Sediment bacterial community is the main driving force for nutrient cycling and energy transfer in aquatic ecosystem. A thorough understanding of the community's spatiotemporal variation is critical for us to understand the mechanisms of cycling and transfer. Here, we investigated the sediment bacterial community structures and their relations with environmental factors, using Lake Taihu as a model system to explore the dependence of biodiversity upon trophic level and seasonality. To combat the limitations of conventional techniques, we employed Illumina MiSeq Sequencing and Le FS e cladogram to obtain a more comprehensive view of the bacterial taxonomy and their variations of spatiotemporal distribution. The results uncovered a 1,000‐fold increase in the total amount of sequences harvested and a reverse relationship between trophic level and the bacterial diversity in most seasons of a year. A total of 65 phyla, 221 classes, 436 orders, 624 families, and 864 genera were identified in the study area. Delta‐proteobacteria and gamma‐proteobacteria prevailed in spring/summer and winter, respectively, regardless trophic conditions; meanwhile, the two classes dominated in the eutrophication and mesotrophication lake regions, respectively, but exclusively in the Fall. For LE fSe analysis, bacterial taxon that showed the strongest seasonal or spatial variation, majority had the highest abundance in spring/summer or medium eutrophication region, respectively. Pearson's correlation analysis indicated that 5 major phyla and 18 sub‐phylogenetic groups showed significant correlation with trophic status. Canonical correspondence analysis further revealed that porewater NH ₄ ⁺ ‐N as well as sediment TOM and NO _x ‐N are likely the dominant environmental factors affecting bacterial community compositions.

Identifying nitrogen-transforming genes and the microbial community in the lacustrine sedimentary environment is critical for revealing nitrogen cycle processes in eutrophic lakes. In this study, we examined the diversity and abundance of ammonia-oxidizing bacteria (AOB), ammonia-oxidizing archaea (AOA), denitrifying bacteria (DNB), and anammox bacteria (AAOB) in different trophic status regions of Lake Taihu using the amoA, Arch-amoA, nirS, and hzo genes as functional markers. Quantitative Polymerase Chain Reaction (qPCR) results indicated that the abundance of the nirS gene was the highest, while the amoA gene had the lowest abundance in all regions. Except for the primary inflow area of Lake Taihu, Arch-amoA gene abundance was higher than the hzo gene in three lake bays, and the abundance of the nirS gene increased with decreasing trophic status. The opposite pattern was observed for the amoA, Arch-amoA, and hzo genes. Phylogenetic analyses showed that the predominant AOB and AOA were Nitrosomonas and Nitrosopumilus maritimus, respectively, and the proportion of Nitrosomonas in the eutrophic region (87.9%) was higher than that in the mesotrophic region (71.1%). Brocadia and Anammoxoglobus were the two predominant AAOB in Lake Taihu. Five novel unknown phylotypes of AAOB were observed, and Cluster AAOB-B was only observed in the inflow area with a proportion of 32%. In the DNB community, Flavobacterium occurred at a higher proportion (22.6–38.2%) in all regions, the proportion of Arthrobacter in the mesotrophic region (3.6%) was significantly lower than that in the eutrophic region (15.6%), and the proportions of Cluster DNB-E in the inflow area (24.5%) was significantly higher than that in the lake bay (7.3%). The canonical correspondence analysis demonstrated that the substrate concentration in sedimentary environments, such as NO_x^--N in the sediment, NH₄⁺-N in the pore water, and the total organic matter, were the key factors that determined the nitrogen-transforming microbial community. However, the temperature was also a predominant factor affecting the AOA and AAOB communities.

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.

Ecological compensation is an effective means to adjust relationships among stakeholders in order to conserve and/or sustainably use ecosystem services. The current ecological compensation standards (ECS) do not well reflect the differences in ecological, social, and economic development. Thus, we took a typical urbanization area (the Suzhou–Wuxi–Changzhou region) in China as an example, because of its prominent contradiction between rapid socio-economic development and fragile ecosystem. Combined with the ecological, economic, and social conditions, the methods of ecosystem service value (ESV) evaluation, cluster analysis, and scenario analysis were used to propose an optimized spatial zoning method and optimal development scenario. Then, the ECS by different zones were determined by using ESV assessment, cost-benefit analysis, and contingent valuation method. The results showed that (1) the regions were divided into two categories, with a total of four zones: ESV output areas (synergetic development zones (SDZ) and ecological conservation zones (ECZ)) and ESV input areas (ecological degradation zones (EDegZ) and economic development zones (EDevZ)); (2) among five scenarios, the best development mode in the future was the protection and development scenario, which was consistent with the existing planning; and (3) the ECS for the SDZ should be paid about 1.94 billion Yuan/year, the ECZ should be paid about 0.80 billion Yuan/year, the ECS for the EDegZ should pay about 2.20–2.25 million Yuan/year, and the EDevZ should pay about 0.06–7.33 million Yuan/year. By feasibility analysis, the ESV input areas were fully capable of inter-regional compensation, which could promote the effective purchase of ecological services. The developed evaluation framework of ECS in this study can accurately provide a scientific basis for the determination of ecological compensation regulations and policies in the future.