Fixed-Parameter Complexity of Minimum Profile Problems

The profile of a graph is an integer-valued parameter defined via vertex orderings; it is known that the profile of a graph equals the smallest number of edges of an interval supergraph. Since computing the profile of a graph is an NP-hard problem, we consider parameterized versions of the problem. Namely, we study the problem of deciding whether the profile of a connected graph of order n is at most n−1+k, considering k as the parameter; this is a parameterization above guaranteed value, since n−1 is a tight lower bound for the profile. We present two fixed-parameter algorithms for this problem. The first algorithm is based on a forbidden subgraph characterization of interval graphs. The second algorithm is based on two simple kernelization rules which allow us to produce a kernel with linear number of vertices and edges. For showing the correctness of the second algorithm we need to establish structural properties of graphs with small profile which are of independent interest. A preliminary version of the paper is published in Proc. IWPEC 2006, LNCS vol. 4169, 60–71.     

The high-conductance state of cortical networks

We studied the dynamics of large networks of spiking neurons with conductance-based (nonlinear) synapses and compared them to networks with current-based (linear) synapses. For systems with sparse and inhibition-dominated recurrent connectivity, weak external inputs induced asynchronous irregular firing at low rates. Membrane potentials fluctuated a few millivolts below threshold, and membrane conductances were increased by a factor 2 to 5 with respect to the resting state. This combination of parameters characterizes the ongoing spiking activity typically recorded in the cortex in vivo. Many aspects of the asynchronous irregular state in conductance-based networks could be sufficiently well characterized with a simple numerical mean field approach. In particular, it correctly predicted an intriguing property of conductance-based networks that does not appear to be shared by current-based models: they exhibit states of low-rate asynchronous irregular activity that persist for some period of time even in the absence of external inputs and without cortical pacemakers. Simulations of larger networks (up to 350,000 neurons) demonstrated that the survival time of self-sustained activity increases exponentially with network size.     

Zeolite based trace humidity sensor for high temperature applications in hydrogen atmosphere

We present a humidity sensor based on H-ZSM-5 type zeolite that is suitable to detect traces of humidity (10–110 ppm_V) under harsh conditions, e.g. reducing atmosphere (H₂) and high temperature (up to 600 °C). By means of complex impedance spectroscopy (IS) we show that the zeolite sensor responds linearly towards minimal changes in humidity. Therefore this result indicates that the zeolite sensor is capable to detect traces of humidity in processes where high temperatures in a hydrogen environment are required.     

Near optimum sampling design and an efficient algorithm for single tone frequency estimation

Work on single tone frequency estimation has focused on uniformly sampled data. However, it has been shown that, for a given number of samples, more information on the frequency of a signal can be gained by non-uniform sampling schemes [M. Wieler, S. Trittler, F.A. Hamprecht, Optimal design for single tone frequency estimation, Digital Signal Process., in press]. Unfortunately, an optimum sampling pattern (that, for example, minimizes the Cramér–Rao bound) does not automatically have a fast and simple algorithm for frequency estimation associated with it. For application in an interferometric measurement system, an algorithm is needed that gathers as much information as possible from a low number of samples, while at the same time keeping the computational effort sufficiently low to process millions of time series in a few seconds. This paper proposes a simple approximation to the optimum sampling pattern by using uniformly sampled blocks of data and further proposes to estimate phase and frequency in each of these blocks and to exploit these intermediate results in the final estimation. An approach to do so is investigated in detail. Results are compared to the Cramér–Rao bound (CRB), and it is shown that this algorithm almost reaches this limit on the variance of unbiased estimators, at a computational complexity lower than that of a typical FFT-based approach. For $M=32$ samples and a signal-to-noise ratio of 10, the standard deviation of the frequency estimate is lower by more than 50% compared to uniform sampling. In addition, the algorithm can easily be applied to poorly characterized systems, e.g. systems for which the noise is not known exactly. Finally, we demonstrate that the proposed strategy yields results that are within 3% of the theoretically achievable accuracy for the theoretically optimum sampling pattern.     

Functionally essential, invariant glutamate near the C-terminus of strand {beta}5 in various ({alpha}/{beta})8-barrel enzymes as a possible indicator of their evolutionary relatedness

Twelve different (/ß)₈-barrel enzymes belonging tothree structurally distinct families were found to contain,near the C-terminus of their strand ß5, a conservedinvariant glutamic acid residue that plays an important functionalrole in each of these enzymes. The search was based on the ideathat a conserved sequence region of an (/ß)₈-barrelenzyme should be more or less conserved also in the equivalentpart of the structure of the other enzymes with this foldingmotif owing to their mutual evolutionary relatedness. For thispurpose, the sequence region around the well conserved fifthß-strand of a-amylase containing catalytic glutamate(Glu230, Aspergillus oryzae -amylase numbering), was used asthe sequence-structural template. The isolated sequence stretchesof the 12 (/ß)₈-barrels are discussed from both thesequence-structural and the evolutionary point of view, theinvariant glutamate residue being proposed to be a joining featureof the studied group of enzymes remaining from their ancestral(/ß)₈-barrel     

