Alberto Sols Biomedical Research Institute
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The future of artificial intelligence: Insights from recent Delphi studies
Get PDFWe review thirteen Delphi studies on the future of Artificial Intelligence (AI), published between 2014 and 2024. Using the Delphi method, an iterative approach that refines expert insights through multiple rounds, these studies provide foresight into AI’s technological advancements, societal impacts, and policy implications across various sectors. For example, Delphi studies in healthcare foresee significant advancements in AI driven diagnostics and personalized medicine, while in manufacturing, AI is anticipated to enhance human robot collaboration and supply chain optimization. AI’s impact on journalism and photography shows promise in automating processes and enriching immersive storytelling, although issues like data privacy and algorithmic bias are raised. This review emphasizes a primary focus on technology trajectories, examining anticipated developments and timelines, while also considering broader strategic foresight aspects. General challenges identified include equitable access, the need for robust data governance, and workforce upskilling to integrate AI responsibly. By synthesizing insights across these studies, we provide a structured overview of both opportunities and limitations in AI development, offering guidance for stakeholders to navigate AI’s complexities and capitalize on its potential responsibly. In addition, we propose methodological recommendations, such as standardizing expert selection and diversifying perspectives to improve the quality of future Delphi studiesThe research was funded by the Margarita Salas Fellowship from the Autonomous University of Madri
A two-stage progressive deep segmentation network for tumor detection in breast ultrasound images
No full textSegmenting tumorous regions in breast ultrasound images is a challenging problem due to
several factors, including the relatively low contrast of the available images, the presence
of speckle noise, and the considerable variations in breast mass sizes and shapes. Current
methods are not precise enough and prone to misdetections. An efficient deep neural model
is proposed for automatically segmenting tumorous regions in breast ultrasound images.
The model is constituted by two consecutive encoder-decoder (autoencoder) networks. The
first autoencoder extracts a preliminary binary mask from the given image. The second
autoencoder refines that mask after concatenating it with the original image. The encoders
within each autoencoder can be defined by applying any state-of-the-art network. In addition,
cost-sensitive learning has been used in order to focalize training on the segmentation errors of
the minority class (tumor). Semantic segmentation based on advanced deep learning methods
is thus applied in order to enhance tumor segmentation in breast ultrasound images. The
proposed model offers advanced capabilities for automated segmentation with the aim of
helping physicians identify and diagnose tumors using state-of-the-art techniques. This model
outperforms recent tumor segmentation methods in the experiments conducted on two public
datasets of breast ultrasound images (UDIAT and BUSI). The largest improvement for both
datasets was achieved by using CoAtNet as baseline model (Dice index equal to 84.49% and
78.94%, respectively)The Spanish Government partly supported this research through Project
TED2021-130081B-C21, and Project PDC2022-133383-I0
MoSe2xTe2-2x alloy for hydrogen gas detection
Get PDFThis study presents one of the first detailed investigation into the out-of-plane growth of two-dimensional MoSe2xTe2–2x for three distinct Se:Te compositions, yielding the samples MoSe0.46Te1.55, MoSe0.55Te1.45, and MoSe0.64Te1.35. Scanning electron microscopy reveals the morphological evolution, showcasing nanoflakes predominantly grown in a random out-of-plane orientation. Raman spectroscopy confirms the crystal structure of MoSe2xTe2–2x samples, with no detectable traces of MoO3 or MoO2 precursors. Distinctive peaks validate the presence of MoSe2xTe2–2x, while spectral shifts suggest at compositional variations. Conductive-Atomic Force Microscopy (C-AFM) was used to quantify current flow between a diamond-doped AFM tip and the sample. Additionally, the resistances of compositions MoSe0.46Te1.55, MoSe0.55Te1.45, and MoSe0.64Te1.35 were characterized, yielding median values of 108.0 Ω, 25.4 kΩ, and 240.0 kΩ, respectively, indicating diverse electrical properties associated with Se content. Notably, the sample with the highest Se content exhibits increased resistivity as anticipated, consistent with XRD spectra results. Finally, gas-sensing performance is evaluated by assessing electrical resistance variations before and after exposure to H2 gas at various temperatures, revealing significant changes indicative of enhanced charge flow upon H2 exposureThis work has been funded by the Young Researchers project of the Comunidad de Madrid and Universidad Autónoma de Madrid under grant code SI3/PJI/2021-00428. The authors acknowledge the service from the MiNa Laboratory at IMN and the funding from CM (project SpaceTec, S2013/ICE2822). L.V. acknowledges financial support from project PID2023-149077OB-C31 funded by MCIN/AEI/10.13039/ 50110001103
Creating multisensory spaces to explore, know and learn about the world. Immersive artistic practices to promote early stimulation in infants and babies with functional diversity
Get PDFEl presente artículo recoge un conjunto de experiencias artísticas inmersivas llevadas a cabo
en un centro de atención temprana y orientadas a la estimulación de bebés e infantes con diversidad
