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The availability of nitrogen (N) is crucial for both the productivity of terrestrial and aquatic ecosystems globally. However, the overuse of artificial fertilizers and the energy required to fix nitrogen have pushed the global nitrogen cycle (N-cycle) past its safe operating limits, leading to severe nitrogen pollution and the production of significant amounts of greenhouse gas nitrous oxide (N2O). The anaerobic ammonium oxidation (anammox) mechanism can counteract the release of ammonium and N2O in many oxygen-limited situations, assisting in the restoration of the homeostasis of the Earth’s N biogeochemistry. In this work, we looked into the characteristics of the anammox hotspots’ distribution across various types of ecosystems worldwide. Anammox hotspots are present at diverse oxic–anoxic interfaces in terrestrial systems, and they are most prevalent at the oxic–anoxic transition zone in aquatic ecosystems. Based on the discovery of an anammox hotspot capable of oxidizing ammonium anoxically into N2 without N2O by-product, we then designed an innovative concept and technical routes of nature-based anammox hotspot geoengineering for climate change, biodiversity loss, and efficient utilization of water resources. After 15 years of actual use, anammox hotspot geoengineering has proven to be effective in ensuring clean drinking water, regulating the climate, fostering plant and animal diversity, and enhancing long-term environmental quality. The sustainable biogeoengineering of anammox could be a workable natural remedy to resolve the conflicts between environmental pollution and food security connected to N management.

Abstract The estuarine plastisphere, a novel ecological habitat in the Anthropocene, has garnered global concerns. Recent geochemical evidence has pointed out its potential role in influencing nitrogen biogeochemistry. However, the biogeochemical significance of the plastisphere and its mechanisms regulating nitrogen cycling remain elusive. Using 15N- and 13C-labelling coupled with metagenomics and metatranscriptomics, here we unveil that the plastisphere likely acts as an underappreciated nitrifying niche in estuarine ecosystems, exhibiting a 0.9 ~ 12-fold higher activity of bacteria-mediated nitrification compared to surrounding seawater and other biofilms (stone, wood and glass biofilms). The shift of active nitrifiers from O2-sensitive nitrifiers in the seawater to nitrifiers with versatile metabolisms in the plastisphere, combined with the potential interspecific cooperation of nitrifying substrate exchange observed among the plastisphere nitrifiers, collectively results in the unique nitrifying niche. Our findings highlight the plastisphere as an emerging nitrifying niche in estuarine environment, and deepen the mechanistic understanding of its contribution to marine biogeochemistry.

Corona virus disease 2019 (COVID-19) has exerted a profound adverse impact on human health. Studies have demonstrated that aerosol transmission is one of the major transmission routes of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Pathogenic microorganisms such as SARS-CoV-2 can survive in the air and cause widespread infection among people. Early monitoring of pathogenic microorganism transmission in the atmosphere and accurate epidemic prediction are the frontier guarantee for preventing large-scale epidemic outbreaks. Monitoring of pathogenic microorganisms in the air, especially in densely populated areas, may raise the possibility to detect viruses before people are widely infected and contain the epidemic at an earlier stage. The multi-scale coupled accurate epidemic prediction system can provide support for governments to analyze the epidemic situation, allocate health resources, and formulate epidemic response policies. This review first elaborates on the effects of the atmospheric environment on pathogenic microorganism transmission, which lays a theoretical foundation for the monitoring and prediction of epidemic development. Secondly, the monitoring technique development and the necessity of monitoring pathogenic microorganisms in the atmosphere are summarized and emphasized. Subsequently, this review introduces the major epidemic prediction methods and highlights the significance to realize a multi-scale coupled epidemic prediction system by strengthening the multidisciplinary cooperation of epidemiology, atmospheric sciences, environmental sciences, sociology, demography, etc. By summarizing the achievements and challenges in monitoring and prediction of pathogenic microorganism transmission in the atmosphere, this review proposes suggestions for epidemic response, namely, the establishment of an integrated monitoring and prediction platform for pathogenic microorganism transmission in the atmosphere.

