ALBERT

Description: Carbon dioxide deep geological storage, especially in deep saline aquifers, is one of the preferred technological options to mitigate the effects of greenhouse gases emissions. Thus, in the last decade, studies characterising the behaviour of potential CO2 deep geological storage sites along with thorough safety assessments have been considered essential in order to minimise the risks associated with these sites. The study of natural analogues represents the best source of reliable information about the expected hydrogeochemical processes involved in the CO2 storage in such deep saline aquifers. In this work, a comprehensive study of the hydrogeochemical features and processes taking place at the natural analogue of the Alicún de las Torres thermal system (Betic Cordillera) has been conducted. Thus, the main water/CO2/rock interaction processes occurring at the thermal system have been identified, quantified and modelled, and a principle conclusion is that the hydrogeochemical evolution of the thermal system is controlled by a global dedolomitization process triggered by gypsum dissolution. This geochemical process generates a different geochemical environment to that which would result from the exclusive dissolution of carbonates from the deep aquifer, which is generally considered as the direct result of CO2 injection in a deep carbonate aquifer. Therefore, discounting of the dedolomitization process in any CO2 deep geological storage may lead to erroneous conclusions. This process will also influence the porosity evolution of the CO2 storage formation, which is a very relevant parameter when evaluating a reservoir for CO2 storage. The geothermometric calculation performed in this work leads to estimate that the thermal water reservoir is located between 650 and 800 m depth, which is very close to the minimum required to inject CO2 in a deep geological storage. It is clear that the proper characterisation of the features and hydrogeochemical processes taking place at a natural system analogous to a man-made deep geological storage will provide useful conceptual, semi-quantitative and even quantitative information about the processes and consequences that may occur at the artificial storage system.

Description: This dataset includes 11 regional EUNIS-classified habitat maps (100-1000 km) and associated confidence maps that were created as a project milestone (Nr. 12) of the EU H2020 project 'iAtlantic'. The 12 iAtlantic regions encompass 1. Subpolar Mid-Atlantic Ridge, off Iceland MFRI, 2. Rockall Trough to PAP, 3. Central mid-Atlantic Ridge, 4. NW Atlantic, Gully Canyon, 5. Sargasso Sea, 6. Eastern Tropical North Atlantic, Cape Verde, 7. Equatorial Atlantic, Romanche Fracture Zone, 8. Slope & margin off Angola & Congo Lobe, 9. Benguela Current, Walvis Ridge to South Africa, 10. Brazil margin & Santos and Campos Basin, 11. Vitória-Trindade Seamount Chain and 12. Malvinas Current. For each of the regions 2-12, a shapefile of polygons classified according to the 2022 EUNIS classification level 3 and a second shapefile of the same polygons attributed with their confidence level according to the MESH Accuracy & Confidence Working approach was created. EUNIS classifications combined biozone and substrate data. Biozones were assigned from bathymetry. Where MBES was not available, GEBCO bathymetry was used. Substrate data were extracted from pre-existing geological/substrate mapping efforts and converted to EUNIS classifications via cross walks or, where substrate data were limited, substrate layers were modelled using Random Forest. The EUNIS habitat map for Region 4 was based on the pre-existing surficial geology compilation of the Scotian Shelf bioregion compiled by the Geological Survey of Canada. The EUNIS habitat map for Region 9 was based on the pre-existing South African habitat map that uses a modified IUCN hierarchical classification system. No additional information to that used in the EUSeaMap was available for Region 1. Therefore, shapefiles were not created for Region 1.

Description: This dataset includes 11 regional EUNIS-classified habitat maps (100-1000 km) and associated confidence maps that were created as a project milestone (Nr. 12) of the EU H2020 project 'iAtlantic'. The 12 iAtlantic regions encompass 1. Subpolar Mid-Atlantic Ridge, off Iceland MFRI, 2. Rockall Trough to PAP, 3. Central mid-Atlantic Ridge, 4. NW Atlantic, Gully Canyon, 5. Sargasso Sea, 6. Eastern Tropical North Atlantic, Cape Verde, 7. Equatorial Atlantic, Romanche Fracture Zone, 8. Slope & margin off Angola & Congo Lobe, 9. Benguela Current, Walvis Ridge to South Africa, 10. Brazil margin & Santos and Campos Basin, 11. Vitória-Trindade Seamount Chain and 12. Malvinas Current. For each of the regions 2-12, a shapefile of polygons classified according to the 2022 EUNIS classification level 3 and a second shapefile of the same polygons attributed with their confidence level according to the MESH Accuracy & Confidence Working approach was created. EUNIS classifications combined biozone and substrate data. Biozones were assigned from bathymetry. Where MBES was not available, GEBCO bathymetry was used. Substrate data were extracted from pre-existing geological/substrate mapping efforts and converted to EUNIS classifications via cross walks or, where substrate data were limited, substrate layers were modelled using Random Forest. The EUNIS habitat map for Region 4 was based on the pre-existing surficial geology compilation of the Scotian Shelf bioregion compiled by the Geological Survey of Canada. The EUNIS habitat map for Region 9 was based on the pre-existing South African habitat map that uses a modified IUCN hierarchical classification system. No additional information to that used in the EUSeaMap was available for Region 1. Therefore, shapefiles were not created for Region 1.

