Neotipificación de Thalictrum maritimum Dufour (Ranunculaceae), planta endémica y amenazada del este peninsular ibérico

Se designa un neótipo para Thalictrum maritimum Dufour (Ranunculaceae), especie descrita para la Albufera de Valencia (España). El espécimen seleccionado como tipo procede de la localidad clásica de la especie.A neotypus of Thalictrum maritimum Dufour (Ranunculaceae), species from the Albufera of Valencia (Spain) is designated here. The specimen selected as type has been collected in the classical locality of this species

Factores de riesgo en la EPOC

La enfermedad pulmonar obstructiva crónica es de origen multifactorial y se desarrolla gracias a la interacción de diferentes factores de riesgo. La mayor parte de la evidencia de la influencia de éstos factores en el desarrollo de la EPOC proviene de estudios epidemiológicos transversales que, en lugar de relación causa-efecto, sugieren asociaciones entre distintos factores de riesgo y esta enfermedad. Estos estudios indican la existencia de 2 tipos de factores de riesgo: un primer grupo, marcadores de riesgo, sobre los que no se puede intervenir, y que dependen del huésped o paciente, y un segundo grupo, o factores de riesgo propiamente dichos, sobre los que sí se puede actuar, que están ligados a la exposición medioambiental

Aspectos sintéticos sobre la flora vascular del Sistema Ibérico

Se presentan y comentan los datos sintéticos sobre las plantas vas-culares de las que se dispone de datos sobre su presencia en el Sistema Ibérico, afectando al número de especies total y sus sinónimos, a géneros y familias mejor representados, autores más implicados en las propuestas de los taxones, países de las localidades clásicas (y provincias en España), publicaciones más implicadas en los nombres y producción taxonómica por décadas, por países y ciudades.Several synthetic data about of the vascular flora of the Iberian System (NE Spain) are presented and commented. This information concerns to the total number of species and their synonyms, to the genera and families better represented, authors most involved in the proposed taxa, the classic localities countries and provinces in Spain, publications more involved in the names and taxonomic production for decades by countries and cities

Thymus vulgaris subsp. mansanetianus subsp. nov. (Lamiaceae)

Se describe una nueva subespecie de Thymus vulgaris L. (Lamiaceae); Th. vulgaris subsp. mansanetianus subsp. nov., caracterizada por presentar un hábito postrado, tallos estoloníferos, decumbentes y radicantes, hojas muy estrechas y una floración otoñal

Unsupervised recognition and prediction of daily patterns in heating loads in buildings

This paper presents a multistep methodology combining unsupervised and supervised learning techniques for the identification of the daily heating energy consumption patterns in buildings. The relevant number of typical profiles is obtained through unsupervised clustering processes. Then Classification and Regression Trees are used to predict the profile type corresponding to external variables, including calendar and climatic variables, from any given day. The methodology is tested with a variety of datasets for three different buildings with different uses connected to the district heating network in Tartu (Estonia). The three buildings under analysis present different energy behaviors (residential, kindergarten and commercial buildings). The paper shows that unsupervised clustering is effective for pattern recognition since the results from the classification and regression trees match the results from the unsupervised clustering. Three main patterns have been identified in each building, seasonality and daily mean temperature being the variables that have the greatest effect. The results concluded that the best classification accuracy is obtained with a small number of clusters with a classification accuracy from 0.7 to 0.85, approximately.The authors would like to thank GREN Eesti [44] for providing data from the substations for academic purposes. The authors would like to acknowledge the Spanish Ministry of Science and Innovation (MICINN) for funding through the Sweet-TES research project (RTI2018-099557-B-C22). This project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 768567

Data driven model for heat load prediction in buildings connected to District Heating by using smart heat meters

An accurate characterization and prediction of heat loads in buildings connected to a District Heating (DH) network is crucial for the effective operation of these systems. The high variability of the heat production process of DH networks with low supply temperatures and derived from the incorporation of different heat sources increases the need for heat demand prediction models. This paper presents a novel data-driven model for the characterization and prediction of heating demand in buildings connected to a DH network. This model is built on the so-called Q-algorithm and fed with real data from 42 smart energy meters located in 42 buildings connected to the DH in Tartu (Estonia). These meters deliver heat consumption data with a 1-h frequency. Heat load profiles are analysed, and a model based on supervised clustering methods in combination with multiple variable regression is proposed. The model makes use of four climatic variables, including outdoor ambient temperature, global solar radiation and wind speed and direction, combined with time factors and data from smart meters. The model is designed for deployment over large sets of the building stock, and thus aims to forecast heat load regardless of the construction characteristics or final use of the building. The low computational cost required by this algorithm enables its integration into machines with no special requirements due to the equations governing the model. The data-driven model is evaluated both statistically and from an engineering or energetic point of view. R2 values from 0.70 to 0.99 are obtained for daily data resolution and R2 values up to 0.95 for hourly data resolution. Hourly results are very promising for more than 90% of the buildings under study.European Commission, RELaTED: h2020, GA nº 76856

Data driven model for heat load prediction in buildings connected to District Heating by using smart heat meters

[EN] An accurate characterization and prediction of heat loads in buildings connected to a District Heating (DH) network is crucial for the effective operation of these systems. The high variability of the heat production process of DH networks with low supply temperatures and derived from the incorporation of different heat sources increases the need for heat demand prediction models. This paper presents a novel data-driven model for the characterization and prediction of heating demand in buildings connected to a DH network. This model is built on the so-called Q-algorithm and fed with real data from 42 smart energy meters located in 42 buildings connected to the DH in Tartu (Estonia). These meters deliver heat consumption data with a 1-h frequency. Heat load profiles are analysed, and a model based on supervised clustering methods in combination with multiple variable regression is proposed. The model makes use of four climatic variables, including outdoor ambient temperature, global solar radiation and wind speed and direction, combined with time factors and data from smart meters. The model is designed for deployment over large sets of the building stock, and thus aims to forecast heat load regardless of the construction characteristics or final use of the building. The low computational cost required by this algorithm enables its integration into machines with no special requirements due to the equations governing the model. The data-driven model is evaluated both statistically and from an engineering or energetic point of view. R-2 values from 0.70 to 0.99 are obtained for daily data resolution and R-2 values up to 0.95 for hourly data resolution. Hourly results are very promising for more than 90% of the buildings under study. (This study has been carried out in the context of RELaTED project. This project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 768567. This publication reflects only the authors' views and neither the Agency nor the Commission are responsible for any use that may be made of the information contained therein

Revised typifications of four Léon Dufour's names

The typifications of four Léon Dufours's names are revised. The lectotype of Centaurea scorpiurifolia (Compositae) is designated from original material preserved in the ?Herbier Léon Dufour? at BORD (available at: Archives départementales des Landes). The neotypes of Centaurea stenophylla and Thalictrum maritimum (Ranunculaceae) were designated from material preserve at P and VAL herbaria, respectively. However, original material has been found at BORD. Therefore, the lectotypes of C. stenophylla and T. maritimum are designated in this work according to Art. 9.19(a) of the International Code of Nomenclature for algae, fungi, and plants (ICN). On the other hand, the lectotype of Centaurea dracunculifolia is also designated from original material preserved at BORD.