Insomnia among coronavirus disease 2019 survivors: A single-center cross-sectional study.

Since the coronavirus disease 2019 (COVID-19) epidemic, insomnia has become one of the longer COVID-19 symptoms. This study aimed to investigate insomnia among COVID-19 survivors and explore the occurrence and influencing factors of insomnia. A cross-sectional study was performed from December 2022 to February 2023 through an online questionnaire star survey with 8 questions. The insomnia severity index scale (ISI) was used to assess the severity of insomnia. Univariate analysis was used to analyze the factors related to COVID-19 infection. A total of 564 participants (183 males and 381 females) were surveyed in the present study. The prevalence of insomnia was 63.12%. Among these insomnia patients, there were 202 (35.82%) with sub-threshold symptoms, 116 (20.57%) with moderate symptoms, and 38 (6.74%) with severe symptoms. Univariate analysis indicated that there were statistically significant differences in the prevalence of insomnia among COVID-19 survivors of different ages, occupations, and educational levels (P < .05). Of the 356 insomnia patients, 185 (51.97%) did not take any measures against insomnia, while those who took drugs only, physical exercise only, drugs and physical exercise, and other measures were 90 (25.28%), 42 (11.80%), 17 (4.78%), and 22 (6.18%), respectively. Additionally, of the 107 insomnia patients with drug therapy, 17 (15.89%) took estazolam, 16 (14.95%) took alprazolam, 39 (36.45%) took zopiclone, and 35 (32.71%) took other drugs to improve insomnia symptoms. The prevalence of insomnia symptoms remains high among COVID-19 survivors in China. Education level and occupation may be the influencing factors. Unfortunately, most patients with insomnia do not take corresponding treatment measures.

Precipitate Evolution in 22Cr25NiWCuCo(Nb) Austenitic Heat-Resistant Stainless Steel during Heat Treatment at 1200 °C.

In this study, 22Cr25NiWCuCo(Nb) heat-resistant steel specimens with high Cr and Ni contents were adopted to investigate the effect of Nb content on thermal and precipitation behavior. Differential scanning calorimetry profiles revealed that the melting point of the 22Cr25NiWCuCo(Nb) steel specimens decreased slightly with the Nb content. After heat treatment at 1200 °C for 2 h, the precipitates dissolved in a Nb-free steel matrix. In addition, the Z phase (CrNb(C, N)) and MX (Nb(C, N), (Cr, Fe)(C, N), and NbC) could be observed in the Nb-containing steel specimens. The amount and volume fraction of the precipitates increased with the Nb content, and the precipitates were distributed heterogeneously along the grain boundary and inside the grain. Even when the heat treatment duration was extended to 6 h, the austenitic grain size and precipitates became coarser; the volume fraction of the precipitates also increased at 1200 °C. The Z phase, rather than the MX phase, became the dominant precipitates at this temperature.

[Baseline correction method for Raman spectroscopy based on B-spline fitting].

Baseline drift is a widespread phenomenon in modern spectroscopy instrumentation, which would bring a very negative impact to the feature extraction of spectrum signal, and the baseline correction method is an important means to solve the problem, which is also the important part of Raman signal preprocessing. The general principle of baseline drift elimination is using the fitting method to the fit the baseline. The traditional fitting method is polynomial fitting, but this method is prone to over-fitting and under-fitting, and the fitting order is difficult to be determined. In this paper, the traditional method is improved; the B-spline fitting method is used to approach the baseline of Raman signal through constant iteration The advantages of B-spline, namely low-order and smoothness, can help the method overcome the shortcomings of polynomial method. In the experiments, the Raman signal of malachite green and rhodamine B were detected, and then the proposed method and traditional method were applied to perform baseline correction Experimental results showed that the proposed method can eliminate the Raman signal baseline drift effectively without over- and under-fitting, and the same order can be used in both positions where large or small baseline drift occurred. Therefore, the proposed method provided more accurate and reliable information for the further analysis of spectral data.