Line Bisection in the Split Brain.

The authors examined line bisection in 4 patients with resection of the corpus callosum and in 22 control participants. The control participants showed a leftward bias, especially with the left hand, implying right-hemispheric dominance in spatial attention. Two patients with anterior callosotomy showed similar biases, suggesting that the anterior callosum plays only a small role. A patient with complete callosotomy showed a strong right bias, regardless of hand use. A patient with posterior callosotomy showed the opposite pattern: a strong left bias, regardless of hand use. These data suggest that the posterior corpus callosum normally plays a role in line bisection and that the resection of the posterior corpus callosum produces consistent bias. The direction of the bias depends on which hemisphere assumes control. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

PicSOM – content-based image retrieval with self-organizing maps

We have developed a novel system for content-based image retrieval in large, unannotated databases. The system is called PicSOM, and it is based on tree structured self-organizing maps (TS-SOMs). Given a set of reference images, PicSOM is able to retrieve another set of images which are similar to the given ones. Each TS-SOM is formed with a different image feature representation like color, texture, or shape. A new technique introduced in PicSOM facilitates automatic combination of responses from multiple TS-SOMs and their hierarchical levels. This mechanism adapts to the user's preferences in selecting which images resemble each other. Thus, the mechanism implements a relevance feedback technique on content-based image retrieval. The image queries are performed through the World Wide Web and the queries are iteratively refined as the system exposes more images to the user.     

Contact Behavior of Microcrystalline Cellulose Pellets Depending on their Water Content

Microcrystalline cellulose pellets for oral drug delivery are often produced by a combined wet extrusion-spheronization process. During the entire process, the cylindrical as well as the spherical pellets are exposed to various stresses resulting in a change of their shape and size due to plastic deformation and breakage. In this work, the effect of moisture content of pellets on their mechanical behavior is studied. In static compression tests, the strong influence of water content on deformation behavior of pellets is confirmed. Moreover, impact tests are performed using a setup consisting of three high-speed cameras to record pellet-wall collisions. Material properties, such as stiffness, restitution coefficient, breakage force, and displacement, were analyzed depending on the water content.     

Machine learning and Design of Experiments: Alternative approaches or complementary methodologies for quality improvement?

Machine Learning (ML), or the ability of self-learning computer algorithms to autonomously structure and interpret data, is a methodological approach to solve complicated optimization problems based on abundant data. ML is recently gaining momentum as algorithmic applications, computing potency, and available data sets increased manifold over the past two decades, providing an information-rich environment in which human reasoning can partially be replaced by computer reasoning. In this paper, we want to assess the implications of ML for Design of Experiments (DoE), a statistical methodology widely used in Quality Management for quantifying effects and interactions of factors with influence on the production quality or the process yield. We specifically want to assess the future role and importance of DoE: Will it remain unaltered by ML, will it be made obsolete, or will it be reinforced? With this, we want to contribute to the discussion of the future use of traditional Quality Management methodologies in production, as our ML assessment can in principle be applied to other statistical methodologies as well. While we are convinced that ML will heavily impact the field of Quality Management and its predominant set of statistical methodologies, we find reason to expect that this impact will be a mutual one. As this is the first paper addressing the joint force potential of the two methodologies ML and DoE, we expect a range of follow-up papers being written on the subject and a spark in specialized applications addressing DoE's ML-enhanced vital functionality for process improvements.     

Assessment of residential battery storage systems and operation strategies considering battery aging

With the increasing popularity of combining residential photovoltaic systems with battery storages, research, industry, and customers look for ways to determine if such an investment is economically profitable. Simulation programs may serve to predict the profitability and lifetime of the system. In this paper, we use techno-economic analysis with a specific account of battery degradation to determine profitability and lifetime of a residential photovoltaic (PV) battery system under different energy management and tariff regimes. This work presents two case studies: the first being a techno-economic comparison for a residential PV-battery system in New South Wales, Australia and Germany, and the second analyzing the profitability and degradation impact of three different operation strategies for a battery storage in Australia. The results reveal that site-specific conditions (i.e., geographical and energy-economic constraints) may have a significant impact on the ideal system configuration and ultimately the anticipated battery lifetime. Furthermore, statistical analysis of different storage operation strategies applied to various prosumer load and generation profiles reveals the effects of storage dispatch strategies on battery aging.     

CrasyDSE: A framework for solving Dyson–Schwinger equations

Dyson–Schwinger equations are important tools for non-perturbative analyses of quantum field theories. For example, they are very useful for investigations in quantum chromodynamics and related theories. However, sometimes progress is impeded by the complexity of the equations. Thus automating parts of the calculations will certainly be helpful in future investigations. In this article we present a framework for such an automation based on a C++ code that can deal with a large number of Green functions. Since also the creation of the expressions for the integrals of the Dyson–Schwinger equations needs to be automated, we defer this task to a Mathematica notebook. We illustrate the complete workflow with an example from Yang–Mills theory coupled to a fundamental scalar field that has been investigated recently. As a second example we calculate the propagators of pure Yang–Mills theory. Our code can serve as a basis for many further investigations where the equations are too complicated to tackle by hand. It also can easily be combined with DoFun, a program for the derivation of Dyson–Schwinger equations.¹Program summaryProgram title: CrasyDSECatalogue identifier: AEMY _v1_0Program summary URL: http://cpc.cs.qub.ac.uk/summaries/AEMY_v1_0.htmlProgram obtainable from: CPC Program Library, Queen’s University, Belfast, N. IrelandLicensing provisions: Standard CPC licence, http://cpc.cs.qub.ac.uk/licence/licence.htmlNo. of lines in distributed program, including test data, etc.: 49030No. of bytes in distributed program, including test data, etc.: 303958Distribution format: tar.gzProgramming language: Mathematica 8 and higher, C++.Computer: All on which Mathematica and C++ are available.Operating system: All on which Mathematica and C++ are available (Windows, Unix, Mac OS).Classification: 11.1, 11.4, 11.5, 11.6.Nature of problem: Solve (large) systems of Dyson–Schwinger equations numerically.Solution method: Create C++ functions in Mathematica to be used for the numeric code in C++. This code uses structures to handle large numbers of Green functions.Unusual features: Provides a tool to convert Mathematica expressions into C++ expressions including conversion of function names.Running time: Depending on the complexity of the investigated system solving the equations numerically can take seconds on a desktop PC to hours on a cluster.     

Plasma modification and deposition on inner tube faces by pulsed DC discharges

The modification of inner surfaces of tubes becomes increasingly important in many fluid transport applications. However, long tubes require special plasma techniques for a surface functionalization on the nanometer scale without damaging the bulk. This work focuses on pulsed DC glow discharges in inert argon, reactive oxygen, nitrogen and air plasma as well as polymerizing acetylene plasma revealing strong influences to the wetting behavior by the total duration of the plasma discharge time (plasma on-time). While the investigated polymers polyamide, polycarbonate, polyethylene terephtalate, and thermoplastic polyurethane react very differently on the inert and reactive etching (activation) plasma, the deposition of plasma polymer films (polymer-like carbon with decreasing cross-linking at lower plasma intensity and >50% hydrogen content) suppresses all substrate influences.