Unipolar plasma electrolytic oxidation: Waveform optimisation for corrosion resistance of commercially pure titanium

This study deals with the anodisation of titanium grade 2 in 0.5-M sulphuric acid using a pulsed signal in a unipolar regime. The electrical parameters investigated are voltage, frequency and duty cycle. The use of duty cycles with a high percentage of anodic polarisation (90%), combined with high frequencies (1000 Hz) and the higher voltage tested (220 V), favoured the establishment of a plasma regime involving strong dielectric discharges, allowing the growth of thicker oxides but with rough architecture. The corrosion resistance of the formed film has been characterised by potentiodynamic tests in 0.5-M NaBr for localised corrosion resistance and by immersion tests in 10% v/v sulphuric acid solution for a uniform corrosion assessment. Current–time curves, visual observations and electron microscope analysis (scanning electron microscopy, energy-dispersive X-ray spectroscopy) were the tools selected to provide a correlation between technological parameters and oxide growth mechanism. For localised and uniform corrosion, anodisation at 220 V with a high level of anodic polarisation (90%) and frequency (1000 Hz) was verified to be particularly advantageous.     

Weak Delocalization in Graphene on a Ferromagnetic Insulating Film

Graphene has been predicted to develop a magnetic moment by proximity effect when placed on a ferromagnetic film, a promise that could open exciting possibilities in the fields of spintronics and magnetic data recording. In this work, the interplay between the magnetoresistance of graphene and the magnetization of an underlying ferromagnetic insulating film is studied in detail. A clear correlation between both magnitudes is observed but through a careful modeling of the magnetization and the weak localization measurements, that such correspondence can be explained by the effects of the magnetic stray fields arising from the ferromagnetic insulator is found. The results emphasize the complexity arising at the interface between magnetic and 2D materials.     

Imaging of the optic disc and retinal nerve fiber layer: the effects of age, optic disc area, refractive error, and gender.

We cross-sectionally examined the relationship between age, optic disc area, refraction, and gender and optic disc topography and retinal nerve fiber layer (RNFL) measurements, using optical imaging techniques. One eye from each of 155 Caucasian subjects (age range 23.0-80.8 y) without ocular pathology was included. Measurements were obtained by using the Heidelberg Retina Tomography (HRT), the GDx Nerve Fiber Analyzer, and the Optical Coherence Tomograph (OCT). The effects of age were small (R2 < 17%) and were limited to specific HRT, GDx, and OCT parameters. Disc area was significantly associated with most HRT parameters and isolated GDx and OCT parameters. Refraction and gender were not significantly associated with any optic disc or RNFL parameters. Although effects of age on the optic disc and RNFL are small, they should be considered in monitoring ocular disease. Optic disc area should be considered when cross-sectionally evaluating disc topography and, to a lesser extent, RNFL thickness.     

Inbreeding in Swiss Braunvieh and its influence on breeding values predicted from a repeatability animal model.

Inbreeding coefficients were computed for 910,444 animals of the Swiss Braunvieh population. Of the animals born in 1984, 71.5% were inbred with 67.9, 3.4, and .2% having inbreeding coefficients between greater than 0 and 5%, greater than 5 to 10%, and greater than 10%, respectively. The average inbreeding coefficient was 1.14% but, for animals with both parents and at least one grandparent known, it was 1.67%. Breeding values for total milk, fat, and protein yields and for fat and protein percentages were predicted using a repeatability animal model including a regression on the inbreeding coefficient. Phenotypic performance was sizeably depressed for milk yield only (-26 kg/% of inbreeding or 2.4% of the phenotypic standard deviation). Adjusting for inbreeding increased the estimated genetic trend slightly. Inbreeding is only partially accounted for when it is ignored in the construction of the inverse of the numerator relationship matrix. This effect was investigated by comparing predicted breeding values from a model including the complete matrix with predicted breeding values from a model including a matrix constructed with inbreeding ignored. Only .8% of all predicted breeding values were affected by more than +/- 5.5 kg. The maximum difference observed was 55.3 kg. The observed average absolute differences between the breeding values of offspring predicted with the two models increased with inbreeding of parents.     

