Detection and classification of aircraft fixation elements during manufacturing processes using a convolutional neural network

The aerospace sector is one of the main economic drivers that strengthens our present, constitutes our future and is a source of competitiveness and innovation with great technological development capacity. In particular, the objective of manufacturers on assembly lines is to automate the entire process by using digital technologies as part of the transition toward Industry 4.0. In advanced manufacturing processes, artificial vision systems are interesting because their performance influences the liability and productivity of manufacturing processes. Therefore, developing and validating accurate, reliable and flexible vision systems in uncontrolled industrial environments is a critical issue. This research deals with the detection and classification of fasteners in a real, uncontrolled environment for an aeronautical manufacturing process, using machine learning techniques based on convolutional neural networks. Our system achieves 98.3% accuracy in a processing time of 0.8 ms per image. The results reveal that the machine learning paradigm based on a neural network in an industrial environment is capable of accurately and reliably estimating mechanical parameters to improve the performance and flexibility of advanced manufacturing processing of large parts with structural responsibility.This publication was carried out as part of the project Nuevas Uniones de estructuras aeronáuticas reference number IDI-20180754. This project has been supported by the Spanish Ministry of Ciencia e Innovación and Centre for Industrial Technological Development (CDTI)

Factores de riesgo del abandono escolar desde la perspectiva del profesorado de Educación Secundaria Obligatoria en Andalucía (España)

El abandono escolar temprano (AET), supone un importante problema social al que hay que hacer frente. La Unión Europea marca como meta para 2020 situar la proporción de abandonos prematuros de la educación y formación por debajo del 10 % (Consejo de la Unión Europea, 2009). España es uno de los países de la Unión con una de las tasas más altas (en 2013 era del 23,6%) agravándose en algunas comunidades, como en el caso de Andalucía -tasa en torno al 28%- (INE, 2015). El presente artículo recoge los resultados del proyecto “Alumnado en riesgo de abandono” desarrollado en dicha comunidad. Analizamos los factores de riesgo de abandono escolar que el profesorado de esta comunidad identifica respecto al rendimiento académico, grado de compromiso y actitudes del alumnado en relación al estudio y comportamiento en el aula. La metodología empleada ha sido de carácter mixto, aunque en este trabajo presentamos los resultados de los análisis cuantitativos de los datos obtenidos mediante un cuestionario aplicado a una muestra representativa de 283 profesores/as. El valor de este estudio reside en haber logrado una radiografía, desde la visión del profesorado, de los posibles factores de riesgo de abandono en la población de alumnos de secundaria andaluces. Conocer dichos riesgos, nos va a permitir plantear medidas de prevención e intervención. Los mayores riesgos identificados, respecto al alumnado que tiene más probabilidad de abandonar prematuramente, son: tener dificultades en las materias instrumentales (matemáticas, lengua,…), no gustarles lo que trabajan en el instituto, bajos niveles de compromiso con el estudio y mostrar comportamientos inadecuados en el aula.High school dropout represents an important social issue, which governments and social institutions should face up. European Union countries have committed to reducing the average share of early school leavers to less than 10% by 2020 (European Union Commission, 2009). Spain has one of the highest dropouts’ rates of EU, being worse in some of Spain’s regions. This is the case of Andalusia community, which has around a 28% of early school leavers (INE, 2015). In the present work we describe some results of the research project “Students at risk for school dropout” which has been developed in Andalusia Community Spain. We used mixed methods, although in this work we only present quantitative results obtained through questionnaires, administered to 283 teachers (which is a representative sample). The value of this study lies in the fact that we have mapped the reality of secondary students in Andalusia (Spain), identifying risk factors. This research has also provided us with a range of aspects that could help us tackle and prevent early dropout. We analyzed risk factors identified by Middle School Teachers in several areas: academic performance, degree of commitment, attitudes and interest in school, and classroom behavior. Our main goal has been to study in depth, which are the most important risk factors, to establish new lines of action to decrease these data and prevent new cases. We conclude that the main risk factors are: difficulties in critical subjects (mathematics, language,…), dislike studying, low engagement and inappropriate classroom behavior

Improving cognition in school children and adolescents through exergames. A systematic review and practical guide

