High spatial resolution remote sensing images contain rich information, it is therefore very important to study their semantic segmentation. Traditional machine learning methods appear low accuracy and efficiency when used for segmenting high-resolution remote sensing images. In recent years, the deep learning method has developed rapidly and has become the mainstream method of image semantic segmentation. Some scholars have introduced SegNet, Deeplabv3+, U-Net and other neural networks into remote sensing image semantic segmentation, but these networks have only limited effect in remote sensing image semantic segmentation. This paper improves the U-Net network for semantic segmentation of remote sensing images. Firstly, an improved convolutional attention module channel interaction and spatial group attention module (CISGAM) is embedded in the feature extraction stage of the U-Net network, so that the network can obtain more effective features; secondly, a residual module is used in the decoding layer to replace the ordinary convolutional layer to avoid the degradation of the model. In addition, we use an attention pyramid pooling module (APPM) with CISGAM to connect the encoder and decoder of U-Net to enhance the network's extraction of multi-scale features. Finally, experiments are carried out on the UC Merced dataset with 0.3m resolution and the GID dataset with 1m resolution. Compared with the original networks such as U-Net and Deeplabv3+, the mean intersection over union （MIoU) of our method on the UCM dataset has increased by 14.56% and 8.72%, and the mean pixel accuracy （MPA) has increased by 12.71% and 8.24%, respectively. In the classification results on the GID dataset, the classification accuracy of waters, buildings and other objects has also been greatly improved. Compared with the original CBAM and PPM, the CISGAM and APPM also achieve certain performance improvement. The experimental results show that the feasibility and robustness of the model is stronger than traditional networks, and it can improve the accuracy of semantic segmentation of high-resolution remote sensing images through stronger feature extraction capabilities, hence providing a new approach for intelligent interpretation of high-resolution remote sensing images.

Road traffic accidents occur frequently and have significant impacts on life, property, and society. However, existing researches on road traffic accident risk pay little attention on establishing an effective road network model that accurately describes the transmission characteristics of traffic accident risk. As a result, the accuracy of risk evaluation is limited. To address this issue, we propose a network geographically weighted regression method based on arrival time cost. We conduct experiments using data from roads, traffic violations, traffic accidents, and urban points of interest (POIs) in a city from 2018 to 2020. The experimental results demonstrate that the network geographically weighted regression method, based on arrival time cost, incorporates the propagation nature of traffic accident risk on the road. It significantly reduces evaluation errors and effectively evaluates road traffic accident risk and its influencing factors. The downtown area of the city exhibits high accident risk, primarily concentrated at intersections with heavy traffic flow and certain road points with inadequate transportation facilities. The impact of different types of road traffic violations and urban POIs on the risk of road traffic accidents varies significantly and exhibits strong spatial heterogeneity.

With the rapid increase of data size, the defects of traditional spatial indexing become more and more apparent. In comparison, learning indexing is based on data distribution. Its volume will not expand with the increase of the amount of data, and can achieve better performance without performing hierarchical comparison. Nevertheless, there are still two difficulties in applying the idea of learning indexing to spatial data: (1) How to choose appropriate dimension reduction method to sort the spatial data. (2) How to simplify data distribution of the dimension reduced data and make it easy to fit. This paper proposes a new type of grid mixed cluster partition learning indexing (grid-ml) based on the idea of learning indexing. In view of the above two difficulties, grid-ml uses z curve to reduce the dimension, and deals with the jumping problem with double-layer grid structure. Then, the improved K-means clustering method is used to simplify data distribution. The results show that grid-ml builds fast with small spatial storage volume, and can query fast as well, demonstrating significant advantages over the traditional spatial indexing approach.

We study the properties of McCoy rings with a doubly mapping, give some examples of this class rings, investigate their extensions and the *-nil McCoy property of *-skew polynomial rings. We showed that (1) Let *-ring R satisfy nil(R[x]) = nil(R)[x]. Then R is *-nil McCoy if and only if R[x] is *-nil McCoy; (2) Let R[x; *] be *-skew polynomial ring. If R is *-revisible, then R[x; *] is *-nil McCoy.

Two simple graphs are commutative if there exists a labelling of their vertices such that their adjacency matrices can commute. This paper gives three necessary conditions ensuring the commutativity of certain graphs from Perron vectors, the number of main eigenvalues, the regularity of graphs. Then we construct new commutative graphs by graph Kronecker product, Cartesian product and circulant matrix. Finally, for two commutative graphs, we provide two algorithms that can express one adjacency matrix as the matrix polynomial of another adjacency matrix with distinct eigenvalues, and compare their merits. Commutative graphs sharing common eigenvectors are essential to the study of spectral graph theory.

For a positive integer n≥2, what is the minimum number of ticks to be engraved on an unscaled ruler of length n to measure all lengths from 1 to n. This is an unsolved problem of ruler with least number of scales. This paper clarifies the relationship between ruler with the least number of scales and the minimal graceful graph, and a combined difference recursive algorithm for calculating all the least scale values of ruler with the least number of scales is given. This algorithm calculates that the length is 3 to all the minimum scale values of the most scale-saving ruler of 40, and combined with the graph theory model, the minimum scale values of ruler with least number of scales with lengths from 41 to 82 are given.

A Holling-Ⅱ predator-prey diffusion model with fear effect and modification under homogeneous Dirichlet boundary conditions is studied. Firstly, the prior estimation of the model is given by applying the maximum value principle and comparison theorem. Then, by calculating the fixed point index of the cone map, the sufficient condition for the existence of the positive solution is obtained. When H is sufficiently large, the stability of the positive solution is discussed according to the linear stability theory. Finally, by means of spectral analysis and branching theorem, the existence and stability of local branching solutions are discussed with m as the branching parameter.

