The 3D printing of a polymeric electrochemical cell body and its characterisation

An undivided flow cell was designed and constructed using additive manufacturing technology and its mass transport characteristics were evaluated using the reduction of ferricyanide, hexacyanoferrate (III) ions at a nickel surface. The dimensionless mass transfer correlation Sh = aRebScdLee was obtained using the convective-diffusion limiting current observed in linear sweep voltammetry; this correlation compared closely with that reported in the literature from traditionally machined plane parallel rectangular flow channel reactors. The ability of 3D printer technology, aided by computational graphics, to rapidly and conveniently design, manufacture and re-design the geometrical characteristics of the flow cell ishighlighted

Experimental Assessment of a Forward-Collision Warning System Fusing Deep Learning and Decentralized Radio Sensing

This paper presents the idea of an automatic forward-collision warning system based on a decentralized radio sensing (RS) approach. In this framework, a vehicle in receiving mode employs a continuous waveform (CW) transmitted by a second vehicle as a probe signal to detect oncoming vehicles and warn the driver of a potential forward collision. Such a CW can easily be incorporated as a pilot signal within the data frame of current multicarrier vehicular communication systems. Detection of oncoming vehicles is performed by a deep learning (DL) module that analyzes the features of the Doppler signature imprinted on the CW probe signal by a rapidly approaching vehicle. This decentralized CW RS approach was assessed experimentally using data collected by a series of field trials conducted in a two-lanes high-speed highway. Detection performance was evaluated for two different DL models: a long short-term memory network and a convolutional neural network. The obtained results demonstrate the feasibility of the envisioned forward-collision warning system based on the fusion of DL and decentralized CW RS

Human bony labyrinth is an indicator of population history and dispersal from Africa.

The dispersal of modern humans from Africa is now well documented with genetic data that track population history, as well as gene flow between populations. Phenetic skeletal data, such as cranial and pelvic morphologies, also exhibit a dispersal-from-Africa signal, which, however, tends to be blurred by the effects of local adaptation and in vivo phenotypic plasticity, and that is often deteriorated by postmortem damage to skeletal remains. These complexities raise the question of which skeletal structures most effectively track neutral population history. The cavity system of the inner ear (the so-called bony labyrinth) is a good candidate structure for such analyses. It is already fully formed by birth, which minimizes postnatal phenotypic plasticity, and it is generally well preserved in archaeological samples. Here we use morphometric data of the bony labyrinth to show that it is a surprisingly good marker of the global dispersal of modern humans from Africa. Labyrinthine morphology tracks genetic distances and geography in accordance with an isolation-by-distance model with dispersal from Africa. Our data further indicate that the neutral-like pattern of variation is compatible with stabilizing selection on labyrinth morphology. Given the increasingly important role of the petrous bone for ancient DNA recovery from archaeological specimens, we encourage researchers to acquire 3D morphological data of the inner ear structures before any invasive sampling. Such data will constitute an important archive of phenotypic variation in present and past populations, and will permit individual-based genotype-phenotype comparisons

Meta-learning recommendation of default hyper-parameter values for SVMs in classifications tasks

Machine learning algorithms have been investigated in several scenarios, one of them is the data classification. The predictive performance of the models induced by these algorithms is usually strongly affected by the values used for their hyper-parameters. Different approaches to define these values have been proposed, like the use of default values and optimization techniques. Although default values can result in models with good predictive performance, different implementations of the same machine learning algorithms use different default values, leading to models with clearly different predictive performance for the same dataset. Optimization techniques have been used to search for hyper-parameter values able to maximize the predictive performance of induced models for a given dataset, but with the drawback of a high computational cost. A compromise is to use an optimization technique to search for values that are suitable for a wide spectrum of datasets. This paper investigates the use of meta-learning to recommend default values for the induction of Support Vector Machine models for a new classification dataset. We compare the default values suggested by the Weka and LibSVM tools with default values optimized by meta-heuristics on a large range of datasets. This study covers only classification task, but we believe that similar ideas could be used in other related tasks. According to the experimental results, meta-models can accurately predict whether tool suggested or optimized default values should be used.CAPESCNPqSão Paulo Research Foundation (FAPESP) (grant#2012/23114-9

A para-differential renormalization technique for nonlinear dispersive equations

For \alpha \in (1,2) we prove that the initial-value problem \partial_t u+D^\alpha\partial_x u+\partial_x(u^2/2)=0 on \mathbb{R}_x\times\mathbb{R}_t; u(0)=\phi, is globally well-posed in the space of real-valued L^2-functions. We use a frequency dependent renormalization method to control the strong low-high frequency interactions.Comment: 42 pages, no figure

HII regions in the CALIFA survey: I. Catalog presentation

We present a new catalogue of H II regions based on the integral field spectroscopy (IFS) data of the extended CALIFA and PISCO samples. The selection of H II regions was based on two assumptions: a clumpy structure with high contrast of H α emission and an underlying stellar population comprising young stars. The catalogue provides the spectroscopic information of 26 408 individual regions corresponding to 924 galaxies, including the flux intensities and equivalent widths of 51 emission lines covering the wavelength range between 3745 and 7200 Å. To our knowledge, this is the largest catalogue of spectroscopic properties of H II regions. We explore a new approach to decontaminate the emission lines from diffuse ionized gas contribution. This diffuse gas correction was estimated to correct every emission line within the considered spectral range. With the catalogue of H II regions corrected, new demarcation lines are proposed for the classical diagnostic diagrams. Finally, we study the properties of the underlying stellar populations of the H II regions. It was found that there is a direct relationship between the ionization conditions on the nebulae and the properties of stellar populations besides the physicals condition on the ionized regions.Fil: Espinosa Ponce, Carlos. Universidad Nacional Autónoma de México; MéxicoFil: Sánchez, S. F.. Universidad Nacional Autónoma de México; MéxicoFil: Morisset, C.. Universidad Nacional Autónoma de México; MéxicoFil: Barrera Ballesteros, J. K.. Universidad Nacional Autónoma de México; MéxicoFil: Galbany, Lluís. Universidad de Granada; EspañaFil: García Benito, Rubén. Instituto de Astrofísica de Andalucía; España. Consejo Superior de Investigaciones Científicas; EspañaFil: Lacerda, E. A. D.. Universidad Nacional Autónoma de México; MéxicoFil: Mast, Damian. Archivo del Observatorio Astronomico de Cordoba ; Observatorio Astronomico de Cordoba ; Rectorado ; Universidad Nacional de Cordoba; . Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba; Argentin

Inducing the cosmological constant from five-dimensional Weyl space

We investigate the possibility of inducing the cosmological constant from extra dimensions by embedding our four-dimensional Riemannian space-time into a five-dimensional Weyl integrable space. Following approach of the induced matter theory we show that when we go down from five to four dimensions, the Weyl field may contribute both to the induced energy-tensor as well as to the cosmological constant, or more generally, it may generate a time-dependent cosmological parameter. As an application, we construct a simple cosmological model which has some interesting properties.Comment: 7 page

Multidrug resistant pulmonary tuberculosis treatment regimens and patient outcomes: an individual patient data meta-analysis of 9,153 patients.

Treatment of multidrug resistant tuberculosis (MDR-TB) is lengthy, toxic, expensive, and has generally poor outcomes. We undertook an individual patient data meta-analysis to assess the impact on outcomes of the type, number, and duration of drugs used to treat MDR-TB