Recent studies and reviews have shown the positive effects of exergames (EXs) on physical activity (PA) and fitness in children and adolescents. Nevertheless, their effects on cognition have been scarcely explored, and no previous review has focussed on this relationship. The purpose of the research reported on here was to analyse the acute and chronic effects of the use of different EXs on the cognition of young people aged 6 to 18 years, to review potential confounders, and to elaborate a practical guide to using EXs in schools or extracurricular contexts. Studies were identified from 4 databases (Pubmed, SportDiscus, ProQuest and Web of Science) from January 2008 through January 2018. Thirteen studies met the inclusion criteria. All the studies showed a positive effect of EXs on cognition. The review showed an acute improvement effect on executive functions (EFs) (visual attention, mental processing, working memory, response inhibition, and motor planning) and chronic benefits on mathematical calculation, self-concept, classroom behaviour, and on parental and interpersonal relationships. Only 5 studies used confounders. EXs are an effective and motivating tool to improve cognition in young people aged 6 to 18 years. Didactic recommendations to use EXs in school or extracurricular contexts are provided in this article. Keywords: academic performance; active video games; acute and chronic effects; cognitive performance; executive functions;  exergames; learning; motivation; physical activity; physical educatio

Ajuste de valores de precipitaciones mensuales estimados por satélites TRMM y GPM en seis estaciones climáticas de las provincias de Jujuy y Salta

El objetivo del presente trabajo fue comparar diferentes modelos lineales por mínimos cuadrados generalizados para ajustar los valores de precipitaciones mensuales estimados por 2 modelos generados por algoritmos predictivos a partir de datos de sensores remotos pertenecientes a los proyectos TRMM (Misión de Medición de Lluvias Tropicales) y GPM (Medición Global de Precipitación) disponibles en el entorno web Giovanni NASA. Para el estudio se emplearon las precipitaciones mensuales registradas en seis estaciones climáticas de Jujuy y Salta durante 18 años (datos provistos y consistidos por el Servicio Meteorológico Nacional), y las precipitaciones correspondientes estimadas por los sistemas TRMM y GPM en esos puntos. Para ello, a través de la técnica bootstrap o de re-muestreo con reposición, se evaluaron diferentes modelos alternativos en los que la variable respuesta fue la precipitación mensual observada superficie, y la variable regresora fue la precipitación mensual estimada (por sistema TRMM y GPM), pudiendo incluir o no los factores Mes y/o Estación Climática dentro de los efectos fijos. No se detectó auto-correlación en las observaciones, pero sí heterocedasticidad (método Breusch-Pagan), motivo por el cual los modelos comparados presentaron un ajuste ponderado por varianzas heterogéneas. Para cada modelo, se calculó su valor de AIC, el error cuadrático medio (RMSE) y el sesgo (MBE), los cuales fueron comparados para la selección del mejor modelo de ajuste. Los modelos que incluyeron únicamente la variable regresora precipitación mensual estimada dentro de los efectos fijos con ajuste de heterocedasticidad por mes y/o por estación climática, fueron los que mejor ajustaron las estimaciones. Como criterio adicional, el modelo con ajuste de heterocedasticidad por mes únicamente (sin incluir la estación climática) fue considerado el más óptimo, ya que permite la versatilidad de ajustar valores estimados de precipitaciones mensuales por los sistemas TRMM y GPM en sitios próximos a las estaciones climáticas.Fil: Solis, Juan Manuel. Universidad Nacional de Jujuy. Centro de Estudios en Bioestadística, Bioinformática y Agromática; Argentina.Fil: Alabar, Fabio. Universidad Nacional de Jujuy. Centro de Estudios en Bioestadística, Bioinformática y Agromática; Argentina.Fil: Ruiz, Sebastián León. Universidad Nacional de Jujuy. Centro de Estudios en Bioestadística, Bioinformática y Agromática; Argentina.Fil: Hurtado, Rafael. Universidad Nacional de Jujuy; Argentina

