Dissolved organic carbon in sediments of the central Arctic Ocean collected during POLARSTERN cruise ARK-XXVII/3 from August-September 2012

Marine organic matter (OM) sinks from surface waters to the seafloor via the biological pump. Benthic communities, which use this sedimented OM as energy and carbon source, produce dissolved organic matter (DOM) in the process of remineralization, enriching the sediment porewater with fresh DOM compounds. We hypothesized that in the oligotrophic deep Arctic basin the molecular signal of freshly deposited primary produced OM is restricted to the surface sediment pore waters which should differ from bottom water and deeper sediment pore water in DOM composition. This study focused on: 1) the molecular composition of the DOM in sediment pore waters of the deep Eurasian Arctic basins, 2) whether the signal of marine vs. terrigenous DOM is represented by different compounds preserved in the sediment pore waters and 3) whether there is any relation between Arctic Ocean ice cover and DOM composition. Molecular data, obtained via 15 Tesla Fourier transform ion cyclotron resonance mass spectrometer, were correlated with environmental parameters by partial least square analysis. The fresher marine detrital OM signal from surface waters was limited to pore waters from < 5 cm sediment depth. The productive ice margin stations showed higher abundances of peptides, unsaturated aliphatics and saturated fatty acids formulae, indicative of fresh OM/pigments deposition, compared to northernmost stations which had stronger aromatic signals. This study contributes to the understanding of the coupling between the Arctic Ocean productivity and its depositional regime, and how it will be altered in response to sea ice retreat and increasing river runoff.

Histoire ecclesiastique : pour servir de continuation à celle de ... l'Abbé Fleury.

Mode of access: Internet.

Traité de l'organe de l'ouie : contenant la structure, les usages & les maladies de toutes les parties de l'oreille /

Mode of access: Internet.

The role of forest certification for the conservation of biodiversity and sustainability of cork oak woodlands

Doutoramento em Engenharia Florestal e dos Recursos Naturais - Instituto Superior de Agronomia - UL

Effect of organic amendments on the afforestation performance of plant species on degraded semiarid conditions

Vegetation plays a fundamental role in soil conservation, so it is common to consider an increase in vegetation cover as one of the techniques to mitigate the effects of desertification in Mediterranean forest environments. There are two factors limiting the establishment and growth of seedlings in dry environments: (i) an excessive radiation and, (ii) the limited availability of water during the summer drought. During an afforestation plan, soil preparation is always necessary to reduce sapling mortality. The goal of this study was to analyze the effect of various organic amendments on soil according to chemical and hydrological properties, and to assess the effects of these parameters on an afforestal proposal under Mediterranean climate conditions. Five amendments were applied in an experimental set of plots: straw mulching (SM); mulch with chipped branches of Aleppo Pine (PM); TerraCotten hydroabsobent polymers (HP); sewage sludge (RU); sheep manure (SH) and control (C). Plots were afforested following the same spatial pattern, and amendments were mixed with the soil at the rate 10 Mg ha -1 . Under bare soil conditions (C), most of mortalities occurred during the summer period of the first year. A substantial positive effect of SM, PM and HP on the survival rates have been clearly observed. Conversely, when the soil was amended with SH, the survival rate quickly decreased or remained more or less constant regarding to C. In this study, the lack of differences on chemical properties indicates that there may exist other reasons to justify the differences that were found in the pattern of vegetation. However, regarding to the hydrological properties some differences have been found. In C, soils were registered below the wilting point during 4 months a year, and therefore, in the area of water unusable by plants. These months were coinciding with the summer Mediterranean drought and can justify the high mortality found on plants. Conversely, in SM, PM and HP, soil moisture remained below the wilting point less period than C and, the plant available water was also higher. In these treatments, the survival sapling rates measured were the highest. SH showed water holding capacity slightly more limited than C. For this treatment, the survival sapling rates measured were the lowest. In conclusion, from a land management standpoint, the PM, SM and HP have been proved as a significant method to reduce sapling mortality rates during the Mediterranean summer drought.

Studies of CMS data access patterns with machine learning techniques

This thesis presents a study of the Grid data access patterns in distributed analysis in the CMS experiment at the LHC accelerator. This study ranges from the deep analysis of the historical patterns of access to the most relevant data types in CMS, to the exploitation of a supervised Machine Learning classification system to set-up a machinery able to eventually predict future data access patterns - i.e. the so-called dataset “popularity” of the CMS datasets on the Grid - with focus on specific data types. All the CMS workflows run on the Worldwide LHC Computing Grid (WCG) computing centers (Tiers), and in particular the distributed analysis systems sustains hundreds of users and applications submitted every day. These applications (or “jobs”) access different data types hosted on disk storage systems at a large set of WLCG Tiers. The detailed study of how this data is accessed, in terms of data types, hosting Tiers, and different time periods, allows to gain precious insight on storage occupancy over time and different access patterns, and ultimately to extract suggested actions based on this information (e.g. targetted disk clean-up and/or data replication). In this sense, the application of Machine Learning techniques allows to learn from past data and to gain predictability potential for the future CMS data access patterns. Chapter 1 provides an introduction to High Energy Physics at the LHC. Chapter 2 describes the CMS Computing Model, with special focus on the data management sector, also discussing the concept of dataset popularity. Chapter 3 describes the study of CMS data access patterns with different depth levels. Chapter 4 offers a brief introduction to basic machine learning concepts and gives an introduction to its application in CMS and discuss the results obtained by using this approach in the context of this thesis.