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.

Four development stages of collective intelligence

The new generation of artificial intelligence(AI)research initiated by Chinese scholars conforms to the needs of a new information environment changes,and strives to advance traditional artificial intelligence(AI 1.0)to a new stage of AI 2.0.As one of the important components of AI,collective intelligence(CI 1.0),i.e.,swarm intelligence,is developing to the stage of CI 2.0(crowd intelligence).Through in-depth analysis and informative argumentation,it is found that an incompatibility exists between CI 1.0 and CI 2.0.Therefore,CI 1.5 is introduced to build a bridge between the above two stages,which is based on biocollaborative behavioral mimicry.CI 1.5 is the transition from CI 1.0 to CI 2.0,which contributes to the compatibility of the two stages.Then,a new interpretation of the meta-synthesis of wisdom proposed by Qian Xuesen is given.The meta-synthesis of wisdom,as an improvement of crowd intelligence,is an advanced stage of bionic intelligence,i.e.,CI 3.0.It is pointed out that the dual-wheel drive of large language models and big data with deep uncertainty is an evolutionary path from CI 2.0 to CI 3.0,and some elaboration is made.As a result,we propose four development stages(CI 1.0,CI 1.5,CI 2.0,and CI 3.0),which form a complete framework for the development of CI.These different stages are progressively improved and have good compatibility.Due to the dominant role of cooperation in the development stages of CI,three types of cooperation in CI are discussed:indirect regulatory cooperation in lower organisms,direct communicative cooperation in higher organisms,and shared intention based collaboration in humans.Labor division is the main form of achieving cooperation and,for this reason,this paper investigates the relationship between the complexity of behavior and types of labor division.Finally,based on the overall understanding of the four development stages of CI,the future development direction and research issues of CI are explored.

Prediction based traffic management in a metropolitan area

In recent years,modern metropolitan areas are the main indicators of economic growth of nation.In metropolitan areas,number and frequency of vehicles have increased tremendously,and they create issues,like traffic congestion,accidents,environmental pollution,economical losses and unnecessary waste of fuel.In this paper,we propose traffic management system based on the prediction information to reduce the above mentioned issues in a metropolitan area.The proposed traffic management system makes use of static and mobile agents,where the static agent available at region creates and dispatches mobile agents to zones in a metropolitan area.The migrated mobile agents use emergent intelligence technique to collect and share traffic flow parameters(speed and density),historical data,resource information,spatio-temporal data and so on,and are analyzes the static agent.The emergent intelligence technique at static agent uses analyzed,historical and spatio-temporal data for monitoring and predicting the expected patterns of traffic density(commuters and vehicles)and travel times in each zone and region.The static agent optimizes predicted and analyzed data for choosing optimal routes to divert the traffic,in order to ensure smooth traffic flow and reduce frequency of occurrence of traffic congestion,reduce traffic density and travel time.The performance analysis is performed in realistic scenario by integrating NS2,SUMO,OpenStreatMap(OSM)and MOVE tool.The effectiveness of the proposed approach has been compared with the existing approach.