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The separation of silver and gold from oxidized or sulfide minerals is largely carried out by a process called stock dissolution, which produces a rich solution called leachate. Once this solution is obtained, it is processed to obtain the silver or gold contained in it using already standardized large-scale procedures that use activated carbon and zinc powder, which require the use of special areas, procedures and materials to carry out the separation called desorption. This causes the procedure to be slower, longer and more complicated, which creates a complicated task for obtaining silver and gold from valuable minerals. To solve this, it is intended to improve the recovery of valuable metals from the leachate through the use of an electrochemical cell, for which the required materials and equipment will be prepared, to use a cell for electrodeposition, with the purpose of recovering valuable metals contained in the leaching of metallic minerals. The electrodeposition process will be used to optimize the recovery of valuable metals, instead of other procedures that currently exist, such as flotation and dilution of values with sodium (NaCN-) cyanide. An electrolytic cell will be built of plastic material, the anode that will be used will be made of copper wire and the cathode a rectangular aluminum sheet, both electrodes will be immersed in the cell within the “rich” solution of the leachate that contains diluted gold and silver. in ionic form. Sodium cyanide (NaCN-) will function as an electrolyte and will be fed with an electrical current to an electrolytic cell by a charger that provides an output current of 3.6 V ~ 600 mA and input currents of 120 V ~ 60 Hz or 0. 1 A with which the valuable metals of gold and silver will be deposited on the anode. Several electrodeposition runs will be carried out in the experimental design, with the aim of achieving an optimal procedure in the recovery of valuable metals., La separación de plata y oro de minerales oxidados o sulfurosos se lleva a cabo mayormente mediante un proceso denominado disolución de valores, el cual produce una solución rica la cual se llama lixiviado. Una vez que se tiene esta solución se procesa para obtener la plata o el oro contenida en ella utilizando procedimientos ya estandarizados a gran escala que utilizan carbón activado y polvo de zinc, los cuales requieren el uso de áreas, procedimientos y materiales especiales para realizarse la separación denominada desorción. Esto ocasiona que el procedimiento sea más tardado, largo y complicado lo que ocasiona una tarea complicada para la obtención de plata y oro de minerales valiosos. Para solucionar esto, se pretende mejorar la recuperación de los metales valiosos del lixiviado mediante el uso de una celda electroquímica, para lo cual se prepararán los materiales y equipos requeridos, para utilizar una celda para electrodeposición, con la finalidad de recuperar metales valiosos contenidos en el lixiviado de minerales metálicos. Se utilizará el proceso de electrodeposición para optimizar la recuperación de metales valiosos, en lugar de otros procedimientos que existen actualmente, como son la flotación y la dilución de valores con (NaCN-) cianuro de sodio. Se construirá una celda electrolítica de material plástico, el ánodo que se utilizará será de alambre de cobre y el cátodo una lámina rectangular de aluminio, ambos electrodos se sumergirán en la celda dentro de la solución “rica” del lixiviado que contiene oro y plata diluidos en forma iónica. El cianuro de sodio (NaCN-) funcionará como electrolito y se alimentará con una corriente eléctrica a una celda electrolítica mediante un cargador que proporciona una corriente de salida de 3.6 V ~ 600 mA y Corrientes de entrada de 120 V ~ 60 Hz o 0,1 A con lo que se logrará depositar en el ánodo los metales valiosos de oro y plata. Se realizarán varias corridas de electrodeposición en el diseño experimental, con la f

Understanding black box models has become paramount as systems based on opaque Artificial Intelligence (AI) continue to flourish in diverse real-world applications. In response, Explainable AI (XAI) has emerged as a field of research with practical and ethical benefits across various domains. This paper highlights the advancements in XAI and its application in real-world scenarios and addresses the ongoing challenges within XAI, emphasizing the need for broader perspectives and collaborative efforts. We bring together experts from diverse fields to identify open problems, striving to synchronize research agendas and accelerate XAI in practical applications. By fostering collaborative discussion and interdisciplinary cooperation, we aim to propel XAI forward, contributing to its continued success. We aim to develop a comprehensive proposal for advancing XAI. To achieve this goal, we present a manifesto of 28 open problems categorized into nine categories. These challenges encapsulate the complexities and nuances of XAI and offer a road map for future research. For each problem, we provide promising research directions in the hope of harnessing the collective intelligence of interested stakeholders.

Understanding black box models has become paramount as systems based on opaque Artificial Intelligence (AI) continue to flourish in diverse real-world applications. In response, Explainable AI (XAI) has emerged as a field of research with practical and ethical benefits across various domains. This paper highlights the advancements in XAI and its application in real-world scenarios and addresses the ongoing challenges within XAI, emphasizing the need for broader perspectives and collaborative efforts. We bring together experts from diverse fields to identify open problems, striving to synchronize research agendas and accelerate XAI in practical applications. By fostering collaborative discussion and interdisciplinary cooperation, we aim to propel XAI forward, contributing to its continued success. We aim to develop a comprehensive proposal for advancing XAI. To achieve this goal, we present a manifesto of 28 open problems categorized into nine categories. These challenges encapsulate the complexities and nuances of XAI and offer a road map for future research. For each problem, we provide promising research directions in the hope of harnessing the collective intelligence of interested stakeholders.

Social Media and Internet have the potential to be exploited as a source of opinion to enrich Decision Making solutions. Crowd Decision Making (CDM) is a methodology able to infer opinions and decisions from plain texts, such as reviews published in social media platforms, by means of Sentiment Analysis. Currently, the emergence and potential of Large Language Models (LLMs) lead us to explore new scenarios of automatically understand written texts, also known as natural language processing. This paper analyzes the use of ChatGPT based on prompt design strategies to assist in CDM processes to extract opinions and make decisions. We integrate ChatGPT in CDM processes as a flexible tool that infer the opinions expressed in texts, providing numerical or linguistic evaluations where the decision making models are based on the prompt design strategies. We include a multi-criteria decision making scenario with a category ontology for criteria. We also consider ChatGPT as an end-to-end CDM model able to provide a general opinion and score on the alternatives. We conduct empirical experiments on real data extracted from TripAdvisor, the TripR-2020Large dataset. The analysis of results show a promising branch for developing quality decision making models using ChatGPT. Finally, we discuss the challenges of consistency, sensitivity and explainability associated to the use of LLMs in CDM processes, raising open questions for future studies.

Classic Delphi and Fuzzy Delphi methods are used to test content validity of a data collection tools such as questionnaires. Fuzzy Delphi takes the opinion issued by judges from a linguistic perspective reducing ambiguity in opinions by using fuzzy numbers. We propose an extension named 2-Tuple Fuzzy Linguistic Delphi method to deal with scenarios in which judges show different expertise degrees by using fuzzy multigranular semantics of the linguistic terms and to obtain intermediate and final results expressed by 2-tuple linguistic values. The key idea of our proposal is to validate the full questionnaire by means of the evaluation of its parts, defining the validity of each item as a Decision Making problem. Taking the opinion of experts, we measure the degree of consensus, the degree of consistency, and the linguistic score of each item, in order to detect those items that affect, positively or negatively, the quality of the instrument. Considering the real need to evaluate a b-learning educational experience with a consensual questionnaire, we present a Decision Making model for questionnaire validation that solve it. Additionally, we contribute to this consensus reaching problem by developing an online tool under GPL v3 license. The software visualizes the collective valuations for each iteration and assists to determine which parts of the questionnaire should be modified to reach a consensual solution., Comment: project at https://github.com/rosanamontes/teranga.go. arXiv admin note: substantial text overlap with arXiv:2402.01775

Explainable Artificial Intelligence (XAI) is a pivotal research domain aimed at understanding the operational mechanisms of AI systems, particularly those considered ``black boxes'' due to their complex, opaque nature. XAI seeks to make these AI systems more understandable and trustworthy, providing insight into their decision-making processes. By producing clear and comprehensible explanations, XAI enables users, practitioners, and stakeholders to trust a model's decisions. This work analyses the value of data morphology strategies in generating counterfactual explanations. It introduces the Overlap Number of Balls Model-Agnostic CounterFactuals (ONB-MACF) method, a model-agnostic counterfactual generator that leverages data morphology to estimate a model's decision boundaries. The ONB-MACF method constructs hyperspheres in the data space whose covered points share a class, mapping the decision boundary. Counterfactuals are then generated by incrementally adjusting an instance's attributes towards the nearest alternate-class hypersphere, crossing the decision boundary with minimal modifications. By design, the ONB-MACF method generates feasible and sparse counterfactuals that follow the data distribution. Our comprehensive benchmark from a double perspective (quantitative and qualitative) shows that the ONB-MACF method outperforms existing state-of-the-art counterfactual generation methods across multiple quality metrics on diverse tabular datasets. This supports our hypothesis, showcasing the potential of data-morphology-based explainability strategies for trustworthy AI., Comment: 21 pages, 6 figures. Submitted to Information Sciences

