Environmental assessment of central solar heating plants with seasonal storage located in Spain

Renewable energies can play a very important role in the development of a new energy model contributing effectively towards a more sustainable development in the mid and long term. In this context Central Solar Heating Plants with Seasonal Storage (CSHPSS) are able to provide space heating and Domestic Hot Water (DHW) to residential buildings with high solar fractions (>50%). These systems are already being used in Central and Northern Europe, as well as in Canada, where there is an important experience in district heating systems. The study presented herein presents an environmental assessment, applying the Life Cycle Assessment (LCA) method, of a CSHPSS, which should cover the space heating and DHW demand of 500 dwellings of 100 m2, located in Zaragoza, Spain. Environmental burdens through the life cycle of the system are estimated based on greenhouse gas emissions, and comprehensive environmental indicators as the ReCiPe and Cumulative Energy Demand (CED). These indicators allow to evaluate the reduction of the environmental load achieved by the CSHPSS analyzed with respect to conventional space heating and DHW systems, as well as to identify the most critical aspects from the environmental perspective. In this article, the environmental behavior of the CSHPSS is decoupled into the two demands covered, heating and DHW, in order to quantify the environmental impact of each generation system. A detailed life cycle inventory is presented with the aim of promoting the development of increasingly efficient technologies from the environmental point of view, not only in the operation phase but also in the construction of the equipment. Furthermore, an in-depth analysis is performed to evaluate the variation of the environmental impact depending on the climatic conditions. The CSHPSS is also dimensioned in different Spanish cities and a LCA is carried out for nine locations. The results can help different stakeholders to make decisions in order to optimize the renewable energy generation systems taking in account its whole life cycle and to point out the necessity to evaluate the environmental impact essentially in the production phase for all renewable energy systems

PND44 Validation of the Fabry Outcome Survey (FOS) Paediatric Health and Pain Questionnaire

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A multiscale orchestrated computational framework to reveal emergent phenomena in neuroblastoma

Neuroblastoma is a complex and aggressive type of cancer that affects children. Current treatments involve a combination of surgery, chemotherapy, radiotherapy, and stem cell transplantation. However, treatment outcomes vary due to the heterogeneous nature of the disease. Computational models have been used to analyse data, simulate biological processes, and predict disease progression and treatment outcomes. While continuum cancer models capture the overall behaviour of tumours, and agent-based models represent the complex behaviour of individual cells, multiscale models represent interactions at different organisational levels, providing a more comprehensive understanding of the system. In 2018, the PRIMAGE consortium was formed to build a cloud-based decision support system for neuroblastoma, including a multi-scale model for patient-specific simulations of disease progression. In this work we have developed this multi-scale model that includes data such as patient's tumour geometry, cellularity, vascularization, genetics and type of chemotherapy treatment, and integrated it into an online platform that runs the simulations on a high-performance computation cluster using Onedata and Kubernetes technologies. This infrastructure will allow clinicians to optimise treatment regimens and reduce the number of costly and time-consuming clinical trials. This manuscript outlines the challenging framework's model architecture, data workflow, hypothesis, and resources employed in its development

P-Stereogenic Phosphines for the Stabilisation of Metal Nanoparticles. A Surface State Study

Palladium and ruthenium nanoparticles have been prepared following the organometallic precursor decomposition methodology, under dihydrogen pressure and in the presence of borane protected P-stereogenic phosphines. NMR (Nuclear Magnetic Resonance) monitoring of the corresponding syntheses has permitted to determine the optimal metal/ligand ratio for leading to small and well-dispersed nanoparticles. Exchange ligand reactions of the as-prepared materials have proven the strong interaction of the phosphines with the metal surface; only oxidative treatment using hydrogen peroxide could release the phosphine-based stabiliser from the metal surface. Pd and Ru nanoparticles have been evaluated in hydrogenation reactions, confirming the robustness of the stabilisers, which selectively permitted the hydrogenation of exocyclic C=C bonds, preventing the coordination of the aromatic rings and as a result, their hydrogenation

