Microcalorimetry for characterization of film formation and cure of coatings and adhesives

A new microcalorimeter with eight parallel channels using robust, low cost sensors for characterization of coatings and adhesives is described and first experiments on coatings and adhesives are presented. The calorimetric sensors are based on thin glass plates (20 mm × 20 mm, thickness 150 μm) with heater and thermocouple sputtered on the surfaces (calorimetric active area of about 9 mm²). The setup allows heating and cooling experiments as well as isothermal measurements in the temperature-modulated mode with up to eight sensors in parallel. The measured quantities are the real (C^′_p) and imaginary part (C^″_p) of the complex heat capacity (), the related absolute value of the heat capacity () and the heat flow . An industrial computer (NI PXI system) with specific software for calibration and data recording controls the electronic components. Sensors can be embedded in a temperature controlled oven (heating and cooling by Peltier elements) or alternatively in a climatic cabinet with controlled temperature and humidity.

The method has been applied successfully to monitoring of film formation of aqueous polymer dispersions (styrene-acrylate copolymer) and curing of coatings.     

Green Tax Reforms and Computational Economics A Do-it-yourself Approach

The double or even triple dividend hypothesis of green tax reforms has beena major issue of dispute in both the scientific community and the politicalarena during the last decade. Theoretical analysis has provided a number ofimportant qualitative insights to the debate but lacks of actual policyrelevance due to very restrictive assumptions. Applied research that takes thestep from stylized analytical to complex numerical models usually comes as ablackbox to non-expert modelers. This paper aims at bridging the gap betweenstylized theoretical work and numerical analysis. We develop a flexible,interactive simulation model which is accessible underhttp://brw.zew.de. Users can specify their own green tax reforms andevaluate the induced economic and environmental effects. Based on illustrativesimulations, we demonstrate the usefulness of our do-it-yourself approach fora better understanding of the double (triple) dividend hypothesis.     

Approximate variational inference based on a finite sample of Gaussian latent variables

Variational methods are employed in situations where exact Bayesian inference becomes intractable due to the difficulty in performing certain integrals. Typically, variational methods postulate a tractable posterior and formulate a lower bound on the desired integral to be approximated, e.g. marginal likelihood. The lower bound is then optimised with respect to its free parameters, the so-called variational parameters. However, this is not always possible as for certain integrals it is very challenging (or tedious) to come up with a suitable lower bound. Here, we propose a simple scheme that overcomes some of the awkward cases where the usual variational treatment becomes difficult. The scheme relies on a rewriting of the lower bound on the model log-likelihood. We demonstrate the proposed scheme on a number of synthetic and real examples, as well as on a real geophysical model for which the standard variational approaches are inapplicable.     

Figure-ground separation by cue integration

This letter presents an improved cue integration approach to reliably separate coherent moving objects from their background scene in video sequences. The proposed method uses a probabilistic framework to unify bottom-up and top-down cues in a parallel, "democratic" fashion. The algorithm makes use of a modified Bayes rule where each pixel's posterior probabilities of figure or ground layer assignment are derived from likelihood models of three bottom-up cues and a prior model provided by a top-down cue. Each cue is treated as independent evidence for figure-ground separation. They compete with and complement each other dynamically by adjusting relative weights from frame to frame according to cue quality measured against the overall integration. At the same time, the likelihood or prior models of individual cues adapt toward the integrated result. These mechanisms enable the system to organize under the influence of visual scene structure without manual intervention. A novel contribution here is the incorporation of a top-down cue. It improves the system's robustness and accuracy and helps handle difficult and ambiguous situations, such as abrupt lighting changes or occlusion among multiple objects. Results on various video sequences are demonstrated and discussed. (Video demos are available at http://organic.usc.edu:8376/ approximately tangx/neco/index.html .).     

Study on the replication quality of micro-structures in the injection molding process with dynamical tool tempering systems

The injection molding of micro-structures is a promising mass-production method for a broad range of materials. However, the replication quality of these structures depends significantly on the heat flow during the filling stage. In this paper, the filling and heat transfer of v-groove and random structures below 5 μm is investigated with the help of an AFM (atomic force microscope) and thermo couples. A numerical model is developed to predict the filling of surface structures during the filling and packing stage. The model implies the use of simple fully developed flow models taking the power-law material model into account. This permits investigation into which ways several processing parameters affect the polymer flow in the surface structures. The mold wall temperature, which has significant effects on the polymer flow, is varied by using a variothermal mold temperature control system to validate the model proposed.     

Artists portray human faces with the Fourier statistics of complex natural scenes

When artists portray human faces, they generally endow their portraits with properties that render the faces esthetically more pleasing. To obtain insight into the changes introduced by artists, we compared Fourier power spectra in photographs of faces and in portraits by artists. Our analysis was restricted to a large set of monochrome or lightly colored portraits from various Western cultures and revealed a paradoxical result. Although face photographs are not scale-invariant, artists draw human faces with statistical properties that deviate from the face photographs and approximate the scale-invariant, fractal-like properties of complex natural scenes. This result cannot be explained by systematic differences in the complexity of patterns surrounding the faces or by reproduction artifacts. In particular, a moderate change in gamma gradation has little influence on the results. Moreover, the scale-invariant rendering of faces in artists' portraits was found to be independent of cultural variables, such as century of origin or artistic techniques. We suggest that artists have implicit knowledge of image statistics and prefer natural scene statistics (or some other rules associated with them) in their creations. Fractal-like statistics have been demonstrated previously in other forms of visual art and may be a general attribute of esthetic visual stimuli.