A superconvergent hybridisable discontinuous Galerkin method for linear elasticity

The first superconvergent hybridisable discontinuous Galerkin method for linear elastic problems capable of using the same degree of approximation for both the primal and mixed variables is presented. The key feature of the method is the strong imposition of the symmetry of the stress tensor by means of the well known and extensively used Voigt notation, circumventing the use of complex mathematical concepts to enforce the symmetry of the stress tensor either weakly or strongly. A novel procedure to construct element by element a superconvergent postprocessed displacement is proposed. Contrary to other hybridisable discontinuous Galerkin formulations, the methodology proposed here is able to produce a superconvergent displacement field for low‐order approximations. The resulting method is robust and locking‐free in the nearly incompressible limit. An extensive set of numerical examples is utilised to provide evidence of the optimality of the method and its superconvergent properties in two and three dimensions and for different element types.     

Classification of motor imagery electroencephalography signals using spiking neurons with different input encoding strategies

Motor imagery-based brain–computer interfaces decode users’ intentions from the electroencephalogram; however, poor spatial resolution makes automatic recognition of these intentions a challenging task. New classification approaches with low computational costs and high classification performances need to be developed in order to increase the number of users benefitted by these systems. On the other hand, spiking neuron models, which are mathematical abstractions of real neurons, have shown good performances in several classification tasks, making these models suitable for motor imagery classification. In this work, two different encoding strategies for spiking neuron models, applied to the classification of motor imagery time–frequency features of stroke patients and healthy subjects, were evaluated. Classification performances and computational costs of spiking neuron models were compared against those of linear discriminant analysis, support vector machines and artificial neural networks. Results showed that a time-varying encoding strategy is more suitable for motor imagery classification, and its implementation computational cost is low. Therefore, a spiking neuron model with a time-varying encoding strategy could increase the number of potential users of brain–computer interfaces.

     

Web-scale provenance reconstruction of implicit information diffusion on social media

Fast, massive, and viral data diffused on social media affects a large share of the online population, and thus, the (prospective) information diffusion mechanisms behind it are of great interest to researchers. The (retrospective) provenance of such data is equally important because it contributes to the understanding of the relevance and trustworthiness of the information. Furthermore, computing provenance in a timely way is crucial for particular use cases and practitioners, such as online journalists that promptly need to assess specific pieces of information. Social media currently provide insufficient mechanisms for provenance tracking, publication and generation, while state-of-the-art on social media research focuses mainly on explicit diffusion mechanisms (like retweets in Twitter or reshares in Facebook).The implicit diffusion mechanisms remain understudied due to the difficulties of being captured and properly understood. From a technical side, the state of the art for provenance reconstruction evaluates small datasets after the fact, sidestepping requirements for scale and speed of current social media data. In this paper, we investigate the mechanisms of implicit information diffusion by computing its fine-grained provenance. We prove that explicit mechanisms are insufficient to capture influence and our analysis unravels a significant part of implicit interactions and influence in social media. Our approach works incrementally and can be scaled up to cover a truly Web-scale scenario like major events. We can process datasets consisting of up to several millions of messages on a single machine at rates that cover bursty behaviour, without compromising result quality. By doing that, we provide to online journalists and social media users in general, fine grained provenance reconstruction which sheds lights on implicit interactions not captured by social media providers. These results are provided in an online fashion which also allows for fast relevance and trustworthiness assessment.     

Correction to: Uniform and scalable sampling of highly configurable systems

Empirical Software Engineering - Testing Internet of Things (IoT) systems is challenging. This is not only because of the various aspects of IoT systems, such as software, hardware, and network...     

Language and Gender Classification of Speech Files Using Supervised Machine Learning Methods

Many language identification (LID) systems are based on language models using techniques that consider the fluctuation of speech over time. Considering these fluctuations necessitates longer recording intervals to obtain reasonable accuracy. Our research extracts features from short recording intervals to enable successful classification of spoken language. The feature extraction process is based on frames of 20 ms, whereas most previous LIDs presented results based on much longer frames (3?s or longer). We defined and implemented 200 features divided into four feature sets: cepstrum features, RASTA features, spectrum features, and waveform features. We applied eight machine learning (ML) methods on the features that were extracted from a corpus containing speech files in 10 languages from the Oregon Graduate Institute (OGI) telephone speech database and compared their performances using extensive experimental evaluation. The best optimized classification results were achieved by random forest (RF): from 76.29% on 10 languages to 89.18% on 2 languages. These results are better or comparable to the state-of-the-art results for the OGI database. Another set of experiments that was performed was gender classification from 2 to 10 languages. The accuracy and the F measure values for the RF method for all the language experiments were greater than or equal to 90.05%.     

