Reconfigurable intelligent surface (RIS) is a new and revolutionary technology to achieve spectrum-, energy- and cost-efficient wireless networks. This paper studies the resource allocation for RIS-empowered device-to-device (D2D) communication underlaying a cellular network, in which an RIS is employed to enhance desired signals and suppress interference between paired D2D and cellular links. We maximize the overall network’s spectrum efficiency (SE) and energy efficiency (EE), respectively, by jointly optimizing the spectrum reuse indicators, the transmit power, the RIS’s passive beamforming and the BS’s receive beamforming. To solve both mixed-integer non-linear programming problems, we first propose an efficient and low-complexity user-pairing scheme based on relative channel strength to determine the spectrum reuse indicators. Other variables are then optimized to maximize the SE by an iterative algorithm, based on the techniques of alternating optimization, successive convex approximation, Lagrangian dual transform and quadratic transform. The EE-maximization problem is solved by an alternating algorithm integrated with Dinkelbach’s method. Numerical results show that the proposed design achieves significant SE and EE enhancements compared to traditional underlay D2D network without RIS, relay-assisted D2D network and other benchmarks.

The critical point of rock mass transition from stable to unstable states is significant for the prevention and control of rock engineering hazards. This study explored the precursor characteristics of instability in sandstone containing prefabricated fractures and intact sandstone based on the critical slowing down (CSD) theory. The results demonstrated that as the input energy drove the dissipation energy dynamic system toward the critical point of two states, the CSD phenomenon appeared, which was manifested by the sudden increase in CSD time series metrics (skewness, kurtosis, and coefficient of variation) and autoregressive model metrics (variance and autocorrelation coefficient). The CSD characteristics of different dissipation energy dynamical systems were different, resulting in different times of CSD phenomena for distinct systems. In addition, the angle of the sandstone fracture affected the characteristics of the dissipation energy dynamical system and hence the appearance time of the precursor information. The precursor time of the sandstone containing 45° parallel fractures appeared earliest among all rock samples, and the ratio of the appearance time of the precursor point to the failure time of all rock samples reached more than 80%, which had good timeliness. The stress at the precursor point reached more than 80% of the peak stress, which corresponded to the plastic stage in the loading process, indicating that the CSD indicators constitute an effective monitoring index, which can provide an important reference for the stability monitoring and early warning of the field rock project.

The deformation of landslides is a non-linear dynamic and complex process due to the impacts of both inherent and external factors. Understanding the basis of landslide deformation is essential to prevent damage to properties and losses of life. To forecast the landslides displacement, a hybrid machine learning model is proposed, in which the Variational Modal Decomposition (VMD) is implemented to decompose the measured total surface displacement into the trend and periodic components. The Double Exponential Smoothing algorithm (DES) and Extreme Learning Machine (ELM) were adopted to predict the trend and the periodic displacement, respectively. Particle Swarm Optimization (PSO) algorithm was selected to obtain the optimal ELM model. The proposed method and implementation procedures were illustrated by a step-like landslide in the Three Gorges Reservoir area. For comparison, Least Square Support Vector Machine (LSSVM) and Convolutional Neutral Network–Gated Recurrent Unit (CNN–GRU) were also conducted with the same dataset to forecast the periodic component. The application results show that DES-PSO-ELM outperformed the other two methods in landslide displacement prediction, with RMSE, MAE, MAPE, and R² values of 1.295mm, 0.998 mm, 0.008%, and 0.999, respectively.