950 resultados para Functional feeding groups


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Polymeric nanocomposites have been shown to possess superior electrical insulation properties compared to traditional filled-resins. However, poor dispersion uniformity and insufficient filler-matrix interaction can adversely affect insulation properties of nanocomposites. In this study, the use of plasma polymerization is proposed to coat poly(ethylene oxide) polymer layers on silica nanoparticles. It is shown that better dispersion is achieved and C-O bonds are created between the surface functional groups of the nanoparticles and the host epoxy polymer. Electrical insulation tests demonstrate that the nanocomposites with plasma polymerized silica nanoparticles feature better resistance against electrical treeing, lower dielectric constant, and also mitigated space charge built-up. Therefore, plasma polymerization offers a promising fabrication technique to further improve the synthesis of nanocomposite dielectrics with superior electrical insulation properties.

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The species from Australia in the genera Carientothrips and Nesothrips are reviewed and an illustrated key is provided. Carientothrips is distinguished based on the unusual form of the maxillary palps. Two species, badius Hood comb.n. and capricornis Mound comb.n., are transferred to Nesothrips from Carientothrips; and Nesothrips melinus Mound syn.n. is synonymised with Carientothrips miskoi Mound. In Carientothrips the following six new species are described: alienatus sp.n., calami sp.n., horni sp.n., palumai sp.n., snowi sp.n., tasmanica sp.n.; while flavitibia Moulton stat.rev. is recalled from synonymy with C. mjobergi (Karny). In Nesothrips four new species are described: barrowi sp.n., brigalowi sp.n., coorongi sp.n., rossi sp.n.; while rhizophorae (Girault) syn.n. is placed as a synonym of minor Bagnall.

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Plasmas, the 4th state of matter, uniformly transform natural precursors with different chemical composition in solid, liquid, and gas states into the same functional vertical graphenes in a single-step process within a few minutes. Functional vertical graphenes show reliable biosensing properties, strong binding with proteins, and improved adhesion to substrates.

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Nanocomposite dielectrics hold a promising future for the next generation of insulation materials because of their excellent physical, chemical, and dielectric properties. In the presented study, we investigate the use of plasma processing technology to further enhance the dielectric performance of epoxy resin/SiO2 nanocomposite materials. The SiO2 nanoparticles are treated with atmospheric-pressure non-equilibrium plasma prior to being added into the epoxy resin host. Fourier transform infrared spectroscopy (FTIR) results reveal the effects of the plasma process on the surface functional groups of the treated nanoparticles. Scanning electron microscopy (SEM) results show that the plasma treatment appreciably improves the dispersion uniformity of nanoparticles in the host polymer. With respect to insulation performance, the epoxy/plasma-treated SiO2 specimen shows a 29% longer endurance time than the epoxy/untreated SiO2 nanocomposite under electrical aging. The Weibull plots of the dielectric breakdown field intensity suggest that the breakdown strength of the nanocomposite with the plasma pre-treatment on the nanoparticles is improved by 23.3%.

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Here we report on the synthesis of caesium doped graphene oxide (GO-Cs) and its application to the development of a novel NO2 gas sensor. The GO, synthesized by oxidation of graphite through chemical treatment, was doped with Cs by thermal solid-state reaction. The samples, dispersed in DI water by sonication, have been drop-casted on standard interdigitated Pt electrodes. The response of both pristine and Cs doped GO to NO2 at room temperature is studied by varying the gas concentration. The developed GO-Cs sensor shows a higher response to NO2 than the pristine GO based sensor due to the oxygen functional groups. The detection limit measured with GO-Cs sensor is ≈90 ppb.

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Synthesis of various functional nanoassemblies, by using a combination of low-pressure reactive plasma-enhanced chemical deposition and plasma-assisted rf magnetron sputtering deposition is reported. This paper details how selective generation and manipulation of the required building blocks and management of unwanted nanoparticle contaminants, can be used for plasma-aided nanofabrication of carbon nanotip microemitter structures, ultra-high aspect ratio semiconductor nanowires, ordered quantum dot arrays, and microporous hydroxyapatite bioceramics. Emerging challenges of the plasma-aided synthesis of functional nanofilms and nanoassemblies are also discussed.

