Differential Cyclic Voltammetry - a Novel Technique for Selective and Simultaneous Detection using Redox Cycling Based Sensors

Redox cycling (RC) is an effect that is used to amplify electrochemical signals. However, traditional techniques such as cyclic voltammetry (CV) do not provide clear insight for a mixture of multiple redox couples while RC is applied. Thus, we have developed a new measurement technique which delivers electrochemical spectra of all reversible redox couples present based on concentrations and standard potentials. This technique has been named differential cyclic voltammetry (DCV). We have fabricated micrometer-sized interdigitated electrode (IDE) sensors to conduct DCV measurements in mixtures of 1mM catechol and 4mM [Ru(NH3)6]Cl3. To simulate the electrochemical behavior of these sensors we have also developed a finite element model (FEM) in Comsol®. The\ud experimental data corresponds to the calculated spectra obtained from simulations. Additionally, the measured spectra can be used to easily derive standard potentials and concentrations simultaneously and selectively.\u

The Canonical Perfect Bose Gas in Casimir Boxes

We study the problem of Bose-Einstein condensation in the perfect Bose gas in the canonical ensemble, in anisotropically dilated rectangular parallelpipeds (Casimir boxes). We prove that in the canonical ensemble for these anisotropic boxes there is the same type of generalized Bose-Einstein condensation as in the grand-canonical ensemble for the equivalent geometry. However the amount of condensate in the individual states is different in some cases and so are the fluctuations.Comment: 23 page

Competitive exception learning using fuzzy frequency distributions

A competitive exception learning algorithm for finding a non-linear mapping is proposed which puts the emphasis on the discovery of the important exceptions rather than the main rules. To do so,we first cluster the output space using a competitive fuzzy clustering algorithm and derive a fuzzy frequency distribution describing the general, average system's output behavior. Next, we look for a fuzzy partitioning of the input space in such away that the corresponding fuzzy output frequency distributions `deviate at most' from the average one as found in the first step. In this way, the most important `exceptional regions' in the input-output relation are determined. Using the joint input-output fuzzy frequency distributions, the complete input-output function as extracted from the data, can be expressed mathematically. In addition, the exceptions encountered can be collected and described as a set of fuzzy if-then-else-rules. Besides presenting a theoretical description of the new exception learning algorithm, we report on the outcomes of certain practical simulations

Interstellar extinction and the distribution of stellar populations in the direction of the ultra-deep Chandra Galactic field

We studied the stellar population in the central 6.6x6.6arcmin,region of the ultra-deep (1Msec) Chandra Galactic field - the "Chandra bulge field" (CBF) approximately 1.5 degrees away from the Galactic Center - using the Hubble Space Telescope ACS/WFC blue (F435W) and red (F625W) images. We mainly focus on the behavior of red clump giants - a distinct stellar population, which is known to have an essentially constant intrinsic luminosity and color. By studying the variation in the position of the red clump giants on a spatially resolved color-magnitude diagram, we confirm the anomalous total-to-selective extinction ratio, as reported in previous work for other Galactic bulge fields. We show that the interstellar extinction in this area is = 4 on average, but varies significantly between ~3-5 on angular scales as small as 1 arcminute. Using the distribution of red clump giants in an extinction-corrected color-magnitude diagram, we constrain the shape of a stellar-mass distribution model in the direction of this ultra-deep Chandra field, which will be used in a future analysis of the population of X-ray sources. We also show that the adopted model for the stellar density distribution predicts an infrared surface brightness in the direction of the "Chandra bulge field" in good agreement (i.e. within ~15%) with the actual measurements derived from the Spitzer/IRAC observations.Comment: 9 pages, 9 figures. Accepted for publication in A&

Financial Markets Analysis by Probabilistic Fuzzy Modelling

For successful trading in financial markets, it is important to develop financial models where one can identify different states of the market for modifying one???s actions. In this paper, we propose to use probabilistic fuzzy systems for this purpose. We concentrate on Takagi???Sugeno (TS) probabilistic fuzzy systems that combine interpretability of fuzzy systems with the statistical properties of probabilistic systems. We start by recapitulating the general architecture of TS probabilistic fuzzy rule-based systems and summarize the corresponding reasoning schemes. We mention how probabilities can be estimated from a given data set and how a probability distribution can be approximated by a fuzzy histogram. We apply our methodology for financial time series analysis and demonstrate how a probabilistic TS fuzzy system can be identified, assuming that a linguistic term set is given. We illustrate the interpretability of such a system by inspecting the rule bases of our models

Kinetic study of an on-chip isocyanate derivatization reaction by on-line nano-esi ms

A high-throughput method is presented for the study of reaction kinetics by nano- electrospray ionization mass spectrometry (nano-ESI MS). The reaction of propyl isocyanate (2), benzyl isocyanate (3), and toluene-2,4-diisocyanate (4) with 4-nitro-7- piperazino-2,1,3-benzoxadiazole (NBDPZ) (1) to yield the corresponding urea derivatives (5) was carried out in a continuous flow glass microchip. Real-time monitoring of the reactions was done by nano-ESI MS. Rate constants of 1.6 ␣ 104 M-1 min-1, 5.2 ␣ 104 M-1 min-1, and 2.5 ␣ 104 M-1 min-1 were determined for isocyanate 2, 3 and 4, respectively

Relative Distress and Return Distribution Characteristics of Japanese Stocks, a Fuzzy-Probabilistic Approach

In this article, we demonstrate that a direct relation exists between the context of Japanese firms indicating relative distress and conditional return distribution properties. We map cross-sectional vectors with company characteristics on vectors with return feature vectors, using a fuzzy identification technique called Competitive Exception Learning Algorithm (CELA)1. In this study we use company characteristics that follow from capital structure theory and we relate the recognized conditional return properties to this theory. Using the rules identified by this mapping procedure this approach enables us to make conditional predictions regarding the probability of a stock's or a group of stocks' return series for different return distribution classes (actually return indices). Using these findings, one may construct conditional indices that may serve as benchmarks. These would be particularly useful for tracking and portfolio management

Probabilistic and Statistical Fuzzy Set Foundations of Competitive Exception Learning

Recently, a Competitive Exception Learning Algorithm (CELA) was introduced [1, 2]. This algorithm establishes an optimal mapping from a (continuous) M-dimensional input sample space to an N-dime