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In this work, we propose and study a new amensalism system with Allee effect on the first species. First, we investigate the existence and stability of all possible coexistence equilibrium points and boundary equilibrium points of this system. Then, applying the Sotomayor theorem, we prove that there exists a saddle-node bifurcation under some suitable parameter conditions. Finally, we provide a specific example with corresponding numerical simulations to further demonstrate our theoretical results.

Multi-driver interactions shape estuarine wetland evolution, yet the intricate evolution patterns and their controlling factors their spatiotemporal dynamics remain inadequately understood. This study employs high-resolution satellite data (1985–2020) and 3S technology (overall classification accuracy: 92.44%, Kappa coefficient: 0.9132) to reveal the development of tidal creeks and vegetation evolution patterns of the Dongtan wetland. Our findings indicate a transition in the development of tidal creeks and vegetation from a natural stage to an artificial intervention stage. Northern regions exhibited severe degradation of both vegetation and tidal creeks influenced by reclamation, contrasting with southern recovery post-restoration. This disparity highlights the varied responses to human activities across different areas of the Dongtan wetland. Notably, the introduction of the invasive species Spartina alterniflora has negatively impacted the habitat of native vegetation. The interaction mechanism between vegetation and tidal creeks manifest as: vegetation constrains tidal creek development through substrate stabilization, wave dissipation, and sediment retention, while tidal creeks modulate physicochemical properties of the substrate hydrological connectivity and seed dispersal, affecting vegetation zonation and community structures. Human activities exert dual modulation effects on the Dongtan wetland, driving its phase transition from natural to artificial landscapes, with artificial landscapes exhibiting the most dynamic landscape type through reclamation and ecological restoration projects. Our findings enhance the understanding of the mechanisms underlying estuarine wetland development and inform strategies for restoring healthy estuarine wetland ecosystems.

Stringent emission reduction goals and rising demand for clean maritime fuels have elevated green methanol as a promising low-cost alternative, which is supported by a mature and easily replicable technology. Here, we investigate the technical feasibility, economics, and development of offshore wind-powered green methanol production and supply to maritime transport within the European Union (EU). We analyze various system configurations, incorporating diverse electricity sources and carbon capture technologies, optimizing them to minimize the levelized cost of methanol (LCOM). The impact of the EU Emissions Trading System and FuelEU Maritime Regulation on the greenhouse gas emissions costs of conventional fuels is also assessed to evaluate the economic competitiveness of green methanol. Additionally, we forecast future LCOM changing trends based on technology trends and regulation restrictions. Results indicate that the produced green methanol can fully meet the EU regulations for renewable fuels. Furthermore, it has the potential to become cost-competitive with conventional fuels after 2030 and is expected to be uniformly less expensive by 2035. For different stakeholders, we present a series of technical and policy recommendations. Offshore wind-powered green methanol could achieve cost parity with conventional marine fuels by 2030−2035 under EU regulations, enabling sustainable shipping through scalable technology and policy incentives.

The effects of intercritical quenching on the microstructure evolution and mechanical performance of Cr–Ni–Mo–V steel with a banded structure are studied. It is found that the intercritical quenching temperature has a significant effect on the morphology, distribution, and relative amount of ferrite/martensite, as well as the carbide precipitates upon tempering treatment. It is indicated that owing to the initial banded structure of Cr-Ni-Mo-V steel, the ferrite formation in intercritical heat treatment also exhibits a banded distribution. With the increase in quenching temperature, the proportion of ferrite in the Cr-Ni-Mo-V steel decreases from 30 ± 3.2 vol.% to 18 ± 2.8 vol.%. Tempering treatment has no significant effect on the distribution characteristics of ferrite, but it promotes the recovery of martensite laths and the precipitation of carbides. The mechanical properties of Cr-Ni-Mo-V steel are determined by both the changes in ferrite content induced by intercritical quenching and the evolution of carbide types during tempering. Delamination cracks are observed on the fracture surface, which is attributed to the lamellar microstructure, improving the plasticity of Cr-Ni-Mo-V steel through stress dispersion and a multi-stage energy absorption mechanism.

Graph convolutional networks (GCNs), an emerging type of neural network model on graphs, have presented state-of-the-art performance on the node classification task. However, recent studies show that neural networks are vulnerable to the small but deliberate perturbations on input features. And GCNs could be more sensitive to the perturbations since the perturbations from neighbor nodes exacerbate the impact on a target node through the convolution. Adversarial training (AT) is a regularization technique that has been shown capable of improving the robustness of the model against perturbations on image classification. However, directly adopting AT on GCNs is less effective since AT regards examples as independent of each other and does not consider the impact from connected examples. In this work, we explore AT on graph and propose a graph-specific AT method, Directional Graph Adversarial Training (DGAT), which incorporates the graph structure into the adversarial process and automatically identifies the impact of perturbations from neighbor nodes. Concretely, we consider the impact from the connected nodes to define the neighbor perturbation which restricts the perturbation direction on node features towards their neighbor nodes, and additionally introduce an adversarial regularizer to defend the worst-case perturbations. In this way, DGAT can resist the impact of worst-case adversarial perturbations and reduce the impact of perturbations from neighbor nodes. Extensive experiments demonstrate that DGAT can effectively improve the robustness and generalization performance of GCNs. Specially, GCNs with DGAT can provide better performance when there are rare few labels available for training.

