QoS metrics-in-the-loop for endowing runtime self-adaptation to robotic software architectures

Multimedia Tools and Applications - The design of robots capable of operating autonomously in changing and unstructured environments, requires using complex software architectures in which,...     

Nickel phosphonate MOF as efficient water splitting photocatalyst

A novel microporous two-dimensional(2D)Ni-based phosphonate metal-organic framework(MOF;denoted as IEF-13)has been successfully synthesized by a simple and green hydrothermal method and fully characterized using a combination of experimental and computational techniques.Structure resolution by single-crystal X-ray diffraction reveals that IEF-13 crystallizes in the triclinic space group Pi having bi-octahedra nickel nodes and a photo/electroactive tritopic phosphonate ligand.Remarkably,this material exhibits coordinatively unsaturated nickel(II)sites,free-P0₃H₂and-P0₃H acidic groups,a C0₂accessible microporosity,and an exceptional thermal and chemical stability.Further,its in-deep optoelectronic characterization evidences a photoresponse suitable for photocatalysis.In this sense,the photocatalytic activity for challenging H₂generation and overall water splitting in absence of any co-catalyst using UV-Vis irradiation and simulated sunlight has been evaluated,constituting the first report for a phosphonate-MOF photocatalyst.IEF-13 is able to produce up to 2,200 fimol of H₂per gram using methanol as sacrificial agent,exhibiting stability,maintaining its crystal structure and allowing its recycling.Even more,170μmol of H₂per gram were produced using IEF-13 as photocatalyst in the absence of any co-catalyst for the overall water splitting,being this reaction limited by the 0₂reduction.The present work opens new avenues for further optimization of the photocatalytic activity in this type of multifunctional materials.     

A note on the variance of rank-based selection strategies for genetic algorithms and genetic programming

This paper evaluates different forms of rank-based selection that are used with genetic algorithms and genetic programming. Many types of rank based selection have exactly the same expected value in terms of the sampling rate allocated to each member of the population. However, the variance associated with that sampling rate can vary depending on how selection is implemented. We examine two forms of tournament selection and compare these to linear rank-based selection using an explicit formula. Because selective pressure has a direct impact on population diversity, we also examine the interaction between selective pressure and different mutation strategies.     

Poly(vinylidene fluoride) with zinc oxide and carbon nanotubes applied to pressure sheath layers in oil and gas pipelines

In this work, PVDF composites containing 0.2% (m/m) of carbon nanotubes (MWCNTs), PVDF with 5.0% (m/m) of zinc oxide (ZnO), and composites containing both particles in the same contents in the matrix were melt processed in a mini-extruder machine with double screws, using the counter-rotation mode. Composites were characterized by scanning electron microscopy (SEM), Fourier-transform infrared spectroscopy (FTIR), thermogravimetric analysis (TGA), differential scanning calorimetry (DSC), X-ray diffraction (XRD), dynamic-mechanical analysis (DMA), and contact angle tests (CA). The samples presented the predominance of the α phase, with an increased degree of crystallinity as well as an increase in dimensional stability by incorporating both fillers, showing a synergistic effect between these particles, as shown on FTIR, DSC, and XRD results. SEM images showed a good dispersion of high aspect ratio particles. In general, DMA and TGA analysis showed that composites had not decreased their thermal and mechanical performance when compared to neat PVDF. Results of CA analysis showed an increase in the hydrophobicity of the sample containing MWCNTs. Permeability tests were also performed using a differential pressure system, combining high temperature and pressure, obtaining permeability measures and time lag. This work presents an alternative of composite materials, suggesting its application in the internal pressure sheath layers of oil and gas flexible pipes.     

