Applications of the SMS method to the design of compact optics

New ultra-thin optical designs are presented that comprise discontinuous optical sections (called channels) working in parallel (multichanneling) to provide the desired optical function. Aplanatic (a particular case of SMS-design) multichannel designs are also shown and used to explain more easily the design procedure. Typically, these multichannel devices are at least formed by three optical surfaces: one of them has discontinuities in the shape, a second one may have discontinuities in its derivative while the third one is smooth. The number of discontinuities is the same in the two first surfaces: Each channel is defined by the smooth surfaces in between the discontinuities, so that the surfaces forming each separate channel are all smooth. No diffractive effects are considered

Flexibility markets to procure system services. CoordiNet project

The article describes the objectives of the CoordiNet project, the proposed coordination schemes and architecture to allow TSOs and DSOs to procure systems services in a coordinated manner, and the platforms being developed in the three demonstrators of the project (Spain, Sweden, Greece).The efficiency and reliability of electricity systems depend, among other aspects, on an efficient collaboration between the different market participants, which require updating the roles of all agents involved. The CoordiNet project, co-funded by the EU, intends to demonstrate how TSOs and DSOs can act in a coordinated manner, to purchase and activate system services, promote the cooperation of all actors and eliminate barriers for the active participation of DERs in the market. The results of the project will help to design scalable tools and methodologies for system operators and third parties to safely connect, manage and coordinate flexibility providers. This paper describes the developments in CoordiNet to ensure the interoperability of the different markets and platforms developed by TSOs and DSOs across Europe.European Commissions' H2020 under grant agreement No 824414

Recursos flexibles para la operación de las redes de distribución y transporte: proyecto CoordiNet

La transición energética requiere de una adecuada coordinación entre el TSO y el DSO para el desarrollo y adquisición de nuevos servicios de flexibilidad en todos los niveles de tensión, a fin de integrar de forma segura y eficiente un volumen creciente de generación renovable en las redes de distribución y transporte. En este sentido, el proyecto CoordiNet está desarrollando diez proyectos de demostración en España, Grecia y Suecia, donde se analizarán diferentes alternativas para la compra de los nuevos productos y servicios de flexibilidad definidos en el proyecto. La presente comunicación presenta en detalle los demostradores españoles, liderados por Endesa y en los que participan Iberdrola y Red Eléctrica de España, además de varias empresas, centros de investigación y universidades.Este proyecto ha recibido financiación del programa de investigación e innovación Horizon 2020 de la Unión Europea en virtud del acuerdo de subvención Nº 824414

ICT architectures for TSO-DSO coordination and data exchange: a European perspective

The coordination between system operators is a key element for the decarbonization of the power system. Over the past few years, many EU-funded research projects have addressed the challenges of Transmission System Operators (TSO) and Distribution System Operators (DSO) coordination by implementing different data exchange architectures. This paper presents a review of the ICT architectures implemented for the main coordination schemes demonstrated in such projects. The main used technologies are analyzed, considering the type of data exchanged and the communication link. Finally, the paper presents the different gaps and challenges on TSO-DSO coordination related to ICT architectures that must still be faced, paying especial attention to the expected contribution of the EU-funded OneNet project on this topic. IEEECoordiNet H202

Imaging systems application of multichannel configurations

While multichannel configurations are well established for non-imaging applications, they have not been used yet for imaging applications. In this paper we present for the first time some of multichannel designs for imaging systems. The multichannel comprises discontinuous optical sections which are called channels. The phase-space representation of the bundle of rays going from the object to the image is discontinuous between channels. This phase-space ray-bundle flow is divided in as many paths as channels there are but it is a single wavefront both at the source and the target. Typically, these multichannel systems are at least formed by three optical surfaces: two of them have discontinuities (either in the shape or in the shape derivative) while the last is a smooth one. Optical surfaces discontinuities cause at the phase space the wave front split in separate paths. The number of discontinuities is the same in the two first surfaces: Each channel is defined by the smooth surfaces in between discontinuities, so the surfaces forming each separate channel are all smooth. Aplanatic multichannel designs are also shown and used to explain the design procedure

Depresión resistente al tratamiento como manifestación de adenoma de glándula suprarrenal. : Reporte de caso.

