原位孔内剪切试验在路基边坡支护设计中的应用

为了对边坡的合理设计提供严密的参考依据,采用原位孔内剪切试验测试了某路基边坡填土的抗剪强度,对不同工况下的边坡支护效果开展数值模拟分析.结果表明,该路基边坡填土压实质量欠佳,呈现轻微的应变硬化特征,其内摩擦角为24°,黏聚力为12 kPa.采用桩基托梁挡墙支护后,边坡最大横向位移可以降低76.5％～84.5％,且随着桩长的增加,边坡变形逐渐减小,但减小幅度呈下降趋势;边坡全局应变水平相比支护前也有所减小,局部最大剪应变明显降低,滑弧特征被削弱.因此,可采用原位孔内剪切试验较为准确地获取边坡填土的强度参数,使得边坡支护设计更合理.     

Side-Channel Attacks in a Real Scenario

Existing Side-Channel Attacks(SCAs) have several limitations and, rather than to be real attack methods,can only be considered to be security evaluation methods. Their limitations are mainly related to the sampling conditions, such as the trigger signal embedded in the source code of the encryption device, and the acquisition device that serves as the encryption-device controller. Apart from it being very difficult for an attacker to add a trigger into the original design before making an attack or to control the encryption device, there is a big gap in the capacity of existing SCAs to pose real threats to cipher devices. In this paper, we propose a new method, the sliding window SCA(SW-SCA), which can be applied in scenarios in which the acquisition device is independent of the encryption device and for which the encryption source code requires no trigger signal or modification. First,we describe the main issues in existing SCAs, then we theoretically analyze the effectiveness and complexity of our proposed SW-SCA —a method that can incorporate a sliding-window mechanism into almost all of the existing non-profiled SCAs. The experimental results for both simulated and physical traces verify the effectiveness of the SW-SCA and the appropriateness of its theoretical complexity.     

动车所调车作业计划优化模型与算法

本文在分析动车组一级修作业流程特点的基础上,将动车所调车作业计划优化问题构建为整数规划模型并设计了融合"股道均衡分配规则""股道无效占用时间最小化规则""冲突消解策略"的启发式算法对问题进行求解,以动车所实际案例为背景验证了模型和算法的有效性。结果表明,所提出优化模型和算法是有效的;所提启发式算法相比三类常见的调度方法具有明显的优越性。     

基于机械对称性的夹具对称性及应用

在总结机械对称性理论体系的基础上,对将其应用到夹具领域进行了探索,提出了由夹具结构对称性、原理对称性、功能对称性和时空对称性组成的夹具对称性概念体系及层次结构.给出了各种夹具对称性的定义,并用实际例子说明了它们在夹具系统的存在和作用原理.探讨了夹具对称性在夹具系统的应用思路与方法,特别研究了将夹具功能对称性应用到夹具设计问题,建立了夹具功能模型和夹具功能集及夹具结构集,给出了从夹具功能出发获得夹具结构的算法,为计算机辅助夹具设计提供了又一条可行的有效途径.     

Spatial dependence model with feature difference

This paper presents a new spatial dependence model with an adjustment of feature difference. The model accounts for the spatial autocorrelation in both the outcome variables and residuals. The feature difference adjustment in the model helps to emphasize feature changes across neighboring units, while suppressing unobserved covariates that are present in the same neighborhood. The prediction at a given unit incorporates components that depend on the differences between the values of its main features and those of its neighboring units. In contrast to conventional spatial regression models, our model does not require a comprehensive list of global covariates necessary to estimate the outcome variable at the unit, as common macro-level covariates are differenced away in the regression analysis. Using the real estate market data in Hong Kong, we applied Gibbs sampling to determine the posterior distribution of each model parameter. The result of our empirical analysis confirms that the adjustment of feature difference with an inclusion of the spatial error autocorrelation produces better out-of-sample prediction performance than other conventional spatial dependence models. In addition, our empirical analysis can identify components with more significant contributions.     

Incorporating textual and management factors into financial distress prediction: A comparative study of machine learning methods

Financial distress prediction (FDP) has been widely considered as a promising approach to reducing financial losses. While financial information comprises the traditional factors involved in FDP, nonfinancial factors have also been examined in recent studies. In light of this, the purpose of this study is to explore the integrated factors and multiple models that can improve the predictive performance of FDP models. This study proposes an FDP framework to reveal the financial distress features of listed Chinese companies, incorporating financial, management, and textual factors, and evaluating the prediction performance of multiple models in different time spans. To develop this framework, this study employs the wrapper-based feature selection method to extract valuable features, and then constructs multiple single classifiers, ensemble classifiers, and deep learning models in order to predict financial distress. The experiment results indicate that management and textual factors can supplement traditional financial factors in FDP, especially textual ones. This study also discovers that integrated factors collected 4 years prior to the predicted benchmark year enable a more accurate prediction, and the ensemble classifiers and deep learning models developed can achieve satisfactory FDP performance. This study makes a novel contribution as it expands the predictive factors of financial distress and provides new findings that can have important implications for providing early warning signals of financial risk.     

Processing and characterization of the microstructure and mechanical properties of Al6061–TiB2 composite

In the present research, aluminum metal matrix composites were processed by the stir casting technique. The effects of TiB2 rein-forcement particles, severe plastic deformation through accumulative roll bonding (ARB), and aging treatment on the microstructural charac-teristics and mechanical properties were also evaluated. Uniaxial tensile tests and microhardness measurements were conducted, and the micro-structural characteristics were investigated. Notably, the important problems associated with cast samples, including nonuniformity of the rein-forcement particles and high porosity content, were solved through the ARB process. At the initial stage, particle-free zones, as well as particle clusters, were observed on the microstructure of the composite. However, after the ARB process, fracturing phenomena occurred in brittle ceramic particles, followed by breaking down of the fragments into fine particles as the number of rolling cycles increased. Subsequently, com-posites with a uniform distribution of particles were produced. Moreover, the tensile strength and microhardness of the ARB-processed com-posites increased with the increase in the reinforcement mass fraction. However, their ductility exhibited a different trend. With post-deforma-tion aging treatment (T6), the mechanical properties of composites were improved because of the formation of fine Mg2Si precipitates.