Evaluation of sustainability using fuzzy association rules mining

Several technologies have been emerging for aiding the management of sustainability. Yet most of them are in development stages. The complex manufacturing systems necessitate the validation of flexible solutions using advanced managerial tools and techniques. This article reports the utilization of fuzzy association rule mining (FARM) approach which enables the decision makers to make flexible decisions for evaluating sustainability in the presence of attributes such as Cost, Market share performance, Profitability, Quality, Toxicity, Legislative factors, Social cohesion, Trade opportunities, and flexibility. The approach has been used for sustainability evaluation in an Indian rotary switches manufacturing organization. The experiences of the conduct of this research indicate that sustainability evaluation can be performed without constraints by the decision makers.     

Supplier selection using fuzzy association rules mining approach

Owing to ill-structured, dynamic environments and the presence of multiple decision-makers with conflicting viewpoints, comprehension, analysis and support of the supplier evaluation process becomes more and more difficult. Moreover, with the complexities of issues such as the role of leadership, the influence of group formation, and analysis of disagreements, it cannot be predictable that there will ever exist a solution to cope with all imprecise, multi-criteria/multi-actor situations. A fuzzy association rules-based approach may be suited for the judgement of human subjects. In this paper, we develop an approach based on Fuzzy Association Rule Mining to support the decision makers by enhancing the flexibility in making decisions for evaluating suppliers with both tangibles and intangibles attributes. Also, by checking the fuzzy classification rules, the goal of knowledge acquisition can be achieved in a framework in which assessments could be established without constraints, and consequently checked and compared in several details. The efficacy and intricacy of the proposed model for finding fuzzy association rules from the database for supplier assessment is demonstrated with the help of numerical examples.     

关联规则在提取典型焊接类型中的应用

为了从白车身工艺规划数据库中提取潜在的、有价值的焊接工艺知识,应用关联规则提取了典型焊接类型。首先建立了提取典型焊接类型的关联规则模型,然后对影响焊接类型的各种属性分类,并根据焊接工艺要求对数值属性离散化,采用Apriori算法提取频繁属性项集。将分属不同属性的项连接生成候选项集,以焊接类型作为规则的后件,根据最小置信度获取强规则。最后对某白车身的焊接数据进行处理,为了能够提取合适的焊接类型,采用较低的支持度和较高的置信度,获取的结果对焊接类型的选择有很强的参考价值。这种方法有助于实现白车身工艺规划的智能化,提高了工艺规划的效率。     

Mining highly correlated least association rules using scalable trie-based algorithm

Development of least association rules (ARs) mining algorithms is one of the more challenging areas in data mining. Exclusive measurements, complexity and excessive computational cost are the main obstacles as compared to frequent pattern mining. Indeed, most previous studies still use the Apriori-like algorithms. To address this issue, this article proposes a new correlation measurement called definite factor (DF) and a scalable trie-based algorithm named significant least pattern growth (SLP-Growth). This algorithm generates the least patterns based on interval support and finally determines it significances using DF. Experiments with the real datasets show that the SLP-Growth can discover highly positive correlated and significant of least ARs. Indeed, it also outperforms the fast frequent pattern-Growth algorithm up to two times, thus verifying its efficiency.     

Evolutionary association rules for total ozone content modeling from satellite observations

In this paper we propose an evolutionary method of association rules discovery (EQAR, Evolutionary Quantitative Association Rules) that extends a recently published algorithm by the authors and we describe its application to a problem of Total Ozone Content (TOC) modeling in the Iberian Peninsula. We use TOC data from the Total Ozone Mapping Spectrometer (TOMS) on board the NASA Nimbus-7 satellite measured at three locations (Lisbon, Madrid and Murcia) of the Iberian Peninsula. As prediction variables for the association rules we consider several meteorological variables, such as Outgoing Long-wave Radiation (OLR), Temperature at 50 hPa level, Tropopause height, and wind vertical velocity component at 200 hPa. We show that the best association rules obtained by EQAR are able to accurate modeling the TOC data in the three locations considered, providing results which agree to previous works in the literature.     

