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Ranked preference data arise when a set of judges rank, in order of their preference, a set of objects. Such data arise in preferential voting systems and market research surveys. Covariate data associated with the judges are also often recorded. Such covariate data should be used in conjunction with preference data when drawing inferences about judges.

To cluster a population of judges, the population is modeled as a collection of homogeneous groups. The Plackett-Luce model for ranked data is employed to model a judge's ranked preferences within a group. A mixture of Plackett- Luce models is employed to model the population of judges, where each component in the mixture represents a group of judges.

Mixture of experts models provide a framework in which covariates are included in mixture models. Covariates are included through the mixing proportions and the component density parameters. A mixture of experts model for ranked preference data is developed by combining a mixture of experts model and a mixture of Plackett-Luce models. Particular attention is given to the manner in which covariates enter the model. The mixing proportions and group specific parameters are potentially dependent on covariates. Model selection procedures are employed to choose optimal models.

Model parameters are estimated via the ‘EMM algorithm’, a hybrid of the expectation–maximization and the minorization–maximization algorithms. Examples are provided through a menu survey and through Irish election data. Results indicate mixture modeling using covariates is insightful when examining a population of judges who express preferences.

An important disadvantage of conducting adhesives is their inferior heat conductivity when compared with soft solder such as Sn60Pb40. Thermal simulations, however, show that, by using thinner layers of adhesive than of solder, the module's thermal resistance does not increase greatly. Test modules with four different silver filled epoxy adhesives and tin/lead solder were manufactured. These test modules contained power diodes, 30 A, 1000 V, die bonded onto Ag/Pt thick film conductors on alumina. The die bond adhesive layer thicknesses were typically 30 or 40 μm. For die bond solder layers the thickness was 90 μm. The alumina substrates were connected to 3 mm thick copper plates with filled epoxy or silicone adhesive. The thickness of these layers was 150 μm or 50 μm, respectively. Thermal resistance of the structures was measured. The results showed that good adhesion between joined surfaces is essential for optimised heat flow. The heat conductivity of an adhesive was only a secondary factor affecting the structure's thermal resistance. When the adhesive joint is of good quality, the replacement of solder with conductive adhesives does not increase the module's thermal resistance any more than as shown by the simulations. It should, however, be remembered that the printing of thin (< 20 μm) uniform layers is not always possible.

The purpose of this paper is to develop an unsupervised classification algorithm including feature selection for industrial product classification with the basic philosophy of a supervised Mahalanobis‐Taguchi System (MTS).

Two novel unsupervised classification algorithms called Unsupervised Mahalanobis Distance Classifier (UNMDC) are developed based on Mahalanobis' distance for identifying “abnormals” as individuals (or, groups) including feature selection. The identification of “abnormals” is based on the concept of threshold value in MTS and the distribution property of Mahalanobis‐D².

The performance of this algorithm, in terms of its efficiency and effectiveness, has been studied thoroughly for three different types of steel product on the basis of its composition and processing parameters. Performance in future diagnosis on the basis of useful features by the new scheme is found quite satisfactory.

This new algorithm is able to identify the set of significant features, which appears to be always a larger class than that of MTS. In industrial environment, this algorithm can be implemented for continuous monitoring of “abnormal” situations along with the general concept of screening “abnormals” either as individuals or as groups during sampling.

The concept of determining threshold for diagnostic purpose is algorithm dependent and independent of the domain knowledge, hence much more flexible in large domain. Multi‐class separation and feature selection in case of detection of abnormals are the special merits of this algorithm.

The study aims to cluster the travellers based on their social media interactions as well as to find the different segments with similar and dissimilar categories according to traveller's choice. The study also aims to understand the behaviour of clusters of the travellers towards destination selection and accordingly make the tour packages in order to improve tourists' satisfaction and gain viable benefits.

Agglomerative hierarchical clustering with Ward's minimum variance linkage algorithm and model-based clustering with parameterized finite Gaussian mixture models has been implemented to achieve the respective goals. The dimension reduction (DR) technique was introduced for better visualizing clustering structure obtained from a finite mixture of Gaussian densities.

