Protecting and preserving clear barrier layers for flexible packaging materials In‐line thermal evaporation of organic topcoat layer

The end market for transparent flexible barrier films is larger than for metallized films. Presently, the market is still dominated by polymeric barrier layers but the used chemicals may be harmful for the environment. An alternative would be transparent thin layers deposited by vacuum deposition techniques using reactive processes. Ceramic materials like silicon oxide or aluminum oxide are used having a film thickness of just ～10 nm, a coating uniformity of +/?5% across and along the film at a barrier performance below 2.0 sccm/m²d for oxygen transmission rate (OTR) and below 1.0 g/m²d for water vapor transmission rate (WVTR) on PET substrates. In this paper, details will be provided about the deposition processes for these barrier layers using thermal evaporation, plasma‐assisted thermal evaporation as well as deposition by electron beam evaporation. An important factor for these high barrier transparent coatings is also to withstand the downstream processes in the whole packaging stream like slitting, lamination, printing etc. One solution is to protect the barrier layers by a Topcoat. For example, off‐line deposition of lacquers is used in field but the market penetration is low due to high process and material costs. An in‐situ Topcoat deposition is a smart solution to overcome this issue saving time and costs. Such an approach will be also described in the presentation and the impact on the performance of the final package will be discussed.     

Atom probe tomography of nanoscale architectures in functional materials for electronic and photonic applications

Microscopy has played a central role in the advancement of nanoscience and nanotechnology by enabling the direct visualization of nanoscale structure, leading to predictive models of novel physical behaviors. Electronic and photonic device technologies, whose features and performance are often improved through miniaturization, have particularly benefited from new capabilities in the characterization of material structure and composition. This paper reviews recent applications of atom probe tomography to semiconducting materials with nanoscale architectures that are designed to impart novel properties and device functionality by virtue of their shape and size. A review is necessary because rapid advances in atom probe instrumentation and analysis in the last decade have greatly expanded the utility of atom probe tomography to address scientific questions and technical questions in this area. The paper is organized in terms of the surface topologies of nanoscale architectures. We begin with nominally planar interfaces including thin film heterostructures and superlattices with open surfaces. Distinctive capabilities in the analysis of interfaces are introduced, as are challenges arising from measurement artifacts. We then discuss nanowires and nanowire heterostructures with surfaces that are closed along one dimension, for which atom probe tomography has provided unique and important understandings on the doping processes. Finally, we consider nanocrystals and quantum dots with completely closed surfaces. Along the way, current challenges and opportunities for atom probe tomography are highlighted, and the reader is directed to complementary reviews of more technical aspects of atom probe analysis.     

Evolution of the trappin multigene family in the Suidae

The purpose of the study was to examine the effects of manipulating lung volume (LV) on phonatory and articulatory kinematic behavior during sentence production in healthy adults. Five men and five women repeated the sentence "I sell a sapapple again" under five LV conditions. These included (1) speaking normally, (2) speaking after exhaling most of the air from the lungs, (3) speaking at end expiratory level (EEL), (4) speaking after a maximal inhalation, and (5) speaking after a maximal inhalation while attempting to maintain as normal a mode of speech as possible. From a multichannel recording, measures were made of LV, sound pressure level (SPL), fundamental frequency (F0) and semitone standard deviation (STSD), and upper and lower lip displacements and peak velocities. When compared with the reference condition, the sentence was spoken significantly more quickly at the lowest LV. SPL increased significantly for the high LV condition, as did the women's F0 and STSD. Upper lip displacements and peak velocities generally decreased for LVs other than the reference condition. Lower lip movements showed inconsistent changes as a function of LV. Adjustments to the LV for speech led to SPL and F0 changes consistent with a coordinated control of the respiratory system and the larynx. However, less consistent effects were observed in the articulatory kinematic measures, possibly because of a less direct biomechanical and neural control linkage between respiratory and articulatory structures.     

On Schematological Equivalence of Dataflow Networks

We consider the problem of determining when two dataflow networks with uninterpreted nodes always have the same input-output behavior. We define a set of behavior-preserving transformations on networks and show that this set is “schematologically complete”; i.e., networks have the same input-output behavior under all interpretations if and only if they can be transformed into isomorphic networks. As a by product, we obtain a polynomial algorithm for deciding schematological equivalence of dataflow networks.     

Infrastructure Federation Through Virtualized Delegation of Resources and Services

Infrastructure federation is becoming an increasingly important issue for modern Distributed Computing Infrastructures (DCIs): Dynamic elasticity of quasi-static Grid environments, incorporation of special-purpose resources into commoditized Cloud infrastructures, cross-community collaboration for increasingly diverging areas of modern e-Science, and Cloud Bursting pose major challenges on the technical level for many resource and middleware providers. Especially with respect to increasing costs of operating data centers, the intelligent yet automated and secure sharing of resources is a key factor for success. With the D-Grid Scheduler Interoperability (DGSI) project within the German D-Grid Initiative, we provide a strategic technology for the automatically negotiated, SLA-secured, dynamically provisioned federation of resources and services for Grid-and Cloud-type infrastructures. This goal is achieved by complementing current DCI schedulers with the ability to federate infrastructure for the temporary leasing of resources and rechanneling of workloads. In this work, we describe the overall architecture and SLA-secured negotiation protocols within DGSI and depict an advanced mechanism for resource delegation through means of dynamically provisioned, virtualized middleware. Through this methodology, we provide the technological foundation for intelligent capacity planning and workload management in a cross-infrastructure fashion.     

On the versatility of radial basis function neural networks: A case study in the field of intrusion detection

Classifiers based on radial basis function neural networks have a number of useful properties that can be exploited in many practical applications. Using sample data, it is possible to adjust their parameters (weights), to optimize their structure, and to select appropriate input features (attributes). Moreover, interpretable rules can be extracted from a trained classifier and input samples can be identified that cannot be classified with a sufficient degree of “certainty”. These properties support an analysis of radial basis function classifiers and allow for an adaption to “novel” kinds of input samples in a real-world application. In this article, we outline these properties and show how they can be exploited in the field of intrusion detection (detection of network-based misuse). Intrusion detection plays an increasingly important role in securing computer networks. In this case study, we first compare the classification abilities of radial basis function classifiers, multilayer perceptrons, the neuro-fuzzy system NEFCLASS, decision trees, classifying fuzzy-k-means, support vector machines, Bayesian networks, and nearest neighbor classifiers. Then, we investigate the interpretability and understandability of the best paradigms found in the previous step. We show how structure optimization and feature selection for radial basis function classifiers can be done by means of evolutionary algorithms and compare this approach to decision trees optimized using certain pruning techniques. Finally, we demonstrate that radial basis function classifiers are basically able to detect novel attack types. The many advantageous properties of radial basis function classifiers could certainly be exploited in other application fields in a similar way.     

Smooth velocity approximation for constrained systems in real-time simulation

The rapidly increasing complexity of multi-body system models in applications like vehicle dynamics, robotics and bio-mechanics requires qualitative new solution methods to slash computing times for the dynamical simulation.