupling active and reactive power control by making the grid X/R ratio high, (ii) maximum transferable power through the feeder, (iii) stability concern and (iv) precise reactive power sharing in different operating modes as well as smooth transition from connected mode to islanded mode (IM). To this end, a novel method is proposed to determine the reactive power reference of distributed generation (DG) units according to their contribution in reactive power sharing in IM. In addition, simulation in MATLAB/Simulink environment is conducted to assess the performance of the control system

Deep Reinforcement Learning Based Joint 3D Navigation and Phase Shift Control for Mobile Internet of Vehicles Assisted by RIS-equipped UAVs

Unmanned aerial vehicles (UAVs) are utilized to improve the performance of wireless communication networks (WCNs), notably, in the context of Internet-of-things (IoT). However, the application of UAVs, as active aerial base stations (BSs)/relays, is questionable in the fifth-generation (5G) WCNs with quasi-optic millimeter wave (mmWave) and beyond in 6G (visible light) WCNs. Because path loss is high in 5G/6G networks that attenuate, even, the line-of-sight (LoS) communicating signals propagated by UAVs. Besides, the limited energy/size/weight of UAVs makes it cost-deficient to design aerial multi-input/output BSs for active beamforming to strengthen the signals. Equipping UAVs with the reconfigurable intelligent surface (RIS), a passive component, can help to address the problems with UAV-assisted communication in 5G and optical 6G networks. We propose adopting the RIS-equipped UAV (RISeUAV) to provide aerial LoS service and facilitate communication for mobile Internet-of-vehicles (IoVs) in an obstructed dense urban area covered by 5G/6G. RISeUAV-aided wireless communication facilitates vehicle-to-vehicle/everything communication for IoVs for updating IoT information required for sensor fusion and autonomous driving. However, autonomous navigation of RISeUAV for this purpose is a multilateral problem and is computationally challenging for being optimally implemented in real-time. We intelligently automated RISeUAV navigation using deep reinforcement learning to address the optimality and time complexity issues. Simulation results show the effectiveness of the method