Adaptive management has very important practical significance for climate change adaptation and will play a great role in climate change risk mitigation in agricultural, forestry, and pastoral areas of China. Based on the theory of adaptive management, this paper selects Yuanping City from Shanxi Province, Qingyuan County from Liaoning Province, and Kulun Banner from Inner Mongolia as representative cases in agricultural, forestry, and pastoral areas, respectively, to carry out field research, and it uses 1970–2017 meteorological station data to apply vulnerability assessment and climate element change trend analysis, combined with the meteorological hazards data, and explore the adaptive management measures for agricultural, forestry, and pastoral areas in China. The conclusions are as follows: (1) the vulnerability of precipitation in Yuanping is high, the increase in temperature and drought and floods are the most important factors affecting crop yields in agricultural areas, and the key to climate change adaptive management lies in the awareness of farmers of climate change risks and the institutional guarantee of the government; (2) Qingyuan has high temperature vulnerability, and the forest areas have relatively strong adaptive capacity to climate change, but climate change will significantly affect the forest’s carbon sequestration function, and the focus of climate change adaptive management in the forestry sector is on engineering and technology field practices; (3) Kulun has the highest vulnerability to climate change, the frequent meteorological disasters seriously impact livestock development, and climate change adaptive management in pastoral areas relies on the participation of pastoralists’ local knowledge and also needs the support of the government and society.

As an important artificial implant material, the corrosion resistance of NiTi shape memory alloy is closely related to the machined surface quality. In this paper, the multiple analysis methods concerning potentiodynamic polarization, impedance spectrum and corrosion morphology are used to analyze the corrosion resistance of the alloy. The potentiodynamic polarization and impedance spectrum test results show that the conductivity and corrosion current density of electrochemical polishing surface decrease, and the polarization resistance and corrosion potential increase compared with milling. After electrochemical polishing, the surface roughness of the milling sample is decreased, and the NiTi alloy of austenite phase is transformed into TiO₂, which improves the corrosion resistance of the alloy. In addition, there are pitting corrosion, hole corrosion and crevice corrosion morphology on the milling surface, while the pitting corrosion and hole corrosion exist on the electrochemical polishing surface. The corrosion morphology verified the analysis of potentiodynamic polarization and impedance spectrum. The multiple analysis method proposed in this paper can be used as a more accurate evaluation method for the corrosion resistance of alloy surface, avoiding the error of analysis results caused by the impedance spectrum equivalent circuit and potentiodynamic polarization following Tafel relationship.

With the ever-increasing living standards of the people in recent years, more and more people have entered the army of tourism, and our country's tourism industry has achieved unprecedented development. A series of travel portal websites such as Ctrip.com, Qunar, and Malacca have emerged. A large amount of tourist information is presented to users, but at the same time it has led to the blind choice of users. Massive data have overwhelmed the information that users are really interested in. The emergence of tourism recommendation systems helps users solve this problem. Against the above background, this paper designs and implements a tourism recommendation system based on data mining. From the perspective of mining the similarity between users, the similarity between users is calculated through the collaborative filtering algorithm, and then the attractions visited by users with higher similarity are recommended.

Efficient and accurate extraction of pavement crack edges is a crucial prerequisite for the identification of structural road defects. However, existing deep learning methods primarily focus on object detection and segmentation, which makes it challenging to accurately capture the fine-grained edge information of cracks. To address this issue, we propose a dilated convolution and edge fusion network (DCEF) for crack edge detection. The proposed DCEF incorporates a dilated context-aware module to expand the receptive field without increasing the number of parameters, allowing the simultaneous capture of local details and global context. A dual attention module is integrated to highlight crack-relevant features and suppress background noise by modeling spatial and channel-wise dependencies. Furthermore, we design an edge-aware fusion module based on depthwise separable convolutions to efficiently integrate multiscale features while significantly reducing computational complexity. Experimental results in a dedicated crack dataset demonstrate that our method achieves optimal dataset scale and optimal image scale scores of 0.821 and 0.905, respectively, outperforming several state-of-the-art methods. These results validate the effectiveness and practicality of the proposed approach.