Upcycling of polypropylene with various concentrations of peroxydicarbonate and dilauroyl peroxide and two processing steps

The influence of two peroxides (peroxydicarbonate/dilauroyl peroxide) with various concentrations (10–200 mmol/kg PP) and their effective opportunity to introduce long chain branched (LCB) were investigated. The dependence of a single and double extrusion step and the changes of the properties were studied. Experiments were carried out in a single screw extruder at 180°C for the first extrusion step (modification) and at 240°C for the second extrusion step (processing simulation). Melt flow rate and dynamic rheological properties were studied at a measuring temperature of 230°C. For the definitive determination of long chain branched polypropylene (LCB-PP) served the extensional rheology measurements. The mechanical properties were examined via tensile test and impact tensile test. Summarized, LCB (melt strength) could be observed via extensional rheology for all modified specimens and the mechanical properties were maintained or even improved for the modified samples. Particularly, samples containing dilauroyl peroxide display excellent mechanical properties in this study.     

When simulated environments make the difference: the effectiveness of different types of training of car service procedures

An empirical analysis was performed to compare the effectiveness of different approaches to training a set of procedural skills to a sample of novice trainees. Sixty-five participants were randomly assigned to one of the following three training groups: (1) learning-by-doing in a 3D desktop virtual environment, (2) learning-by-observing a video (show-and-tell) explanation of the procedures, and (3) trial-and-error. In each group, participants were trained on two car service procedures. Participants were recalled to perform a procedure either 2 or 4 weeks after the training. The results showed that: (1) participants trained through the virtual approach of learning-by-doing performed both procedures significantly better (i.e. p < .05 in terms of errors and time) than people of non-virtual groups, (2) the virtual training group, after a period of non-use, were more effective than non-virtual training (i.e. p < .05) in their ability to recover their skills, (3) after a (simulated) long period from the training—i.e. up to 12 weeks—people who experienced 3D environments consistently performed better than people who received other kinds of training. The results also suggested that independently from the training group, trainees’ visuospatial abilities were a predictor of performance, at least for the complex service procedure, adj R ² = .460, and that post-training performances of people trained through virtual learning-by-doing are not affected by learning styles. Finally, a strong relationship (p < .001, R ² = .441) was identified between usability and trust in the use of the virtual training tool—i.e. the more the system was perceived as usable, the more it was perceived as trustable to acquire the competences.     

A Review of Element-Based Galerkin Methods for Numerical Weather Prediction: Finite Elements,Spectral Elements,and Discontinuous Galerkin

Numerical weather prediction (NWP) is in a period of transition. As resolutions increase, global models are moving towards fully nonhydrostatic dynamical cores, with the local and global models using the same governing equations; therefore we have reached a point where it will be necessary to use a single model for both applications. The new dynamical cores at the heart of these unified models are designed to scale efficiently on clusters with hundreds of thousands or even millions of CPU cores and GPUs. Operational and research NWP codes currently use a wide range of numerical methods: finite differences, spectral transform, finite volumes and, increasingly, finite/spectral elements and discontinuous Galerkin, which constitute element-based Galerkin (EBG) methods. Due to their important role in this transition, will EBGs be the dominant power behind NWP in the next 10 years, or will they just be one of many methods to choose from? One decade after the review of numerical methods for atmospheric modeling by Steppeler et al. (Meteorol Atmos Phys 82:287–301, 2003), this review discusses EBG methods as a viable numerical approach for the next-generation NWP models. One well-known weakness of EBG methods is the generation of unphysical oscillations in advection-dominated flows; special attention is hence devoted to dissipation-based stabilization methods. Since EBGs are geometrically flexible and allow both conforming and non-conforming meshes, as well as grid adaptivity, this review is concluded with a short overview of how mesh generation and dynamic mesh refinement are becoming as important for atmospheric modeling as they have been for engineering applications for many years.     

On vector averaging over the unit hypersphere

Sample averaging is a commonly used way to smooth out irregularities of data and to get rid of random fluctuations in measurements analysis. In adaptive signal processing, where an adaptive system learns its own parameters in order to perform a predefined task, the learnt parameters-pattern may depend on the initial learning state and on the fluctuations of the statistical features of the input signals to the system. In adaptive system learning, averaging may be employed as a method to merge several learnt parameters-patterns in order to get a better representative pattern. Even in the case of scalar parameters, the concept of averaging is not uniquely defined as scalar parameters spaces may exhibit a rich structure to be dealt with. The case of multiple parameter patterns where single parameters are mutually constrained to each other may exhibit an even richer structure. In the present paper, we deal with the case of parameters-patterns belonging to the unit hypersphere and develop an averaging technique based on the differential geometrical structure of such a curved space. Numerical experiments illustrate the behavior of the developed averaging algorithm.     

