Evaluation of dynamic modulus measurement for C/C-SiC composites at different temperatures

The determination of elastic properties at application temperature is fundamental for the design of fibre reinforced ceramic composite components. An attractive method to characterize the flexural modulus at room and high temperature under specific atmosphere is the nondestructive Resonant Frequency Damping Analysis (RFDA). The objective of this paper was to evaluate and validate the modulus measurement via RFDA for orthotropic C/C-SiC composites at the application temperature. At room temperature flexural moduli of C/C-SiC with 0/90° reinforcement were measured under quasi-static 4-point bending loads and compared with dynamic moduli measured via RFDA longitudinally to fibre direction. The dynamic modulus of C/C-SiC was then measured via RFDA up to 1250°C under flowing inert gas and showed an increase with temperature which fitted with literature values. The measured fundamental frequencies were finally compared to those resulting from numerical modal analyses. Dynamic and quasi-static flexural moduli are comparable and the numerical analyses proved that bending modes are correctly modeled by means of dynamic modulus measured via RFDA. The nondestructive RFDA as well as the numerical modeling approach are suitable for evaluation of C/C-SiC and may be transferred to other fibre reinforced ceramic composite materials.     

APP软件性能效率研究

性能效率是APP软件的重要质量属性，但目前缺乏APP软件性能效率的通用模型。分析了APP软件的性能特征，基于ISO/IEC 25010标准提出了APP软件的性能效率模型，定义了APP软件性能效率的子特性和度量指标。基于提出的APP软件性能效率模型，通过实验对APP软件的性能效率进行了度量及相关分析。     

甲苯+乙醇+1-癸基-3-甲基咪唑醋酸盐物系等压气液相平衡数据的测定及关联

在常压下测定了甲苯+乙醇+1-癸基-3-甲基咪唑醋酸盐([C_(10)MIM][OAc])三元物系的等压气液相平衡数据,使用Aspen软件中NRTL模型对三元气液相平衡数据进行关联,通过计算得到方程参数。实验结果表明,[C_(10)MIM][OAc]的加入对甲苯有显著的盐析效应,[C_(10)MIM][OAc]摩尔分数越大效果越明显,当[C_(10)MIM][OAc]摩尔分数达到0.15时甲苯-乙醇的共沸现象完全消失。由NRTL模型计算出[C_(10)MIM][OAc]打破甲苯-乙醇体系共沸的最小摩尔分数为0.139 7。[C_(10)MIM][OAc]可以用作分离甲苯-乙醇共沸体系的萃取剂。     

The location and sizing of urban freight loading/unloading lay-by areas

The volume of freight vehicles operating within city boundaries is steadily increasing, which contributes to road congestion, especially in urban areas with a parking deficit. The proper identification of the location and size of commercial parking lay-by areas, where delivery vehicles can park for a limited time to perform loading/unloading operations, can relieve road congestion. Compared to the existing literature, this paper presents an improved two-stage approach, which includes a mathematical programme and a simulation model for determining the location and sizing of lay-by areas. The robustness and soundness of the results from a methodological and practical point of view are discussed using an example application.     

Intelligent Kano classification of product features based on customer reviews

Product features can be classified into different categories based on customer opinions. The rapid development of artificial intelligence and machine learning paves the way toward computational analysis of customer reviews for opinion mining. This paper presents an Intelligent Kano framework to extract, quantify, and classify different product features based on customer reviews. The framework is enabled by a novel integration of multiple artificial intelligence and machine learning techniques such as sentiment analysis and anomaly detection. A case study is conducted to validate the framework’s effectiveness. Over 12,000 customer reviews on two coffee machines are analyzed for the classification.     

区分站点状态的两级轮询控制系统

针对提高轮询控制模型工作效率和区分网络优先级的问题，提出了区分站点忙闲状态的完全-门限两级轮询控制模型（ETTPSS）。模型以两级优先级为基础，依据站点的忙闲状态采用并行处理方式只对忙站点进行信息分组发送服务。模型既能区分传输服务优先级又能避开对无信息分组的空闲站点的查询，从而提高了模型资源利用率和工作效率。运用概率母函数与马尔可夫链相结合的方法对该模型进行理论分析研究，精确解析了模型各个重要性能参数。仿真实验结果表明，仿真值与理论值近似相等，说明理论分析正确合理。与普通轮询模型相比，该模型性能大幅度提高。     

Components and Development in Big Data System: A Survey

With the growth of distributed computing systems, the modern Big Data analysis platform products often have diversified characteristics. It is hard for users to make decisions when they are in early contact with Big Data platforms. In this paper, we discussed the design principles and research directions of modern Big Data platforms by presenting research in modern Big Data products. We provided a detailed review and comparison of several state-of-the-art frameworks and concluded into a typical structure with five horizontal and one vertical. According to this structure, this paper presents the components and modern optimization technologies developed for Big Data, which helps to choose the most suitable components and architecture from various Big Data technologies based on requirements.     

A thermal-aware VM consolidation mechanism with outage avoidance

Efficient energy and temperature management techniques are essential elements for operators of cloud data centers. Dynamic virtual machine (VM) consolidation using live migration techniques presents a great opportunity for cloud service providers to adaptively reduce energy consumption and optimize their resource utilization. In recent studies, power consumption readings of individual physical hosts were chosen as the main monitoring parameters in their allocation policies, whereas very few have considered host temperature, which has shown to have a negative impact on server reliability, as a migration criterion. In this work, a thermal-aware VM consolidation mechanism is proposed for resource allocation optimization and server reliability assurance. We consider the variability in host temperature as a migration criterion to avoid outage incidents via having better VM consolidations. Extensive simulation results obtained from CloudSim show the promising performance of the proposed mechanism in energy saving while reducing the number of server outage incidents due to fluctuations in host temperature.