Innovación docente en el EEES de cara a la práctica profesional a través del aprendizaje basado en proyectos

En este artículo se describe nuestra experiencia en la docencia de Arquitectura e Ingeniería de Computadores en el Máster en Informática de la Universidade da Coruña, en la cual concurrían las circunstancias de titulación EEES de nueva implantación y un número reducido de alumnos. La orientación profesionalizante del máster nos motivó a explorar en innovación docente de cara a la práctica profesional, fundamentalmente a través de metologías de aprendizaje basado en proyectos (project-based learning) combinado con las acciones de: (1) sustitución de docencia teórica por trabajos académicamente dirigidos; (2) impartición de seminarios profesionales; (3) uso de técnicas de role playing; y (4) desarrollo de habilidades comunicativas. La valoración global es que esta metodología y sus acciones asociadas han resultado tremendamente positivas en la docencia de la materia.Peer Reviewe

Exploratory Data Analysis and Data Envelopment Analysis of Urban Rail Transit

[Abstract] This paper deals with the efficiency and sustainability of urban rail transit (URT) using exploratory data analytics (EDA) and data envelopment analysis (DEA). The first stage of the proposed methodology is EDA with already available indicators (e.g., the number of stations and passengers), and suggested indicators (e.g., weekly frequencies, link occupancy rates, and CO2 footprint per journey) to directly characterize the efficiency and sustainability of this transport mode. The second stage is to assess the efficiency of URT with two original models, based on a thorough selection of input and output variables, which is one of the key contributions of EDA to this methodology. The first model compares URT against other urban transport modes, applicable to route personalization, and the second scores the efficiency of URT lines. The main outcome of this paper is the proposed methodology, which has been experimentally validated using open data from the Transport for London (TfL) URT network and additional sources.Ministerio de Economía, Industria y Competitividad; TIN2016-75845-PAgencia Estatal de Investigación; SNEO-20161147Xunta de Galicia; ED431G2019/01Xunta de Galicia; ED431C 2017/04Xunta de Galicia; ED431G2019/0

Exploratory data analysis and data envelopment analysis of construction and demolition waste management in the European Economic Area

This paper deals with the e ciency and sustainability of Construction and Demolition Waste (CDW) management in 30 Member States of the European Economic Area (EEA) (the 28 European Union countries plus Norway and Iceland) for the period 2010–2016 using Exploratory Data Analytics (EDA) and Data Envelopment Analysis (DEA). The first stage of the proposed methodology is EDA with already available (the CDW recovery rate) and suggested indicators (e.g., building stock characterization, dwelling occupancy ratio, macroeconomic ratios and CDW breakdown) to characterize the e ciency and sustainability of CDW management. The second stage is to assess the e ciency of countries using DEA through two original CDW production models, one for sustainability, measuring the e ciency of the construction sector for reducing itsCDW, and the second a model to score the e ciency of maximizing the CDW recovery rate. The main outcome of the paper is the proposed methodology, which is a candidate for replacing current indicators in order to evaluate the performance of CDW policy, due to is adaptive nature, promoting the continuous improvement and overcoming the limitations of the poor quality of metrics, data and parametric indicators. The methodology has been experimentally validated using Eurostat data for 30 Member States of EEA, ranking them according to the two DEA model scores, to point out the countries considered e cient among those of their scale, as a reference for sustainable and e cient practices

Exploratory Data Analysis and Data Envelopment Analysis of Construction and Demolition Waste Management in the European Economic Area

