Non-comoving baryons and cold dark matter in cosmic voids

We examine the fully relativistic evolution of cosmic voids constituted by baryons and cold dark matter (CDM), represented by two non-comoving dust sources in a $\Lambda$CDM background. For this purpose, we consider numerical solutions of Einstein's field equations in a fluid-flow representation adapted to spherical symmetry and multiple components. We present a simple example that explores the frame-dependence of the local expansion and the Hubble flow for this mixture of two dusts, revealing that the relative velocity between the sources yields a significantly different evolution in comparison with that of the two sources in a common 4-velocity (which reduces to a Lemaitre-Tolman-Bondi model). In particular, significant modifications arise for the density contrast depth and void size, as well as in the amplitude of the surrounding over-densities. We show that an adequate model of a frame-dependent evolution that incorporates initial conditions from peculiar velocities and large-scale density contrast observations may contribute to understand the discrepancy between the local value of $H_0$ and that inferred from the CMB.Comment: Discussion of the evolution of baryon-CDM relative velocity added. Other minor but important corrections were incorporated. Version accepted for publication in EPJ

NORA: Scalable OWL reasoner based on NoSQL databasesand Apache Spark

Reasoning is the process of inferring new knowledge and identifying inconsis-tencies within ontologies. Traditional techniques often prove inadequate whenreasoning over large Knowledge Bases containing millions or billions of facts.This article introduces NORA, a persistent and scalable OWL reasoner built ontop of Apache Spark, designed to address the challenges of reasoning over exten-sive and complex ontologies. NORA exploits the scalability of NoSQL databasesto effectively apply inference rules to Big Data ontologies with large ABoxes. Tofacilitatescalablereasoning,OWLdata,includingclassandpropertyhierarchiesand instances, are materialized in the Apache Cassandra database. Spark pro-grams are then evaluated iteratively, uncovering new implicit knowledge fromthe dataset and leading to enhanced performance and more efficient reasoningover large-scale ontologies. NORA has undergone a thorough evaluation withdifferent benchmarking ontologies of varying sizes to assess the scalability of thedeveloped solution.Funding for open access charge: Universidad de Málaga / CBUA This work has been partially funded by grant (funded by MCIN/AEI/10.13039/501100011033/) PID2020-112540RB-C41,AETHER-UMA (A smart data holistic approach for context-aware data analytics: semantics and context exploita-tion). Antonio Benítez-Hidalgo is supported by Grant PRE2018-084280 (Spanish Ministry of Science, Innovation andUniversities)

SALON ontology for the formal description of Sequence Alignments.

Background. Information provided by high-throughput sequencing platforms allows the collection of content-rich data about bio- logical sequences and their context. Sequence alignment is a bioinformatics approach to identifying regions of similarity in DNA, RNA, or protein sequences. However, there is no consensus about the specific common terminology and representation for sequence alignments. Thus, automatically linking the wide existing knowledge about the sequences with the alignments is challenging. Results. The Sequence Alignment Ontology (SALON) defines a helpful vocabulary for representing and semantically annotating pairwise and multiple sequence alignments. SALON is an OWL 2 ontology that supports automated reasoning for alignments validation and retrieving complementary information from public databases under the Open Linked Data approach. This will reduce the effort needed by scientists to interpret the sequence alignment results. Conclusions. SALON defines a full range of controlled terminology in the domain of sequence alignments. It can be used as a mediated schema to integrate data from different sources and validate acquired knowledge.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

TITAN: A knowledge-based platform for Big Data workflow management

Modern applications of Big Data are transcending from being scalable solutions of data processing and analysis, to now provide advanced functionalities with the ability to exploit and understand the underpinning knowledge. This change is promoting the development of tools in the intersection of data processing, data analysis, knowledge extraction and management. In this paper, we propose TITAN, a software platform for managing all the life cycle of science workflows from deployment to execution in the context of Big Data applications. This platform is characterised by a design and operation mode driven by semantics at different levels: data sources, problem domain and workflow components. The proposed platform is developed upon an ontological framework of meta-data consistently managing processes and models and taking advantage of domain knowledge. TITAN comprises a well-grounded stack of Big Data technologies including Apache Kafka for inter-component communication, Apache Avro for data serialisation and Apache Spark for data analytics. A series of use cases are conducted for validation, which comprises workflow composition and semantic meta-data management in academic and real-world fields of human activity recognition and land use monitoring from satellite images.Universidad de Málaga. Andalucía TECH

