Work-Related Violence Research Project: Overview and Survey Module and Focus Group Findings

[Excerpt] The main goal of the contract was to provide ILAB with a newly developed set of high quality research tools (i.e., new survey questions module and related focus group protocols) and corresponding methodological recommendations to meet ILAB’s needs for collecting nationally representative, gender-disaggregated data on the prevalence, nature, and possible consequences of adult (18 years of age and older) WRV, including gender-based violence (or GBV) to the extent practicable. ILAB is particularly interested in the formal and informal sectors of one or more of the following Spanish-speaking Central American countries: Panama, Honduras, Nicaragua, El Salvador, Guatemala, and Costa Rica

Impacto del ozono troposférico sobre la anatomía foliar de Abies pinsapo Boiss. I: estudio de la distribución de daños

Impacto del ozono troposférico sobre la anatomía foliar de Abies pinsapo Boiss. I: Estudio de la distribución de daños. Con el fin de elaborar unos criterios unificados para la evaluación del impacto del ozono troposférico y compararlos con la respuesta de las poblaciones naturales de Sierra Bermeja y Sierra de las Nieves, se han reproducido en cámaras Open Top, ambientes filtrados y enriquecidos con ozono para realizar una caracterización microscópica del daño que produce el ozono troposférico sobre Abies pinsapo Boiss. Los resultados demuestran que pese a que la morfología foliar permanece inalterada tras someter durante un mes en condiciones de 30 ppb durante 8 horas al día, a nivel tisular se han producido daños de consideración que afectan de este endemismoTropospheric ozone injury on the foliar anatomy of Abies pinsapo Boiss. I: Study of damage distribution. With the aim of develop unified criteria to evaluate the tropospheric ozone injury and compare with responses in Abies pinsapo Boiss. in natural populations in Sierra Bermeja and Sierra de las Nieves, it has been reproduced in Open Top Chambers conditions of filtered air and addition of controlled amounts of ozone. Results show that despite leaf morphology remains unaltered after a treatment with 30 ppb 8 hours per day, at tissue level there have been injuries that affect this endemis

Detección de inercia sectorial en salidas a bolsa mediante modelos arima y redes neuronales

En este trabajo se explora la posibilidad de existencia de mercados segmentados en las salidas a bolsa que pudiesen reflejarse en inercia a corto plazo. Se propone que el rendimiento inicial de las acciones pertenecientes a los sectores tecnológico, de telecomunicaciones y medios de comunicación por un lado y el del resto, por otro, podría estar relacionado con la rentabilidad inicial de otras acciones pertenecientes al mismo sector. Para contrastar ésto, se analizan una serie de índices diarios que son objeto de predicción mediante modelos ARIMA y redes neuronales artificiales. Los resultados aportan indicios de presencia de inercia y de que esta afecta de forma distinta en función del área de actividad.In this work, we explore the possible existence of segmented short-term serial dependence in IPOs. We propose that average first-day underpricing of TMT companies might be affected by the average initial return of the companies taken public in the same sector over the previous days. In order to analyse this, we create a set of indexes to be predicted using artificial neural networks and ARIMA models. Their forecasting ability suggests that both the existence of inertia and a segmented market cannot be ruled out.Publicad

The influence of market deregulation on fast food consumption and body mass index : a cross-national time series analysis

Peer reviewe

Comparison of Conventional Hybrid and CTC/Attention Decoders for Continuous Visual Speech Recognition

Thanks to the rise of deep learning and the availability of large-scale audio-visual databases, recent advances have been achieved in Visual Speech Recognition (VSR). Similar to other speech processing tasks, these end-to-end VSR systems are usually based on encoder-decoder architectures. While encoders are somewhat general, multiple decoding approaches have been explored, such as the conventional hybrid model based on Deep Neural Networks combined with Hidden Markov Models (DNN-HMM) or the Connectionist Temporal Classification (CTC) paradigm. However, there are languages and tasks in which data is scarce, and in this situation, there is not a clear comparison between different types of decoders. Therefore, we focused our study on how the conventional DNN-HMM decoder and its state-of-the-art CTC/Attention counterpart behave depending on the amount of data used for their estimation. We also analyzed to what extent our visual speech features were able to adapt to scenarios for which they were not explicitly trained, either considering a similar dataset or another collected for a different language. Results showed that the conventional paradigm reached recognition rates that improve the CTC/Attention model in data-scarcity scenarios along with a reduced training time and fewer parameters.Comment: Accepted at the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING

Svq: a proposal for still image coding in mpeg 4 - snhc

A technique for efficient coding of homogeneous textures is presented here. The technique is based on the use of Stochastic Vector Quantization and provides very high compression with graceful degradation. To encode the image, a linear prediction filter is computed. Then, the prediction error is encoded using a Stochastic Vector Quantization approach. To decode the image, the prediction error is decoded first and then filtered as a whole using the prediction filter, thus avoiding the block effect found in conventional VQ. The approach has been proposed as a still image coding technique in MPEG 4 SNHC. Comparisons with the Video VM of MPEG 4 are also presentedPeer ReviewedPostprint (published version

Networks Underpinning Symbiosis Revealed Through Cross-Species eQTL Mapping.

Organisms engage in extensive cross-species molecular dialog, yet the underlying molecular actors are known for only a few interactions. Many techniques have been designed to uncover genes involved in signaling between organisms. Typically, these focus on only one of the partners. We developed an expression quantitative trait locus (eQTL) mapping-based approach to identify cause-and-effect relationships between genes from two partners engaged in an interspecific interaction. We demonstrated the approach by assaying expression of 98 isogenic plants (Medicago truncatula), each inoculated with a genetically distinct line of the diploid parasitic nematode Meloidogyne hapla With this design, systematic differences in gene expression across host plants could be mapped to genetic polymorphisms of their infecting parasites. The effects of parasite genotypes on plant gene expression were often substantial, with up to 90-fold (P = 3.2 × 10-52) changes in expression levels caused by individual parasite loci. Mapped loci included a number of pleiotropic sites, including one 87-kb parasite locus that modulated expression of >60 host genes. The 213 host genes identified were substantially enriched for transcription factors. We distilled higher-order connections between polymorphisms and genes from both species via network inference. To replicate our results and test whether effects were conserved across a broader host range, we performed a confirmatory experiment using M. hapla-infected tomato. This revealed that homologous genes were similarly affected. Finally, to validate the broader utility of cross-species eQTL mapping, we applied the strategy to data from a Salmonella infection study, successfully identifying polymorphisms in the human genome affecting bacterial expression

Projected changes in extreme daily precipitation linked to changes in precipitable water and vertical velocity in CMIP6 models

Agencia Estatal de Investigación | Ref. PID2021-122314OB-I00Xunta de Galicia | Ref. ED431C2021/44Universidade de Vigo/CISU