Oxygen tolerance capacity of upflow anaerobic solid-state(UASS) with anaerobic filter(AF) system

In order to investigate the oxygen tolerance capacity of upflow anaerobic solid-state（UASS）with anaerobic filter（AF） system, the effect of microaeration on thermophilic anaerobic digestion of maize straw was investigated under batch conditions and in the UASS with AF system. Aeration intensities of 0–431 m L O2/gvswere conducted as pretreatment under batch conditions. Aeration pretreatment obviously enhanced anaerobic digestion and an aeration intensity of 431 m L O2/gvsincreased the methane yield by 82.2%. Aeration intensities of 0–355 m L O2/gvswere conducted in the process liquor circulation of the UASS with AF system. Dissolved oxygen（DO） of UASS and AF reactors kept around 1.39 ±0.27 and 0.99 ± 0.38 mg/L, respectively. p H was relatively stable around 7.11 ± 0.04. Volatile fatty acids and soluble chemical oxygen demand concentration in UASS reactor were higher than those in AF reactor. Methane yield of the whole system was almost stable at 85 ± 7 m L/gvs as aeration intensity increased step by step. The UASS with AF system showed good oxygen tolerance capacity.

Energy-efficient joint UAV secure communication and 3D trajectory optimization assisted by reconfigurable intelligent surfaces in the presence of eavesdroppers

We consider a scenario where an unmanned aerial vehicle(UAV),a typical unmanned aerial system(UAS),transmits confidential data to a moving ground target in the presence of multiple eavesdroppers.Multiple friendly reconfigurable intelligent surfaces(RISs) help to secure the UAV-target communication and improve the energy efficiency of the UAV.We formulate an optimization problem to minimize the energy consumption of the UAV,subject to the mobility constraint of the UAV and that the achievable secrecy rate at the target is over a given threshold.We present an online planning method following the framework of model predictive control(MPC) to jointly optimize the motion of the UAV and the configurations of the RISs.The effectiveness of the proposed method is validated via computer simulations.

自主无人系统的具身认知智能框架

自主无人系统是一类具有自主认知、运动规划、自主决策和推理能力的智能系统,其目标是在有限甚至没有人工参与的情况下完成复杂开放动态场景中的通用任务。针对自主无人系统在跨域协同任务上往往面临协同感知效率低、自组网通信可靠性差、资源调度流程慢、任务分配易冲突等一系列问题,探讨了融合大模型和生成式人工智能技术,构建了“大模型+自主无人系统+人工智能生成内容”为一体的自主无人系统“算-控-测”具身认知智能架构,以推动自主无人系统具身认知智能应用落地。

UAVs as remote sensing platforms in plant ecology:review of applications and challenges

Aims Unmanned aerial vehicles(UAVs),i.e.drones,have recently emerged as cost-effective and flexible tools for acquiring remote sensing data with fine spatial and temporal resolution.It provides a new method and opportunity for plant ecologists to study issues from individual to regional scales.However,as a new method,UAVs remote sensing applications in plant ecology are still challenged.The needs of plant ecology research and the application development of UAVs remote sensing should be better integrated.Methods This report provides a comprehensive review of UAV-based remote sensing applications in plant ecology to synthesize prospects of applying drones to advance plant ecology research.Important Findings Of the 400 references,59%were published in remote sensing journals rather than in plant ecology journals,reflecting a substantial gap between the interests of remote sensing experts and plant ecologists.Most of the studies focused on UAV remote sensing’s technical aspects,such as data processing and remote sensing inversion,with little attention on answering ecological questions.There were 61%of studies involved community-scale research.RGB and multispectral cameras were the most used sensors(75%).More ecologically meaningful parameters can be extracted from UAV data to better understand the canopy surface irregularity and community heterogeneity,identify geometrical characteristics of canopy gaps and construct canopy chemical assemblies from living vegetation volumes.More cooperation between plant ecologists and remote sensing experts is needed to promote UAV remote sensing in advancing plant ecology research.