Organic matter is the material basis for shales to generate hydrocarbon, as well as the main reservoir space and seepage channel for shale gas. When the thermal evolution degree is consistent, the organic carbon content in present shales is subject to the abundance of primitive sedimentary organic matter. Deep geofluids significantly influence the sedimentary organic matter’s enrichment, but the mechanism remains unclear. This paper is aimed at determining how hydrothermal and volcanic activities affected the enrichment of sedimentary organic matter by studying lower Cambrian shales in the lower Yangtze region and upper Ordovician-lower Silurian shales. Oxidation-reduction and biological productivity are used as indicators in the study. The result shows that hydrothermal or volcanic activities affected the enrichment of sedimentary organic matter by influencing climate changes and the nutrients’ sources on the waterbody’s surface and reducing water at the bottom. In the lower Cambrian shales of the Wangyinpu Formation in the lower Yangtze region, hydrothermal origin caused excess silicon. During the sedimentary period of the lower and middle-upper Wangyinpu Formation, vigorous hydrothermal activities increased the biological productivity on the waterbody’s surface and intensified the reducibility at the bottom of the waterbody, which enabled the rich sedimentary organic matter to be well preserved. During the sedimentary period of the lower upper Ordovician Wufeng Formation and the lower Silurian Longmaxi Formation in the upper Yangtze region, frequent volcanic activities caused high biological productivity on the waterbody surface and strong reducibility at the bottom of the waterbody. As a result, the abundant organic matter deposited from the water surface can be well preserved. During the sedimentary period of the upper Longmaxi Formation, volcanic activities died down gradually then disappeared, causing the biological productivity on the water surface to decrease. Besides, the small amount of organic matter deposited from the water surface was destroyed due to oxidation.

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Herein, the effect of Ti addition on the formation and evolution of inclusions in high‐Al transformation‐induced plasticity (TRIP) steel is investigated by performing a series of laboratory experiments and thermodynamic calculations for different quantities and sequences of Ti addition. Before the addition of Al and Ti, the main inclusions are spherical Mn–Si–Al–O oxide particles. The addition of Al transforms the Mn–Si–Al–O inclusions to Al2O3 inclusion. After the subsequent addition of 0.02 wt% Ti, the main inclusions are Al2O3, AlN, Al2O3–AlN, Al2O3–TiN, and Al2O3–AlN–TiN. However, after the subsequent addition of 0.05 or 0.12 wt% Ti to molten steel, the main inclusions are Al2O3, TiN, and Al2O3–TiN, and almost no AlN is observed. Using different addition sequences of Al and Ti to high‐Al TRIP steel does not result in significant differences in the types of inclusions. Adding Ti to molten steel does not transform Al2O3 to Ti‐oxides, whereas Al addition causes Ti‐oxides to be transformed to Al2O3. Importantly, adding over 0.05 wt% Ti to TRIP steel prevents the formation of AlN inclusion.

COVID-19 outbreaks in China in late December 2019, then in the United States (US) in early 2020. In the initial wave of diffusion, the virus respectively took 14 and 33 days to spread across the provinces/states in the Chinese mainland and the coterminous US, during which there are 43% and 70% zero entries in the space-time series for China and US respectively, indicating a zero-inflated count process. A logistic growth curve as a function of the number of days since the first case appeared in each of these countries accurately portrays the national aggregate per capita rates of infection for both. This paper presents two space-time model specifications, one based upon the generalized linear mixed model, and the other upon Moran eigenvector space-time filtering, to describe the spread of COVID-19 in the initial 19 and 58 days across the Chinese mainland and the coterminous US, respectively. Results from these case studies show both models shed new light on the role of spatial structures in COVID-19 diffusion, models that can forecast new cases in subsequent days. A principal finding is that describing the spatiotemporal diffusion of COVID-19 benefits from including a hierarchical structural component to supplement the commonly employed contagion component.