Spatial-Temporal Evolution Patterns and Influencing Factors of Hotels in Yellow River Basin from 2012 to 2022

Governmental attention towards the high-quality development of the Yellow River basin has brought new development opportunities for the hotel industry. This study aims to reveal the spatial-temporal evolution patterns and influencing factors of hotels in the Yellow River Basin from 2012 to 2022, based on economic, social, and physical geographic data of 190,000 hotels in the Yellow River flowing. With the help of a GIS technology system, the spatial-temporal evolution patterns of all hotels, star hotels, and ordinary hotels were explored, respectively. Then, the significant influencing factors of these patterns were revealed by using geographic detector and Person correlation analysis. The following conclusions were drawn: (1) the overall scale of the hotel industry in the Yellow River Basin expanded year by year, achieving rapid growth from 2016, and fluctuating around 2020 due to the impact of the novel coronavirus epidemic;the overall spatial distribution had significant regional differences, showing the structural characteristics of "southeast more, northwest less”;(2) there was a great difference in the degree of spatial autocorrelation agglomeration among prefecture-level cities, and the degree of agglomeration of both the hotel industry as a whole and general hotels decreased year by year, showing a random distribution in 2022;star hotels were always distributed randomly. Additionally, a strong synergistic correlation was shown between the number of ordinary hotels and the number of star hotels in local space;(3) overall, the development of the hotel industry was significantly affected by seven factors: structural force, macro force, ecological force, internal power, consumption power, intermediary power, and external power. There were differences in the forces acting on different types of hotels, which gives a pattern recognition in-depth.

Analysis of a maritime transport chain with information asymmetry and disruption risk.

Maritime transport chain is facing huge information asymmetry after the outbreak of major emergencies, such as COVID-19 epidemic. The previous literature has proved that information investing and information sharing are two effective tactics to relieve information asymmetry between supply chain nodes, and help them improve the performance of the supply chain. This paper assumes random demand disruption is the main cause of the information asymmetry in a maritime transportation chain. To explore how the random demand disruption and channel competition jointly impact operational decisions in a dual-channel maritime transport chain composed of one port, two carriers and shippers, we construct a game-theoretical basic model, and proposed two strategies, i.e., information investing and information sharing. Several significant managerial insights are derived. First, we find that inaccurate disruption information leads to inaccurate decisions and huge losses; Second, investing in precise information benefits the port only if the chain members are optimistic about the market, and improves the revenue of the carrier who invested in information if the investment cost is reasonable; Third, accepting information sharing benefits the port only when the precise disruption and the distortion of information are relatively large, as well as the misappropriate rate is relatively small; and only when the port is pessimistic about the market or the channel competition is weak, sharing information may hurt the carrier who invested in information. Finally, the strength of the channel competition will enhance the impact of information inaccuracy on the maritime transport chain.

Dynamic-Fusion-Based Federated Learning for COVID-19 Detection.

Medical diagnostic image analysis (e.g., CT scan or X-Ray) using machine learning is an efficient and accurate way to detect COVID-19 infections. However, the sharing of diagnostic images across medical institutions is usually prohibited due to patients' privacy concerns. This causes the issue of insufficient data sets for training the image classification model. Federated learning is an emerging privacy-preserving machine learning paradigm that produces an unbiased global model based on the received local model updates trained by clients without exchanging clients' local data. Nevertheless, the default setting of federated learning introduces a huge communication cost of transferring model updates and can hardly ensure model performance when severe data heterogeneity of clients exists. To improve communication efficiency and model performance, in this article, we propose a novel dynamic fusion-based federated learning approach for medical diagnostic image analysis to detect COVID-19 infections. First, we design an architecture for dynamic fusion-based federated learning systems to analyze medical diagnostic images. Furthermore, we present a dynamic fusion method to dynamically decide the participating clients according to their local model performance and schedule the model fusion based on participating clients' training time. In addition, we summarize a category of medical diagnostic image data sets for COVID-19 detection, which can be used by the machine learning community for image analysis. The evaluation results show that the proposed approach is feasible and performs better than the default setting of federated learning in terms of model performance, communication efficiency, and fault tolerance.

The Study of Healthy Benefits and Design Strategy of Urban Residential Green Space

The outbreak of COVID-19 in 2020 has made the health of urban populations as a key research issue in various disciplines. Residential green space as one of the most basic units of urban ecological environment has potential benefits to people’s health. Based on the analysis of affecting factors of urban residents’ heath, this paper discusses the benefits of residential green space on people’s health and its working mechanism through literature research, and at last, proposes the design strategy of residential green space based on improving residents’ health. In conclusion, to activate the healthy benefits of residential green space, it should be planned reasonably, and the greening rate and amenity facility of residential green space need to be improved too.

Clinical evaluation of a rapid colloidal gold immunochromatography assay for SARS-Cov-2 IgM/IgG.

BACKGROUND: Since December 2019, there had been an outbreak of COVID-19 in Wuhan, China. At present, diagnosis COVID-19 were based on real-time RT-PCR, which have to be performed in biosafe laboratory and is unsatisfactory for suspect case screening. Therefore, there is an urgent need for rapid diagnostic test for COVID-19. OBJECTIVE: To evaluate the diagnostic performance and clinical utility of the colloidal gold immunochromatography assay for SARS-Cov-2 specific IgM/IgG anti-body detection in suspected COVID-19 cases. METHODS: In the prospective cohort, 150 patients with fever or respiratory symptoms were enrolled in Taizhou Public Health Medical Center, Taizhou Hospital, Zhejiang province, China, between January 20 to February 2, 2020. All patients were tested by the colloidal gold immunochromatography assay for COVID-19. At least two samples of each patient were collected for RT-PCR assay analysis, and the PCR results were performed as the reference standard of diagnosis. Meanwhile 26 heathy blood donor were recruited. The sensitivity and specificity of the immunochromatography assay test were evaluated. Subgroup analysis were performed with respect to age, sex, period from symptom onset and clinical severity. RESULTS: The immunochromatography assay test had 69 positive result in the 97 PCR-positive cases, achieving sensitivity 71.1% [95% CI 0.609-0.797], and had 2 positive result in the 53 PCR-negative cases, achieving specificity 96.2% [95% CI 0.859-0.993]. In 26 healthy donor blood samples, the immunochromatography assay had 0 positive result. In subgroup analysis, the sensitivity was significantly higher in patients with symptoms more than 14 days 95.2% [95% CI 0.741-0.998] and patients with severe clinical condition 86.0% [95% CI 0.640-0.970]. CONCLUSIONS: The colloidal gold immunochromatography assay for SARS-Cov-2 specific IgM/IgG anti-body had 71.1% sensitivity and 96.2% specificity in this population, showing the potential for a useful rapid diagnosis test for COVID-19. Further investigations should be done to evaluate this assay in variety of clinical settings and populations.