Analysis of the Partial Nitrification/Anammox Performance and Microbial Structure of Low C/N Wastewater by A²/O Process

Given the carbon limitation of low C/N wastewater, the improvement of nitrogen-removal efficiency remains a challenging task of municipal wastewater treatment plants (WWTPs) in China. In this study, a partial nitrification/anammox (PN/A) system was established to facilitate the anaerobic-anoxic-aerobic (A2/O) treatment of low C/N (C/N = 3) wastewater with insufficient carbon sources. Effects of dissolved oxygen (DO) concentration and internal reflux ratio on nitrogen-removal efficiency and pathway were investigated. Under the optimal DO (0.5–0.8 mg·L−1) and internal reflux ratio (250%), the highly efficient NH4+-N removal (97.21%) and TN removal (80.92%) were achieved based on PN/A. Moreover, the relative abundance of ammonia-oxidizing bacteria (Nitrosomonas) was 3 times higher than the abundance of nitrite-oxidizing bacteria (Nitrospira) in phase V, which was the main cause of PN in the reactor. Anaerobic ammonia-oxidizing bacteria (Candidatus Brocadia, Pirellula, and Gemmata) were also found and considered as the key microbes involved in anammox. This study reports that the A2/O process can achieve advanced nitrogen removal of low C/N wastewater based on PN/A by optimizing conventional process parameters. The outcomes of this study may provide practical engineering applications as a reference for nitrogen removal based on the A2/O process

UAV-assisted Cooperative Communications with Power-splitting Information and Power Transfer

In this paper, we focus on a UAV-assisted cooperative communication system with simultaneous wireless information and power transfer (SWIPT), where the UAV serves as a mobile relay and is powered by radio signal from the source via power-splitting mechanism. We study the end-to-end cooperative throughput maximization problem by optimizing the UAV’s power profile, power-splitting ratio profile and trajectory for both amplify-and-forward (AF) and decode-and-forward (DF) protocols. The problem is decomposed into two subproblems: profile optimization and trajectory optimization. The former one is solved via dual decomposition and the latter one is solved via successive convex optimization. Then the cooperative throughput is optimized by alternately solving the two subproblems. Simulation results show that with the proposed optimal solution, choice for the UAV’s power profile and power-splitting ratio profile is more long-sighted than two greedy strategies and successive optimization for trajectory design can converge in a few rounds of iteration. The proposed optimal solution outperforms not only mobile and static greedy strategies, but also a similar solution from an existing work without consideration of SWIPT with performance gain up to 30%. Moreover, w

Resource Allocation and Basestation Placement in Cellular Networks with Wireless Powered UAVs

In this paper, we focus on a downlink cellular network, where multiple UAVs serve as aerial basestations to provide wireless connectivity to ground users through frequency division multi-access (FDMA) scheme. The UAVs are exclusively powered by a wireless charging station located on the ground following save-then-transmit protocol. In such a cellular network joint optimization for user association, resource allocation and basesation placement is investigated to maximize the downlink sum rate. The problem is formulated as a mixed integer optimization problem and is thus challenging to solve. We propose an efficient solution based on alternate optimization by iteratively solving one of the three subproblems at a time and an algorithm based on penalty method and successive convex optimization to binarize the association indicators. Numerical result shows that the downlink sum rate cannot be always enhanced by deploying more UAVs due to non-negligible tradeoff between energy/communication sources and co-channel interference

UAV-assisted Cooperative Communications with Power-splitting Information and Power Transfer

In this paper, we focus on a UAV-assisted cooperative communication system with simultaneous wireless information and power transfer (SWIPT), where the UAV serves as a mobile relay and is powered by radio signal from the source via power-splitting mechanism. We study the end-to-end cooperative throughput maximization problem by optimizing the UAV’s power profile, power-splitting ratio profile and trajectory for both amplify-and-forward (AF) and decode-and-forward (DF) protocols. The problem is decomposed into two subproblems: profile optimization and trajectory optimization. The former one is solved via dual decomposition and the latter one is solved via successive convex optimization. Then the cooperative throughput is optimized by alternately solving the two subproblems. Simulation results show that with the proposed optimal solution, choice for the UAV’s power profile and power-splitting ratio profile is more long-sighted than two greedy strategies and successive optimization for trajectory design can converge in a few rounds of iteration. The proposed optimal solution outperforms not only mobile and static greedy strategies, but also a similar solution from an existing work without consideration of SWIPT with performance gain up to 30%. Moreover, we also show that the convergence speed of the proposed algorithm is acceptable even with high improvement requirement

UAV-Assisted Cooperative Communications with Time-Sharing Information and Power Transfer

In this paper, we focus on a UAV-assisted cooperative communication system based on simultaneous wireless information and power transfer (SWIPT), where the UAV serves as a relay and its transmission capability is partly powered by radio signal from the source via the time-sharing mechanism. We study the end-to-end cooperative throughput maximization problem by optimizing the UAV's decision profile, power profile and trajectory for both amplify-and-forward (AF) and decode-and-forward (DF) protocols. The problem is decomposed into three optimization subproblems for decision profile, power profile and trajectory, and solved through alternating optimization, by which each of the subproblems is solved with the other two fixed. A binarization algorithm is further proposed to make the decision profile feasible. We show that the proposed solution outperforms not only two SWIPT-based strategies, but also a similar solution from an existing work without consideration for SWIPT. In addition, results indicate that the proposed algorithm performs efficiently in both optimality and convergence

Design Principles for SuCESsFul Biosensors: Specific Fluorophore/Analyte Binding and Minimization of Fluorophore/Scaffold Interactions

Quantifying protein location and concentration is critical for understanding function in situ. Scaffold conjugated to environment-sensitive fluorophore (SuCESsFul) biosensors, in which a reporting fluorophore is conjugated to a binding scaffold, can, in principle, detect analytes of interest with high temporal and spatial resolution. However, their adoption has been limited due to the extensive empirical screening required for their development. We sought to establish design principles for this class of biosensor by characterizing over 400 biosensors based on various protein analytes, binding proteins, and fluorophores. We found that the brightest readouts are attained when a specific binding pocket for the fluorophore is present on the analyte. Also, interaction of the fluorophore with the binding protein it is conjugated to can raise background fluorescence, considerably limiting sensor dynamic range. Exploiting these two concepts, we designed biosensors that attain a 100-fold increase in fluorescence upon binding to analyte, an order of magnitude improvement over the previously best-reported SuCESsFul biosensor. These design principles should facilitate the development of improved SuCESsFul biosensors. Keywords: solvatochromism; Sso7d scaffold; sensors; protein engineering; directed evolutionNational Science Foundation (U.S.) (Grant MCB-115803)National Institutes of Health (U.S.) (Grant U54CA112967)National Cancer Institute (U.S.) (Grant U54CA112967)National Institutes of Health (U.S.) (Grant R01 EB 010246