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The development, operation, and applications of two configurations of an integrated plasma-aided nanofabrication facility (IPANF) comprising low-frequency inductively coupled plasma-assisted, low-pressure, multiple-target RF magnetron sputtering plasma source, are reported. The two configurations of the plasma source have different arrangements of the RF inductive coil: a conventional external flat spiral "pancake" coil and an in-house developed internal antenna comprising two orthogonal RF current sheets. The internal antenna configuration generates a "unidirectional" RF current that deeply penetrates into the plasma bulk and results in an excellent uniformity of the plasma over large areas and volumes. The IPANF has been employed for various applications, including low-temperature plasma-enhanced chemical vapor deposition of vertically aligned single-crystalline carbon nanotips, growth of ultra-high aspect ratio semiconductor nanowires, assembly of optoelectronically important Si, SiC, and Al1-xInxN quantum dots, and plasma-based synthesis of bioactive hydroxyapatite for orthopedic implants.

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Uniformity of postprocessing of large-area, dense nanostructure arrays is currently one of the greatest challenges in nanoscience and nanofabrication. One of the major issues is to achieve a high level of control in specie fluxes to specific surface areas of the nanostructures. As suggested by the numerical experiments in this work, this goal can be achieved by manipulating microscopic ion fluxes by varying the plasma sheath and nanorod array parameters. The dynamics of ion-assisted deposition of functional monolayer coatings onto two-dimensional carbon nanorod arrays in a hydrogen plasma is simulated by using a multiscale hybrid numerical simulation. The numerical results show evidence of a strong correlation between the aspect ratios and nanopattern positioning of the nanorods, plasma sheath width, and densities and distributions of microscopic ion fluxes. When the spacing between the nanorods and/or their aspect ratios are larger, and/or the plasma sheath is wider, the density of microscopic ion current flowing to each of the individual nanorods increases, thus reducing the time required to apply a functional monolayer coating down to 11 s for a 7-μm-wide sheath, and to 5 s for a 50-μm-wide sheath. The computed monolayer coating development time is consistent with previous experimental reports on plasma-assisted functionalization of related carbon nanostructures [B. N. Khare et al., Appl. Phys. Lett. 81, 5237 (2002)]. The results are generic in that they can be applied to a broader range of plasma-based processes and nanostructures, and contribute to the development of deterministic strategies of postprocessing and functionalization of various nanoarrays for nanoelectronic, biomedical, and other emerging applications.

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Brain decoding of functional Magnetic Resonance Imaging data is a pattern analysis task that links brain activity patterns to the experimental conditions. Classifiers predict the neural states from the spatial and temporal pattern of brain activity extracted from multiple voxels in the functional images in a certain period of time. The prediction results offer insight into the nature of neural representations and cognitive mechanisms and the classification accuracy determines our confidence in understanding the relationship between brain activity and stimuli. In this paper, we compared the efficacy of three machine learning algorithms: neural network, support vector machines, and conditional random field to decode the visual stimuli or neural cognitive states from functional Magnetic Resonance data. Leave-one-out cross validation was performed to quantify the generalization accuracy of each algorithm on unseen data. The results indicated support vector machine and conditional random field have comparable performance and the potential of the latter is worthy of further investigation.

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Studies of semantic impairment arising from brain disease suggest that the anterior temporal lobes are critical for semantic abilities in humans; yet activation of these regions is rarely reported in functional imaging studies of healthy controls performing semantic tasks. Here, we combined neuropsychological and PET functional imaging data to show that when healthy subjects identify concepts at a specific level, the regions activated correspond to the site of maximal atrophy in patients with relatively pure semantic impairment. The stimuli were color photographs of common animals or vehicles, and the task was category verification at specific (e.g., robin), intermediate (e.g., bird), or general (e.g., animal) levels. Specific, relative to general, categorization activated the antero-lateral temporal cortices bilaterally, despite matching of these experimental conditions for difficulty. Critically, in patients with atrophy in precisely these areas, the most pronounced deficit was in the retrieval of specific semantic information.

