Subtraction: More than an Algorithm?

[EN] One of the aims of compulsory education is for students to adequately handle basic maths, owing to its importance in their future professional and personal lives. However, mechanical knowledge of an algorithm may not be sufficient to train future citizens with critical and creative thinking if it is not accompanied by a comprehensive understanding of the concept. In this regard, existing research shows that a high percentage of students in primary education commit errors when they attempt subtraction. However, little is known about whether adults perform the same calculations correctly. In this context, 535 university students completed a questionnaire composed of 20 subtractions. The results showed that only one quarter of respondents performed the subtractions correctly. Analysis of error type showed that the most frequent mistakes corresponded to the systematic errors made by primary-level students. This may indicate that the types of errors committed during early learning persist over time, implying that subtraction may not have been adequately taught. New educational approaches and initiatives are required to encourage the teaching and learning of subtraction in a more reasoned and critical manner during early learning

Bluetongue virus serotypes 1 and 4 in red deer, Spain

We studied the potential of red deer as bluetongue maintenance hosts and sentinels. Deer maintained detectable bluetongue virus (BTV) serotype 4 RNA for 1 year after the virus was cleared from livestock. However, the virus was not transmitted to yearlings. BTV serotype 1 RNA was detected in red deer immediately after its first detection in cattle.This study was funded by the Spanish Ministry of Natural, Rural and Marine Environment (RASVE 274/2007, and an agreement between Organismo Autónomo de Parques Nacionales (OAPN), Dirección General de Recursos Agricolas y Ganaderos (DGRAG), and Consejo Superior de Investigaciones Cientificas (CSIC). F.R.-F. is supported by a postdoctoral contract of the Instituto de Salud Carlos III of the Spanish government.Peer Reviewe

Bibliometric analysis of articles related to knowledge organization published in Spanish journals listed on the Web of Science

[Resumen] Teniendo en cuenta el 20 aniversario del Capítulo Español de ISKO hemos querido ver cómo se refleja el tema de la organización del conocimiento en las dos revistas españolas sobre documentación indizadas en la Web of Science (WoS): El Profesional de la Información (EPI) y la Revista Española de Documentación Científica (REDC). Para ello se han recuperado las referencias de los artículos de estas dos revistas y se han estudiado los datos recopilados poniendo especial atención en dos aspectos principales: el análisis de los temas tratados en dichos artículos a través del estudio de las palabras clave y el análisis de los autores a través de sus afiliaciones y procedencia geográfica. A partir de los resultados obtenidos se ha podido determinar el grado de representación de esta materia en estas dos revistas, así como los tipos de colaboraciones entre los autores y las instituciones que más publican sobre este tema.[Abstract] To mark the 20th anniversary of the Spanish chapter of the ISKO, we wanted to see how the subject of knowledge organization was covered by the two Spanish documentation journals listed on the Web of Science (WoS): El Profesional de la Información (EPI) and Revista Española de Documentación Científica (REDC). The complete list of articles published in these journals was downloaded and analyzed, with special attention to two main aspects: a keyword analysis of themes dealt with in the published articles, and an analysis of author affiliations and geographic origin. From this analysis it has been possible to determine the degree to which the subject of knowledge organization is represented in these two journals, the types of collaborations between authors, and the institutions that publish most in this area

Generating IFC-compliant models and structural graphs of truss bridges from dense point clouds

The IFC schema has been evolving towards the infrastructure domain. Furthermore, the use of laser scanning technologies as means to digitalize and monitor infrastructures has also significantly increased. This work presents an automated modelling approach for truss bridges that utilizes laser scanning data as its source for geometrical information. The methodology takes a partially instance-segmented point cloud of a truss bridge and generates both an IFC-compliant information model of the truss and the corresponding structural graph. This process uses bounding boxes and their collisions to overcome the missing data from the partial segmentation to create the truss model, as well as to identify the nodes that connect the different truss members. The methodology was tested on a use case made of 272 members and obtained the truss model and structural graph files.Universidade de Vigo | Ref. PREUVIGO-21Agencia Estatal de Investigación | Ref. FJC2020–046370-IAgencia Estatal de Investigación | Ref. PID2021-124236OB-C33Agencia Estatal de Investigación | Ref. FJC2020–046370-IFinanciado para publicación en acceso aberto: Universidade de Vigo/CISU

Evaluation of MALDI biotyper interpretation criteria for accurate identification of nontuberculous mycobacteria

Identification of mycobacteria by matrix-assisted laser desorption ionization-time of flight mass spectrometry (MALDI-TOF MS) requires not only a good protein extraction protocol but also an adequate cutoff score in order to provide reliable results. The aim of this study was to assess the cutoff scores proposed by the MALDI-TOF MS system for mycobacterial identification. A total of 693 clinical isolates from a liquid medium and 760 from a solid medium were analyzed, encompassing 67 different species of nontuberculous mycobacteria (NTM). MALDI-TOF MS identified 558 (80.5%) isolates from the liquid medium and 712 (93.7%) isolates from the solid medium with scores of ≥1.60. Among these, four (0.7%) misidentifications were obtained from the liquid medium and four (0.5%) from the solid medium. With regard to species diversity, MALDI-TOF MS successfully identified 64 (95.5%) different species, while PCR-reverse hybridization (GenoType Mycobacterium CM and AS assays) identified 24 (35.8%) different species. With MALDI-TOF MS scores of ≥2, all isolates were correctly identified, and with scores in the range from 1.60 to 1.99, most isolates were correctly identified, except for Mycobacterium angelicum, M. parascrofulaceum, M. peregrinum, M. porcinum, and M. gastri. In conclusion, MALDI-TOF MS is a useful method for identifying a large diversity of NTM species. A score threshold of 1.60 proved useful for identifying almost all the isolates tested; only a few species required a higher score (≥2.00) to obtain a valid definitive identification

