Attitudinal choice models with applications in human decision making

A new family of attitudinal discrete choice models is proposed by considering the attitudinal character and the weight vector, both of which are specific to a decision maker (DM). Given the attribute values of different alternatives, the proposed models give varying choice probabilities, as per the DM's-specific attitudinal character and the weight vector. It is also shown that the conventional discrete choice models are the special cases of the proposed attitudinal models. The proposed choice models are also generalized through an additional parameter to add to their capabilities. An application on real data is included to demonstrate their usefulness in the real world.     

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。     

基于查询的新闻多文档自动摘要技术研究

针对新闻文本领域,该文提出一种基于查询的自动文本摘要技术,更加有针对性地满足用户信息需求。根据句子的TF-IDF、与查询句的相似度等要素,计算句子权重,并根据句子指示的时间给定不同的时序权重系数,使得最近发生的新闻内容具有更高的权重,最后使用最大边界相关的方法选择摘要句。通过与基于TF-IDF、Text-Rank、LDA等六种方法的对比,该摘要方法ROUGE评测指标上优于其他方法。从结合评测结果及摘要示例可以看出,该文提出的方法可以有效地从新闻文档集中摘取核心信息,满足用户查询内容的信息需求。     

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。     

A platform for P2P agent-based collaborative applications

The operational environment can be a valuable source of information about the behavior of software applications and their usage context. Although a single instance of an application has limited evidence of the range of the possible behaviors and situations that might be experienced in the field, the collective knowledge composed by the evidence gathered by the many instances of a same application running in several diverse user environments (eg, a browser) might be an invaluable source of information. This information can be exploited by applications able to autonomously analyze how they behave in the field and adjust their behavior accordingly. Augmenting applications with the capability to collaborate and directly share information about their behavior is challenging because it requires the definition of a fully decentralized and dependable networked infrastructure whose nodes are the user machines. The nodes of the infrastructure must be collaborative, to share information, and autonomous, to exploit the available information to change their behavior, for instance, to better accommodate the needs of the users to prevent known problems. This paper describes the initial results that we obtained with the design and the development of an infrastructure that can enable the execution of collaborative scenarios in a fully decentralized way. Our idea is to combine the agent-based paradigm, which is well suited to design collaborative and autonomous nodes, and the peer-to-peer paradigm, which is well suited to design distributed and dynamic network infrastructures. To demonstrate our idea, we augmented the popular JADE agent-based platform with a software layer that supports both the creation of a fully decentralized peer-to-peer network of JADE platforms and the execution of services within that network, thus enabling JADE multiagent systems (MASs) to behave as peer-to-peer networks. The resulting platform can be used to study the design of collaborative applications running in the field.     

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.