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The purpose of this study is to quantify the proportion and flow path of the water diversion from Yangtze River (YRD) into Taihu Lake. Based on the analysis of rainfall and data of Taihu basin in the recent 30 years, a 1-D hydrodynamic model of the main inflow river network area of Taihu basin was constructed, coupled the convection-diffusion model with conservative material, the characteristics of YRD and the water inflow into Taihu Lake (WITL) in three typical years were calculated. The results show that the YRD has shown a significant upward trend in the past 30 years, accounting for 26.4, 35.6 and 42% of the total WITL in three typical years of wet, normal and dry conditions respectively. From the perspective of space, Taige River is the largest river in the western part of the lake that is affected by the river diversion (35%–72%), and Wuxi River is the smallest (1–3%). In addition, the primary flow path of YRD to Taihu Lake was through the Wuyi River and Lake Gehu from the water diversion station west of the Zao River. HIGHLIGHTS A one-dimensional hydrodynamic model was constructed to simulate the flow process in the plain river network area of Taihu Lake basin.; Coupled with the convection-diffusion model of conservative material, the proportion of water diversion from Yangtze River in the water inflow from Taihu Lake is calculated.;

The anomaly detection and health monitoring of dams have attracted increasing attention. To detect the temporal and spatial anomalies of the dam, a novel spatial–temporal variational autoencoder is proposed. The proposed model is based on the sequential variational autoencoder, and its backbone is fulfilled by the recurrent neural network and graph convolutional network to capture the temporal and spatial features in both the generative and inference models. To obtain a normal pattern, we made an assumption that the normal values should be temporally smooth and spatially similar. Then, the smoothness and similarity-inducing operations are used in the framework of the proposed model. Through the addition of smoothness and similarity losses in sequential variational autoencoder, the proposed model can obtain a temporally smooth and spatially similar pattern. For verification, an arch dam is taken as an example. Through comparison among six baseline models, the proposed model detects the temporal and spatial anomalies accurately and stably. [ABSTRACT FROM AUTHOR]

• The physical properties of several complex Fe-Si phases are studied by DFT. • The structural stability is verified by formation enthalpy, phonon and elastic constants. • The magnetic, electronic, and mechanical properties are discussed. • Anomalous phonon behavior is observed in the Fe 24 Si 5 phase. • The Young's modulus, shear modulus and Debye temperature decrease with the increase of Fe/Si ratio. The formation enthalpy, electronic structure, lattice dynamics and elasticity of several complex ordered iron-rich silicides (Fe 11 Si 5 , Fe 13 Si 3 , Fe 17 Si 12 and Fe 24 Si 5) are calculated from the first-principles calculations. It is found that the Debye temperature Θ D of the system decreases with the increase of the ratio of the number of iron atoms to the number of silicon atoms in the same structure. The softening of low-energy acoustic phonons is more obvious with the increase of iron atoms. The elastic constants C 11 and C 44 , Young's modulus E and shear modulus G of the system decrease with the increase of iron atoms. An anomalous phenomenon occurs in the high frequency region of Fe 24 Si 5. The contribution of Fe1 atoms with smaller magnetic moment to the phonon density of states is much larger than that of the Si and other Fe atoms. This indicates that the local metal bond strength between Fe1-Fe1 atoms is stronger. [ABSTRACT FROM AUTHOR]

Microbial secondary metabolites are a rich source for pharmaceutical discoveries and play crucial ecological functions. While tools exist to identify secondary metabolite clusters in genomes, precise sequence-to-function mapping remains challenging because neither function nor substrate specificity of biosynthesis enzymes can accurately be predicted. Here, we developed a knowledge-guided bioinformatic pipeline to solve these issues. We analyzed 1928 genomes of Pseudomonas bacteria and focused on iron-scavenging pyoverdines as model metabolites. Our pipeline predicted 188 chemically different pyoverdines with nearly 100% structural accuracy and the presence of 94 distinct receptor groups required for the uptake of iron-loaded pyoverdines. Our pipeline unveils an enormous yet overlooked diversity of siderophores (151 new structures) and receptors (91 new groups). Our approach, combining feature sequence with phylogenetic approaches, is extendable to other metabolites and microbial genera, and thus emerges as powerful tool to reconstruct bacterial secondary metabolism pathways based on sequence data., Competing Interests: SG, YS, KR, LB, DZ, RH, RX, JS, AJ, VF, XB, ZW, RK, ZL No competing interests declared, (© 2024, Gu, Shao et al.)