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The stability evaluation of deep foundation pit slopes is crucial, including the solution of the factor of safety and the location of the critical slip surface. Prestressed anchor cables and underground diaphragm walls are commonly used to reinforce pit slopes. By means of the dimensional increase technique and the global analysis approach of slope stability, a nonlinear optimization problem is defined for the stability analysis of pit slopes. In the optimization problem, the ordinates of discrete points on the slip surface and the factor of safety are both the decision variables, the objective function is the factor of security, and the constraints are the equilibrium equations and the convexity of the slip surface. Because the objective function is linear and the constraint functions are polynomials of degree three at most, the optimization problem is a classical optimization one that conventional optimization techniques can solve without recourse to modern optimization techniques such as the AI technique. Examples suggest that the proposed procedure is far more efficient and stable than the optimization models based on the methods of slides. At last, applications are demonstrated to ongoing deep foundation pit slopes situated at Tong Zhou – Beijing sub-center. [ABSTRACT FROM AUTHOR]

Existing prototype learning methods provide limited interpretation on which patches from input images are similar to the corresponding prototypes. Moreover, these methods do not consider the diversities among the prototypes, which leads to low classification accuracy. To address these problems, this paper proposes Characteristic Prototype Network (CDPNet) with clear interpretation of local regions and characteristic. The network designs the feature prototype to represent the discriminative feature and the characteristic prototype to characterize the prototype's properties among different individuals. In addition, two novel strategies, dynamic region learning and similarity score minimization among similar intra-class prototypes, are designed to learn the prototypes so as to improve their diversity. Therefore, CDPNet can explain which kind of characteristic within the image is the most important one for classification tasks. The experimental results on well-known datasets show that CDPNet can provide clearer interpretations and obtain state-of-the-art classification performance in prototype learning. • We propose a novel interpretable prototype learning method for classification. • The prototype learning mechanism in the previous research is analyzed and the drawbacks are revealed. • This paper presents a new strategy to learn the prototype within the dynamic region by similarity score. [ABSTRACT FROM AUTHOR]

Breast cancer is one of the most common malignant tumors among female. How to effectively discriminate the category of the cancers using the key features/factors is very important in the diagnosis and prediction. In this paper, Jointly Sparse Discriminant Analysis (JSDA) is proposed to explore the key factors in breast cancer and extract the key features for improving the accuracy in diagnosis and prediction. JSDA introduces the jointly sparse regular term (i.e. L 2 , 1 -norms term) to the criterion. A convergent iterative algorithm is designed to solve the optimization problem. It is shown that the proposed JSDA algorithm not only can learn the jointly sparse discriminant vectors to explore the key factors of the breast cancer in cancer pathologic diagnosis, but also can improve the diagnosis accuracy compared with the classical feature extraction and discriminant analysis algorithm. Experimental results on breast cancer datasets indicate that JSDA outperforms some well-known subspace learning algorithms in prediction accuracy, not matter they are non-sparse or sparse, particularly in the cases of small sample sizes. Data analysis shows that the key factors of the breast cancer explored by the JSDA are consistent with the practical experience. [ABSTRACT FROM AUTHOR]

Abstract: Application of cell-–biomaterial systems in regenerative medicine can be facilitated by their successful low temperature preservation. Vitrification, which avoids ice crystal formation by amorphous solidification, is an emerging approach to cryopreservation. Developing vitrification strategy, effective cryopreservation of alginate–fibrin beads with porcine mesenchymal stromal cells has been achieved in this study. The cell–biomaterial constructs were pre-cultured for 20 days before cryopreservation, allowing for cell proliferation and construct stabilization. Ethylene glycol (EG) was employed as the basic cryoprotectant for two equilibration solutions. Successful cryopreservation of the constructs was achieved using vitrification solution composed of penetrating (EG MW 62Da) and non-penetrating (sucrose MW 342Da) cryoprotectants. Stepwise procedure of introduction to and removal of cryoprotectants was brief; direct plunging into liquid nitrogen was applied. Cell viability, evaluated by combining live/death staining and confocal laser microscopy, was similar for both control and vitrified cells in the beads. No detectable damage of microstructure of cryopreserved beads was found as shown by scanning electron microscopy. Both osteogenically induced control and vitrified cells in the constructs were equally capable of mineral production and deposition. There was no statistically significant difference in metabolic activity and proliferation between both groups during the entire culture period. Our study leads to the conclusion that the developed cryopreservation protocol allowed to maintain the integrity of the beads while preserving the ability of the pig bone marrow derived mesenchymal stromal cells to proliferate and subsequently differentiate; demonstrating that vitrification is a promising approach for cryopreser-vation of “ready-to-use” cell–biomaterial constructs. [Copyright &y& Elsevier]