To enhance the adaptability of the grey Bernoulli model to various real data series, a grey Bernoulli model based on Conformable fractional order derivatives is proposed by taking advantage of the fractional order calculus in describing complex systems. It is found that the proposed model can be converted to some classical grey prediction models by replacing its structural parameters, which reflects its uniformity. Moreover, the particle swarm optimization algorithm is used to solve the planning model to obtain the optimal hyperparameters of the proposed model. The experimental results show that the two evaluation indicators of the proposed model are superior to the five competing algorithms, which confirms the validity and feasibility of the new model.

In this paper, the phenomenon of phase transition is studied in an asymmetric tristable model driven by Lévy noise and Gaussian white noise. First, the fourth-order Runge-Kutta algorithm is used to simulate the stationary probability density of the system. Then we observe the shape of the stationary probability density curve by adjusting the system parameters and noise parameters. It has been found that asymmetric parameter, additive noise intensity, multiplicative noise intensity, the stability index, and the skewness parameter can all induce phase transition. Moreover, the influence of additive noise intensity and multiplicative noise intensity on the number and height of stationary probability density peaks is opposite. We also found that under the same asymmetric parameters, the influence of positive and negative skewness parameters on the stationary probability density is different.

The Rio del Rey basin, located in the eastern margin of the Niger Delta Basin in West Africa, develops large-scale mud diapirs. There are some disputes about the activity time and mechanism of the diapirs. Based on the detailed interpretation and mapping of the 3D seismic profile of the Rio del Rey basin, the distribution characteristics of the mud diapir on the top surface of the Miocene Nguti structural layer are redefined. The results show that: (1) mud diapir on the top of the Nguti structural layer can be divided into the northern buried area, the central pierced area, and the southern pierced area; (2) the mud diapir in the northern buried area was formed before the deposition of the Nguti structural layer, and was a passive mud diapir controlled by early sedimentation. The mud diapir in the central pierced area was formed before the deposition of the Pliocene S0 structural layer, and was an active mud diapir developed under the action of gravity sliding structures. The mud diapir in the southern pierced area was formed after the deposition of the Nguti structural layer, and was a thrust mud diapir controlled by thrust structures at the front edge of the gravity structure; (3) the mud diapir as a whole is a tongue shaped body protruding southward, divided into two segments: the west segment along a NNW-NW direction, and the east segment along a NNE-NE direction.

Tourism total factor productivity, as an important indicator to measure the high-quality development of tourism, has become a research hotspot. Although there have been a large amount of researches on indicators that affect tourism total factor productivity, the impact mechanism and effect of informatization on tourism total factor productivity have not yet been clarified. Based on a systematic analysis of the impact mechanism of informatization on tourism total factor productivity and its decomposition factors: tourism technical progress and tourism technical efficiency, this paper constructs a measurement system of informatization development level integrating multi-source data, and analyzes the dynamic evolution process of tourism total factor productivity and its decomposition factors in 30 provinces of China from 2010 to 2019 using the DEA-Malmquist index. The threshold panel model is used for the study. The results show that: (1) During the research period, China's informatization level has been increasing year by year, and the gap between provinces has been narrowing, with the spatial characteristics of hot spots in the east and cold spots in the west. (2) Tourism total factor productivity showed a continuous upward trend, with significant contributions from tourism technical progress, but tourism technical efficiency declined. (3) Informatization has a significant nonlinear promoting effect on tourism total factor productivity. Informatization has a positive impact on tourism total factor productivity through positive promotion of tourism technical progress, and has a threshold effect on tourism total factor productivity through nonlinear inhibition of tourism technical efficiency.

In this study, 11 traditional settlements (TS) in the East Qinling Mountain area were surveyed, and their formation, evolution, landscape gene and image expression were studied, and the ancient people's cognition of the relationship between human and land contained in TS was discussed. The main conclusions are as follows: the TS in the East Qinling Mountain area can be divided into two types according to whether it is native or not, named immigrant TS or native TS. the immigrant TS also contains four subclasses according to their formation reason, namely, the TS because of eluding the war or turbulence, transforming from troops, moving to show one's ambition, production development. The TS evolution in the East Qinling Mountain area is influenced by the immigration of other ethnic groups, the invasion of bandits, and the change of production mode and life style. From the perspective of landscape gene, the TS in the East Qinling Mountain area include natural landscape gene and human landscape gene. The TS landscape depends on nature macroscopically and transforms nature microscopically, showing the characteristics of building in accordance with the situation of mountains, near to water, being surrounded by solid wall and being maintained by families. Corresponding to the landscape images of Chinese TS, to some extent the image of shanshui and ecological are the reflection of the ancient people's views on nature. The clan image and the image of seeking good fortune are essentially the reflection of the ancients' thinking about survival and development, while the basic image of the TS in the East Qinling Mountain area is "to worship literature and to be kind-hearted, surrounded by well ecological mountain and water".

In this study, we analyzed the differences in the effects of different forest vacation activities and personality traits on participants' behavioral intentions through two quasi-natural experiments (experimental group-forest therapy and control group-forest tourism), and found that:(1) personality traits of participants in two groups of forest vacation activities were categorized as resilient, overcontroler, and emotional;(2) there were significant differences in the results of behavioral intentions among the different class of personality traits, and the overall distribution of resilient > overcontroler > emotional type groups was shown; (3) under the interaction effect of the two groups of forest vacation activities and the three personality traits, the results showed that the behavioral intention was influenced by both the class of personality traits and the forest vacation activities synergistic effects. The findings of this study expand the study of the subjective influences on behavioral intentions of forest therapists at the theoretical level, and at the practical level, they help to deepen the forest therapy products and provide empirical evidence for the rational arrangement of experiential contents.