Untersuchungen zur Charakterisierung des Mischsystems Sojaöl/epoxidiertes Sojaöl mittels FTIR/h-ATR-Spektroskopie

Characterization of Soybean Oil/Epoxidised Soybean Oil Mixtures by FTIR/h-ATR-Spectroscopy Controlling of technical production process requires a rapid method of analysis. This article describes a Fourier transform-infrared/horizontal attentuated total reflectance (FTIR/h-ATR) method to determine the iodine value and the degree of epoxidation in binary mixtures of soybean oil and epoxidised soybean oil. This analytical method will also be tested regarding the chemical reaction of epoxidation of soybean oil.     

Decision-theoretic planning with generalized first-order decision diagrams

Many tasks in AI require representation and manipulation of complex functions. First-Order Decision Diagrams (FODD) are a compact knowledge representation expressing functions over relational structures. They represent numerical functions that, when constrained to the Boolean range, use only existential quantification. Previous work has developed a set of operations for composition and for removing redundancies in FODDs, thus keeping them compact, and showed how to successfully employ FODDs for solving large-scale stochastic planning problems through the formalism of relational Markov decision processes (RMDP). In this paper, we introduce several new ideas enhancing the applicability of FODDs. More specifically, we first introduce Generalized FODDs (GFODD) and composition operations for them, generalizing FODDs to arbitrary quantification. Second, we develop a novel approach for reducing (G)FODDs using model checking. This yields – for the first time – a reduction that maximally reduces the diagram for the FODD case and provides a sound reduction procedure for GFODDs. Finally we show how GFODDs can be used in principle to solve RMDPs with arbitrary quantification, and develop a complete solution for the case where the reward function is specified using an arbitrary number of existential quantifiers followed by an arbitrary number of universal quantifiers.     

Descriptive matrix factorization for sustainability Adopting the principle of opposites

Climate change, the global energy footprint, and strategies for sustainable development have become topics of considerable political and public interest. The public debate is informed by an exponentially growing amount of data and there are diverse partisan interest when it comes to interpretation. We therefore believe that data analysis methods are called for that provide results which are intuitively understandable even to non-experts. Moreover, such methods should be efficient so that non-experts users can perform their own analysis at low expense in order to understand the effects of different parameters and influential factors. In this paper, we discuss a new technique for factorizing data matrices that meets both these requirements. The basic idea is to represent a set of data by means of convex combinations of extreme data points. This often accommodates human cognition. In contrast to established factorization methods, the approach presented in this paper can also determine over-complete bases. At the same time, convex combinations allow for highly efficient matrix factorization. Based on techniques adopted from the field of distance geometry, we derive a linear time algorithm to determine suitable basis vectors for factorization. By means of the example of several environmental and developmental data sets we discuss the performance and characteristics of the proposed approach and validate that significant efficiency gains are obtainable without performance decreases compared to existing convexity constrained approaches.     

Acylierter Weizenkleber als Cobindemittel in Papierstreichfarben

Acylated Wheat Gluten as a Cobinder in Paper Sizing. Wheat gluten cannot be used in its native form for technical application because of its structural properties. Therefore it is necessary to modify Gluten in a chemical manner to satify industrial norms. Different chemical conditions and their influence over qualities of produkt are the subject of the following report. Regarding the results of our experiments we can show that chemical modified wheat gluten complies such requirements which are demanded for industrial Cobinders like viscosity, picking-up-resistence and water retention. Our modified wheat gluten partly possesses better qualities than industrial standard.     

