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Through excellent absorption and transformation, the macrophyte Myriophyllum (M.) aquaticum can considerably remove phosphorus from wastewater. The results of changes in growth rate, chlorophyll content, and roots number and length showed that M. aquaticum could cope better with high phosphorus stress compared with low phosphorus stress. Transcriptome and differentially expressed genes (DEGs) analyses revealed that, when exposed to phosphorus stresses at various concentrations, the roots were more active than the leaves, with more DEGs regulated. M. aquaticum also showed different gene expression and pathway regulatory patterns when exposed to low phosphorus and high phosphorus stresses. M. aquaticum's capacity to cope with phosphorus stress was maybe due to its improved ability to regulate metabolic pathways such as photosynthesis, oxidative stress reduction, phosphorus metabolism, signal transduction, secondary metabolites biosynthesis, and energy metabolism. In general, M. aquaticum has a complex and interconnected regulatory network that deals efficiently with phosphorus stress to varying degrees. This is the first time that the mechanisms of M. aquaticum in sustaining phosphorus stress have been fully examined at the transcriptome level using high-throughput sequencing analysis, which may indicate the direction of follow-up research and have some guiding value for its future applications. [ABSTRACT FROM AUTHOR]

Air target threat assessment is a key issue in air defense operations. Aiming at the shortcomings of traditional threat assessment methods, such as one-sided, subjective, and low-accuracy, a new method of air target threat assessment based on gray neural network model (GNNM) optimized by improved moth flame optimization (IMFO) algorithm is proposed. The model fully combines with excellent optimization performance of IMFO with powerful learning performance of GNNM. Finally, the model is trained and evaluated using the target threat database data. The simulation results show that compared with the GNNM model and the MFO-GNNM model, the proposed model has a mean square error of only 0.0012 when conducting threat assessment, which has higher accuracy and evaluates 25 groups of targets in 10 milliseconds, which meets real-time requirements. Therefore, the model can be effectively used for air target threat assessment. [ABSTRACT FROM AUTHOR]

Abstract Ozonation has been demonstrated to be an efficient method of water treatment. In this study, the degradation of 20 mg/L of sulfadimethoxine (SDM) in different water matrices during ozonation was investigated. At pH 7.0, 100% removal of SDM was achieved by ozonation within 10 min. The degradation of SDM was more pronounced at acidic pH than under ambient environmental conditions, and was also dependent on different water matrices. Both direct and indirect oxidation of SDM by ozone were observed, and it was also shown that both ozone molecules and hydroxyl radicals were involved in the SDM degradation process, whereas it was found that the saturated ring of SDM made it O 3 -recalcitrant. Seven transformation products (TPs) were identified during SDM ozonation, allowing three degradation pathways to be proposed. Additionally, the main reaction sites, including N (7) and C (2) on the aniline ring, and the __S__N__ bond, were confirmed both experimentally and theoretically. The toxicity evolution during the degradation process was investigated, and the results showed no toxic intermediate products obtained during ozonation. Graphical abstract Image 1 Highlights • 20 mg/L of SDM was completely degraded by ozonation in 10 min at pH 7.0. • 7 products were determined by LC-Q-TOF-MS and FED calculations. • Oxidation of NH 2 group, hydroxylation and S N bond cleavage were observed. • Toxicity predictions showed no toxic intermediate products were formed from ozonation. [ABSTRACT FROM AUTHOR]

Anaerobic ammonium oxidation (Anammox) is expected to play a promising role in treating low C/N ratio wastewater. In this study, nitrogen removal performance and the effects of extracellular polymeric substances (EPS) on anammox activity were investigated in two different reactors, followed by analysis of 16S rRNA high-throughput sequencing. A sequencing batch reactor (SBR) and an up-flow anammox sludge blanket reactor (UASB) operated for 133 days, with total nitrogen removal efficiencies of 82.09 ± 4.76% and 87.91 ± 2.59% in the phase 3, respectively. The EPS concentration in UASB was about 1.6 times higher than that in SBR. Also, in the batch tests, especially the anammox-EPS of UASB (A U -EPS) additions contributed to better nitrogen removal capability for anammox bacteria (AnAOB). The relative abundance of AnAOB in UASB was observed to reach 19.68% in the end, accounting for a relatively high degree in the whole community. In SBR, the effluent NH 4 +-N concentration increased when increasing the substrate concentration, followed by the decrease of the relative abundance of AnAOB. Thus, the different feeding strategies would lead to different granulation styles, further influencing the activity and stability of anammox system. This research aims to get a better understanding of the different mechanisms associated with AnAOB enrichment strategies. [Display omitted] • Addition of Anammox-EPS effectively enhanced the anammox activity. • Adding the Anammox-EPS of UASB led to a higher SAA. • Compared to SBR, there was a higher biomass retention ability in UASB. [ABSTRACT FROM AUTHOR]

A system with multiple cooperating unmanned aerial vehicles (multi-UAVs) can use its advantages to accomplish complicated tasks. Recent developments in deep reinforcement learning (DRL) offer good prospects for decision-making for multi-UAV systems. However, the safety and training efficiencies of DRL still need to be improved before practical use. This study presents a transfer-safe soft actor-critic (TSSAC) for multi-UAV decision-making. Decision-making by each UAV is modeled with a constrained Markov decision process (CMDP), in which safety is constrained to maximize the return. The soft actor-critic-Lagrangian (SAC-Lagrangian) algorithm is combined with a modified Lagrangian multiplier in the CMDP model. Moreover, parameter-based transfer learning is used to enable cooperative and efficient training of the tasks to the multi-UAVs. Simulation experiments indicate that the proposed method can improve the safety and training efficiencies and allow the UAVs to adapt to a dynamic scenario., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 Yue, Yang, Zhang and Zuo.)