In the realm of Artificial Intelligence (AI), the need for privacy and security in data processing has become paramount. As AI applications continue to expand, the collection and handling of sensitive data raise concerns about individual privacy protection. Federated Learning (FL) emerges as a promising solution to address these challenges by enabling decentralized model training on local devices, thus preserving data privacy. This paper introduces FLEX: a FLEXible Federated Learning Framework designed to provide maximum flexibility in FL research experiments. By offering customizable features for data distribution, privacy parameters, and communication strategies, FLEX empowers researchers to innovate and develop novel FL techniques. The framework also includes libraries for specific FL implementations including: (1) anomalies, (2) blockchain, (3) adversarial attacks and defences, (4) natural language processing and (5) decision trees, enhancing its versatility and applicability in various domains. Overall, FLEX represents a significant advancement in FL research, facilitating the development of robust and efficient FL applications., Comment: Submitted to Information Fusion

As Artificial Intelligence systems become integral across domains, the demand for explainability grows. While the effort by the scientific community is focused on obtaining a better explanation for the model, it is important not to ignore the potential of this explanation process to improve training as well. While existing efforts primarily focus on generating and evaluating explanations for black-box models, there remains a critical gap in directly enhancing models through these evaluations. This paper introduces SHIELD (Selective Hidden Input Evaluation for Learning Dynamics), a regularization technique for explainable artificial intelligence designed to improve model quality by concealing portions of input data and assessing the resulting discrepancy in predictions. In contrast to conventional approaches, SHIELD regularization seamlessly integrates into the objective function, enhancing model explainability while also improving performance. Experimental validation on benchmark datasets underscores SHIELD's effectiveness in improving Artificial Intelligence model explainability and overall performance. This establishes SHIELD regularization as a promising pathway for developing transparent and reliable Artificial Intelligence regularization techniques., Comment: 18 pages, 8 figures

Trustworthy Artificial Intelligence solutions are essential in today's data-driven applications, prioritizing principles such as robustness, safety, transparency, explainability, and privacy among others. This has led to the emergence of Federated Learning as a solution for privacy and distributed machine learning. While decision trees, as self-explanatory models, are ideal for collaborative model training across multiple devices in resource-constrained environments such as federated learning environments for injecting interpretability in these models. Decision tree structure makes the aggregation in a federated learning environment not trivial. They require techniques that can merge their decision paths without introducing bias or overfitting while keeping the aggregated decision trees robust and generalizable. In this paper, we propose an Interpretable Client Decision Tree Aggregation process for Federated Learning scenarios that keeps the interpretability and the precision of the base decision trees used for the aggregation. This model is based on aggregating multiple decision paths of the decision trees and can be used on different decision tree types, such as ID3 and CART. We carry out the experiments within four datasets, and the analysis shows that the tree built with the model improves the local models, and outperforms the state-of-the-art., Comment: Submitted to Information Science Journal

La energía hidroeléctrica es una de las fuentes de energía renovable más desarrolladas y utilizadas en términos de capacidad instalada y fiabilidad; por lo tanto, es uno de los métodos de producción de electricidad con mayor contribución a nivel mundial y con un nivel de madurez tecnológica esencial. En los últimos años, el sector hidroeléctrico ha pasado de las grandes a las pequeñas centrales, debido a las limitaciones de los recursos hídricos y a los efectos del cambio climático. Además, aunque menos utilizadas, las turbomáquinas hidráulicas reversibles se postulan como alternativas para compensar la demanda de tecnologías de energías renovables intermitentes y de difícil previsión. Este artículo presenta el desarrollo e implementación de una plataforma digital basada en mediciones en tiempo real para la monitorización, registro y análisis en los dominios de tiempo y frecuencia de magnitudes hidromecánicas, dinámicas y eléctricas. Los datos resultantes son adecuados para la caracterización de las máquinas hidráulicas, y su diagnóstico técnico, permitiendo la detección de fenómenos complejos que pueden ocurrir durante su funcionamiento. Para ilustrar algunas de las capacidades de la plataforma, se realizaron pruebas experimentales en una bomba centrífuga específica de baja velocidad, a través de su curva característica de cuatro cuadrantes. La atención se centró en la medición de la fluctuación de la presión y la aceleración (vibraciones) durante el funcionamiento en su punto de mejor rendimiento (BEP) y en un punto de funcionamiento fuera de diseño en el que se detectaron inestabilidades hidrodinámicas. Por último, se exponen los análisis y resultados, y se presentan las características., Hydropower is one of the most developed and utilized renewable energy sources in terms of installed capacity and reliability; therefore, it is one of the electricity production methods with the highest contribution worldwide and with an essential level of technological maturity. In recent years, the hydropower sector has shifted from large to small plants, due to water resource constraints and the effects of climate change. In addition, although less used, reversible hydro turbomachines are postulated as alternatives to offset the demand for intermittent and difficult to forecast renewable energy technologies. This paper presents the development and implementation of a digital platform based on real-time measurements for monitoring, recording, and analysis in the time and frequency domains of hydromechanical, dynamic, and electrical magnitudes. The resulting data are suitable for the characterization of hydraulic machines, and their technical diagnosis, allowing the detection of complex phenomena that may occur during their operation. To illustrate some of the capabilities of the platform, experimental tests were performed on a specific low-speed centrifugal pump, through its four-quadrant characteristic curve. The focus was on the measurement of pressure fluctuation and acceleration (vibrations) during operation at its best efficiency point (BEP) and at an off-design operating point where hydrodynamic instabilities were detected. Finally, the analysis and results are exposed, and the characteristics are presented.

Actualmente en Colombia se aumenta la demanda de energía eléctrica y la red de generación con la que cuenta el país tiene varios años de operación, considerando que la mayoría de las centrales hidroeléctricas fueron construidas entre 1960 y 1980. De acuerdo con lo anterior, es de suma importancia contar con una metodología aplicable, a más de la mitad de las centrales hidroeléctricas presentes en Colombia las cuales generan energía por medio de turbinas tipo Pelton, para modernizar los equipos mecánicos y alargar la vida útil de la central con el fin de garantizar una generación de energía más eficiente y sin paradas de emergencia por fallos en los equipos. En este documento se presenta una descripción de los sistemas y equipos instalados en las válvulas esféricas de centrales hidroeléctricas con turbinas tipo Pelton, su funcionamiento y una metodología detallada que entrega como resultado una estructura documental para su proceso de licitación., Currently in Colombia the demand for electricity is increasing and the generation network that the country has several years of operation, considering that most of the hydroelectric plants were built between 1960 and 1980. According to the above, it is extremely important to have a methodology applicable to more than half of the hydroelectric powerplants present in Colombia, which generate energy through Pelton-type turbines, to modernize the mechanical equipment and extend the useful life of the plant to guarantee a more efficient energy generation and without emergency stops due to equipment failures. This document presents a description of the systems and equipment installed in the spherical valves of hydroelectric plants with Pelton-type turbines, their operation and a detailed methodology that results in a documentary structure for the bidding process.

Evolutionary Computation algorithms have been used to solve optimization problems in relation with architectural, hyper-parameter or training configuration, forging the field known today as Neural Architecture Search. These algorithms have been combined with other techniques such as the pruning of Neural Networks, which reduces the complexity of the network, and the Transfer Learning, which lets the import of knowledge from another problem related to the one at hand. The usage of several criteria to evaluate the quality of the evolutionary proposals is also a common case, in which the performance and complexity of the network are the most used criteria. This work proposes MO-EvoPruneDeepTL, a multi-objective evolutionary pruning algorithm. MO-EvoPruneDeepTL uses Transfer Learning to adapt the last layers of Deep Neural Networks, by replacing them with sparse layers evolved by a genetic algorithm, which guides the evolution based in the performance, complexity and robustness of the network, being the robustness a great quality indicator for the evolved models. We carry out different experiments with several datasets to assess the benefits of our proposal. Results show that our proposal achieves promising results in all the objectives, and direct relation are presented among them. The experiments also show that the most influential neurons help us explain which parts of the input images are the most relevant for the prediction of the pruned neural network. Lastly, by virtue of the diversity within the Pareto front of pruning patterns produced by the proposal, it is shown that an ensemble of differently pruned models improves the overall performance and robustness of the trained networks., Comment: 28 pages, 11 figures

Trustworthy Artificial Intelligence (AI) is based on seven technical requirements sustained over three main pillars that should be met throughout the system's entire life cycle: it should be (1) lawful, (2) ethical, and (3) robust, both from a technical and a social perspective. However, attaining truly trustworthy AI concerns a wider vision that comprises the trustworthiness of all processes and actors that are part of the system's life cycle, and considers previous aspects from different lenses. A more holistic vision contemplates four essential axes: the global principles for ethical use and development of AI-based systems, a philosophical take on AI ethics, a risk-based approach to AI regulation, and the mentioned pillars and requirements. The seven requirements (human agency and oversight; robustness and safety; privacy and data governance; transparency; diversity, non-discrimination and fairness; societal and environmental wellbeing; and accountability) are analyzed from a triple perspective: What each requirement for trustworthy AI is, Why it is needed, and How each requirement can be implemented in practice. On the other hand, a practical approach to implement trustworthy AI systems allows defining the concept of responsibility of AI-based systems facing the law, through a given auditing process. Therefore, a responsible AI system is the resulting notion we introduce in this work, and a concept of utmost necessity that can be realized through auditing processes, subject to the challenges posed by the use of regulatory sandboxes. Our multidisciplinary vision of trustworthy AI culminates in a debate on the diverging views published lately about the future of AI. Our reflections in this matter conclude that regulation is a key for reaching a consensus among these views, and that trustworthy and responsible AI systems will be crucial for the present and future of our society., Comment: 30 pages, 5 figures, under second review

As systems based on opaque Artificial Intelligence (AI) continue to flourish in diverse real-world applications, understanding these black box models has become paramount. In response, Explainable AI (XAI) has emerged as a field of research with practical and ethical benefits across various domains. This paper not only highlights the advancements in XAI and its application in real-world scenarios but also addresses the ongoing challenges within XAI, emphasizing the need for broader perspectives and collaborative efforts. We bring together experts from diverse fields to identify open problems, striving to synchronize research agendas and accelerate XAI in practical applications. By fostering collaborative discussion and interdisciplinary cooperation, we aim to propel XAI forward, contributing to its continued success. Our goal is to put forward a comprehensive proposal for advancing XAI. To achieve this goal, we present a manifesto of 27 open problems categorized into nine categories. These challenges encapsulate the complexities and nuances of XAI and offer a road map for future research. For each problem, we provide promising research directions in the hope of harnessing the collective intelligence of interested stakeholders.

