Old stellar population synthesis: New age and mass estimates for Mayall II = G1

Mayall II = G1 is one of the most luminous globular clusters (GCs) in M31. Here, we determine its age and mass by comparing multicolor photometry with theoretical stellar population synthesis models. Based on far- and near-ultraviolet GALEX photometry, broad-band UBVRI, and infrared JHK_s 2MASS data, we construct the most extensive spectral energy distribution of G1 to date, spanning the wavelength range from 1538 to 20,000 A. A quantitative comparison with a variety of simple stellar population (SSP) models yields a mean age that is consistent with G1 being among the oldest building blocks of M31 and having formed within ~1.7 Gyr after the Big Bang. Irrespective of the SSP model or stellar initial mass function adopted, the resulting mass estimates (of order $10^7 M_\odot$) indicate that G1 is one of the most massive GCs in the Local Group. However, we speculate that the cluster's exceptionally high mass suggests that it may not be a genuine GC. We also derive that G1 may contain, on average, $(1.65\pm0.63)\times10^2 L_\odot$ far-ultraviolet-bright, hot, extreme horizontal-branch stars, depending on the SSP model adopted. On a generic level, we demonstrate that extensive multi-passband photometry coupled with SSP analysis enables one to obtain age estimates for old SSPs to a similar accuracy as from integrated spectroscopy or resolved stellar photometry, provided that some of the free parameters can be constrained independently.Comment: Accepted for Publication in RAA, 12 pages, 1 figure, 2 table

Changements côtiers et inondations suite au passage d’un ouragan extrême (Irma, 2017) aux Petites Antilles

Cette étude porte sur les changements côtiers et les inondations suite au passage d’un ouragan de catégorie 5 (Irma) les 5 et 6 septembre 2017 sur les îles de Saint-Martin et Saint-Barthélemy aux Antilles. Deux missions de terrain à t+2 mois et t+8 mois sur les deux îles ont permis d’analyser les impacts de l’ouragan Irma sur des côtes basses particulièrement sensibles aux événements météo-marins extrêmes et aux pressions anthropiques. Le retour d’expérience a été réalisé sur les côtes les plus touchées par l’évènement. Pour comparer les impacts de l’ouragan Irma et les interactions avec les systèmes côtiers et les infrastructures côtières, nous avons choisi d’analyser des côtes peu urbanisées et des littoraux densément aménagés. La méthode a reposé sur l’analyse d’images satellites avant le passage de l’ouragan Irma et l’analyse d’images drones post-évènement. Elle s’est aussi appuyée sur des observations géomorphologiques, la mesure des hauteurs de vagues et la cartographie des espaces inondées. Les systèmes côtiers ont répondu très différemment en fonction du degré d’artificialisation de la côte, révélant des changements côtiers variés, des transferts sédimentaires perturbés et une influence sur les hauteurs d’eau maximales à la côte.This study deals with the coastal changes, flooding and damage after the passage of a category 5 hurricane (Irma) on 6 September 2017 over the islands of Saint-Martin and Saint-Barthélemy in the Lesser Antilles. Two field work were made 2 and 8 months after the catastrophe over the two islands. It made it possible to analyze the impacts of Hurricane Irma on the low-lying shores that are particularly susceptible to extreme cyclonic events and anthropogenic stressors. The field work was made on the coasts most affected by the cyclonic event. To compare impacts of hurricane Irma and interactions with coastal systems and coastal infrastructure, we chose to analyze undeveloped to highly urbanized coasts. The method was based on the analysis of satellite images and Unmanned Aerial Vehicle surveys. It also relied on qualitative observations, geomorphological and sedimentary surveys and the measurement of wave run up and the mapping of flooded areas. The coastal system revealed a variety of morpho-sedimentary responses on both the natural and highly urbanized coasts, showing varied coastal changes, perturbed sedimentary transfers and the effects of coastal structures and street on flow channeling and on water level increas

Le processus de production du risque « submersion marine » en zone littorale : l’exemple des territoires « Xynthia »

