In recent years, people pay more and more attention to travel safety and travel risks. Tourism risk perception is a quantitative assessment of tourism security. Destination risk perception of tourists directly affects tourists purchase intention. The asymmetry of the objective existence of tourist safety information and the subjective perception of tourists determines that tourists are extremely sensitive to travel risks. The inevitability of tourism risks requests that tourists have a certain level knowledge of destination environment. This study aimed to systematically review existing researches of tourism risk perception. The study shows: (1) Tourism risk perception includes three views, which were subjective feelings, objective evaluation and the cognition of exceeding the threshold portion of the negative consequences or negative impact that may occur during travel. (2) The subjective factors of tourism risk perception were the physical characteristics and psychological processes. The objective factors include physical risk, economic risk, equipment risk, social risk, psychological risk, time risk and opportunity loss. (3) The multi-dimensional model and the two-factor model were the two main types of risk perception assessment models. The survey (with interviews) and mathematical statistical analysis were the main research methods. Besides, this article highlights three points. (1) There is a certain critical value for travel risk perception of tourists; (2) cognitive ability is an important factor affecting the level of tourists objective risk perception; and (3) quantitative assessment of tourism risk perception level is helpful to the tourism decision making and destination management.

The rapid development of urbanization and industrialization in coastal China in the past 20 years has exerted a huge squeezing effect on agricultural land use. The phenomenon of non-grain production on cultivated land (NGP) is very common, seriously threatening the protection of high-quality arable land and national food security. In order to find out the overall situation regarding NGP on cultivated land in coastal China, this study revealed the spatial differentiation of NGP and its main driving factors by spatial autocorrelation analysis, multiple linear regression models and geographically weighted regression analysis (GWR). The results show that: (1) in 2018, the non-grain cultivated land area of 11 provinces along the coast of China was about 15.82 × 106 hm2, accounting for 33.65% of the total cultivated land area. (2) The NGP rate in 11 provinces gradually decreased from south to north, but the NGP area showed two peak centers in Guangxi province and Shandong province, then decreased gradually outwards. (3) The low economic benefit of the planting industry (per capita GDP and urban-to-rural disposable income ratio) was the most important driving force, leading to the spatial differentiation of NGP, while the number of rural laborers and land transfer areas also acted as the main driving factors for the spatial differentiation of NGP. However, the influence of each driving factor has obvious spatial heterogeneity. The non-grained area and the non-grain production rate at the municipal level were completely different from those at the provincial level, and the spatial heterogeneity was more prominent. In the future, the local government should control the disorganized spread of NGP, scientifically set the bottom line of NGP, reduce the external pressure of NGP, regulate multi-party land transfer behavior, and strengthen land-use responsibilities. This study can provide a scientific foundation for adjusting land-use planning and cultivated land protection policies in China and other developing countries.

Vegetation recovery is an important marker of ecosystem health in the mining area. Clarifying the influence of vegetation recovery on the characteristics of soil microbial community and its assembly process can improve our understanding of the ecological resilience and self-maintaining mechanism in the open-pit mining area. For this purpose, we employed MiSeq high-throughput sequencing coupled with null model analysis to determine the composition, molecular ecological network characteristics, key bacterial and fungal clusters, and the assembly mechanism of the soil microbial communities in shrubs (BL), coniferous forest (CF), broad-leaved forests (BF), mixed forest (MF), and the control plot (CK, the poplar plantation nearby that had been continuously grown for over 30 a without disturbance). The results showed that the vegetation restoration model had a significant influence on the α-diversity of the microbial community (p < 0.05). Compared with CK, Sobs and Shannon index of MF and CF have increased by 35.29, 3.50, and 25.18%, 1.05%, respectively, whereas there was no significant difference in the α-diversity of fungal community among different vegetation restoration types, Actinobacteria, Chloroflexi, Proteobacteria, and Acidobacteria were the dominant phyla. The diversity of the first two phyla was significantly higher than those of CK. However, the diversity of the last two phyla was dramatically lower than those of CK (p < 0.05). Ascomycota and Basidiomycota were dominant phyla in the fungal community. The abundance and diversity of Ascomycota were significantly higher than those of CK, while the abundance and diversity of the latter were considerably lower than those of CK (p < 0.05). The stochastic process governed the assembly of the soil microbial community, and the contribution rate to the bacterial community construction of CK, CF, BF, and MF was 100.0%. Except for MF, where the soil fungal community assembly was governed by the deterministic process, all other fungal communities were governed by the stochastic process. Proteobacteria and Acidobacteria are key taxa of the bacterial network, while Mortierellales, Thelebolales, Chaetothyriales, and Hypocreales are the key taxa of the fungal network. All these results might provide the theoretical foundation for restoring the fragile ecosystem in the global mining region.

In order to take into account the physical health of athletes and the quality of sports training, an optimization modeling and simulation method for the relationship between high-intensity training and sports injuries of athletes is proposed. The research of the specific content of the method is based on the two-dimensional and three-dimensional registration principles. In the research process, the program is written strictly according to the registration principle, and the correctness of the method is effectively tested by means of experimental methods. Digital image reconstruction measures based on 3D models may be based on ray tracing methods. The experimental results show that the number of accurate cases of this method is 77, and the accuracy rate is 96.3%. The proposed method can formulate a scientific and effective injury prevention strategy for athletes during high-intensity training.

Li-Xia-river Wetlands make up the biggest freshwater marsh in East China. Over the last decades, social and economic developments have dramatically altered the natural wetlands landscape. Mitigating land use conflict is beneficial to protect wetlands, maintain ecosystem services, and coordinate local socioeconomic development. This study employed multi-source data and GIS-based approaches to construct a composite index model with the purpose of quantitatively evaluating the intensity of land use conflict in Li-Xia-river Wetlands from 1978 to 2018. The results showed that the percentage of the wetlands’ area declined from 20.3% to 15.6%, with an overall reduction rate of 23.2%. The mean index of land use conflict increased from 0.15 to 0.35, which suggests that the conflict intensity changed from “no conflict” to “mild conflict.” The number of severe conflict units increased by about 25 times. A conspicuous spatial variation of land use conflict was observed across different periods, although taking land for agricultural activities was the overriding reason for wetlands reduction. However, in recent years, urban sprawl has posed the greatest threat to Li-Xia-river Wetlands. Coordinating land use conflict and formulating a practical strategy are the initial imperative steps to mitigate the threat to wetlands.