Most applications of Artificial Intelligence (AI) are designed for a confined and specific task. However, there are many scenarios that call for a more general AI, capable of solving a wide array of tasks without being specifically designed for them. The term General-Purpose Artificial Intelligence Systems (GPAIS) has been defined to refer to these AI systems. To date, the possibility of an Artificial General Intelligence, powerful enough to perform any intellectual task as if it were human, or even improve it, has remained an aspiration, fiction, and considered a risk for our society. Whilst we might still be far from achieving that, GPAIS is a reality and sitting at the forefront of AI research. This work discusses existing definitions for GPAIS and proposes a new definition that allows for a gradual differentiation among types of GPAIS according to their properties and limitations. We distinguish between closed-world and open-world GPAIS, characterising their degree of autonomy and ability based on several factors such as adaptation to new tasks, competence in domains not intentionally trained for, ability to learn from few data, or proactive acknowledgment of their own limitations. We propose a taxonomy of approaches to realise GPAIS, describing research trends such as the use of AI techniques to improve another AI (AI-powered AI) or (single) foundation models. As a prime example, we delve into GenAI, aligning them with the concepts presented in the taxonomy. We explore multi-modality, which involves fusing various types of data sources to expand the capabilities of GPAIS. Through the proposed definition and taxonomy, our aim is to facilitate research collaboration across different areas that are tackling general purpose tasks, as they share many common aspects. Finally, we discuss the state of GPAIS, prospects, societal implications, and the need for regulation and governance.

Land use and land cover (LULC) mapping are of paramount importance to monitor and understand the structure and dynamics of the Earth system. One of the most promising ways to create accurate global LULC maps is by building good quality state-of-the-art machine learning models. Building such models requires large and global datasets of annotated time series of satellite images, which are not available yet. This paper presents TimeSpec4LULC (https://doi.org/10.5281/zenodo.5913554; Khaldi et al., 2022), a smart open-source global dataset of multispectral time series for 29 LULC classes ready to train machine learning models. TimeSpec4LULC was built based on the seven spectral bands of the MODIS sensors at 500 m resolution, from 2000 to 2021, and was annotated using spatial–temporal agreement across the 15 global LULC products available in Google Earth Engine (GEE). The 22-year monthly time series of the seven bands were created globally by (1) applying different spatial–temporal quality assessment filters on MODIS Terra and Aqua satellites; (2) aggregating their original 8 d temporal granularity into monthly composites; (3) merging Terra + Aqua data into a combined time series; and (4) extracting, at the pixel level, 6 076 531 time series of size 262 for the seven bands along with a set of metadata: geographic coordinates, country and departmental divisions, spatial–temporal consistency across LULC products, temporal data availability, and the global human modification index. A balanced subset of the original dataset was also provided by selecting 1000 evenly distributed samples from each class such that they are representative of the entire globe. To assess the annotation quality of the dataset, a sample of pixels, evenly distributed around the world from each LULC class, was selected and validated by experts using very high resolution images from both Google Earth and Bing Maps imagery. This smartly, pre-processed, and annotated dataset is targeted towards scientific

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Covid-19 outbreak forced the governments around the world to decide schools closure. In Brazil this circumstance lasted almost two years because a government wrong management had been marked by delays and lack implementation of public policies. The outbreak context brought important challenges for children and their families. This paper carried out a characterization and analysis of a questionnaire to assess the perception of fifteen families about care, socialization/learning, feeding and development of autonomy in shared context with children during the pandemic in a public day care center. Results show that families faced difficulties in socializing and playing. Feeding practices and use of objects-utensils during mealtimes and care had better indicators. However, difficulties in autonomy, learning, and anxiety increased. As a conclusion, the impact of pandemic changes must be evaluated, revealing actions that the school will have to carry out in the future, especially on children's development/learning processes., El brote de Covid-19 obligó gobiernos de todo el mundo cerrar las escuelas. En Brasil esta circunstancia duró casi dos años debido a la gestión del gobierno, marcada por retrasos y falta de políticas públicas. El contexto del brote trajo desafíos para niños y familias. Este artículo caracteriza y analiza un cuestionario para evaluar la percepción que quince familias tuvieron sobre cuidado, socialización/aprendizaje, alimentación y desarrollo de la autonomía de los niños durante en una escuela infantil. Los resultados muestran que las familias enfrentaron dificultades para socializar y jugar. Las prácticas de alimentación y el uso de objetos-utensilios durante las comidas y el cuidado tuvieron mejores indicadores. Aumentaron las dificultades en la autonomía, el aprendizaje y la ansiedad. Como conclusión, se debe evaluar el impacto de los cambios de la pandemia, revelando acciones que la escuela deberá realizar en el futuro, especialmente en los procesos de desarrollo/aprendizaje de los niños.

Peruvian sign language (LSP) is a visual-gestural native language of Peru used by the deaf community of the country. This paper aims to present preliminary findings about relative clauses in this language. Relative clauses are grammatical constructions that consist in a nominal element and a subordinate clause that modifies that nominal element. For the present investigation, we interviewed 4 LSP signers and we documented the relative clauses they realized. Thereby, we have found that LSP has postnominal relative clauses with both internal and external heads. Moreover, we have found a resumptive pronoun in this type of construction. Finally, we present an analysis of our data using the raising analysis of relative clauses., Resumen en LSP: https://youtu.be/us1UxyT5QtU La lengua de señas peruana (LSP) es una lengua viso-gestual originaria del Perú utilizada por la comunidad sorda del país. Este artículo tiene como objetivo presentar hallazgos preliminares sobre las cláusulas relativas de esta lengua. Las cláusulas relativas son construcciones gramaticales que consisten en un elemento nominal y en una cláusula subordinada que modifica a este elemento nominal. Para la presente investigación entrevistamos a cuatro señantes de LSP y documentamos las cláusulas relativas que realizaron. Así, hemos encontrado que la LSP posee cláusulas relativas post-nominales con cabeza interna y externa. Además, hemos encontrado un pronombre reasuntivo en este tipo de construcciones. Finalmente, presentamos un análisis de nuestros datos según el raising analysis de cláusulas relativas.

Explainable artificial intelligence is proposed to provide explanations for reasoning performed by an Artificial Intelligence. There is no consensus on how to evaluate the quality of these explanations, since even the definition of explanation itself is not clear in the literature. In particular, for the widely known Local Linear Explanations, there are qualitative proposals for the evaluation of explanations, although they suffer from theoretical inconsistencies. The case of image is even more problematic, where a visual explanation seems to explain a decision while detecting edges is what it really does. There are a large number of metrics in the literature specialized in quantitatively measuring different qualitative aspects so we should be able to develop metrics capable of measuring in a robust and correct way the desirable aspects of the explanations. In this paper, we propose a procedure called REVEL to evaluate different aspects concerning the quality of explanations with a theoretically coherent development. This procedure has several advances in the state of the art: it standardizes the concepts of explanation and develops a series of metrics not only to be able to compare between them but also to obtain absolute information regarding the explanation itself. The experiments have been carried out on image four datasets as benchmark where we show REVEL's descriptive and analytical power.

