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ObjectiveTo explore the impact of an airway stepwise management strategy in the treatment of hospital acquired pneumonia (HAP) in the ultra elderly critically ill patients.MethodsClinical data on the ultra eldely(≥80 years old) HAP patients at the department of Critied Care Medicine, Peking Union Medical College Hospital from January 2021 to April 2023 was retrospective collected, and the enrolled patients were divided into an observation group (January 2022-April 2023) and control group (January 2021-December 2021) according to the airway stepwise management strategy. In the control group, conventional airway manage- ment was used, and in the observation group, an airway stepwise management strategy from simple to complex and from noninvasive to invasive was used. The oxygenation indices before and after airway interventiont, tracheal intubation/tracheostomy rate, and invasive ventilator usage rate was compared in both groups.ResultsA total of 61 HAP patients who met the inclusion and exclusion criteria were selected, including 31 in the observation group and 30 in the control group. Compared with before airway intervention, the arterial partial pressure of carbon dioxide gradually decreased, arterial partial pressure of oxygen and oxygenation index gradually increased at 12 h, 48 h and 72 h of airway intervention(all P < 0.05). Compared with the control group, the observation group had lower arterial partial pressure of carbon dioxide, and higher arterial partial pressure of oxygen and oxygenation index(all P < 0.05), and the rate of tracheal intubation/incision (35.5% vs. 66.7%, P=0.015) and the rate of invasive ventilator use (41.9% vs. 73.3%, P=0.013) were lower in the observation group at 48 h of airway intervention.ConclusionsThe application of an airway stepwise management strategy in the ultra elderly HAP patients can significantly improve oxygenation status and reduce iatrogenic trauma.

After occurrence of natural disasters,active distribution network (ADN) can promptly restore power supply to some critical loads through tie-line switching and flexible distributed generation (DG),and thus the fault risk is effectively mitigated. A data-driven multi-dimensional intelligent forecast approach for the fault risk levels in ADNs is proposed in this paper. Firstly,a feature selection method based on Chi-square test (χ2) and Pearson correlation coeffects is developed to analyze the strength of fault correlation factors from multiple dimensions and the optimal set of fault features is obtained. Then,an optimal network reconfiguration model is established for the damaged ADNs considering DG integration,and consequently the heterogeneity of the line importance can be taken into account which provides a solid foundation for the classification of fault risks. Furthermore,an intelligent forecast model for ADN fault risk levels is established based on extreme gradient boostig (XGBoost) algorithm. Finally,the numerical tests on IEEE RBTS Bus6 distribution network demonstrate that the proposed approach achieves a predication accuracy 3.17% higher than back propagation (BP) neural network does. The proposed approach has good generalization capability,thus providing an important basis for the fault risk management in ADNs to effectively reduce the fault loss.

The widespread application of electric vehicles (EVs) is a positive force driving green development. However, their widespread penetration also poses significant challenges and threats to the security and stable operation of the power grid. To address this urgent issue, this article constructs a bi-level optimal dispatching model fostering collaboration between electric vehicle aggregators and the distribution network. The upper-level optimization targets the minimization of peak-valley differences in the distribution network via considerably arranging power outputs of gas turbines, while the lower-level one focuses on reducing the charging expense of EV aggregators via efficient charging transfer. Note that the charging expense is not only composed of electric cost but also a dynamic carbon emission factor-based cost, which contributes to the electricity economy and carbon reduction concurrently. A geometric mean optimizer (GMO) is introduced to solve the mode. Its efficiency is evaluated against three typical algorithms, i.e., genetic algorithm, great-wall construction algorithm, and optimization algorithm based on an extended IEEE 33-bus system with different charging behaviors of EVs on both a typical weekday and weekend. Simulation results demonstrate that the GMO outperforms other competitive algorithms in accuracy and stability. The peak-valley difference between the distribution network and the total cost of EV aggregators can be decreased by over 98% and 76%, respectively. [ABSTRACT FROM AUTHOR]