Prediction of Anti-Glioblastoma Drug-Decorated Nanoparticle Delivery Systems Using Molecular Descriptors and Machine Learning

The theoretical prediction of drug-decorated nanoparticles (DDNPs) has become a very important task in medical applications. For the current paper, Perturbation Theory Machine Learning (PTML) models were built to predict the probability of different pairs of drugs and nanoparticles creating DDNP complexes with anti-glioblastoma activity. PTML models use the perturbations of molecular descriptors of drugs and nanoparticles as inputs in experimental conditions. The raw dataset was obtained by mixing the nanoparticle experimental data with drug assays from the ChEMBL database. Ten types of machine learning methods have been tested. Only 41 features have been selected for 855,129 drug-nanoparticle complexes. The best model was obtained with the Bagging classifier, an ensemble meta-estimator based on 20 decision trees, with an area under the receiver operating characteristic curve (AUROC) of 0.96, and an accuracy of 87% (test subset). This model could be useful for the virtual screening of nanoparticle-drug complexes in glioblastoma. All the calculations can be reproduced with the datasets and python scripts, which are freely available as a GitHub repository from authors. View Full-TextThe APC was funded by IKERDATA, S.L. under grant 3/12/DP/2021/00102—Area 1: Development of innovative business projects, from Provincial Council of Vizcaya (BEAZ for the Creation of Innovative Business Innovative business ventures)

TITAN: A knowledge-based platform for Big Data workflow management

Modern applications of Big Data are transcending from being scalable solutions of data processing and analysis, to now provide advanced functionalities with the ability to exploit and understand the underpinning knowledge. This change is promoting the development of tools in the intersection of data processing, data analysis, knowledge extraction and management. In this paper, we propose TITAN, a software platform for managing all the life cycle of science workflows from deployment to execution in the context of Big Data applications. This platform is characterised by a design and operation mode driven by semantics at different levels: data sources, problem domain and workflow components. The proposed platform is developed upon an ontological framework of meta-data consistently managing processes and models and taking advantage of domain knowledge. TITAN comprises a well-grounded stack of Big Data technologies including Apache Kafka for inter-component communication, Apache Avro for data serialisation and Apache Spark for data analytics. A series of use cases are conducted for validation, which comprises workflow composition and semantic meta-data management in academic and real-world fields of human activity recognition and land use monitoring from satellite images.This work has been partially funded by the Spanish Ministry of Science and Innovation via Grant PID2020 112540RB-C41 (AEI/FEDER, UE) and Andalusian PAIDI program with grant P18-RT-2799. Funding for open access charge: Universidad de Málaga / CBUA

Homérica: estudiar y vivir el mundo griego (2020-2021)

Formación del alumno en la Antigüedad Clásica en doble orientación didáctica: teórica y teatral-artística, en conjunción. La teórica consiste en jornadas de conferencias y la práctica en un montaje teatral mitológico, a cargo de alumnos y profesores

GreenSenti-IA: A Workflow Approach for Biodiversity Analysis in Urban Green Area Monitoring.

Nowadays, efficient urban planning cannot be conceived without carefully considering its ecological footprint. In particular, the smart design and monitoring of urban green areas is an important challenge in modern cities to promote human well-being and to mitigate other linked issues, such as urban heat, rainwater infiltration, and native biodiversity loss. The analysis of land use and land cover evolution oriented to green areas in cities is a key task in this direction, which entails the integration and management of large volumes of earth observation spatial data from remote sensors, near sensor web technologies, as well as geopositioned information collected by citizens. These geospatial sensing data should be duly integrated, analysed, visualised and shared to enable citizens to take advantage of them as they cannot deal with them as raw data. Therefore, the generation and deployment of big data computational platforms are crucial to support advanced services for urban environmental monitoring, which provide citizens with the means to harness satellite and spatial information. In this work, we present Green-Senti, a spatial big data framework oriented to urban green area monitoring, which is based on the acquisition and processing of Sentinel-1/2 images. This also includes incorporating services for the collection, integration, analysis and sharing of additional data, such as geopositioned land tastings, web visualization and linked open data repositories. The architectural design and implementation of the proposal are described, with special emphasis on the services and APIs generated to allow interoperability with external platforms. This proposal takes advantage of MongoDB and HDFS to organize and distribute all the integrated data in a Hadoop Cluster. Data analysis provided includes the calculation of several monitoring indicators, such as NDVI, NDWI and GVMI.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech