The effect of oxygen content on the manganese sulfide in the process of cooling and heat treatment is studied. The experimental results show that the changes of oxygen content have a great effect on the formation and growth of sulfide in free-cutting steel. And the morphology of sulfides transforms from Type II to type I in cooling process with the increase of oxygen content contribute to the formation of some oxides during initial solidification stage, which results in the formation of MnS from eutectic reaction to monotectic reaction. Oxygen content has a great effect on nucleation through decreasing nucleation work, increasing the oxygen content helps heterogeneous nucleation process. In slow cooling and heat treatment process, with the increase of oxygen content, sulfide inclusion coarsening rate is increasing and size of inclusion is remarkably magnifying, using Ostwald ripening theory.
Objective To characterize hypertensive patients living in metropolitan cities in China. Methods This was a cross-sectional survey conducted in Beijing, Shanghai and Guangzhou. The eligibility criteria included outpatients 35-85 years of age with a systolic blood pressure (SBP) of ! 140 mmHg or a diastolic blood pressure (DBP) of ! 90 mmHg or both and/or patients receiving antihypertensive medications. The patients' demographic characteristics, medical history and findings of physical examinations, laboratory tests and cardiovascular imaging (i.e., ultrasonic cardiogram) were included in the survey. Risk stratification and the rate of hypertension control were evaluated. Results A total of 25,336 individuals were surveyed, of which 79.1% were from cardiology clinics and 51.8% were male hypertensives. The average SBP/DBP was 139.3±18.6/82.3±12.0 mmHg. The mean age was 63.6±11.5 years. The mean BMI was 25.1±3.8 kg/m 2 . Among the men, 55.9% had a waist circumference of >90 cm. Among the women, 50.9% had a waist circumference of >85 cm. The percentages of patients with diabetes mellitus, heart disease and cerebral vascular disease were 20.3%, 39.2% and 10.4%, respectively. The smoking rate was 17.6%. Overall, 60.9% of the patients were in the very high risk group. While 97.7% of the patients were receiving antihypertensive drug therapy, only 40.2% had controlled SBP/DBP (i.e., under 140/90 mmHg). The control rate was statistically higher in Beijing and Shanghai than in Guangzhou and among older patients than among younger patients (43% among the patients >75 years of age vs. 28.1% among the patients 35-45 years of age). Conclusion In Beijing, Shanghai and Guangzhou, most hypertensive patients have various cardiovascular risk factors and cardiovascular diseases. High blood pressure is not under appropriate control in all cases, especially among young hypertensives and patients living in Guangzhou city. Approaches designed to target multiple risk factors and concomitant cardiovascular diseases and boost the hypertension control rate are warranted.
Prompt surveillance and forecasting of COVID-19 spread are of critical importance for slowing down the pandemic and for the success of any public mitigation efforts. However, as with any infectious disease with rapid transmission and high virulence, lack of COVID-19 observations for near-real-time forecasting is still the key challenge obstructing operational disease prediction and control. In this context, we can follow the two approaches to forecasting COVID-19 dynamics: based on mechanistic models and based on machine learning. Mechanistic models are better in capturing an epidemiological curve, using a low amount of data, and describing the overall trajectory of the disease dynamics, hence, providing long-term insights into where the disease might go. Machine learning, in turn, can provide more precise data-driven forecasts especially in the short-term horizons, while suffering from limited interpretability and usually requiring backlog history on the infectious disease. We propose a unified reinforcement learning framework that combines the two approaches. That is, long-term trajectory forecasts are used in machine learning techniques to forecast local variability which is not captured by the mechanistic model.
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