Automatic recognition of accessible pedestrian signals

Accessible pedestrian signals (APS) enhance accessibility in streets around the world. Recent attempts to extend the use of APS to people with visual and audible impairments have emerged from the area of audio signal processing. Even though few authors have studied the detection of APS by sound, comprehensive literature in Biology has been published to detect other simple sounds like birds and frogs calls. Since these calls exhibit the same periodic and modulated nature as APS, many of these approaches can be adapted for this purpose. We present an algorithm that follows this approach. The algorithm was evaluated using a collection of 79 recordings gathered from streets in San Jose, Costa Rica, where an APS system will be implemented. Three types of sounds were available: low-pitch chirps, high-pitch chirps and cuckoo-like sounds. The results showed 91% precision, 80% recall, 83% F-measure, and 90% specificity.UCR::Vicerrectoría de Investigación::Unidades de Investigación::Ingeniería::Centro de Investigaciones en Tecnologías de Información y Comunicación (CITIC)UCR::Vicerrectoría de Docencia::Ingeniería::Facultad de Ingeniería::Escuela de Ciencias de la Computación e Informátic

Results of the JET real-time disruption predictor in the ITER-like wall campaigns

The impact of disruptions in JET became even more important with the replacement of the previous Carbon Fiber Composite (CFC) wall with a more fragile full metal ITER-like wall (ILW). The development of robust disruption mitigation systems is crucial for JET (and also for ITER). Moreover, a reliable real-time (RT) disruption predictor is a pre-requisite to any mitigation method. The Advance Predictor Of DISruptions (APODIS) has been installed in the JET Real-Time Data Network (RTDN) for the RT recognition of disruptions. The predictor operates with the new ILW but it has been trained only with discharges belonging to campaigns with the CFC wall. 7 realtime signals are used to characterize the plasma status (disruptive or non-disruptive) at regular intervals of 1 ms. After the first 3 JET ILW campaigns (991 discharges), the success rate of the predictor is 98.36% (alarms are triggered in average 426 ms before the disruptions). The false alarm and missed alarm rates are 0.92% and 1.64%

Implementation of the disruption predictor APODIS in JET Real Time Network using the MARTe framework

Disruptions in tokamaks devices are unavoidable, and they can have a significant impact on machine integrity. So it is very important have mechanisms to predict this phenomenon. Disruption prediction is a very complex task, not only because it is a multi-dimensional problem, but also because in order to be effective, it has to detect well in advance the actual disruptive event, in order to be able to use successful mitigation strategies. With these constraints in mind a real-time disruption predictor has been developed to be used in JET tokamak. The predictor has been designed to run in the Multithreaded Application Real-Time executor (MARTe) framework. The predictor ?Advanced Predictor Of DISruptions? (APODIS) is based on Support Vector Machine (SVM)

Metabolic adaptations in spontaneously immortalized PGC-1a knock-out mouse embryonic fibroblasts increase their oncogenic potential

Trabajo presentado en la SEBBN 19 Madrid, celebrada en Madrid del 16 al 19 de julio de 2019.PGC-1a controls, to a large extent, the capacity of cells to respond to changing nutritional requirements and energetic demands. The key role of metabolic reprogramming in tumor development has highlighted the potential role of PGC-1a in cancer. To investigate how loss of PGC-1a activity in primary cells impacts the oncogenic characteristics of spontaneously immortalized cells, and the mechanisms involved, we used the classic 3T3 protocol to generate spontaneously immortalized mouse embryonic fibroblasts (iMEFs) from wild-type (WT) and PGC-1a knockout (KO) mice and analyzed their oncogenic potential in vivo and in vitro. We found that PGC-1a KO iMEFs formed larger and more proliferative primary tumors than WT counterparts, and fostered the formation of lung metastasis by B16 melanoma cells. These characteristics were associated with the reduced capacity of KO iMEFs to respond to cell contact inhibition, in addition to an increased ability to form colonies in soft agar, an enhanced migratory capacity, and a reduced growth factor dependence. The mechanistic basis of this phenotype is likely associated with the observed higher levels of nuclear b-catenin and c-myc in KO iMEFs. Evaluation of the metabolic adaptations of the immortalized cell lines identified a decrease in oxidative metabolism and an increase in glycolytic flux in KO iMEFs, which were also more dependent on glutamine for their survival. Furthermore, glucose oxidation and tricarboxylic acid cycle forward flux were reduced in KO iMEF, resulting in the induction of compensatory anaplerotic pathways. Indeed, analysisi of aminoacid and lipid patterns supported the efficient use of tricarboxylic acid cycle intermediates to synthesize lipids and proteins to support elevated cell growth rates. All these characteristics have been observed in aggressive tumors and support a tumor suppressor role for PGC-1a, restraining metabolic adaptations in cancer