Mitigating Detrimental Effect of Self-Doping Near the Anode in Highly Efficient Organic Solar Cells

Poly(3,4-ethylenedioxythiophene):polystyrene sulfonate (PEDOT:PSS) has been one of the most established hole transport layers (HTL) in organic solar cells (OSCs) for several decades. However, the presence of PSS⁻ ions is known to deteriorate device performance via a number of mechanisms including diffusion to the HTL-active layer interface and unwanted local chemical reactions. In this study, it is shown that PSS⁻ ions can also result in local p-doping in the high efficiency donor:non-fullerene acceptor blends – resulting in photocurrent loss. To address these issues, a facile and effective approach is reported to improve the OSC performance through a two-component hole transport layer (HTL) consisting of a self-assembled monolayer of 2PACz ([2-(9H-Carbazol-9-yl)ethyl]phosphonic acid) and PEDOT:PSS. The power conversion efficiency (PCE) of 17.1% using devices with PEDOT:PSS HTL improved to 17.7% when the PEDOT:PSS/2PACz two-component HTL is used. The improved performance is attributed to the overlaid 2PACz layer preventing the formation of an intermixed p-doped PSS⁻ ion rich region (≈5–10 nm) at the bulk heterojunction-HTL contact interface, resulting in decreased recombination losses and improved stability. Moreover, the 2PACz monolayer is also found to reduce electrical shunts that ultimately yield improved performance in large area devices with PCE enhanced from 12.3% to 13.3% in 1 cm² cells.     

A graph-based framework for model-driven optimization facilitating impact analysis of mutation operator properties

Optimization problems in software engineering typically deal with structures as they occur in the design and maintenance of software systems. In model-driven optimization (MDO), domain-specific models are used to represent these structures while evolutionary algorithms are often used to solve optimization problems. However, designing appropriate models and evolutionary algorithms to represent and evolve structures is not always straightforward. Domain experts often need deep knowledge of how to configure an evolutionary algorithm. This makes the use of model-driven meta-heuristic search difficult and expensive. We present a graph-based framework for MDO that identifies and clarifies core concepts and relies on mutation operators to specify evolutionary change. This framework is intended to help domain experts develop and study evolutionary algorithms based on domain-specific models and operators. In addition, it can help in clarifying the critical factors for conducting reproducible experiments in MDO. Based on the framework, we are able to take a first step toward identifying and studying important properties of evolutionary operators in the context of MDO. As a showcase, we investigate the impact of soundness and completeness at the level of mutation operator sets on the effectiveness and efficiency of evolutionary algorithms.

     

Iron working at Zambana el Vato (TN), Italy

ABSTRACT

The workshop of Zambana el Vato (region Trentino, Northern Italy), is dated to the period between the 7th-6th and the 5th century BC. Iron working activities are clearly recognizable from the various finds. Among them there are working slag, heated clay, fragments of hearth or forge, hammerscale and more residues that can be referred to iron technology. A number of selected specimens were sectioned and mounted for photomicroscopy to identify the structure and some of the mounted samples were also examined by scanning electron microscopy (SEM) using both a back scattered electron detector and energy dispersive (EDS) x-ray analysis. This paper presents the results of these studies. The hearths were regularly repaired, as their fragments were found mixed with working slag. The hammerscale samples indicate that there were three iron-working areas. The fragments of forge with traces of tuyeres indicate that bellows were employed. Refining slag was identified among the debris. This is particularly significant as for the moment no iron refining centers are known in this area.     

Powell–Sabin B‐splines and unstructured standard T‐splines for the solution of the Kirchhoff–Love plate theory exploiting Bézier extraction

The equations that govern Kirchhoff–Love plate theory are solved using quadratic Powell–Sabin B‐splines and unstructured standard T‐splines. Bézier extraction is exploited to make the formulation computationally efficient. Because quadratic Powell–Sabin B‐splines result in ‐continuous shape functions, they are of sufficiently high continuity to capture Kirchhoff–Love plate theory when cast in a weak form. Unlike non‐uniform rational B‐splines (NURBS), which are commonly used in isogeometric analysis, Powell–Sabin B‐splines do not necessarily capture the geometry exactly. However, the fact that they are defined on triangles instead of on quadrilaterals increases their flexibility in meshing and can make them competitive with respect to NURBS, as no bending strip method for joined NURBS patches is needed. This paper further illustrates how unstructured T‐splines can be modified such that they are ‐continuous around extraordinary points, and that the blending functions fulfil the partition of unity property. The performance of quadratic NURBS, unstructured T‐splines, Powell–Sabin B‐splines and NURBS‐to‐NURPS (non‐uniform rational Powell–Sabin B‐splines, which are obtained by a transformation from a NURBS patch) is compared in a study of a circular plate. Copyright © 2015 John Wiley & Sons, Ltd.