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In the global effort to reduce CO 2 emissions, the concurrent enhancement of pollutant degradation and reductions in fossil fuel consumption are pivotal aspects of microalgae-mediated wastewater treatment. Clarifying the degradation mechanisms of bacteria and microalgae during pollutant treatment, as well as regulatory biolipid production, could enhance process sustainability. The synergistic and inhibitory relationships between microalgae and bacteria are introduced in this paper. The different stimulators that can regulate microalgal biolipid accumulation are also reviewed. Wastewater treatment technologies that utilize microalgae and bacteria in laboratories and open ponds are described to outline their application in treating heavy metal-containing wastewater, animal husbandry wastewater, pharmaceutical wastewater, and textile dye wastewater. Finally, the major requirements to scale up the cascade utilization of biomass and energy recovery are summarized to improve the development of biological wastewater treatment. [Display omitted] • The synergistic and antagonistic interactions between bacteria and microalgae are summarized. • Different stimulators that could regulate microalgal biolipid accumulation are also reviewed. • Wastewater treatment technologies that utilize microalgae and bacteria in the laboratory and open ponds are described. • The trends in microalgae-bacterial consortium in multistage energy production are proposed. [ABSTRACT FROM AUTHOR]

This article analyzes the robustness and timeliness for cooperative lossy communications in unmanned aerial vehicle (UAV) networks. The analytical framework consists of two steps: 1) calculating the outage probability, and 2) characterizing the Age-of-Information (AoI) for a given outage probability. Initially, we determine the outage probability based on the Shannon's lossy source-channel separation theorem. Numerical results indicate that joint decoding reduces the outage probability, and the system can achieve higher diversity order for less stringent distortion requirement. Then, we derive a closed-form expression of the lower bound on the average AoI for the communication system where outage events are constrained to an acceptable level. Moreover, we conduct a series of simulations for verifying the lower bound on the average AoI and evaluating the impact of UAV locations on the average AoI. It is demonstrated that the lower bound is tight when the server utilization ratio is either relatively busy or idle. For the case with a relatively large outage probability, we propose an intermittent transmission scheme to refine the AoI performance without extra energy consumption. The effectiveness of the intermittent transmission scheme for reducing the average AoI is verified by both the simulations and the lower bound analysis. [ABSTRACT FROM AUTHOR]

Attracted by the advantages of multi-access edge computing (MEC) and non-orthogonal multiple access (NOMA), this article studies the resource allocation problem of a NOMA-MEC system in an ultra-dense network (UDN), where each user may opt for offloading tasks to the MEC server when it is computationally intensive. Our optimization goal is to minimize the system computation cost, concerning the energy consumption and task delay of users. In order to tackle the non-convexity issue of the objective function, we decouple this problem into two sub-problems: user clustering as well as jointly power and computation resource allocation. Firstly, we propose a user clustering matching (UCM) algorithm exploiting the differences in channel gains of users. Then, relying on the mean-field game (MFG) framework, we solve the resource allocation problem for intensive user deployment, using the novel deep deterministic policy gradient (DDPG) method, which is termed by a mean-field-deep deterministic policy gradient (MF-DDPG) algorithm. Finally, a jointly iterative optimization algorithm (JIOA) of UCM and MF-DDPG is proposed to minimize the computation cost of users. The simulation results demonstrate that the proposed algorithm exhibits rapid convergence, and is capable of efficiently reducing both the energy consumption and task delay of users. [ABSTRACT FROM AUTHOR]

As an emerging technology in 5G, ultra-dense unmanned aerial vehicles (UAVs) network can significantly improve the system capacity and networks coverage. However, it is still a challenge to reduce interference and improve energy efficiency (EE) of UAVs. In this paper, we investigate a downlink power control problem to maximize the EE in an ultra-dense UAV network. Firstly, the power control problem is formulated as a discrete mean field game (MFG) to imitate the interactions among a large number of UAVs, and then the MFG framework is transformed into a Markov decision process (MDP) to obtain the equilibrium solution of the MFG due to the dense deployment of UAVs. Specifically, a deep reinforcement learning-based MFG (DRL-MFG) algorithm is proposed to suppress the interference and maximize the EE by using deep neural networks (DNN) to explore the optimal power strategy for UAVs. The numerical results show that the UAVs can effectively interact with the environment to obtain the optimal power control strategy. Compared with the benchmarks algorithms, the DRL-MFG algorithm converges faster to the solution of MFG and improves the EE of UAVs. Moreover, the impact of the transmit power on EE under the different heights of the UAVs is also analyzed. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) technology has recently drawn more attention due to its ability to achieve the interconnections of massive physic devices. However, how to provide a reliable power supply to energy-constrained devices and improve the energy efficiency in the wireless powered IoT (WP-IoT) is a twofold challenge. In this paper, we develop a novel wireless power transmission (WPT) system, where an unmanned aerial vehicle (UAV) equipped with radio frequency energy transmitter charges the IoT devices. A machine learning framework of echo state networks together with an improved ${k}$ -means clustering algorithm is used to predict the energy consumption and cluster all the sensor nodes at the next period, thus automatically determining the charging strategy. The energy obtained from the UAV by WPT supports the IoT devices to communicate with each other. In order to improve the energy efficiency of the WP-IoT system, the interference mitigation problem is modeled as a mean field game, where an optimal power control policy is presented to adapt and analyze the large number of sensor nodes randomly deployed in WP-IoT. The numerical results verify that our proposed dynamic charging policy effectively reduces the data packet loss rate, and that the optimal power control policy greatly mitigates the interference, and improve the energy efficiency of the whole network. [ABSTRACT FROM AUTHOR]