This paper deals with the efficiency and sustainability of Construction and Demolition Waste (CDW) management in 30 Member States of the European Economic Area (EEA) (the 28 European Union countries plus Norway and Iceland) for the period 2010–2016 using Exploratory Data Analytics (EDA) and Data Envelopment Analysis (DEA). The first stage of the proposed methodology is EDA with already available (the CDW recovery rate) and suggested indicators (e.g., building stock characterization, dwelling occupancy ratio, macroeconomic ratios and CDW breakdown) to characterize the efficiency and sustainability of CDW management. The second stage is to assess the efficiency of countries using DEA through two original CDW production models, one for sustainability, measuring the efficiency of the construction sector for reducing its CDW, and the second a model to score the efficiency of maximizing the CDW recovery rate. The main outcome of the paper is the proposed methodology, which is a candidate for replacing current indicators in order to evaluate the performance of CDW policy, due to is adaptive nature, promoting the continuous improvement and overcoming the limitations of the poor quality of metrics, data and parametric indicators. The methodology has been experimentally validated using Eurostat data for 30 Member States of EEA, ranking them according to the two DEA model scores, to point out the countries considered efficient among those of their scale, as a reference for sustainable and efficient practicesThis research was funded by the Ministry of Economy, Industry and Competitiveness of Spain, Project TIN2016-75845-P (AEI/FEDER/EU) and SNEO-20161147 (CDTI) and by Xunta de Galicia and FEDER funds of the EU (Centro de Investigación de Galicia accreditation 2019–2022, ref. ED431G2019/01, and Consolidation Programme of Competitive Reference Groups, ref. ED431C 2017/04)S

Serverless-like platform for container-based YARN clusters

Financiado para publicación en acceso aberto: Universidade da Coruña/CISUG[Abstract]: Serverless computing is an emerging paradigm that has gained a lot of relevance in recent years, as it allows users to consume computing resources without worrying about the underlying infrastructure and pay only for what they actually use. Most current services that implement this paradigm typically rely on the Function-as-a-Service (FaaS) model, which works perfectly for simple applications based on stateless functions triggered by specific events. However, these services are not designed to run more complex applications with intricate interactions, usually presenting a significant degree of configuration difficulty and/or low ability to customise the execution environment. They also tend to be designed for short and simple workloads, with some services even limiting their maximum runtime to just a few minutes. In this paper, we present a platform based on Hadoop YARN oriented to the execution of Big Data workloads in a containerised and serverless way, so that the resources allocated to such containers are automatically and dynamically scaled according to their actual usage. An experimental evaluation has been carried out to compare our serverless-like platform with a standard YARN deployment when executing Big Data workloads concurrently. Our results have shown experimental evidence of enhancing both performance and overall resource efficiency, providing runtime reductions and resource usage improvements of up to 41% and 50%, respectively.This work was supported by grants PID2019-104184RB-I00, PDC20 21-121309-I00, TED2021-129177B-I00 and PID2022-136435NB-I00, funded by the Ministry of Science and Innovation of Spain, MCIN/AEI/ 10.13039/501100011033 (PDC2021 and TED2021 also funded by ‘‘NextGenerationEU’’/PRTR, and PID2022 by ‘‘ERDF A way of making Europe’’, EU). It was also supported by Xunta de Galicia, Spain (predoctoral fellowship ED481A 2022/067). Funding for open access charge: Universidade da Coruña/CISUG.Xunta de Galicia; ED481A 2022/06

Dihydroceramide accumulation mediates cytotoxic autophagy of cancer cells via autolysosome destabilization

Autophagy is considered primarily a cell survival process, although it can also lead to cell death. However, the factors that dictate the shift between these 2 opposite outcomes remain largely unknown. In this work, we used Δ9-tetrahydrocannabinol (THC, the main active component of marijuana, a compound that triggers autophagy-mediated cancer cell death) and nutrient deprivation (an autophagic stimulus that triggers cytoprotective autophagy) to investigate the precise molecular mechanisms responsible for the activation of cytotoxic autophagy in cancer cells. By using a wide array of experimental approaches we show that THC (but not nutrient deprivation) increases the dihydroceramide:ceramide ratio in the endoplasmic reticulum of glioma cells, and this alteration is directed to autophagosomes and autolysosomes to promote lysosomal membrane permeabilization, cathepsin release and the subsequent activation of apoptotic cell death. These findings pave the way to clarify the regulatory mechanisms that determine the selective activation of autophagy-mediated cancer cell death