Description: This dataset includes 11 regional EUNIS-classified habitat maps (100-1000 km) and associated confidence maps that were created as a project milestone (Nr. 12) of the EU H2020 project 'iAtlantic'. The 12 iAtlantic regions encompass 1. Subpolar Mid-Atlantic Ridge, off Iceland MFRI, 2. Rockall Trough to PAP, 3. Central mid-Atlantic Ridge, 4. NW Atlantic, Gully Canyon, 5. Sargasso Sea, 6. Eastern Tropical North Atlantic, Cape Verde, 7. Equatorial Atlantic, Romanche Fracture Zone, 8. Slope & margin off Angola & Congo Lobe, 9. Benguela Current, Walvis Ridge to South Africa, 10. Brazil margin & Santos and Campos Basin, 11. Vitória-Trindade Seamount Chain and 12. Malvinas Current. For each of the regions 2-12, a shapefile of polygons classified according to the 2022 EUNIS classification level 3 and a second shapefile of the same polygons attributed with their confidence level according to the MESH Accuracy & Confidence Working approach was created. EUNIS classifications combined biozone and substrate data. Biozones were assigned from bathymetry. Where MBES was not available, GEBCO bathymetry was used. Substrate data were extracted from pre-existing geological/substrate mapping efforts and converted to EUNIS classifications via cross walks or, where substrate data were limited, substrate layers were modelled using Random Forest. No additional information to that used in the EUSeaMap was available for region 1. Therefore, shapefiles were not created for region 1.

Description: Single-nucleotide polymorphisms (SNPs) in the protein phosphatase and actin regulator 1 gene (PHACTR1) have been associated with susceptibility to develop several diseases, including cardiovascular disease. The purpose of this study was to evaluate the role of two polymorphisms (rs2026458 and rs9349379) of the PHACTR1 gene in the susceptibility to the risk of developing premature coronary artery disease (CAD) in the Mexican population. The genotype analysis was performed using 5’exonuclease TaqMan genotyping assays in a group of 994 patients with premature CAD and 703 controls. A similar genotype distribution of rs2026458 was observed in both groups; however, under an additive model adjusted by age, body mass index, type 2 diabetes mellitus, smoking, dyslipidemia, and hypertension, the rs9349379 G allele was associated with a higher risk for developing premature CAD (odds ratio (OR) = 1.22, 95% confidence interval (CI) = 1.03–1.46, p-value (p) = 0.024). The two PHACTR1 polymorphisms were not in linkage disequilibrium. In summary, our results suggest that the PHACTR1 rs9349379 polymorphism plays an important role in the risk of developing premature CAD in the Mexican population.

Description: This paper addresses the problem of computing the family of two-filiform Lie algebra laws of dimension nine using three Lie algebra properties converted into matrix form properties: Jacobi identity, nilpotence and quasi-filiform property. The interest in this family is broad, both within the academic community and the industrial engineering community, since nilpotent Lie algebras are applied in traditional mechanical dynamic problems and current scientific disciplines. The conditions of being quasi-filiform and nilpotent are applied carefully and in several stages, and appropriate changes of the basis are achieved in an iterative and interactive process of simplification. This has been implemented by means of the development of more than thirty Maple modules. The process has led from the first family formulation, with 64 parameters and 215 constraints, to a family of 16 parameters and 17 constraints. This structure theorem permits the exhaustive classification of the quasi-filiform nilpotent Lie algebras of dimension nine with current computational methodologies.