Abstract Global riverine nitrous oxide (N2O) emissions have increased more than 4-fold in the last century. It has been estimated that the hyporheic zones in small streams alone may contribute approximately 85% of these N2O emissions. However, the mechanisms and pathways controlling hyporheic N2O production in stream ecosystems remain unknown. Here, we report that ammonia-derived pathways, rather than the nitrate-derived pathways, are the dominant hyporheic N2O sources (69.6 ± 2.1%) in agricultural streams around the world. The N2O fluxes are mainly in positive correlation with ammonia. The potential N2O metabolic pathways of metagenome-assembled genomes (MAGs) provides evidence that nitrifying bacteria contain greater abundances of N2O production-related genes than denitrifying bacteria. Taken together, this study highlights the importance of mitigating agriculturally derived ammonium in low-order agricultural streams in controlling N2O emissions. Global models of riverine ecosystems need to better represent ammonia-derived pathways for accurately estimating and predicting riverine N2O emissions.

Abstract The uncertain stability of deep reinforcement learning training on complex tasks impedes its development and deployment, especially in intelligent vehicles, such as intelligent surface vessels and self-driving cars. Complex and varied environmental states puzzle training of decision-making networks. Here we propose an elastic adaptive deep reinforcement learning algorithm to address these challenges and achieve autonomous navigation in intelligent vehicles. Our method trains the decision-making network over the function and optimization learning stages, in which the state space and action space of autonomous navigation tasks are pruned by choosing classic states and actions to reduce data similarity, facilitating more stable training. We introduce a task-adaptive observed behaviour classification technique in the function learning stage to divide state and action spaces into subspaces and identify classic states and actions. In which the classic states and actions are accumulated as the training dataset that enhances its training efficiency. In the subsequent optimization learning stage, the decision-making network is refined through meticulous exploration and accumulation of datasets. The proposed elastic adaptive deep reinforcement learning enables the decision-making network to effectively learn from complex state and action spaces, leading to more efficient training compared to traditional deep reinforcement learning approaches. Simulation results demonstrate the remarkable effectiveness of our method in training decision-making networks for intelligent vehicles. The findings validate that our method provides reliable and efficient training for decision-making networks in intelligent vehicles. Moreover, our method exhibits stability in training other tasks characterized by continuous state and action spaces.

This paper addresses the issue of course keeping control (CKC) for unmanned surface vehicles (USVs) under network environments, where various challenges, such as network resource constraints and discontinuities of course and yaw caused by data transmission, are taken into account. To tackle the issue of network resource constraints, an event-sampled scheme is developed to obtain the course data, and a novel event-sampled adaptive neural-network-based state observer (NN–SO) is developed to achieve the state reconstruction of discontinuous yaw. Using a backstepping design method, an event-sampled mechanism, and an adaptive NN–SO, an adaptive neural output feedback (ANOF) control law is designed, where the dynamic surface control technique is introduced to solve the design issue caused by the intermission course data. Moreover, an event-triggered mechanism (ETM) is established in a controller–actuator (C–A) channel and a dual-channel event-triggered adaptive neural output feedback control (ETANOFC) solution is proposed. The theoretical results show that all signals in the closed-loop control system (CLCS) are bounded. The effectiveness is verified through numerical simulations.

