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Credit scoring is a crucial task within risk management for any company in the financial sector. On the one hand, it is in the self-interest of banks to avoid approving credits to customers who probably default. On the other hand, regulators require strict risk management systems from banks to protect their customers and, from “too big to fail institutions”, to avoid bankruptcy with negative impacts on an economy as a whole. However, credit scoring is also expensive and time-consuming. So, any possible method, like three-way decisions, to further increase its efficiency, is worth a try. We propose a two-step approach based on three-way decisions. Customers whose credit applications can be approved or rejected right away are decided in a first step. For the remaining credit applications, additional information is gathered in a second step. Hence, these decisions are more expensive than the ones in the first step. In our paper, we present a methodology to apply three-way decisions with probabilistic rough sets for credit scoring and an extensive case study with more than 7000 credit applications from Chilean micro-enterprises.

The anisotropic coefficients of thermal expansion and the peritectic transformation of orthorhombic-Hf6Ta2O17 to tetragonal-HfO2 plus liquid at 2250 °C have been studied by in-situ X-ray powder diffraction from room temperature to complete melting (∼2450 °C) in air, using a quadrupole lamp furnace (QLF) and a conical nozzle levitator (CNL) equipped with a CO2 laser. The topotactic, peritectic transformation has been fully described by extracting the orientation relationship, lattice variant deformation and a motif (grouping) of cations that relates the two structures at the transformation temperature. The calculation of these two important parameters as well as identification of the motif is facilitated by a knowledge of the anisotropic coefficients of thermal expansion as a function of temperature. Symmetry decomposition has been performed to show that the orthorhombic-Hf6Ta2O17 and tetragonal-HfO2 structures are simply related by polyhedral rotations and loss of 1 mol of oxygen.

Information Sciences is a leading international journal in computer science launched in 1968, so becoming fifty years old in 2018. In order to celebrate its anniversary, this study presents a bibliometric overview of the leading publication and citation trends occurring in the journal. The aim of the work is to identify the most relevant authors, institutions, countries, and analyze their evolution through time. The paper uses the Web of Science Core Collection in order to search for the bibliographic information. Our study also develops a graphical mapping of the bibliometric material by using the visualization of similarities (VOS) viewer. With this software, the work analyzes bibliographic coupling, citation and co-citation analysis, co-authorship, and co-occurrence of keywords. The results underline the significant growth of the journal through time and its international diversity having publications from countries all over the world.

Knowledge of thermal expansion and high temperature phase transformations is essential for prediction and interpretation of materials behavior under the extreme conditions of high temperature and intense radiation encountered in nuclear reactors. Structure and thermal expansion of Lu2O3 and Yb2O3 were studied in oxygen and argon atmospheres up to their melting temperatures using synchrotron X-ray diffraction on laser heated levitated samples. Both oxides retained the cubic bixbyite C-type structure in oxygen and argon to melting. In contrast to fluorite-type structures, the increase in the unit cell parameter of Yb2O3 and Lu2O3 with temperature is linear within experimental error from room temperature to the melting point, with mean thermal expansion coefficients (8.5 ± 0.6) · 10−6 K−1 and (7.7 ± 0.6) · 10−6 K−1, respectively. There is no indication of a superionic (Bredig) transition in the C-type structure or of a previously suggested Yb2O3 phase transformation to hexagonal phase prior to melting.

Demand forecasting and capacity management are complicated tasks for emergency healthcare services due to the uncertainty, complex relationships, and high public exposure involved. Published research does not show integrated solutions to these tasks. Thus, the objective of this paper is to present results from three hospitals that show the feasibility of routinely applying integrated forecasting and capacity management with advanced operations research tools. After testing several forecasting methods, neural networks and support vector regression provided the best results in terms of variance and accuracy. Based on this forecasting, a logic for managing hospital capacity was designed and implemented. This logic includes the comparison between the forecasted demand and the available medical resources and a stochastic simulation model to assess the performance of different configurations of facilities and resources. The logic also provides hospital managers with a decision tool for determining the number and distribution of medical resources on emergency services based on a cost/benefit analysis of resources and service improvement. Such results support the task of assigning doctors to different kinds of boxes, defining their work schedules, and considering additional doctors. The contribution of this paper consists of an integrated solution designed to implement the abovementioned logic. This solution combines forecasting, simulation for capacity management, process design, and IT support, facilitating the practical routine use of complex models. The integration explicitly considers a solution that also has adaptation capabilities to facilitate use under changing conditions. The solution is also general and admits adaptation and extension to other services. Thus, we have already performed similar work for ambulatory and surgical services.