HPC in Java: Experiences in Implementing the NAS Parallel Benchmarks

International audienceThis paper reports on the design, implementation and benchmarking of a Java version of the Nas Parallel Benchmarks. We first briefly describe the implementation and the performance pitfalls. We then compare the overall performance of the Fortran MPI (PGI) version with a Java implementation using the ProActive middleware for distribution. All Java experiments were conducted on virtual machines with different vendors and versions. We show that the performance varies with the type of computation but also with the Java Virtual Machine, no single one providing the best performance in all experiments. We also show that the performance of the Java version is close to the Fortran one on computational intensive benchmarks. However, on some communications intensive benchmarks, the Java version exhibits scalability issues, even when using a high performance socket implementation (JFS)

Running scientific codes on amazon EC2: a performance analysis of five high-end instances

Amazon Web Services (AWS) is a well-known public Infrastructure-as-a-Service (IaaS) provider whose Elastic Computing Cloud (EC2) o ering includes some instances, known as cluster instances, aimed at High-Performance Computing (HPC) applications. In previous work, authors have shown that the scalability of HPC communication-intensive applications does not bene t from using higher computational power cluster instances as much as it could be expected. Cost analysis recommends using lower computational power cluster instances unless high memory requirements preclude their use. Moreover, it has been observed that scalability is very poor when more than one instance is used due to network virtualization overhead. Based on those results, this paper gives more insight into the performance of running scienti c applications on the Amazon EC2 platform evaluating ve (of which two have been recently released) of the higher computational power instances in terms of single instance performance, intra-VM (Virtual Machine) scalability and cost-e ciency. The evaluation has been carried out using both an HPC benchmark suite and a real High-Troughput Computing (HTC) application.Facultad de Informátic

HPC in Java: Experiences in Implementing the NAS Parallel Benchmarks

International audienceThis paper reports on the design, implementation and benchmarking of a Java version of the Nas Parallel Benchmarks. We first briefly describe the implementation and the performance pitfalls. We then compare the overall performance of the Fortran MPI (PGI) version with a Java implementation using the ProActive middleware for distribution. All Java experiments were conducted on virtual machines with different vendors and versions. We show that the performance varies with the type of computation but also with the Java Virtual Machine, no single one providing the best performance in all experiments. We also show that the performance of the Java version is close to the Fortran one on computational intensive benchmarks. However, on some communications intensive benchmarks, the Java version exhibits scalability issues, even when using a high performance socket implementation (JFS)

Clonal chromosomal mosaicism and loss of chromosome Y in elderly men increase vulnerability for SARS-CoV-2

The pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2, COVID-19) had an estimated overall case fatality ratio of 1.38% (pre-vaccination), being 53% higher in males and increasing exponentially with age. Among 9578 individuals diagnosed with COVID-19 in the SCOURGE study, we found 133 cases (1.42%) with detectable clonal mosaicism for chromosome alterations (mCA) and 226 males (5.08%) with acquired loss of chromosome Y (LOY). Individuals with clonal mosaic events (mCA and/or LOY) showed a 54% increase in the risk of COVID-19 lethality. LOY is associated with transcriptomic biomarkers of immune dysfunction, pro-coagulation activity and cardiovascular risk. Interferon-induced genes involved in the initial immune response to SARS-CoV-2 are also down-regulated in LOY. Thus, mCA and LOY underlie at least part of the sex-biased severity and mortality of COVID-19 in aging patients. Given its potential therapeutic and prognostic relevance, evaluation of clonal mosaicism should be implemented as biomarker of COVID-19 severity in elderly people. Among 9578 individuals diagnosed with COVID-19 in the SCOURGE study, individuals with clonal mosaic events (clonal mosaicism for chromosome alterations and/or loss of chromosome Y) showed an increased risk of COVID-19 lethality