Lipids and Rheological Properties of Starch. Part II: The Effect of Granule Surface Material on Viscosity of Wheat Starch

The extraction of wheat starch with ethanol reduced the protein content from 0.4 to 0.3%. Wheat starch extracted with 1% SDS containing 1% 2-ME or with 1% SDS gave no staining with amido black, indicating that most of starch surface protein has been separated. TLC of lipids extracted from starch without gelatinization showed that ethanol extracted considerable amounts of starch lipids such as lysophosphatidyl choline, lysophosphatidyl ethanolamine and free fatty acids. After extraction with SDS and especially with SDS + 1% 2-ME only some starch granules were deformed. In this starch some changes have been observed also on the granules surface by REM. The extraction resulted also in considerable changes in rheological properties of extracted starches. The starch samples were characterized thermodynamically also.     

Exploiting symmetries for scaling loopy belief propagation and relational training

Judging by the increasing impact of machine learning on large-scale data analysis in the last decade, one can anticipate a substantial growth in diversity of the machine learning applications for “big data” over the next decade. This exciting new opportunity, however, also raises many challenges. One of them is scaling inference within and training of graphical models. Typical ways to address this scaling issue are inference by approximate message passing, stochastic gradients, and MapReduce, among others. Often, we encounter inference and training problems with symmetries and redundancies in the graph structure. A prominent example are relational models that capture complexity. Exploiting these symmetries, however, has not been considered for scaling yet. In this paper, we show that inference and training can indeed benefit from exploiting symmetries. Specifically, we show that (loopy) belief propagation (BP) can be lifted. That is, a model is compressed by grouping nodes together that send and receive identical messages so that a modified BP running on the lifted graph yields the same marginals as BP on the original one, but often in a fraction of time. By establishing a link between lifting and radix sort, we show that lifting is MapReduce-able. Still, in many if not most situations training relational models will not benefit from this (scalable) lifting: symmetries within models easily break since variables become correlated by virtue of depending asymmetrically on evidence. An appealing idea for such situations is to train and recombine local models. This breaks long-range dependencies and allows to exploit lifting within and across the local training tasks. Moreover, it naturally paves the way for the first scalable lifted training approaches based on stochastic gradients, both in an online and a MapReduced fashion. On several datasets, the online training, for instance, converges to the same quality solution over an order of magnitude faster, simply because it starts optimizing long before having seen the entire mega-example even once.     

Parathyroid hormone-related protein in patients with primary breast cancer and eucalcemia

Parathyroid hormone-related protein (PTHrP) is a causative factor of humoral hypercalcemia in breast cancer and other malignancies. We studied circulating PTHrP levels with three different immunoassays directed against different parts of the PTHrP molecule in 48 patients with breast cancer and eucalcemia. The methods used were: (a) a RIA with antibodies directed toward the midregion (63-78); (b) an immunofluorometric assay with two antibodies against 1-34 and 38-67; and (c) an immunoradiometric assay with antibodies against 1-40 and 1-72. Although most patients had PTHrP levels indistinguishable from normal when measured by all three methods, four patients had increased serum levels in the IFMA. PTHrP was detected by immunohistochemistry in tumors from nearly all patients. One patient with elevated PTHrP in plasma measured by IFMA showed intense staining of tumor by immunohistochemistry; the tumor was histologically graded as III (severe) and was the largest of all tumors in this patient group. The IFMA can identify increased serum PTHrP in some patients with breast cancer who are not hypercalcemic. This assay may be especially useful in screening patients for this tumor during a relative early phase of the disease.     

Spectrum Determination of Terahertz Sources Using Fabry-Perot Interferometer and Bolometer Detector

We describe a method to determine the radiation spectrum of terahertz sources using a transmitted Fabry-Perot interferometer and a bolometer detector. Our novel Fabry-Perot spectrometer can measure the spectrum of an unknown broadband sub-terahertz and terahertz source. The spectrometer does not need to be pre-tuned. We develop a new algorithm to support the measurements using this spectrometer. Our technique allows us to measure the spectrum over more than an octave, and our numerical algorithm is very stable and robust, providing for an accurate spectrum determination. Two Gunn oscillation sources with the main frequencies at 200 GHz and 600 GHz are used to test the proposed method. The spectrum extracted from the measured data shows that this method is accurate and reliable.