Airway segmentation is crucial for the examination, diagnosis, and prognosis of lung diseases, while its manual delineation is unduly burdensome. To alleviate this time-consuming and potentially subjective manual procedure, researchers have proposed methods to automatically segment airways from computerized tomography (CT) images. However, some small-sized airway branches (e.g., bronchus and terminal bronchioles) significantly aggravate the difficulty of automatic segmentation by machine learning models. In particular, the variance of voxel values and the severe data imbalance in airway branches make the computational module prone to discontinuous and false-negative predictions. especially for cohorts with different lung diseases. Attention mechanism has shown the capacity to segment complex structures, while fuzzy logic can reduce the uncertainty in feature representations. Therefore, the integration of deep attention networks and fuzzy theory, given by the fuzzy attention layer, should be an escalated solution for better generalization and robustness. This paper presents an efficient method for airway segmentation, comprising a novel fuzzy attention neural network and a comprehensive loss function to enhance the spatial continuity of airway segmentation. The deep fuzzy set is formulated by a set of voxels in the feature map and a learnable Gaussian membership function. Different from the existing attention mechanism, the proposed channel-specific fuzzy attention addresses the issue of heterogeneous features in different channels. Furthermore, a novel evaluation metric is proposed to assess both the continuity and completeness of airway structures. The efficiency, generalization and robustness of the proposed method have been proved by training on normal lung disease while testing on datasets of lung cancer, COVID-19 and pulmonary fibrosis., Comment: 12 pages, 5 figures, Submitted to IEEE TNNLS

Land-Use and Land-Cover (LULC) mapping is relevant for many applications, from Earth system and climate modelling to territorial and urban planning. Global LULC products are continuously developing as remote sensing data and methods grow. However, there still exists low consistency among LULC products due to low accuracy in some regions and LULC types. Here, we introduce Sentinel2GlobalLULC, a Sentinel-2 RGB image dataset, built from the spatial-temporal consensus of up to 15 global LULC maps available in Google Earth Engine. Sentinel2GlobalLULC v2.1 contains 194877 single-class RGB image tiles organized into 29 LULC classes. Each image is a 224 × 224 pixels tile at 10 × 10 m resolution built as a cloud-free composite from Sentinel-2 images acquired between June 2015 and October 2020. Metadata includes a unique LULC annotation per image, together with level of consensus, reverse geo-referencing, global human modification index, and number of dates used in the composite. Sentinel2GlobalLULC is designed for training deep learning models aiming to build precise and robust global or regional LULC maps.

Land use and land cover (LULC) mapping are of paramount importance to monitor and understand the structure and dynamics of the Earth system. One of the most promising ways to create accurate global LULC maps is by building good quality state-of-the-art machine learning models. Building such models requires large and global datasets of annotated time series of satellite images, which are not available yet. This paper presents TimeSpec4LULC (https://doi.org/10.5281/zenodo.5913554; Khaldi et al., 2022), a smart open-source global dataset of multispectral time series for 29 LULC classes ready to train machine learning models. TimeSpec4LULC was built based on the seven spectral bands of the MODIS sensors at 500 m resolution, from 2000 to 2021, and was annotated using spatial–temporal agreement across the 15 global LULC products available in Google Earth Engine (GEE). The 22-year monthly time series of the seven bands were created globally by (1) applying different spatial–temporal quality assessment filters on MODIS Terra and Aqua satellites; (2) aggregating their original 8 d temporal granularity into monthly composites; (3) merging Terra + Aqua data into a combined time series; and (4) extracting, at the pixel level, 6 076 531 time series of size 262 for the seven bands along with a set of metadata: geographic coordinates, country and departmental divisions, spatial–temporal consistency across LULC products, temporal data availability, and the global human modification index. A balanced subset of the original dataset was also provided by selecting 1000 evenly distributed samples from each class such that they are representative of the entire globe. To assess the annotation quality of the dataset, a sample of pixels, evenly distributed around the world from each LULC class, was selected and validated by experts using very high resolution images from both Google Earth and Bing Maps imagery. This smartly, pre-processed, and annotated dataset is targeted towards scientific

There is a broad consensus on the importance of deep learning models in tasks involving complex data. Often, an adequate understanding of these models is required when focusing on the transparency of decisions in human-critical applications. Besides other explainability techniques, trustworthiness can be achieved by using counterfactuals, like the way a human becomes familiar with an unknown process: by understanding the hypothetical circumstances under which the output changes. In this work we argue that automated counterfactual generation should regard several aspects of the produced adversarial instances, not only their adversarial capability. To this end, we present a novel framework for the generation of counterfactual examples which formulates its goal as a multi-objective optimization problem balancing three different objectives: 1) plausibility, i.e., the likeliness of the counterfactual of being possible as per the distribution of the input data; 2) intensity of the changes to the original input; and 3) adversarial power, namely, the variability of the model's output induced by the counterfactual. The framework departs from a target model to be audited and uses a Generative Adversarial Network to model the distribution of input data, together with a multi-objective solver for the discovery of counterfactuals balancing among these objectives. The utility of the framework is showcased over six classification tasks comprising image and three-dimensional data. The experiments verify that the framework unveils counterfactuals that comply with intuition, increasing the trustworthiness of the user, and leading to further insights, such as the detection of bias and data misrepresentation., Comment: 52 pages, 14 figures, under review

Support vector machines (SVMs) are popular learning algorithms to deal with binary classification problems. They traditionally assume equal misclassification costs for each class; however, real-world problems may have an uneven class distribution. This article introduces EBCS-SVM: evolutionary bilevel cost-sensitive SVMs. EBCS-SVM handles imbalanced classification problems by simultaneously learning the support vectors and optimizing the SVM hyperparameters, which comprise the kernel parameter and misclassification costs. The resulting optimization problem is a bilevel problem, where the lower level determines the support vectors and the upper level the hyperparameters. This optimization problem is solved using an evolutionary algorithm (EA) at the upper level and sequential minimal optimization (SMO) at the lower level. These two methods work in a nested fashion, that is, the optimal support vectors help guide the search of the hyperparameters, and the lower level is initialized based on previous successful solutions. The proposed method is assessed using 70 datasets of imbalanced classification and compared with several state-of-the-art methods. The experimental results, supported by a Bayesian test, provided evidence of the effectiveness of EBCS-SVM when working with highly imbalanced datasets., Comment: Copyright 2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works

In recent years, Deep Learning models have shown a great performance in complex optimization problems. They generally require large training datasets, which is a limitation in most practical cases. Transfer learning allows importing the first layers of a pre-trained architecture and connecting them to fully-connected layers to adapt them to a new problem. Consequently, the configuration of the these layers becomes crucial for the performance of the model. Unfortunately, the optimization of these models is usually a computationally demanding task. One strategy to optimize Deep Learning models is the pruning scheme. Pruning methods are focused on reducing the complexity of the network, assuming an expected performance penalty of the model once pruned. However, the pruning could potentially be used to improve the performance, using an optimization algorithm to identify and eventually remove unnecessary connections among neurons. This work proposes EvoPruneDeepTL, an evolutionary pruning model for Transfer Learning based Deep Neural Networks which replaces the last fully-connected layers with sparse layers optimized by a genetic algorithm. Depending on its solution encoding strategy, our proposed model can either perform optimized pruning or feature selection over the densely connected part of the neural network. We carry out different experiments with several datasets to assess the benefits of our proposal. Results show the contribution of EvoPruneDeepTL and feature selection to the overall computational efficiency of the network as a result of the optimization process. In particular, the accuracy is improved, reducing at the same time the number of active neurons in the final layers.

Federated learning is a machine learning paradigm that emerges as a solution to the privacy-preservation demands in artificial intelligence. As machine learning, federated learning is threatened by adversarial attacks against the integrity of the learning model and the privacy of data via a distributed approach to tackle local and global learning. This weak point is exacerbated by the inaccessibility of data in federated learning, which makes harder the protection against adversarial attacks and evidences the need to furtherance the research on defence methods to make federated learning a real solution for safeguarding data privacy. In this paper, we present an extensive review of the threats of federated learning, as well as as their corresponding countermeasures, attacks versus defences. This survey provides a taxonomy of adversarial attacks and a taxonomy of defence methods that depict a general picture of this vulnerability of federated learning and how to overcome it. Likewise, we expound guidelines for selecting the most adequate defence method according to the category of the adversarial attack. Besides, we carry out an extensive experimental study from which we draw further conclusions about the behaviour of attacks and defences and the guidelines for selecting the most adequate defence method according to the category of the adversarial attack. This study is finished leading to meditated learned lessons and challenges.

Removing the bias and variance of multicentre data has always been a challenge in large scale digital healthcare studies, which requires the ability to integrate clinical features extracted from data acquired by different scanners and protocols to improve stability and robustness. Previous studies have described various computational approaches to fuse single modality multicentre datasets. However, these surveys rarely focused on evaluation metrics and lacked a checklist for computational data harmonisation studies. In this systematic review, we summarise the computational data harmonisation approaches for multi-modality data in the digital healthcare field, including harmonisation strategies and evaluation metrics based on different theories. In addition, a comprehensive checklist that summarises common practices for data harmonisation studies is proposed to guide researchers to report their research findings more effectively. Last but not least, flowcharts presenting possible ways for methodology and metric selection are proposed and the limitations of different methods have been surveyed for future research., Comment: 54 pages, 14 figures, accepted by the Information Fusion journal

The combination of convolutional and recurrent neural networks is a promising framework that allows the extraction of high-quality spatio-temporal features together with its temporal dependencies, which is key for time series prediction problems such as forecasting, classification or anomaly detection, amongst others. In this paper, the TSFEDL library is introduced. It compiles 20 state-of-the-art methods for both time series feature extraction and prediction, employing convolutional and recurrent deep neural networks for its use in several data mining tasks. The library is built upon a set of Tensorflow+Keras and PyTorch modules under the AGPLv3 license. The performance validation of the architectures included in this proposal confirms the usefulness of this Python package., Comment: 26 pages, 33 figures