Cushing's syndrome is the result of exposure to excessive amounts of glucocorticoids over a long period of time. Its clinical manifestations range from classic signs like full moon facies, cervical fat pads or purple striae; to nonspecific findings which can go unnoticed and lead to misdiagnosis. Among the most challenging Cushing syndrome cases are those of a clinical presentation based on psychiatric and nonspecific symptoms; however, it is essential to keep this syndrome in mind as a differential diagnosis in order to achieve early diagnosis and treatment. This article presents the case of a 62 year old female who consults for a second opinion after being diagnosed with treatment resistant depression, in whom, through physical examination, the diagnosis is guided towards a primary endogenous Cushing's syndrome. El síndrome de Cushing es el resultado de la exposición a una cantidad excesiva de glucocorticoides por un largo periodo de tiempo. Sus manifestaciones clínicas rondan desde hallazgos muy característicos como las facies de luna llena, el acúmulo de grasa dorsocervical o las estrías purpúreas; hasta presentaciones poco específicas que pueden pasar desapercibidas y llevar a diagnósticos erróneos. Dentro de los casos más retadores se encuentran aquellos cuya presentación clínica se basa en manifestaciones psiquiátricas y síntomas inespecíficos; sin embargo, es esencial tener este síndrome en mente como diagnóstico diferencial con el fin de captar la mayor cantidad de casos de forma temprana y ofrecerles el tratamiento adecuado. Se presenta un caso de una mujer de 62 años quien consulta por una segunda opinión tras un diagnóstico de depresión sin respuesta al tratamiento, en quien por medio de examen físico se logra orientar el diagnóstico hacia un síndrome de cushing endógeno primario.&nbsp

TSO-DSO-Customer coordination for purchasing flexibility system services: Challenges and lessons learned from a demonstration in Sweden

This paper presents a real-word implementation of a TSO-DSO-customer coordination framework for the use of flexibility to support system operation. First, we describe the general requirements for TSO-DSO-customer coordination, including potential coordination schemes, actors and roles and the required architecture. Then, we particularise those general requirements for a real-world demonstration in Sweden, aiming to avoid congestions in the grid during the high-demand winter season. In the light of current congestion management rules and existing markets in Sweden, we describe an integration path to newly defined flexibility markets in support of new tools that we developed for this application. The results show that the use of flexibility can reduce the congestion costs while enhancing the secure operation of the system. Additionally, we discuss challenges and lessons learned from the demonstration, including the importance of the engagement between stakeholders, the role of availability remuneration, and the paramount importance of defining appropriate technical requirements and market timings.This work has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement nº 824414

SHARDS frontier fields: physical properties of a low-mass Lyα emitter at z = 5.75

