High efficiency and high linearity power amplifier design

The optimum high-frequency Class-F loading conditions are inferred, accounting for the effects of actual output device behavior, and deriving useful charts for an effective design. The important role of the biasing point selection is stressed, demonstrating that it must be different from the Class-B theoretical one to get the expected improvement. The IMD behavior of the Class-F amplifier is presented and the large-signal sweet-spot origin in the IMD output characteristics is discussed, together with possible strategies to improve intermodulation distortion performances. The control of the sweet spot position is demonstrated via proper terminating impedances, both at fundamental and harmonic frequencies and low frequencies

Substrate-controlled Michael additions of titanium enolates from chiral α-benzyloxy ketones to conjugated nitroalkenes

Lewis acid-mediated substrate-controlled reactions of the titanium(IV) enolates of chiral a-benzyloxy ketones with conjugated nitroalkenes give the 2,4-anti-4,5-syn Michael adducts in good yields and diastereomeric ratios. The supplementary Lewis acid plays a key role in the outcome of these transformations, probably as a consequence of the formation of bimetallic enolates that increase the reactivity of the enolate and direct the approach of the nitroalkene. Importantly, the most appropriate Lewis acid depends on the electrophilic partner: TiCl4 is the most suitable Lewis acid for b-aryl nitroalkenes while the best results for b-alkyl nitroalkenes are obtained with SnCl4. Finally, the nitro group of the resultant compounds can be converted into the corresponding amino, oxime, and nitrile groups under mild conditions, which permits the synthesis of a variety of enantiomerically pure derivatives with excellent yields

Application of Artificial Intelligence Algorithms Within the Medical Context for Non-Specialized Users: the CARTIER-IA Platform

The use of advanced algorithms and models such as Machine Learning, Deep Learning and other related approaches of Artificial Intelligence have grown in their use given their benefits in different contexts. One of these contexts is the medical domain, as these algorithms can support disease detection, image segmentation and other multiple tasks. However, it is necessary to organize and arrange the different data resources involved in these scenarios and tackle the heterogeneity of data sources. This work presents the CARTIER-IA platform: a platform for the management of medical data and imaging. The goal of this project focuses on providing a friendly and usable interface to organize structured data, to visualize and edit medical images, and to apply Artificial Intelligence algorithms on the stored resources. One of the challenges of the platform design is to ease these complex tasks in a way that non-AI-specialized users could benefit from the application of AI algorithms without further training. Two use cases of AI application within the platform are provided, as well as a heuristic evaluation to assess the usability of the first version of CARTIER-IA. Year of Publication 2021 Journal International Journal of Interactive Multimedia and Artificial Intelligence Volume 6 Issue Regular Issue Number 6 Number of Pages 46-53 Date Published 06/2021 ISSN Number 1989-1660 URL https://www.ijimai.org/journal/sites/default/files/2021-05/ijimai_6_6_5.pdf DOI 10.9781/ijimai.2021.05.005 DOI Google Scholar BibTeX EndNote X3 XML EndNote 7 XML Endnote tagged Marc RIS Attachment ijimai_6_6_5.pdf 932.11 K

From thermal to electroactive graphene nanofluids

Here, we describe selected work on the development and study of nanofluids based on graphene and reduced graphene oxide both in aqueous and organic electrolytes. A thorough study of thermal properties of graphene in amide organic solvents (N,N-dimethylformamide, N,N-dimethylacetamide, and N-methyl-2-pyrrolidone) showed a substantial increase of thermal conductivity and specific heat upon graphene integration in those solvents. In addition to these thermal studies, our group has also pioneered a distinct line of work on electroactive nanofluids for energy storage. In this case, reduced graphene oxide (rGO) nanofluids in aqueous electrolytes were studied and characterized by cyclic voltammetry and charge-discharge cycles (i.e., in new flow cells). In addition, hybrid configurations (both hybrid nanofluid materials and hybrid cells combining faradaic and capacitive activities) were studied and are summarized here

KoopaML, a Machine Learning platform for medical data analysis

Machine Learning allows facing complex tasks related to data analysis with big datasets. This Artificial Intelligence branch allows not technical contexts to get benefits related to data processing and analysis. In particular, in medicine, medical professionals are increasingly interested in Machine Learning to identify patterns in clinical cases and make predictions regarding health issues. However, many do not have the necessary programming or technological skills to perform these tasks. Many different tools focus on developing Machine Learning pipelines, from libraries for developers and data scientists to visual tools for experts or platforms to learn. However, we have identified some requirements in the medical context that raise the need to create a customized platform adapted to end-user found in this context. This work describes the design process and the first version of KoopaML, an ML platform to bridge the data science gaps of physicians while automatizing Machine Learning pipelines. The platform is focused on enhanced interactivity to improve the engagement of physicians while still providing all the benefits derived from the introduction of Machine Learning pipelines in medical departments, as well as integrated ongoing training during the use of the tool’s features

Investigating the potential of Sentinel-2 configuration to predict the quality of Mediterranean permanent grasslands in open woodlands

The assessment of pasture quality in permanent grasslands is essential for their conservation and management, as it can contribute to making real-time decisions for livestock management. In this study, we assessed the potential of Sentinel-2 configuration to predict forage quality in high diverse Mediterranean permanent grasslands of open woodlands. We evaluated the performance of Partial Least Squares Regression (PLS) models to predict crude protein (CP), neutral detergent fibre (NDF), acid detergent fibre (ADF) and enzyme digestibility of organic matter (EDOM) by using three different reflectance datasets: (i) laboratory measurements of reflectance of dry and ground pasture samples re-sampled to Sentinel-2 configuration (Spec-lab) (ii) field in-situ measurements of grasslands canopy reflectance resampled to Sentinel-2 configuration (Spec-field); (iii) and Bottom Of Atmosphere Sentinel-2 imagery. For the three reflectance datasets, the models to predict CP content showed moderate performance and predictive ability. Mean R2test = 0.68 were obtained using Spec-lab data, mean R2test decreased by 0.11 with Spec-field and by 0.18 when Sentinel-2 reflectance was used. Statistics for NDF showed worse predictions than those obtained for CP: predictions produced with Spec-lab showed mean R2test = 0.64 and mean RPDtest = 1.73. The mean values of R2test = 0.50 and RPDtest = 1.54 using Sentinel-2 BOA reflectance were marginally better than the values obtained with Spec-field (mean R2test = 0.48, mean RPDtest = 1.43). For ADF and EDOM, only predictions made with Spec-lab produced acceptable results. Bands from the red-edge region, especially band 5, and the SWIR regions showed the highest contribution to estimating CP and NDF. Bands 2, blue and 4, red also seem to be important. The implementation of field spectroscopy in combination with Sentinel-2 imagery proved to be feasible to produce forage quality maps and to develop larger datasets. This study contributes to increasing knowledge of the potential and applicability of Sentinel-2 to predict the quality of Mediterranean permanent grasslands in open woodlands

Parallel evolutionary biclustering of short-term electric energy consumption

Presentación realizada en el marco del Proyecto PINV18-661: Análisis de la eficiencia energética en edificios no residenciales mediante técnicas metaheurísticas y de inteligencia artificial.CONACYT - Consejo Nacional de Ciencias y TecnologíaPROCIENCI

Application and modeling of GaN FET in 1MHz large signal bandwidth power supply for radio frequency power amplifier

In this paper, implementation and testing of non- commercial GaN HEMT in a simple buck converter for envelope amplifier in ET and EER transmission techn iques has been done. Comparing to the prototypes with commercially available EPC1014 and 1015 GaN HEMTs, experimentally demonstrated power supply provided better thermal management and increased the switching frequency up to 25MHz. 64QAM signal with 1MHz of large signal bandw idth and 10.5dB of Peak to Average Power Ratio was gener ated, using the switching frequency of 20MHz. The obtaine defficiency was 38% including the driving circuit an d the total losses breakdown showed that switching power losses in the HEMT are the dominant ones. In addition to this, some basic physical modeling has been done, in order to provide an insight on the correlation between the electrical characteristics of the GaN HEMT and physical design parameters. This is the first step in the optimization of the HEMT design for this particular application

Integrando escalas y métodos LTER para comprender la dinámica global de un espacio protegido de montaña: el Parque Nacional de Ordesa y Monte Perdido.