Quantifying scientific collaboration impact by exploiting collaboration-citation network

Despite the growing interest in exploring the collaboration patterns and the structure of collaboration networks, the impact of collaboration associated with time-varying scholarly networks is hardly known. This paper investigates collaboration and productivity in a science career and quantifies the impact of collaboration in the collaboration-citation network. Moreover, this paper also investigates collaboration patterns and examines the typical duration of research collaborations. A SCIRank model is proposed to quantify the impact of scientific collaboration, which not only reveals the impact of co-author pairs but also identifies scholarly papers with the outstanding impact that leads to Nobel Prize awards.

     

Measuring small absorptions by exploiting photothermal self-phase modulation

We present a method for the measurement of small optical absorption coefficients. The method exploits the deformation of cavity Airy peaks that occur if the cavity contains an absorbing material with a nonzero thermorefractive coefficient dn/dT or a nonzero expansion coefficient α(th). Light absorption leads to a local temperature change and to an intensity-dependent phase shift, i.e., to a photothermal self-phase modulation. The absorption coefficient is derived from a comparison of time-resolved measurements with a numerical time-domain simulation applying a Markov-chain Monte Carlo algorithm. We apply our method to the absorption coefficient of lithium niobate doped with 7 mol. % magnesium oxide and derive a value of α(LN) = (5.9 ± 0.9) × 10(-4)/cm. Our method should also apply to materials with much lower absorption coefficients. Based on our modeling, we estimate that, with cavity finesse values of the order of 10(4), absorption coefficients of as low as 10(-8)/cm can be measured.     

Interestingness of association rules in data mining: Issues relevant to e-commerce

The ubiquitous low-cost connectivity synonymous with the internet has changed the competitive business environment by dissolving traditional sources of competitive advantage based on size, location and the like. In this level playing field, firms are forced to compete on the basis of knowledge. Data mining tools and techniques provide e-commerce applications with novel and significant knowledge. This knowledge can be leveraged to gain competitive advantage. However, the automated nature of data mining algorithms may result in a glut of patterns — the sheer numbers of which contribute to incomprehensibility. Importance of automated methods that address this immensity problem, particularly with respect to practical application of data mining results, cannot be overstated. We first examine different approaches to address this problem citing their applicability to e-commerce whenever appropriate. We then provide a detailed survey of one important approach, namely interestingness measure, and discuss its relevance in e-commerce applications such as personalization in recommender systems. Study of current literature brings out important issues that reveal many promising avenues for future research. We conclude by reiterating the importance of post-processing methods in data mining for effective and efficient deployment of e-commerce solutions.     

Gain ratio based fuzzy weighted association rule mining classifier for medical diagnostic interface

The health care environment still needs knowledge based discovery for handling wealth of data. Extraction of the potential causes of the diseases is the most important factor for medical data mining. Fuzzy association rule mining is well-performed better than traditional classifiers but it suffers from the exponential growth of the rules produced. In the past, we have proposed an information gain based fuzzy association rule mining algorithm for extracting both association rules and member-ship functions of medical data to reduce the rules. It used a ranking based weight value to identify the potential attribute. When we take a large number of distinct values, the computation of information gain value is not feasible. In this paper, an enhanced approach, called gain ratio based fuzzy weighted association rule mining, is thus proposed for distinct diseases and also increase the learning time of the previous one. Experimental results show that there is a marginal improvement in the attribute selection process and also improvement in the classifier accuracy. The system has been implemented in Java platform and verified by using benchmark data from the UCI machine learning repository.     

Important citation identification by exploiting the syntactic and contextual information of citations

Scientometrics - Citations are not equally important. Researchers presented different models and techniques to identify important citations. However, the features used in these work are relatively...     

Scattered data approximation by regular grid weighted smoothing

Scattered data approximation refers to the computation of a multi-dimensional function from measurements obtained from scattered spatial locations. For this problem, the class of methods that adopt a roughness minimization are the best performing ones. These methods are called variational methods and they are capable of handling contrasting levels of sample density. These methods express the required solution as a continuous model containing a weighted sum of thin-plate spline or radial basis functions with centres aligned to the measurement locations, and the weights are specified by a linear system of equations. The main hurdle in this type of method is that the linear system is ill-conditioned. Further, getting the weights that are parameters of the continuous model representing the solution is only a part of the effort. Getting a regular grid image requires re-sampling of the continuous model, which is typically expensive. We develop a computationally efficient and numerically stable method based on roughness minimization. The method leads to an algorithm that uses standard regular grid array operations only, which makes it attractive for parallelization. We demonstrate experimentally that we get these computational advantages only with a little compromise in performance when compared with thin-plate spline methods.     

用核糖体工程技术开拓海洋微生物药用资源的研究

利用核糖体工程技术对海洋中无活性的放线菌B3054进行诱导,获得了突变菌株B3054／M,其形态、菌丝体颜色都与母体菌株有显著的差异,发酵液经HPLC分析发现,代谢产物有明显的变化,特别是突变株产生了能抑制小鼠乳腺癌tsFT210细胞周期活性的活性代谢产物。这是首次利用核糖体工程技术诱变得到的具有细胞周期抑制活性的海洋放线菌菌株,这一研究结果表明,利用该技术对自然界的无活性微生物进行诱导来拓展微生物药用资源是可行的。     

样条加权残数法分析薄壳

本文用加权残数法分析薄壳,以五次样条函数构造试函数。我们用离散型最小二乘法分析了扁壳和变厚度圆柱壳的轴对称弯曲问题,对等厚度圆柱壳的轴对称弯曲问题分别采用了配点和配线法进行计算此较。各个问题都编制了程序,通过实例计算并与精确解比较,证明上述方法程序编制简单,输入数据少,耗用机时少,计算精度高,较为经济、实用。     

Local estimation of failure probability function by weighted approach

In the reliability-based design of engineering systems, it is often required to evaluate the failure probability for different values of distribution parameters involved in the specification of design configuration. The failure probability as a function of the distribution parameters is referred as the ‘failure probability function (FPF)’ in this work. From first principles, this problem requires repeated reliability analyses to estimate the failure probability for different distribution parameter values, which is a computationally expensive task. A “weighted approach” is proposed in this work to locally evaluate the FPF efficiently by means of a single simulation. The basic idea is to rewrite the failure probability estimate for a given set of random samples in simulation as a function of the distribution parameters. It is shown that the FPF can be written as a weighted sum of sample values. The latter must be evaluated by system analysis (the most time-consuming task) but they do not depend on the distribution. Direct Monte Carlo simulation, importance sampling and Subset Simulation are incorporated under the proposed approach. Examples are given to illustrate their application.     

The association between kinematic risky driving among parents and their teenage children: Moderation by shared personality characteristics

This study examined the driving behavior of 42 parent–teenager dyads for 18 months, under naturalistic driving conditions. At baseline participants’ personality characteristics were assessed. Objective risky driving measures (kinematic risky driving) were captured by accelerometers for the duration of the study. To estimate teenage and parent correlations in kinematic risky driving, separate Poisson regression models were fit for teenagers and parents. Standardized residuals were computed for each trip for each individual. Correlations were obtained by estimating the Spearman rank correlations of the individual average residuals across teenagers and parents. The bootstrap technique was used to estimate the standard errors associated with the parent–teenager correlations. The overall correlation between teenage and parent kinematic risky driving for the 18-month study period was positive, but weak (r = 0.18). When the association between parent and teenagers’ risky driving was adjusted for shared personality characteristics, the correlation reduced to 0.09. Although interesting, the 95% confidence intervals on the difference between these two estimates overlapped zero. We conclude that the weak similarity in parent–teen kinematic risky driving was partly explained by shared personality characteristics.     