We analyze the properties of a multiply-imaged Lyman-alpha (Lya) emitter at z=5.75 identified through SHARDS Frontier Fields intermediate-band imaging of the Hubble Frontier Fields (HFF) cluster Abell 370. The source, A370-L57, has low intrinsic luminosity (M_UV~-16.5), steep UV spectral index (\beta=-2.4+/-0.1), and extreme rest-frame equivalent width of Lya (EW(Lya)=420+180-120 \AA). Two different gravitational lens models predict high magnification (\mu~10--16) for the two detected counter-images, separated by 7", while a predicted third counter-image (\mu~3--4) is undetected. We find differences of ~50% in magnification between the two lens models, quantifying our current systematic uncertainties. Integral field spectroscopy of A370-L57 with MUSE shows a narrow (FWHM=204+/-10 km/s) and asymmetric Lya profile with an integrated luminosity L(Lya)~10^42 erg/s. The morphology in the HST bands comprises a compact clump (r_e<100 pc) that dominates the Lya and continuum emission and several fainter clumps at projected distances <1 kpc that coincide with an extension of the Lya emission in the SHARDS F823W17 and MUSE observations. The latter could be part of the same galaxy or an interacting companion. We find no evidence of contribution from AGN to the Lya emission. Fitting of the spectral energy distribution with stellar population models favors a very young (t<10 Myr), low mass (M*~10^6.5 Msun), and metal poor (Z<4x10^-3) stellar population. Its modest star formation rate (SFR~1.0 Msun/yr) implies high specific SFR (sSFR~2.5x10^-7 yr^-1) and SFR density (Sigma_SFR ~ 7-35 Msun/yr/kpc^2). The properties of A370-L57 make it a good representative of the population of galaxies responsible for cosmic reionization.Comment: 14 pages, 8 figures, 4 tables. Accepted for publication in Ap

Soil organic carbon stocks in native forest of Argentina: a useful surrogate for mitigation and conservation planning under climate variability