[ES] El presente trabajo plantea el diseño y la implementación de un sistema de adquisición de datos biométrico para la evaluación de pilotos de competición. Este sistema permitirá la monitorización en tiempo real de las señales biométricas del piloto. Con dichos datos se pretende interpretar, mediante la fusión de sensores obtenida, un estado global psicofísico del piloto durante los diferentes eventos que transcurren en la realización de las pruebas orientadas a las situaciones de conducción que sufre un piloto de competición en pista. Este sistema se va a poner en práctica en el simulador G-UPV de conducción profesional para pilotos de competición, perteneciente al Instituto de Diseño y Fabricación (IDF), el cual cuenta con un cockpit monocasco apoyado en una plataforma dinámica de 6 GDL, con la que se pretende emular la aceleración que experimenta un piloto en una pista real, además de las condiciones del asfalto. Con este sistema se pretende medir e interpretar una combinación de señales que se consideran potencialmente relevantes para la determinación del estado psicofísico del piloto, entre las cuales se incluyen señales de electrocardiograma, electromiograma, electrodermograma, ritmo respiratorio, temperatura corporal, saturación oxígeno en sangre y la aceleración que experimenta el piloto en los ejes xyz, [EN] The present work proposes the design and implementation of a biometric data acquisition system for the evaluation of competition pilots. This system will allow real-time monitoring of the pilot's biometric signals. With these data, the aim is to interpret, by means of the sensor fusion obtained, a global psychophysical state of the pilot during the different events that take place in carrying out tests aimed at the driving situations suffered by a competition pilot on the track. This system will be implemented in the G-UPV professional driving simulator for competition pilots, belonging to the Institute of Design and Manufacturing (IDF), which has a monocoque cockpit supported on a dynamic 6 GDL platform, with which is intended to emulate the acceleration experienced by a pilot on a real track, in addition to the asphalt conditions. This system aims to measure and interpret a combination of signals that are considered potentially relevant for the determination of the pilot's psychophysical state, including electrocardiogram, electromyogram, electrodermogram, respiratory rhythm, body temperature, blood oxygen saturation and the acceleration that pilot experiences on the xyz axes.

[ES] La búsqueda de nuevos compuestos químicos con propiedades específicas representa uno de los retos más significativos, no sólo para la industria químico-farmacéutica, sino para el mundo científico en general. Los boratos son compuestos con muy buenas prestaciones mecánicas y ópticas que, dopados con tierras raras adquieren propiedades luminiscentes únicas. Para la aplicación de estas propiedades extraordinarias en un sensor de altas prestaciones es necesario garantizar la estabilidad estructural del compuesto y analizar su comportamiento bajo las condiciones de operación. En este trabajo se analiza la estabilidad estructural del borato de tántalo (TaBO4) en un intervalo de presiones desde presión atmosférica hasta 69.3 gigapascales. Para ello, se realizan experimentos de difracción de rayos X de compresión dinámica en los sincrotrones ALBA y DIAMOND light source. Los datos obtenidos son analizados mediante un refinamiento Rietveld. Los resultados validan el potencial de este material como un posible candidato para la construcción de sensores de alta presión., [EN] Research in new advanced materials is a crucial enterprise, not only for the chemical and pharmaceutical industries, but for scientific community. Borates are chemical compounds with very interesting mechanical and optical properties. When doped with lanthanoids or actinoids, borates acquire unique luminescent properties. To leverage such properties in a highperformance pressure sensor, it is necessary to analyze the properties of the material under relevant load conditions and to ensure its stability. In this work, we study the structural evolution of tantalum borate (TaBO4) across a wide pressure range, from atmospheric pressure up to 69.3 gigapascal. To this end, X-ray diffraction experiments under dynamic compression were conducted at the ALBA and DIAMOND light source synchrotron. The data generated were analyzed using a Rietveld refinement strategy. Our results validate the potential of tantalum borate for use as a high-pressure sensor, [CA] La cerca de nous compostos químics amb propietats cada vegada més específiques representa un dels reptes més significatius no sols per a la industria químico-farmecèutica, sinó per al món científic en general. Els borats són compostos amb molt bones prestacions mecàniques i òptiques que, dopats amb terres rares adquireixen propietats luminescents úniques. Per a l’aplicació d’aquestes propietats extraordinàries en un sensor d’elevades prestacions és necessari assegurar la estabilitat estructural del compost i analitzar el comportament d’aquest baix les condicions d’operació. En aquest treball s’analitza la estabilitat estructural del borat de tàntal (TaBO4) en un interval de pressions des de la pressió atmosfèrica fins a 69.3 gigapascals. Amb tal fi, es realitzen experiments de difracció de raigs X de compressió dinàmica en los sincrotrones ALBA y DIAMOND light source. Les dades obtingudes són analitzades amb un refinament Rietveld. Els resultats validen el potencial d’aquest material com a un plausible candidat per a la construcció de sensors d’altes pressions.

[ES] La búsqueda de nuevos compuestos químicos con propiedades específicas representa uno de los retos más significativos, no sólo para la industria químico-farmacéutica, sino para el mundo científico en general. Los boratos son compuestos con muy buenas prestaciones mecánicas y ópticas que, dopados con tierras raras adquieren propiedades luminiscentes únicas. Para la aplicación de estas propiedades extraordinarias en un sensor de altas prestaciones es necesario garantizar la estabilidad estructural del compuesto y analizar su comportamiento bajo las condiciones de operación. En este trabajo se analiza la estabilidad estructural del borato de tántalo (TaBO4) en un intervalo de presiones desde presión atmosférica hasta 69.3 gigapascales. Para ello, se realizan experimentos de difracción de rayos X de compresión dinámica en los sincrotrones ALBA y DIAMOND light source. Los datos obtenidos son analizados mediante un refinamiento Rietveld. Los resultados validan el potencial de este material como un posible candidato para la construcción de sensores de alta presión., [EN] Research in new advanced materials is a crucial enterprise, not only for the chemical and pharmaceutical industries, but for scientific community. Borates are chemical compounds with very interesting mechanical and optical properties. When doped with lanthanoids or actinoids, borates acquire unique luminescent properties. To leverage such properties in a highperformance pressure sensor, it is necessary to analyze the properties of the material under relevant load conditions and to ensure its stability. In this work, we study the structural evolution of tantalum borate (TaBO4) across a wide pressure range, from atmospheric pressure up to 69.3 gigapascal. To this end, X-ray diffraction experiments under dynamic compression were conducted at the ALBA and DIAMOND light source synchrotron. The data generated were analyzed using a Rietveld refinement strategy. Our results validate the potential of tantalum borate for use as a high-pressure sensor, [CA] La cerca de nous compostos químics amb propietats cada vegada més específiques representa un dels reptes més significatius no sols per a la industria químico-farmecèutica, sinó per al món científic en general. Els borats són compostos amb molt bones prestacions mecàniques i òptiques que, dopats amb terres rares adquireixen propietats luminescents úniques. Per a l’aplicació d’aquestes propietats extraordinàries en un sensor d’elevades prestacions és necessari assegurar la estabilitat estructural del compost i analitzar el comportament d’aquest baix les condicions d’operació. En aquest treball s’analitza la estabilitat estructural del borat de tàntal (TaBO4) en un interval de pressions des de la pressió atmosfèrica fins a 69.3 gigapascals. Amb tal fi, es realitzen experiments de difracció de raigs X de compressió dinàmica en los sincrotrones ALBA y DIAMOND light source. Les dades obtingudes són analitzades amb un refinament Rietveld. Els resultats validen el potencial d’aquest material com a un plausible candidat per a la construcció de sensors d’altes pressions.

[ES] El presente proyecto es de ejecución material, al tratarse de un producto industrial. Este proyecto tiene por objeto el diseño de dos mecanismos de presurización para una celda de yunques de diamante. Los dos mecanismos (uno en el exterior y otro en el interior de un criostato) tienen que ajustar con cuatro tornillos situados en la parte superior de la celda que deben ser girados de forma simultánea para ejercer la presión en el interior de la misma. Estos cuatro tornillos, que atraviesan la parte superior y se enroscan en la parte inferior, juntan entre si las dos partes (superior e inferior) de las que se compone la celda.

[ES] El presente proyecto es de ejecución material, al tratarse de un producto industrial. Este proyecto tiene por objeto el diseño de dos mecanismos de presurización para una celda de yunques de diamante. Los dos mecanismos (uno en el exterior y otro en el interior de un criostato) tienen que ajustar con cuatro tornillos situados en la parte superior de la celda que deben ser girados de forma simultánea para ejercer la presión en el interior de la misma. Estos cuatro tornillos, que atraviesan la parte superior y se enroscan en la parte inferior, juntan entre si las dos partes (superior e inferior) de las que se compone la celda.

