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Background. Type 2 diabetes (T2D) as a worldwide chronic disease combined with the COVID-19 pandemic prompts the need for improving the management of hospitalized COVID-19 patients with preexisting T2D to reduce complications and the risk of death. This study aimed to identify clinical factors associated with COVID-19 outcomes specifically targeted at T2D patients and build an individualized risk prediction nomogram for risk stratification and early clinical intervention to reduce mortality. Methods. In this retrospective study, the clinical characteristics of 382 confirmed COVID-19 patients, consisting of 108 with and 274 without preexisting T2D, from January 8 to March 7, 2020, in Tianyou Hospital in Wuhan, China, were collected and analyzed. Univariate and multivariate Cox regression models were performed to identify specific clinical factors associated with mortality of COVID-19 patients with T2D. An individualized risk prediction nomogram was developed and evaluated by discrimination and calibration. Results. Nearly 15% (16/108) of hospitalized COVID-19 patients with T2D died. Twelve risk factors predictive of mortality were identified. Older age (HR = 1.076, 95% CI = 1.014–1.143, p=0.016), elevated glucose level (HR = 1.153, 95% CI = 1.038–1.28, p=0.0079), increased serum amyloid A (SAA) (HR = 1.007, 95% CI = 1.001–1.014, p=0.022), diabetes treatment with only oral diabetes medication (HR = 0.152, 95%CI = 0.032–0.73, p=0.0036), and oral medication plus insulin (HR = 0.095, 95%CI = 0.019–0.462, p=0.019) were independent prognostic factors. A nomogram based on these prognostic factors was built for early prediction of 7-day, 14-day, and 21-day survival of diabetes patients. High concordance index (C-index) was achieved, and the calibration curves showed the model had good prediction ability within three weeks of COVID-19 onset. Conclusions. By incorporating specific prognostic factors, this study provided a user-friendly graphical risk prediction tool for clinicians to quickly identify high-risk T2D patients hospitalized for COVID-19.

This paper presents a lumped equivalent circuit model of the nonreciprocal magnetoelectric tunable microwave band-pass filter. The reciprocal coupled-line circuit is based on the converse magnetoelectric effect of magnetoelectric composites, includes the electrical tunable equivalent factor of the piezoelectric layer, and is established by the introduced lumped elements, such as radiation capacitance, radiation inductance, and coupling inductance, according to the transmission characteristics of the electromagnetic wave and magnetostatic wave in an inverted-L-shaped microstrip line and ferrite slab. The nonreciprocal transmission property of the filter is described by the introduced T-shaped circuit containing controlled sources. Finally, the lumped equivalent circuit of a nonreciprocal magnetoelectric tunable microwave band-pass filter is given and the lumped parameters are also expressed. When the deviation angles of the ferrite slab are respectively 0° and 45°, the corresponding magnetoelectric devices are respectively a reciprocal device and a nonreciprocal device. The curves of S parameter obtained by the lumped equivalent circuit model and electromagnetic simulation are in good agreement with the experimental results. When the deviation angle is between 0° and 45°, the maximum value of the S parameter predicted by the lumped equivalent circuit model is in good agreement with the experimental result. The comparison results of the paper show that the lumped equivalent circuit model is valid. Further, the effect of some key material parameters on the performance of devices is predicted by the lumped equivalent circuit model. The research can provide the theoretical basis for the design and application of nonreciprocal magnetoelectric tunable devices. [ABSTRACT FROM AUTHOR]

Advanced detection of roadway in coal mine is an important prerequisite for safe mining of roadway. Multiple solutions and large range of abnormal detection are the problems that puzzle advanced detection of roadway, and the research and application of multi-data fusion technology for advanced detection of roadway can effectively solve such problems. The principle and types of data fusion are introduced, and the research progress of data fusion is discussed. Focusing on the advanced detection technology of coal mine roadway, this paper further analyzes the research progress and current situation of roadway multi-data fusion, and points out its existing problems and development direction, which provides ideas for different types of advanced detection data fusion application of roadway in the future.