Fever Outpatient Service of General Hospital Applies “1 + 3 + 3” Emergency Management Mode to Deal with COVID-19 Pandemic

Purpose: To discuss the effect of “1 + 3 + 3” emergency management mode to deal with COVID-19 pandemic in fever outpatient service of general hospitals. Method: This paper analyzes and summarizes the problems encountered by fever outpatient service in dealing with the COVID-19 pandemic from three aspects of “One Team”, “Three-Key” Control and “Three Mosts”. Results: The application of “1 + 3 + 3” emergency management mode can effectively boost the orderliness and efficiency of fever outpatient service in dealing with COVID-19 pandemic. Conclusion: The “1 + 3 + 3” emergency management mode provides a new management mode and idea for dealing with COVID-19 pandemic, so the fever outpatient service of general hospitals can better improve the national overall anti-pandemic situation.

Clinical Effect of Intelligent Emergency Nursing Mode in Patients with Severe Traumatic Brain Injury

Objective: Severe traumatic brain injury (sTBI) is one of the common acute and critical diseases in neurosurgery. So we aim to explore the clinical effectiveness of an intelligent emergency care model in patients with severe traumatic brain injury. Methods: Eighty patients with severe traumatic brain injury (sTBI) who were treated in Zhuji People’s Hospital of Zhejiang Province from January 2019 to December 2021 were selected as the study subjects. The patients were divided into an observation group and a control group with 40 patients in each group according to the random number table method. Patients in the control group received conventional first-aid nursing mode intervention, and the intelligent emergency nursing mode was used for the observation group based on the control group. Comparisons were conducted between the two groups on the time of arrival to the emergency room, the time from the emergency room to the operating room, Glasgow Coma Scale (GCS) score before surgery, GCS score when leaving the Intensive Care Unit (ICU), the average length of ICU stay, the average length of hospital stay, the total hospital costs. Results: The time of arrival to the emergency room, the time from the emergency room to the operating room, the average length of ICU stay, the average length of hospital stay, and the total hospital costs in the observation group were significantly lower than those in the control group, and the differences were statistically significant (All P Conclusion: Intelligent emergency nursing mode can shorten the time of sTBI rescue, the length of ICU stay, and the average length of hospital stay, reduce the total hospitalization cost, improve the prognosis, with good efficacy, reduce the total cost of hospitalization, and improve the prognosis with better efficacy.

Stability Monitoring and Control of Generation Based on the Synchronized Measurements in Nodes of Its Connection

Synchronized distributed measurements of mode parameters create a technical feasibility for development and implementing new technologies of control the mode stability and the admissibility of EPS （electric power system） mode. Discussion will focus on different models obtained from data synchronized measurements for operational and automatic emergency control without EPS being totally controlled. According to the proposed technology, the generator＇s output power restrictions are determined in real-time by the terms a static stability using the generators＇ mode model as a multipole with connection nodes of generators＇ electromotive forces （the matrix of SMA （self and mutual admittances） of electromotive forces of generators）. Potential applications of the technology are distribution network with the main substation and generators of commensurable capacity, and transmission network with large power plants （generators） distributed into the network. The one-level control system for all of generators with defining the generator＇s power limits relative to the main substation is implemented in the first case. In the second case, the two-level control system is brought in, based on the separation of large and small generation motion. The results of the method and technology efficiency verification are shown in the paper, by both computer simulations of the power system modes and its physical model.

A binary choice model for adoption of an emerging travel mode with unique service features

In the era of emerging technologies, the transportation system is witnessing the introduction of innovative mobility services, such as autonomous vehicles, which possess unique service features that cannot be seen from conventional travel modes. To facilitate the understanding of the behavioral impacts and the adoption of innovative mobilities, a novel binary weibit model with an oddball alternative (BW-O) is developed for the binary choice between conventional and emerging mobilities. The BW-O model explicitly considers the unprecedented (or unique) service features of emerging travel modes while retaining the closed-form choice probability. This study empirically illustrates the application of the BW-O model in the mode choice context. The desirable properties of the BW-O model compared to the existing binary choice models are discussed both theoretically and empirically. In the binary mode choice problem with an emerging travel mode, the unique service features of the emerging mode can lead to the “oddball” effect and “superstar” effect, which play a critical role in the travel behavior and mode adoption. The BW-O model inherently captures both effects by considering a higher perception variance for the emerging mode and asymmetric choice probabilities between different modes. Thus, as revealed by the empirical results, the BW-O model outperforms the basic binary weibit model in terms of both model fit and predictive power. The developed BW-O model is not only applicable to the mode choice problem in transportation systems, but also opens a door for more general class-imbalanced binary choice contexts where an alternative has additional attractiveness and asymmetric choice probability.