Abstract As a famous linear manifold learning method, orthogonal neighborhood preserving projections (ONPP) is able to provide a set of orthogonal projections for dimensionality reduction. However, a problem of ONPP is that it takes the L 2 -norm as the basic measurement and therefore tends to be sensitive to the outliers or the variations of the data. Aiming at strengthening the robustness of the conventional method ONPP, in this paper, a robust and sparse dimensionality reduction method based on linear reconstruction, called Robust Jointly Sparse Embedding (RJSE), is proposed by introducing L 2, 1 -norm as the basic measurement and regularization term. We design a simple iterative algorithm to obtain the optimal solution of the proposed robust and sparse dimensionality reduction model. Experiments on four benchmark data sets demonstrate the competitive performance of the proposed method compared with the state-of-the-art methods. [ABSTRACT FROM AUTHOR]

• A multiview method, GRMDA, is proposed for extracting the robust and sparse features, which reconstructs within-class scatter term and between-class scatter term in multiview scenario using L 2 , 1 norm to enhance robustness. • We conduct the analysis of small-class problem and adopt another criterion inspired by MMC to solve this problem. In terms of overfitting problem, the L 2 , 1 norm is also imposed on the regularization term for extracting sparse features. • The paper presents an iterative algorithm to compute optimal linear transform for each view. The poof of convergence is provided, and the computational complexity is analyzed. Several experiments were conducted to evaluate the performance of GRMDA Multiview data leads to the demand for classifying samples from various views, and the large gap between different views makes the classification task challenging. Recently, researchers have extended linear discriminant analysis (LDA) to multi-view scenarios. However, the extended methods are generally associated with the small-class problem, that is, the projection size is limited by the number of classes. In addition, they are sensitive to variations in images or outliers. To solve these problems, this study proposes a generalized robust multiview discriminant analysis (GRMDA) to obtain a linear transform for each view and for learning multiview jointly sparse discriminant common subspace. GRMDA aims to achieve both maximal between-class and minimal within-class variation for data from multiple views in a common space. Instead of formulating the ratio trace problem, we reformulate GRMDA inspired by maximum margin criterion (MMC) to address the small-class problem. Moreover, the proposed method achieves stronger robustness by reconstructing the within-class and between-class scatter terms from the definition of L 2 , 1 norm. Furthermore, GRMDA ensures joint sparsity using the L 2 , 1 norm-based regularization term. Additionally, we present an iterative algorithm, convergence proof, and complexity analysis. Experiments on six popular databases, that is, COIL100, USPS/MNIST, Extended Yale Face B, AR, BBCSport, and multiple feature datasets, were conducted to evaluate the performance of GRMDA against the state-of-the-art multiview methods. The experimental results demonstrate that the proposed method can achieve a significant performance with strong robustness and fast convergence. [ABSTRACT FROM AUTHOR]

[Display omitted] For deep-buried shield tunnels in dry cohesionless soils, it is critical to determine the support pressure acting on a tunnel face due to the significant soil arching effect during excavation. In this paper, a new analytical model of the multi-layer parabolic bearing arch is proposed to predict the limit support pressure based on the limit equilibrium method and the wedge model. The model consists of the multi-layer parabolic bearing arch, the friction arch and the wedge. According to the different buried depth of tunnel, the tunnel state is divided into shallow buried zone, transition zone and deep buried zone. By assuming the multi-layer parabolic soil arch as a three-hinged structural arch with reasonable arch axis, the load transfer expression of multi-layer parabolic soil arch is derived in transition zone and deep buried zone respectively. Then, the rationality and effectiveness of the proposed model were verified by comparing with model tests, existing theoretical methods and numerical model. Finally, the effects from the internal friction angle φ of soil on the inclination angle β , tunnel state and limit support pressure were also discussed. It is shown that with the increase of internal friction angle φ , the boundaries in between the shallow zone and the deep buried zone declines linearly, the inclination angle β increases linearly and the limit support pressure decreases nonlinearly. This study provides a significant reference for predicting the value of limit support pressure acting on the tunnel face in dry cohesionless soils. [ABSTRACT FROM AUTHOR]