Tesis por compendio, [EN] The dimensionality of the system plays a decisive role in the behavior of the electronic dynamics of interacting electrons. In particular, the quasi-2D dimensionality is responsible for the unusual behavior observed in graphene-like materials and layered van-der-Waal systems. Moreover, such effects are also observed for superconducting materials of high critical temperature, even in the normal state, due to their low-dimensionality. The experimental study of graphene triggered a growing attention to respective electronic properties, because the honeycomb lattice defines a band structure with two nodal points in the Brillouin zone which determines a relativistic Dirac-type electronic dynamics. Within a theoretical framework, many properties of single-layer graphene have been studied to allow further characterization of this material. These properties are unconventional due to the unique band structure of graphene, which is described in terms of Dirac fermions, creating links with certain theories of particle physics. In fact, several theoretical groups have employed phenomenological models inspired in quantum cromodynamics (i.e. Nambu-Jona Lassino and Gross-Neveu models) applied to the study of graphene properties. These properties are responsible for the unusual phenomena, such as the fractional Hall effect, which allows the possibility for magnetic catalysis of an excitonic gap, ferromagnetism and superconductivity. The research of high critical temperature superconductors with impurity centers is significant for understanding the underlying physics of such disordered systems. While the cuprate family present insulating properties in the pristine state, the undoped iron pnictides (i.e. LaOFeAs) show a semi-metallic behavior. Inspite these diferences, both compounds are layered structures, where the superconducting state is supported by a quasi-2D square lattice. While for iron pnictides this state is formed by the FeAs layer, the cuprate superconducting state, [ES] La dimensionalidad de un sistema juega un papel fundamental en la conducta de la dinámica de los electrones que interactúan. En particular, la dimensionalidad cuasi-2D es responsable del comportamiento inusual observado en materiales de tipo grafeno y sistemas laminares basados en enlaces de tipo van der Waals. Además, estos efectos también se observan en materiales superconductores de alta temperatura crítica, incluso en el estado normal, debido a su baja dimensionalidad. El estudio experimental del grafeno provocó una atención creciente a sus propie-dades electrónicas, porque su estructura en forma de panal de abejas da lugar a una estructura de bandas con dos puntos nodales en la zona de Brillouin que determina una dinámica electrónica relativista de tipo Dirac. En el plano teórico, muchas propiedades del grafeno de una sola capa se han estudiado para permitir una mayor caracterización de este material. Estas propiedades son poco convencionales debido a la singular estructura de bandas del grafeno, que se describe en términos de fermiones de Dirac, lo que crea vínculos con ciertas teorías de la física de partículas. De hecho, varios grupos teóricos han empleado modelos fenomenológicos inspirados en la cromodinámica cuántica (es decir, los modelos Nambu-Jona Lassino y Gross-Neveu) aplicados al estudio de las propiedades del grafeno. Estas propiedades son responsables de inusuales fenómenos, como el efecto Hall fraccionario, que permite la posibilidad de catálisis magnética de un gap excitónico, ferromagnetismo y superconductividad. La investigación de superconductores de alta temperatura crítica con centros de impurezas es importante para comprender la física subyacente de tales sistemas desordenados. Mientras que la familia de los cupratos presenta propiedades aislantes en estado prístino, los pnictogenuros de hierro sin dopar (es decir, LaOFeAs) muestran un comportamiento semimetálico. A pesar de estas diferencias, ambos compuestos son estructuras en capas, [CAT] La dimensionalitat d'un sistema juga un paper fonamental en la conducta de la dinámica dels electrons que interactúen. En particular, la dimensionalitat cuasi-2D és responsable del comportament inusual observat a materials de tipus grafè i sistemes laminars basats en enllaços de tipus van der Waals. A més a més, aquestos efectes també s'observen a materials superconductors d'alta temperatura crítica, inclús al seu estat normal, degut a la seua baixa dimensionalitat. L'estudi experimental del grafè va produir una atenció creixent a les seues propietats electròniques, perque la seua estructura en forma de panal d'abelles dona lloc a una estructura de bandes amb dos punts nodals a la zona de Brillouin que determinen una dinámica electrónica relativista de tipus Dirac. Al planol teòric, moltes propietats del grafè d'una sola capa s'han estudiat per a permetre una major caracterizació d'aquest material. Aquestes propietat són poc convencionals degut a la singular estructura de bandes del grafè, que es descriu mitjançant fermions de Dirac. Aquestos fermions permeten establir víncles amb certes teories de la física de particles. De fet, alguns grups teòrics han empleat models fenomenològics inspirats a la cromodinàmica quàntica (es a dir, els models Nambu-Jona Lassino i Gross-Neveu) aplicats a l'estudi de les propietats del grafè. Aquestes propietats són responsables d'inusuals fenómens, com l'efecte Hall fraccionari, que permet la possibilitat de catálisi magnètica d'un gap excitònic, ferromagnetisme i superconductivitat. La investigació de superconductors d'alta temperatura crítica amb centres d'impureses és important per a comprendre la física subjacent de tals sistemes desordenats. Mentre que la família dels cuprats presenta propietats aïllants en estat pristí, els pnictogenurs de ferro sense dopar (és a dir, LaOFeAs) mostren un comportament semimetálico. Malgrat aquestes diferències, tots dos compostos són estructures en capes, on l'estat superconductor està rec

[ES] El presente trabajo plantea el diseño y la implementación de un sistema de adquisición de datos biométrico para la evaluación de pilotos de competición. Este sistema permitirá la monitorización en tiempo real de las señales biométricas del piloto. Con dichos datos se pretende interpretar, mediante la fusión de sensores obtenida, un estado global psicofísico del piloto durante los diferentes eventos que transcurren en la realización de las pruebas orientadas a las situaciones de conducción que sufre un piloto de competición en pista. Este sistema se va a poner en práctica en el simulador G-UPV de conducción profesional para pilotos de competición, perteneciente al Instituto de Diseño y Fabricación (IDF), el cual cuenta con un cockpit monocasco apoyado en una plataforma dinámica de 6 GDL, con la que se pretende emular la aceleración que experimenta un piloto en una pista real, además de las condiciones del asfalto. Con este sistema se pretende medir e interpretar una combinación de señales que se consideran potencialmente relevantes para la determinación del estado psicofísico del piloto, entre las cuales se incluyen señales de electrocardiograma, electromiograma, electrodermograma, ritmo respiratorio, temperatura corporal, saturación oxígeno en sangre y la aceleración que experimenta el piloto en los ejes xyz, [EN] The present work proposes the design and implementation of a biometric data acquisition system for the evaluation of competition pilots. This system will allow real-time monitoring of the pilot's biometric signals. With these data, the aim is to interpret, by means of the sensor fusion obtained, a global psychophysical state of the pilot during the different events that take place in carrying out tests aimed at the driving situations suffered by a competition pilot on the track. This system will be implemented in the G-UPV professional driving simulator for competition pilots, belonging to the Institute of Design and Manufacturing (IDF), which has a monocoque cockpit supported on a dynamic 6 GDL platform, with which is intended to emulate the acceleration experienced by a pilot on a real track, in addition to the asphalt conditions. This system aims to measure and interpret a combination of signals that are considered potentially relevant for the determination of the pilot's psychophysical state, including electrocardiogram, electromyogram, electrodermogram, respiratory rhythm, body temperature, blood oxygen saturation and the acceleration that pilot experiences on the xyz axes.

[ES] La búsqueda de nuevos compuestos químicos con propiedades específicas representa uno de los retos más significativos, no sólo para la industria químico-farmacéutica, sino para el mundo científico en general. Los boratos son compuestos con muy buenas prestaciones mecánicas y ópticas que, dopados con tierras raras adquieren propiedades luminiscentes únicas. Para la aplicación de estas propiedades extraordinarias en un sensor de altas prestaciones es necesario garantizar la estabilidad estructural del compuesto y analizar su comportamiento bajo las condiciones de operación. En este trabajo se analiza la estabilidad estructural del borato de tántalo (TaBO4) en un intervalo de presiones desde presión atmosférica hasta 69.3 gigapascales. Para ello, se realizan experimentos de difracción de rayos X de compresión dinámica en los sincrotrones ALBA y DIAMOND light source. Los datos obtenidos son analizados mediante un refinamiento Rietveld. Los resultados validan el potencial de este material como un posible candidato para la construcción de sensores de alta presión., [EN] Research in new advanced materials is a crucial enterprise, not only for the chemical and pharmaceutical industries, but for scientific community. Borates are chemical compounds with very interesting mechanical and optical properties. When doped with lanthanoids or actinoids, borates acquire unique luminescent properties. To leverage such properties in a highperformance pressure sensor, it is necessary to analyze the properties of the material under relevant load conditions and to ensure its stability. In this work, we study the structural evolution of tantalum borate (TaBO4) across a wide pressure range, from atmospheric pressure up to 69.3 gigapascal. To this end, X-ray diffraction experiments under dynamic compression were conducted at the ALBA and DIAMOND light source synchrotron. The data generated were analyzed using a Rietveld refinement strategy. Our results validate the potential of tantalum borate for use as a high-pressure sensor, [CA] La cerca de nous compostos químics amb propietats cada vegada més específiques representa un dels reptes més significatius no sols per a la industria químico-farmecèutica, sinó per al món científic en general. Els borats són compostos amb molt bones prestacions mecàniques i òptiques que, dopats amb terres rares adquireixen propietats luminescents úniques. Per a l’aplicació d’aquestes propietats extraordinàries en un sensor d’elevades prestacions és necessari assegurar la estabilitat estructural del compost i analitzar el comportament d’aquest baix les condicions d’operació. En aquest treball s’analitza la estabilitat estructural del borat de tàntal (TaBO4) en un interval de pressions des de la pressió atmosfèrica fins a 69.3 gigapascals. Amb tal fi, es realitzen experiments de difracció de raigs X de compressió dinàmica en los sincrotrones ALBA y DIAMOND light source. Les dades obtingudes són analitzades amb un refinament Rietveld. Els resultats validen el potencial d’aquest material com a un plausible candidat per a la construcció de sensors d’altes pressions.