Molecular Hydrogen and [FeII] in Active Galactic Nuclei

(Abridge) Near-infrared spectroscopy is used to study the kinematics and excitation mechanisms of the H2 and [FeII] gas in a sample of AGN. The H2 lines are unresolved in all objects in which they were detected while the [FeII] lines have widths implying gas velocities of up to 650 km/s. This suggests that, very likely, the H2 and [FeII] emission does not originate from the same parcel of gas. Molecular H2 were detected in 90% of the sample, including PG objects, indicating detectavel amounts of molecular material even in objects with low levels of circumnuclear starburst activity. The data favors thermal excitation for the H2 lines. Indeed, in NGC3227, Mrk766, NGC4051 and NGC4151, the molecular emission is found to be purely thermal. This result is also confirmed by the rather similar vibrational and rotational temperatures in the objects for which they were derived. [FeII] lines are detected in all of the AGN. The [FeII] 1.254mu/Pa-beta ratio is compatible with excitation of the [FeII] by the active nucleus, but in Mrk 766 it implies a stellar origin. A correlation between H2/Br-gamma and [FeII]/Pa-beta is found. We confirm that it is a useful diagnostic tool in the NIR to separate emitting line objects by their level of nuclear activity. X-ray excitation models are able to explain the observed H2 and part of the [FeII] emission. Most likely, a combination of X-ray heating, shocks driven by the radio jet, and circumnuclear star formation contributes, in different proportions, to the H2 and [FeII] emission. In most of our spectra, the [FeII] 1.257mu/1.644mu ratio is found to be 30% lower than the intrinsic value based on current atomic data. This implies either than the extinction towards the [FeII] emitting clouds is very similar in most objects or there are possible inaccuracies in the A-values in the [FeII] transitions.Comment: 18 pages, 6 figures, Accepted for publication in Astronomy & Astrophysic

The Infrared Nuclear Emission of Seyfert Galaxies on Parsec Scales: Testing the Clumpy Torus models

We present subarcsecond resolution mid-infrared (mid-IR) photometry in the wavelength range from 8 to 20 micron of eighteen Seyfert galaxies, reporting high spatial resolution nuclear fluxes for the entire sample. We construct spectral energy distributions (SEDs) that the AGN dominates adding near-IR measurements from the literature at similar angular resolution. The IR SEDs of intermediate-type Seyferts are flatter and present higher 10 to 18 micron ratios than those of Seyfert 2. We fit the individual SEDs with clumpy torus models using the in-house-developed BayesClumpy tool. The models reproduce the high spatial resolution measurements. Regardless of the Seyfert type, even with high spatial resolution data, near- to mid-IR SED fitting poorly constrains the radial extent of the torus. For the Seyfert 2, we find that edge-on geometries are more probable than face-on views, with a number of clouds along equatorial rays of N = 5-15. The 10 micron silicate feature is generally modeled in shallow absorption. For the intermediate-type Seyferts, N and the inclination angle of the torus are lower than those of the Seyfert 2 nuclei, with the silicate feature appearing in weak emission or absent. The columns of material responsible for the X-ray absorption are larger than those inferred from the model fits for most of the galaxies, which is consistent with X-ray absorbing gas being located within the dust sublimation radius whereas the mid-IR flux arises from an area farther from the accretion disc. The fits yield both the bolometric luminosity of the intrinsic AGN and the torus integrated luminosity, from which we derive the reprocessing efficiency of the torus. In the models, the outer radial extent of the torus scales with the AGN luminosity, and we find the tori to be confined to scales less than 5 pc.Comment: 26 pages, 8 figures, 9 tables. Accepted for publication in Ap

A multiscale orchestrated computational framework to reveal emergent phenomena in neuroblastoma

Neuroblastoma is a complex and aggressive type of cancer that affects children. Current treatments involve a combination of surgery, chemotherapy, radiotherapy, and stem cell transplantation. However, treatment outcomes vary due to the heterogeneous nature of the disease. Computational models have been used to analyse data, simulate biological processes, and predict disease progression and treatment outcomes. While continuum cancer models capture the overall behaviour of tumours, and agent-based models represent the complex behaviour of individual cells, multiscale models represent interactions at different organisational levels, providing a more comprehensive understanding of the system. In 2018, the PRIMAGE consortium was formed to build a cloud-based decision support system for neuroblastoma, including a multi-scale model for patient-specific simulations of disease progression. In this work we have developed this multi-scale model that includes data such as patient's tumour geometry, cellularity, vascularization, genetics and type of chemotherapy treatment, and integrated it into an online platform that runs the simulations on a high-performance computation cluster using Onedata and Kubernetes technologies. This infrastructure will allow clinicians to optimise treatment regimens and reduce the number of costly and time-consuming clinical trials. This manuscript outlines the challenging framework's model architecture, data workflow, hypothesis, and resources employed in its development