In this paper, a novel event-triggered composite learning finite-time control scheme is presented for underactuated marine surface vehicles (MSVs) trajectory tracking under unknown dynamics and unknown time-varying disturbances. Line-of-sight (LOS) tracking control method is employed to address the underactuation problem of MSVs. The neural networks (NNs) are untilized to approximate unknown dynamics. The serial-parallel estimation model is employed to construct the prediction error, and the prediction errors and tracking errors are fused with construct the NN weights updating. Combining the result of approximation information, the disturbances observers can be created to achieve disturbance estimation. Fractional power technology is artistically introduced to realize the finite-time trajectory tracking control of MSVs based on composite learning. The proposed control scheme ensures the simultaneous realization of high precision tracking performance and unknown information approximation. Moreover, an event-triggered mechanism is introduced to reduce the transmission load and the execution rate of actuators. It is proved that the proposed control scheme ensures all error signals of the MSVs trajectory tracking control system can converge to the neighborhood of zero within a finite time. Finally, the simulation results on an MSV verify the effectiveness and superiority of the proposed control scheme.

The unmanned aerial manipulator (UAM) is a kind of aerial robot that combines a quadrotor aircraft and an onboard manipulator. This paper focuses on the problems of structure design, system modeling, and motion control of an UAM applied for water sampling. A novel, light, cable-driven UAM has been designed. The drive motors installed in the base transmit the force and motion remotely through cables, which can reduce the inertia ratio of the manipulator. The Newton–Euler method and Lagrangian method are adopted to establish the quadrotor model and manipulator model, respectively. External disturbances, model uncertainty, and joint flexibility are also accounted for in the two submodels. The quadrotor and manipulator are controlled separately to ensure the overall accurate aerial operation of the UAM. Specifically, a backstepping control method is designed with the disturbance observer (BC-DOB) technique for the position loop and attitude loop control of the quadrotor. A backstepping integral fast terminal sliding mode control based on the linear extended state observer (BIFTSMC-LESO) has been developed for the manipulator to provide precise manipulation. The DOB and LESO serve as compensators to estimate the external disturbances and model uncertainty. The Lyapunov theory is used to ensure the stability of the two controllers. Three simulation cases are conducted to test the superior performance of the proposed quadrotor controller and manipulator controller. All the results show that the proposed controllers provide better performances than other traditional controllers, which can complete the task of water quality sampling well.

In this paper, a robust adaptive finite-time (FT) tracking control scheme is proposed for Euler-Lagrange systems (ELSs) subject to nonparametric uncertainties, unknown disturbances and input saturation. In the design procedure, a Gaussian error function is utilized to approximate the input saturation nonlinearity. Following that, by employing the natural property that the upper bound of model parameters uncertainties is linear-in-parameters, the lumped uncertain term caused by uncertain model parameters and external disturbances is formulated by a linear-parametric form with a single parameter. And then, a novel robust adaptive tracking control law is designed to resolve the tracking control problem of uncertain ELSs. The proposed control scheme is featured by FT convergence rate, and robustness against uncertainties and unknown disturbances. Furthermore, the robust adaptive FT tracking control scheme is insensitive to the character of the uncertainties, and is with low computational burden and easy to implement in engineering applications. And its rigorous stability is analyzed with the aid of the Lyapunov stability theory, and its effectiveness is verified by simulation results and comparison.

LoRa is a low-power long-range IoT standard that uses the chirp spread spectrum technique, and we have strived to further extend its coverage by utilizing the direct device-to-device (D2D) links to construct a multi-hop relay network. In LoRa, the spreading factor (SF) is an important parameter, which not only provides great flexibility between the data rate and sensitivity but also presents a new dimension for multiple accesses. Our approach to improving the capacity of a multihop LoRa network is to attempt to off-load the data traffic into several subnets by utilizing this multiple-access dimension. Each subnet rooted at a sink node is allocated a specific SF on the basis of network clustering. This enables packet transmission in parallel with multiple SFs to become feasible. To allow such parallel transmissions, our considerations are: 1) ensuring the connectivity of all subnets; 2) off-loading the traffic according to the number of nodes, data rates, and network topologies of each subnet; and 3) shortening the airtime of each subnet by reducing the hop count. Toward these objectives, we present a tree-based SF clustering algorithm (TSCA) to conduct SF allocation in a multihop LoRa network. The TSCA focuses on balancing the airtime between the subnets while ensuring connectivity. Furthermore, we use simulations to show that our approach can significantly increase the network performance compared with other approaches. We additionally deploy a real-chip experiment to evaluate the feasibility of parallel transmission in practice.

