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Objective To analyze the clinical characteristics of patients in makeshift COVID-19 hospital and discuss how to establish and optimize the transfer system for a large number of critical cases. Methods From April 10 to May 24 of 2022, 659 patients with COVID-19 who needed to be transferred due to emergency in makeshift hospital located in National Convention and Exhibition Center were included, and their basic information, including the age, gender, underlying diseases and reasons for transfer, were analyzed. According to the key elements of the transfer process, a fast and efficient transfer system was established. Results Among the 659 transferred patients, 131 patients had respiratory system related diseases whose mean age was 58.47(95%CI: 55.62~61.33), with a male to female ratio of 1∶0.37(95/36). There were 528 patients had non-respiratory system diseases, whose mean age was 52.57(95%CI: 50.87~54.28), with a male to female ratio of 1∶1.01 (263/265). The types of diseases of the transferred patients mainly were 86 cases of endocrine diseases, 69 cases of cardiovascular system diseases, 34 cases of fever, 38 cases of nervous system diseases, 36 cases of tumor diseases, 34 cases of trauma, 32 cases of digestive system diseases and 199 cases of other diseases. Conclusion The effective treatment of patients in critical condition in makeshift hospital depends on the linkage mechanism run effectively, and the establishment of a fast, smooth, efficient and safe transport system can improve the treatment level of critical cases in large makeshift hospital.

Objective To construct an XGBoost prediction model to predict disease severity of COVID-19 based on clinical characteristics dataset of COVID-19 patients. Methods A total of 347 laboratory-confirmed COVID-19 patients with complete medical information admitted from Feb 10 to April 5, 2020 were screened from the medical record system of Huoshenshan Hospital. Firstly, 21 features with significant differences were screened out as input features for the training model. Bayesian optimization was performed on the constructed XGBoost model to adjust the parameters, and the optimal combination of features was filtered based on feature importance. To further analyze the positive and negative effects of the numerical size of each feature on the prediction results, each feature importance was quantified and attributed by using SHapley Additive exPlanations (SHAP). Finally, the performance of the XGBoost prediction model was evaluated, and the model was compared and discussed with other machine learning methods, including support vector machine (SVM), naïve Bayes (NB), logical regression (LR), and k-nearest neighbors (KNN). Results In this study, 21 features with significant differences between the severe and non-severe groups were selected for training and validation. The optimal subset with 10 features in the k-nearest neighbor model obtained the highest value of area under curve (AUC) among the 4 models in the validation set. XGBoost and support vector machine were better than other machine learning methods in terms of prediction performance (AUC: 0.942 0, and 0.959 4 on the test set, respectively), and the training speed of XGBoost was significantly faster. Conclusion A prediction model based on XGBoost is successfully built to achieve early prediction of disease severity of COVID-19 patients.

The effects of plasma density on the plasmaspheric hiss amplitude and its spatial and temporal scale sizes are investigated using data from Van Allen Probes A and B. The plasmaspheric hiss amplitude and scale sizes are found to be more affected by the small‐scale density variations than the overall density profile of the plasmasphere. In detail, our results suggest that both the hiss wave amplitude and its scale sizes are (a) related to the total electron density when the density is less than ∼1,000 cm−3; (b) independent of the total electron density when the density is greater than ∼1,000 cm−3; and (c) better correlated with the detrended density (removing the background plasmasphere density profile from the total electron density) regardless of the density value. Key Points: Effects of plasma density on the spatial and temporal scale sizes of plasmaspheric hiss amplitude is statistically investigatedThe plasmaspheric hiss amplitude is better correlated with the detrended electron density than the total electron densityThe spatial and temporal scale sizes of plasmaspheric hiss are mainly related to that of the relatively small‐scale density variations [ABSTRACT FROM AUTHOR]

Bicoherence analysis is statistically performed for the oxygen ion cyclotron harmonic (OCH) waves observed by Van Allen Probes. While OCH waves and so‐called electromagnetic ion cyclotron harmonics have indistinguishable spectral characteristics in observation, they are believed to be excited by different mechanisms with the latter being suggested to arise from nonlinear wave‐wave coupling. Our result reveals that most of the OCH wave events have small bicoherence indices, so they are unlikely excited by wave‐wave coupling. The result is not much affected by the parameter choice in the bicoherence analysis, although increasing the subinterval length and/or reducing the subinterval overlap increases the chance of getting large bicoherence index values. Furthermore, the examination of two special events with large bicoherence indices shows that the observed phase relationship among different wave components does not align with the expectation from wave‐wave coupling. The events cannot be convincingly confirmed to be generated through wave‐wave coupling. Plain Language Summary: This paper presents a statistical bicoherence analysis study of the oxygen ion cyclotron harmonic (OCH) waves observed by Van Allen Probes. OCH waves have apparent features similar to electromagnetic ion cyclotron (EMIC) harmonics in observation. Previous studies have suggested that OCH waves are excited through the oxygen ion Bernstein instability but EMIC harmonics are generated by nonlinear wave‐wave coupling. Bicoherence analysis is commonly employed for assessing wave‐wave coupling. The results of our bicoherence analysis indicate that most of the OCH events have bicoherence indices less than 0.5, so they are unlikely generated through wave‐wave coupling. The study also demonstrates that the parameter choice in bicoherence analysis affects the calculated index values, but the index values remain mostly less than 0.5 for the various reasonable parameter choices tested. In addition, a detailed analysis of the phase relationship among various electric and magnetic wave components in two specific OCH events reveals that, despite their large bicoherence indices, the observed phase relationship does not align with what would be expected if the waves are generated from wave‐wave coupling. This reminds us that having a large bicoherence index is a necessary but not sufficient condition to prove that waves are generated by wave‐wave coupling. Key Points: Most of the oxygen ion cyclotron harmonic (OCH) waves observed by Van Allen Probes have small bicoherence indicesIncreasing the subinterval length and/or reducing the subinterval overlap increases the chance of getting large bicoherence index valuesExamination of two special OCH wave events with large bicoherence indices does not support them being generated from wave‐wave coupling [ABSTRACT FROM AUTHOR]

