Temperature-dependent exciton-related transition energies mediated by carrier concentrations in unintentionally Al-doped ZnO films

The authors reported on a carrier-concentration mediation of exciton-related radiative transition energies in Al-doped ZnO films utilizing temperature-dependent (TD) photoluminescence and TD Hall-effect characterizations. The transition energies of free and donor bound excitons consistently change with the measured TD carrier concentrations. Such a carrier-concentration mediation effect can be well described from the view of heavy-doping-induced free-carrier screening and band gap renormalization effects. This study gives an important development to the currently known optical properties of ZnO materials.This research is supported by the State Key Program for Basic Research of China under Grant No. 2011CB302003, National Natural Science Foundation of China (Nos. 61025020, 60990312, and 61274058), Basic Research Program of Jiangsu Province (BK2011437), and the Priority Academic Program Development of Jiangsu Higher Education Institutions

Thermal pretreatment of sapphire substrates prior to ZnO buffer layer growth

The properties of ZnO buffer layers grown via metal-organic chemical vapor deposition (MOCVD) on sapphire substrates after various thermal pretreatments are systematically investigated. High-temperature pretreatments lead to significant modifications of the sapphire surface, which result in enhanced growth nucleation and a consequent improvement of the surface morphology and quality of the ZnO layers. The evolution of the surface morphology as seen by atomic force microscopy indicates an obvious growth mode transition from three-dimensional to quasi-two-dimensional as the pretreatment temperature increases. A minimum surface roughness is obtained when the pretreatment temperature reaches 1150 °C, implying that a high-temperature pretreatment at 1150 °C or above may lead to a conversion of the surface polarity from O-face to Zn-face, similar to processes in GaN material growth via MOCVD. By analyzing the evolution of the film properties as a function of pretreatment temperature, the optimal condition has been determined to be at 1150 °C. This study indicates that a high-temperature pretreatment is crucial to grow high-quality ZnO on sapphire substrates by MOCVD.This research was supported by the State Key Program for Basic Research of China under Grant No. 2011CB302003, National Natural Science Foundation of China (Nos. 61025020, 60990312, and 61274058), Basic Research Program of Jiangsu Province (BK2011437), and the Priority Academic Program Development of Jiangsu Higher Education Institutions

Vertical Segregation and Phylogenetic Characterization of Ammonia-Oxidizing Bacteria and Archaea in the Sediment of a Freshwater Aquaculture Pond

Pond aquaculture is the major freshwater aquaculture method in China. Ammonia-oxidizing communities inhabiting pond sediments play an important role in controlling culture water quality. However, the distribution and activities of ammonia-oxidizing microbial communities along sediment profiles are poorly understood in this specific environment. Vertical variations in the abundance, transcription, potential ammonia oxidizing rate, and community composition of ammonia-oxidizing bacteria (AOB) and ammonia-oxidizing archaea (AOA) in sediment samples (0–50 cm depth) collected from a freshwater aquaculture pond were investigated. The concentrations of the AOA amoA gene were higher than those of the AOB by an order of magnitude, which suggested that AOA, as opposed to AOB, were the numerically predominant ammonia-oxidizing organisms in the surface sediment. This could be attributed to the fact that AOA are more resistant to low levels of dissolved oxygen. However, the concentrations of the AOB amoA mRNA were higher than those of the AOA by 2.5- to 39.9-fold in surface sediments (0–10 cm depth), which suggests that the oxidation of ammonia was mainly performed by AOB in the surface sediments, and by AOA in the deeper sediments, where only AOA could be detected. Clone libraries of AOA and AOB amoA sequences indicated that the diversity of AOA and AOB decreased with increasing depth. The AOB community consisted of two groups: the Nitrosospira and Nitrosomonas clusters, and Nitrosomonas were predominant in the freshwater pond sediment. All AOA amoA gene sequences in the 0–2 cm deep sediment were grouped into the Nitrososphaera cluster, while other AOA sequences in deeper sediments (10–15 and 20–25 cm depths) were grouped into the Nitrosopumilus cluster