Temporal contexts: Effective text classification in evolving document collections

The management of a huge and growing amount of information available nowadays makes Automatic Document Classification (ADC), besides crucial, a very challenging task. Furthermore, the dynamics inherent to classification problems, mainly on the Web, make this task even more challenging. Despite this fact, the actual impact of such temporal evolution on ADC is still poorly understood in the literature. In this context, this work concerns to evaluate, characterize and exploit the temporal evolution to improve ADC techniques. As first contribution we highlight the proposal of a pragmatical methodology for evaluating the temporal evolution in ADC domains. Through this methodology, we can identify measurable factors associated to ADC models degradation over time. Going a step further, based on such analyzes, we propose effective and efficient strategies to make current techniques more robust to natural shifts over time. We present a strategy, named temporal context selection, for selecting portions of the training set that minimize those factors. Our second contribution consists of proposing a general algorithm, called Chronos, for determining such contexts. By instantiating Chronos, we are able to reduce uncertainty and improve the overall classification accuracy. Empirical evaluations of heuristic instantiations of the algorithm, named WindowsChronos and FilterChronos, on two real document collections demonstrate the usefulness of our proposal. Comparing them against state-of-the-art ADC algorithms shows that selecting temporal contexts allows improvements on the classification accuracy up to 10%. Finally, we highlight the applicability and the generality of our proposal in practice, pointing out this study as a promising research direction.     

Influence of H2/NH3 mixtures on the composition of SiCNYO and SiCNAl(O) nanopowders

We performed pyrolysis of SiCNAlH and SiCNYOH nanopowder precursors under a reactive atmosphere (Ar/NH₃/H₂) with various compositions of ammonia (NH₃) and dihydrogen (H₂) to diminish C content, which is deleterious for thermal stability and sintering of the powders. This paper continues a previous work on the fabrication of an Si₃N₄/SiC composite without free C by studying the effect of H₂ on the C/N atomic ratio of the powder. We studied the influence of the nature of the gaseous mixture (Ar/NH₃/H₂) on the powder composition. Elemental analysis showed that the introduction of H₂ in the pyrolysis atmosphere limited the decomposition of NH₃ and allowed for control of the C/N ratio. This behaviour can be explained by the structural evolution observed by ²⁹Si NMR spectrometry but also by Fourier transform infrared and Raman spectroscopy. An Si₃N₄/SiC composite, with traces of free C, was obtained after post-pyrolysis heat treatment of the powders synthesized with 10 wt.% of H₂ and 25 wt.% NH₃.     

Effect of ageing and annealing on the mechanical behaviour and biodegradability of a poly(3‐hydroxybutyrate) and poly(ethylene‐co‐methyl‐acrylate‐co‐glycidyl‐methacrylate)blend

Poly(3‐hydroxybutyrate) (PHB) undergoes an ageing process that contributes to its remarkable fragility. Blending it with an elastomer is a possibility to increase toughness. In this work, the mechanical properties of a 70/30 wt% blend of PHB and poly(ethylene‐co‐methyl‐acrylate‐co‐glycidyl‐methacrylate) were studied over time. The phenomenon of ageing affected the blend, which lost its ductility and became fragile days after its processing. Differential scanning calorimetry and small angle X‐ray scattering analyses showed that this drop in mechanical properties was due to changes in the crystalline structure of the matrix. Annealing reduced fragility, increased toughness and prevented a re‐ageing of the blend. Biodegradation in soil was also more intense for annealed samples. © 2013 Society of Chemical Industry     

Monitoring of induction motor load by neural network techniques

This paper deals with the electric tracing of the load variation of an induction machine supplied by the mains. A load problem, like a torque dip, affects the machine supply current and consequently it should be possible to use the current pattern to detect features of the torque pattern, using the machine itself as a torque sensor. But current signature depends on many phenomena and misunderstandings are possible. At first the effect of different load anomalies on current spectrum, in comparison with other machine problems like rotor asymmetries, are investigated. Reference is made to low frequency torque disturbances, which cause a quasistationary machine behavior. Simplified relationships, validated by simulation results and by experimental results, are developed to address the current spectrum features. In order to detect on-line anomalies, a current signature extraction is performed by the time-frequency spectrum approach. This method allows the detection of random faults as well. Finally it is shown that a neural network approach can help the torque pattern recognition, improving the interpretation of machine anomalies effects