A coupled damage-plasticity DEM bond contact model for highly porous rocks

In view of the significant stress loss induced by structural collapse when simulating high-porous soft rocks using traditional damage bond models in DEM ( discrete element method) modelling, a novel damage bond contact model is proposed to capture the ductile failure of high-porous cemented soft rocks. To address the unrealistic physical contact distribution resulting from the use of spherical particles in DEM modelling and consider the physical presence of broken bonds, far-field interaction is introduced between grains when two untouched particles reach a specific activation gap, enabling the generation of stable, highly porous open structure samples while using spherical DEM particles. The final results demonstrate that this newly developed model facilitates the transition from the purely elastic rock-like behaviour stage to the transitional ductile failure stage of porous soft rocks, as well as reproduces the softening/hardening response of soft rocks under different confinements

Simulation of pile installation in chalk:Discrete and continuum approaches

Chalk is a type of porous rock formed from cemented calcite grains. It is widely found in areas across the UK, and is present beneath the North Sea where offshore wind turbines are being installed. Large piles are often driven into chalk to support these turbines. However, the installation process can cause the intact rock below the pile to crush, creating a putty-like material with different mechanical properties from the original rock. This unpredictability has made it difficult to design piles that are appropriate for use in chalk. This paper presents two approaches to modelling open-ended pile installation in chalk. The first approach is based on the Discrete Element Method (DEM), which represents the rock as separate particles bonded together. A new contact model is proposed for highly porous rocks. The second approach uses the Geotechnical Particle Finite Element Method (GPFEM), which has been adapted to account for the large displacements and nonlinearities of the problem. With GPFEM the coupled hydromechanical effects developing during pile installation are investigated using a robust and mesh independent implementation of an elasto-plastic constitutive model at large strains. With DEM the micromechanical features of pile plugging are explored and the mechanisms behind radial stress distributions inside and outside the plug are unveiled. Although both approaches have their challenges, they have been successful in modelling pile installation experiments at model scale. This offers the potential for a closer examination and improved understanding of the mechanisms underlying open-ended pile installation in chal

A coupled damage-plasticity DEM bond contact model for highly porous rocks

In view of the significant stress loss induced by structural collapse when simulating high-porous soft rocks using traditional damage bond models in DEM ( discrete element method) modelling, a novel damage bond contact model is proposed to capture the ductile failure of high-porous cemented soft rocks. To address the unrealistic physical contact distribution resulting from the use of spherical particles in DEM modelling and consider the physical presence of broken bonds, far-field interaction is introduced between grains when two untouched particles reach a specific activation gap, enabling the generation of stable, highly porous open structure samples while using spherical DEM particles. The final results demonstrate that this newly developed model facilitates the transition from the purely elastic rock-like behaviour stage to the transitional ductile failure stage of porous soft rocks, as well as reproduces the softening/hardening response of soft rocks under different confinements

Research on seismic signals for vehicle targets and recognition by data fusion

This paper researches seismic signals of typical vehicle targets in order to extract features and to recognize vehicle targets. As a data fusion method, the technique of artificial neural networks combined with genetic algorithm(ANNCGA) is applied for recognition of seismic signals that belong to different kinds of vehicle targets. The technique of ANNCGA and its architecture have been presented. The algorithm had been used for classification and recognition of seismic signals of vehicle targets in the outdoor environment. Through experiments, it can be proven that seismic properties of target acquired are correct, ANNCGA data fusion method is effective to solve the problem of target recognition. <br /

The Newsvendor Problem with Different Delivery Time, Resalable Returns, and an Additional Order

In a B2C scenario, the retailer is confronted with two kinds of demand. One requires an immediate delivery after placing an order, while the other prefers a delayed shipment due to some personal reasons. Considering demands for different delivery time, we explore a newsvendor model with resalable returns and an additional order to optimize the procurement decision under a stochastic demand distribution. The impact of the proportion of the instant delivery needs and the return rate on the order quantity and the expected profit is illustrated through numerical tests. It is shown that the expected profit decreases as the ratios of immediate delivery needs and returned products increase. Besides, if the sum of the percentage of the instant delivery needs and the return rate is less than 1, the expected profit is always greater than the result if the sum of them is equal to or greater than 1. Management implications are also discussed

Formamide deionized accelerates the somatic embryogenesis of Cunninghamia lanceolata

Aim of the study: To improve the efficiency of the somatic embryogenesis (SE) in Cunninghamia lanceolata. Area of the study: The study was conducted at Nanjing Forestry University (Nanjing, China). Material and methods: Immature cones of C. lanceolata, genotype 01A1 which was planted in Yangkou State-owned Forest Farm (Fujian, China), were used to induced callus. These calli were used to induce SE, concentration gradients of 0 g/L, 0.01134 g/L, 0.1134 g/L, 1.1134 g/L and 11.34 g/L of FD was added, to explore the optimal concentration for promoting SE of C. lanceolata. Main results: Low concentration of FD promoted the maturation of somatic embryos, while high concentration of FD lead to browning of embryogenic callus. The seedling rate and rooting number of seedlings induced by different concentrations of FD were significantly different. Research highlights: This study may aid in the rapid maturation of C. lanceolata somatic embryos and is useful for accelerated C. lanceolata breeding. Keywords: C. lanceolata; Formamide Deionized; Somatic embryogenesis; Seedling rate. Abbreviations used: FD (Formamide Deionized), FD0 (the concentration of 0 g/L FD), FD0.01134 (the concentration of 0.01134 g/L FD), FD0.1134 (the concentration of 0.1134 g/L FD), FD1.134 (the concentration of 1.134 g/L FD), FD11.34 (the concentration of 11.34 g/L FD)

CellBRF: A Feature Selection Method for Single-Cell Clustering Using Cell Balance and Random Forest

Motivation Single-cell RNA sequencing (scRNA-seq) offers a powerful tool to dissect the complexity of biological tissues through cell sub-population identification in combination with clustering approaches. Feature selection is a critical step for improving the accuracy and interpretability of single-cell clustering. Existing feature selection methods underutilize the discriminatory potential of genes across distinct cell types. We hypothesize that incorporating such information could further boost the performance of single cell clustering. Results We develop CellBRF, a feature selection method that considers genes’ relevance to cell types for single-cell clustering. The key idea is to identify genes that are most important for discriminating cell types through random forests guided by predicted cell labels. Moreover, it proposes a class balancing strategy to mitigate the impact of unbalanced cell type distributions on feature importance evaluation. We benchmark CellBRF on 33 scRNA-seq datasets representing diverse biological scenarios and demonstrate that it substantially outperforms state-of-the-art feature selection methods in terms of clustering accuracy and cell neighborhood consistency. Furthermore, we demonstrate the outstanding performance of our selected features through three case studies on cell differentiation stage identification, non-malignant cell subtype identification, and rare cell identification. CellBRF provides a new and effective tool to boost single-cell clustering accuracy. Availability and implementation All source codes of CellBRF are freely available at https://github.com/xuyp-csu/CellBRF