Application comparison of improved endotracheal tube and ordinary endotracheal tube among critically ill patients

目的  探讨改良气管导管与传统气管导管优越性比较。方法  对25例需要长期呼吸机支持治疗患者随机分为A、B两组，A组（n=12）使用改良气管导管，B组（n=13）使用传统的气管导管，两组患者分别在留管时间、ICU住院时间、憋气缓解率以及更换气管导管费用方面进行比较。结果  两组患者在GCS评分及留管时间方面无统计学意义，P>0.05，但在更换导管费用、ICU入住时间以及憋气缓解率方面，改良气管导管明显优于传统气管导管，P<0.05。结论  改良气管导管可以大大减少头端贴壁的几率，从而减少重症患者憋气发生，同时可以减少患者入住ICU的时间，大大减少住院费用。Objective: To explore the advantages of improved endotracheal tube and to compare it with traditional endotracheal tube. Methods: 25 patients requiring long-term mechanical ventilation patients were randomly divided into two groups: group A and group B. Group A (n=12) use improved endotracheal tube, and traditional endotracheal tube was used for Group B (n=13). The indwelling time, ICU hospitalization time, suffocation remission rate and replacement cost of the endotracheal tube were respectively compared between two groups. Results: GCS score and indwelling catheter time between two groups have no statistical significance, P > 0.05. Improved tracheal catheter is much better than traditional tracheal catheter with regards to catheter replacement cost, time of ICU stay and remission rate of suffocation, P<0.05. Conclusion: The improvement of the endotracheal tube can greatly reduce the rate of head end to stick wall, thus decreasing occurrence of severe shortness of breath, shortening the stay time of the patients in ICU at the same time, and greatly reducing the cost of hospitalization.

Giant Magneto-Optical Sch\"{a}fer-Hubert Effect in Two-Dimensional van der Waals Antiferromagnets \textit{M}PS3_3 (\textit{M}=Mn, Fe, Ni)

The recent discovery of long-range magnetic order in atomically thin films has triggered particular interest in two-dimensional (2D) van der Waals (vdW) magnetic materials. In this paper, we perform a systematic theoretical study of the magneto-optical Sch\"{a}fer-Hubert effect (MOSHE) in 2D vdW antiferromagnetic \textit{M}PS$_3$ (\textit{M} = Mn, Fe, Ni) with multifold intralayer and interlayer magnetic orders. The formula for evaluating the MOSHE in 2D magnets is derived by considering the influence of a non-magnetic substrate. The MOSHE of monolayer and bilayer \textit{M}PS$_3$ are considerably large ($>2^{\circ}$), originating from the strong anisotropy of in-plane optical conductivity. The Sch\"{a}fer-Hubert rotation angles are surprisingly insensitive to the orientations of the N\'{e}el vector, while the Sch\"{a}fer-Hubert ellipticities are identified to be a good criterion to distinguish different interlayer magnetic orders. Our work establishes a theoretical framework for exploring novel 2D vdW magnets and facilitates the promising applications of the 2D \textit{M}PS$_3$ family in antiferromagnetic nanophotonic devices

Physics-informed radial basis network (PIRBN): A local approximation neural network for solving nonlinear PDEs

Our recent intensive study has found that physics-informed neural networks (PINN) tend to be local approximators after training. This observation leads to this novel physics-informed radial basis network (PIRBN), which can maintain the local property throughout the entire training process. Compared to deep neural networks, a PIRBN comprises of only one hidden layer and a radial basis "activation" function. Under appropriate conditions, we demonstrated that the training of PIRBNs using gradient descendent methods can converge to Gaussian processes. Besides, we studied the training dynamics of PIRBN via the neural tangent kernel (NTK) theory. In addition, comprehensive investigations regarding the initialisation strategies of PIRBN were conducted. Based on numerical examples, PIRBN has been demonstrated to be more effective and efficient than PINN in solving PDEs with high-frequency features and ill-posed computational domains. Moreover, the existing PINN numerical techniques, such as adaptive learning, decomposition and different types of loss functions, are applicable to PIRBN. The programs that can regenerate all numerical results can be found at https://github.com/JinshuaiBai/PIRBN.Comment: 48 pages, 26 figure