Hard x-ray monochromator with milli-electron volt bandwidth for high-resolution diffraction studies of diamond crystals

We report on design and performance of a high-resolution x-ray monochromator with a spectral bandwidth of ΔE(X) ? 1.5 meV, which operates at x-ray energies in the vicinity of the backscattering (Bragg) energy E(H) = 13.903 keV of the (008) reflection in diamond. The monochromator is utilized for high-energy-resolution diffraction characterization of diamond crystals as elements of advanced x-ray crystal optics for synchrotrons and x-ray free-electron lasers. The monochromator and the related controls are made portable such that they can be installed and operated at any appropriate synchrotron beamline equipped with a pre-monochromator.     

Dopamine: Just the Right Medicine for Membranes

Mussel‐inspired chemistry has attracted widespread interest in membrane science and technology. Demonstrating the rapid growth of this field over the past several years, substantial progress has been achieved in both mussel‐inspired chemistry and membrane surface engineering based on mussel‐inspired coatings. At this stage, it is valuable to summarize the most recent and distinctive developments, as well as to frame the challenges and opportunities remaining in this field. In this review, recent advances in rapid and controllable deposition of mussel‐inspired coatings, dopamine‐assisted codeposition technology, and photoinitiated grafting directly on mussel‐inspired coatings are presented. Some of these technologies have not yet been employed directly in membrane science. Beyond discussing advances in conventional membrane processes, emerging applications of mussel‐inspired coatings in membranes are discussed, including as a skin layer in nanofiltration, interlayer in metal‐organic framework based membranes, hydrophilic layer in Janus membranes, and protective layer in catalytic membranes. Finally, some critical unsolved challenges are raised in this field and some potential pathways are proposed to address them.     

Nanoscale compositional mapping with gentle forces

Microscopists have always pursued the development of an instrument that combines topography and materials properties analyses at the highest resolution. The measurement of the tiny amount of energy dissipated by a vibrating tip in the proximity of the sample surface has provided atomic force microscopes with a robust and versatile method to determine the morphology and the compositional variations of surfaces in their natural environment. Applications in biology, polymer science and microelectronics illustrate the potential of phase-imaging force microscopy for nanoscale analysis.     

Genetically engineered brain drug delivery vectors: cloning, expression and in vivo application of an anti-transferrin receptor single chain antibodystreptavidin fusion gene and protein

A single chain Fv antibody–streptavidin fusion proteinwas expressed and purified from bacterial inclusion bodies followingcloning of the genes encoding the variable region of the heavychain and light chain of the murine OX26 monoclonal antibodyto the rat transferrin receptor. The latter undergoes receptormediated transcytosis through the brain capillary endothelialwall in vivo, which makes up the blood–brain barrier (BBB);therefore, the OX26 monoclonal antibody and its single chainFv analog may act as brain drug delivery vectors in vivo. Attachmentof biotinylated drugs to the antibody vector is facilitatedby production of the streptavidin fusion protein. The bi-functionalityof the OX26 single chain Fv antibody–streptavidin fusionprotein was retained, as the product both bound biotin and therat transferrin receptor in vitro and in vivo, based on pharmacokineticand brain uptake analyses in anesthetized rats. The attachmentof biotin–polyethyleneglycol–fluorescein to theOX26 single chain Fv antibody–streptavidin fusion proteinresulted in illumination of isolated rat brain capillaries inconfocal fluorescent microscopy. In conclusion, these studiesdemonstrate that genetically engineered single chain Fv antibody–streptavidinfusion proteins may be used for non-invasive neurotherapeuticdelivery to the brain using endogenous BBB transport systemssuch as the transferrin receptor.     

(De)stabilization of Alpha-Synuclein Fibrillary Aggregation by Charged and Uncharged Surfactants

Parkinson’s disease (PD) is the second most common neurodegenerative disorder. An important hallmark of PD involves the pathological aggregation of proteins in structures known as Lewy bodies. The major component of these proteinaceous inclusions is alpha (α)-synuclein. In different conditions, α-synuclein can assume conformations rich in either α-helix or β-sheets. The mechanisms of α-synuclein misfolding, aggregation, and fibrillation remain unknown, but it is thought that β-sheet conformation of α-synuclein is responsible for its associated toxic mechanisms. To gain fundamental insights into the process of α-synuclein misfolding and aggregation, the secondary structure of this protein in the presence of charged and non-charged surfactant solutions was characterized. The selected surfactants were (anionic) sodium dodecyl sulphate (SDS), (cationic) cetyltrimethylammonium chloride (CTAC), and (uncharged) octyl β-D-glucopyranoside (OG). The effect of surfactants in α-synuclein misfolding was assessed by ultra-structural analyses, in vitro aggregation assays, and secondary structure analyses. The α-synuclein aggregation in the presence of negatively charged SDS suggests that SDS-monomer complexes stimulate the aggregation process. A reduction in the electrostatic repulsion between N- and C-terminal and in the hydrophobic interactions between the NAC (non-amyloid beta component) region and the C-terminal seems to be important to undergo aggregation. Fourier transform infrared spectroscopy (FTIR) measurements show that β-sheet structures comprise the assembly of the fibrils.