Les lourdes conséquences des catastrophes naturelles restent (trop) souvent expliquées, à travers les médias notamment, par les phénomènes de changements climatiques, par la défaillance des systèmes de défense ou par le caractère exceptionnel des conditions naturelles comme ce fut le cas lors des événements de février 2010 « Xynthia ». Or de tels phénomènes de submersion marine ont déjà atteint le littoral atlantique par le passé. Certes les dommages matériels et humains étaient moindres mais les conditions naturelles similaires. La différence se trouve dans l’occupation des sols bien plus tournée vers l’urbanisation ces 50 dernières années. Notre objectif est de montrer que loin d’être le fruit d’un phénomène exceptionnel, la submersion marine issue de la tempête Xynthia est l’aboutissement d’un processus de production du risque massif et récent. L’étude a concerné les communes de Charente-Maritime et Vendée qui ont subi des pertes humaines. La densification de l’urbanisation et plus généralement l’aménagement du territoire, même s’ils ne sont pas à l’origine du phénomène, ont accru les enjeux toujours plus vulnérables dans les zones à risques, accentuant ainsi les conséquences matérielles et humaines des phénomènes naturels. La tempête Xynthia a généré une submersion marine d’occurrence sans doute rare mais qui rencontre des dynamiques territoriales très rapides. Un espace quasi désert peut se retrouver en trente ans fortement urbanisé. Cet exemple rappelle que les mesures de prévention du risque en particulier les mesures de réglementation de l’occupation des sols doivent s’apprécier sur la longue durée car leur transgression est quasiment irréversible.The huge aftermaths of disasters remain (too) often explained, through the media, by the climate change or by the failure of flood defenses, as was the storm Xynthia that hit the western part of France in February 2010. However such phenomena of marine flooding linked to windstorm have already struck the Atlantic coast in the past. Material and human damage were lower although natural conditions were quite similar. The difference lies in the urbanization that has widely grown for fifty years. Our goal is to show that the toll of the Xynthia storm is not essentially due to an exceptional marine flooding phenomenon. On the contrary, it is due to the accumulation of vulnerable assets in flood prone zone. The study mainly relies on the seven communes of Charente-Maritime and Vendée which suffered human losses. The densification of urbanization and more generally the lack of land use control led to an anarchic development of assets at risk especially touristic plants and secondary homes. The marine flooding generated by Xynthia Storm has probably a low return period but the low frequency phenomenon has met very rapid territorial dynamics. Thus the process of “production of risk” had been very fast. A quite uninhabited space can be turned into an highly urbanized territory within thirty years. This example reminds that the measures of prevention of risk, especially land use planning must be taken and assessed on the long term because their transgression is irreversible

DAÑOS CATASTRÓFICOS EN CARRETERAS INSULARES [Material gráfico]

Copia digital. Madrid : Ministerio de Educación, Cultura y Deporte, 201

Motivators and Inhibitors for Managing IT Project Knowledge: Findings from Three Exploratory Case Studies

Abstract Managing knowledge on IT projects is an important factor in contributing toward

Using Technology to Enhance a Project Management Course in the United Arab Emirates

Abstract There is little doubt that advances in technology have provided unprecedented opportunities to develop more effective learning platforms, with the aim of ultimately providing better student learning outcomes. However, technological innovations on their own may provide little more than improvements in quality and productivity over previously used methods. Technology, regardless of how advanced the technology is, should never be used as a substitute for good teaching practice, but as a means of enhancing existing good teaching practices. This paper discusses how, over a period of 4 years, technology has been embedded into a project management course within the United Arab Emirates in order to provide students with a more effective learning experience. The various technologies used in this example include videos of a case study project used in the course, a working example of the application software that was developed for the case study, audio recording of lectures and the construction of videos containing power point slides overlaid with previously recorded audio files

Coordinated RhoA signaling at the leading edge and uropod is required for T cell transendothelial migration

A biosensor for the RhoA GTPase illustrates its activation patterns in both the front and rear of migrating lymphocytes

Basalts erupted along the Tongan fore-arc during subduction initiation: evidence from geochronology of dredged rocks from the Tonga fore-arc and trench

A wide variety of different rock types were dredged from the Tonga fore arc and trench between 8000 and 3000 m water depths by the 1996 Boomerang voyage. 40Ar-39Ar whole rock and U-Pb zircon dating suggest that these fore arc rocks were erupted episodically from the Cretaceous to the Pliocene (102 to 2 Ma). The geochemistry suggests that MOR-type basalts and dolerites were erupted in the Cretaceous, that island arc tholeiites were erupted in the Eocene and that back arc basin and island arc tholeiite and boninite were erupted episodically after this time. The ages generally become younger northward suggesting that fore arc crust was created in the south at around 48–52 Ma and was extended northward between 35 and 28 Ma, between 9 and 15 Ma and continuing to the present-day. The episodic formation of the fore arc crust suggested by this data is very different to existing models for fore arc formation based on the Bonin-Marianas arc. The Bonin-Marianas based models postulate that the basaltic fore arc rocks were created between 52 and 49 Ma at the beginning of subduction above a rapidly foundering west-dipping slab. Instead a model where the 52 Ma basalts that are presently in a fore arc position were created in the arc-back arc transition behind the 57–35 Ma Loyalty-Three Kings arc and placed into a fore arc setting after arc reversal following the start of collision with New Caledonia is proposed for the oldest rocks in Tonga. This is followed by growth of the fore arc northward with continued eruption of back arc and boninitic magmas after that time

Multi-scale digital soil mapping with deep learning

We compared different methods of multi-scale terrain feature construction and their relative effectiveness for digital soil mapping with a Deep Learning algorithm. The most common approach for multi-scale feature construction in DSM is to filter terrain attributes based on different neighborhood sizes, however results can be difficult to interpret because the approach is affected by outliers. Alternatively, one can derive the terrain attributes on decomposed elevation data, but the resulting maps can have artefacts rendering the approach undesirable. Here, we introduce ‘mixed scaling’ a new method that overcomes these issues and preserves the landscape features that are identifiable at different scales. The new method also extends the Gaussian pyramid by introducing additional intermediate scales. This minimizes the risk that the scales that are important for soil formation are not available in the model. In our extended implementation of the Gaussian pyramid, we tested four intermediate scales between any two consecutive octaves of the Gaussian pyramid and modelled the data with Deep Learning and Random Forests. We performed the experiments using three different datasets and show that mixed scaling with the extended Gaussian pyramid produced the best performing set of covariates and that modelling with Deep Learning produced the most accurate predictions, which on average were 4–7% more accurate compared to modelling with Random Forests