Analysing the Twitter accounts of licensed Sports gambling operators in Spain: a space for responsible gambling?

Apart from the economic impact of the online gambling industry, the social, public order and health-related consequences of the industry merit analysis to inform appropriate action, regulatory or otherwise. The omnipresence of ICTs, the inability to use technologies properly, along with the growth of online gambling channels, have acted simultaneously as a catalyst for the spread of pathological and problematic gambling. In this context, social networks have become a highly effective platform to instil positive attitudes towards the products of gambling operators. This work uses the Natural Language Processing based web application “GPLSI Social Analytics” to track, in real time, the conversations generated on Twitter about the Spanish domain accounts of the main online sports gambling operators. The findings indicate that most of the messages about these operators are positive and surprise is the predominant emotion associated with them. The notion of responsible online gambling barely receives a mention in the conversations analysed. Given the role of new technologies as access facilitators and potential enhancers of addictive behaviours, it is necessary to adopt measures directed at social networks that guarantee the coexistence of the right to freedom of expression with the protection of the most vulnerable populations

Ethics on the corporate websites of the main advertising agencies in Spain

Although a significant number of studies have been carried out in relation to the ethical criteria of advertising messages in Spain, little or no research has been done on the corporate ethics of advertising agencies. Based on a content analysis methodology, the research presented here provides a twofold account of the ethical dimension of agency self-advertising on the Internet by reading (1) corporate ethics statements and (2) corporate identity statements. The results of such reading disclose that only a minimal percentage of companies is bound by particular ethical commitments and only one advertising agency makes explicit reference to ethical concerns in its corporate identity statemen

Evaluation of a Bayesian Algorithm to Detect Burned Areas in the Canary Islands’ Dry Woodlands and Forests Ecoregion Using MODIS Data

Burned Area (BA) is deemed as a primary variable to understand the Earth’s climate system. Satellite remote sensing data have allowed for the development of various burned area detection algorithms that have been globally applied to and assessed in diverse ecosystems, ranging from tropical to boreal. In this paper, we present a Bayesian algorithm (BY-MODIS) that detects burned areas in a time series of Moderate Resolution Imaging Spectroradiometer (MODIS) images from 2002 to 2012 of the Canary Islands’ dry woodlands and forests ecoregion (Spain). Based on daily image products MODIS, MOD09GQ (250 m), and MOD11A1 (1 km), the surface spectral reflectance and the land surface temperature, respectively, 10 day composites were built using the maximum temperature criterion. Variables used in BY-MODIS were the Global Environment Monitoring Index (GEMI) and Burn Boreal Forest Index (BBFI), alongside the NIR spectral band, all of which refer to the previous year and the year the fire took place in. Reference polygons for the 14 fires exceeding 100 hectares and identified within the period under analysis were developed using both post-fire LANDSAT images and official information from the forest fires national database by the Ministry of Agriculture and Fisheries, Food and Environment of Spain (MAPAMA). The results obtained by BY-MODIS can be compared to those by official burned area products, MCD45A1 and MCD64A1. Despite that the best overall results correspond to MCD64A1, BY-MODIS proved to be an alternative for burned area mapping in the Canary Islands, a region with a great topographic complexity and diverse types of ecosystems. The total burned area detected by the BY-MODIS classifier was 64.9% of the MAPAMA reference data, and 78.6% according to data obtained from the LANDSAT images, with the lowest average commission error (11%) out of the three products and a correlation (R2) of 0.82. The Bayesian algorithm—originally developed to detect burned areas in North American boreal forests using AVHRR archival data Long-Term Data Record—can be successfully applied to a lower latitude forest ecosystem totally different from the boreal ecosystem and using daily time series of satellite images from MODIS with a 250 m spatial resolution, as long as a set of training areas adequately characterising the dynamics of the forest canopy affected by the fire is defined

La "seca" de olivos jóvenes II: identificación y patogenicidad de los hongos asociados con podredumbres radicales

This work deals with the identification of fungal isolates associated with root rot, the major factor included in the «drying and death syndrome» («seca») of young olive trees in Andalucía, southern Spain. Fungi associated with damping-off in olive tree nurseries were also identified. Several isolates from every fungal species were tested for pathogenicity in nursery plants and rooted cuttings of olive cultivar 'Picual'. Pathogenicity tests demonstrated that five fungal species -Cylindrocarpon destructans, Phytophthora megasperma, P. palmivora, Pythium irregulare and Sclerotium rolfsii- were clearly pathogenic to olive trees and reproduced symptoms of root rot and foliar wilting. Other fungal species associated with root rot of olive trees in the field or in the nurseries, including Fusarium acuminatum, F. eumartii, F. oxysporum, F. solani, Macrophomina phaseolina and Rhizoctonia solani, were weakly or not pathogenic. Pathogenicity of Phytophthora megasperma, P. palmivora and Pythium irregulare depended on soil water content, since isolates tested only caused extensive root rot and sudden plant death when the soil was continuously waterlogged. The high frequency of P. megasperma (part I) and its dependence for pathogenicity on soil water content suggest that this pathogen may play an important role in the well known sensitivity of young olive trees to «root asphyxiation».En este trabajo se han identificado los aislados fúngicos asociados a las podredumbres radiculares de olivo, el factor más importante incluido bajo la denominación genérica de «seca» de olivos jóvenes en Andalucía, así como los aislados asociados a la muerte de plántulas («damping-off») en viveros de olivo. La patogenicidad de aislados seleccionados de cada especie fúngica se evaluó en plantones y estaquillas enraizadas de olivo del cultivar «Picual», en condiciones parcialmente controladas. De todas las especies ensayadas, cinco mostraron claramente su patogenicidad en olivo: Cylindrocarpon destructans, Phytophthora megasperma, P. palmivora, Pythium irregulare y Sclerotium rolfsii, reproduciendo los síntomas de podredumbre radicular y marchitez foliar en las plantas inoculadas. Otras especies asociadas a podredumbres radiculares en campo y vivero {Fusarium acuminatum, F. eumartii, F. oxysporum, F. solani, Macrophomina phaseolina y Rhizoctonia solani) resultaron débilmente o nada patogénicas. La patogenicidad de Phytophthora megasperma, P. palmivora y Pythium irregulare resultó dependiente del contenido hídrico del suelo, ya que los aislados ensayados sólo causaron necrosis extensas del sistema radicular y muerte de las plantas inoculadas en condiciones de saturación continua del suelo. La elevada frecuencia de aislamiento de Phytophthora megasperma en suelos encharcados (parte I) y su dependencia patogénica del exceso de agua en el suelo sugieren que este hongo puede jugar un papel importante en la generalmente aceptada sensibilidad de los olivos jóvenes a la «asfixia radicular»

SemTab 2019: Resources to Benchmark Tabular Data to Knowledge Graph Matching Systems

Tabular data to Knowledge Graph matching is the process of assigning semantic tags from knowledge graphs (e.g., Wikidata or DBpedia) to the elements of a table. This task is a challenging problem for various reasons, including the lack of metadata (e.g., table and column names), the noisiness, heterogeneity, incompleteness and ambiguity in the data. The results of this task provide significant insights about potentially highly valuable tabular data, as recent works have shown, enabling a new family of data analytics and data science applications. Despite significant amount of work on various flavors of this problem, there is a lack of a common framework to conduct a systematic evaluation of state-of-the-art systems. The creation of the Semantic Web Challenge on Tabular Data to Knowledge Graph Matching (SemTab) aims at filling this gap. In this paper, we report about the datasets, infrastructure and lessons learned from the first edition of the SemTab challenge