江苏海洋大学

Soil erosion remains a pervasive global environmental challenge, with spatial mapping serving as a cornerstone for conserving regional soil and mitigating land degradation. Current mapping efforts predominantly focus on single types of erosion (e.g., erosion by wind or water), leaving a large void in regional-scale assessments of compound soil erosion. Through a dataset of 137Cs-derived erosion rates extracted from nearly 1000 research articles, the first spatial distribution map of China's compound soil erosion was produced via a random-forest machine-learning approach, elucidating its primary driving factors. The modelled national average rate of soil erosion was 17.8 t ha−1 y−1, with a total annual soil loss of approximately 21.4 Pg y−1, a finding that challenges traditional estimates derived from single erosion type. Spatially, higher values were found in the northern and northwestern regions, whereas lower values dominated the eastern and southern areas. Soil, climate, and vegetation were the dominant controllers, contributing 30.2, 28.6, and 25.8%, respectively, while topography and anthropogenic activity each accounted for 7.7%. Specifically, soil organic matter, runoff, silt content, standard deviation of the normalized difference vegetation index, wind speed, and population density were identified as the six most influential predictors. The impacts of these factors exhibit threshold effects and spatial heterogeneity, indicating that distinct mechanisms govern soil erosion across different regions. These findings provide critical support for understanding the spatial distribution of compound erosion and offer a scientific basis for formulating macro-scale erosion mitigation policies in China.

To elucidate the reaction mechanism and kinetic characteristics of organic sulfur in coal during the process of coal spontaneous combustion, diphenyl sulfone was selected as the model compound in this study. A combination of thermogravimetry/differential scanning calorimetry (TG/DSC) and gas chromatography-mass spectrometry (GC-MS), together with the Flynn-Wall-Ozawa kinetic method and quantum chemical calculations, was adopted to investigate the competitive mechanism between its pyrolysis and oxidation reactions. Experimentally, programmed temperature experiments were conducted under different atmospheres via TG/DSC to obtain the thermodynamic and kinetic characteristics of diphenyl sulfone, while GC-MS was applied for the real-time monitoring and compositional analysis of its volatile products. Theoretically, quantum chemical calculations were performed to further reveal the potential reaction pathways and energy profiles of the reactions. The results showed that the stepwise cleavage of C-S bonds was the dominant pathway for the pyrolysis of diphenyl sulfone, with the theoretical activation energy (73.91 kJ/mol) of the rate-determining step being in excellent agreement with the experimental value (73.51 kJ/mol); the theoretically calculated reaction rate constant was of the same order of magnitude as the experimental one. GC-MS results under nitrogen atmosphere also indicated that the characteristic peak of diphenyl sulfone attenuated gradually with the increase in temperature, whereas the relative abundance of biphenyl, the main pyrolysis product, increased significantly and became the primary component of gaseous products at temperatures above 300 degrees C. For the oxidation of diphenyl sulfone, the dominant reaction pathway was the formation of a cyclic transition state via the attack of oxygen molecules, followed by the generation of diphenyl ether and sulfur dioxide, and this process was accompanied by a significant exothermic effect. GC-MS analysis under air atmosphere confirmed that diphenyl ether and hydroxydiphenyl ether were the characteristic oxidation products of diphenyl sulfone, whose contents exhibited regular variations with temperature; this result further verified the rationality of the theoretically proposed oxidation reaction pathway. In addition, the analysis in this study revealed that during the oxidation of diphenyl sulfone, the dominant reaction regime of the system gradually shifted from oxidation-dominated at the low-temperature stage (Ea=59.21 kJ/mol) to pyrolysis-dominated at the medium and high-temperature stages (with the activation energy rising to 89.86 kJ/mol) as the temperature increased. By integrating experimental analysis with quantum chemical theoretical research, this study systematically elaborates the reaction mechanisms and dynamic competitive behavior of pyrolysis and oxidation for diphenyl sulfone during coal spontaneous combustion, which provides theoretical and methodological references for revealing the intrinsic mechanism of coal spontaneous combustion.

