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In engineering practices such as backfill mining, the existence and characteristics of the rock-backfill interface significantly influence the propagation of explosive stress waves and the dynamic response of the rock mass. This study aims to reveal how, among these characteristics, different backfill media and their wave impedance differences with the rock specifically affect these processes. To this end, physical experiments were combined with numerical simulations, utilizing Digital Image Correlation (DIC) technology to capture strain field evolution under blasting, and LS-DYNA was employed for numerical analysis. The research indicates that the wave impedance difference between the rock and the backfill medium is key to controlling stress wave propagation and energy distribution. When the wave impedances are similar, stress wave transmission is dominant, leading to higher peak strain values at various points and a more uniform action of the stress wave on the rock mass, resulting in relatively uniform final failure. When the wave impedance difference is large, interface reflection is enhanced, forming a significant “blocking effect”; in this case, although the initial peak strain may be lower, the continuous action of the interface can lead to greater final strain and plastic deformation in the rock mass. Numerical simulations further show that the greater the wave impedance difference, the more pronounced the “guiding” and “blocking” effects of the backfill on crack propagation. This leads to intensified damage and crack accumulation in the rock mass adjacent to the backfill due to energy concentration, which also results in a higher fractal dimension of the cracks. Therefore, the type of backfill medium directly determines the strain response characteristics and final damage patterns of the rock mass by modulating the reflection and transmission behavior of waves at the interface. These findings have practical guiding significance for optimizing backfill material selection and blasting parameter design in backfill mining.

Ordinary differential equation (ODE) models are widely used to model dynamic processes in many scientific fields. Parameter estimation is usually a challenging problem, especially in nonlinear ODE models. The most popular method, nonlinear least square estimation, is shown to be strongly sensitive to outliers. In this paper, robust estimation of parameters using M-estimators is proposed, and their asymptotic properties are obtained under some regular conditions. The authors also provide a method to adjust Huber parameter automatically according to the observations. Moreover, a method is presented to estimate the initial values of parameters and state variables. The efficiency and robustness are well balanced in Huber estimators, which is demonstrated via numerical simulations and chlorides data analysis.

To provide the necessary security and privacy privileges for registered users of biometric systems, a novel template protection method for generating cancelable palmprint features based on randomized cuckoo hashing and minHash is proposed in this paper. Firstly, the orthogonal features of palmprint are extracted using the anisotropic filter. Then, a novel randomized cuckoo hashing is applied on the binary palmprint feature as a means of first layer security. Randomized cuckoo hashing composes of two hash tables; before hashing, each column of original biometric template is filtered with a random matrix to improve discriminative ability. For the randomized cuckoo hashing with the same positions, we use different Gray coding methods instead, which improves irreversibility. Furthermore, to improve the efficiency and resist unlinkability attacks, another layer of privacy protection, MinHash is adopted. Experimental results on PolyU database show that our method can nearly preserve the original verification performance. The irreversibility, unlinkability and revocability properties are examined experimentally. Also, the privacy of palmprint protection scheme is analyzed under the Brute-force Attack, False Accept Attack and Birthday attack.

A novel cancelable palmprint templates generation scheme is proposed in this paper. Firstly, the Gabor filter and chaotic matrix are used to extract palmprint features. It is then arranged into a row vector and divided into equal size blocks. These blocks are converted to corresponding decimals and mapped to look up tables, forming final cancelable palmprint features based on the selected check bits. Finally, collaborative representation based classification with regularized least square is used for classification. Experimental results on the Hong Kong PolyU Palmprint Database verify that the proposed cancelable templates can achieve very high performance and security levels. Meanwhile, it can also satisfy the needs of real-time applications.