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The early Cambrian is a critical interval of dramatic oceanic and biochemical changes in geological history. The black shale deposits, which are rich in Mo, Ni, V, and platinum group elements (PGE), are a reflection of that interval. Among all known Cambrian black shale deposits in South China, the vanadium deposits are poorly constrained by geochronology. The newly discovered tuff layer in the Sansui Bagong vanadium deposit in Guizhou Province can provide excellent constraints on the age of vanadium deposits. In this study, we obtain a new zircon U–Pb isotopic age, which can constrain the age of the vanadium deposit. This tuff occurs in the middle part of the ore bed, and the age of the tuff layer can reflect the mineralization age of the V deposit. Laser ablation inductively coupled plasma mass spectrometry (LA-ICP-MS) is used in this study to obtain the zircon U–Pb age of the tuff and indicates that the event of volcanic activity took place at 520.9 ± 1 Ma. This age is close to the boundary between the Cambrian Terreneuvian and Series 2. This result provides a good constraint on the age of the vanadium deposits in South China and makes the vanadium deposits to be comparable with other Mo–Ni–PGE deposits. The alteration of organic carbon isotope (δ 13Corg) values can reflect changes in the marine environment and is widely used in stratigraphic correlation. The analysis of the δ 13Corg values of the ore bed in the present study reveals a positive excursion at the bottom of the deposit and a negative excursion in the V-enriched layer. The δ 13Corg values in the Bagong section are comparable to those in the Xiaotan and Longbizui sections. The ages of the Sansui vanadium deposit constrained by the U–Pb isotopic age of the tuff and the δ 13Corg values are consistent. All of the data lead us to infer that the vanadium deposit formed at approximately 521 Ma.

During the Ediacaran–Cambrian transition, a series of stratal continuous and well-preserved siliceous rock and phosphorite assemblages developed in Qingzhen, Guizhou Province, China, facilitating research on the biological evolution, marine chemistry, and palaeoecological environment of this period. Therefore, we investigated the paleontology, trace and rare earth elements, total organic carbon, total sulfur content, and Zn isotopes of the phosphorus-bearing rock series in the Taozichong Formation of the Cambrian period in Qingzhen. Geochemical analysis reveals that the sedimentary rocks in this area were formed in the oxygen-rich seawater environment and were not affected by high-temperature hydrothermal activity. The upwelling ocean current provided abundant rare earth elements and other nutrient elements, as well as conditions for the prosperity of biota in Qingzhen. In addition, the δ66Zn value (−0.21‰–0.41‰ range and 0.17‰ mean) in the Qingzhen phosphorous rock series was much lower than that in seawater, indicating a strong level of biological productivity. The variation trend of δ13C, δ18O, and δ66Zn exhibited four stages and three obvious drift events. The results suggest that climate change during this period led to the intermittent flourishing and extinction of organisms, which triggered the negative drift of δ13C and δ18O in the ocean, resulting in a coordinated response of δ66Zn. The unique ecological environment of the Taozichong Formation in Qingzhen also provides favorable conditions for the population continuation of Ediacaran-type benthic soft-bodied metazoon dominated by discoid fossils, Shaanxilithes, worm fossils, and sponge fossils in the Cambrian strata, as well as participation in the global biological explosion events. The study area provides new insights for rebuilding the global Ediacaran–Cambrian ecosystem during the transition period. [ABSTRACT FROM AUTHOR]

Accurate forecasting of traffic flow is crucial for intelligent traffic control and guidance. It is very challenging to forecast the traffic flow due to the high non‐linearity, complexity and dynamicity of the data. Most existing forecasting methods focus on designing complicated graph neural network architectures to capture the spatio‐temporal features of traffic data with the help of predefined graphs. However, traffic data exhibit a strong spatial dependency, which means that there are often complex correlations between nodes in a road network topology graph. Moreover, the spatial correlations of the road network change over time. To capture the properties of the road network topology graph, a novel spatio‐temporal adaptive graph convolutional networks model (STAGCN) based on deep learning is proposed. Utilize adaptive graph generation block to capture the static and dynamic structures of the traffic road network respectively, and they are integrated to construct an adaptive road network topology graph. Then the spatio‐temporal features of the traffic data are captured using spatio‐temporal convolution blocks. Experiments were conducted on publicly available traffic datasets for freeway traffic in California, USA, and the results showed that the prediction accuracy of the STAGCN model outperformed multiple baseline methods. [ABSTRACT FROM AUTHOR]