Description: This work summarizes the activities carried out by the SMOS (Soil Moisture and Ocean Salinity) Barcelona Expert Center (SMOS-BEC) team in conjunction with the CIALE/Universidad de Salamanca team, within the framework of the European Space Agency (ESA) CALIMAS project in preparation for the SMOS mission and during its first year of operation. Under these activities several studies were performed, ranging from Level 1 (calibration and image reconstruction) to Level 4 (land pixel disaggregation techniques, by means of data fusion with higher resolution data from optical/infrared sensors). Validation of SMOS salinity products by means of surface drifters developed ad-hoc, and soil moisture products over the REMEDHUS site (Zamora, Spain) are also presented. Results of other preparatory activities carried out to improve the performance of eventual SMOS follow-on missions are presented, including GNSS-R to infer the sea state correction needed for improved ocean salinity retrievals and land surface parameters. Results from CALIMAS show a satisfactory performance of the MIRAS instrument, the accuracy and efficiency of the algorithms implemented in the ground data processors, and explore the limits of spatial resolution of soil moisture products using data fusion, as well as the feasibility of GNSS-R techniques for sea state determination and soil moisture monitoring.

Description: The presence of smoke is the first symptom of fire; therefore to achieve early fire detection, accurate and quick estimation of the presence of smoke is very important. In this paper we propose an algorithm to detect the presence of smoke using video sequences captured by Internet Protocol (IP) cameras, in which important features of smoke, such as color, motion and growth properties are employed. For an efficient smoke detection in the IP camera platform, a detection algorithm must operate directly in the Discrete Cosine Transform (DCT) domain to reduce computational cost, avoiding a complete decoding process required for algorithms that operate in spatial domain. In the proposed algorithm the DCT Inter-transformation technique is used to increase the detection accuracy without inverse DCT operation. In the proposed scheme, firstly the candidate smoke regions are estimated using motion and color smoke properties; next using morphological operations the noise is reduced. Finally the growth properties of the candidate smoke regions are furthermore analyzed through time using the connected component labeling technique. Evaluation results show that a feasible smoke detection method with false negative and false positive error rates approximately equal to 4% and 2%, respectively, is obtained.

Description: Sensors, Vol. 18, Pages 2993: A System for In-Line 3D Inspection without Hidden Surfaces Sensors doi: 10.3390/s18092993 Authors: Juan-Carlos Perez-Cortes Alberto J. Perez Sergio Saez-Barona Jose-Luis Guardiola Ismael Salvador This work presents a 3D scanner able to reconstruct a complete object without occlusions, including its surface appearance. The technique presents a number of differences in relation to current scanners: it does not require mechanical handling like robot arms or spinning plates, it is free of occlusions since the scanned part is not resting on any surface and, unlike stereo-based methods, the object does not need to have visual singularities on its surface. This system, among other applications, allows its integration in production lines that require the inspection of a large volume of parts or products, especially if there is an important variability of the objects to be inspected, since there is no mechanical manipulation. The scanner consists of a variable number of industrial quality cameras conveniently distributed so that they can capture all the surfaces of the object without any blind spot. The object is dropped through the common visual field of all the cameras, so no surface or tool occludes the views that are captured simultaneously when the part is in the center of the visible volume. A carving procedure that uses the silhouettes segmented from each image gives rise to a volumetric representation and, by means of isosurface generation techniques, to a 3D model. These techniques have certain limitations on the reconstruction of object regions with particular geometric configurations. Estimating the inherent maximum error in each area is important to bound the precision of the reconstruction. A number of experiments are presented reporting the differences between ideal and reconstructed objects in the system.

Description: Sensors, Vol. 18, Pages 1380: Social Sentiment Sensor in Twitter for Predicting Cyber-Attacks Using ℓ1 Regularization Sensors doi: 10.3390/s18051380 Authors: Aldo Hernandez-Suarez Gabriel Sanchez-Perez Karina Toscano-Medina Victor Martinez-Hernandez Hector Perez-Meana Jesus Olivares-Mercado Victor Sanchez In recent years, online social media information has been the subject of study in several data science fields due to its impact on users as a communication and expression channel. Data gathered from online platforms such as Twitter has the potential to facilitate research over social phenomena based on sentiment analysis, which usually employs Natural Language Processing and Machine Learning techniques to interpret sentimental tendencies related to users’ opinions and make predictions about real events. Cyber-attacks are not isolated from opinion subjectivity on online social networks. Various security attacks are performed by hacker activists motivated by reactions from polemic social events. In this paper, a methodology for tracking social data that can trigger cyber-attacks is developed. Our main contribution lies in the monthly prediction of tweets with content related to security attacks and the incidents detected based on ℓ 1 regularization.