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Abstract Objective To report initial results of a planned multicenter year‐long prospective study examining the risk and impact of COVID‐19 among persons with neuroinflammatory disorders (NID), particularly multiple sclerosis (MS). Methods In April 2020, we deployed online questionnaires to individuals in their home environment to assess the prevalence and potential risk factors of suspected COVID‐19 in persons with NID (PwNID) and change in their neurological care. Results Our cohort included 1115 participants (630 NID, 98% MS; 485 reference) as of 30 April 2020. 202 (18%) participants, residing in areas with high COVID‐19 case prevalence, met the April 2020 CDC symptom criteria for suspected COVID‐19, but only 4% of all participants received testing given testing shortages. Among all participants, those with suspected COVID‐19 were younger, more racially diverse, and reported more depression and liver disease. PwNID had the same rate of suspected COVID‐19 as the reference group. Early changes in disease management included telemedicine visits in 21% and treatment changes in 9% of PwNID. After adjusting for potential confounders, increasing neurological disability was associated with a greater likelihood of suspected COVID‐19 (ORadj = 1.45, 1.17–1.84). Interpretations Our study of real‐time, patient‐reported experience during the COVID‐19 pandemic complements physician‐reported MS case registries which capture an excess of severe cases. Overall, PwNID seem to have a risk of suspected COVID‐19 similar to the reference population.

Abstract In this article, a powerful method to synthesize uniform CaCO3 rod bundles with a high mass fraction of aragonite from carbide slag in large‐scale production via a two‐step leaching‐precipitation process is reported. The effects of the addition of ethylene glycol and reaction temperature on the structure and morphology of the prepared CaCO3 samples were investigated and then demonstrated by the experimental results. It is beneficial to increase the mass fraction of aragonite in CaCO3 samples and ensure the formation of regular rod bundles in morphology when the addition of ethylene glycol is 30 mL and the reaction temperature is 80 °C. The formation mechanism of the CaCO3 rod bundles was preliminarily discussed according to the Muffins–Sekerka theory.

Abstract Gyro‐remanent magnetization (GRM) is a frequently occurring yet unwanted remanence contamination for certain samples during alternating field (AF) demagnetization of the natural remanent magnetization. The origin and detailed properties of GRM have not yet been fully understood. In this study, systematic rock magnetic analyses were conducted on marine greigite‐bearing samples of Hole U1433A drilled by the IODP Expedition 349 from the South China Sea. Results show that GRM is mostly acquired above ~55 mT AF demagnetization and can be effectively removed by heating to ~400°C during thermal demagnetization but a secondary tail could remain until ~585°C. In addition, no apparent GRM was observed during the AF demagnetization for the 400°C thermally treated samples. These results strongly suggest that GRM is dominantly carried by single domain (SD) greigite but with minor contributions from SD magnetite. Thus, thermal treatment alone or the hybrid demagnetization (i.e., thermal demagnetization at ~400°C first then systematical AF demagnetization) can efficiently avoid the GRM acquisition and be beneficial for relative paleointensity estimation for greigite‐bearing samples. Besides, GRM carried by greigite has a low thermal stability. Our results also show AF demagnetization spectra of anhysteretic remanent magnetization (ARM) could be strongly distorted by GRM effects due to both have a preference of SD particles. Thus, the median destructive field of ARM is improper to be used as a coercivity proxy for greigite‐bearing samples. Instead, the biplot analysis of AF demagnetization of natural remanent magnetization and ARM can be used to evaluate the relative content of greigite.

To improve the computational efficiency of Monte Carlo simulation in composite power systems reliability evaluation, this study presents a method based on the improved estimation of distribution algorithm (EDA) and double cross linked list. Compared to traditional techniques, this method is comprehensively improved in the stage of both sampling and state evaluation. In the sampling stage, the population‐based incremental learning algorithm is presented, where the probability vector is updated based on the distribution characteristics of excellent samples in population of previous generations. Meanwhile, setting a limit to the probabilities of elements in normal state and mutation strategy are introduced, which improves the excellent characteristics of the population. In the state evaluation stage, the state search and match process is speed up by utilising the intelligent storage technology based on the double across linked list. It avoids calling the optimal power flow for the same state repeatedly. Finally, the proposed method is tested in IEEE RTS 79. As the result shows, compared with other methods ever used in reliability evaluation, this method is not only more efficient in computation but also more accurate. Thus, the proposed method is proved to be reliable and effective.