For a fast calculation of vehicle-track dynamics and wheel-rail contact mechanics, wheel-rail contact geometric gap is usually idealised in elliptic or nonelliptic form. These two idealisations deviate from the actual one if the lateral combined curvature within the contact patch is not constant or the yaw angle of wheelset exists. The influence of these idealisations on contact solution has not yet been deeply understood, and thus the accuracy of simplified contact modelling applied to vehicle-track dynamics and wheel-rail contact mechanics remains uncertain. This paper presents a numerical methodology to treat 3D wheel-rail rolling contact, in which the asymmetric geometric gap due to yaw angle is fully taken into account. The attention of this work is placed on investigating the effect of geometric gap idealisation on wheel-rail contact force, rolling contact solution, and wear distribution. It can help with the effective wheel-rail contact modelling on the computation of both vehicle-track dynamics and wheel-rail contact mechanics.

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To improve the nursing effect in patients after thoracic surgery, this paper proposes a refined intervention method in the operating room based on traditional operating room nursing and applies this method to the nursing of patients after thoracic surgery. Moreover, this paper improves the traditional neural network algorithm and uses the deep neural network algorithm to process test data. In addition, it includes patients accepted by the hospital as samples for test analysis and formulates detailed intervention methods for the operating room. Finally, this paper collects the corresponding test data by setting up test and control groups and visually displays the data using mathematical statistics. The statistical parameters of the experiment in this paper include the quality of recovery, complications, satisfaction score, and recovery effect. The comparative test shows that the refined intervention in the operating room based on the neural network proposed in this paper can achieve a certain effect in the postoperative nursing of thoracic surgery, effectively promote the quality of recovery, and reduce the possibility of complications.

Thoracic surgery is the main surgical method for the treatment of respiratory diseases and lung diseases, but infections caused by improper care are prone to occur during the operation, which can induce pulmonary edema and lung injury and affect the effect of the operation and the subsequent recovery. Therefore, it is necessary to control the disease in time and adopt more scientific and comprehensive nursing measures. Based on the neural network algorithm, this paper constructs a neural network-based factor analysis model and applies the operating room management nursing to postoperative infection nursing after thoracic surgery and verifies the effect through the neural network model. The statistical parameters in this article mainly include the postoperative infection rate of thoracic surgery, patient satisfaction, postoperative rehabilitation effect, and complications. Through statistical analysis, it can be known that operating room management and nursing can play an important role in postoperative infection nursing after thoracic surgery, effectively reducing postoperative infection nursing after thoracic surgery, and improving the recovery effect of patients after infection.