Considering that the state transfer rules between nodes in existing rumor propagation models are mostly based on a single propagation mechanism, and most of the models have a single way of refuting rumors, in this paper, a new SEIOR rumor propagation model (ignorant (S), hesitators (E), spreaders (I), rumor debunkers (O), immunizers (R)) is proposed by introducing hesitators and rumor debunkers and combining different rumor-refuting ways of rumor debunkers. Firstly, the dynamics process of the propagation model is described by using the mean-field equations. Secondly, the equilibrium point and the basic regeneration number of the model are solved, and the existence and stability of the equilibrium point are discussed. Then, numerical simulations are used to analyze the effects of different factors on rumor propagation patterns. The results show that the rumor-spreading rate ${\alpha }_2$ has the greatest effect on rumor propagation. With the increase in ${\alpha }_2$, the degree of influence of the hesitator-converting rate ${\alpha }_1$ on the scale of rumor propagation first increases and then decreases. Different rumor-refuting ways have different inhibiting effects on the spread of rumors. Finally, based on the results of the theoretical proving and numerical analysis, some targeted measures to control the spread of rumors are proposed.

The rapid development of urbanization has led to increasing uncertainties related to urban safety risks, which has brought certain challenges to the sustainable development of cities. The concept of urban resilience has found a new way to improve the ability of a city to absorb and resolve risks. However, the existing literature on the evaluation of urban resilience is mostly developed from a static perspective, lacking a systematic and dynamic understanding of the level of urban resilience. Therefore, this paper takes Hunan Province as the research object, determines the resilience evaluation indicators, collects the data of each indicator by using the observation method and the literature method, then chooses the comprehensive index method and other methods to measure the urban resilience level of Hunan Province in the years of 2010–2021, and observes the dynamic changes in the resilience level. And, we use the GeoDetector model to detect the dominant factors affecting the urban resilience level and the interaction between these factors. The results of this study show that: (1) The level of urban resilience in Hunan Province shows a steady upward trend from 2010 to 2021, but cities with low resilience levels hold a dominant position. Among all subsystems, the level of urban economic resilience is the highest. (2) From 2010 to 2021, the level of urban resilience in Hunan Province indicates a stepwise spatial structure in the spatial pattern, gradually decreasing from east to west. (3) The urban resilience of Hunan Province from 2010 to 2021 has a significant spatial agglomeration effect, mainly manifested as “L-H type” agglomeration and “L-L type” agglomeration. (4) The spatio-temporal differentiation of urban resilience is mainly caused by economic and social factors, while ecological, institutional, and infrastructure factors have a relatively small influence on the level of urban resilience. The interaction of impact factors will have a more significant influence on urban resilience. The research results of this article are of great significance for urban resilience construction in Hunan Province and even the whole country.

The demand for autonomous exploration and mapping of underground environments has significantly increased in recent years. However, accurately localizing and mapping robots in subterranean settings presents notable challenges. This paper presents a tightly coupled LiDAR-Inertial odometry system that combines the NanoGICP point cloud registration method with IMU pre-integration using incremental smoothing and mapping. Specifically, a point cloud affected by dust particles is first filtered out and separated into ground and non-ground point clouds (for ground vehicles). To maintain accuracy in environments with spatial variations, an adaptive voxel filter is employed, which reduces computation time while preserving accuracy. The estimated motion derived from IMU pre-integration is utilized to correct point cloud distortion and provide an initial estimation for LiDAR odometry. Subsequently, a scan-to-map point cloud registration is executed using NanoGICP to obtain a more refined pose estimation. The resulting LiDAR odometry is then employed to estimate the bias of the IMU. We comprehensively evaluated our system on established subterranean datasets. These datasets were collected by two separate teams using different platforms during the DARPA Subterranean (SubT) Challenge. The experimental results demonstrate that our system achieved performance enhancements as high as 50–60% in terms of root mean square error (RMSE).

