Objective To evaluate the prevalence and characteristics of olfactory or gustatory dysfunction in coronavirus disease 2019 (COVID-19) patients. Study Design Multicenter case series. Setting Five tertiary care hospitals (3 in China, 1 in France, 1 in Germany). Subjects and Methods In total, 394 polymerase chain reaction (PCR)–confirmed COVID-19-positive patients were screened, and those with olfactory or gustatory dysfunction were included. Data including demographics, COVID-19 severity, patient outcome, and the incidence and degree of olfactory and/or gustatory dysfunction were collected and analyzed. The Questionnaire of Olfactory Disorders (QOD) and visual analog scale (VAS) were used to quantify olfactory and gustatory dysfunction, respectively. All subjects at 1 hospital (Shanghai) without subjective olfactory complaints underwent objective testing. Results Of 394 screened subjects, 161 (41%) reported olfactory and/or gustatory dysfunction and were included. Incidence of olfactory and/or gustatory disorders in Chinese (n = 239), German (n = 39), and French (n = 116) cohorts was 32%, 69%, and 49%, respectively. The median age of included subjects was 39 years, 92 of 161 (57%) were male, and 10 of 161 (6%) were children. Of included subjects, 10% had only olfactory or gustatory symptoms, and 19% had olfactory and/or gustatory complaints prior to any other COVID-19 symptom. Of subjects with objective olfactory testing, 10 of 90 demonstrated abnormal chemosensory function despite reporting normal subjective olfaction. Forty-three percent (44/102) of subjects with follow-up showed symptomatic improvement in olfaction or gustation. Conclusions Olfactory and/or gustatory disorders may represent early or isolated symptoms of severe acute respiratory syndrome coronavirus 2 infection. They may serve as a useful additional screening criterion, particularly for the identification of patients in the early stages of infection.

interpretation of data, 102 writing, revising it critically. All of them agreement to be accountable for all aspects of 103 the work in ensuring that questions related to the accuracy or integrity of any part of 104 the work are appropriately investigated and resolved. 105 106 Abstract 123

The optimal stopping time is of profound significance in statistics, mathematics and finance, and can be used to derive optimal choices from uncertain problems such as volatile golden market. The paper mainly focuses on studying the problem of optimal stopping time by using the basic theory of Brownian motion and resolving the optimal time node for trading gold at a specified time in which tendency of golden price is known. Brownian motion, one of the basic theories in the stock market, solves probabilistic random problems and helps calculate the time node closet for best-selling time in formula. This paper uses Brownian motion as a research method to calculate optimal stopping time. Then the data of golden price is selected from 2021 to 2022 as a model and used for analysing boundary and retracement state of specific golden price to determine the optimal stopping time that maximal trading revenue generates. Therefore, this paper provides a common method to study best stopping time in bull market to derive the optimal profit. The empirical results verify the feasibility and operability of the optimal stopping time model in the investment market. Investors can use boundary value and retracement value as reference data to sell gold in time in order to avoid huge losses. Investors can also adjust the parameters of the model according to their own investment strategies, so that the calculated results of the model can meet individual needs.

The random-value impulse noise (RVIN) detection approach in image denoising, which is dependent on manually defined detection thresholds or local window information, does not have strong generalization performance and cannot successfully cope with damaged pictures with high noise levels. The fusion of the K-means clustering approach in the noise detection stage is reviewed in this research, and the internal relationship between the flat region and the detail area of the damaged picture is thoroughly explored to suggest an unique two-stage method for gray image denoising. Based on the concept of pixel clustering and grouping, all pixels in the damaged picture are separated into various groups based on gray distance similarity features, and the best detection threshold of each group is solved to identify the noise. In the noise reduction step, a partition decision filter based on the gray value characteristics of pixels in the flat and detail areas is given. For the noise pixels in flat and detail areas, local consensus index (LCI) weighted filter and edge direction filter are designed respectively to recover the pixels damaged by the RVIN. The experimental results show that the accuracy of the proposed noise detection method is more than 90%, and is superior to most mainstream methods. At the same time, the proposed filtering method not only has good noise reduction and generalization performance for natural images and medical images with medium and high noise but also is superior to other advanced filtering technologies in visual effect and objective quality evaluation.