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The rise in shallow groundwater levels is typically triggered by precipitation recharge, exhibiting a certain lag relative to precipitation changes. Therefore, identifying the response mechanism of shallow groundwater levels to precipitation is crucial for clarifying the interaction between precipitation and groundwater. However, the response mechanism of groundwater levels to precipitation is complex and variable, influenced by various hydrogeological and geographical conditions, and often exhibits significant nonlinear characteristics. To address this issue, this study employs methods such as continuous wavelet transform, cross wavelet transform, and wavelet coherence to analyze the response patterns of groundwater levels to precipitation at different wavelet scales in the Datong Basin from 2013 to 2022: (i) At short wavelet scales (10.33~61.96 d), the groundwater level dynamics respond almost instantaneously to extreme rainfall; (ii) At medium wavelet scales(61.96~247.83 d), the precipitation-groundwater recharge process shows characteristics of either rapid recovery or significant delay; (iii) At long wavelet scales (247.83~495.67 d), three potential groundwater processes were identified in the Datong Basin, exhibiting long-term lag responses throughout this study period, with lag times of 11.18 days, 148.75 days, and 151.49 days, respectively. Furthermore, the results indicate that the lag response time of shallow groundwater levels to precipitation is not only related to the wavelet scale but also to the identified depth conditions of different groundwater regions, groundwater extraction intensity, precipitation intensity, and aquifer lithology. This study distinguishes the temporal and spatial response mechanisms of shallow groundwater to precipitation at different wavelet scales, and this information may further aid in understanding the interaction between precipitation and groundwater levels. [ABSTRACT FROM AUTHOR]

Haze is a phenomenon caused by excessive PM2.5 (air-borne particulate matter having a diameter of fewer than 2.5 μm) and other pollutants and results from the interaction between specific climatic conditions and human activities. It significantly impacts human health, transportation, and the natural environment and has aroused widespread concern. However, the influence of haze on human mental health, being hidden and indirect, is often overlooked. When haze pollution occurs, people express their feelings and concerns about haze events on media such as Weibo. At present, few studies focus on haze public opinion, as well as the changing trends in people's discussion of haze since its emergence, which is of great significance for haze response and resource management. Based on the perspective of topic analysis, this study explores the psychological impact of haze on people by exploring the feelings of netizens in haze public opinion and investigates the evolution of people's concerns based on long-term public opinion data. In this study, seven typical provinces and cities in China with severe haze pollution were selected as the research area. Based on data on the "haze" theme from Weibo from 2013 to 2019, first, the microblog posts were preprocessed, and the keyword co-word network was constructed. Second, the Louvain algorithm was used to detect the topic community. Based on this, the cosine similarity was calculated to realize the temporal evolution analysis of topics. The results show that with the development and change in haze pollution, the content and intensity of the topics netizens pay attention to have changed, including five types: merger, split, survival, transformation, and rebirth/extinction. People's attention to haze shows obvious spatial differences, and it is related to the degree of haze pollution, which is bipolar. Areas with severe haze tend to pay more attention to haze itself and its influence, while areas with light haze pay more attention to haze control. The research results can provide valuable insights for governments and relevant departments in guiding public opinion and resource allocation. [ABSTRACT FROM AUTHOR]

Navigation systems are extensively used in everyday life, but the conventional A* algorithm has several limitations in path planning applications within these systems, such as low degrees of freedom in path planning, inadequate consideration of the effects of special regions, and excessive nodes and turns. Addressing these limitations, an enhanced A* algorithm was proposed using regular hexagonal grid mapping. First, the approach to map modeling using hexagonal grids was described. Subsequently, the A* algorithm was refined by optimizing the calculation of movement costs, thus allowing the algorithm to integrate environmental data more effectively and flexibly adjust node costs while ensuring path optimality. A quantitative method was also introduced to assess map complexity and adaptive heuristics that decrease the number of traversed nodes and increase the search speed. Moreover, a turning penalty measure was implemented to minimize unnecessary turns on the planned paths. Simulation results confirmed that the improved A* algorithm exhibits superior performance, which can dynamically adjust movement costs, enhance search efficiency, reduce turns, improve overall path planning quality, and solve critical path planning issues in navigation systems, greatly aiding the development and design of these systems and making them better suited to meet modern navigation requirements. [ABSTRACT FROM AUTHOR]