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PURPOSE To compare diffusion-weighted functional magnetic resonance imaging (DfMRI), a novel alternative to the blood oxygenation level-dependent (BOLD) contrast, in a functional MRI experiment. MATERIALS AND METHODS Nine participants viewed contrast reversing (7.5 Hz) black-and-white checkerboard stimuli using block and event-related paradigms. DfMRI (b = 1800 mm/s2 ) and BOLD sequences were acquired. Four parameters describing the observed signal were assessed: percent signal change, spatial extent of the activation, the Euclidean distance between peak voxel locations, and the time-to-peak of the best fitting impulse response for different paradigms and sequences. RESULTS The BOLD conditions showed a higher percent signal change relative to DfMRI; however, event-related DfMRI showed the strongest group activation (t = 21.23, P < 0.0005). Activation was more diffuse and spatially closer to the BOLD response for DfMRI when the block design was used. DfMRIevent showed the shortest TTP (4.4 +/- 0.88 sec). CONCLUSION The hemodynamic contribution to DfMRI may increase with the use of block designs.

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Adopting both the resource-based view and dynamic capability theory this study advances the contention that firms must possess both resources and capabilities at a superior level to achieve superior customer and product performance. To examine this contention this study investigates the individual effect of the complementarity between marketing resources and capability and complementarity between innovation resources and capability on customer and product performance respectively. The results of a survey of 171 B2B manufacturing firms show a significant main effect for complementarity between marketing resources–capability and complementarity between innovation resources–capability on customer and product performance. The findings also show that complementarity marketing resources–capability has a stronger positive relationship with customer performance than with product performance, while complementarity between innovation resources–capability has a stronger positive relationship with product performance than with customer performance.

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We consider the following problem: a user stores encrypted documents on an untrusted server, and wishes to retrieve all documents containing some keywords without any loss of data confidentiality. Conjunctive keyword searches on encrypted data have been studied by numerous researchers over the past few years, and all existing schemes use keyword fields as compulsory information. This however is impractical for many applications. In this paper, we propose a scheme of keyword field-free conjunctive keyword searches on encrypted data, which affirmatively answers an open problem asked by Golle et al. at ACNS 2004. Furthermore, the proposed scheme is extended to the dynamic group setting. Security analysis of our constructions is given in the paper.

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We study the natural problem of secure n-party computation (in the passive, computationally unbounded attack model) of the n-product function f G (x 1,...,x n ) = x 1 ·x 2 ⋯ x n in an arbitrary finite group (G,·), where the input of party P i is x i  ∈ G for i = 1,...,n. For flexibility, we are interested in protocols for f G which require only black-box access to the group G (i.e. the only computations performed by players in the protocol are a group operation, a group inverse, or sampling a uniformly random group element). Our results are as follows. First, on the negative side, we show that if (G,·) is non-abelian and n ≥ 4, then no ⌈n/2⌉-private protocol for computing f G exists. Second, on the positive side, we initiate an approach for construction of black-box protocols for f G based on k-of-k threshold secret sharing schemes, which are efficiently implementable over any black-box group G. We reduce the problem of constructing such protocols to a combinatorial colouring problem in planar graphs. We then give two constructions for such graph colourings. Our first colouring construction gives a protocol with optimal collusion resistance t < n/2, but has exponential communication complexity O(n*2t+1^2/t) group elements (this construction easily extends to general adversary structures). Our second probabilistic colouring construction gives a protocol with (close to optimal) collusion resistance t < n/μ for a graph-related constant μ ≤ 2.948, and has efficient communication complexity O(n*t^2) group elements. Furthermore, we believe that our results can be improved by further study of the associated combinatorial problems.

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We propose a reliable and ubiquitous group key distribution scheme that is suitable for ad hoc networks. The scheme has self-initialisation and self-securing features. The former feature allows a cooperation of an arbitrary number of nodes to initialise the system, and it also allows node admission to be performed in a decentralised fashion. The latter feature allows a group member to determine the group key remotely while maintaining the system security. We also consider a decentralised solution of establishing secure point-to-point communication. The solution allows a new node to establish a secure channel with every existing node if it has pre-existing secure channels with a threshold number of the existing nodes.