Los centros de Formación Profesional en España y su rol en el proceso de validación de competencias

Esta investigación tiene el objetivo de identificar el rol que tienen los centros integrados de formación profesional y los centros de referencia nacional en el procedimiento de acreditación de competencias profesionales adquiridas a través de vías no formales e informales de formación. A lo largo de esta investigación descriptiva se hace una revisión en profundidad de la bibliografía relativa al objeto de estudio y se identifican los parámetros a través de los cuales se estructura el desarrollo del procedimiento de acreditación de competencias profesionales a nivel europeo (principios y directrices europeas) y nacional (legislación). Estos parámetros son contrastados con los datos más actuales del procedimiento en España y con la información relativa a estos centros, recopilados a través de cuestionario y entrevistas. Los principales resultados de esta investigación muestran que estos centros cuentan con ciertas limitaciones para desarrollar el procedimiento como recurso educativo abierto.Departamento de PedagogíaDoctorado en Investigación Transdisciplinar en Educació

Informe sobre el procedimiento de validación de competencias en Castilla y León (2012)

Este documento es un análisis descriptivo del procedimiento de acreditación de competencias profesionales adquiridas a través de la experiencia laboral o vías formativas no formales. Este proceso tiene su nacimiento en Europa y ha ido desarrollándose en España conforme a directivas europeas. Su objetivo es el de acreditar una serie de competencias profesionales que tiene el individuo pero que no están reconocidas de manera oficial. Con la acreditación de estas competencias se le facilitará la obtención de un certificado de profesionalidad o en su caso un título de formación profesional. Analizaremos las diferentes fases que conlleva este procedimiento, desde las etapas previas que lo confeccionan, hasta las fases de asesoramiento y evaluación, donde el candidato tiene mayor implicación. Este proceso se ha llevado a cabo en la comunidad autónoma de Castilla y León, y se corresponde principalmente a la acreditación de las competencias adquiridas por la experiencia en el sector de los servicios socioculturales y a la comunidad.Máster en Psicopedagogí

Microarrays as Platform for Multiplex Assays in Biomarker and Drug Discovery

Despite the tremendous advances in the understanding of the molecular mechanisms and the complexity of the diseases is one of the present challenges for the scientific community; then, novel strategies are required to be designed and developed for effective strategies for early diagnosis and treatment. As many cellular alterations are observed at protein level, high-throughput assays are dramatically needed for biomarker discovery. Herein, we describe advantages and limitations of protein microarrays, as proteomics strategy useful for multiplex and high-throughput protein characterization in clinical samples. Finally, a few examples are discussed; mostly of them related to currently disease biomarkers already identified in proximal fluids by protein arrays are discussed

Nurturing a Digital Learning Environment for Adults 55+

Being digitally competent means having competences in all areas of DigComp: Information and data literacy, Communication and collaboration, Digital content creation, Safety and Problem-solving. More than other demographic categories, adults 55+ have a wide range of levels of digitalization. Depending on their level of competences, individuals may join self-administered online courses to improve their skills, or they may need guidance from adult educators. Taking into consideration the above situation and willing to address adult learners regardless of their initial skill levels, the proposed educational programme is carefully designed for both: self-administrated and educator-led training. It comprises five totally innovative courses that can be separately taught or can be integrated into a complex programme delivered by adult education organizations. These courses are the result of an ERASMUS+ project “Digital Facilitator for Adults 55+”. Chapter 1 introduces the methodology for designing attractive and engaging educational materials for adults’ digital skills improvement. The methodology clarifies the inputs, the development process and the expected results. An ample explanation of the five phases of the 5E instructional strategy is presented to help adult educators build a sequence of coherent and engaging learning stages. With this approach, learners are supported to think, work, gather ideas, identify their own skill levels and needs, analyse their progress, and communicate with others under the guidance of educators. Following up on the proposed methodology, in Chapter 2 researchers from Formative Footprint (Spain), TEAM4Excellence (Romania), Voluntariat Pentru Viata (Romania) and Saricam Halk Egitimi Merkezi (Turkey) developed five course modules in line with the DIGCOMP - Digital Competence Framework for Citizens. These modules address the competence areas of information and data literacy, communication and collaboration, digital content creation, safety, and problem-solving. Each course module comprises digital textbooks, videos, interactive activities and means for evaluation developed using the 5E instructional model strategy. Understanding that accessibility is one of the main components of lifelong learning education, Chapter 3 of the manual provides an overview of the integration of educational materials, tools, instruments, video tutorials as well as DIFA55+ web app in the digital educational ecosystem. Finally, the authors formulate recommendations for usability and transferability that go beyond individuals, ensuring that educational materials are user-friendly and effective while making it easier to apply successful pedagogical approaches in other complementary educational contexts or projects.Grant Agreement—2021-1-RO01-KA220-ADU-000035297, Digital Facilitator for Adults 55

