On the synthesis of metadata tags for HTML files

RDFa, JSON-LD, Microdata, and Microformats allow to endow the data in HTML files with metadata tags that help software agents understand them. Unluckily, there are many HTML files that do not have any metadata tags, which has motivated many authors to work on proposals to synthesize them. But they have some problems: the authors either provide an overall picture of their designs without too many details on the techniques behind the scenes or focus on the techniques but do not describe the design of the software systems that support them; many of them cannot deal with data that are encoded using semistructured formats like forms, listings, or tables; and the few proposals that can work on tables can deal with horizontal listings only. In this article, we describe the design of a system that overcomes the previous limitations using a novel embedding approach that has proven to outperform four state-of-the-art techniques on a repository with randomly selected HTML files from 40 different sites. According to our experimental analysis, our proposal can achieve an F₁ score that outperforms the others by 10.14% ; this difference was confirmed to be statistically significant at the standard confidence level.     

Torii: An aspect-based sentiment analysis system that can mine conditions

Aspect-based sentiment analysis systems are a kind of text-mining systems that specialize in summarizing the sentiment that a collection of reviews convey regarding some aspects of an item. There are many cases in which users write their reviews using conditional sentences; in such cases, mining the conditions so that they can be analyzed is very important not to misinterpret the corresponding sentiment summaries. Unfortunately, current commercial systems or research systems neglect conditions; current frameworks and toolkits do not provide any components to mine them; furthermore, the proposals in the literature are insufficient because they are based on handcrafted patterns that fall short regarding recall or machine learning procedures that are tightly bound with a specific language and require too much configuration. In this article, we present Torii, which is a system that loads a collection of reviews, discovers the aspects on which they report, and summarizes the sentiment that is conveyed on them taking into account the existing conditions, if any. We also describe its architecture, our approach to mine conditions, and our experimental analysis on a large multilingual data set with reviews from multiple categories. To the best of our knowledge, Torii is the first proposal that addresses aspect-based sentiment analysis taking conditions into account.     

Correction to: EKLT: Asynchronous Photometric Feature Tracking Using Events and Frames

International Journal of Computer Vision - The original version of this article was unfortunately omitted to publish the footnote “The best result per row is highlighted in bold” in...     

VOCs and PAHs emissions from creosote-treated wood in a field storage area

In this study, the emissions of volatile organic compounds (VOCs, in this case aromatic hydrocarbons containing one benzene ring and furans) and polycyclic aromatic hydrocarbons (PAHs) from wood recently treated with creosote are examined. The VOCs and PAHs were identified and quantified in the gas phase. Additionally, the PAHs were quantified in the particulate phase. Glass multi-sorbent tubes (Carbotrap, Carbopack X, Carboxen-569) were used to hold the VOCs. The analysis was performed using automatic thermal desorption (ATD) coupled with capillary gas chromatography/mass spectrometry (GC/MS). PAHs vapours were collected on XAD-2 resin, and particulate matter was collected on glass fibre filters. The PAHs were analysed using GC/MS. The main components of the vapours released from the creosote-treated wood were naphthalene, toluene, m+p-xylene, ethylbenzene, o-xylene, isopropylbenzene, benzene and 2-methylnaphthalene. VOCs emission concentrations ranged from 35 mg m(-3) of air on the day of treatment to 5 mg m(-3) eight days later. PAHs emission concentrations ranged from 28 microg m(-3) of air on the day of treatment to 4 microg m(-3) eight days later. The air concentrations of PAHs in particulate matter were composed predominantly of benzo[b+j]fluoranthene, benzo[a]anthracene, chrysene, fluoranthene, benzo[e]pyrene and 1-methylnaphthalene. The emission concentrations of particulate polycyclic aromatic hydrocarbons varied between 0.2 and 43.5 ng m(-3). Finally, the emission factors of VOCs and PAHs were determined.     

Training realisation and training demand in the Rioja wine sector in Spain

Training has been identified as an effective tool in the development of human resources, especially in the current context of change. This work aims to analyse the realisation and demand on training courses in the Rioja wine sector in Spain. A representative sample of companies in the sector was interviewed using a questionnaire. Two types of analysis were conducted: univariate and a factorial analysis of variance. These analyses allow using a hypothesis test in which three independent variables are controlled – zone, ownership, and winery size. The results show that the realisation and demand for training are focused on topics related to wine production, and not on marketing. No significant differences were found regarding the realisation and demand for training and the independent variables.     

