ATLAS Simulation readiness for first data at LHC

The commissioning phase for the ATLAS experiment, in preparation for the new LHC machine to switch on, has presented challenges to nearly every aspect of the software development. The ATLAS simulation program, as a part of this phase, is now operational and fully functional within the ATLAS common software framework, Athena. The latest developments are directed towards enhanced versatility to cope with the increasing needs of developers and users and ease of use for the large ATLAS community, now with more than 2000 potential users. Emphasis in this talk is on recently added functionality recently added, validation and production strategy, and improved robustness and maintainability

Improving Latin American soil information database for digital soil mapping enhances its usability and scalability

Spatial soil databases can help model complex phenomena in which soils are decisive, for example, evaluating agricultural potential or estimating carbon storage capacity. The Soil Information System for Latin America and the Caribbean, SISLAC, is a regional initiative promoted by the FAO's South American Soil Partnership to contribute to the sustainable management of soil. SISLAC includes data coming from 49,084 soil profiles distributed unevenly across the continent, making it the region's largest soil database. However, some problems hinder its usages, such as the quality of the data and its high dimensionality. The objective of this research is twofold. First, to evaluate the quality of SISLAC and its data values and generate a new, improved version that meets the minimum quality requirements to be used by different interests or practical applications. Second, to demonstrate the potential of improved soil profile databases to generate more accurate information on soil properties, by conducting a case study to estimate the spatial variability of the percentage of soil organic carbon using 192 profiles in a 1473 km2 region located in the department of Valle del Cauca, Colombia. The findings show that 15 percent of the existing soil profiles had an inaccurate description of the diagnostic horizons. Further correction of an 4.5 additional percent of existing inconsistencies improved overall data quality. The improved database consists of 41,691 profiles and is available for public use at ttps://doi.org/10.5281/zenodo.6540710 (Díaz-Guadarrama, S. & Guevara, M., 2022). The updated profiles were segmented using algorithms for quantitative pedology to estimate the spatial variability. We generated segments one centimeter thick along with each soil profile data, then the values of these segments were adjusted using a spline-type function to enhance vertical continuity and reliability. Vertical variability was estimated up to 150 cm in-depth, while ordinary kriging predicts horizontal variability at three depth intervals, 0 to 5, 5 to 15, and 15 to 30 cm, at 250 m-spatial resolution, following the standards of the GlobalSoilMap project. Finally, the leave-one-out cross validation provides information for evaluating the kriging model performance, obtaining values for the RMSE index between 1.77% and 1.79% and the R2 index greater than 0.5. The results show the usability of SISLAC database to generate spatial information on soil properties and suggest further efforts to collect a more significant amount of data to guide sustainable soil management.Fil: Diaz Guadamarra, Sergio. Universidad Nacional de Colombia. Facultad de Ciencias Agrarias. Departamento de Agronomía; ColombiaFil: Lizarazo, Iván. Universidad Nacional de Colombia. Facultad de Ciencias Agrarias. Departamento de Agronomía; ColombiaFil: Guevara, Mario. Universidad Nacional Autónoma de México. Campus Juriquilla. Centro de Geociencias; MéxicoFil: Guevara, Mario. Universidad Nacional Autónoma de México.Campus Juriquilla. Centro de Geociencias; México. United States Department of Agriculture. Soil Salinity National Laboratory, Estados UnidosFil: Angelini, Marcos Esteban. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Suelos; Argentina. Wageningen University. Soil Geography and Landscape Group; Países Bajos. International Soil Reference and Information Centre. World Soil Information; Países BajosFil: Araujo Carrillo, Gustavo A. Corporación Colombiana de Investigación Agropecuaria AGROSAVIA; ColombiaFil: Argeñal, Jainer. Universidad Nacional Autónoma de Honduras. Facultad de Ciencias; Honduras.Fil: Armas, Daphne. Universidad de Almería. Departamento de Agronomía, Edif. CITEIIB, España.Fil: Balsa, Rafael A. Ministerio de Desarrollo Agrario y Riego. Dirección General de Asuntos Ambientales Agrarios, Perú.Fil: Bolivar, Adriana. Instituto Geográfico Agustín Codazzi. Subdirección Agrología; ColombiaFil: Bustamante, Nelson. Servicio Agrícola y Ganadero; Chile.Fil: Dart, Ricardo O. Embrapa Solos; BrasilFil: Dell Acqua, Martín. Ministerio de Ganadería, Agricultura y Pesca. Dirección General de Recursos Naturales; UruguayFil: Lencina, Arnulfo. Universidad Nacional de Asunción. Facultad de Ciencias Agrarias; ParaguayFil: Figueredo, Hernán. Sociedad Boliviana de la Ciencia del Suelo; Bolivia.Fil: Fontes, Fernando. Ministerio de Ganadería, Agricultura y Pesca. Dirección General de Recursos Naturales; UruguayFil: Gutierrez Diaz, Joan S. Aarhus University. Faculty of Science and Technology,.Department of Agroecology; DinamarcaFil: Jiménez, Wilmer. Ministerio de Agricultura y Ganadería; Ecuador.Fil: Rodriguez, Dario Martin. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Suelos; ArgentinaFil: Schulz, Guillermo. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Suelos; ArgentinaFil: Tenti Vuegen, Leonardo Mauricio. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Suelos; Argentin

No silver bullet for digital soil mapping: country-specific soil organic carbon estimates across Latin America.

