On the assimilation of SWOT type data into 2D shallow-water models

In river hydraulics, assimilation of water level measurements at gauging stations is well controlled, while assimilation of images is still delicate. In the present talk, we address the richness of satellite mapped information to constrain a 2D shallow-water model, but also related difficulties. 2D shallow models may be necessary for small scale modelling in particular for low-water and flood plain flows. Since in both cases, the dynamics of the wet dry front is essential, one has to elaborate robust and accurate solvers. In this contribution we introduce robust second order, stable finite volume scheme [CoMaMoViDaLa]. Comparisons of real like tests cases with more classical solvers highlight the importance of an accurate flood plain modelling. A preliminary inverse study is presented in a flood plain flow case, [LaMo] [HoLaMoPu]. As a first step, a 0th order data processing model improves observation operator and produces more reliable water level derived from rough measurements [PuRa]. Then, both model and flow behaviours can be better understood thanks to variational sensitivities based on a gradient computation and adjoint equations. It can reveal several difficulties that a model designer has to tackle. Next, a 4D-Var data assimilation algorithm used with spatialized data leads to improved model calibration and potentially leads to identify river discharges. All the algorithms are implemented into DassFlow software (Fortran, MPI, adjoint) [Da]. All these results and experiments (accurate wet-dry front dynamics, sensitivities analysis, identification of discharges and calibration of model) are currently performed in view to use data from the future SWOT mission

L’open data au prisme des Communs : enjeux éthiques et professionnels en bibliothèque

Mémoire de fin d\u27étude du diplôme de conservateur, promotion 27, portant sur l’open data au prisme des Communs

Marginalisation, discrimination and the health of Latino immigrant day labourers in a central North Carolina community

The morbidity and mortality of Latino immigrants in the United States (US) stem from a complex mix of policy, culture, discrimination, and economics. Immigrants working as day labourers may be particularly vulnerable to the negative influences of these social factors due to limited access to social, financial, and legal resources. We aimed to understand how the health of male Latino day labourers in North Carolina, US is influenced by their experiences interacting with their community and perceptions of their social environment. To respond to our research questions, we conducted three focus groups (n=9, n =10, n=10) and a photovoice project (n=5) with Latino male immigrants between October 2013 and March 2014. We conducted a thematic analysis of transcripts from the discussions in the focus groups and the group of Photovoice participants. We found that men's health and well-being were primarily shaped by their experiences and feelings of discrimination and marginalization. We identified three main links between discrimination/marginalization and poor health: (1) dangerous work resulted in workplace injuries or illnesses, (2) unsteady employment caused stress, anxiety and insufficient funds for health care, and (3) exclusionary policies and treatment resulted in limited healthcare accessibility. Health promotion with Latino immigrant men in new settlement areas could benefit from community-building activities, addressing discrimination, augmenting the reach of formal health care, and building upon the informal mechanisms that immigrants rely on to meet their health needs. Reforms to immigration and labour policies are also essential to addressing these structural barriers to health for these men

Diseño de un drone para mapeo de zonas vulnerables a desastres naturales mediante uso de sensor de Lidar

Los últimos desastres naturales han mostrado qué tan vulnerable es el Perú, prueba de ello son los números a nivel nacional del impacto que tuvo el fenómeno del Niño Costero en el 2017: 138 personas fallecidas, 1 782 316 personas entre afectadas y damnificadas, 413 983 viviendas entre destruidas y afectadas, 234 51 kilómetros de carretera entre destruida y afectada, y 131 611 hectáreas de cultivo entre destruidas y afectadas, y todo ello pudo evitarse, o en su defecto reducir su impacto teniendo una adecuada gestión de riesgos y política de prevención. La presente tesis consiste en el diseño de un drone con un sensor LiDAR acoplado, el cual permitiría escanear zonas de interés y de difícil acceso para un topógrafo y obtener una nube de puntos, mediante la cual se puedan realizar simulaciones de desastres naturales, y así cuantificar posibles daños e identificar rutas y zonas seguras. La presente tesis tiene como objetivo general el diseño de un drone que pueda llevar como carga útil un sensor LiDAR y los componentes necesarios para su funcionamiento. Ello involucra realizar previamente una investigación acerca de cómo funciona tanto el drone como el LiDAR, además de sistemas que se puedan encontrar en el mercado, para luego empezar con la selección de componentes y el diseño propiamente dicho, tanto mecánico, electrónico y de control, y ello implica realizar cálculos y simulaciones, que fueron objetivos específicos de la tesis. El sistema diseñado permite la adquisición de datos de un terreno en forma de una nube de puntos mediante el uso del sensor LiDAR, además de que este sistema puede ser usado de forma manual o de forma autónoma, tiene una duración de 20.9 minutos, soporta vientos de hasta 7m/s y podrá ser monitoreado desde una estación en tierra, como una laptop, caracterizándose por ser de bajo costo en comparación a los modelos comerciales que existen en la actualidad