[ES] El presente trabajo plantea el diseño y la implementación de un sistema de adquisición de datos biométrico para la evaluación de pilotos de competición. Este sistema permitirá la monitorización en tiempo real de las señales biométricas del piloto. Con dichos datos se pretende interpretar, mediante la fusión de sensores obtenida, un estado global psicofísico del piloto durante los diferentes eventos que transcurren en la realización de las pruebas orientadas a las situaciones de conducción que sufre un piloto de competición en pista. Este sistema se va a poner en práctica en el simulador G-UPV de conducción profesional para pilotos de competición, perteneciente al Instituto de Diseño y Fabricación (IDF), el cual cuenta con un cockpit monocasco apoyado en una plataforma dinámica de 6 GDL, con la que se pretende emular la aceleración que experimenta un piloto en una pista real, además de las condiciones del asfalto. Con este sistema se pretende medir e interpretar una combinación de señales que se consideran potencialmente relevantes para la determinación del estado psicofísico del piloto, entre las cuales se incluyen señales de electrocardiograma, electromiograma, electrodermograma, ritmo respiratorio, temperatura corporal, saturación oxígeno en sangre y la aceleración que experimenta el piloto en los ejes xyz, [EN] The present work proposes the design and implementation of a biometric data acquisition system for the evaluation of competition pilots. This system will allow real-time monitoring of the pilot's biometric signals. With these data, the aim is to interpret, by means of the sensor fusion obtained, a global psychophysical state of the pilot during the different events that take place in carrying out tests aimed at the driving situations suffered by a competition pilot on the track. This system will be implemented in the G-UPV professional driving simulator for competition pilots, belonging to the Institute of Design and Manufacturing (IDF), which has a monocoque cockpit supported on a dynamic 6 GDL platform, with which is intended to emulate the acceleration experienced by a pilot on a real track, in addition to the asphalt conditions. This system aims to measure and interpret a combination of signals that are considered potentially relevant for the determination of the pilot's psychophysical state, including electrocardiogram, electromyogram, electrodermogram, respiratory rhythm, body temperature, blood oxygen saturation and the acceleration that pilot experiences on the xyz axes.

Este artículo muestra el análisis de los impactos de las inversiones en activos de largo plazo, deuda en moneda extranjera y de la paridad peso-dólar en la probabilidad de obtener ganancias cambiarias durante el periodo de Covid-19, en comparación con el periodo previo (1990-2019). Mediante especificaciones logit, se encuentra que antes de la pandemia los activos intangibles, la inversión en activos fijos durante los doce meses recientes, la paridad peso-dólar y la deuda en dólares reducen la probabilidad de obtener ganancias cambiarias; únicamente las inversiones en inmuebles, planta y equipo tiene un efecto positivo en la probabilidad de obtener ganancias cambiarias en el caso de una depreciación monetaria. Durante el periodo asociado con la contingencia, únicamente fueron significativas las inversiones recientes en activos fijos, manteniendo la relación inversa observada en el periodo previo.

Este artículo muestra el análisis de los impactos de las inversiones en activos de largo plazo, deuda en moneda extranjera y de la paridad peso-dólar en la probabilidad de obtener ganancias cambiarias durante el periodo de Covid-19, en comparación con el periodo previo (1990-2019). Mediante especificaciones logit, se encuentra que antes de la pandemia los activos intangibles, la inversión en activos fijos durante los doce meses recientes, la paridad peso-dólar y la deuda en dólares reducen la probabilidad de obtener ganancias cambiarias; únicamente las inversiones en inmuebles, planta y equipo tiene un efecto positivo en la probabilidad de obtener ganancias cambiarias en el caso de una depreciación monetaria. Durante el periodo asociado con la contingencia, únicamente fueron significativas las inversiones recientes en activos fijos, manteniendo la relación inversa observada en el periodo previo.

Given the crisis caused by the Covid-19 pandemic, it is necessary to determine if there is fiscal space to carry out an anti-cyclical policy in Mexico that in the short term will facilitate economic recovery. In this paper we seek to empirically approximate the existence of fiscal space by estimating the sustainability gap of the primary balance, making use of the forecasts of a cointegrated macroeconomic model. Specifically, the gap is estimated using the trend forecast of nominal GDP, the nominal interest rate, and the primary balance in the coming months. Our findings suggest that there is not enough fiscal space to implement an expansionary policy based on public debt over the next year, even though GDP growth will remain on a gradual recovery path and the interest rate could remain longer. lower than before the crisis. Thus, public spending must be managed efficiently to avoid a large increase in the deficit and greater risks to financial stability., Dada la crisis por la pandemia de Covid-19, se requiere determinar si existe espacio fiscal para llevar a cabo una política anticíclica en México que en el corto plazo facilite la recuperación económica. En este artículo buscamos aproximar empíricamente la existencia de espacio fiscal mediante la estimación de la brecha de sostenibilidad del balance primario, haciendo uso de los pronósticos de un modelo macroeconómico cointegrado. Específicamente, se estima la brecha usando el pronóstico tendencial del PIB nominal, la tasa de interés nominal y el saldo primario en los próximos meses. Nuestros hallazgos sugieren que no hay espacio fiscal suficiente para implementar una política expansiva basada en deuda pública durante el próximo año, incluso a pesar de que el crecimiento del PIB se mantendrá en una ruta de recuperación gradual y de que la tasa de interés pudiera mantenerse más baja que antes de la crisis. Así, el gasto público deberá administrarse de manera eficiente para evitar un gran aumento en el déficit y mayores riesgos a la estabilidad financiera.

Dada la crisis por la pandemia de Covid-19, se requiere determinar si existe espacio fiscal para llevar a cabo una política anticíclica en México que en el corto plazo facilite la recuperación económica. En este artículo buscamos aproximar empíricamente la existencia de espacio fiscal mediante la estimación de la brecha de sostenibilidad del balance primario, haciendo uso de los pronósticos de un modelo macroeconómico cointegrado. Específicamente, se estima la brecha usando el pronóstico tendencial del PIB nominal, la tasa de interés nominal y el saldo primario en los próximos meses. Nuestros hallazgos sugieren que no hay espacio fiscal suficiente para implementar una política expansiva basada en deuda pública durante el próximo año, incluso a pesar de que el crecimiento del PIB se mantendrá en una ruta de recuperación gradual y de que la tasa de interés pudiera mantenerse más baja que antes de la crisis. Así, el gasto público deberá administrarse de manera eficiente para evitar un gran aumento en el déficit y mayores riesgos a la estabilidad financiera. TRANSLATE with x EnglishArabicHebrewPolishBulgarianHindiPortugueseCatalanHmong DawRomanianChinese SimplifiedHungarianRussianChinese TraditionalIndonesianSlovakCzechItalianSlovenianDanishJapaneseSpanishDutchKlingonSwedishEnglishKoreanThaiEstonianLatvianTurkishFinnishLithuanianUkrainianFrenchMalayUrduGermanMalteseVietnameseGreekNorwegianWelshHaitian CreolePersian // TRANSLATE with COPY THE URL BELOW Back EMBED THE SNIPPET BELOW IN YOUR SITE Enable collaborative features and customize widget: Bing Webmaster PortalBack

Given the crisis caused by the Covid-19 pandemic, it is necessary to determine if there is fiscal space to carry out an anti-cyclical policy in Mexico that in the short term will facilitate economic recovery. In this paper we seek to empirically approximate the existence of fiscal space by estimating the sustainability gap of the primary balance, making use of the forecasts of a cointegrated macroeconomic model. Specifically, the gap is estimated using the trend forecast of nominal GDP, the nominal interest rate, and the primary balance in the coming months. Our findings suggest that there is not enough fiscal space to implement an expansionary policy based on public debt over the next year, even though GDP growth will remain on a gradual recovery path and the interest rate could remain longer. lower than before the crisis. Thus, public spending must be managed efficiently to avoid a large increase in the deficit and greater risks to financial stability., Dada la crisis por la pandemia de Covid-19, se requiere determinar si existe espacio fiscal para llevar a cabo una política anticíclica en México que en el corto plazo facilite la recuperación económica. En este artículo buscamos aproximar empíricamente la existencia de espacio fiscal mediante la estimación de la brecha de sostenibilidad del balance primario, haciendo uso de los pronósticos de un modelo macroeconómico cointegrado. Específicamente, se estima la brecha usando el pronóstico tendencial del PIB nominal, la tasa de interés nominal y el saldo primario en los próximos meses. Nuestros hallazgos sugieren que no hay espacio fiscal suficiente para implementar una política expansiva basada en deuda pública durante el próximo año, incluso a pesar de que el crecimiento del PIB se mantendrá en una ruta de recuperación gradual y de que la tasa de interés pudiera mantenerse más baja que antes de la crisis. Así, el gasto público deberá administrarse de manera eficiente para evitar un gran aumento en el déficit y mayores riesgos a la estabilidad financiera.