Urban Infrastructure Vulnerability to Climate-Induced Risks: A Probabilistic Modeling Approach Using Remote Sensing as a Tool in Urban Planning

[EN] In our contemporary cities, infrastructures face a diverse range of risks, including those caused by climatic events. The availability of monitoring technologies such as remote sensing has opened up new possibilities to address or mitigate these risks. Satellite images allow the analysis of terrain over time, fostering probabilistic models to support the adoption of data-driven urban planning. This study focuses on the exploration of various satellite data sources, including nighttime land surface temperature (LST) from Landsat-8, as well as ground motion data derived from techniques such as MT-InSAR, Sentinel-1, and the proximity of urban infrastructure to water. Using information from the Local Climate Zones (LCZs) and the current land use of each building in the study area, the economic and climatic implications of any changes in the current features of the soil are evaluated. Through the construction of a Bayesian Network model, synthetic datasets are generated to identify areas and quantify risk in Barcelona. The results of this model were also compared with a Multiple Linear Regression model, concluding that the use of the Bayesian Network model provides crucial information for urban managers. It enables adopting proactive measures to reduce negative impacts on infrastructures by reducing or eliminating possible urban disparities.This work has been funded by the Spanish Ministry of Science and Innovation through the PONT3 project Ref. PID2021-124236OB-C33 and through the grant PRE2019-087331 for the training of predoctoral researchers.Rodríguez-Antuñano, I.; Barros-González, B.; Martínez-Sánchez, J.; Riveiro, B. (2024). Urban Infrastructure Vulnerability to Climate-Induced Risks: A Probabilistic Modeling Approach Using Remote Sensing as a Tool in Urban Planning. Infrastructures. 9(7). https://doi.org/10.3390/infrastructures90701079

Learning from failure propagation in steel truss bridges

Although truss-type bridge collapses usually have catastrophic consequences, their analysis present opportunities for improving different aspects in the field of bridge engineering, such as structural assessment, structural health monitoring, maintenance and conservation or even design strategies. As the world experiences more extreme events, efforts have been made to design more resilient bridges that can withstand local failures. Forensic techniques have contributed to understanding the causes and risk factors of bridge failures, and the creation of collapse databases has provided valuable insights for preventing such failures. However, these databases often focus on the hazards and do not provide information on initial damage and how it propagates, which is essential for improving the progressive collapse resistance of truss-type bridges. The main novelty of this paper is to present a methodology to identify triggering events leading to progressive collapse on truss-type bridges. It is the first time that a methodology includes a novel database which collects detailed information on initial damages and its propagation, as well as the consequences of the collapse. The methodology was implemented by collecting information from 25 case studies present in the literature. Results have allowed to identify most frequent initial constituted damages states or failures (ICDS) leading progressive collapse. In terms of consequences, results were thoroughly analysed and compared with predictions from different casualty models. The findings showed that the proposed methodology serves as an effective tool for identifying the triggering events of progressive collapse in truss-type bridges.Agencia Estatal de Investigación | Ref. PID2021-124236OBAgencia Estatal de Investigación | Ref. FJC2020–046370-IUniversidade de Vigo/CISU

Initiation and propagation of failures in steel truss bridges

Bridge collapses are catastrophic events with countless consequences. However, bridge engineering has progressed thanks to the knowledge acquired analyzing collapsed structures. In this direction, modern forensic techniques allow detecting weaknesses and vulnerable zones in the structural systems. It has been demonstrated that the data related to bridge failures has been fundamental for engineers to propose and update theories, concepts, and designs in bridge engineering. This paper presents a methodology to analyze the initial damage and its propagation on steel truss bridges. The first part of the paper presents a comprehensive review of state‐of‐the‐art and scientific challenges. The methodology is described in detail in the second part of the paper; it comprises two main tasks that are further divided into several activities. This methodology was developed as part of the “Pont3” project and has proven to be of great value in gaining a better understanding of how progressive collapse occurs in steel truss bridges. By using this methodology, it is possible to detect initial damage and evaluate the structural behavior of steel truss bridges, which will ultimately lead to safer and more reliable structures.Universidade de Vigo/CISU

Multimodal deep learning for point cloud panoptic segmentation of railway environments

The demand for transportation asset digitalisation has significantly increased over the years. For this purpose, mobile mapping systems (MMSs) are among the most popular technologies that allow capturing high precision three-dimensional point clouds of the infrastructure. In this paper, a multimodal deep learning methodology is presented for panoptic segmentation of the railway infrastructure. The methodology takes advantage of image rasterisation of the point clouds to perform a rough segmentation and discard more than 80% of points that are not relevant to the infrastructure. With this approach, the computational requirements for processing the remaining point cloud are highly reduced, allowing the process of dense point clouds in short periods of time. A 90 km-long railway scenario was used for training and testing. The proposed methodology is two times faster than the current state-of-the-art for the same point cloud density, and pole-like object segmentation metrics are improved.Fundación BBVAAgencia Estatal de Investigación | Ref. PID2019-108816RB-I00Ministerio de Universidades | Ref. FPU20/01024Universidade de Vigo/CISU