Predictive models for the characterization of internal defects in additive materials from active thermography sequences supported by machine learning methods

The present article addresses a generation of predictive models that assesses the thickness and length of internal defects in additive manufacturing materials. These modes use data from the application of active transient thermography numerical simulation. In this manner, the raised procedure is an ad-hoc hybrid method that integrates finite element simulation and machine learning models using different predictive feature sets and characteristics (i.e., regression, Gaussian regression, support vector machines, multilayer perceptron, and random forest). The performance results for each model were statistically analyzed, evaluated, and compared in terms of predictive performance, processing time, and outlier sensibility to facilitate the choice of a predictive method to obtain the thickness and length of an internal defect from thermographic monitoring. The best model to predictdefect thickness with six thermal features was interaction linear regression. To make predictive models for defect length and thickness, the best model was Gaussian process regression. However, models such as support vector machines also had significative advantages in terms of processing time and adequate performance for certain feature sets. In this way, the results showed that the predictive capability of some types of algorithms could allow for the detection and measurement of internal defects in materials produced by additive manufacturing using active thermography as a non-destructive test.This research was funded by Ministry of Science and Innovation, Government of Spain, through the research project titled Fusion of non-destructive technologies and numerical simulation methods for the inspection and monitoring of joints in new materials and additive manufacturing processes (FaTIMA) with code RTI2018-099850-B-I00. The authors are grateful to the Fundación Universidad de Salamanca for the indirect support provided by the ITACA proof-of-concept project (PC_TCUE_18-20_047), being this helpful for some of the purposes of this article

Step heating thermography supported by machine learning and simulation for internal defect size measurement in additive manufacturing

A methodology based on step-heating thermography for predicting the length dimension of small defects in additive manufacturing from temperature data measured on thermal images is proposed. Regression learners were applied with different configurations to predict the length of the defects. These algorithms were trained using large datasets generated with Finite Element Method simulations. The different predictive methods obtained were optimized using Bayesian inference. Using predictive methods generated and based on intrinsic performance results, knowing the material characteristics, the defect length can be predicted from single temperature data in defect and non-defect zone. Thus, the developed algorithms were implemented in a laboratory set-up carried out on ad-hoc manufactured parts of Nylon and polylactic acid which include induced defects with different sizes and thicknesses. Using the trained algorithm, the deviation of the predicted results for the defect size varied between 13% and 37% for PLA and between 13% and 36% for Nylon.This research has been funded by Ministry of Science and Innovation (Government of Spain) through the research project titled Fusion of nondestructive technologies and numerical simulation methods for the inspection and monitoring of joints in new materials and additive manufacturing processes (FaTIMA) with code RTI2018-099850-B-I00

Practices in Vehicle Engineering: MATLAB programming for processing experimental data in tire tests

