饱和块状混合回填土地基的一维固结分析

用块状渣土置换软弱地基和回填低洼谷地等是处置工程渣土的有效途径。为了分析饱和块状混合回填土地基的固结性状，运用混合物理论建立了其一维固结模型。首先，假定块状土固相和充填土固相之间满足等应变条件，获得了饱和块状混合回填土中各相应变与块状土孔隙变形和充填土孔隙变形的关系式。其次，在小应变条件下，根据自由能势函数方程建立了饱和块状混合回填土的一维线弹性本构方程，再结合达西定律和应力平衡方程获得了一维固结控制方程。再次，利用分离变量法得到一维固结解析解，通过退化本文模型与已有模型进行对比，验证了本文模型的正确性。最后，基于所得解析解，分析了充填土孔隙渗透系数、块状土孔隙渗透系数以及流体交换参数等因素对饱和块状混合回填土地基固结性状的影响。分析结果表明：充填土孔隙渗透系数对饱和块状混合回填土地基整体固结性状起主导作用；在固结初期，块状土超孔压会有一定程度的上升，且3个参数具有相似的作用机理。     

Method for calculating horizontal drain induced non-linear and large strain degree of consolidation

Consolidating dredged clay slurries using a combination of vacuum pressure and prefabricated horizontal drains (PHDs) is widely used in practice. This is a large strain problem, but there is no existing large strain theory for PHD induced consolidation. A method with explicit equations has been proposed to consider both mechanical and geometrical non-linearities in analyzing PHD induced consolidation. The method considers stepwise variation of properties using imaginary time concept. An imaginary time is determined by the condition of continuity of degree of consolidation before and after changing the properties. Then the method was applied to analyze a large scale model test of vacuum consolidation with PHD. Two analyses were conducted. One considered both the mechanical and geometrical non-linearity (large strain), and another only considered mechanical non-linearity (small strain). The results of large strain analysis agree with the measured settlement curve and excess pore pressures well. While the small strain analysis under-estimated the rate of consolidation significantly. The results from this study indicate that for analyzing PHD induced consolidation of clay slurries considering the effect of large strain is important, and the proposed method can be a useful design tool.     

Learn decision trees with deep visual primitives

In this paper, we strive to propose a self-interpretable framework, termed PrimitiveTree, that incorporates deep visual primitives condensed from deep features with a conventional decision tree, bridging the gap between deep features extracted from deep neural networks (DNNs) and trees’ transparent decision-making processes. Specifically, we utilize a codebook, which embeds the continuous deep features into a finite discrete space (deep visual primitives) to distill the most common semantic information. The decision tree adopts the spatial location information and the mapped primitives to present the decision-making process of the deep features in a tree hierarchy. Moreover, the trained interpretable PrimitiveTree can inversely explain the constituents of the deep features, highlighting the most critical and semantic-rich image patches attributing to the final predictions of the given DNN. Extensive experiments and visualization results validate the effectiveness and interpretability of our method.     

Analyzing dissipation effects on the non-classicality witnessing by a qubit via the entangled state representation

ABSTRACT

In this paper, we study detection of the state non-classicality for a quantum harmonic oscillator by a qubit in the presence of dissipation effects. We show that dissipation can enhance the effectiveness of the method in case of using the corrected form of the related nonclassicality witness. Such an improvement is attributed to the fact that dissipation leads to probing a surface, instead of a curve, of the complex plane for non-classicality condition on normally-ordered characteristic function.     

飞灰固结体在沥青混合料中应用研究

通过对新型固结剂固化飞灰生成的固结体研磨成微粉全部或部分替代矿粉作为沥青混合料进行试验探究。试验结果表明,当微粉替代矿粉时,混合料的物理性能、水稳定性及高温稳定性得到了提高,确定了微粉替代矿粉最佳替代率。微粉在沥青混合料中重金属的存在状态采用多级提取的方法进行分析,试验结果进一步证明了微粉中重金属在沥青混合料中的长期稳定性。为焚烧垃圾飞灰固化稳定化和资源化利用提供了一定的理论指导。     

基于伪布尔模型和启发式算法求解无容量设施选址问题

采用伪布尔模型和启发式算法来求解无容量设施选址问题。首先给出了问题的伪布尔(pseudo-Boolean)表示，然后基于Khumawala规则对问题进行预处理，最后提出两种启发式分支准则来求解问题。实验结果表明所提算法简单有效。     

Examining the role of regional culture and geographical distances on the representation of unfamiliar foods in a continental-size country

This study provides an experimental-exploratory investigation about the role of regional culture and Euclidean distances on the consumers’ representation of edible insects in Brazil, a country with an extensive geographical surface. Seven hundred and eighty participants were recruited on the streets of eight cities from different Brazilian states: Manaus in Amazonas; Porto Velho in Rondônia; Macapá in Amapá; Cuiabá in Mato Grosso; Aracaju in Sergipe; Rio de Janeiro in Rio de Janeiro; Campinas in São Paulo; and Santa Maria in Rio Grande do Sul. These participating cities were considered from their cultural identity differences and geographical distances. Through a continual restricted word association task, participants were instructed to promptly verbalize the first five terms that came to their minds when stimulated with the expression “food made with edible insects”. Following, they had to score the valence of each term they produced. The dictionaries produced in each city were compared and classified into groups using the Ellegård’s index. Each group presented distinct ways of expression and attitude with respect to the inductive expression. Basically, Brazil was divided into two main groups according to their representation of edible insects: one consisted by the cities situated near the shore of the Atlantic Ocean, which present a cultural formation influenced by the European immigrants; and the other comprised the cities from the continental region that have strong cultural influence from the Amerindians. Thus, the cultural formation was more decisive to explain the similar representations among the cities than their geographical proximity. Given that, to effectively introduce a novel food in a country with varied regional culture, the marketing strategy should be focused on the values and beliefs of their culture subgroups instead of a single strategy for the whole country.     

Hierarchical learning using deep optimum-path forest

Bag-of-Visual Words (BoVW) and deep learning techniques have been widely used in several domains, which include computer-assisted medical diagnoses. In this work, we are interested in developing tools for the automatic identification of Parkinson’s disease using machine learning and the concept of BoVW. The proposed approach concerns a hierarchical-based learning technique to design visual dictionaries through the Deep Optimum-Path Forest classifier. The proposed method was evaluated in six datasets derived from data collected from individuals when performing handwriting exams. Experimental results showed the potential of the technique, with robust achievements.