A total of 980 travellers have been clustered into 8 different interest groups according to their tourism destinations selection across East Asia based on individual social media feedback. For selecting the optimal number of clusters as well as the behaviour of the interested travellers groups, both these proposed methods have shown remarkable similarities. DR technique ensures the reduction in dimensionality with seven directions, of which the first two directions explained 95% of total variability.

Tourism organizations focus on marketing efforts to promote the most attractive benefits to the clusters of travellers. By segmenting travellers of East Asia into homogeneous groups, it is feasible to choose a similar area to test different marketing techniques. Finally, it can be identified to which segments, new respondents or potential clients belong; consequently, the tourism organizations can design the tour packages.

The study has uniqueness in two aspects. Firstly, the study empirically revealed tourists' experience and behavioural intention to select tourism destinations and secondly, it finds quantifiable insights into the tourism phenomenon in East Asia, which helps tourism organizations to understand the buying behaviours of tourists' segments. Finally, the application of clustering algorithms to achieve the purpose of this study and the findings are very new in the literature on tourism, to understand the tourist behaviour towards destination selection based on social media reviews.

This paper presents a mixture of linear models (or hedonic regressions) for defining housing submarkets. Two different mixture models are considered: the first model allows all the regression coefficients to vary among the clusters (random coefficients); and the second model allows only the intercept term to change (random intercept). The model with a random intercept can be seen as a linear mixed model where the random effects distribution is estimated via non-parametric maximum likelihood (NPML). The models are illustrated using a real data set of 293 properties in Pamplona, Spain. These mixture models provide a classification of the dwellings into homogeneous groups that determine the structure of the submarkets.

Mashups have been studied extensively in the literature; nevertheless, the large body of work in this area focuses on service/data level integration and leaves UI level integration, hence UI mashups, almost unexplored. The latter generates digital environments in which participating sources exist as individual entities; member applications and data sources share the same graphical space particularly in the form of widgets. However, the true integration can only be realized through enabling widgets to be responsive to the events happening in each other. The authors call such an integration “widget orchestration” and the resulting application “mashup by orchestration”. This article aims to explore and address challenges regarding the realization of widget‐based UI mashups and UI level integration, prominently in terms of widget orchestration, and to assess their suitability for building web‐based personal environments.

The authors provide a holistic view on mashups and a theoretical grounding for widget‐based personal environments. The authors identify the following challenges: widget interoperability, end‐user data mobility as a basis for manual widget orchestration, user behavior mining – for extracting behavioral patterns – as a basis for automated widget orchestration, and infrastructure. The authors introduce functional widget interfaces for application interoperability, exploit semantic web technologies for data interoperability, and realize end‐user data mobility on top of this interoperability framework. The authors employ semantically enhanced workflow/process mining techniques, along with Petri nets as a formal ground, for user behavior mining. The authors outline a reference platform and architecture that is compliant with the authors' strategies, and extend W3C widget specification respectively – prominently with a communication channel – to foster standardization. The authors evaluate their solution approaches regarding interoperability and infrastructure through a qualitative comparison with respect to existing literature, and provide a computational evaluation of the behavior mining approach. The authors realize a prototype for a widget‐based personal learning environment for foreign language learning to demonstrate the feasibility of their solution strategies. The prototype is also used as a basis for the end‐user assessment of widget‐based personal environments and widget orchestration.

The evaluation results suggest that the interoperability framework, platform, and architecture have certain advantages over existing approaches, and the proposed behavior mining techniques are adequate for the extraction of behavioral patterns. User assessments show that widget‐based UI mashups with orchestration (i.e. mashups by orchestration) are promising for the creation of personal environments as well as for an enhanced user experience.

This article provides an extensive exploration of mashups by orchestration and their role in the creation of personal environments. Key challenges are described, along with novel solution strategies to meet them.