[ES] En este trabajo, se pretende involucrar al estudiante en el proceso de medición y procesamiento de los datos en las prácticas de laboratorio de neumáticos en la asignatura de ingeniería de vehículos. El procesamiento de los datos, debido a su complejidad suele omitirse, sin embargo, esta propuesta enseña a los estudiantes a tratar con el ruido de los sensores, el alcance y la precisión de cada medición. Partiendo de una plantilla MATLAB diseñaran métodos de filtrado y procesamiento de datos.[EN] The aim of this work is to involve the student in the measurement and data processing of tire laboratory experiments in the course of vehicle engineering. Data processing, due to its complexity, is often omitted, however, this proposal teaches students to deal with sensor noise, range, and accuracy of each measurement. By using a MATLAB template, they will design data filtering and processing methods.Pérez Fernández, J.; Alcázar Vargas, M.; Sánchez Andrades, I.; Carabias Acosta, E.; Castillo Aguilar, JJ. (2023). Prácticas en Ingeniería de Vehículos: Programación en MATLAB del procesado de datos experimentales en ensayos de neumáticos. Modelling in Science Education and Learning. 16(2):13-19. https://doi.org/10.4995/msel.2023.19078131916

Deciphering biomarkers for leptomeningeal metastasis in malignant hemopathies (Lymphoma/Leukemia) patients by comprehensive multipronged proteomics characterization of cerebrospinal fluid

In the present work, leptomeningeal disease, a very destructive form of systemic cancer, was characterized from several proteomics points of view. This pathology involves the invasion of the leptomeninges by malignant tumor cells. The tumor spreads to the central nervous system through the cerebrospinal fluid (CSF) and has a very grim prognosis; the average life expectancy of patients who suffer it does not exceed 3 months. The early diagnosis of leptomeningeal disease is a challenge because, in most of the cases, it is an asymptomatic pathology. When the symptoms are clear, the disease is already in the very advanced stages and life expectancy is low. Consequently, there is a pressing need to determine useful CSF proteins to help in the diagnosis and/or prognosis of this disease. For this purpose, a systematic and exhaustive proteomics characterization of CSF by multipronged proteomics approaches was performed to determine different protein profiles as potential biomarkers. Proteins such as PTPRC, SERPINC1, sCD44, sCD14, ANPEP, SPP1, FCGR1A, C9, sCD19, and sCD34, among others, and their functional analysis, reveals that most of them are linked to the pathology and are not detected on normal CSF. Finally, a panel of biomarkers was verified by a prediction model for leptomeningeal disease, showing new insights into the research for potential biomarkers that are easy to translate into the clinic for the diagnosis of this devastating disease.We gratefully acknowledge financial support from the Spanish Health Institute, Carlos III (ISCIII), for the grants: FIS PI14/01538, FIS PI17/01930 and CB16/12/00400. We also acknowledge Fondos FEDER (EU) and Junta Castilla-León (COVID-19 grant COV20EDU/00187). The Proteomics Unit belongs to ProteoRed, PRB3-ISCIII, supported by grant PT17/0019/0023 of the PE I + D + I2017-2020, funded by ISCIII and FEDER—Norma Galicia is supported by the CONACYT Program. P. Juanes-Velasco is supported by JCYL PhD Program “Nos Impulsa-JCYL” and scholarship JCYLEDU/601/2020

Estimación de la resistencia a la rodadura en neumáticos mediante banco de ensayos de tracción-compresión

La resistencia a la rodadura constituye alrededor del 20 % del consumo energético en automóviles. Más del 90 % del impacto medioambiental de los neumáticos se atribuye a la energía perdida en la rodadura. Cada vez más, los fabricantes de vehículos y de neumáticos invierten recursos para minimizar esta energía desaprovechada. Actualmente, con objeto de maximizar la autonomía en los vehículos eléctricos, se impone el uso de los conocidos “green tire”, que son neumáticos con una resistencia a la rodadura muy inferior a la convencional. En el sector de los camiones, las cifras son aún mayores, pues la resistencia a la rodadura supera el 30 % del consumo energético. El origen de esta pérdida de energía es debido, fundamentalmente, al comportamiento viscoelástico que presenta la goma de los neumáticos. En este trabajo, se propone una metodología novedosa para estimar la resistencia a la rodadura en neumáticos, empleando para ello un banco de ensayo de tracción-compresión.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech