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“Cualquier proyecto es una ventana de promoción para la ciudad de Madrid y nosotros lo consideramos como tal”. Una entrevista a Raúl Torquemada y Víctor Aertsen de la Madrid Film Office
No full textLa entrevista al director y al responsable de la comunicación de la Madrid Film Office toma en consideración la posición singular de la ciudad de Madrid dentro de la cinematografía en España: como ciudad con tres film offices (la de la ciudad, la de la Comunidad Autónoma y la de España) así como numerosas instituciones estatales y privadas vinculadas a la historia, al presente y al futuro del cine español. Madrid es, además, sede de grandes productoras internacionales y, con diferencia, la ciudad española donde más películas, series y otros productos audiovisuales se ruedan. La tarea de la Film Office de la ciudad consiste en promocionar los rodajes de nuevas producciones y dar a conocer también localizaciones nuevas dentro de Madrid, conectar a ellos con las distintas instituciones y fortalecer la reputación de Madrid como ciudad cinematográfica.
The interview with the director and the head of communications of the Madrid Film Office takes into account the unique position of the city of Madrid within the Spanish film industry: as a city with three film offices (the city’s, the Autonomous Community’s and the Spain’s) as well as numerous state and private institutions linked to the history, present and future of Spanish cinema. Madrid is also the headquarters of major international production companies and by far the Spanish city where most films, series and other audiovisual products are shot. The task of the city’s Film Office is to promote the filming of new productions and to publicise new locations in Madrid, to connect them with the different institutions and to strengthen Madrid’s reputation as a film city.
The interview with the director and the head of communications of the Madrid Film Office takes into account the unique position of the city of Madrid within the Spanish film industry: as a city with three film offices (the city’s, the Autonomous Community’s and the Spain’s) as well as numerous state and private institutions linked to the history, present and future of Spanish cinema. Madrid is also the headquarters of major international production companies and by far the Spanish city where most films, series and other audiovisual products are shot. The task of the city’s Film Office is to promote the filming of new productions and to publicise new locations in Madrid, to connect them with the different institutions and to strengthen Madrid’s reputation as a film city
Environmental assessment of the use of ground olive stones in mortars. Reduction of CO2 emissions and production of sustainable mortars for buildings
Get PDFStrengthening the evidence for seasonal intertidal exploitation in Mesolithic Europe and new insights into Early Holocene environmental conditions in the Bay of Biscay from the oxygen isotope composition of Phorcus lineatus (da Costa, 1778) shells
Get PDFMás de 200 años de cooperación: anécdotas y conceptos claros.
No full textEsta ponencia reúne la voz de cuatro expertos con más de dos siglos de experiencia acumulada en la enseñanza y práctica del aprendizaje cooperativo en Educación Física. A través de preguntas clave y anécdotas reales, se revisan conceptos, se aclaran confusiones comunes y se comparten estrategias para diferenciar la cooperación real de la mera colaboración o la competición encubierta. Las intervenciones subrayan la importancia del lenguaje, la coherencia metodológica y la continuidad de un enfoque cooperativo que trascienda la clase y transforme la convivencia diaria. El texto invita a reflexionar sobre la profundidad y el potencial del aprendizaje cooperativo como motor de cambio, tanto dentro como fuera de la escuel
On an Incremental Version of the Chebyshev Method for the Matrix P-Th Root
No full textThe aim of this paper is to present an improvement of the incremental Newton methodproposed by Iannazzo [SIAM J. Matrix Anal. Appl., 28:2 (2006), 503–523] for approx-imating the principal p-th root of a matrix. We construct and analyze an incrementalChebyshev method with better numerical behavior. We present a convergence and nu-merical analysis of the method, where we compare it with the corresponding incrementalNewton method. The new method has order of convergence three and is stable and moreefficient than the incremental Newton metho
Losing Water by Storing It: The Oversighted Side of Intensive Water Regulation and Damming
Get PDFReservoirs are crucial for managing water resources but they may also promote enhanced waterevaporation inland. Here we analyze the historical trends, causal attribution, and future projections of regionalevaporative losses across 362 Spanish reservoirs, representing 94% of the nation's storage capacity, in one of themost heavily dammed countries in the world. There is a consistent annual increase in evaporation of27.7 hm3 year. While climate dynamics and warming have played a role in this trend, our researchdemonstrate that the impact of new reservoir construction and fluctuations in available water surface area havebeen much more influential (22 and 7 times greater, respectively). Anticipated evaporative losses by the end ofthe 21st century under a high greenhouse gases emissions scenario are expected to be 35% higher than thoseregistered during the observational period. Our projections suggest that warming will increasingly driveevaporation, yet the available water surface will remain a critical determinant
Morphological image analysis for determining bunch grape characteristics: A case study on bunch weight in Cabernet-Sauvignon
No full textMorphological image analysis is a powerful technique used in various fields, including agriculture, to quantitatively assess the physical characteristics of objects. In viticulture, the accurate assessment of grapevine characteristics is essential for optimizing crop management and improving the quality of wine production. Among these characteristics, bunch weight is a critical factor influencing vine health, yield potential, and the quality of grapes harvested. Accurate vineyard yield estimation is crucial for the wine industry as it enables optimization in harvest planning, winery management, and marketing strategies (Victorino et al., 2022). However, the significant spatial and temporal variability within vineyards complicates precise predictions of grape bunch weight (Bramley et al., 2011). Conventional methods, such as manual grape bunch sampling, are destructive, labour-intensive, and prone to significant errors that can exceed 30%, depending on the sampling technique used and vineyard heterogeneity (Dunn & Martin, 2008). To overcome these limitations, sensor-based technologies, particularly image analysis, have shown great potential in addressing these challenges. These tools enable the inspection of a large number of grape bunches within a short time, reducing reliance on extrapolations and errors associated with variability (Liu & Zeng, 2020). Non-contact measurements based on two-dimensional image processing have been proven to be useful in the detection of several key agricultural traits, especially in single fruits with regular and uncomplicated shapes, such as apples and apricots (e.g., Khojastehnazhand et al., 2019; Wu et al., 2019). Other studies also present the potential of these techniques for more complicated fruit shapes, such as grape bunches, where the shape and size are highly dependent of the cultivar, viticulture practices and edaphoclimatic conditions. Diago et al., (2014) demonstrated that features such as the projected area of the bunch, the number of visible berries, and the perimeter are key predictors of bunch weight in two-dimensional analyses, achieving significant correlations across various cultivars. Moreover, the use of two-dimensional imaging has become an effective tool for the automatic segmentation of grape bunches and the counting of visible berries (Aquino et al., 2018; Milella et al., 2018). Advanced methods, such as algorithms based on convolutional neural networks, have significantly improved segmentation and counting under field conditions, bringing these technologies closer to practical applications in commercial vineyards (Liu & Zeng, 2020). However, the dependence of these correlations on the cultivar remains a challenge, as differences in bunch architecture and environmental conditions significantly affect the accuracy of proposed models (Tello et al., 2015; Victorino et al., 2022). Despite these advances, several open questions persist regarding image-based weight estimation. These include berry occlusion, variability in image capture conditions, and cultivar dependence, all which limit model generalization (Diago et al., 2014; Victorino et al., 2022)
Automated detection of downy mildew in vineyards using explainable deep learning
No full textTraditional methods for identifying downy mildew in commercial vineyards are often labour-intensive, subjective, and time-consuming. Artificial intelligence offers an opportunity to streamline and standardize this process, increasing sampling efficiency and consistency. This study presents an interpretable, automated approach to detect downy mildew symptoms in vineyard conditions. RGB images of grapevine canopies were collected across multiple commercial vineyards using a ground-based mobile phenotyping platform. Image analysis was conducted using a sliding window technique to classify sub-images into zones with and without symptoms. Techniques such as transfer learning, fine-tuning, and data augmentation were used to automate classification, comparing the performance of convolutional neural networks (CNNs) and vision transformers (ViTs). The trained model, integrated into the sliding window system, accurately localized symptomatic areas, with predictions interpreted through explainable artificial intelligence (XAI). The EfficientNetV2S model achieved a classification accuracy of 91% and an F1-score of 0.92 in localizing symptomatic zones. This approach enables reliable and interpretable downy mildew detection under diverse field conditions, representing a significant advancement in precision vineyard protection