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The stiffened plate is one type of representative thin-walled structure consisting of plates reinforced by varying stiffeners to strengthen its resistance to buckling deformation. However, the mechanism by which stiffeners could efficiently improve the buckling resistance of such structures has not been well reported. Therefore, to investigate the mechanism and predict buckling behavior of the structures accurately, a refined Reddy-type global-local stiffened plate model (RGLS) is established firstly, which can fulfill continuous condition of in-plane displacement and transverse shear stress among the adjacent layers between the plate and stiffeners. In conjunction with the established model, a refined triangular element which can accommodate the continuous condition of transverse shear stresses at the interface between the plate and the stiffeners is constructed. Subsequently, on the basis of the unidirectional stiffened sandwich structures, the symmetric grid-stiffened sandwich structures are proposed. The buckling behavior of grid-stiffened structures is further explored by the present element. A full experimental study and a system of numerical examples have been performed to assess the reliability of the established elements. These examples demonstrate that the buckling loads could be captured by the developed element with high computational efficiency and accuracy. However, the models will significantly overrate buckling loads of the stiffened sandwich structures without considering the continuum condition of the transverse stresses at the plate-stiffener interface. [ABSTRACT FROM AUTHOR]

Objective: To examine whether certain Medicare Advantage (MA) plan characteristics are associated with driving beneficiaries to providers that generate fewer avoidable hospital stays. Data Sources: This paper primarily used 2018–2019 MA encounter data and traditional Medicare (TM) claims data for a nationally representative 20% sample of Medicare beneficiaries. Study Design: For each plan design aspect—plan type, carrier, star rating, and network breadth—we estimated two adjusted Poisson regressions of avoidable hospital stays: one without clinician fixed effects and the other with. We calculated the difference between the coefficients to evaluate the extent to which patient sorting affected avoidable hospital stays relative to TM. Data Extraction Methods: Our sample included Medicare beneficiaries 65 years and older who were continuously enrolled in either MA or TM during 2018–2019. Beneficiaries in our sample had one or more chronic, ambulatory care‐sensitive conditions. Principal Findings: Patient sorting can be attributed to certain characteristics of plan design aspects. For plan type, HMOs account for 86%, with PPOs accounting for only 14%. For carriers, Humana and smaller carriers account for 89%. For star ratings, high‐star contracts account for 94%, with other stars only accounting for 6%. By network design, narrow network plan‐counties explained 20% of the patient sorting effect. Conclusions: While MA plans were found to be associated with driving beneficiaries to providers that generate fewer avoidable hospital stays, the effect is not homogeneous across the characteristics of MA plans. HMOs and high‐star contracts are drivers of this MA phenomenon. [ABSTRACT FROM AUTHOR]

Precipitation is a vital physical parameter of the earth surface system, and accurate estimation of spatiotemporal patterns of precipitation is essential for flood disaster monitoring, drought monitoring, and water management. However, regional precipitation, which is derived solely from rain gauges, remote sensing, and weather radar, is subject to large uncertainties, especially for topographically complex mountain areas. Multi-source precipitation data fusion is a practical method for achieving high-accuracy and high-resolution precipitation information. This study proposes an XGBoost-based geostatistical fusion method (XGBoost) for combining information from ground-based measurements, radar precipitation, and other auxiliary parameters, to improve the accuracy of the spatiotemporal distribution of precipitation in geographically complex mountain areas. In the XGBoost-based geostatistical fusion model, radar precipitation and terrestrial parameters, which include longitude, latitude, digital elevation model data, aspect, slope, enhanced vegetation index, and distance from the coastline, are considered as the independent variables. The XGBoost-based geostatistical fusion model was applied to a regional rainstorm event that lasted from August 26th to 30th, 2018, in northern Guangdong using daily measurements from 206 rain gauges and 51 stations for model training and validation. The fused results were further compared with the results obtained from the multiple linear regression kriging method (LM). Validation using ground-based precipitation measurements was applied for different data fusion methods based on the coefficient of determination (R2), Mean Absolute Error (MAE), and Root-Mean-Square Error (RMSE). The experimental results indicated that: (1) The ground-based precipitation data were positively associated with radar precipitation, and the correlation coefficient between the ground-based precipitation data and the terrestrial parameters varied significantly with measurement time over the regional rainstorm event. (2) The XGBoost produced 1 km precipitation prediction with higher accuracy than the LM before residual correction. (3) The accuracy of fused precipitation with the XGBoost-based geostatistical method was reduced after residual correction, but the accuracy of the LM was increased. The XGBoost-based geostatistical method produced 1-km precipitation with lower accuracy than the TsHARP utility on August 27th and 29th; however, in general, the XGBoost-based geostatistical method outperformed the LM because the nonlinear relationships between the ground-based precipitation data and the independent variables were considered in XGBoost. (4) The XGBoost-based geostatistical method captured the differences in precipitation for different land cover patterns and produced the spatial details of fused precipitation over the complex mountain areas.

With more than 26 million confirmed cases and over two million case-fatalities worldwide, the coronavirus disease (COVID-19) pandemic has transformed the dynamics of human lives globally. It has been designated as a pandemic by the World Health Organization. The COVID-19 virus can be transmitted through droplets, aerosols, or direct contact. It possesses evident characteristics of human-to-human transmission. Additionally, COVID-19 is a highly pathogenic new coronavirus, and people are prone to serious respiratory diseases resulting in high mortality after becoming infected. It has posed a great security threat to the entire human society and caused hundreds of billions of economic losses. The novel coronavirus disease 2019 (COVID-19) epidemic spread from Wuhan to all other cities in China before Spring Festival, causing serious public health issues and preventing the growth of the social economy. Analyzing the spatial-temporal spread pattern of COVID-19 can support the prevention of the epidemic. Thus, this study aims to analyze the temporal-spatial spread characteristics of COVID-19 in Hubei Province. First, a regression model with variables of migration big data (mobility scale index (MSI) and traffic intensity) is employed to explore the temporal pattern of the spread of the epidemic. Second, the spatial spread characteristics of COVID-19 are analyzed using a regression model comprising transportation information (primary and secondary road transportation networks) and social economic information (2018 GDP data). The results illustrate the following. First, the regression model based on population migration data and daily COVID-19 cases in each city was significant (Sig.=0.00), with R2 up to 0.715, indicating that the independent variable could explain the dependent variable. As indicated by the standardized coefficient results, MSI (0.85) has a greater impact on the daily new cases in each city. Second, the cumulative infection rate per 10000 people was positively correlated with the number of medical institutions and GDP with correlation coefficients of 0.689 and 0.774, respectively, Sig. was less than 0.05. However, it was not correlated with the number of beds (Sig. > 0.05). Third, the spatial regression model based on the traffic network, socio-economic data and cumulative infection rate of ten thousand people in each city of Hubei was also significant. The independent variables in the model can explain the variability of 67.2% of the dependent variables. The results of the standardized coefficient show that the GDP ratio of each city has a greater impact on the model. The results of the study are expected to provide scientific data support for the government and epidemic prevention workers to formulate efficient epidemic prevention and policy decisions. In conclusion, the model fit of multiple regression on the time scale is better than that on the spatial scale. Population migration has the greatest impact on the spread of the epidemic. That is, population mobility has a greater effect on the prevention and control of epidemic situations. The results of the study are expected to provide scientific data support for the government on formulating epidemic prevention policies.