Country-specific soil organic carbon (SOC) estimates are the baseline for the Global SOC Map of the Global Soil Partnership (GSOCmap-GSP). This endeavor is key to explaining the uncertainty of global SOC estimates but requires harmonizing heterogeneous datasets and building country-specific capacities for digital soil mapping (DSM).We identified country-specific predictors for SOC and tested the performance of five predictive algorithms for mapping SOC across Latin America. The algorithms included support vector machines (SVMs), random forest (RF), kernel-weighted nearest neighbors (KK), partial least squares regression (PL), and regression kriging based on stepwise multiple linear models (RK). Country-specific training data and SOC predictors (5 x 5 km pixel resolution) were obtained from ISRIC - World Soil Information. Temperature, soil type, vegetation indices, and topographic constraints were the best predictors for SOC, but country-specific predictors and their respective weights varied across Latin America. We compared a large diversity of country-specific datasets and models, and were able to explain SOC variability in a range between ~ 1 and ~ 60 %, with no universal predictive algorithm among countries. A regional (n = 11 268 SOC estimates) ensemble of these five algorithms was able to explain ~ 39% of SOC variability from repeated 5-fold cross-validation.We report a combined SOC stock of 77.8 +- 43.6 Pg (uncertainty represented by the full conditional response of independent model residuals) across Latin America. SOC stocks were higher in tropical forests (30 +- 16.5 Pg) and croplands (13 +- 8.1 Pg). Country-specific and regional ensembles revealed spatial discrepancies across geopolitical borders, higher elevations, and coastal plains, but provided similar regional stocks (77.8 +- 42.2 and 76.8 +- 45.1 Pg, respectively). These results are conservative compared to global estimates (e.g., SoilGrids250m 185.8 Pg, the Harmonized World Soil Database 138.4 Pg, or the GSOCmap-GSP 99.7 Pg). Countries with large area (i.e., Brazil, Bolivia, Mexico, Peru) and large spatial SOC heterogeneity had lower SOC stocks per unit area and larger uncertainty in their predictions. We highlight that expert opinion is needed to set boundary prediction limits to avoid unrealistically high modeling estimates. For maximizing explained variance while minimizing prediction bias, the selection of predictive algorithms for SOC mapping should consider density of available data and variability of country-specific environmental gradients. This study highlights the large degree of spatial uncertainty in SOC estimates across Latin America. We provide a framework for improving country-specific mapping efforts and reducing current discrepancy of global, regional, and country-specific SOC estimates

Quick Minds Slowed Down: Effects of Rotation and Stimulus Category on the Attentional Blink

BACKGROUND: Most people show a remarkable deficit to report the second of two targets when presented in close temporal succession, reflecting an attentional restriction known as the 'attentional blink' (AB). However, there are large individual differences in the magnitude of the effect, with some people showing no such attentional restrictions. METHODOLOGY/PRINCIPAL FINDINGS: Here we present behavioral and electrophysiological evidence suggesting that these 'non-blinkers' can use alphanumeric category information to select targets at an early processing stage. When such information was unavailable and target selection could only be based on information that is processed relatively late (rotation), even non-blinkers show a substantial AB. Electrophysiologically, in non-blinkers this resulted in enhanced distractor-related prefrontal brain activity, as well as delayed target-related occipito-parietal activity (P3). CONCLUSION/SIGNIFICANCE: These findings shed new light on possible strategic mechanisms that may underlie individual differences in AB magnitude and provide intriguing clues as to how temporal restrictions as reflected in the AB can be overcome

Recommendations and guidelines from the ISMRM Diffusion Study Group for preclinical diffusion MRI: Part 1 -- In vivo small-animal imaging

The value of in vivo preclinical diffusion MRI (dMRI) is substantial. Small-animal dMRI has been used for methodological development and validation, characterizing the biological basis of diffusion phenomena, and comparative anatomy. Many of the influential works in this field were first performed in small animals or ex vivo samples. The steps from animal setup and monitoring, to acquisition, analysis, and interpretation are complex, with many decisions that may ultimately affect what questions can be answered using the data. This work aims to serve as a reference, presenting selected recommendations and guidelines from the diffusion community, on best practices for preclinical dMRI of in vivo animals. In each section, we also highlight areas for which no guidelines exist (and why), and where future work should focus. We first describe the value that small animal imaging adds to the field of dMRI, followed by general considerations and foundational knowledge that must be considered when designing experiments. We briefly describe differences in animal species and disease models and discuss how they are appropriate for different studies. We then give guidelines for in vivo acquisition protocols, including decisions on hardware, animal preparation, imaging sequences and data processing, including pre-processing, model-fitting, and tractography. Finally, we provide an online resource which lists publicly available preclinical dMRI datasets and software packages, to promote responsible and reproducible research. An overarching goal herein is to enhance the rigor and reproducibility of small animal dMRI acquisitions and analyses, and thereby advance biomedical knowledge.Comment: 69 pages, 6 figures, 1 tabl

Flood monitoring in urban areas: Statistical vs. neurofuzzy approach

The paper aims at investigating different classification and segmentation tools for flood monitoring using satellite SAR images. To this aim, two different approaches, namely statistical segmentation and neurofuzzy classification and compared and discussed. The methods show, in general, the possibility to provide to a good extent accurate maps of the flooded areas using simple processing schemes. This stresses the effectiveness of satellite SAR images for real-time flood monitoring

The status of the ATLAS simulation project for the LHC startup

The commissioning phase for the ATLAS experiment, being now so imminent the LHC startup, is a challenge for every development performed up to now. Along this line, the simulation program is nowadays operational and running with full functionality within the ATLAS common framework Athena. The latest developments concern an enhanced versatility, to cope with the increasing needs of developers and users, an easiness in use by a big community such as ATLAS reaching now more than 2000 potential users. Emphasis in this talk is towards the new functionality recently added, validation and production strategy as well as improved robustness and maintainability