The complexity of microelectronic circuits, their scale of integration and clock speed requirements have been increasing steadily. All these changes have the effect of increasing the power density of the microcircuits. ICs with a power of several watts and an area of over a square centimetre are quite common. Thus, there is more heat generated per device at die, component and substrate‐attach levels of electronic packaging. In order to maintain reliability of finished products, the junction temperature of the constituent devices must be kept low. It has been demonstrated that thermal management can be one key to lowering the cost and increasing the performance life of microelectronic products. The cost‐effectiveness of lowering device temperature has been demonstrated to be dramatic compared with the cost of thermal management materials. Proper thermal management of advanced microelectronic devices has to be addressed at all levels. One should address the problem from the basic level of die‐attach, through component‐attach, and eventually substrate‐attach to thermal drains. Thermal management is almost invariably coupled with a thermally induced stress problem. The increase in temperature at the device level also means a larger fluctuation of temperature from the ambient. Each cycle of on‐off for the device represents one thermal cycle. Stress‐induced failure due to coefficient of thermal expansion (CTE) mismatch is much more acute for higher power devices. In this paper, the authors address the issue of thermally induced stress on the microelectronic product at all levels of packaging, with major emphasis on component and substrate levels. Various ways and examples of reducing or eliminating this stress, which is a major cause of device failures, will be demonstrated. One of the proven methods is through the use of low Tg epoxies with high thermal stability.

The purpose of this paper is to critically review the latest approaches for capturing and explaining heterogeneity in partial least squares (PLS) path modelling and to classify these into a methodological taxonomy. Furthermore, several areas for future research effort are introduced in order to stimulate ongoing development in this important research field.

Different approaches to treat heterogeneity in PLS path models are introduced, critically evaluated and classified into a methodological taxonomy. Future research directions are derived from a comparison of benefits and limitations of the procedures.

The review reveals that finite mixture‐PLS can be regarded as the most comprehensive and commonly used procedure for capturing heterogeneity within a PLS path modelling framework. However, further research is necessary to explore the capabilities and limitations of the approach.

Directions for additional research, common to most latent class detection procedures include the verification and comparison of available approaches, the handling of large data sets, the allowance of varying structures of path models, the profiling of segments and the problem of model selection.

Whereas modelling heterogeneity in covariance structure analysis has been studied for several years, research interest has only recently been devoted to the question of clustering in PLS path modelling. This is the first contribution which critically consolidates available approaches, discloses problematic aspects and addresses significant areas for future research.

Prospect theory is now widely accepted as the dominant model of choice under risk, but has not been fully incorporated into applied research because of uncertainty about how to include population-level parameter estimates. The purpose of this paper is to characterize heterogeneity across people to lay a foundation for future applied research.

The paper uses elicitation data from field experiments in Vietnam to fit a finite Gaussian mixture model using the expectation maximization algorithm. Applied results are simulated for investment allocations under myopic loss aversion.

The authors find that about 20 percent of the sample is classified as extremely loss averse, while the rest of the population is only mildly loss averse. This implies a bimodal distribution of loss aversion in the population.

The data set is only moderately sized: 181 subjects. Future research will be needed to extend these results out of sample, and to other regions.

This paper provides empirical evidence that heterogeneity matters in prospect theory modeling. It highlights how policy makers might be misled by assuming that average prospect theory parameters are typical within the population.

The purpose of the present paper is to quantify and analyze the strain-rate dependence of the yield stress for both unfilled acrylonitrile-butadiene-styrene (ABS) and short carbon fiber-reinforced ABS (CF-ABS) materials, fabricated via material extrusion additive manufacturing (ME-AM). Two distinct and opposite infill orientation angles were used to attain anisotropy effects.

Tensile test samples were printed with two different infill orientation angles. Uniaxial tensile tests were performed at five different constant linear strain rates. Apparent densities were measured to compensate for the voided structure. Scanning electron microscope fractography images were analyzed. An Eyring-type flow rule was evaluated for predicting the strain-rate-dependent yield stress.

Anisotropy was detected not only for the yield stresses but also for its strain-rate dependence. The short carbon fiber-filled material exhibited higher anisotropy than neat ABS material using the same ME-AM processing parameters. It seems that fiber and molecular orientation influence the strain-rate dependence. The Eyring-type flow rule can adequately describe the yield kinetics of ME-AM components, showing thermorheologically simple behavior.

A polymer’s viscoelastic behavior is paramount to be able to predict a component’s ultimate failure behavior. The results in this manuscript are important initial findings that can help to further develop predictive numerical tools for ME-AM technology. This is especially relevant because of the inherent anisotropy that ME-AM polymer components show. Furthermore, short carbon fiber-filled ABS enhanced anisotropy effects during ME-AM, which have not been measured previously.