We present a novel soft clustering approach based on Support Vector Clustering.Data points outside the clusters found form a fuzzy boundary region.Clusters with any shape as well as outliers can be identified.Membership degrees were calculated in a natural way. Support Vector Clustering (SVC) is an important density-based clustering algorithm which can be applied in many real world applications given its ability to handle arbitrary cluster silhouettes and detect the number of classes without any prior knowledge. However, if outliers are present in the data, the algorithm leaves them unclassified, assigning a zero membership degree which leads to all these objects being treated in the same way, thus losing important information about the data set. In order to overcome these limitations, we present a novel extension of this clustering algorithm, called Rough-Fuzzy Support Vector Clustering (RFSVC), that obtains rough-fuzzy clusters using the support vectors as cluster representatives. The cluster structure is characterized by two main components: a lower approximation, and a fuzzy boundary. The membership degrees of the elements in the fuzzy boundary are calculated based on their closeness to the support vectors that represent a specific cluster, while the lower approximation is built by the data points which lie inside the hyper-sphere obtained in the training phase of the SVC algorithm. Our computational experiments verify the strength of the proposed approach compared to alternative soft clustering techniques, showing its potential for detecting outliers and computing membership degrees for clusters with any silhouette.

One of the most common methods used in the social network analysis of criminal groups is node importance evaluation, which focuses on the links between network members to identify likely crime suspects. Because such traditional node evaluators do not take full advantage of group members' individual criminal propensities, a new evaluator called the social network criminal suspect evaluator (SNCSE) is proposed. SNCSE incorporates members' individual criminal propensities into the node importance evaluation and employs a novel perspective based on concepts of human and social capital, an ego network structure, and an analogy between social interaction and field theory. SNCSE is applied to solve two real-world problems. Its effectiveness is compared with that of traditional evaluators. The results show that integrating criminal propensity into network analysis enables the more accurate identification of key suspects compared to alternative evaluators.

Understanding criminal groups as social networks has led to the design of powerful systems for decision support in criminal investigative work. Tools using the methods of social network analysis have proven particularly effective in the identification of associations between individuals whose relationships are not otherwise evident. This identification is typically based on the links between individuals and does not account for other relevant information, such as individual attributes. The present study proposes a new model for identifying criminal associations that incorporates this type of data. Built around a linear association model, this approach identifies the principal association between two individuals. Assuming one of the individuals as the crime planner, the approach can be used to maximize his/her utility function. The model is compared with an existing algorithm for identifying associations using a real dataset provided by the Public Prosecutor's Office of Region del Biobio-Chile. The results demonstrate the proposed model's effectiveness and flexibility in generating different association alternatives, a particularly useful feature that contributes to the more efficient use of criminal investigation resources.

In a model introduced by Weitzman an agent called Pandora opens boxes sequentially, in whatever order she likes, discovers prizes within, and optimally stops. Her aim is to maximize the expected value of the greatest discovered prize, minus the costs of opening the boxes. The solution, using the so-called Pandora rule, is attractive and has many applications. However, it does not address applications in which the payoff depends on all discovered prizes, rather than just the best of them, nor is it easy to say whether or not some generalized Pandora rule might do so. Here, we establish a sense in which it cannot. We discover that if a generalized Pandora rule is to be optimal for some more general utility, and all model parameters, then the problem can be solved via a second problem having Weitzman's form of utility.

