Sedimentation in the Three Gorges Reservoir (TGR) has been studied with a primary focus on the amount due to the decrease in sediment inflow caused by water and soil conservation projects and sediment retention of recently constructed upstream cascade reservoirs. In this study, the rate and distribution of sedimentation in the TGR were investigated. A total of 1.6 billion tons of sediment was trapped in the TGR from 2003 to 2015. The sediment retention rate increased from 60% during the initial operation stage between 2003 and 2005 to 82% during the normal operation stage between 2006 and 2015. Sediment finer than 0.008 and 0.016 mm contributed 44 and 61% of the total sedimentation, respectively, and the median size of the sedimentation was 0.01 mm. The spatial distribution of the sedimentation was discontinuous, and 80% of the sedimentation occurred in one-third of the backwater region, with primary deposition zones being at the wide reservoir reaches. The sedimentation was assessed by the sediment transport capacity, which was embedded in the 1-D sediment transport and bed deformation equations. Due to the impoundment of the TGR, the sediment transport capacities decreased significantly at the wide reservoir reaches where flocculation occurred, resulting in deposition. However, the sediment transport capacities were still greater than the sediment concentrations at the gorges, which will be the fixed stretches with no deposition. The surplus sediment transport capacity at the gorge stretches would not allow cumulative sedimentation in the future, leading to a spatially discontinuous equilibrium sedimentation along the TGR instead of the predicted spatially continuous distribution. A uniform bed slope is not expected under the new sedimentation equilibrium state, and the actual sedimentation will be considerably less than the initially predicted value, generating a sustainable storage volume with about 84.7% of the initial volume, which will facilitate the engineering functions of the TGR significantly.

The Three Gorges Project has benefited shipping development in the upper Yangtze River by improving the waterway channel and thus increasing ship load and port handling capacity. However, the Three Gorges lockage freight volume exceeded the designed capacity in 2011 (19 years sooner than expected), becoming a bottleneck for shipping development. For the predicted freight volume, building of a second ship lock with transit capacity of 150 million tons is suggested, and before it is built, ship organization should be optimized to increase transit capacity.

With the increasing integration of artificial intelligence into education, traditional instructional models in Hydraulic Engineering are shifting toward competence- and performance-oriented pedagogy under the New Engineering framework. Rooted in constructivist and learner-centered theories, this study examines how AI-assisted versus traditional instruction influences learning behaviors, competence development, and academic achievement in engineering education through a quasi-experimental study involving 102 undergraduate students. Results indicate that while the AI-assisted group achieved significantly higher Midterm Report Scores and PPT Presentation Scores, no significant difference was observed in Final Exam Scores between the two groups. Multivariate regression and latent profile analysis reveal that AI-assisted instruction enhances Classroom Participation, Data Processing Ability, and Comprehensive Analytical Ability, yet falls short in fostering Practical Problem-solving Ability compared to traditional instruction. Path analysis further indicates that AI-assisted instruction improves Academic Achievement indirectly by promoting Learning Behaviors, which in turn foster Competence Development, ultimately contributing to improved Academic Achievement. By addressing a critical gap in the literature on the mechanisms of AI integration in engineering education, this study underscores the importance of optimizing learning processes rather than merely pursuing outcome enhancement, offering theoretical and practical insights for AI-integrated instructional reform in the context of New Engineering education.

Citrus orchards receive increasing amounts of nitrogen fertilizer for the purpose of optimal yields and good quality of citrus fruits. Although the effects of increased nitrogen fertilizer on citrus fruit trees have been reasonably well studied, few comparable studies have examined impacts on soil bacterial communities and diversity, even though they play critical roles in orchard ecosystem functioning. In our study, Illumina MiSeq sequencing was used to investigate bacterial community structure and diversity under 5-years long-term nitrogen fertilization gradients (N0, N1, N2, N3, N4, and N5) in citrus orchard soils. The sequencing result showed that nitrogen fertilizer addition increased bacterial diversity along the N0 to N3 gradient but decreased bacterial diversity along the N3 to N5 gradient. The increase in the nitrogen fertilizer rate altered bacterial community composition by increasing the relative abundance of Delta-proteobacteria, Nitrospirae, SBR1093, and Latescibacteria and decreasing the relative abundance of Alpha-proteobacteria. Finally, regression analysis revealed that bacterial diversity and the relative abundance of Nitrosomonadales, Rhodobiaceae, Gemmatimonas, and Variibacter exhibited a significant positive correlation with citrus yield. The study revealed that a reasonable nitrogen fertilizer rate applied to citrus orchards could improve bacterial community structure and diversity and increase citrus yield.

The intensification of land use and climate change threatens watershed sustainability. These external disturbances drive complex interactions among components within watershed socio-ecological systems (SESs). Understanding how SESs respond to these changes is crucial for developing effective integrated watershed management strategies. Nevertheless, the nonlinear responses of these systems to such changes remain poorly understood. To fill this gap, this study proposes a network analysis method focusing on the Yangtze River Basin to construct an SES network comprising six dimensions, revealing the response of coupled relationships among network elements to climate and land-use change. The results showed that changes in land-use dynamics (LUD) and the standardized precipitation evapotranspiration index (SPEI) altered the link count and importance of network nodes, with notable shifts in vegetation and landscape nodes. Importantly, a strong nonlinear response of the LUD and SPEI to the coupled relationship between SES elements was observed, and critical thresholds were identified for all network attributes. Furthermore, compared to the SPEI threshold, the LUD threshold was stable at 0.24, demonstrating stronger robustness. This study provides a new perspective for understanding watershed SESs, and has important implications for sustainable ecosystem management and planning.