Landslides are among the most frequent and destructive geological disasters, causing significant loss of life and property. Research on landslide prediction is crucial for minimizing disaster-related losses, ensuring public safety, and developing reliable early warning systems and preparedness strategies. Existing prediction technologies are broadly classified into mechanical modeling methods and mathematical modeling methods. Compared to mechanical modeling, mathematical modeling relies on data analysis, enabling the use of existing monitoring data without requiring detailed geological information. Among these, the grey model stands out for its high accuracy with minimal information requirements, as well as its simplicity and ease of implementation. Based on these advantages, grey model-based landslide prediction has emerged as a key research focus. This paper proposes a dynamic landslide warning model that integrates the concept of risk with grey system theory, using a small amount of real-time monitoring data. Experimental results demonstrate the model's effectiveness in accurately predicting landslides and providing timely warnings for unstable slopes.

Granting users precise access rights is one of the purposes of access control technologies. With the increasing requirements of fine-grained authorization, too strict or too loose access control models may cause many problems. In this paper, aiming at insufficient authorizations in text databases, we propose a risk-aware topic-based access control (RTBAC) model, which uses topics to represent the content relationships between users and data. The RTBAC model also uses risk technologies to grant users corresponding access rights based on their historical behaviours and their access requests. The RTBAC model is a fine-grained access control model, and the authorization of RTBAC can reach the paragraph level. Experimental results show that RTBAC is an efficient access control model and the access control granularity of the RTBAC model is more than 3 times that of the existing content-based access control models.

Source address verification is to prevent nodes on the same link from deceiving each other’s IP addresses, to prevent hackers from gaining the trust of each other’s terminals, and then controlling the terminals, thus avoiding the penetration of attacks. Based on this, this paper proposes an inter-domain source IPv6 address encryption and verification scheme based on HMAC (HMAC-SAV). The verification scheme verifies the origin of data from transmission path and source/destination-end and identifies the authenticity of data packets. It makes up for the shortcomings of traditional Ingress/Egress filtering, thus preventing IP address spoofing attacks. Through the test vector evaluation of the HMAC-SAV scheme, it is proved that this scheme is a feasible and effective source address verification scheme with multiple verification methods.

Coral mud is widely distributed in the South China Sea. Studying deformation laws of coral mud is important to the infrastructure development in the South China Sea. In this article, a one-dimensional oedometer test of coral mud under near-natural conditions is performed. The exponential fitting model, Burgers model and ODGM model are used to analyse the time-strain relationship of coral mud under different vertical pressures. The self-fitting accuracy and prediction accuracy of the three models under different vertical pressures are compared. Then, the number of data points used in the model was reduced, and the associated change in accuracy and model stability of the three models were analysed. The study found that the ODGM model has high self-fitting, high prediction accuracy, and a high model stability when the number of data points is reduced, which describe the long-term nonlinear deformation of coral mud more accurately.

Settlement issues at airports pose a significant threat to operational safety, particularly in coastal regions, where land reclamation introduces unique challenges. The complexities of marine foundations, the difficulties in investigating reclaimed land, and the heightened risks of excessive settlement require timely and accurate monitoring and prediction to effectively identify risks and minimize unnecessary maintenance costs. To address these challenges, this study introduces a dynamic prediction model based on grey system theory, enhanced by a variable-size sliding window mechanism that continuously integrates the latest monitoring data. Validation using datasets from Kansai International Airport and Xiamen Xiang’an International Airport demonstrates that the model improves prediction accuracy by over 20% compared to existing models. Additionally, an exponential forecasting mechanism for long-term settlement prediction is developed and verified with data from Pudong International Airport. The proposed model demonstrates robust predictive capabilities across both long-term and short-term forecasting scenarios.