The Impact of Roof Pitch and Ceiling Insulation on Cooling Load of Naturally-Ventilated Attics

A 2D unsteady computational fluid dynamics (CFD) model is employed to simulate buoyancy-driven turbulent ventilation in attics with different pitch values and ceiling insulation levels under summer conditions. The impacts of roof pitch and ceiling insulation on the cooling load of gable-roof residential buildings are investigated based on the simulation of turbulent air flow and natural convection heat transfer in attic spaces with roof pitches from 3/12 to 18/12 combined with ceiling insulation levels from R-1.2 to R-40. The modeling results show that the air flows in the attics are steady and exhibit a general streamline pattern that is qualitatively insensitive to the investigated variations of roof pitch and ceiling insulation. Furthermore, it is predicted that the ceiling insulation plays a control role on the attic cooling load and that an increase of roof pitch from 3/12 to 8/12 results in a decrease in the cooling load by around 9% in the investigated cases. The results suggest that the increase of roof pitch alone, without changing other design parameters, has limited impact on attics cooling load and airflow pattern. The research results also suggest both the predicted ventilating mass flow rate and attic cooling load can be satisfactorily correlated by simple relationships in terms of appropriately defined Rayleigh and Nusselt numbers

Multiagent Reinforcement Learning with an Attention Mechanism for Improving Energy Efficiency in LoRa Networks

Long Range (LoRa) wireless technology, characterized by low power consumption and a long communication range, is regarded as one of the enabling technologies for the Industrial Internet of Things (IIoT). However, as the network scale increases, the energy efficiency (EE) of LoRa networks decreases sharply due to severe packet collisions. To address this issue, it is essential to appropriately assign transmission parameters such as the spreading factor and transmission power for each end device (ED). However, due to the sporadic traffic and low duty cycle of LoRa networks, evaluating the system EE performance under different parameter settings is time-consuming. Therefore, we first formulate an analytical model to calculate the system EE. On this basis, we propose a transmission parameter allocation algorithm based on multiagent reinforcement learning (MALoRa) with the aim of maximizing the system EE of LoRa networks. Notably, MALoRa employs an attention mechanism to guide each ED to better learn how much ''attention'' should be given to the parameter assignments for relevant EDs when seeking to improve the system EE. Simulation results demonstrate that MALoRa significantly improves the system EE compared with baseline algorithms with an acceptable degradation in packet delivery rate (PDR).Comment: 6 pages, 3 figures, This paper has been accepted for publication in IEEE Global Communications Conference (GLOBECOM) 202

AoI-aware Sensing Scheduling and Trajectory Optimization for Multi-UAV-assisted Wireless Backscatter Networks

This paper considers multiple unmanned aerial vehicles (UAVs) to assist sensing data transmissions from the ground users (GUs) to a remote base station (BS). Each UAV collects sensing data from the GUs and then forwards the sensing data to the remote BS. The GUs first backscatter their data to the UAVs and then all UAVs forward data to the BS by the nonorthogonal multiple access (NOMA) transmissions. We formulate a multi-stage stochastic optimization problem to minimize the long-term time-averaged age-of-information (AoI) by jointly optimizing the GUs' access control, the UAVs' beamforming, and trajectory planning strategies. To solve this problem, we first model the dynamics of the GUs' AoI statuses by virtual queueing systems, and then propose the AoI-aware sensing scheduling and trajectory optimization (AoI-STO) algorithm. This allows us to transform the multi-stage AoI minimization problem into a series of per-slot control problems by using the Lyapunov optimization framework. In each time slot, the GUs' access control, the UAVs' beamforming, and mobility control strategies are updated by using the block coordinate descent (BCD) method according to the instant GUs' AoI statuses. Simulation results reveal that the proposed AoI-STO algorithm can reduce the overall AoI by more than 50%. The GUs' scheduling fairness is also improved greatly by adapting the GUs' access control compared with typical baseline schemes.Comment: This paper has been accepted by IEEE TV

Bayesian Optimization Enhanced Deep Reinforcement Learning for Trajectory Planning and Network Formation in Multi-UAV Networks