A Markovian–Bayesian Network for Risk Analysis of High Speed and Conventional Railway Lines Integrating Human Errors

The article provides a new Markovian–Bayesian network model to evaluate the probability of accident associated with the circulation of trains along a given high speed or conventional railway line with special consideration to human error. This probability increases as trains pass throughout the different elements encountered along the line. A Bayesian network, made up of a sequence of several connected Bayesian subnetworks, is used. A subnetwork is associated with each element in the line that implies a concentrated risk of accident or produces a change in the driver's attention, such as signals, tunnel, or viaduct entries or exits, etc. Bayesian subnetworks are also used to reproduce segments without signals where some elements add continuous risks, such as rolling stock failures, falling materials, slope slides in cuttings and embankments, etc. All subnetworks are connected with the previous one and some of them are multi‐connected because some consequences are dependent on previous errors. Because driver's attention plays a crucial role, its degradation with driving time and the changes due to seeing light signals or receiving acoustic signals is taken into consideration. The model updates the driver's attention level and accumulates the probability of accident associated with the different elements encountered along the line. This permits us to generate a continuously increasing risk graph that includes continuous and sudden changes indicating where the main risks appear and whether or not an action must be taken by the infrastructure manager. Sensitivity analysis allows the relevant and irrelevant parameters to be identified avoiding wastes of time and money by concentrating safety improvement actions only on the relevant ones. Finally, some examples are used to illustrate the model. In particular, the case of the Orense–Santiago de Compostela line, where a terrible accident took place in 2013.     

Estimation of effective solar irradiation using an unscented Kalman filter in a parabolic-trough field

Measurement of direct solar radiation has been shown to be very useful to improve control performance and disturbance rejection in solar fields by anticipating the effect of sudden changes in solar radiation due to clouds. Since direct solar radiation is measured locally by pyrheliometers, important errors in the estimation of the overall effective solar radiation can be produced when the pyrheliometer is covered by clouds while the rest of the solar field is not or viceversa. Furthermore, estimation of the overall efficiency affected by the reflectivity and absortance of metal tubes is very difficult because only local measurements can be obtained. This work proposes an algorithm for estimating overall solar radiation and efficiency at the field. The algorithm uses an unscented Kalman filter and it is validated by data obtained at the Plataforma Solar of Almer?´a (Spain).     

Very Large Photoconduction Enhancement Upon Self‐Assembly of a New Triindole Derivative in Solution‐Processed Films

A new carbazole‐related small molecule exhibiting self‐assembly into ordered nanostructures in solution‐processed cast films has been synthesized and its charge‐photogeneration and ‐transport properties have been investigated. Large photoconductivity was measured in the amorphous state while an enormous improvement in the photoconduction properties was observed when the molecules spontaneously organized. Photocurrents increased upon self‐assembly by up to four orders of magnitude, mostly due to the drastic enhancement of the charge photogeneration. A greatly favorable arrangement of the aromatic cores in the resulting nanostructures, which were characterized by X‐ray analysis, may explain these improvements. Photocurrents of mA cm^?2, on/off ratios of 10⁴ and quantum efficiencies of unity at low field and light intensity, which are among the best values reported to date, along with the simplicity of fabrication, give this readily‐available organic system great potential for use in plastic optoelectronic devices.     

Production of hydrogen and MWCNTs by methane decomposition over catalysts originated from LaNiO3 perovskite

LaNiO₃ type perovskite was prepared by the “self-combustion” method and was used as catalyst precursor for the methane decomposition reaction at 600 and 700 °C. CH₄ conversion reaches 80% at 700 °C and 65% at 600 °C using pure CH₄. The yield of CNT and H₂ were 2.2 g_CNT g^?1 h^?1 and 8.2 L g^?1 h^?1 at 700 °C respectively after 4 h of reaction. When the reaction is prolonged to 22 h the catalytic activity decreases but the catalyst is still active, the production of hydrogen reaches 63.5 L (STP) per gram of catalyst and the production of MWCNT was equal to 17 g per gram of catalyst.Multi-wall carbon nanotubes were characterized by X-ray diffraction (XRD), surface area (BET), transmission electron microscopy (TEM), scanning electron microscopy (SEM), thermogravimetric analysis (TGA) and Raman spectroscopy. TEM micrographs showed that MWCNT longer than 20 μm were formed with inner diameters ranging from 5 to 16 nm and outer diameters up to about 40 nm.The results obtained here clearly show that the use of the perovskite LaNiO₃ as catalytic precursor is very effective for the simultaneous production of carbon nanotubes and hydrogen.