The aim of this research paper is to show evidence of the influence of economic and fundamental company variables on the exchange rate gains derived from foreign currency mismatches. By means of panel data models, quarterly data of 84 Mexican companies are analyzed during the period 1990-2018. The results show that the US dollar and GDP, as well as six of the fundamental variables are significant, four of the latter studied for the first time. It is recommended to take in account these variables because they can be useful to guide the making decisions and the design of operation, investment and financing strategies to prevent foreign exchange losses. Obviously, the general scope of this study limits its direct applicability for particular cases, rendering necessary to analyze the specific situation based on the findings here reported. In Mexico, the subject had not been studied in the depth presented in this document, nor had these variables been analyzed. It is concluded that foreign exchange gains reflect relevant aspects of the economy, so as the importance of managerial decisions, El objetivo de este artículo de investigación es mostrar la evidencia de la influencia de variables económicas y fundamentales de empresa sobre las ganancias cambiarias derivadas de los descalces de moneda extranjera. Mediante modelos para datos en panel, se analizan datos trimestrales de 84 empresas mexicanas durante el periodo 1990-2018. Los resultados muestran que el dólar estadunidense y el PIB, así como seis de las variables fundamentales son significativas, cuatro de éstas últimas estudiadas por primera vez. Se recomienda considerar estas variables pues pueden ser útiles para orientar la toma de decisiones y el diseño de estrategias de operación, inversión y financiamiento para evitar pérdidas cambiarias. Obviamente, el alcance general de este estudio limita su aplicabilidad directa a casos particulares, haciendo necesario analizar la situación específica partiendo de los hallazgos aquí reportados. En México no se había estudiado el tema con la profundidad que se presenta en este documento, tampoco se habían analizado estas variables. Se concluye que las ganancias cambiarias reflejan aspectos relevantes de la economía, así como la importancia de las decisiones administrativas.

This paper compares the dependency structure of the fall of equity returns in the three largest Latin American countries ́stock markets. The fall caused by the emergence of the Covid-19 pandemic is compared with other episodes of financial crises that occurred since the last decade of the 20th century. Using bivariate Clayton copulas with regimes driven by Markovian changes, the degree of dependence in the lower tail of the distribution is studied, finding different patterns of dependence that suggest that the markets of Brazil and Mexico are more dependent than the Argentinean. A limitation is that the economic recovery derived from the pandemic has not yet ended, so the results in relation to the effects of Covid-19 could only be considered provisional. However, this article contributes to understanding the process of globalization and the possible consequences for the Latin American stock markets’ internationalization and the investment in the region, including portfolio investment., Este artículo muestra el análisis efectuado para comparar la estructura de dependencia de la caída de los rendimientos accionarios de los mercados de los tres países más grandes de América Latina (Argentina, Brasil y México) en los rendimientos del mercado de valores estadounidense. Se compara la caída propiciada por el surgimiento de la pandemia de Covid-19 con otros episodios de crisis financieras acaecidas a partir de la última década del siglo XX. Mediante cópulas bivariadas de Clayton con regímenes conducidos por cambios markovianos, se estudia el grado de la dependencia en la cola inferior de la distribución, encontrando patrones distintos de dependencia que sugieren que los mercados de Brasil y México son más dependientes que el argentino. Una limitante es que no termina aún la recuperación económica derivada de la pandemia, por lo que los resultados en relación con los efectos de la Covid-19 podrían considerarse sólo como provisionales, sin embargo, este artículo contribuye a entender cómo ha sido el proceso de globalización y las posibles consecuencias para la internacionalización de los mercados bursátiles latinoamericanos y la inversión en la región, incluyendo la de portafolio.

Este artículo muestra el análisis de los impactos de las inversiones en activos de largo plazo, deuda en moneda extranjera y de la paridad peso-dólar en la probabilidad de obtener ganancias cambiarias durante el periodo de Covid-19, en comparación con el periodo previo (1990-2019). Mediante especificaciones logit, se encuentra que antes de la pandemia los activos intangibles, la inversión en activos fijos durante los doce meses recientes, la paridad peso-dólar y la deuda en dólares reducen la probabilidad de obtener ganancias cambiarias; únicamente las inversiones en inmuebles, planta y equipo tiene un efecto positivo en la probabilidad de obtener ganancias cambiarias en el caso de una depreciación monetaria. Durante el periodo asociado con la contingencia, únicamente fueron significativas las inversiones recientes en activos fijos, manteniendo la relación inversa observada en el periodo previo.

Este artículo muestra el análisis de los impactos de las inversiones en activos de largo plazo, deuda en moneda extranjera y de la paridad peso-dólar en la probabilidad de obtener ganancias cambiarias durante el periodo de Covid-19, en comparación con el periodo previo (1990-2019). Mediante especificaciones logit, se encuentra que antes de la pandemia los activos intangibles, la inversión en activos fijos durante los doce meses recientes, la paridad peso-dólar y la deuda en dólares reducen la probabilidad de obtener ganancias cambiarias; únicamente las inversiones en inmuebles, planta y equipo tiene un efecto positivo en la probabilidad de obtener ganancias cambiarias en el caso de una depreciación monetaria. Durante el periodo asociado con la contingencia, únicamente fueron significativas las inversiones recientes en activos fijos, manteniendo la relación inversa observada en el periodo previo.

Este artículo presenta los resultados obtenidos en un estudio realizado por la Facultad de Ingeniería Mecánica y Eléctrica de la Universidad Veracruzana (UV) acerca de los saberes mínimos requeridos por estudiantes del área técnica que acceden del nivel medio superior al superior. A través de un examen diagnóstico del área físico-matemática y una revisión curricular, se documenta inicialmente la pertinencia de la forma en que los alumnos se afrontan reiteradamente a la repetición de contenidos desde el bachillerato hasta los primeros semestres de la universidad. Se problematiza la conveniencia y las dificultades que este hecho representa; se pone en evidencia ante las autoridades competentes un ajuste de contenidos temáticos irrepetibles entre las escuelas de nivel bachillerato y las licenciaturas en Ingeniería. El objetivo final es demostrar la necesidad de creación de espacios dentro de los contenidos temáticos en la universidad, incrementar la calidad en los conocimientos y ofrecer una mejor preparación matemática, tecnológica y cultural para el estudiante.

El objetivo de este proyecto es el desarrollo de un transductor de torque de bajo costo con capacidad de realizar el envío de datos, en tiempo real, a múltiples dispositivos, por medio de la aplicación de protocolos de comunicación del internet de las cosas. Para hacer posible este desarrollo, como primer paso fue necesario recopilar información bibliográfica sobre desarrollos similares, el segundo paso consistió en definir los parámetros de funcionamiento bajo los cuales se diseñaría el prototipo. Definidos los parámetros de diseño, como tercer paso, se procedió a hacer una comparación cuantitativa de los diferentes microcontroladores de bajo costo con el fin de determinar cual es el más adecuado, para luego proceder a realizar el diseño conceptual de los diferentes componentes del transductor, mediante la herramienta CAD. Con los diseños preliminares concluidos, se procedió a realizar la solución de los modelos matemáticos que describen el comportamiento del torque y al mismo tiempo, se hizo la simulación por elementos finitos del transductor. Los resultados obtenidos de los modelos matemáticos y los resultados de la simulación, fueron comparados para determinar si la simulación describe los efectos sufridos por el material. Completados los análisis de esfuerzos del transductor se procedió a generar los planos de fabricación y, al mismo tiempo, se inició el desarrollo de las interfaces de comunicación mediante el empleo de protocolo de comunicación del internet de las cosas. Finalmente, se concluyó que el microcontrolador que más se adapta a las necesidades establecidas para el desarrollo del transductor es el RASPBERRY PI ZERO W, porque no necesita módulos externos para el establecimiento de conexiones remotas. Los modelos matemáticos y simulaciones por elementos 11 finitos, demostraron que la aleación de aluminio 6061 T6 es capaz de soportar las condiciones de trabajo planteadas. Además, fue posible establecer comunicación de datos entre el RASPBERRY y un comput, The objective of this project was to develop a low-cost torque transducer that had the ability to send data in real time to multiple devices by applying Internet of Things communication protocols. To begging with the development, it was first necessary to collect bibliographic information to be aware of similar developments, the second step to follow was to define the operating parameters under which the prototype would be designed. Once the design parameters were defined, the quantitative comparison of the different low-cost microcontrollers was carried out to determine which is the most appropriate, with this step concluded, the conceptual design was carried out using the CAD tool of the different components of the transducer. With the preliminary designs completed, we proceeded to carry out the solution of the mathematical models that describe the behavior of the torque and at the same time the finite element simulation of the transducer was performed. The results obtained from the mathematical models and the simulation results were compared to determine if the simulation describes the effects suffered by the material. Once the stress analysis of the transducer was completed, the process of generating the manufacturing drawings began, and at the same time the process of developing the communication interfaces began using the Internet of Things communication protocol. It was concluded that the microcontroller that is most appropriated to the needs is the RASPBERRY PI ZERO W, because it does not require an externals module for establishing remote connections. Mathematical models and finite element simulations showed that the 6061 T6 aluminum alloy is capable of withstanding the proposed working conditions and it was possible to establish data communication between the RASPBERRY and a computer using an interface in LABVIEW using the TCP / IP communication protocol.