Identification Methods of the Deformation Memory Effect in the Stress Region above Crack Initiation Threshold

AbstractDeformation memory effect (DME) is one of the rock memory effects. One important application of the DME is to determine the in situ stress state. Compared to the traditional in situ stress measurements, the methods based on the DME are commercial and permit large number of measurements. Application of DME needs enough reliable identification methods. However, the existing methods sometimes are indistinct and the amount is insufficient. combined with three traditional methods including tangential modulus method, deformation rate analysis (DRA), acoustic emission method, two new potential methods were explored in the stress region above crack initiation threshold. One is based on the fractal dimension, called FD method. Another one is to take advantage of the lateral strain in the DRA method and the FD method, instead of using the axial strain. Based on the contact bond model in PFC2D, numerical model for granite sample was developed and cyclic uniaxial compressions were performed on it. Both the existing methods and new methods were used to detect the DME. The results demonstrate that the FD method is effective and reliable, result by DRA method with lateral strain is better than that with the axial strain, the tangential modulus method is not so distinct as other methods

Reconstruct gene regulatory network using slice pattern model

<p>Abstract</p> <p>Background</p> <p>Gene expression time series array data has become a useful resource for investigating gene functions and the interactions between genes. However, the gene expression arrays are always mixed with noise, and many nonlinear regulatory relationships have been omitted in many linear models. Because of those practical limitations, inference of gene regulatory model from expression data is still far from satisfactory.</p> <p>Results</p> <p>In this study, we present a model-based computational approach, Slice Pattern Model (SPM), to identify gene regulatory network from time series gene expression array data. In order to estimate performances of stability and reliability of our model, an artificial gene network is tested by the traditional linear model and SPM. SPM can handle the multiple transcriptional time lags and more accurately reconstruct the gene network. Using SPM, a 17 time-series gene expression data in yeast cell cycle is retrieved to reconstruct the regulatory network. Under the reliability threshold, <it>θ </it>= 55%, 18 relationships between genes are identified and transcriptional regulatory network is reconstructed. Results from previous studies demonstrate that most of gene relationships identified by SPM are correct.</p> <p>Conclusion</p> <p>With the help of pattern recognition and similarity analysis, the effect of noise has been limited in SPM method. At the same time, genetic algorithm is introduced to optimize parameters of gene network model, which is performed based on a statistic method in our experiments. The results of experiments demonstrate that the gene regulatory model reconstructed using SPM is more stable and reliable than those models coming from traditional linear model.</p

Evaluating the structure characteristics of epikarst at a typical peak cluster depression in Guizhou plateau area using ground penetrating radar attributes

Epikarst, defined as the “skin” of karst environment, is widely developed in southwest China, largely as a result of the subtropical monsoon climate. Typical SW China karst accommodates a dual hydrogeological structure, with surface and subsurface hydrological systems. The epikarst ecosystem of karst environments plays a key role in biogeochemical cycling and energy and material storage and transport. Due to low rates of soil-formation derived from carbonate rock weathering, the soil layer is shallow and scattered, presenting interlocked features within carbonate rock. Research on epikarst structure is primarily based on section field survey with semi-quantitative characterization, often lacking a fully quantitative description of soil-rock structural characteristics. We utilized ground penetrating radar (GPR) attributes to interpret the structure of epikarst at a peak cluster depression in the Guizhou karst plateau. Two typical types of epikarst slope profiles and one peak cluster depression in Maguan Town, Puding County were selected for study. We used MALA GPR equipment with 500 MHz and 50 MHz antennas to acquire data. GPR data was processed conventionally and then average energy attributes, average amplitude attributes and coherence attributes were extracted to interpret the structure of the two epikarst profiles and the soil depth of the depression. The results show that: (i) energy and coherence attributes can highlight the soil-rock structure of the epikarst profiles with relative ease; (ii) compared to the original returned image, the energy attributes visualise the soil and rock medium more effectively; and (iii) the coherence attributes can identify the reflection interface between complete bedrock and bedrock containing fissure and grikes (epikarst). In addition, using the 50 MHz antenna we were able to determine the soil depth in depression with coherence attributes indicating a depth of 3.6 m, very close to the real depth (3.58 m) measured by our auger verification work. GPR attributes provide evidence that the epikarst has developed a large number of fissures filled with soil or other materials, but that the bedrock under the epikarst has few fractures. GPR attributes are therefore helpful for increasing our confidence of studying the structure of slope epikarst structure and depression soil depth