Las técnicas de aprendizaje grupal como parte o fundamento de las asignaturas de grado: propuestas del equipo docente de Trabajo Colaborativo de la UPCT

[SPA]El aprendizaje por competencias en el que se basan las titulaciones de Grado presupone el análisis previo de los objetivos a alcanzar con cada una de las asignaturas que las conforman. Delimitados los objetivos y las competencias genéricas y específicas de una asignatura, el docente debe planificar la tarea de aprendizaje que ha de llevar a su logro, pudiéndose valer para ello de técnicas de aprendizaje colaborativo. La dificultad encontrada a la hora de planificar o poner en práctica tales tareas de aprendizaje lleva a la necesidad de adquirir una cierta formación en su empleo. Es en este contexto, donde se desarrolla la labor del equipo docente de Trabajo Colaborativo de la Universidad Politécnica de Cartagena. La presente comunicación recoge la labor desarrollada por este equipo de trabajo desde su constitución, con las propuestas individuales de cada uno de sus integrantes, en las que se justifica el interés por las nuevas metodologías docentes; así como por la selección de concretas técnicas de aprendizaje en grupo, en función de los objetivos y competencias que se quieren conseguir. [ENG]The learning throughout competences, applied in the new universities degress, involves the previous analysis of the targets in each considered subjetc. Once the aims and the generic and specific competences are delimited for each subjetc, the teacher have to planify the learning procedure to reach this objective. The colabotarive learning techniques are an important tool in this context. The application of these techniquee are not as easy as is expected. The Colaborative Learning team of the Universidad de Cartagena develops an important role in this context. This paper shows the work which has been developed by this group from its beginning. We expose the proposals of each member in which they justify their interest in applying the new teaching tecniques and their learning goals to reach the specific competences of each subjetc.Campus Mare Nostrum, Universidad Politécnica de Cartagena, Universidad de Murcia, Región de Murci

Perinatal asphyxia: current status and approaches towards neuroprotective strategies, with focus on sentinel proteins

Delivery is a stressful and risky event menacing the newborn. The mother-dependent respiration has to be replaced by autonomous pulmonary breathing immediately after delivery. If delayed, it may lead to deficient oxygen supply compromising survival and development of the central nervous system. Lack of oxygen availability gives rise to depletion of NAD+ tissue stores, decrease of ATP formation, weakening of the electron transport pump and anaerobic metabolism and acidosis, leading necessarily to death if oxygenation is not promptly re-established. Re-oxygenation triggers a cascade of compensatory biochemical events to restore function, which may be accompanied by improper homeostasis and oxidative stress. Consequences may be incomplete recovery, or excess reactions that worsen the biological outcome by disturbed metabolism and/or imbalance produced by over-expression of alternative metabolic pathways. Perinatal asphyxia has been associated with severe neurological and psychiatric sequelae with delayed clinical onset. No specific treatments have yet been established. In the clinical setting, after resuscitation of an infant with birth asphyxia, the emphasis is on supportive therapy. Several interventions have been proposed to attenuate secondary neuronal injuries elicited by asphyxia, including hypothermia. Although promising, the clinical efficacy of hypothermia has not been fully demonstrated. It is evident that new approaches are warranted. The purpose of this review is to discuss the concept of sentinel proteins as targets for neuroprotection. Several sentinel proteins have been described to protect the integrity of the genome (e.g. PARP-1; XRCC1; DNA ligase IIIα; DNA polymerase β, ERCC2, DNA-dependent protein kinases). They act by eliciting metabolic cascades leading to (i) activation of cell survival and neurotrophic pathways; (ii) early and delayed programmed cell death, and (iii) promotion of cell proliferation, differentiation, neuritogenesis and synaptogenesis. It is proposed that sentinel proteins can be used as markers for characterising long-term effects of perinatal asphyxia, and as targets for novel therapeutic development and innovative strategies for neonatal care

American College of Rheumatology Provisional Criteria for Clinically Relevant Improvement in Children and Adolescents With Childhood-Onset Systemic Lupus Erythematosus

10.1002/acr.23834ARTHRITIS CARE & RESEARCH715579-59

Fusion of Visible and Thermal Descriptors Using Genetic Algorithms for Face Recognition Systems

