An Indirect Method for Vapor Pressure and Phase Change Enthalpy Determination by Thermogravimetry

Vapor pressure is a fundamental property of a pure substance. This property is the pressure of a compound’s vapor in thermodynamic equilibrium with its condensed phase (solid or liquid). When phase equilibrium condition is met, phase coexistence of a pure substance involves a continuum interplay of vaporization or sublimation to gas and condensation back to their liquid or solid form, respectively. Thermogravimetric analysis (TGA) techniques are based on mass loss determination and are well suited for the study of such phenomena. In this work, it is shown that TGA method using a reference substance is a suitable technique for vapor pressure determination. This method is easy and fast because it involves a series of isothermal segments. In contrast to original Knudsen’s approach, where the use of high vacuum is mandatory, adopting the proposed method a given experimental setup is calibrated under ambient pressure conditions. The theoretical framework of this method is based on a generalization of Langmuir equation of free evaporation: The real strength of the proposed method is the ability to determine the vapor pressure independently of the molecular mass of the vapor. A demonstration of this method has been performed using the Clausius–Clapeyron equation of state to derive the working equation. This algorithm, however, is adaptive and admits the use of other equations of state. The results of a series of experiments with organic molecules indicate that the average difference of the measured and the literature vapor pressure amounts to about 5 %. Vapor pressure determined in this study spans from few mPa up to several kPa. Once the p versus T diagram is obtained, phase transition enthalpy can additionally be calculated from the data.     

Approximation of graph edit distance based on Hausdorff matching

Graph edit distance is a powerful and flexible method for error-tolerant graph matching. Yet it can only be calculated for small graphs in practice due to its exponential time complexity when considering unconstrained graphs. In this paper we propose a quadratic time approximation of graph edit distance based on Hausdorff matching. In a series of experiments we analyze the performance of the proposed Hausdorff edit distance in the context of graph classification and compare it with a cubic time algorithm based on the assignment problem. Investigated applications include nearest neighbor classification of graphs representing letter drawings, fingerprints, and molecular compounds as well as hidden Markov model classification of vector space embedded graphs representing handwriting. In many cases, a substantial speedup is achieved with only a minor loss in accuracy or, in one case, even with a gain in accuracy. Overall, the proposed Hausdorff edit distance shows a promising potential in terms of flexibility, efficiency, and accuracy.     

Behavioral observations of sensory substitution in neonate macaques (Macaca arctoides).

The ability of neonate macaque monkeys to learn to respond to artificial spatial sensory information was studied through the use of compact, head-worn, electronic spatial sonars with audible displays, which translate spatial information into auditory dimensions specifying distance, direction, and surface characteristics. Three animals were born in the dark and raised without vision for 1 to 3 months while wearing either the Binaural Sensory Aid (Animal 1; Kay, 1974) or the Trisensor (Animals 2 and 3; Easton & Jackson, 1983) airborne sonars. Each animal demonstrated alertness to information transmitted by the devices in spontaneous reaching or reinforced discrimination tasks, and more device-related, perceptual-motor activities were observed when the sensors were switched on than when they were switched off. The results show that neonate monkeys can learn effective use of information obtained from sensory substitution devices through unstructured interaction with the environment. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

On the Impact of Using Utilities Rather than Costs for Graph Matching

The concept of graph edit distance constitutes one of the most flexible graph matching paradigms available. The major drawback of graph edit distance, viz. the exponential time complexity, has been recently overcome by means of a reformulation of the edit distance problem to a linear sum assignment problem. However, the substantial speed up of the matching is also accompanied by an approximation error on the distances. Major contribution of this paper is the introduction of a transformation process in order to convert the underlying cost model into a utility model. The benefit of this transformation is that it enables the integration of additional information in the assignment process. We empirically confirm the positive effects of this transformation on five benchmark graph sets with respect to the accuracy and run time of a distance based classifier.     

Graph Classification by Means of Lipschitz Embedding

In pattern recognition and related fields, graph-based representations offer a versatile alternative to the widely used feature vectors. Therefore, an emerging trend of representing objects by graphs can be observed. This trend is intensified by the development of novel approaches in graph-based machine learning, such as graph kernels or graph-embedding techniques. These procedures overcome a major drawback of graphs, which consists of a serious lack of algorithms for classification. This paper is inspired by the idea of representing graphs through dissimilarities and extends our previous work to the more general setting of Lipschitz embeddings. In an experimental evaluation, we empirically confirm that classifiers that rely on the original graph distances can be outperformed by a classification system using the Lipschitz embedded graphs.     

Designing and implementing lightweight kernels for capability computing

In the early 1990s, researchers at Sandia National Laboratories and the University of New Mexico began development of customized system software for massively parallel ‘capability’ computing platforms. These lightweight kernels have proven to be essential for delivering the full power of the underlying hardware to applications. This claim is underscored by the success of several supercomputers, including the Intel Paragon, Intel Accelerated Strategic Computing Initiative Red, and the Cray XT series of systems, each having established a new standard for high‐performance computing upon introduction. In this paper, we describe our approach to lightweight compute node kernel design and discuss the design principles that have guided several generations of implementation and deployment. A broad strategy of operating system specialization has led to a focus on user‐level resource management, deterministic behavior, and scalable system services. The relative importance of each of these areas has changed over the years in response to changes in applications and hardware and system architecture. We detail our approach and the associated principles, describe how our application of these principles has changed over time, and provide design and performance comparisons to contemporaneous supercomputing operating systems. Copyright © 2008 John Wiley & Sons, Ltd.     

Structure-Dynamic Analysis of an Induction Machine Depending on Stator–Housing Coupling

The estimation and the calculation of the acoustic sound of electric machinery are of particular interest nowadays. Various approaches have been presented, relying either on analytical or on numerical models. The analytical models presented here are based on the electromagnetic-field theory. Numerical models are applied to derive the exciting forces stemming from various sources and effects. The numerical results have to be verified. Hence, they are compared with the physically based analytical results. The radiated noise depends directly on the surface deformation of the machine. Therefore, the analysis is focused on the structure-dynamic vibration. The combined analysis presented here allows for the reduction of vibration and noise, optimizing the coupling of the machine's stator and housing. The studied induction machine's housing is mounted with six spiral-steel springs to the stator. With the presented method, the impact of different numbers of springs is analyzed exemplarily.