Construction and practice of a comprehensive nursing skills course with simulation in an RN-BSN program in China: a quasi-experimental study

Background Simulation has been widely used in the teaching of pre-licensed nursing students and has shown positive results. However, there is limited evidence regarding the application of a comprehensive nursing course with simulation for Associated Degree in Nursing (ADN)-prepared nurses with different work experience times. Therefore, this study aimed to evaluate the satisfaction, self-confidence, and perceptions of scenario simulation among Chinese nurses in a comprehensive nursing skills course in an RN-BSN program. Methods A single-group post-test approach was used in the current study. Participants that completed a comprehensive nursing skills course with simulation in an RN-BSN program were enrolled. Results The mean satisfaction, self-confidence, educational practice, and simulation designs scores were rated highly. Self-confidence (p = 0.002), active learning (p = 0.020), collaboration (p = 0.030), support (p = 0.008), and problem-solving (p = 0.007) were significantly higher among students with more work experience compared to those with less experience. Then, four themes were analyzed: enthusiasm for learning, ability to experience different feelings during role-play, hybrid teaching format, and simulation fidelity. Conclusions Results demonstrated that a comprehensive nursing skills course with simulation might improve Chinese ADN-prepared nurses’ satisfaction and self-confidence in learning. Nurses with work experience gave a high rate to the scenario simulation, demonstrating that simulation can be widely applicable for students with different characteristics. Finally, the teaching strategy in the present study can be applied to more RN-BSN programs.

Scenario Simulation of The Geohazard Dynamic Process of Large-Scale Landslides: A Case Study of The Xiaomojiu Landslide Along The Jinsha River

Large-scale landslides often cause severe damage due to their long run-out distances and having disaster chain effects. Scenario simulation has been adopted in the current work to analyze the Xiaomojiu landslide dynamic processes, such as sliding velocity, deposition characteristics, and flood outburst after a landslide-dam failure using Particle Flow Code (PFC-3D) which introduced the changeable friction coefficient and the HEC-RAS software. The landslide characteristics and topography data were obtained via field investigation, whereas high-resolution topographic data (0.17 m) was obtained using an Unmanned Aerial Vehicle (UAV). The results showed that: 1. The landslide presents a scallop shape with a length of 1566 m, a width ranging from 809~1124 m, and an area of 1.34×106 m2. The average thickness and volume of the sliding body is approximately 40 m, 5.1×107 m3. The InSAR deformation analysis showed that the Xiaomojiu landslide has a maximum annual displacement rate of 60 mm/y, and a maximum accumulation deformation of 180 mm since November 25, 2017. 2. From the landslide simulation results, the failure process of the Xiaomojiu landslide lasted for 65 s with a maximum velocity of 78.2 m/s. The deposited area is approximately 2023 m long, 900 m wide, with a maximum height of approximately 149 m. 3. After the landslide blocks the Jinsha River, a landslide-dammed lake with an elevation of 2940 m and a storage capacity of 4.13×109 m3 is formed. The maximum peak flow rate of the breach is 12051.7 m3/s, 43451.4 m3/s, 148635.6 m3/s, and 304544.7 m3/s for the landslide-dammed failure degrees of 15%, 25%, 50%, and 75%, respectively. These results provide a scientific reference for the risk analysis and mitigation of the landslide.

Finder-MCTS: A Cognitive Spectrum Allocation Based on Traveling State Priority and Scenario Simulation in IoV

With the increasing number of intelligent connected vehicles, the problem of scarcity of communication resources has become increasingly obvious. It is a practical issue with important significance to explore a real-time and reliable dynamic spectrum allocation scheme for the vehicle users, while improving the utilization of the available spectrum. However, previous studies have problems such as local optimum, complex parameter setting, learning speed, and poor convergence. Thus, in this paper, we propose a cognitive spectrum allocation method based on traveling state priority and scenario simulation in IoV, named Finder-MCTS. The proposed method integrates offline learning with online search. This method mainly consists of two stages. Initially, Finder-MCTS gives the allocation priority of different vehicle users based on the vehicle’s local driving status and global communication status. Furthermore, Finder-MCTS can search for the approximate optimal allocation solutions quickly online according to the priority and the scenario simulation, while with the offline deep neural network-based environmental state predictor. In the experiment, we use SUMO to simulate the real traffic flows. Numerical results show that our proposed Finder-MCTS has 36.47%, 18.24%, and 9.00% improvement on average than other popular methods in convergence time, link capacity, and channel utilization, respectively. In addition, we verified the effectiveness and advantages of Finder-MCTS compared with two MCTS algorithms’ variations.