Computing shortest paths in 2D and 3D memristive networks

Global optimisation problems in networks often require shortest path length computations to determine the most efficient route. The simplest and most common problem with a shortest path solution is perhaps that of a traditional labyrinth or maze with a single entrance and exit. Many techniques and algorithms have been derived to solve mazes, which often tend to be computationally demanding, especially as the size of maze and number of paths increase. In addition, they are not suitable for performing multiple shortest path computations in mazes with multiple entrance and exit points. Mazes have been proposed to be solved using memristive networks and in this paper we extend the idea to show how networks of memristive elements can be utilised to solve multiple shortest paths in a single network. We also show simulations using memristive circuit elements that demonstrate shortest path computations in both 2D and 3D networks, which could have potential applications in various fields

Representative sampling method for laboratory testing on shear strength of rock joints

The direct shear test is commonly used to determine the shear strength of rock joints. The measured shear strength, however, varies greatly with specimen positions due to the heterogeneity of rock joints. As a result, selecting specimens that represent the overall properties of rock joints is usually difficult. This paper aims to investigate the heterogeneity of rock joints and propose a new sampling method for selecting representative specimens. The roughness and shear strength variations of specimens taken from different positions of a natural rock joint were analyzed, and it was discovered that the heterogeneity of rock joint roughness is responsible for the heterogeneity of shear strength. The limitations of the traditional sampling method based on visual judgment were extensively investigated, revealing that shear strength parameters acquired by the traditional method contain large coefficient of variation (COV) values. To acquire trustworthy shear strength parameters, we proposed a representative sampling method based on the maximum likelihood estimation of the overall properties of rock joints. The number of determined representative specimens increases with the increase of normal stress. Representative specimens determined under low normal stresses can likewise exhibit the overall properties of the rock joint when subjected to high normal stresses. The Mohr-Coulomb and a nonlinear criterion were used to validate the derived representative specimens, demonstrating that the proposed method can produce reliable shear strength parameters and shear strength envelopes. Particularly, the determined representative specimens could derive shear strength parameters with relative errors less than 10% and COV values less than 0.1. The proposed method provides a quantitative and reliable tool for determining representative specimens to obtain reliable shear strength of rock joints

Comparative genome analysis of lignin biosynthesis gene families across the plant kingdom

<p>Abstract</p> <p>Background</p> <p>As a major component of plant cell wall, lignin plays important roles in mechanical support, water transport, and stress responses. As the main cause for the recalcitrance of plant cell wall, lignin modification has been a major task for bioenergy feedstock improvement. The study of the evolution and function of lignin biosynthesis genes thus has two-fold implications. First, the lignin biosynthesis pathway provides an excellent model to study the coordinative evolution of a biochemical pathway in plants. Second, understanding the function and evolution of lignin biosynthesis genes will guide us to develop better strategies for bioenergy feedstock improvement.</p> <p>Results</p> <p>We analyzed lignin biosynthesis genes from fourteen plant species and one symbiotic fungal species. Comprehensive comparative genome analysis was carried out to study the distribution, relatedness, and family expansion of the lignin biosynthesis genes across the plant kingdom. In addition, we also analyzed the comparative synteny map between rice and sorghum to study the evolution of lignin biosynthesis genes within the <it>Poaceae </it>family and the chromosome evolution between the two species. Comprehensive lignin biosynthesis gene expression analysis was performed in rice, poplar and <it>Arabidopsis</it>. The representative data from rice indicates that different fates of gene duplications exist for lignin biosynthesis genes. In addition, we also carried out the biomass composition analysis of nine <it>Arabidopsis </it>mutants with both MBMS analysis and traditional wet chemistry methods. The results were analyzed together with the genomics analysis.</p> <p>Conclusion</p> <p>The research revealed that, among the species analyzed, the complete lignin biosynthesis pathway first appeared in moss; the pathway is absent in green algae. The expansion of lignin biosynthesis gene families correlates with substrate diversity. In addition, we found that the expansion of the gene families mostly occurred after the divergence of monocots and dicots, with the exception of the C4H gene family. Gene expression analysis revealed different fates of gene duplications, largely confirming plants are tolerant to gene dosage effects. The rapid expansion of lignin biosynthesis genes indicated that the translation of transgenic lignin modification strategies from model species to bioenergy feedstock might only be successful between the closely relevant species within the same family.</p

Estimating RQD for Rock Masses Based on a Comprehensive Approach

Rock Quality Designation (RQD) is among the widely used measures of the quality of rock masses and can be derived through Monte Carlo stochastic process-based fracture network simulations. However, repeated simulations can yield variable RQD results. Here, we introduce a four-step approach that incorporates class ratio analysis to estimate the representative RQD, which includes (1) extracting the mean and confidence interval of the RQD sample, in terms of the Confidence Neutrosophic Number Cubic Value (CNNCV), (2) employing class ratio analysis to determine the thresholds of the number of virtual boreholes and that of the number of models for a given size D, beyond which the CNNCV remains substantially unchanged, (3) accepting the CNNCV at the thresholds of the number of models as the representative RQD for the model of size D (RQD(D)) and (4) determining the representative RQD (rRQD), defined as the specific value which, once D exceeds, the RQD(D) does not change significantly. The introduced approach is illustrated with a case study of an open-pit slope in China, and it was tested for its performance. The RQD calculation results of the proposed method and the traditional single-model approach exhibit differences, which diminish with increasing model sizes. At the 95% confidence level, the stable size of the RQD determined by the proposed method is 13 m, compared to 25 m for the single-model approach. This method enhances the accuracy of representative elementary volume predictions by accounting for the diversity in the simulation results of RQDs for the same size. Overall, the introduced approach offers a reliable method for obtaining RQD estimates