Estudo de Base sobre o Sistema Nacional de Seguimento da Qualidade Ambiental - SSQA em Cabo Verde

Porque um sistema de seguimento da qualidade ambiental em Cabo Verde? A vulnerabilidade dos equilíbrios ambientais dominantes em regiões insulares e a fragilidade geral dos recursos naturais sob pressões antrópicas crescentes estão na base da definição e implementação de políticas ambientais imprescindíveis para assegurar um desenvolvimento económico e social sustentável, ou seja, dentro dos limites permitidos pela dinâmica, e pela capacidade de renovação dos recursos naturais. A implementação de tais políticas implica opções e decisões que directa ou indirectamente tem impacto nas componentes ambientais como a água, o solo, o ar, a energia, o próprio homem e a biodiversidade bem como na evolução natural ou induzida dos ecossistemas e processos naturais. Numa perspectiva de desenvolvimento económico e social sintonizada com as capacidades de carga do ambiente, interessa a durabilidade das opções e decisões. Assim um SSQA revela-se como um importante instrumento de seguimento e avaliação do PANA. Este, enquanto instrumento político-estratégico deve ser capaz de moldar as políticas económicas nacionais, regionais, sectoriais e municipais e assegurar que os níveis de desenvolvimento alcançados sejam sustentáveis e capazes de proporcionar maiores índices de eficiência no relacionamento do cabo-verdiano com o seu ambiente, do qual depende e faz parte integrante. O SSQA é entendido como sendo um importante instrumento de gestão ambiental, de ordenamento espacial e temporal das actividades humanas, de avaliação preventiva dos seus impactos e da regulamentação da utilização dos recursos de forma a optimizar os benefícios económicos e sociais que lhes estão subjacentes

Towards a method to anticipate dark matter signals with deep learning at the LHC

We study several simplified dark matter (DM) models and their signatures at the LHC using neural networks. We focus on the usual monojet plus missing transverse energy channel, but to train the algorithms we organize the data in 2D histograms instead of event-by-event arrays. This results in a large performance boost to distinguish between standard model (SM) only and SM plus new physics signals. We use the kinematic monojet features as input data which allow us to describe families of models with a single data sample. We found that the neural network performance does not depend on the simulated number of background events if they are presented as a function of S/pB, for reasonably large B, where S and B are the number of signal and background events per histogram, respectively. This provides flexibility to the method, since testing a particular model in that case only requires knowing the new physics monojet cross section. Furthermore, we also discuss the network performance under incorrect assumptions about the true DM nature. Finally, we propose multimodel classifiers to search and identify new signals in a more general way, for the next LHC runThe work of EA is partially supported by the “Atracción de Talento” program (Modalidad 1) of the Comunidad de Madrid (Spain) under the grant number 2019-T1/TIC-14019 and by the Spanish Research Agency (Agencia Estatal de Investigación) through the grant IFT Centro de Excelencia Severo Ochoa SEV-2016-0597. This work has been also partially supported by CONICET and ANPCyT under projects PICT 2016-0164, PICT 2017-0802, PICT 2017-2751, PICT 2017- 2765, and PICT 2018-0368

A method for approximating optimal statistical significances with machine-learned likelihoods

Machine-learning techniques have become fundamental in high-energy physics and, for new physics searches, it is crucial to know their performance in terms of experimental sensitivity, understood as the statistical significance of the signal-plus-background hypothesis over the background-only one. We present here a simple method that combines the power of current machine-learning techniques to face high-dimensional data with the likelihood-based inference tests used in traditional analyses, which allows us to estimate the sensitivity for both discovery and exclusion limits through a single parameter of interest, the signal strength. Based on supervised learning techniques, it can perform well also with high-dimensional data, when traditional techniques cannot. We apply the method to a toy model first, so we can explore its potential, and then to a LHC study of new physics particles in dijet final states. Considering as the optimal statistical significance the one we would obtain if the true generative functions were known, we show that our method provides a better approximation than the usual naive counting experimental result

