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Because there are many types of accommodation facilities in Jiuzhen Mountain Tourist Resort in Wuhan, the evaluation indexes that affect the accommodation output benefit in Jiuzhen Mountain Tourist Resort, including quality grade, brand effect, government policy, market supply and demand relationship, business subject and consumer habits, should be comprehensively considered. In this paper, the fuzzy comprehensive evaluation method is used to evaluate the outgoing benefits of accommodation products in Jiuzhen Mountain Tourist Resort. The results show that the hotel products within the geographical area have high output efficiency, but there is still the problem of uneven development, so the hotel resources should be optimally allocated, and the service and management should be improved to boost the overall efficiency of the tourism resort accommodation industry. © 2021 ACM.
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INTRODUCTION: Poor sleep quality among college students is a global problem. Chinese college students were required to home quarantine, social distance and participate in online learning during the COVID-19 epidemic. This study aimed to investigate the sleep quality of college students during the epidemic and identify the factors related to poor sleep quality. METHODS: Study participants completed an online survey that included questionnaires about sleep symptoms and lifestyle during the COVID-19 outbreak. The study participants included 3416 college students (mean age 20.4 ± 1.8 years). The Pittsburgh Sleep Quality Index (PSQI) was used to measure sleep quality, and a PSQI score >7 was defined as poor sleep quality. A logistic regression model was used to analyze the factors related to sleep quality. RESULTS: The percentage of college students with poor sleep quality was 15.97 % in southern Anhui province during the COVID-19 pandemic. The majority of the students were female (67.4%) and most were from urban areas (53.9%). Single-parent (adjusted odds ratio [aOR], 1.39;95% CI, 1.02-1.89) domestic violence incidents ≥5×/yr (aOR, 3.68;95% CI, 1.70 to 7.96), nap time >4 hr/d (aOR, 1.90;95% CI, 25-2.90) were significantly associated with poor sleep quality. While knowledge of COVID-19 was prevalent (aOR, 0.71;95% CI, 0.53 to 0.96) light exercise >1 hour/day (aOR, 0.47;95% CI, 0.28 to 0.78), parent-accompanied exercise >3×/wk (aOR, 0.59;95% CI, 0.38 to 0.90) were protective factors against poor sleep quality. CONCLUSIONS: The present study found that college students in single-parent families and students who had experienced domestic violence had a high risk of poor sleep quality during the COVID-19 pandemic in China. College students who were familiar with COVID-19 and had light exercise habits or parent-accompanied exercise habits had better sleep quality. At the time of writing, COVID-19 was still pandemic worldwide, so targeted sleep health interventions must be established to actively guide college students' healthy living habits. In addition, the sleep disorders and other health problems that may occur in college students should be dealt with in advance, and should be part of the routine work of global disease prevention.
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Introduction: Over the last two decades several ECMO survival predictions scores have been developed, with varying internal and external validation. We sought to evaluate the performance of six widely available scores on both our local COVID-19 database and a large international multicenter dataset. Methods: Using an institutional dataset encompassing 15 hospitals in a bi-state region and an international dataset of 42 countries, International Severe Acute Respiratory and emerging Infections Consortium (ISARIC), we evaluated the performance of ECMOnet, Respiratory Extracorporeal Membrane Oxygenation Survival Prediction (RESP), PRedicting dEath for SEvere ARDS on VV-ECMO (PRESERVE), Sequential Organ Failure Assessment (SOFA), Roch and PREdiction of Survival on ECMO Therapy-Score (PRESET) scores in identifying ECMO survival for COVID-19 patients. Results: We identified a total of 67 local and 1,014 ISARIC COVID-19 patients supported on ECMO, with a mortality rates of 48% and 51% respectively. In the local cohort all scores demonstrated poor overall performance with area under the receiver operative curve (AUROC) values between 0.53-0.61;ECMOnet 0.54, RESP 0.53, PRESERVE 0.59, Roch 0.53, PRESET 0.61 and SOFA 0.59. The ISARIC database contained fewer variables, allowing 4 scores to be evaluated. Again, all scores demonstrated poor performance in identifying non-survivors with AUROC between 0.55-0.66;ECMOnet 0.59, Roch 0.66, PRESET 0.55 and SOFA 0.59. Conclusions: Current ECMO prediction scores have poor accuracy and limited clinical utility when applied to both local and international databases of COVID-19 patients. Future work should focus on developing clinically applicable models to identify COVID-19 patients most likely to benefit from ECMO.
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The COVID-19 can be transmitted through airborne droplets, aerosols and other carriers. In order to better reduce people's risk of infection, individuals need to wear masks to prevent the spread of the virus when going out to public places, seeking medical treatment, and taking public transportation. This paper is based on the improved RETINAFACE algorithm, which effectively realizes the detection of mask wearing, and on the basis of this algorithm, realizes the function of judging whether the mask is worn correctly. In the face recognition algorithm, this paper designs a face recognition algorithm with higher accuracy. The system combines a face mask wearing detection algorithm, a mask standard wearing detection algorithm and a face recognition algorithm. In addition, this article adds a voice prompt module to better assist the integrity of the system's functions. The test results of the final experiment show that the system can effectively achieve the purpose of face mask detection and recognition. © 2020 IEEE.