Churn prediction is an important application of classification models that identify those customers most likely to attrite based on their respective characteristics described by e.g. socio-demographic and behavioral variables. Since nowadays more and more of such features are captured and stored in the respective computational systems, an appropriate handling of the resulting information overload becomes a highly relevant issue when it comes to build customer retention systems based on churn prediction models. As a consequence, feature selection is an important step of the respective classifier construction process. Most feature selection techniques; however, are based on statistically inspired validation criteria, which not necessarily lead to models that optimize goals specified by the respective organization. In this paper we propose a profit-driven approach for classifier construction and simultaneous variable selection based on Support Vector Machines. Experimental results show that our models outperform conventional techniques for feature selection achieving superior performance with respect to business-related goals. publisher: Elsevier articletitle: Profit-based feature selection using support vector machines – General framework and an application for customer retention journaltitle: Applied Soft Computing articlelink: http://dx.doi.org/10.1016/j.asoc.2015.05.058 content_type: article copyright: Copyright © 2015 Elsevier B.V. All rights reserved. ispartof: Applied Soft Computing vol:35 pages:740-748 status: published

One of the main tasks of conjoint analysis is to identify consumer preferences about potential products or services. Accordingly, different estimation methods have been proposed to determine the corresponding relevant attributes. Most of these approaches rely on the post-processing of the estimated preferences to establish the importance of such variables. This paper presents new techniques that simultaneously identify consumer preferences and the most relevant attributes. The proposed approaches have two appealing characteristics. Firstly, they are grounded on a support vector machine formulation that has proved important predictive ability in operations management and marketing contexts and secondly they obtain a more parsimonious representation of consumer preferences than traditional models. We report the results of an extensive simulation study that shows that unlike existing methods, our approach can accurately recover the model parameters as well as the relevant attributes. Additionally, we use two conjoint choice experiments whose results show that the proposed techniques have better fit and predictive accuracy than traditional methods and that they additionally provide an improved understanding of customer preferences.

This paper describes the direct measurements of the structure of iron-bearing liquids using a combination of containerless techniques and in-situ high energy x-ray diffraction.These capabilities provide data that is important to help model and optimize processes such as smelting, steel making, and controlling slag chemistry. A successful programme of liquid studies has been undertaken and the Advanced Photon Source using these combined techniques which include the provision of gas mixing and the control of pO2and the changing influence of mixed valance elements. It is possible to combine rapid image acquisition with quenching of liquids to obtain the full diffraction patterns of deeply supercooled liquids and the metastable supercooled liquid regime, where the liquid structures and viscosity change most dramatically, can also be explored.

Feature selection and classification of imbalanced data sets are two of the most interesting machine learning challenges, attracting a growing attention from both, industry and academia. Feature selection addresses the dimensionality reduction problem by determining a subset of available features to build a good model for classification or prediction, while the class-imbalance problem arises when the class distribution is too skewed. Both issues have been independently studied in the literature, and a plethora of methods to address high dimensionality as well as class-imbalance has been proposed. The aim of this work is to simultaneously explore both issues, proposing a family of methods that select those attributes that are relevant for the identification of the target class in binary classification. We propose a backward elimination approach based on successive holdout steps, whose contribution measure is based on a balanced loss function obtained on an independent subset. Our experiments are based on six highly imbalanced microarray data sets, comparing our methods with well-known feature selection techniques, and obtaining a better prediction with consistently fewer relevant features.

This paper presents a new approach for consumer credit scoring, by tailoring a profit-based classification performance measure to credit risk modeling. This performance measure takes into account the expected profits and losses of credit granting and thereby better aligns the model developers’ objectives with those of the lending company. It is based on the Expected Maximum Profit (EMP) measure and is used to find a trade-off between the expected losses – driven by the exposure of the loan and the loss given default – and the operational income given by the loan. Additionally, one of the major advantages of using the proposed measure is that it permits to calculate the optimal cutoff value, which is necessary for model implementation. To test the proposed approach, we use a dataset of loans granted by a government institution, and benchmarked the accuracy and monetary gain of using EMP, accuracy, and the area under the ROC curve as measures for selecting model parameters, and for determining the respective cutoff values. The results show that our proposed profit-based classification measure outperforms the alternative approaches in terms of both accuracy and monetary value in the test set, and that it facilitates model deployment. publisher: Elsevier articletitle: Development and application of consumer credit scoring models using profit-based classification measures journaltitle: European Journal of Operational Research articlelink: http://dx.doi.org/10.1016/j.ejor.2014.04.001 content_type: article copyright: Copyright © 2014 Elsevier B.V. All rights reserved. ispartof: European Journal of Operational Research vol:238 issue:2 pages:505-513 status: published