In this study, we differentiate wet processes from dry processes in shaping the extratropical thermal stratification during the Last Glacial Maximum. Our findings indicate that even during the dry glacial period the influence of wet processes on thermal stratification cannot be overlooked. The applicability of dry and wet baroclinic adjustment theory strongly depends on the seasonality rather than the glaciation as the warm season is characterized by a weaker meridional temperature gradient and increased precipitation than the cold season. Despite that the baroclinic adjustment theory based on effective static stability can be proficiently applied to all seasons, the classical dry baroclinic adjustment theory may be better suited for use during relatively cold seasons. These findings have important implications for understanding processes governing the extratropical thermal stratification, particularly in the context of cold climate. Plain Language Summary: In this study, we examined how different processes affect the formation of the extratropical thermal stratification during the Last Glacial Maximum. We found that both dry and wet processes play a crucial role, with the wet processes being particularly important during warm seasons. Our results suggest that the importance of wet process is largely dependent on the seasonality instead of glaciation. These findings help to improve our understanding of the extratropical thermal stratification and its governing processes, particularly in the context of cold climates. Key Points: The study differentiates wet process from dry process in shaping extratropical thermal stratification during the Last Glacial MaximumEven during the dry glacial period, wet processes still poses a significant influence on thermal stratificationSeasonality governs dry and wet baroclinic adjustment theory applicability, with the dry theory being more suited for cold seasons [ABSTRACT FROM AUTHOR]

Wireless body area networks (WBANs) can be used to realize the real-time monitoring and transmission of health data concerning the human body based on wireless communication technology. With the transmission of these sensitive health data, security and privacy protection issues have become increasingly prominent. Fine-grained authentication allows physicians to run authentication checks of another specific entity according to their identifying attributes. Hence, it plays a key role in preserving the security and privacy of WBANs. In recent years, substantial research has been carried out on fine-grained authentication. However, these studies have put considerable effort into WBAN performances, resulting in weakened security. This paper proposes a fine-grained user authentication and key agreement protocol based on physical unclonable functions (PUFs) while maintaining robust security and performance. This will allow physicians to perform mutual authentication and obtain key agreements with authorized body area sensor nodes according to their identity parameters, such as occupation type and title. We then provide comprehensive security and heuristic analyses to demonstrate the security of the proposed protocol. Finally, the performance comparison shows that the proposed protocol is more robust in security, cost-effective communication, and computational overheads compared to three leading alternatives. [ABSTRACT FROM AUTHOR]

Nanozymes are artificial enzymes that mimic natural enzyme-like activities and exhibit tremendous potential for tumor chemodynamic therapy. However, the development of novel nanozymes with superior catalytic activities for nanotheranostics remains a formidable challenge. Herein, we report a facile synthesis of monodisperse palladium nanosheets (Pd nanosheets) and their assembly on graphene oxide (GO) that enhances the catalytic activities of Pd nanoparticles. Simultaneously, the obtained nanocomposites (rGO-Pd) could be applied as a smart near-infrared (NIR) light-responsive nanotheranostic for near infrared imaging-guided chemodynamic/photothermal combined therapy. Notably, rGO-Pd exhibited high peroxidase mimicking activities, which could catalyze the conversion of intratumoral H2O2 to ˙OH. Impressively, the reactive oxygen species (ROS) generation of rGO-Pd was further remarkably enhanced by the endogenous acidity of the tumor microenvironment and the exogenous NIR light-responsive photothermal effect. These collective properties of the rGO-Pd nanozyme enabled it to be a ROS generation accelerator for photothermally enhanced tumor chemodynamic therapy. Thus, the as-developed rGO-Pd may represent a promising new type of high-performance nanozyme for multifunctional nanotheranostics toward cancer. [ABSTRACT FROM AUTHOR]