Cancer-derived exosomes loaded with ultrathin palladium nanosheets for targeted bioorthogonal catalysis

The transformational impact of bioorthogonal chemistries has inspired new strategies for the in vivo synthesis of bioactive agents through non-natural means. Among these, Pd catalysts have played a prominent role in the growing subfield of bioorthogonal catalysis by producing xenobiotics and uncaging biomolecules in living systems. However, delivering catalysts selectively to specific cell types still lags behind catalyst development. Here, we have developed a bioartificial device comprising cancer-derived exosomes that are loaded with Pd catalysts by a method that enables the controlled assembly of Pd nanosheets directly inside the vesicles. This hybrid system mediates Pd-triggered dealkylation reactions in vitro and inside cells, and displays preferential tropism for their progenitor cells. The use of Trojan exosomes to deliver abiotic catalysts into designated cancer cells creates the opportunity for a new targeted therapy modality; that is, exosome-directed catalyst prodrug therapy, whose first steps are presented herein with the cell-specific release of the anticancer drug panobinostat

Risk factors of early high school dropout: Andalusia middle school teachers’ perspective

El abandono escolar temprano (AET), supone un importante problema social al que hay que hacer frente. La Unión Europea marca como meta para 2020 situar la proporción de abandonos prematuros de la educación y formación por debajo del 10 % (Consejo de la Unión Europea, 2009). España es uno de los países de la Unión con una de las tasas más altas (en 2013 era del 23,6%) agravándose en algunas comunidades, como en el caso de Andalucía -tasa en torno al 28%- (INE, 2015). El presente artículo recoge los resultados del proyecto “Alumnado en riesgo de abandono” desarrollado en dicha comunidad. Analizamos los factores de riesgo de abandono escolar que el profesorado de esta comunidad identifica respecto al rendimiento académico, grado de compromiso y actitudes del alumnado en relación al estudio y comportamiento en el aula. La metodología empleada ha sido de carácter mixto, aunque en este trabajo presentamos los resultados de los análisis cuantitativos de los datos obtenidos mediante un cuestionario aplicado a una muestra representativa de 283 profesores/as. El valor de este estudio reside en haber logrado una radiografía, desde la visión del profesorado, de los posibles factores de riesgo de abandono en la población de alumnos de secundaria andaluces. Conocer dichos riesgos, nos va a permitir plantear medidas de prevención e intervención.Los mayores riesgos identificados, respecto al alumnado que tiene más probabilidad de abandonar prematuramente, son: tener dificultades en las materias instrumentales (matemáticas, lengua,…), no gustarles lo que trabajan en el instituto, bajos niveles de compromiso con el estudio y mostrar comportamientos inadecuados en el aula.High school dropout represents an important social issue, which governments and social institutions should face up. European Union countries have committed to reducing the average share of early school leavers to less than 10% by 2020 (European Union Commission, 2009). Spain has one of the highest dropouts’ rates of EU, being worse in some of Spain’s regions. This is the case of Andalusia community, which has around a 28% of early school leavers (INE, 2015) In the present work we describe some results of the research project “Students at risk for school dropout” which has been developed in Andalusia Community Spain. We used mixed methods, although in this work we only present quantitative results obtained through questionnaires, administered to 283 teachers (which is a representative sample). The value of this study lies in the fact that we have mapped the reality of secondary students in Andalusia (Spain), identifying risk factors. This research has also provided us with a range of aspects that could help us tackle and prevent early dropout. We analyzed risk factors identified by Middle School Teachers in several areas: academic performance, degree of commitment, attitudes and interest in school, and classroom behavior. Our main goal has been to study in depth, which are the most important risk factors, to establish new lines of action to decrease these data and prevent new cases. We conclude that the main risk factors are: difficulties in critical subjects (mathematics, language,…), dislike studying, low engagement and inappropriate classroom behavior.Grupo FORCE (HUM-386). Departamento de Didáctica y Organización Escolar de la Universidad de Granad