Unmanned aerial vehicle (UAV) cloud can greatly enhance the intelligence of unmanned systems by dynamically unloading the compute-intensive applications to cloud. For the uncertain nature of UAV missions and fast-changing environment, different UAV applications may have different quality of service (QoS) requirements. This paper proposes a mixed QoS ensurance and energy-balanced (MQEB) architecture for UAV cloud from a view of control theory, which can support both hard and soft QoS ensurance with the consideration of energy saving. The hard and soft QoS requirements are decoupled by being normalized into a two-level cascaded feedback loop. The former is time slot loop (TS-Loop) to enforce the absolute QoS ensurance for real-time applications, and the latter is contention window loop (CW-Loop) to enforce the plastic QoS ensurance for non-real-time applications. Finally, the back propagating (BP) neuron network is used for parameters’ self-tuning and controller design. The hardware experiments demonstrate the feasibility of MQEB. In heavy load, MQEB has greater throughput and better energy efficiency, and in light load, MQBE has lower total power consumption. [ABSTRACT FROM AUTHOR]

The energy efficiency (EE) of a multi-user multi-relay system with the maximum diversity network coding (MDNC) is studied. We explicitly find the connection among the outage probability, energy consumption, and EE, and formulate the maximizing EE problem under the outage probability constraint. Relay scheduling (RS) and power allocation (PA) are applied to schedule the relay states (transmitting, sleeping, and so on) and optimize the transmitting power under the practical channel and power consumption models. Since the optimization problem is NP hard, to reduce computational complexity, the outage probability is first tightly approximated to a log-convex form. Furthermore, the EE is converted into a subtractive form based on the fractional programming. Then, a convex mixed-integer nonlinear problem is eventually obtained. With a generalized outer approximation algorithm, RS and PA are solved in an iterative manner. The Pareto-optimal curves between the EE and the target outage probability show the EE gains from PA and RS. Moreover, by comparing with the no network coding (NoNC) scenario, we conclude that with the same number of relays, MDNC can lead to EE gains. However, if RS is implemented, NoNC can outperform MDNC in terms of the EE when more relays are needed in the MDNC scheme. [ABSTRACT FROM PUBLISHER]

Abstract: This paper presents the research of a high frequency, high efficiency Solar inverter using silicon carbide power semiconductor device. Compared to silicon power devices, the silicon carbide power device is more suitable for solar power inverter due to its good electric characteristics. This paper presents the design of sic power semiconductor device based solar inverter. The experiment results of a 1kW silicon carbide JFET based inverter showed about 3% efficiency improvement by a silicon carbide JFET-based inverter over silicon IGBT based inverter. [Copyright &y& Elsevier]

Unmanned aerial vehicle (UAV) and reconfigurable intelligent surface (RIS) methods are promising techniques for improving the energy and spectrum efficiency of Fifth Generation/Beyond Fifth Generation (5G/B5G) networks. In order to take advantage of both techniques, we propose an RIS-assisted UAV secure communication scheme, where an UAV is equipped with RIS to facilitate secure transmission. To maximize the average secrecy rate, we jointly optimize the beamforming power, reflect phase shift, and UAV's trajectory. For this non-convex problem, we decompose it into the power beamforming problem, the phase shift optimization problem, and the UAV's trajectory design problem, and proposed an efficient iterative algorithm to solve the problem. The numerical results verify the superiority of the proposed scheme that can improve the average secure transmission rate by about 20% compared to that of Eavesdropping elimination methods. [ABSTRACT FROM AUTHOR]