Policing plays an important role in combating street crime. Though policing actions have a dissuasive impact on criminal behavior, they can also have unpredictable and even undesirable effects such as displacement of crime hot-spots. This paper presents an agent-based simulation model that generates artificial street-crime data which can be used to test different policing strategies in a virtual environment. The model can thus evaluate the strategies’ effectiveness and collateral effects before putting them into practice and provide support for the policing decision-making process. Based on this model, a crime simulator was implemented in Repast Simphony and a series of test simulations on fictitious and real cities were carried out. The proposed formulation was successfully validated, confirming its potential as a powerful tool for the study of street crime.

We introduce an embedded method that simultaneously selects relevant features during classifier construction by penalizing each feature's use in the dual formulation of support vector machines (SVM). This approach called kernel-penalized SVM (KP-SVM) optimizes the shape of an anisotropic RBF Kernel eliminating features that have low relevance for the classifier. Additionally, KP-SVM employs an explicit stopping condition, avoiding the elimination of features that would negatively affect the classifier's performance. We performed experiments on four real-world benchmark problems comparing our approach with well-known feature selection techniques. KP-SVM outperformed the alternative approaches and determined consistently fewer relevant features.

Traditional methodologies for time series prediction take the series to be predicted and split it into training, validation, and test sets. The first one serves to construct forecasting models, the second set for model selection, and the third one is used to evaluate the final model. Different time series approaches such as ARIMA and exponential smoothing, as well as regression techniques such as neural networks and support vector regression, have been successfully used to develop forecasting models. A problem that has not yet received proper attention, however, is how to update such forecasting models when new data arrives, i.e. when a new event of the considered time series occurs. This paper presents a strategy to update support vector regression based forecasting models for time series with seasonal patterns. The basic idea of this updating strategy is to add the most recent data to the training set every time a predefined number of observations takes place. This way, information in new data is taken into account in model construction. The proposed strategy outperforms the respective static version in almost all time series studied in this work, considering three different error measures.

We introduce a novel wrapper Algorithm for Feature Selection, using Support Vector Machines with kernel functions. Our method is based on a sequential backward selection, using the number of errors in a validation subset as the measure to decide which feature to remove in each iteration. We compare our approach with other algorithms like a filter method or Recursive Feature Elimination SVM to demonstrate its effectiveness and efficiency.

I. Leonard Bernstein, MD; James T. Li, MD, PhD; David I. Bernstein, MD; Robert Hamilton, PhD, DABMLI; Sheldon L. Spector, MD; Ricardo Tan, MD; Scott Sicherer, MD; David B. K. Golden, MD; David A. Khan, MD; Richard A. Nicklas, MD; Jay M. Portnoy, MD; Joann Blessing-Moore, MD; Linda Cox, MD; David M. Lang, MD; John Oppenheimer, MD; Christopher C. Randolph, MD; Diane E. Schuller, MD; Stephen A. Tilles, MD; Dana V. Wallace, MD; Estelle Levetin, PhD; and Richard Weber, MD

Aluminate glasses containing 45–71.5 mol% alumina, 10–40 mol% rare earth oxide, and 0–30 mol% silica were synthesized from precursor oxides. The glass transition and crystallization temperatures were determined by differential scanning calorimetry; the structural and mechanical properties were investigated by Raman and Brillouin spectroscopy. The range of the supercooled liquid region varies from ∼40 °C to 200 °C, providing a useful working range for compositions with 5–30 mol% silica. Raman scattering showed the presence of isolated SiO 4 species that strengthen the network-forming structure, enhance glass formation, and stabilize the glass even when they are present at fairly low concentrations. Sound velocities were measured by Brillouin scattering. From these and other values, various elastic moduli were calculated. The moduli increased with both aluminum and rare earth content, as did the hardness of the glasses. Young’s modulus was in the range 118–169 GPa, 60–130% larger than that for pure silica glass.