In this paper, we employ multiple UAVs coordinated by a base station (BS) to help the ground users (GUs) to offload their sensing data. Different UAVs can adapt their trajectories and network formation to expedite data transmissions via multi-hop relaying. The trajectory planning aims to collect all GUs' data, while the UAVs' network formation optimizes the multi-hop UAV network topology to minimize the energy consumption and transmission delay. The joint network formation and trajectory optimization is solved by a two-step iterative approach. Firstly, we devise the adaptive network formation scheme by using a heuristic algorithm to balance the UAVs' energy consumption and data queue size. Then, with the fixed network formation, the UAVs' trajectories are further optimized by using multi-agent deep reinforcement learning without knowing the GUs' traffic demands and spatial distribution. To improve the learning efficiency, we further employ Bayesian optimization to estimate the UAVs' flying decisions based on historical trajectory points. This helps avoid inefficient action explorations and improves the convergence rate in the model training. The simulation results reveal close spatial-temporal couplings between the UAVs' trajectory planning and network formation. Compared with several baselines, our solution can better exploit the UAVs' cooperation in data offloading, thus improving energy efficiency and delay performance.Comment: 15 pages, 10 figures, 2 algorithm

Hierarchical Deep Reinforcement Learning for Age-of-Information Minimization in IRS-aided and Wireless-powered Wireless Networks

In this paper, we focus on a wireless-powered sensor network coordinated by a multi-antenna access point (AP). Each node can generate sensing information and report the latest information to the AP using the energy harvested from the AP's signal beamforming. We aim to minimize the average age-of-information (AoI) by adapting the nodes' transmission scheduling and the transmission control strategies jointly. To reduce the transmission delay, an intelligent reflecting surface (IRS) is used to enhance the channel conditions by controlling the AP's beamforming vector and the IRS's phase shifting matrix. Considering dynamic data arrivals at different sensing nodes, we propose a hierarchical deep reinforcement learning (DRL) framework to for AoI minimization in two steps. The users' transmission scheduling is firstly determined by the outer-loop DRL approach, e.g. the DQN or PPO algorithm, and then the inner-loop optimization is used to adapt either the uplink information transmission or downlink energy transfer to all nodes. A simple and efficient approximation is also proposed to reduce the inner-loop rum time overhead. Numerical results verify that the hierarchical learning framework outperforms typical baselines in terms of the average AoI and proportional fairness among different nodes.Comment: 31 pages, 6 figures, 2 tables, 3 algorithm

Matching-Driven Deep Reinforcement Learning for Energy-Efficient Transmission Parameter Allocation in Multi-Gateway LoRa Networks

Long-range (LoRa) communication technology, distinguished by its low power consumption and long communication range, is widely used in the Internet of Things. Nevertheless, the LoRa MAC layer adopts pure ALOHA for medium access control, which may suffer from severe packet collisions as the network scale expands, consequently reducing the system energy efficiency (EE). To address this issue, it is critical to carefully allocate transmission parameters such as the channel (CH), transmission power (TP) and spreading factor (SF) to each end device (ED). Owing to the low duty cycle and sporadic traffic of LoRa networks, evaluating the system EE under various parameter settings proves to be time-consuming. Consequently, we propose an analytical model aimed at calculating the system EE while fully considering the impact of multiple gateways, duty cycling, quasi-orthogonal SFs and capture effects. On this basis, we investigate a joint CH, SF and TP allocation problem, with the objective of optimizing the system EE for uplink transmissions. Due to the NP-hard complexity of the problem, the optimization problem is decomposed into two subproblems: CH assignment and SF/TP assignment. First, a matching-based algorithm is introduced to address the CH assignment subproblem. Then, an attention-based multiagent reinforcement learning technique is employed to address the SF/TP assignment subproblem for EDs allocated to the same CH, which reduces the number of learning agents to achieve fast convergence. The simulation outcomes indicate that the proposed approach converges quickly under various parameter settings and obtains significantly better system EE than baseline algorithms