Hebei University of Environmental Engineering

Extreme droughts are projected to become more frequent and severe under climate change, posing significant risks to wetland ecosystems and submerged macrophyte communities. We combined field surveys in West Dongting Lake, China, combined with controlled greenhouse experiments to examine how drought intensity (expressed as contrasting soil moisture conditions during drought) and drought timing affect submerged macrophyte species richness, biomass, as well as resilience, mediated through species response in their reproductive strategies. Field observations revealed a sharp decline in clonal species (Hydrilla verticillata, Ceratophyllum demersum, Vallisneria spinulosa) following an extreme drought, while the sexual species Najas marina emerged as dominant. Greenhouse experiments confirmed these patterns and elucidated underlying mechanisms: extreme drought suppressed biomass, leaf area, and seedling re-germination in clonal species, whereas N. marina maintained regeneration via a persistent soil seed bank. Moderate drought enhanced leaf area, consistent with the intermediate disturbance hypothesis, while early drawdowns were most detrimental to growth. Species-specific responses highlight the role of reproductive strategy in drought resilience. These findings underscore the need for climate-adaptive water-level management, including limiting early drawdowns, mitigating extreme drought, and conserving seed banks to sustain biodiversity and ecosystem function under increasing hydroclimatic variability.

Purpose It is necessary but difficult to accurately predict the water levels in front of sluice gates of an open channel water transfer project due to the complex interactions among hydraulic structures. The existing methods have certain shortcomings. For example, although one-dimensional hydrodynamic simulation is technically feasible, little is known about hydrodynamic models for prediction. Another example is that, neural networks can hardly predict the information of nonmonitoring sections. To these problems, this paper presents a novel GRA-NARX and hydrodynamic coupled prediction model (H-GRA-NARX-HPM) that is based on the GRA-NARX (gray relation analysis-nonlinear auto-regressive exogenous) neural network with automatic hyperparameter calibration. Design/methodology/approach Firstly, the GRA is used to determine the correlations of influencing factors and find the optimal influencing factors. Secondly, the selected factors are taken as the input variables of the NARX neural network. Finally, the GRA-NARX neural network with automatic hyperparameter calibration (H-GRA-NARX model) provides accurate 24-h water level prediction to be used as the boundary condition of the hydrodynamic model, and then the H-GRA-NARX-HPM is constructed. Findings The section from the inlet sluice gate of Tang River aqueduct to the outlet sluice gate of Zhang River inverted siphon in the Middle Route of the South-to-North Water Transfer Project, China, is taken as the study area. The water levels before the outlet sluice gate of Anyang River inverted siphon on February 22, 2018 and February 26, 2018, are predicted by the H-GRA-NARX-HPM and then compared with those of the prediction models (GRA-BP-HPM, GRA-NARX-HPM) that use GRA-BP(gray relation analysis-back-propagation) neural network and GRA-NARX neural network prediction information as boundary conditions. The results show that the H-GRA-NARX-HPM has the highest accuracy with MAE values of 0.0028 m and 0.0141 m and MSE values of 1.636 × 10-5 and 2.658 × 10-4 on February 22 and February 26, respectively. In order to verify the universality and applicability of the model, the section from the inlet sluice gate of Ming River aqueduct to the outlet sluice gate of Qili River inverted siphon is taken as another study area. The water levels before the outlet sluice gate of Nansha River inverted siphon on March 18, 2018 and March 19, 2018, are predicted by the H-GRA-NARX-HPM and then also compared with GRA-BP-HPM and GRA-NARX-HPM. The results show that the H-GRA-NARX-HPM has the highest accuracy as well. Originality/value The main contribution of this paper is to propose a novel GRA-NARX and hydrodynamic coupled prediction model (H-GRA-NARX-HPM) which can overcome the main limitations of the individual modelling approaches.

In situ formed synergistic effects of amorphous carbon and MgS catalyzed the hydrogen sorption properties of MgH 2 -CBD.

To enable compute-intensive and delay-sensitive maritime services, unmanned surface vessels (USVs) can offload tasks to mobile edge computing (MEC) servers mounted on unmanned aerial vehicles (UAVs). However, jointly minimizing energy consumption and latency is challenging due to the strong coupling between communication, computation, and mobility under stringent quality-of-service (QoS) requirements. To capture this trade-off, we formulate a weighted energy–delay minimization problem that jointly optimizes one-to-one UAV–USV scheduling, task partitioning, and UAV trajectory. The resulting problem is particularly difficult due to a hybrid discrete–continuous decision space and strong temporal coupling under stringent feasibility constraints. To address this mixed-integer nonconvex optimization problem, we reformulate it as a Markov decision process (MDP) and develop a constraint-aware OU–TD3 algorithm that integrates differentiable scheduling relaxation, feasibility-aware action mapping, and adaptive OU–Gaussian mixed exploration for stable learning in high-dimensional continuous control. We further extend the formulation and solution to a cooperative multi-UAV MEC setting with signal-to-interference-plus-noise ratio (SINR)-coupled interference and coordination constraints. Extensive simulations with statistical evaluation demonstrate stable convergence and up to 54.2% cost reduction over baseline schemes, while maintaining robustness under realistic maritime disturbances.