Dynamic data mining is increasingly attracting attention from the respective research community. On the other hand, users of installed data mining systems are also interested in the related techniques and will be even more since most of these installations will need to be updated in the future. For each data mining technique used, we need different methodologies for dynamic data mining. In this paper, we present a methodology for dynamic data mining based on fuzzy clustering. Using the implementation of the proposed system we show its benefits in two application areas: customer segmentation and traffic management.

Web usage mining is an important and fast developing area of web mining where a lot of research has been done already. Recently, companies got aware of its potentials, especially for applications in marketing. A structured methodology is, however, a crucial requirement for a successful practical application of web usage mining. This publication provides such a methodology that is based on suggestions from literature and own experience from various web mining projects. Its application in a Chilean bank shows how a combined use of data from a data warehouse and web data can contribute to improve marketing activities. The benefits from this project point at the huge potential web usage mining has not only in financial services.

Glasses based on rare earth oxides and aluminum oxide with 0–20 mol% SiO 2 provide a combination of optical properties, mechanical and chemical stability, and process characteristics not available in other oxide materials. Properties of the glasses include: refractive index 1.7–1.8, low dispersion (Abbe number ∼40), high solubility of optically active dopants, homogeneous chemical composition, long fluorescence lifetimes at dopant concentrations up to 5 mol%, broad fluorescence bandwidth, and infrared transmission to ≈5000 nm. The glasses are hard, strong and resist chemical attack and they can be cast in sections 5–10 mm thick. This paper briefly describes glass processing and presents bulk glass properties including results of experiments to study infrared fluorescence at wavelengths ∼1550, ∼2900 and ∼1030 nm from Er- and Yb-doped glasses that were optically pumped at 980 nm.

The main objective of machine diagnosis is the early recognition of mechanical defects in a machine, which is often referred to as preventive maintenance. This may result in the reduction of faults and higher machine availability. Preventive maintenance can be performed periodically in fixed time intervals, by demand due to machine faults, or continuously, depending on the state of a machine. To minimize maintenance and repair time, state-dependent maintenance of machines is usually applied. It assumes precise and reliable monitoring of machine's states, which is often provided by an expert who can distinguish malfunctions in operation from other changes in machine's states. In Fuzzy-Neuro Systems '98 — Computational Intelligence, pp. 98–105, and Int. J. Fuzzy Sets and Systems 105 (1999) 81–90, we have introduced a clustering method for dynamic objects, i.e., objects which are described by (time) trajectories of features. The aim of this paper is to show exemplarily that this method can be used for early recognition of changes in a machine's state and thus for automatic fault detection. Moreover, the method can be applied to automatic feature selection. A major advantage of this method is that it requires less expert knowledge than traditional approaches.

In data analysis, objects are usually represented by feature vectors, each describing a state of an object at a point of time. Most methods for data analysis use only these feature vectors and do not take into account changes over time. They can therefore be called static. But often a “dynamic” approach, which utilizes the feature changes over time, seems to be more appropriate (e.g. supervision of patients in medical care, state-dependent maintenance of machines, classification of shares). In this paper, different criteria for structuring the field of “dynamic data analysis (DDA)” are proposed and one of the relevant approaches is investigated in more detail. This approach considers possible ways to handle dynamics within static methods for data analysis. In doing this, different types of similarity measures for trajectories are defined, which can be used to modify static methods for data analysis. One of the proposed similarity measures has been integrated into the fuzzy c-means. An application example is used to demonstrate the applicability of the modified fuzzy c-means.

Drop solution calorimetry was used to determine the enthalpy of solution in lead borate for three different materials with the chemical composition Y3Al5O12. Crystalline yttrium aluminum garnet (YAG), a crystalline mixture of 3 YAlO3 (perovskite) plus Al2O3 (α-alumina), and a glass, were all synthesized by containerless melting and cooling. The enthalpies of drop-dissolution per mole of Y3Al5O12 were 472.49 ± 4.19, 440.35 ± 3.59, and 196.02 ± 3.39 kJ/mol, respectively, for the garnet, the crystalline mixture, and the glass. The garnet phase is thermodynamically stable with respect to the corresponding mixture of α-alumina and perovskite confirming that the two phase mixture is metastable. The enthalpy of vitrification of YAG is 276.47 ± 5.40 kJ/mol (13.82 ± 0.27 kJ/g atom). This high value for the enthalpy of vitrification is related to the reluctant glass forming ability of the YAG composition. The heat of fusion of Y3Al2O12 is estimated to be 25.8 kJ/g atom or 516 kJ/mol at its melting point, 2240 K.