Patterns of literature citation by undergraduate students and researchers in the veterinary field

A comparative analysis carried out on the literature citation characteristics of two sets of Mexican research documents produced in the veterinary field-the undergraduate thesis and the research journal article-revealed distinct patterns of literature usage on the part of the authors. It is suggested that the differences reflect the relative qualities of the research undertaken by two populations with distinct research competence and experience.     

Identifying crash contributory factors at urban roundabouts and using association rules to explore their relationships to different crash types

The use of roundabouts improves intersection safety by eliminating or altering conflict types, reducing crash severity, and causing drivers to reduce speeds. However, roundabout performances can degrade if precautions are not taken during either the design or the operation phase. Therefore, additional information on the safety of the roundabouts is extremely helpful for planners and designers in identifying existing deficiencies and in refining the design criteria currently being used. The aim of the paper was to investigate the crash contributory factors in 15 urban roundabouts located in Italy and to study the interdependences between these factors.The crash data refer to the period 2003–2008. The identification of the crash contributory factors was based on site inspections and rigorous analyses performed by a team of specialists with a relevant road safety engineering background. Each roundabout was inspected once every year from 2004 to 2009, both in daytime and in nighttime. Overall, 62 different contributory factors and 2156 total contributory factors were identified. In 51 crashes, a single contributory factor was found, whereas in the other 223 crashes, a combination of contributory factors was identified. Given the large amount of data, the interdependences between the contributory factors and between the contributory factors and the different crash types were explored by an association discovery. Association discovery is the identification of sets of items (i.e., crash contributory factors and crash types in our study) that occur together in a given event (i.e., a crash in our study). The rules were filtered by support, confidence, and lift. As a result, 112 association rules were discovered.Overall, numerous contributory factors related to the road and environment deficiencies but not related to the road user or to the vehicle were identified. The most important factors related to geometric design were the radius of deflection and the deviation angle. In existing roundabouts, the improvement of these factors might be quite expensive, but the crucial role of a moderate radius of deflection and a large deviation angle in the design of new roundabouts was stressed. Many of the contributory factors were related to markings and signs, and these factors could be easily removed with low-cost safety measures. Furthermore, because of the association between the markings, signs, and geometric design contributory factors, the study results suggest that the improvement in markings and signs might also have a significant effect in the sites where geometric design deficiencies were identified as contributory factors.     

Finite elements formulated by the weighted discrete least squares method

By use of the least squares error criterion, an alternate finite element formulation is presented. The method is based on the discrete or element-wise minimization of square and weighted differential equation residuals which are expressed in terms of element nodal quantities. In order to overcome the stringent inter-element continuity requirement, a major stumbling block, on the element trial functions two practical schemes are proposed. One is the reduction of the original governing differential equation to a system of equivalent first order differential equations; the other is a method of smoothing discontinous trial functions. The latter essentially relaxes the continuity requirement and yields efficient non-conforming finite elements. This paper also demonstrates the use of constant weights which significantly improves the rates of convergence. Several numerical examples illustrate the proposed method. From these examples, it may be concluded that the use of constant weights and the relaxation of the inter-element continuity requirement are two indispensable features of the weighted discrete least square method.     

Discriminating real objects in radar imaging by exploiting the squared modulus of the continuous wavelet transform

New technique based on continuous wavelet transform (CWT) for classifying objects in synthetic aperture radar (SAR) imaging is presented. The CWT allows to analyse two-dimensional SAR images to highlight the frequency and angular behaviour of the scatterers. This technique allows to build a SAR hyperimage, that is, a four-dimensional data cube which represents for each spatial location (x, y) of the scatterer in the image, its frequency and angular energy behaviour. When analysing different targets, objects or areas in SAR images, it has been recently observed that some scatterers belonging to a same class of objects could have similar frequency and angular energy responses. The previous observations have motivated the determination to exploit these energy responses to discriminate these objects. This discrimination is performed by frequency and angular correlations between the response of a particular scatterer (measured) and those of all the scatterers in the SAR image. Some examples of discrimination from real SAR data are presented and show an interest of the method for target classification and recognition for SAR imaging