PhotoGeo: a photo digital library with spatial-temporal support and self-annotation

The recent popularity of digital cameras has posed a new problem: how to efficiently store and retrieve the very large number of digital photos captured and chaotically stored in multiple locations without any annotation. This paper proposes an infrastructure, called PhotoGeo, which aims at helping users with the people photo annotation, event photo annotation, storage and retrieval of personal digital photos. To achieve the desired objective, PhotoGeo uses new algorithms that make it possible to annotate photos with the key metadata to facilitate their retrieval, such as: the people who were shown in the photo (who); where it was captured (where); the date and time of capture (when); and the event that was captured. The paper concludes with a detailed evaluation of these algorithms.     

Multirate control with incomplete information over Profibus-DP network

When a process field bus-decentralized peripherals (Profibus-DP) network is used in an industrial environment, a deterministic behaviour is usually claimed. However, due to some concerns such as bandwidth limitations, lack of synchronisation among different clocks and existence of time-varying delays, a more complex problem must be faced. This problem implies the transmission of irregular and, even, random sequences of incomplete information. The main consequence of this issue is the appearance of different sampling periods at different network devices. In this paper, this aspect is checked by means of a detailed Profibus-DP timescale study. In addition, in order to deal with the different periods, a delay-dependent dual-rate proportional-integral-derivative control is introduced. Stability for the proposed control system is analysed in terms of linear matrix inequalities.     

EDT Method for Multiple Labelled Objects Subject to Tied Distances

Machine Intelligence Research - The success of new scientific areas can be assessed by their potential for contributing to new theoretical approaches aligned with real-world applications. The...     

Selecting linear algebra kernel composition using response time prediction

Numerical linear algebra libraries provide many kernels that can be composed to perform complex computations. For a given computation, there is typically a large number of functionally equivalent kernel compositions. Some of these compositions achieve better response times than others for particular data and when executed on a particular computer architecture. Previous research provides methods to enumerate (a subset of) these kernel compositions. In this work, we study the problem of determining the composition that yields the lowest response time. Our approach is based on a response time prediction for each candidate combination. While this prediction could in principle be obtained using analytical and/or empirical performance models, developing accurate such models is known to be challenging. Instead, we define a feature space that captures salient properties of kernel combinations and predict response time using supervised machine learning. We experiment with a standard set of machine learning algorithms and identify an effective algorithm for our kernel composition selection problem. Using this algorithm, our approach widely outperforms the strategy that would consist in always using the simplest kernel composition and is often close to the fastest kernel compositions among those evaluated. We quantify the potential benefit of our approach if it were to be implemented as part of an interactive computational tool. We find that although the potential benefit is substantial, a limiting factor is the kernel composition enumeration overhead. Copyright © 2014 John Wiley & Sons, Ltd.     

Online identification of appliances from power consumption data collected by smart meters

The efficient use of resources is a matter of great concern in today’s society, especially in the energy sector. Although the main strategy to decrease energy use has long been focused on supply, over the last few years, there has been a shift to the demand side. Under this new line of action, demand-side management networks have emerged and extended from the household level to larger installations, with the appearance of the concepts of Smart Grids and even Smart Cities. The extended use of Smart Meters for measuring residential electricity consumption facilitates the creation of such intelligent environments. In this context, this article proposes a system which extracts value from the collected consumer information to identify the appliances belonging to that smart environment by means of machine learning techniques. Considering the large amount of information that would be handled when millions of homes were sending data, big data technology has been used. An experiment to evaluate the classification method was carried out with seven devices and three different configurations. The results are also reported, achieving promising results, with recognition rates of 75 % after 1 h of training and 100 % after 4 h.