This paper investigates the automatic berthing problem of underactuated surface vessels in the case of uncertain dynamics and yaw rate limitation, given the importance of yaw rate control and the unmeasurable hydrodynamic parameters of the vessel at low speeds. First, we use the differential homeomorphism coordinate transformation to solve the problem of underactuation. Second, a radial basis function network (RBF) is introduced to approximate unknown nonlinear functions. Third, we apply the barrier Lyapunov function (BLF) approach to limit the yaw rate within a safe range. Fourth, we use dynamic surface control (DSC) technology and minimum learning parameters (MLP) to tackle the differential explosion problems in backstepping and computational complexity. Finally, Lyapunov stability theory proves that signals produced by the designed control scheme are bounded and effective. The simulation results show that, compared with the control scheme without BLF, the proposed method can effectively limit the yaw rate within a specific range and effectively solves the influence of the model uncertainly.

This paper proposes a zero-speed vessel fin stabilizer adaptive neural network control strategy based on a command filter for the problem of large-angle rolling motion caused by adverse sea conditions when a vessel is at low speed down to zero. In order to avoid the adverse effects of the high-frequency part of the marine environment on the vessel rolling control system, a command filter is introduced in the design of the controller and a command filter backstepping control method is designed. An auxiliary dynamic system (ADS) is constructed to correct the feedback error caused by input saturation. Considering that the system has unknown internal parameters and unmodeled dynamics, and is affected by unknown disturbances from the outside, the neural network technology and nonlinear disturbance observer are fused in the proposed design, which not only combines the advantages of the two but also overcomes the limitations of the single technique itself. Through Lyapunov theoretical analysis, the stability of the control system is proved. Finally, the simulation results also verify the effectiveness of the control method.

In this paper, an output feedback fault-tolerant control (FTC) scheme is presented for the dynamic positioning of vessels subjected to external disturbances, dynamic uncertainties, and thruster saturation constraints under thruster faults. A high-gain observer is constructed to acquire the estimates of vessel velocities. An auxiliary dynamic system is introduced to compensate the effect of the thruster faults and saturation constrains. Neural network with iterative updating laws is employed to approximate the lumped uncertainties caused by unknown environmental disturbances and dynamic uncertainties of vessels. Furthermore, the control allocation is accomplished to distribute the FTC forces and moment among individual thrusters. It is theoretically proved that the derived FTC scheme has the capability to maintain the vessel position at the desired values and guarantees that all signals in the closed-loop control system are uniformly ultimately bounded. Finally, the simulation results demonstrate the effectiveness of the proposed control scheme.

In this paper, we strive to construct an efficient multi-hop network based on the sub-GHz low-power wide-area technology. Specifically, we investigate the combination of LoRa, a physical-layer standard that can provide several-kilometer outdoor coverage, and concurrent transmission (CT), a recently proposed multi-hop protocol that can significantly improve the network efficiency. The main contributions of this paper are threefold. 1) Since the CT enhances the network efficiency by allowing synchronized packet collisions, the performance of the physical-layer receiver under such packet collisions needs to be carefully examined to ensure the network reliability. We first extensively evaluate the LoRa receiver performance under CT to verify that LoRa is compatible to CT. Specifically, we find that, due to the time-domain and frequency-domain energy spreading effect, LoRa is robust to the packet collisions resulting from CT. 2) We further find the receiver performance under CT can be further improved by introducing timing offsets between the relaying packets. In view of this, we propose a timing delay insertion method, the offset-CT method, that adds random timing delay before the packets while preventing the timing offset from diverging over the multi-hop network. 3) We conduct proof-of-concept experiments to demonstrate the feasibility of CT-based LoRa multi-hop network and the performance improvement brought by the proposed offset-CT method.