Background The nationally determined contribution (NDC) presented by Argentina within the framework of the Paris Agreement is aligned with the decisions made in the context of the United Nations Framework Convention on Climate Change (UNFCCC) on the reduction of emissions derived from deforestation and forest degradation, as well as forest carbon conservation (REDD+). In addition, climate change constitutes one of the greatest threats to forest biodiversity and ecosystem services. However, the soil organic carbon (SOC) stocks of native forests have not been incorporated into the Forest Reference Emission Levels calculations and for conservation planning under climate variability due to a lack of information. The objectives of this study were: (i) to model SOC stocks to 30 cm of native forests at a national scale using climatic, topographic and vegetation as predictor variables, and (ii) to relate SOC stocks with spatial–temporal remotely sensed indices to determine biodiversity conservation concerns due to threats from high inter‑annual climate variability. Methods We used 1040 forest soil samples (0–30 cm) to generate spatially explicit estimates of SOC native forests in Argentina at a spatial resolution of approximately 200 m. We selected 52 potential predictive environmental covariates, which represent key factors for the spatial distribution of SOC. All covariate maps were uploaded to the Google Earth Engine cloud‑based computing platform for subsequent modelling. To determine the biodiversity threats from high inter‑annual climate variability, we employed the spatial–temporal satellite‑derived indices based on Enhanced Vegetation Index (EVI) and land surface temperature (LST) images from Landsat imagery. Results SOC model (0–30 cm depth) prediction accounted for 69% of the variation of this soil property across the whole native forest coverage in Argentina. Total mean SOC stock reached 2.81 Pg C (2.71–2.84 Pg C with a probability of 90%) for a total area of 460,790 km2, where Chaco forests represented 58.4% of total SOC stored, followed by Andean Patagonian forests (16.7%) and Espinal forests (10.0%). SOC stock model was fitted as a function of regional climate, which greatly influenced forest ecosystems, including precipitation (annual mean precipitation and precipitation of warmest quarter) and temperature (day land surface temperature, seasonality, maximum temperature of warmest month, month of maximum temperature, night land surface temperature, and monthly minimum temperature). Biodiversity was influenced by the SOC levels and the forest regions. Conclusions In the framework of the Kyoto Protocol and REDD+, information derived in the present work from the estimate of SOC in native forests can be incorporated into the annual National Inventory Report of Argentina to assist forest management proposals. It also gives insight into how native forests can be more resilient to reduce the impact of biodiversity loss.EEA Santa CruzFil: Peri, Pablo Luis. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Santa Cruz; Argentina.Fil: Peri, Pablo Luis. Universidad Nacional de la Patagonia Austral; Argentina.Fil: Peri, Pablo Luis. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina.Fil: Gaitan, Juan José. Universidad Nacional de Luján. Buenos Aires; Argentina.Fil: Gaitan, Juan José. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina.Fil: Mastrangelo, Matias Enrique. Universidad Nacional de Mar del Plata. Facultad de Ciencias Agrarias. Grupo de Estudio de Agroecosistemas y Paisajes Rurales; Argentina.Fil: Mastrangelo, Matias Enrique. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina.Fil: Nosetto, Marcelo Daniel. Universidad Nacional de San Luis. Instituto de Matemática Aplicada San Luis. Grupo de Estudios Ambientales; Argentina.Fil: Nosetto, Marcelo Daniel. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina.Fil: Villagra, Pablo Eugenio. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto Argentino de Nivología, Glaciología y Ciencias Ambientales (IANIGLA); Argentina.Fil: Villagra, Pablo Eugenio. Universidad Nacional de Cuyo. Facultad de Ciencias Agrarias; Argentina.Fil: Balducci, Ezequiel. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Yuto; Argentina.Fil: Pinazo, Martín Alcides. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Montecarlo; Argentina.Fil: Eclesia, Roxana Paola. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Paraná; Argentina.Fil: Von Wallis, Alejandra. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Montecarlo; Argentina.Fil: Villarino, Sebastián. Universidad Nacional de Mar del Plata. Facultad de Ciencias Agrarias. Grupo de Estudio de Agroecosistemas y Paisajes Rurales; Argentina.Fil: Villarino, Sebastián. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina.Fil: Alaggia, Francisco Guillermo. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Manfredi. Campo Anexo Villa Dolores; Argentina.Fil: Alaggia, Francisco Guillermo. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina.Fil: Gonzalez-Polo, Marina. Universidad Nacional del Comahue; Argentina.Fil: Gonzalez-Polo, Marina. Consejo Nacional de Investigaciones Científicas y Técnicas. INIBIOMA; Argentina.Fil: Manrique, Silvana M. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Investigaciones en Energía No Convencional. CCT Salta‑Jujuy; Argentina.Fil: Meglioli, Pablo A. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto Argentino de Nivología, Glaciología y Ciencias Ambientales (IANIGLA); Argentina.Fil: Meglioli, Pablo A. Universidad Nacional de Cuyo. Facultad de Ciencias Agrarias; Argentina.Fil: Rodríguez‑Souilla, Julián. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Austral de Investigaciones Científicas (CADIC); Argentina.Fil: Mónaco, Martín H. Ministerio de Ambiente y Desarrollo Sostenible. Dirección Nacional de Bosques; Argentina.Fil: Chaves, Jimena Elizabeth. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Austral de Investigaciones Científicas (CADIC); Argentina.Fil: Medina, Ariel. Ministerio de Ambiente y Desarrollo Sostenible. Dirección Nacional de Bosques; Argentina.Fil: Gasparri, Ignacio. Universidad Nacional de Tucumán. Instituto de Ecología Regional; Argentina.Fil: Gasparri, Ignacio. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina.Fil: Alvarez Arnesi, Eugenio. Universidad Nacional de Rosario. Instituto de Investigaciones en Ciencias Agrarias de Rosario; Argentina.Fil: Alvarez Arnesi, Eugenio. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Santa Fe; Argentina.Fil: Barral, María Paula. Universidad Nacional de Mar del Plata. Facultad de Ciencias Agrarias. Grupo de Estudio de Agroecosistemas y Paisajes Rurales; Argentina.Fil: Barral, María Paula. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina.Fil: Von Müller, Axel. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Esquel Argentina.Fil: Pahr, Norberto Manuel. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Montecarlo; Argentina.Fil: Uribe Echevarría, Josefina. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Quimilí; Argentina.Fil: Fernandez, Pedro Sebastian. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Famaillá; Argentina.Fil: Fernandez, Pedro Sebastian. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Ecología Regional; Argentina.Fil: Morsucci, Marina. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto Argentino de Nivología, Glaciología y Ciencias Ambientales (IANIGLA); Argentina.Fil: Morsucci, Marina. Universidad Nacional de Cuyo. Facultad de Ciencias Agrarias; Argentina.Fil: Lopez, Dardo Ruben. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Manfredi. Campo Anexo Villa Dolores; Argentina.Fil: Lopez, Dardo Ruben. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina.Fil: Cellini, Juan Manuel. Universidad Nacional de la Plata (UNLP). Facultad de Ciencias Naturales y Museo. Laboratorio de Investigaciones en Maderas; Argentina.Fil: Alvarez, Leandro M. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto Argentino de Nivología, Glaciología y Ciencias Ambientales (IANIGLA); Argentina.Fil: Alvarez, Leandro M. Universidad Nacional de Cuyo. Facultad de Ciencias Agrarias; Argentina.Fil: Barberis, Ignacio Martín. Universidad Nacional de Rosario. Instituto de Investigaciones en Ciencias Agrarias de Rosario; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Santa Fe; Argentina.Fil: Barberis, Ignacio Martín. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Santa Fe; Argentina.Fil: Colomb, Hernán Pablo. Ministerio de Ambiente y Desarrollo Sostenible. Dirección Nacional de Bosques; Argentina.Fil: Colomb, Hernán. Administración de Parques Nacionales (APN). Parque Nacional Los Alerces; Argentina.Fil: La Manna, Ludmila. Universidad Nacional de la Patagonia San Juan Bosco. Centro de Estudios Ambientales Integrados (CEAI); Argentina.Fil: La Manna, Ludmila. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina.Fil: Barbaro, Sebastian Ernesto. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Cerro Azul; Argentina.Fil: Blundo, Cecilia. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Ecología Regional; Argentina.Fil: Blundo, Cecilia. Universidad Nacional de Tucumán. Tucumán; Argentina.Fil: Sirimarco, Marina Ximena. Universidad Nacional de Mar del Plata. Grupo de Estudio de Agroecosistemas y Paisajes Rurales (GEAP); Argentina.Fil: Sirimarco, Marina Ximena. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina.Fil: Cavallero, Laura. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Manfredi. Campo Anexo Villa Dolores; Argentina.Fil: Zalazar, Gualberto. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto Argentino de Nivología, Glaciología y Ciencias Ambientales (IANIGLA); Argentina.Fil: Zalazar, Gualberto. Universidad Nacional de Cuyo. Facultad de Ciencias Agrarias; Argentina.Fil: Martínez Pastur, Guillermo José. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Austral de Investigaciones Científicas (CADIC); Argentina