funcional. El objetivo de esta investigación fue averiguar si el uso de prácticas propias del arte contemporáneo, como las instalaciones inmersivas, resultan efectivas para el tratamiento de los infantes y bebés con diversidad funcional, ofreciéndoles nuevos canales de asimilación e incorporación de aprendizajes integrales, minimizando, de esta manera, las consecuencias derivadas de sus diversidades funcionales. La metodología de investigación está basada en el cruce de dos métodos cualitativos interconectados: el estudio de caso y la narración visual a través de una investigación basada en las artes (IBA), que nos han permitido poder observar y comprender el significado de una experiencia llevada a cabo en un escenario real. Los resultados visibilizan que incorporar prácticas artísticas y lúdicas propias del arte contemporáneo son un buen método de estimulación en atención temprana, puesto que proporcionan medios alternativos que facilitan la incorporación de aprendizajes a nivel motor, cognitivo, afectivo, estético y relacional, a través de un ambiente estimulante y seguroThis article gathers a series of immersive artistic experiences carried out in an early care center and oriented to the stimulation of infants and toddlers with functional diversity. The objective of this research was to determine whether the use of contemporary art practices, such as immersive installations, are effective in the treatment of infants and babies with functional diversity, since they offer them new assimilation channels for the incorporation of integral learning, thus minimizing the consequences derived from their functional diversities. The research methodology is based on the crossing of two interconnected qualitative methods: the case study and the visual narrative through arts-based research (ABR), which have allowed us to observe and understand the meaning of an experience carried out in a real scenario. The results show that incorporating artistic and ludic practices of contemporary art are a good method of stimulation in early care, providing alternative means that facilitate the incorporation of motor, cognitive, affective, aesthetic and relational learning, through a stimulating and secure environmen
Active biodegradable packaging films based on the revalorization of food-grade olive oil mill by-products
Get PDFSynthetic packaging is being replaced by biodegradable packaging through the revalorization of food industry by-products. The olive oil (OO) industry, known for producing large quantities of antioxidant-rich by-products, can be a major supplier for sustainable packaging materials. This study aims to valorize a food-grade by-product (defatted flour, DF) from OO extraction produced using a zero-waste strategy that combines expeller press technology and supercritical CO2 extraction. DF and its aqueous extract (DFE) were combined with carboxymethylcellulose (CMC) to create biodegradable bioactive packaging films. DF contains a high content of insoluble dietary fiber (28.4%) and total phenolic compounds (35,000 ppm), including oleuropein, elenolic acid, hydroxytyrosol, and tyrosol (4324, 3603, 1525, and 157 ppm, respectively). This study examined the effects of DF and DFE on the physicochemical and barrier properties of the films, as well as their capacity to delay oxidation in polyunsaturated fatty acid-rich oil. Films with DF and DFE contained high phenolic content (1500 and 1200 ppm, respectively), and their inclusion improved ultraviolet visible barrier capacity. Additionally, oil oxidation was slower when protected by DF- and DFE-based films than when protected with CMC film alone. This allows their use as protective packaging and potential carriers of bioactive oils to enhance the nutritional and functional qualities of packaged food
Geochemical insights into plant uptake of Technology-critical elements: A case study on lettuce from European soils
Get PDFWhile vegetable uptake of traditional metal contaminants is a well-studied pathway to human exposure and risk, a paucity of information exists on the uptake of emerging metal contaminants. This study evaluated the uptake of the Technology-critical elements (TCEs) gallium (Ga), germanium (Ge), niobium (Nb), tantalum (Ta), thallium (Tl), and rare earth elements (REEs) into lettuce cultivated in 21 European urban soils. For comparison, the uptake of cadmium (Cd) was also analysed. First, the uptake was predicted by multiplying soil concentrations with previously established bioconcentration factors (BCFs). Subsequently, multiple regression models incorporating geochemical variables as predictors were used to determine whether prediction accuracy could be improved. A “3-predictor model” incorporated soil TCE concentration, pH, and organic matter (OM), and a “7-predictor model” added data on clay content and the soil concentrations of Fe, Al, and Mn as well. With the exception of Cd, Ge, and Tl, the BCF approach provided unsatisfactory predictions (R2 < 0.5), while the 7-predictor models yielded the best predictions, even when accounting for the greater number of predictors. While the most important predictors of uptake varied somewhat between the TCEs, the concentrations of TCEs in the soil generally explained the largest proportion of the variation. The least influential predictors in our dataset were [Mnsoil], [Fesoil], and soil OM. Incorporating geochemical data generally improved the predictions of uptake by lettuce, and these findings underscore the need for more detailed characterisations of the uptake potential of TCEs by food plants and subsequent consequences for human healt
Routes and transformations in the Maghrebi Space (second-eighth c. AH / eighth-fourteenth c. AD)
Get PDFEsta sección monográfica forma parte de los resultados del proyecto “Tránsitos y migraciones en el norte de África: análisis diacrónico de la población y su entorno (DIANA)”, que se integra en el coordinado MAGNA II (Coord. M. Á. Manzano) “Tránsitos y transformaciones en el espacio y la población magrebíes”, MICIN/AEI/10.13039/501100011033 y FEDER Una manera de hacer Europ
Lugares de culto en la universidad
No full textEste proyecto “El ejercicio de la libertad religiosa en la educación superior: reflexiones, aportes y vivencias”, código PC-23-0021, se financia con la convocatoria 2023, concedido por la Fundación Pluralismo y Convivenci