The discovery of anaerobic ammonia oxidation (anammox) expanded our knowledge on the microbial nitrogen cycle. Previous studies report that anammox bacteria are distributed in a wide range of habitats and plays significant roles in the global nitrogen cycle. However, most studies focus only on individual ecosystems or datasets from public databases. To date, our understanding of how anammox bacteria respond to environmental properties and are distributed in different habitats on a global scale, remain unclear. To explore the global distribution of anammox bacteria, samples were collected from different habitats at different locations globally, including wetlands, drylands, groundwater aquifers and snow from 10 countries across six continents. We then used high-throughput amplicon sequencing targeting the functional gene hydrazine synthase (HZS) and generated community profiles. Results showed that Candidatus Brocadia is detected as the dominant genus on a global scale, accounting for 80.0% to 99.9% of the retrieved sequences in different habitats. The Jettenia-like sequences were the second most abundant group, accounting for no more than 19.9% of the retrieved sequences in all sites. The samples in drylands, wetlands and groundwater aquifers showed similar community composition and diversity, with the snow samples being the most different. Deterministic processes seem stronger in regulating the community composition of anammox bacteria, which is supported by the higher proportion explained by local-scale factors. Groundwater aquifers showed high gene abundance and the most complex co-occurrence network among the four habitat types, suggesting that it might be the preferred habitat of anammox bacteria. There is little competition between anammox bacterial species based on co-occurrence analysis. Hence, we could infer that environmental factors such as anaerobic and stable conditions, instead of substrate limitations, may be vital factors determining the anammox bacteria community. These results provide a better understanding of the global distribution of anammox bacteria and the ecological factors that affect their community structuring in diverse habitats.

Archaea play vital roles in global biogeochemical cycles, particularly in nitrification and methanogenesis. The recovery of archaeal community following disturbance is essential for maintaining the stability of ecosystem function. To examine whether the archaeal community could recover from water flooding and assess the influence of anthropogenic pollution on the autogenic recovery, soil samples from two riparian zones with contrasting pollution background were investigated. Collected samples in each area were divided into three groups of reference, flooding, and recovery according to the flooded state of each site. The results showed that the archaeal abundance was resilient to the disturbances of both water flooding and anthropogenic pollution. More similar community composition and diversity appeared between the recovery and reference groups in the area with low anthropogenic pollution. It indicated that high anthropogenic pollution could result in less resilience of archaeal community. The co-occurrence network further revealed that the archaeal community in the area of low anthropogenic pollution exhibited more associations suggesting a higher ecosystem stability. The better recovery of archaeal community was associated with the high resilience ability. The Nitrososphaerales was the key taxon maintaining the better recovery of the archaeal community from the disturbances due to its high resilience index and quantitative dominance. Overall, archaeal community has the capability of autogenic recovery, the process of which might be intervened by anthropogenic pollution, and then potentially affects the ecosystem functions of the riparian system.

This paper discusses the predefined practical finite-time (PPFT) dynamic positioning (DP) control problem for DP vessels subject to internal/external uncertainties. Those heterogeneity uncertainties are handled by a separatetype treatment approach. The finite-time (FT) DP control is fulfilled by a predefined FT function on the basis of a time-based generator (TBG). Under the dynamic surface control together with the TBG design framework, the convergence time and control accuracy of the DP system can be determined by the designer offline. Meanwhile, the virtual derivation and computational burden problems are dissolved by using a first-order filter and virtual parameter learning technique. To reduce mechanical wear, an event-triggering protocol between the control law and the actuator is built to reduce the operating frequency of the actuator. An event-triggered neuroadaptive PPFT control scheme is presented for DP vessels. The stability of the closed-loop DP control systems is validated via the Lyapunov theorem. Approach efficiency is confirmed by numerical examples. [ABSTRACT FROM AUTHOR]