The current global resource shortage and environmental pollution are becoming increasingly serious, and the development of the new energy vehicle industry has become one of the important issues of the times. In this paper, a nickel–cobalt lithium manganate (NCM) battery for a pure electric vehicle is taken as the research object, a heat dissipation design simulation is carried out using COMSOL software, and a charging heat generation model of the battery pack is established. Combined with the related research on the thermal management technology of the lithium-ion battery, five liquid-cooled temperature control models are designed for thermal management, and their temperature control simulation and effect analysis are carried out. Finally, the performance evaluation system of the thermal management scheme of the lithium-ion battery pack is established based on the analytic network process (ANP) and system dynamics (SD), and the performance of the above five thermal management design models is comprehensively scored and analyzed. The results show that liquid-cooled Models 1 (86.7075) and 5 (89.1055) have the highest overall scores, meeting both the temperature control requirements and the overall thermal management performance, and it is recommended to apply the working condition settings for which they are evaluated as Level I.

The accurate forecasting of metal prices is of great importance to industrial producers as the supply of metal raw materials is a very important part of industrial production. The futures market is subject to many factors, and metal prices are highly volatile. In the past, most of the relevant research has focused only on deterministic point forecasting, with less research performed on interval uncertainty forecasting. Therefore, this paper proposes a novel forecasting model that combines point forecasting and interval forecasting. First, a novel hybrid price point forecasting model was established using Variational Modal Decomposition (VMD) and a Long Short-Term Memory Neural Network (LSTM) based on Sparrow Search Algorithm (SSA) optimization. Then, five distribution functions based on the optimization algorithm were used to fit the time series data patterns and analyze the metal price characteristics, Finally, based on the optimal distribution function and point forecasting results, the forecasting range and confidence level were set to determine the interval forecasting model. The interval forecasting model was validated by inputting the price data of copper and aluminum into the model and obtaining the interval forecasting results. The validation results show that the proposed hybrid forecasting model not only outperforms other comparative models in terms of forecasting accuracy, but also has a better performance in forecasting sharp fluctuations and data peaks, which can provide a more valuable reference for producers and investors.

As the market share of electric vehicles continues to expand, fire accidents due to impacts from the power battery located at the bottom of the electric vehicles are receiving increasing attention. Lithium-ion batteries, as the mainstream choice of power battery for electric vehicles solving the problem that they are prone to thermal runaway due to damage when impacted, are the key to preventing and controlling fire accidents in electric vehicles. To address the protective problem of the bottom power battery of electric vehicles when it is impacted by road debris, two new types of sandwich structures with an enhanced regular hexagonal structure and semicircular arch structure as the core layer, respectively, are innovatively proposed in this article. They are used to protect the bottom power battery of electric vehicles and are compared with the traditional homogeneous protective structure in terms of protective performance. A local finite element simulation (FEM) of an electric vehicle containing the necessary components was established for simulation. Stress distribution, deformation, and energy absorption data for each component of an electric vehicle assembled with a protective structure when subjected to a bottom impact were obtained safely and cost-effectively. Three evaluation coefficients, namely, the cell shape variable (B_cmax), the protective effect parameter (ƒ_PE), and the total energy absorption of the structure (E_a), are proposed to compare and analyze the simulation results of different protective structures under equal mass conditions. The maximum values of the battery deformation of arched sandwich construction and reinforced honeycomb sandwich construction were 0.35 mm and 0.40 mm, respectively, which are much smaller than that of the maximum deformation of the battery under the protection of a homogeneous protective structure, which is 0.62 mm. Their protective effect parameters are 43.55 and 35.48, respectively, which proves that the optimization degree of the protective structure of the bottom of the electric vehicle after the application of the new structure is 35% or more. The total energy absorptions of the two structures are 91.77 J and 87.19 J, respectively, accounting for more than 70% of the kinetic energy in the system, which proves that the deformation of the sandwich structure can effectively absorb the kinetic energy of the collision between the road obstacle and the bottom of the car. The final results show that the arched sandwich structure showed the best impact resistance in the simulation, which can be used for the power battery’s protective structure on the electric vehicle’s bottom. This study fills a gap in local finite element modeling in electric vehicle crash simulations and provides ideas for fire prevention designs of electric vehicle structures.