Coral reefs around Hainan Island in the northern South China Sea represent a marginal reef system exposed to interacting climatic and anthropogenic stresses. This study used an optimized MaxEnt model, remote-sensing-derived coral reef occurrence data, key environmental variables, and CMIP6 climate projections to assess habitat suitability, identify key environmental thresholds associated with suitability change, and examine areas with potential refugial significance. The optimized model showed high predictive performance (mean AUC = 0.947). Bathymetry was the dominant predictor of habitat suitability, while sea surface temperature (SST) and dissolved oxygen (DO) concentration were also important predictors. Predicted suitability declined markedly when water depth exceeded 8.9 m or when multiannual mean SST exceeded 26.8 degrees C. Under current climate conditions, suitable habitat was limited in extent and showed strong spatial heterogeneity. Future projections indicated severe habitat contraction under SSP2-4.5 and SSP5-8.5, whereas under SSP1-1.9 suitable habitat contracted sharply by the 2050s but partially re-emerged by the 2090s. Under SSP1-1.9, parts of eastern Hainan, especially the coastal waters of southern Wenchang, Qionghai, and Wanning, may retain refugial potential. These results help clarify future spatial patterns of habitat persistence and decline, providing a scientific reference for regional conservation prioritization and adaptive management.

Background In recent years, the integration of large language models has brought significant opportunities for advancing personalised learning in higher education. However, little attention has been paid to how students of different performance levels benefit from such tools, especially in creative disciplines. Objectives This study explored how two generative AI models, ChatGPT 4 and DeepSeek, support high- and low-performing college design students in personalised learning and influence their AI-assisted design performance, creative self-efficacy, and engagement. Methods This study adopts a comparative quasi-experimental design to examine the differential effects of two generative AI tools, ChatGPT and DeepSeek, on personalised learning outcomes in design education. Rather than serving as a passive control, both tools function as AI-assisted learning conditions with distinct interaction styles and feedback structures. Results Results show that, compared with conventional creative methods, personalised learning based on both GAIs significantly improves student outcomes. ChatGPT users demonstrated higher creative self-efficacy, while DeepSeek users exhibited greater engagement and AI-assisted design performance. Although self-efficacy and engagement were influenced by students' initial performance levels, AI-assisted design performance improved consistently across groups. Semi-structured interviews revealed that high-performing students used ChatGPT to expand imagination and experiment with abstract concepts, while low-performing students benefited from DeepSeek's structured prompts for clearer direction. Conclusions These different effects can be understood using Cognitive Load Theory, which shows that DeepSeek's organised outputs helped low-performing students focus better, while ChatGPT's open-ended feedback encouraged creative thinking for high-performing learners. Overall, the findings underscore the necessity of differentiated AI integration strategies in design education, taking into account learner characteristics and instructional goals.

Shallow lakes often exhibit two distinct ecological states: clear water dominated by macrophytes or turbid water dominated by phytoplankton, with both states strongly influenced by phosphorus availability. However, the specific mechanisms by which phosphorus utilization sustains phytoplankton communities in these contrasting states are not yet fully understood. For 1 year, we conducted monthly surveys of a macrophytic lake and an algal lake in Wuhan, China to examine how phosphorus availability and utilization strategies shaped phytoplankton community dynamics. The algal lake had notably higher phosphorus concentrations, especially in the sediments, with elevated levels of iron-bound phosphorus and a higher equilibrium phosphorus concentration, which indicated a larger potentially bioavailable phosphorus pool. In contrast, the macrophytic lake contained more organic phosphorus, including acid-soluble and sodium hydroxide-extractable phosphates. In the algal lake, there were significant correlations between sediment phosphorus adsorption parameters and dominant phytoplankton such as Chroococcus and Merismopedia. In the macrophytic lake, chlorophyll-a concentrations were positively correlated with alkaline phosphatase activity, which suggested a greater dependence on organic phosphorus hydrolysis. Structural equation modeling revealed that phytoplankton in the macrophytic lake relied primarily on organic phosphorus hydrolysis, whereas phytoplankton in the algal lake benefited from anaerobic release of inorganic phosphorus from the sediments. These contrasting phosphorus utilization strategies shaped species dominance, which not only influenced phytoplankton community composition but also serve as valuable ecological indicators of nutrient dynamics and lake health. Overall, our findings provide a foundation for eutrophication management and ecosystem restoration.