Bi2O3 vaporization from ZnO-based varistors

Bi₂O₃-doped ZnO ceramic varistors are usually sintered at temperatures near to 1200 °C in the presence of a Bi-rich liquid phase, which is partially vaporized during the sintering process. Volatilization of bismuth oxide depends on the total surface area in direct contact to the reaction atmosphere and this in turn is related to the area/volume ratio of the ceramic compact. This loss of Bi₂O₃ has a significant role on the development of the varistor microstructure and more specifically ZnO grain growth, which is strongly enhanced by the presence of the liquid phase, should be particularly affected. In the present paper, X-ray fluorescence analysis is performed to describe the Bi₂O₃ vaporization profile as a function of distance to the outer surface, taking into account its influence on microstructural evolution.     

An integrated emotional and physiological assessment for VR-based active shooter incident experiments

Unfortunately, active shooter incidents are on the rise in the United States. With the recent technological advancements, virtual reality (VR) experiments could serve as an effective method to prepare civilians and law enforcement personnel for such scenarios. However, for VR experiments to be effective for active shooter training and research, such experiments must be able to evoke emotional and physiological responses as live active shooter drills and events do. The objective of this study is thus to test the effectiveness of an active shooter VR experiment on emotional and physiological responses. Additionally, we consider different locomotion techniques (i.e., walk-in-place and controller) and explore their impact on users’ sense of presence. The results suggest that the VR active shooter experiment in this study can induce emotional arousal and increase heart rate of the participants immersed in the virtual environment. Furthermore, compared to the controller, the walk-in-place technique resulted in a higher emotional arousal in terms of negative emotions and a stronger sense of presence. The study presents a foundation for future active shooter experiments as it supports the ecological validity using VR for active shooter incident related work for the purposes of training or research.     

Intelligent fault diagnosis for rolling bearings based on graph shift regularization with directed graphs

Graph shift regularization is a new and effective graph-based semi-supervised classification method, but its performance is closely related to the representation graphs. Since directed graphs can convey more information about the relationship between vertices than undirected graphs, an intelligent method called graph shift regularization with directed graphs (GSR-D) is presented for fault diagnosis of rolling bearings. For greatly improving the diagnosis performance of GSR-D, a directed and weighted k-nearest neighbor graph is first constructed by treating each sample (i.e., each vibration signal segment) as a vertex, in which the similarity between samples is measured by cosine distance instead of the commonly used Euclidean distance, and the edge weights are also defined by cosine distance instead of the commonly used heat kernel. Then, the labels of samples are considered as the graph signals indexed by the vertices of the representation graph. Finally, the states of unlabeled samples are predicted by finding a graph signal that has minimal total variation and satisfies the constraint given by labeled samples as much as possible. Experimental results indicate that GSR-D is better and more stable than the standard convolutional neural network and support vector machine in rolling bearing fault diagnosis, and GSR-D only has two tuning parameters with certain robustness.     

Performance Evaluation Indicator (PEI): A new paradigm to evaluate the competence of machine learning classifiers in predicting rockmass conditions

To illustrate an unprejudiced comparison among machine learning classifiers established on proprietary databases, and to guarantee the validity and robustness of these classifiers, a Performance Evaluation Indicator (PEI) and the corresponding failure criterion are proposed in this study. Three types of machine learning classifiers, including the strictly binary classifier, the normal multiclass classifier and the misclassification cost-sensitive classifier, are trained on four datasets recorded from a water drainage TBM project. The results indicate that: (1) the PEI successfully compares the competence of classifiers under different scenarios by isolating the effects of different overlapping-degree of rockmass classes, and (2) the cost-sensitive algorithm is warranted to classify rockmasses when the ratio of inter-class classes is more than 8:1. The contributions of this research are to fill the gap in performance evaluations of a classifier for imbalanced training data, and to identify the best situation to apply this classifier.     

A Building Information Model enabled Multiple Traveling Salesman Problem for building interior patrols

During building emergencies, an effective and visible primary search plan enhances situation awareness and enables a more efficient rescue mission. The aim of the primary search during an emergency is the rapid screening of every space in the building to identify locations of victims and their conditions. Afterwards, first responders can plan for the rescue of those victims. To provide a timely draw up of interior patrol routes and assign rescue teams to conduct the primary search, this study formulates the problem as a multiple traveling salesman problem (M-TSP) where the comprehensive building interior network is given by the building information models (BIMs), while the total traveling costs (lengths) of every rescue team is minimized. To meet the requirement of real-time patrol routes optimization, we employed the branch-and-price algorithm for the enhancement of computation efficiency. In addition, a heuristic method was introduced to provide timely solutions for large-scale networks. A case study is conducted for a single-floor convention center. We utilized BIM to construct a network of nodes and arcs where the decision model requires as input, and the branch-and-price algorithm finds the optimal patrol. The resulting patrol routes can be visualized and serve as guide for rescue teams to conduct the primary search. The integrated approach proposed in this study is practical and can expedite search and rescue missions.     

Sunway supercomputer architecture towards exascale computing: analysis and practice

Science China Information Sciences -     

一类时滞乙肝病毒模型的稳定性分析

研究了具有时滞和非线性发生率的乙肝病毒模型的稳定性及Hopf分支问题.利用微分不等式技巧,讨论了系统解的正性和有界性.利用泛函微分方程的稳定性理论,通过分析模型在平衡点处超越特征方程根的分布情况,讨论了时滞对平衡点稳定性的影响.研究结果表明:时滞不影响无病平衡点的稳定性;时滞会诱发地方病平衡点的稳定性,并且在其附近产生小振幅的周期解.     

微分模的Gorenstein投射和内射复形

考虑微分模的Gorenstein同调理论,证明了一个微分模复形C:=((Cn,θn),dn)是Gorenstein投射的(或Gorenstein内射的)当且仅当每个层次的微分模(Cn,θn)都是Gorenstein投射的(或Gorenstein内射的),并且给出了微分模复形的Gorenstein投射维数和Gorenstein内射维数的刻画.     

求解4G网络建站问题进化多目标算法

根据4G建站模型,设计了一种针对重点区域进行杂交、变异的进化多目标算法.该算法能有效减少个体进行杂交和变异时编码的长度、降低复杂度,使处理4G建站模型的进化多目标算法性能大大提高;在杂交变异之后,再根据约束条件对非重点区域进行搜索,在非重点区域中,激活能够最大程度覆盖了未被覆盖的测试点的候选基站,以此类推,直到全部激活的基站的覆盖率满足模型覆盖率约束为止,既保证了生成的解均为有效解,又使得在激活尽可能少基站的情况下覆盖到尽可能多的测试点.最后用极大极小方法求解出了一组最优解集.模拟4G建站问题的计算机仿真表明该算法非常有效.