Warped Radion Dark Matter

Warped scenarios offer an appealing solution to the hierarchy problem. We consider a non-trivial deformation of the basic Randall-Sundrum framework that has a KK-parity symmetry. This leads to a stable particle beyond the Standard Model, that is generically expected to be the first KK-parity odd excitation of the radion field. We consider the viability of the KK-radion as a DM candidate in the context of thermal and non-thermal production in the early universe. In the thermal case, the KK-radion can account for the observed DM density when the radion decay constant is in the natural multi-TeV range. We also explore the effects of coannihilations with the first KK excitation of the RH top, as well as the effects of radion-Higgs mixing, which imply mixing between the KK-radion and a KK-Higgs (both being KK-parity odd). The non-thermal scenario, with a high radion decay constant, can also lead to a viable scenario provided the reheat temperature and the radion decay constant take appropriate values, although the reheat temperature should not be much higher than the TeV scale. Direct detection is found to be feasible if the DM has a small (KK-parity odd) Higgs admixture. Indirect detection via a photon signal from the galactic center is an interesting possibility, while the positron and neutrino fluxes from KK-radion annihilations are expected to be rather small. Colliders can probe characteristic aspects of the DM sector of warped scenarios with KK-parity, such as the degeneracy between the radion and the KK-radion (DM) modes.Comment: 43 pages, 16 figures; added reference

Warped Universal Extra Dimensions

We consider a 5D warped scenario with a KK-parity symmetry, where the non-trivial warping arises from the dynamics that stabilizes the size of the extra dimension. Generically, the lightest Kaluza-Klein (KK) particle is the first excitation of the radion field, while the next-to-lightest Kaluza-Klein particle is either the first excitation of the (RH) top quark or the first KK-parity odd Higgs. All these masses are expected to be of order the electroweak scale. We present simple analytical expressions for the masses and wavefunctions of the lowest lying KK modes, and derive the Feynman rules necessary for phenomenological applications. The framework allows to interpolate between a strongly warped scenario a la Randall-Sundrum (RS), and a weakly warped scenario that shares properties of both RS and Universal Extra Dimensions models.Comment: 41 pages, 13 figures. Minor comments added. Published versio

patrimonio intelectual

Actas de congresoLas VI Jornadas se realizaron con la exposición de ponencias que se incluyeron en cuatro ejes temáticos, que se desarrollaron de modo sucesivo para facilitar la asistencia, el intercambio y el debate, distribuidos en tres jornadas. Los ejes temáticos abordados fueron: 1. La enseñanza como proyecto de investigación. Recursos de enseñanza-aprendizaje como mejoras de la calidad educativa. 2. La experimentación como proyecto de investigación. Del ensayo a la aplicabilidad territorial, urbana, arquitectónica y de diseño industrial. 3. Tiempo y espacio como proyecto de investigación. Sentido, destino y usos del patrimonio construido y simbólico. 4. Idea constructiva, formulación y ejecución como proyecto de investigación. Búsqueda y elaboración de resultados que conforman los proyectos de la arquitectura y el diseño

The association of DNA Repair with breast cancer risk in women. A comparative observational study

<p>Abstract</p> <p>Background</p> <p>Previous studies have found a link between a low DNA repair capacity (DRC) level and increased cancer risk. Our aim was to assess the statistical association of DRC level and breast cancer (BC) using a case–control epidemiological study in a Hispanic community.</p> <p>Methods</p> <p>We conducted a comparative observational study to assess the validity of DRC in detecting BC in 824 women throughout Puerto Rico. Over a 6-year period, we compared 285 women newly diagnosed with BC to 539 without BC. DRC levels were measured in lymphocytes by means of a host-cell reactivation assay. We assessed the sensitivity, specificity, and association using the receiver operating characteristic curve analysis. Multiple logistic regression-adjusted odds ratios were estimated with 95% confidence level to measure the strength of the association of DRC and BC after adjusting for all confounders simultaneously.</p> <p>Results</p> <p>Compared to women without cancer, women with BC showed an average decrease of 60% in their DRC levels (<it>p</it> < 0.001). Validity of the association of DRC as a measure of BC risk showed a sensitivity of 83.2% and specificity of 77.6% (<it>p</it> < 0.0001).</p> <p>Conclusions</p> <p>Our results support the usefulness of DRC level as a measure of BC risk. Additional studies in other populations are needed to further verify its usefulness.</p