Sound wave velocities have been measured for CaAl2O4 glass and for two compositions along the Y2O3–Al2O3 join by Brillouin scattering spectroscopy. The sound speeds in these aluminate glasses range from 6.96 ± 0.16 km/s (vL) and 3.81 ± 0.05 km/s (vT). The corresponding bulk moduli are K=79 GPa for CaAl2O4 glass, and 113 ± 5 GPa for the Y2O3–Al2O3 glasses. The latter moduli are much larger than for most silicate or aluminosilicate glasses. The low compressibility is due to the lack of traditional network-forming cations in the yttrium aluminate glass so that all Al–O and Y–O bonds contribute equally in resisting compression.

The effects of oxygen and nitrogen additions on the optical properties of pure liquid zirconium were investigated under containerless conditions using rotating analyzer ellipsometry. Liquid zirconium was electromagnetically levitated and melted, and metered flows of oxygen and nitrogen were added to the melt in separate experiments. The optical constants ( n and k ) and the normal spectral emissivity ( e λ ) of the liquid at λ = 633 nm were measured over the duration of the experiments. The measured values of n and k decreased and e λ increased with the O and N concentrations for the single-phase liquids. Metastable Zr O solutions with 135% of the liquidus O-atom concentration were achieved at a temperature of 2260 K. Precipitation of a solid phase from the Zr N liquid at 2290 K occurred at a concentration of 7.6 at.% N. Based on these results, a new method using in situ ellipsometric measurements is described for the accurate determination of liquidus concentrations and temperatures. Dissolved oxygen and nitrogen were found to influence the optical properties of liquid zirconium in a manner proportional to their respective valences.

Dynamic data mining has gained increasing attention in the last decade. It addresses changing data structures which can be observed in many real-life applications, e.g. buying behavior of customers. As opposed to classical, i.e. static data mining where the challenge is to discover pattern inherent in given data sets, in dynamic data mining the challenge is to understand - and in some cases even predict - how such pattern will change over time. Since changes in general lead to uncertainty, the appropriate approaches for uncertainty modeling are needed in order to capture, model, and predict the respective phenomena considered in dynamic environments. As a consequence, the combination of dynamic data mining and soft computing is a very promising research area. The proposed algorithm consists of a dynamic clustering cycle when the data set will be refreshed from time to time. Within this cycle criteria check if the newly arrived data have structurally changed in comparison to the data already analyzed. If yes, appropriate actions are triggered, in particular an update of the initial settings of the cluster algorithm. As we will show, rough clustering offers strong tools to detect such changing data structures. To evaluate the proposed dynamic rough clustering algorithm it has been applied to synthetic as well as to real-world data sets where it provides new insights regarding the underlying dynamic phenomena.

Many industrial problems require adequate interpretation of data which are present in the respective situations. For example process monitoring, diagnosis, quality control, and prediction are some of these tasks. All the related problems have in common that a large amount of data describing the respective area exists. But in most cases the information contained in the data is not used sufficiently. Since the above described problems have different characteristics a multitude of methods for analysing the existing data is needed to solve the related problems. In this article we give an overview over advanced methods for data analysis, present a software tool which supports the application of these methods, and show some industrial realizations to emphasize the benefits of advanced data analysis.

Entrepreneurship education has two effects: it improves students’ entrepreneurial skills and provides impetus to those suited to entrepreneurship while discouraging others. While entrepreneurship education helps students to make a vocational decision its effects may conflict for those unsuited to entrepreneurship. We show that vocational and skill formation effects of entrepreneurship education can be identified empirically using a structural equation model. While conflicting effects of vocational and skill directed course content are observed in some individuals, overall these types of content are complements. We discuss how our results extend recent research and provide implications for the design of entrepreneurship courses.