The aim of this article is to present a new face recognition system based on the fusion of visible and thermal features obtained from the most current local matching descriptors by maximizing face recognition rates through the use of genetic algorithms. The article considers a comparison of the performance of the proposed fusion methodology against five current face recognition methods and classic fusion techniques used commonly in the literature. These were selected by considering their performance in face recognition. The five local matching methods and the proposed fusion methodology are evaluated using the standard visible/thermal database, the Equinox database, along with a new database, the PUCV-VTF, designed for visible-thermal studies in face recognition and described for the first time in this work. The latter is created considering visible and thermal image sensors with different real-world conditions, such as variations in illumination, facial expression, pose, occlusion, etc. The main conclusions of this article are that two variants of the proposed fusion methodology surpass current face recognition methods and the classic fusion techniques reported in the literature, attaining recognition rates of over 97% and 99% for the Equinox and PUCV-VTF databases, respectively. The fusion methodology is very robust to illumination and expression changes, as it combines thermal and visible information efficiently by using genetic algorithms, thus allowing it to choose optimal face areas where one spectrum is more representative than the other

Automated Detection and Analysis of Massive Mining Waste Deposits Using Sentinel-2 Satellite Imagery and Artificial Intelligence

International audienceThis article presents a method to detect and segment mine waste deposits, specifically waste rock dumps and leaching wasted dumps, in Sentinel-2 satellite imagery using artificial intelligence. This challenging task has important implications for mining companies and regulators like the National Geology and Mining Service in Chile. Challenges include limited knowledge of mine waste deposit numbers, as well as logistical and technical difficulties in conducting inspections and surveying physical stability parameters. The proposed method combines YOLOv7 object detection with a vision transformer classifier to locate mine waste deposits, as well as a deep generative model for data augmentation to enhance detection and segmentation accuracy. The ViT classifier achieved 98% accuracy in differentiating five satellite imagery scene types, while the YOLOv7 model achieved an average precision of 81% for detection and 79% for segmentation of mine waste deposits. Finally, the model was used to calculate mine waste deposit areas, with an absolute error of 6.6% compared to Google Earth API results

An Easy to Use Deep Reinforcement Learning Library for AI Mobile Robots in Isaac Sim

The use of mobile robots for personal and industrial uses is becoming popular. Currently, many robot simulators with high-graphical capabilities can be used by engineering to develop and test these robots such as Isaac Sim. However, using that simulator to train mobile robots with the deep reinforcement learning paradigm can be very difficult and time-consuming if one wants to develop a custom experiment, requiring an understanding of several libraries and APIs to use them together correctly. The proposed work aims to create a library that conceals configuration problems in creating robots, environments, and training scenarios, reducing the time dedicated to code. Every developed method is equivalent to sixty-five lines of code at maximum and five at minimum. That brings time saving in simulated experiments and data collection, thus reducing the time to produce and test viable algorithms for robots in the industry or academy

Un asunto entre privados : El caso Celco - Mehuín y la construcción de problemas públicos (1995-2010)

La presente investigación titulada “Un asunto entre privados: el Caso CELCO - Mehuín y la construcción de problemas públicos”, aborda el proceso de definición de problemas públicos en el ámbito socioambiental y su establecimiento en la agenda, desde una perspectiva de política pública. Bajo la pregunta ¿cómo se define un problema público en el ámbito medioambiental?, la investigación se lleva a cabo a través del análisis de una problemática particular, que es la ocurrida entre la empresa Celulosa Arauco y Constitución S.A. (CELCO) y la comunidad costera de Mehuín, región de Los Ríos, Provincia de Valdivia, entre los años 1995 a 2010. El interés del estudio es vincular los postulados teóricos en materia de definición problemas públicos con la praxis

Fusion of Visible and Thermal Descriptors Using Genetic Algorithms for Face Recognition Systems

The aim of this article is to present a new face recognition system based on the fusion of visible and thermal features obtained from the most current local matching descriptors by maximizing face recognition rates through the use of genetic algorithms. The article considers a comparison of the performance of the proposed fusion methodology against five current face recognition methods and classic fusion techniques used commonly in the literature. These were selected by considering their performance in face recognition. The five local matching methods and the proposed fusion methodology are evaluated using the standard visible/thermal database, the Equinox database, along with a new database, the PUCV-VTF, designed for visible-thermal studies in face recognition and described for the first time in this work. The latter is created considering visible and thermal image sensors with different real-world conditions, such as variations in illumination, facial expression, pose, occlusion, etc. The main conclusions of this article are that two variants of the proposed fusion methodology surpass current face recognition methods and the classic fusion techniques reported in the literature, attaining recognition rates of over 97% and 99% for the Equinox and PUCV-VTF databases, respectively. The fusion methodology is very robust to illumination and expression changes, as it combines thermal and visible information efficiently by using genetic algorithms, thus allowing it to choose optimal face areas where one spectrum is more representative than the other