Support vector classification of proteomic profile spectra based on feature extraction with the bi-orthogonal discrete wavelet transform

Automatic classification of high-resolution mass spectrometry data has increasing potential to support physicians in diagnosis of diseases like cancer. The proteomic data exhibit variations among different disease states. A precise and reliable classification of mass spectra is essential for a successful diagnosis and treatment. The underlying process to obtain such reliable classification results is a crucial point. In this paper such a method is explained and a corresponding semi automatic parameterization procedure is derived. Thereby a simple straightforward classification procedure to assign mass spectra to a particular disease state is derived. The method is based on an initial preprocessing stage of the whole set of spectra followed by the bi-orthogonal discrete wavelet transform (DWT) for feature extraction. The approximation coefficients calculated from the scaling function exhibit a high peak pattern matching property and feature a denoising of the spectrum. The discriminating coefficients, selected by the Kolmogorov–Smirnov test are finally used as features for training and testing a support vector machine with both a linear and a radial basis kernel. For comparison the peak areas obtained with the it ClinProt-System ¹ [33] were analyzed using the same support vector machines. The introduced approach was evaluated on clinical MALDI-MS data sets with two classes each originating from cancer studies. The cross validated error rates using the wavelet coefficients where better than those obtained from the peak areas².     

Viscoelastic material design with negative stiffness components using topology optimization

An application of topology optimization to design viscoelastic composite materials with elastic moduli that soften with frequency is presented. The material is a two-phase composite whose first constituent is isotropic and viscoelastic while the other is an orthotropic material with negative stiffness but stable. A concept for this material based on a lumped parameter model is used. The performance of the topology optimization approach in this context is illustrated using three examples.     

Toward a formal specification of multimedia synchronization scenarios

This paper introduces time stream Pétri nets (Ts treamPn), a model for the formal specification of multimedia synchronization scenarios. This new model extends time Pétri nets to formally describe the timed behaviour of multimedia objects and streams in asynchronous distributed systems. The proposed approach uses time intervals to label the arcs exiting from the places of the net, and typed transitions to define different firing rules. This model allows a complete and accurate specification of synchronization constraints between multimedia streams and can be used at different levels of granularity.     

Heavy Electron Superconductors in Terms of Block Spin Phenomenology

We investigate the relationship between superconductivity and spin glasses, which were observed to be coexistent in heavy fermion superconductors (HFSs). We begin by explaining the phase of spin glass using concepts of finite-sized block spin. We then introduce the phase of superconductivity in HFSs as collective Cooper pairing, that is, the pairing of block spins with net spin = 1/2, each of which is comprised of a large number of random spins that together produce a majority spin direction. The superconducting Bardeen-Cooper-Schrieffer (BCS)-type phonon-mediated electron-electron interaction is substituted for the BCS-type electron-electron interaction mediated by phonon-enhanced spin flips previously suggested by us. An effective charge of any value stemming from an electron attached to the flux of an electric field can be referred to as a composite charge. The distinguished difference between ours and the original BCS model is the following: (i) another BCS-type interaction, (ii) bare electrons that are substituted for block spins with net spin = 1/2 and net charge = e in the presence of electric fields, and (iii) Fermi-Dirac distributions that are replaced by a new distribution called as Brillouin distribution.     

Rational Design and Synthesis of Extremely Efficient Macroporous CoSe2–CNT Composite Microspheres for Hydrogen Evolution Reaction

Uniquely structured CoSe₂–carbon nanotube (CNT) composite microspheres with optimized morphology for the hydrogen‐evolution reaction (HER) are prepared by spray pyrolysis and subsequent selenization. The ultrafine CoSe₂ nanocrystals uniformly decorate the entire macroporous CNT backbone in CoSe₂–CNT composite microspheres. The macroporous CNT backbone strongly improves the electrocatalytic activity of CoSe₂ by improving the electrical conductivity and minimizing the growth of CoSe₂ nanocrystals during the synthesis process. In addition, the macroporous structure resulting from the CNT backbone improves the electrocatalytic activity of the CoSe₂–CNT microspheres by increasing the removal rate of generated H₂ and minimizing the polarization of the electrode during HER. The CoSe₂–CNT composite microspheres demonstrate excellent catalytic activity for HER in an acidic medium (10 mA cm^?2 at an overpotential of ≈174 mV). The bare CoSe₂ powders exhibit moderate HER activity, with an overpotential of 226 mV at 10 mA cm^?2. The Tafel slopes for the CoSe₂–CNT composite and bare CoSe₂ powders are 37.8 and 58.9 mV dec^?1, respectively. The CoSe₂–CNT composite microspheres have a slightly larger Tafel slope than that of commercial carbon‐supported platinum nanoparticles, which is 30.2 mV dec^–1.     

Using dynamic demand information and zoning for the storage of non-uniform density stock keeping units

The warehouse order-picking operation is one of the most labour-intense activities that has an important impact on responsiveness and efficiency of the supply chain. An understanding of the impact of the simultaneous effects of customer demand patterns and order clustering, considering physical restrictions in product storage, is critical for improving operational performance. Storage restrictions may include storing non-uniform density stock keeping units (SKUs) whose dimensions and weight constrain the order-picking operation given that a priority must be followed. In this paper, a heuristic optimisation based on a quadratic integer programming is employed to generate a layout solution that considers customer demand patterns and order clustering. A simulation model is used to investigate the effects of creating and implementing these layout solutions in conjunction with density zones to account for restrictions in non-uniform density SKUs. Results from combining layout optimisation heuristics and density zoning indicate statistical significant differences between assignments that ignore the aforementioned factors and those that recognise it.