Clonal chromosomal mosaicism and loss of chromosome Y in elderly men increase vulnerability for SARS-CoV-2

The pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2, COVID-19) had an estimated overall case fatality ratio of 1.38% (pre-vaccination), being 53% higher in males and increasing exponentially with age. Among 9578 individuals diagnosed with COVID-19 in the SCOURGE study, we found 133 cases (1.42%) with detectable clonal mosaicism for chromosome alterations (mCA) and 226 males (5.08%) with acquired loss of chromosome Y (LOY). Individuals with clonal mosaic events (mCA and/or LOY) showed a 54% increase in the risk of COVID-19 lethality. LOY is associated with transcriptomic biomarkers of immune dysfunction, pro-coagulation activity and cardiovascular risk. Interferon-induced genes involved in the initial immune response to SARS-CoV-2 are also down-regulated in LOY. Thus, mCA and LOY underlie at least part of the sex-biased severity and mortality of COVID-19 in aging patients. Given its potential therapeutic and prognostic relevance, evaluation of clonal mosaicism should be implemented as biomarker of COVID-19 severity in elderly people. Among 9578 individuals diagnosed with COVID-19 in the SCOURGE study, individuals with clonal mosaic events (clonal mosaicism for chromosome alterations and/or loss of chromosome Y) showed an increased risk of COVID-19 lethality