The silence of the library: Environment, situational norm, and social behavior.

On the basis of the idea that situational norms are mentally represented as associations between environments and normative behavior, it was proposed that an environment can automatically direct normative behavior. More specifically, when situational norms are well-established (e.g., when entering the library, one should be silent), an environment is capable of automatically activating mental representations of normative behavior and the behavior itself. In these experiments, participants were exposed to pictures of environments, and effects on accessibility of representations of normative behavior and on actual behavior were assessed. Results indicated that representations of behavior and actual behavior itself are activated automatically when (a) goals to visit the environment are active and (b) strong associations between environment and normative behavior are established. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

A farm level approach to define successful mitigation strategies for GHG emissions from ruminant livestock systems

Ruminant livestock systems are a significant source of greenhouse gases (GHGs). Thus far, mitigation options for GHG emissions mainly focused on a single gas, and are treated as isolated activities. The present paper proposes a framework for a farm level approach for the full accounting of GHG emissions. The methodology accounts for the relevant direct and indirect emissions of methane, nitrous oxide and carbon dioxide, including carbon sequestration. Furthermore, the potential trade-off with ammonia volatilisation and nitrate leaching are taken into account. A ruminant livestock farm is represented with a conceptual model consisting of five pools: animal, manure, soil, crop and feed. The carbon and nitrogen inputs, throughputs and outputs are described, and the direct emissions are related to the carbon and nitrogen flows. The indirect emissions included in the methodology are mainly carbon dioxide emissions from energy use and nitrous oxide emissions related to imported resources and nitrogen losses. The whole farm approach is illustrated with a case of two dairy farms with contrasting livestock density and grassland management. It is shown that the inclusion of carbon sequestration and all indirect emissions have a major impact on the GHG budget of the farm. For one farm, the effect of four mitigation options on the GHG emissions was quantified. It was concluded that a whole farm approach of full accounting contributes to a better insight in the interactions between the carbon and nitrogen flows and the resulting emissions, within and outside the farm boundaries. Consequently, the methodology can be used to develop efficient and effective mitigation strategies.     

Improving active Mealy machine learning for protocol conformance testing

Using a well-known industrial case study from the verification literature, the bounded retransmission protocol, we show how active learning can be used to establish the correctness of protocol implementation I relative to a given reference implementation R. Using active learning, we learn a model M _R of reference implementation R, which serves as input for a model-based testing tool that checks conformance of implementation I to M _R . In addition, we also explore an alternative approach in which we learn a model M _I of implementation I, which is compared to model M _R using an equivalence checker. Our work uses a unique combination of software tools for model construction (Uppaal), active learning (LearnLib, Tomte), model-based testing (JTorX, TorXakis) and verification (CADP, MRMC). We show how these tools can be used for learning models of and revealing errors in implementations, present the new notion of a conformance oracle, and demonstrate how conformance oracles can be used to speed up conformance checking.     

Neural Network Method for Solving Partial Differential Equations

A method is presented to solve partial differential equations (pde's) and its boundary and/or initial conditions by using neural networks. It uses the fact that multiple input, single output, single hidden layer feedforward networks with a linear output layer with no bias are capable of arbitrarily well approximating arbitrary functions and its derivatives, which is proven by a number of authors and well known in literature. Knowledge about the pde and its boundary and/or initial conditions is incorporated into the structures and the training sets of several neural networks. In this way we obtain networks of which some are specifically structured. To find the solution of the pde and its boundary and/or initial conditions we have to train all obtained networks simultaneously. Therefore we use an evolutionary algorithm to train the networks. We demonstrate the working of our method by applying it to two problems.     

Priming and authorship ascription: When nonconscious goals turn into conscious experiences of self-agency.

The conscious experience of self-agency (i.e., the feeling that one causes one’s own actions and their outcomes) is fundamental to human self-perception. Four experiments explored how experienced self-agency arises from a match between nonconsciously activated outcome representations and the subsequent production of the outcome and explored specifically how implicit motivation to produce the outcome may impinge on this process. Participants stopped a rapidly presented sequence of colors on a computer screen. Subsequently, they were presented with what could be the color on which they had stopped the sequence or a color that was randomly chosen by the computer. Agency ratings after each trial revealed that priming outcomes (a specific color) just before the outcome was produced enhanced experienced self-agency. Importantly, priming outcomes relatively far in advance also augmented self-agency, but only if the outcome was attached to positive affect and thus operating as a nonconscious goal maintaining the outcome representation active over time. As such, these studies show how the mechanisms underlying nonconscious goal pursuit promote experiences of self-agency, thus integrating 2 lines of research that so far have led separate lives. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Dynamic Simulation of Planar Flexible Link Manipulators using Adaptive Modal Integration

In this paper a modal integration method is proposed for analyzingthe dynamic behavior of multi-link planar flexible manipulators. Anon-linear finite element method is employed to derive theequations of motion in terms of a mixed set of generalizedcoordinates of the manipulator with rigid links and deformationparameters that characterize flexible deformations of the links.Using a perturbation method the vibrational motion of themanipulator is modeled as a first-order perturbation of thenon-linear nominal rigid link motion. For that purpose theflexible dynamic manipulator model is split into two parts. Arigidified model describes the nominal rigid link motion. Alinear system linearized about the nominal trajectory describesthe vibrational motion. In order to reduce the dimension of thelinearized system, a modal reduction technique is proposed. Thenmodal integration can be applied using only a small number of lowfrequency modes. The mode-acceleration concept is used to accountfor the pseudo static contribution of the high frequency modes.Applied to the motion of a manipulator mechanism the method isreferred to as adaptive modal integration since thetime-varying nature of the mode shape functions is taken intoaccount.A flexible two-link manipulator is analyzed to illustrate theperformance of the solution method. Comparisons between solutionsutilizing non-linear and perturbation analyzes with and withoutmodal integration show a good agreement. In a simulation with onlya few modes the accuracy is kept, whereas the computation time isdrastically reduced.     

Above-threshold leakage in semiconductor lasers: An analyticalphysical model

We present an analytical physical model for above-threshold leakage in semiconductor lasers. The model can be applied to estimate whether heterobarrier lowering and accompanying overbarrier leakage are within reach of having serious deleterious effects on laser performance. The model uses two-dimensional fully self-consistent numerical equations that arise from comprehensive systems of partial coupled differential equations. The effect of temperature and doping on laser efficiency is analyzed for two lasers, one designed for operation at 1.3 μm and the other at 1.55 μm. Both devices are assumed to be built in the InGaAsP-InP material system. We show that, even in a 1.55-μm laser, overbarrier leakage can cause severe performance degradation at typical operating temperatures and doping levels, and we argue that overbarrier leakage deserves to be treated as a potential threat to laser performance at telecommunication wavelengths     

A multiplex ligation detection assay for the characterization of Salmonella enterica strains

A proof of principle of a multi-target assay for genotyping Salmonella has been developed targeting 62 genomic marker sequences of Salmonella related to pathogenicity. The assay is based on multiplex ligation detection reaction (LDR) followed by customized ArrayTube? microarray detection. The feasibility of the developed assay was verified in a method comparison study with conventional PCR using 16 Salmonella 'test' strains comprising eight serovars. Subsequently, the feasibility of the LDR microarray assay was also tested by analyzing 41 strains belonging to 23 serovars. With the exception of four serovars each serovar was characterized by a unique virulence associated gene repertoire. The LDR microarray platform proved to be a convenient, rapid and easy to use tool with potential in tracing a Salmonella contamination in the food chain, for outbreak studies, and to provide data for risk assessors that support bio-traceability models.