The coronavirus disease 2019 (COVID-19) and its mutant viruses are still wreaking global havoc over the last two years, but the impact of human activity on the transmission of the pandemic is difficult to ascertain. Estimating human dynamic spatiotemporal distribution can help in our understanding of how to mitigate COVID-19 spread, which can help in maintaining urban health within a county and between counties within a country. This distribution can be computed using the Volunteered Geographic Information (VGI) of the citizens in conjunction with other variables, such as climatic conditions, and used to analyze how human's daily density distribution quantitatively affects COVID-19 transmission. Based on the estimated population density, when the population density increases daily by 1 person/km2 in a county or prefectural-level administrative unit with an average size of 26,000 km2, the county would have an additional 3.6 confirmed cases and 0.054 death cases after 5 days, which is the illness onset time for a new COVID-19 case. After 14 days, which is the maximum incubation period of the COVID-19 virus, there would be 5 new confirmed cases and 0.092 death cases. However, in neighboring regions, there can be 0.96 fewer people infected with COVID-19 on average per day as a result of strong intervention of local and neighboring authorities. The primary innovation and contribution are that this is the first quantitative assessment of the impacts of dynamic population density on the COVID-19 pandemic. Additionally, the direct and indirect effects of the impact are estimated using spatial panel models. The models that control the unobserved factors improve the reliability of the estimation, as validated by random experiments and the use of the Baidu migration dataset. [ABSTRACT FROM AUTHOR]

In order to enhance the safety of mountain road driving, an algorithm for safe route planning in mountainous area based on fuzzy random forest is proposed. The basic idea of the algorithm is to increase the road impedance in the unsafe areas and reduce the passing rate of the roads with high impedance, that is, the roads with low safety. In this paper, the road rank, slope, visibility and road undulation were selected as the main factors affecting safe driving, and the eigenvalue of corresponding factors and safe driving speed of typical road sections were collected. The mapping model between the factors affecting the safe driving and the safe driving speed was constructed by random forest algorithm, and the safe driving speed of non-sampled road sections was estimated with fuzzy reasoning method. Further the impedance of the road network was constructed based on safe driving speed of the whole road network, finally the planning route of safe driving in mountainous area was obtained. Taking the mountain road network in Qinshui County, Jincheng City, Shanxi Province as an example, the rationality of the proposed algorithm was verified. The results indicate that the planning scheme could effectively avoid the road section with steep slope and poor field of view and provide a more reasonable route for drivers to travel safely in mountainous area. [ABSTRACT FROM AUTHOR]

Finding a means to extract water body information efficiently and accurately from high-resolution remote sensing images has been an important research direction in the field of water body extraction in recent years. However, shadows from buildings and other obstacles interfere with the accuracy of water body extraction. To address this problem, this paper proposes a neural network method incorporating an attention mechanism for water body extraction. This paper is based on the U-Net convolutional neural network and adds the squeeze-and-excitation module of SENet, an attention mechanism, to the downsampling process of the U-Net network. The module weights the feature maps so that the network focuses more on the features of the water body information and thus reduces attention to the shadow features from buildings and other features, thus improving the accuracy of image segmentation. The dropout and batch normalization layers are also added to improve the generalization ability and stability of the model. In this paper, a water extraction network SE-CU-Net model is presented to overcome the shadowing effect from buildings and other features. Using GF-2 images of Jiangsu province as the data source, the recognition results of this paper are compared with Dense-Net, Res-Net, Seg-Net, U-net, SVM, and RF. Through the comparison experiments, the model of this paper can not only better overcome the influence of shadows from buildings and other features, but it also has a stronger recognition ability and recognition effect. The average ASCR, Precision, mIoU, OA, F1-Score and kappa coefficients in the three tested areas reached 98.27%, 97.17%, 89.33%, 98.2%, 89.3% and 0.883, respectively, with significantly higher accuracy than the other six classical methods, verifying the effectiveness of the model in overcoming the influence of shadows from buildings and other features in water body extraction research. [ABSTRACT FROM AUTHOR]