Asymptotic switched composite adaptive control with application to robotic interaction tasks

This article proposes a new switched adaptive control design for uncertain switched systems with composite (time-driven and state-dependent) switching and shows its applicability in switched impedance control. A composite switched adaptive control design, consisting of the direct switched adaptive control and the indirect switched adaptive control counterpart, is developed to improve the control performance. Specifically, a new stability condition for composite switching is proposed by making use of differential matrix equations and Sylvester matrix equations, which are a generalization of Lyapunov matrix equations. The design results in a time-varying multiple Lyapunov function that is decreasing at the switching instants. From the theoretical point of view, the relevance of this work is the construction of the adaptive laws that guarantee asymptotic tracking error and asymptotic estimation for the direct and indirect switched adaptive control loops, respectively. From the practical point of view, the relevance of this work is validated in a new switched impedance control for the robot interaction with uncertain and discontinuous environments.     

Swarm Intelligence Approaches for Grid Load Balancing

With the rapid growth of data and computational needs, distributed systems and computational Grids are gaining more and more attention. The huge amount of computations a Grid can fulfill in a specific amount of time cannot be performed by the best supercomputers. However, Grid performance can still be improved by making sure all the resources available in the Grid are utilized optimally using a good load balancing algorithm. This research proposes two new distributed swarm intelligence inspired load balancing algorithms. One algorithm is based on ant colony optimization and the other algorithm is based on particle swarm optimization. A simulation of the proposed approaches using a Grid simulation toolkit (GridSim) is conducted. The performance of the algorithms are evaluated using performance criteria such as makespan and load balancing level. A comparison of our proposed approaches with a classical approach called State Broadcast Algorithm and two random approaches is provided. Experimental results show the proposed algorithms perform very well in a Grid environment. Especially the application of particle swarm optimization, can yield better performance results in many scenarios than the ant colony approach.     

Evaluation of artery visualizations for heart disease diagnosis

Heart disease is the number one killer in the United States, and finding indicators of the disease at an early stage is critical for treatment and prevention. In this paper we evaluate visualization techniques that enable the diagnosis of coronary artery disease. A key physical quantity of medical interest is endothelial shear stress (ESS). Low ESS has been associated with sites of lesion formation and rapid progression of disease in the coronary arteries. Having effective visualizations of a patient's ESS data is vital for the quick and thorough non-invasive evaluation by a cardiologist. We present a task taxonomy for hemodynamics based on a formative user study with domain experts. Based on the results of this study we developed HemoVis, an interactive visualization application for heart disease diagnosis that uses a novel 2D tree diagram representation of coronary artery trees. We present the results of a formal quantitative user study with domain experts that evaluates the effect of 2D versus 3D artery representations and of color maps on identifying regions of low ESS. We show statistically significant results demonstrating that our 2D visualizations are more accurate and efficient than 3D representations, and that a perceptually appropriate color map leads to fewer diagnostic mistakes than a rainbow color map.     

A microvalve for hybrid microfluidic systems

A hybrid valve for lab on chip applications is presented. The valve is assembled by bonding poly (methyl methacrylate), PMMA, and silicon-based elastomers. The process used to promote the hybrid bonding includes the deposition of an organosilane (TMSPM) on the thermoplastic polymer, PMMA to interface PMMA and elastomers. For this study, a membrane in ELASTOSIL^? is bonded in correspondence of the end of two microfluidic channels of a fabricated PMMA microfluidic chip. Prior the bonding, a plasma etching process has been used to remove the TMSPM in a confined circular area. This process made possible to bond selectively the edge of a membrane leaving free to move its central part. Actuating the membrane with an external positive pressure or vacuum is possible, respectively, to obstruct or to connect the microfluidic channels. The microvalve may be simply integrated in microfluidic devices and permits the control of microvolumes of fluid in processes such as transport, separation, and mixing. The deposition of the TMSPM, the bonding of the valve and its actuation has been characterized and tested. The flow rate control of liquids through the valve has been characterized. The results have been discussed and commented. The valve can stand up to 14 psi without showing leakages.     

Supporting Different Dimensions of Adaptability in Workflow Modeling

This paper discusses the theme of workflow systemadaptability in relation to process modeling. Startingfrom a discussion on the role of formal constructs incooperation, the main claim is that adaptabilityinvolves different dimensions of process modeling.These dimensions concern the possibility to flexiblycombine a rich set of basic categories in order toobtain the most suitable language for modeling thetarget business process and the work practices aroundit; to take into account various levels of visibilityof the contexts of definition and use of a processmodel; and finally to allow for temporary as well aspermanent modifications of the process itself.Ariadne, a notation conceived for the above purpose,is illustrated by means of a working example.Moreover, the paper presents the main designprinciples governing Ariadne's implementation.     

High-Energy Physics on the Grid: the ATLAS and CMS Experience

In this paper we present the experience of the ATLAS and CMS High-Energy Physics (HEP) experiments at the Large Hadron Collider (LHC) with the LCG/EGEE Grid infrastructure. The activity developed around the following two main lines: large-scale physics and detector simulations and end-user analysis. The LCG/EGEE Grid infrastructure offers a large amount of computing and storage resources and is growing very rapidly. It provides the natural environment for large-scale physics and detector simulations. Also, the analysis of these detector simulation data (and in the near future of the reconstructed data from physics collisions) requires efficient end-users access to Grid resources. In this paper, the main findings and lessons learned in terms of performance, robustness and scalability of the whole system are discussed in detail.