Nano-Thermal Transport Array: An Instrument for Combinatorial Measurements of Heat Transfer in Nanoscale Films

The nano-Thermal Transport Array is a silicon-based micromachined device for measuring the thermal properties of nanoscale materials in a high-throughput methodology. The device contains an array of thermal sensors, each one of which consists of a silicon nitride membrane and a tungsten heating element that also serves as a temperature gauge. The thermal behavior of the sensors is described with an analytical model. The assumptions underlying this model and its accuracy are checked using the finite element method. The analytical model is used in a data reduction scheme that relates experimental quantities to materials properties. Measured properties include thermal effusivity, thermal conductivity, and heat capacity. While the array is specifically designed for combinatorial analysis, here we demonstrate the capabilities of the device with a high-throughput study of copper multi-layer films as a function of film thickness, ranging from 15 to 470 nm. Thermal conductivity results show good agreement with earlier models predicting the conductivity based on electron scattering at interfaces.Engineering and Applied Science

Interest Groups in Multi-Level Contexts: European Integration as Cross-Cutting Issue in Party-Interest Group Contacts

Policy-specific actor-constellations consisting of party- and group-representatives commonly drive the effective establishment of new policy programmes or changes in existing policies. In the EU multi-level system, the creation of such constellations is complicated because it practically requires consensus on two dimensions: the European public policy at stake and the issue of European integration. This means that, for interest groups with interests in particular policy domains, and with limited interest in the actual issue of European integration, non-Eurosceptic parties must be their main ally in their policy battles. We hypothesise that interest groups with relevant European domain-specific interests will ally with non-Eurosceptic parties, whereas interest groups whose interests are hardly affected by the European policy process will have party-political allies across the full range of positions on European integration. We assess this argument on the basis of an elite-survey of interest group leaders and study group-party dyads in several European countries (i.e., Belgium, Lithuania, Italy, Netherlands, Poland, and Slovenia) in a large number of policy domains. Our dependent variable is the group-party dyad and the main independent variables are the European policy interests of the group and the level of Euroscepticism of the party. We broadly find support for our hypotheses. The findings of our study speak to the debate concerning the implications of the politicisation of European integration and, more specifically, the way in which party-political polarisation of Europe may divide domestic interest group systems and potentially drive group and party systems apart

Pricing Liquidity Risk with Heterogeneous Investment Horizons

We develop a new asset pricing model with stochastic transaction costs and investors with heterogenous horizons. Short-term investors hold only liquid assets in equilibrium. This generates segmentation effects in the pricing of liquid versus illiquid assets. Specifically, the liquidity (risk) premia of illiquid assets are determined by the heterogeneity in investor horizons and by the correlation between liquid and illiquid assets. We estimate our model for the cross-section of U.S. stocks and find that it fits average returns substantially better than a standard liquidity CAPM. Allowing for heterogenous horizons also leads to much larger estimates for the liquidity premia

Update on stents: Recent studies on the TAXUS® stent system in small vessels

Small vessel size (<3 mm) has been identified as an independent predictive factor of restenosis after percutaneous coronary intervention when using bare metal stents (BMS). It remains controversial whether BMS placement in small vessels has an advantage over balloon angioplasty in terms of angiographic and clinical outcomes. The advent of drug eluting stents (DES), either paclitaxel-eluting stents (PES) or sirolimus-eluting stents (SES), has strongly impacted interventional cardiology by significantly reducing restenosis and the need for repeat revascularization. Therefore, it was also expected that DES could substantially reduce restenosis in smaller vessels. However, even in the DES era, small vessel size remains an independent predictor of angiographic and clinical restenosis. To date, only a few studies systematically investigate the clinical effect of DES placement in small vessels. In addition, some potential issues with the use of DES have been raised, such as late stent thrombosis and late restenosis. In order to (i) establish the superiority of DES over BMS; (ii) verify the efficacy and safety of DES; and (iii) critically assess the superiority of one DES over the other in patients with small coronary arteries, further multicenter, randomized clinical trials with larger sample size are warranted

Optimal Scaling of Interaction Effects in Generalized Linear Models

Multiplicative interaction models, such as Goodman's RC(M) association models, can be a useful tool for analyzing the content of interaction effects. However, most models for interaction effects are only suitable for data sets with two or three predictor variables. Here, we discuss an optimal scaling model for analyzing the content of interaction effects in generalized linear models with any number of categorical predictor variables. This model, which we call the optimal scaling of interactions (OSI) model, is a parsimonious, one-dimensional multiplicative interaction model. We discuss how the model can be used to visually interpret the interaction effects. Two empirical data sets are used to show how the results of the model can

Fuzzy clustering with Minkowski distance

Distances in the well known fuzzy c-means algorithm of Bezdek (1973) are measured by the squared Euclidean distance. Other distances have been used as well in fuzzy clustering. For example, Jajuga (1991) proposed to use the L_1-distance and Bobrowski and Bezdek (1991) also used the L_infty-distance. For the more general case of Minkowski distance and the case of using a root of the squared Minkowski distance, Groenen and Jajuga (2001) introduced a majorization algorithm to minimize the error. One of the advantages of iterative majorization is that it is a guaranteed descent algorithm, so that every iteration reduces the error until convergence is reached. However, their algorithm was limited to the case of Minkowski parameter between 1 and 2, that is, between the L_1-distance and the Euclidean distance. Here, we extend their majorization algorithm to any Minkowski distance with Minkowski parameter greater than (or equal to) 1. This extension also includes the case of the L_infty-distance. We also investigate how well this algorithm performs and present an empirical application

A scanning AC calorimetry technique for the analysis of nano-scale quantities of materials

We present a scanning AC nanocalorimetry method that enables calorimetry measurements at heating and cooling rates that vary from isothermal to 2 × 10^3 K/s, thus bridging the gap between traditional scanning calorimetry of bulk materials and nanocalorimetry. The method relies on a micromachined nanocalorimetry sensor with a serpentine heating element that is sensitive enough to make measurements on thin-film samples and composition libraries. The ability to perform calorimetry over such a broad range of scanning rates makes it an ideal tool to characterize the kinetics of phase transformations or to explore the behavior of materials far from equilibrium. We demonstrate the technique by performing measurements on thin-film samples of Sn, In, and Bi with thicknesses ranging from 100 to 300 nm. The experimental heat capacities and melting temperatures agree well with literature values. The measured heat capacities are insensitive to the applied AC frequency, scan rate, and heat loss to the environment over a broad range of experimental parameters

Identifying Unknown Response Styles: A Latent-Class Bilinear Multinomial Logit Model

Respondents can vary significantly in the way they use rating scales. Specifically, respondents can exhibit varying degrees of response style, which threatens the validity of the responses. The purpose of this article is to investigate to what extent rating scale responses show response style and substantive content of the item. The authors develop a novel model that accounts for possibly unknown kinds of response styles, content of the items, and background characteristics of respondents. By imposing a bilinear structure on the parameters of a multinomial logit model, the authors can visually distinguish the effects on the response behavior of both the characteristics of a respondent and the content of the item. This approach is combined with finite mixture modeling, so that two separate segmentations of the respondents are obtained: one for response style and one for item content. This latent-class bilinear multinomial logit (LC-BML) model is applied to a cross-national data set. The results show that item content is highly influential in explaining response behavior and reveal the presence of several response styles, including the prominent response styles acquiescence and extreme response style