The spectral emissivities, refractive indices, and extinction coefficients of pure liquid uranium and zirconium were measured versus temperature by He-Ne laser polarimetry at a wavelength of 632.8 nm. The experiments were conducted under containerless conditions using electromagnetic levitation and heating supplemented by CO2 laser beam heating. Clean liquid metal surfaces were achieved by heating the specimens to high temperatures at which oxides evaporated and nitrides decomposed. Results were obtained for liquid uranium and zirconium in the temperature ranges 2000–2800 K and 2000–2600 K, respectively and included data for liquid zirconium undercooled by 125 K. The spectral emissivity of zirconium was equal to 0.345 and was independent of temperature. The spectral emissivity of uranium increased with temperature from 0.272 at 2000 K to 0.294 at 2800 K. The melting temperature of zirconium was determined from its emissivity and apparent melting temperature to be 2125±11 K , in good agreement with values in the literature.

Entrepreneurship education ranks highly on policy agendas in Europe and the US, but little research is available to assess its impacts. In this context it is of primary importance to understand whether entrepreneurship education raises intentions to be entrepreneurial generally or whether it helps students determine how well suited they are for entrepreneurship. We develop a theoretical model of Bayesian learning in which entrepreneurship education generates signals which help students to evaluate their own aptitude for entrepreneurial tasks. We derive predictions from the model and test them using data from a compulsory entrepreneurship course at a German university. Using survey responses from 189 students ex ante and ex post, we find that entrepreneurial propensity declined somewhat in spite of generally good evaluations of the class. Our tests of Bayesian updating provide support for the notion that students receive valuable signals and learn about their own type in the entrepreneurship course.

Optical properties and spectral emissivities of liquid silicon, titanium, niobium, and zirconium were investigated by HeNe laser polarimetry at 632.8 nm. The metals were of a high purity and, except for zirconium, clean. The more demanding environmental requirements for eliminating oxide or nitride phases from zirconium were not met. Containerless conditions were achieved by electromagnetic levitation and heating. CO2 laser beam heating was also used to extend the temperature range for stable levitation and to heat solid silicon to form the metallic liquid phase. Corrections to previously reported calorimetric measurements of the heat capacity of liquid niobium were derived from the measured temperature dependence of its spectral emissivity. Property measurements were obtained for supercooled liquid silicon and supercooling of liquid zirconium was accomplished. The purification of liquid metals and the extension of this work on liquids to the measurement of thermodynamic properties and phase equilibria are discussed.

Three methods for noncontact temperature measurement are presented. Ideal gas thermometry is realized by using laser-induced fluorescence to measure the concentration of mercury atoms in a Hg-Ar mixture in the vicinity of hot specimens. Emission polarimetry is investigated by measuring the spatially resolved intensities of polarized light from a hot tungsten sphere. Laser polarimetry is used to measure the optical properties, emissivity, and, in combination with optical pyrometry, the temperature of electromagnetically levitated liquid aluminum. The precision of temperature measurements based on the ideal gas law is + or - 2.6 percent at 1500-2000 K. The polarized emission technique is found to have the capability to determine optical properties and/or spectral emissivities of specimens over a wide range of wavelengths with quite simple instruments.

The importance of research and development has been increasing steadily during the last decades and it will grow further in the future. As a consequence, models which can support the planning process for R&D become also more numerous and sophisticated. This contribution first reviews the existing literature in these areas. Starting from a rather basic model, the structure of planning models for R&D is developed. The main focus is on models which use the mathematical programming framework. Special attention is given to the modelling of uncertainty which is particularly important in R&D planning. Future developments which seem to be desirable and necessary are considered as extensions of models described before.

Support from the Chilean Fondecyt project 1040926 and the Millennium Science Institute ‘‘Complex Engineering Systems’’ (www.sistemasdeingenieria.cl) is greatly acknowledged.

This paper discusses and meditates about problems and solutions on man-machine interfaces. The incorporation of the user and the designer, local operating models and system theory, standarization efforts and a special example on design are presented and annotated. A way to connect low-level partial solutions and integrated systems is exhibited.

A short survey of models and strategies in the area of graphics dialogue programming is given together with information and analysis about new ways aiming towards user requirements and application efficiency.