The development of lightweight steels with high specific strength is critical for automotive applications and energy savings. This study aimed to develop a high-performance lightweight steel with high specific strength by designing an alloy composition and optimizing thermomechanical processing. A novel Fe-28.6Mn-10.2Al-1.1C-3.2Ni-3.9Cr (wt.%) steel was investigated, focusing on microstructural evolution, mechanical properties, and strengthening mechanisms. The steel was processed through hot-rolling, solution treatment, cold-rolling, and subsequent annealing. Microstructural characterization revealed a dual-phase matrix of austenite and ferrite (6.8 vol.%), with B2 precipitates distributed at the grain boundaries and within the austenite matrix, alongside nanoscale κ-carbides (<10 nm). Short-time annealing resulted in the finer austenite grains (~1.1 μm) and the higher volume fraction (5.0%) of intragranular B2 precipitates with a smaller size (~0.18 μm), while long-time annealing promoted the coarsening of austenite grains (~1.6 μm) and the growth of intergranular B2 particles (~0.9 μm). This steel achieved yield strengths of 1130~1218 MPa and tensile strengths of 1360~1397 MPa through multiple strengthening mechanisms, including solid solution strengthening, grain boundary strengthening, dislocation strengthening, and precipitation strengthening.

Urban airspace, characterized by densely packed high-rise buildings, presents complex and dynamically changing environmental conditions. It brings potential risks to UAV flights, such as the risk of collision and accidental entry into no-fly zones. Currently, mainstream path planning algorithms, including the PSO algorithm, have issues such as a tendency to converge to local optimal solutions and poor stability. In this study, an improved particle swarm optimization algorithm (LGPSO) is proposed to address these problems. This algorithm redefines path planning as an optimization problem, constructing a cost function that incorporates safety requirements and operational constraints for UAVs. Stochastic inertia weights are added to balance the global and local search capabilities. In addition, asymmetric learning factors are introduced to direct the particles more precisely towards the optimal position. An enhanced Lévy flight strategy is used to improve the exploration ability, and a greedy algorithm evaluation strategy is designed to evaluate the path more quickly. The configuration space is efficiently searched using the corresponding particle positions and UAV parameters. The experiments, which involved mapping complex urban environments with 3D modeling tools, were carried out by simulations in MATLAB R2023b to assess their algorithmic performance. The results show that the LGPSO algorithm improves by 23% over the classical PSO algorithm and 18% over the GAPSO algorithm in the optimal path distance under guaranteed security. The LGPSO algorithm shows significant improvements in stability and route planning, providing an effective solution for UAV path planning in complex environments.

Biogenic compounds are important materials for drug discovery and chemical biology. In this work, we report a quasi-biogenic molecule generator (QBMG) to compose virtual quasi-biogenic compound libraries by means of gated recurrent unit recurrent neural networks. The library includes stereo-chemical properties, which are crucial features of natural products. QMBG can reproduce the property distribution of the underlying training set, while being able to generate realistic, novel molecules outside of the training set. Furthermore, these compounds are associated with known bioactivities. A focused compound library based on a given chemotype/scaffold can also be generated by this approach combining transfer learning technology. This approach can be used to generate virtual compound libraries for pharmaceutical lead identification and optimization.

The influence of Cr addition on the microstructure and tensile properties of Fe-25Mn-10Al-1.2C lightweight steel was investigated. The characteristics of the microstructures and deformation behavior were carried out through X-ray diffraction (XRD), scanning electron microscopy (SEM), transmission electron microscopy (TEM), electron backscatter diffraction (EBSD), and room temperature tensile testing. Fe-20Mn-12Al-1.5C steel without Cr exhibited a fully austenitic single phase. With the addition of Cr, the volume fraction of ferrite continuously increased. When the content of Cr exceeded 5 wt%, the precipitation of Cr7C3 carbides was observed. In the steel with 5 wt% Cr, the quantity of κ carbides remarkably decreased, indicating that the addition of 5 wt% Cr significantly inhibited the nucleation of κ-carbides. As the Cr content increases from 0 wt% to 5 wt%, the austenite grain sizes were 8.8 μm and 2.5 μm, respectively, demonstrating that Cr alloying is an effective method of grain refinement. Tensile strength increased slightly while elongation decreased with increasing Cr content. As the Cr content exceeded 5 wt%, the yield strength increased but the elongation drastically decreased. The steel with 2.5 wt% Cr achieved a synergistic improvement in strength and ductility, exhibiting the best tensile performance.

The determination of quantitative relationship between soil dielectric constant and water content is an important basis for measuring soil water content based on ground penetrating radar (GPR) technology. The calculation of soil volumetric water content using GPR technology is usually based on the classic Topp formula. However, there are large errors between measured values and calculated values when using the formula, and it cannot be flexibly applied to different media. To solve these problems, first, a combination of GPR and shallow drilling is used to calibrate the wave velocity to obtain an accurate dielectric constant. Then, combined with experimental moisture content, the intelligent group algorithm is applied to accurately build mathematical models of the relative dielectric constant and volumetric water content, and the Topp formula is revised for sand and clay media. Compared with the classic Topp formula, the average error rate of sand is decreased by nearly 15.8%, the average error rate of clay is decreased by 31.75%. The calculation accuracy of the formula has been greatly improved. It proves that the revised model is accurate, and at the same time, it proves the rationality of the method of using GPR wave velocity calibration method to accurately calculate the volumetric water content.