Los espacios protegidos, por el hecho de albergar una gran geo-biodiversidad y asegurar una baja intervención humana, constituyen lugares muy adecuados para el seguimiento de organismos y procesos a escala ecológica, así como para la obtención de series temporales largas a escala geológica. En el marco de la red LTER-España, el Parque Nacional de Ordesa y Monte Perdido (PNOMP) y el Instituto Pirenaico de Ecología-CSIC están impulsando estudios para la detección de cambios a distintas escalas mediante variados métodos y aproximaciones. Destacamos aquí los más consolidados, entre los que se encuentran los análisis de registros de sedimentos en lagos, espeleotemas en cuevas, la dinámica de uno de los pocos glaciares activos de la Península ibérica, el análisis físico-químico de aguas corrientes e ibones de alta montaña, el registro del cambio climático actual en árboles longevos, la afección que éste ejerce sobre masas actuales de pinos en el límite superior del bosque y de abetales en zonas húmedas, la matorralización de algunos pastos y los procesos mecanicistas que subyacen, la reorganización de la diversidad florística en pastos tras el abandono paulatino o drástico de la ganadería, la biodiversidad de las comunidades alpinas y la dinámica poblacional de especies amenazadas o indicadoras de hábitats o de motores de cambio global. Los seguimientos ecológicos actuales muestran que tanto el cambio climático como el de usos del suelo están teniendo una considerable trascendencia en la fisionomía y la estructura de algunos de los ambientes más icónicos y frecuentes del parque (deterioro del glaciar, termofilización de la flora en cumbres alpinas, densificación del bosque en su límite superior, pérdida de productividad en algunos pastos supraforestales, etc.). También sugieren una importante variabilidad espacial en los procesos (por ej. en el PNOMP conviven pastos matorralizados y pastos muy estables), y evidencian que los cambios observados no siempre siguen los paradigmas establecidos (por ej., las especies amenazadas mantienen dinámicas poblacionales estables). La integración de resultados parciales proporcionados por cada aproximación relativiza la importancia de las percepciones que cada estudio destaca por separado, y permite medir los cambios actuales en el marco de referencia de los cambios a escala geológica.Predecir la resistencia y resiliencia de los ecosistemas o las poblaciones de seres vivos para enfrentarse a los futuros cambios ambientales es complicado, no sólo por la falta de conocimientos disponibles sino también porque las respuestas que observamos no siempre son tan rápidas o lineales como se espera. La modelización constituye una herramienta cada vez más utilizada, pero requiere de evidencias reales para validar sus pronósticos, por lo que la observación de los procesos que actúan en el PNOMP ha de incluir un esfuerzo continuado de monitorización multiescalar y multidisciplinar de los distintos componentes de la geo, hidro-, crio- y biosfera, sin olvidar el componente humano. Entender la complejidad supone conectar las interacciones que existen entre todos los sistemas y ponderar sus efectos según las escalas de trabajo

Magnetic ionic plastic crystal: choline[FeCl4]

A novel organic ionic plastic crystal (OIPC) based on a quaternary ammonium cation and a tetrachloroferrate anion has been synthesized with the intention of combining the properties of the ionic plastic crystal and the magnetism originating from the iron incorporated in the anion. The thermal analysis of the obtained OIPC showed a solid?solid phase transition below room temperature and a high melting point above 220 1C, indicating their plastic crystalline behaviour over a wide temperature range, as well as thermal stability up to approximately 200 1C. The magnetization measurements show the presence of three-dimensional antiferromagnetic ordering below 4 K. The results from electrochemical characterization display a solid-state ionic conduction sufficiently high and stable (between 10 2.7 and 10 3.6 S cm 1 from 20 to 180 1C) for electrochemical applications