Adaptive Comfort Models Applied to Existing Dwellings in Mediterranean Climate Considering Global Warming

Comfort analysis of existing naturally ventilated buildings located in mild climates, such as the ones in the Mediterranean zones, offer room for a reduction in the present and future energy consumption. Regarding Spain, most of the present building stock was built before energy standards were mandatory, let alone considerations about global warming or adaptive comfort. In this context, this research aims at assessing adaptive thermal comfort of inhabitants of extant apartments building in the South of Spain per EN 15251:2007 and ASHRAE 55-2013. The case study is statistically representative housing built in 1973. On-site monitoring of comfort conditions and computer simulations for present conditions have been carried out, clarifying the degree of adaptive comfort at present time. After that, additional simulations for 2020, 2050, and 2080 are performed to check whether this dwelling will be able to provide comfort considering a change in climate conditions. As a result, the study concludes that levels of adaptive comfort can be considered satisfactory at present time in these dwellings, but not in the future, when discomfort associated with hot conditions will be recurrent. These results provide a hint to foresee how extant dwellings, and also dwellers, should adapt to a change in environmental conditions

Mapping gas around massive galaxies: cross-correlation of DES Y3 galaxies and Compton-y maps from SPT and Planck

Artículo escrito por un elevado número de autores, solo se referencian el que aparece en primer lugar, el nombre del grupo de colaboración, si le hubiere, y los autores pertenecientes a la UAMWe cross-correlate positions of galaxies measured in data from the first three years of the Dark Energy Survey with Compton-y maps generated using data from the South Pole Telescope (SPT) and the Planck mission. We model this cross-correlation measurement together with the galaxy autocorrelation to constrain the distribution of gas in the Universe. We measure the hydrostatic mass bias or, equivalently, the mean halo bias-weighted electron pressure (bh Pe), using large-scale information. We find (bh Pe) to be [0.16+−000403, 0.28+−000504, 0.45+−001006, 0.54+−000708, 0.61+−000608, 0.63+−000807] meV cm−3 at redshifts z ∼ [0.30, 0.46, 0.62, 0.77, 0.89, 0.97]. These values are consistent with previous work where measurements exist in the redshift range. We also constrain the mean gas profile using small-scale information, enabled by the high-resolution of the SPT data. We compare our measurements to different parametrized profiles based on the cosmo-OWLS hydrodynamical simulations. We find that our data are consistent with the simulation that assumes an AGN heating temperature of 108.5 K but are incompatible with the model that assumes an AGN heating temperature of 108.0 K. These comparisons indicate that the data prefer a higher value of electron pressure than the simulations within r500c of the galaxies’ haloesThe DES participants from Spanish institutions are partially supported by MICINN under grants ESP2017-89838, PGC2018-094773, PGC2018-102021, SEV-2016-0588, SEV-2016-0597, and MDM-2015-0509, some of which include ERDF funds from the European Union. IFAE is partially funded by the CERCA program of the Generalitat de Catalunya. Research leading to these results has received funding from the European Research Council under the European Union’s Seventh Framework Program (FP7/2007-2013) including ERC grant agreements 240672, 291329, and 306478. We acknowledge support from the Brazilian Instituto Nacional de Ciencia e Tecnologia (INCT) do e-Universo (CNPq grant 465376/2014-2

Higgs-dilaton cosmology: From the early to the late Universe

We consider a minimal scale-invariant extension of the standard model of particle physics combined with unimodular gravity formulated in [M. Shaposhnikov and D. Zenhausern, Phys. Lett. B 671, 187 (2009).]. This theory is able to describe not only an inflationary stage, related to the standard model Higgs field, but also a late period of dark-energy domination, associated with an almost massless dilaton. A number of parameters can be fixed by inflationary physics, allowing us to make specific predictions for any subsequent period. In particular, we derive a relation between the tilt of the primordial spectrum of scalar fluctuations, ns, and the present value of the equation of state parameter of dark energy (DE), wDE0. We find bounds for the scalar tilt, ns-1. The relation between ns and wDE0 allows us to use the current observational bounds on ns to further constrain the dark-energy equation of state to 0<1+wDE0<0.02, which is to be confronted with future dark-energy surveysWe acknowledge financial support from the Madrid Regional Government (CAM) under the Program No. HEPHACOS P-ESP-00346 and MICINN under Grant No. AYA2009-13936-C06-06. We also participate in the Consolider-Ingenio 2010 PAU (CSD2007-00060), as well as in the European Union Marie Curie Network UniverseNet under Contract No. MRTN-CT-2006-035863. J. R. would like to acknowledge financial support from UAM/CSIC. The work of M. S. and D. Z. was supported by the Swiss National Science Foundation and by the Tomalla Foundatio

Non-local gradients in bounded domains motivated by continuum mechanics: Fundamental theorem of calculus and embeddings

In this article, we develop a new set of results based on a non-local gradient jointly inspired by the Riesz s s -fractional gradient and peridynamics, in the sense that its integration domain depends on a ball of radius δ > 0 δ > 0 (horizon of interaction among particles, in the terminology of peridynamics), while keeping at the same time the singularity of the Riesz potential in its integration kernel. Accordingly, we define a functional space suitable for non-local models in calculus of variations and partial differential equations. Our motivation is to develop the proper functional analysis framework to tackle non-local models in continuum mechanics, which requires working with bounded domains, while retaining the good mathematical properties of Riesz s s -fractional gradients. This functional space is defined consistently with Sobolev and Bessel fractional ones: we consider the closure of smooth functions under the natural norm obtained as the sum of the Lp norms of the function and its non-local gradient. Among the results showed in this investigation, we highlight a non-local version of the fundamental theorem of calculus (namely, a representation formula where a function can be recovered from its non-local gradient), which allows us to prove inequalities in the spirit of Poincaré, Morrey, Trudinger, and Hardy as well as the corresponding compact embeddings. These results are enough to show the existence of minimizers of general energy functionals under the assumption of convexity. Equilibrium conditions in this non-local situation are also established, and those can be viewed as a new class of non-local partial differential equations in bounded domains.This work has been supported by the Agencia Estatal de Investigación of the Spanish Ministry of Research and Innovation, through projects PID2020-116207GB-I00 (J.C.B. and J.C.), PID2021- 124195NB-C32 and the Severo Ochoa Programme for Centres of Excellence in R&D CEX2019-000904-S (C.M.- C.), by Junta de Comunidades de Castilla-La Mancha through project SBPLY/19/180501/000110 and European Regional Development Fund 2018/11744 (J.C.B. and J.C.), by the Madrid Government (Comunidad de Madrid, Spain) under the multi-annual Agreement with UAM in the line for the Excellence of the University Research Staff in the context of the V PRICIT (Regional Programme of Research and Technological Innovation) (C.M.-C.), by the ERC Advanced Grant 834728 (C.M.-C.), and by Fundación Ramón Areces (J.C.

A regional machine learning method to outperform temperature-based Reference Evapotranspiration estimations in Southern Spain

Accurate estimations of reference evapotranspiration (ET0) are crucial for determining crop water requirements and designing an adequate irrigation scheduling to optimize the use of water. In this work, a new clustering method to outperform the accuracy of ET0 estimations only using temperature variables has been developed and assessed, based on the multifractal properties of the Diurnal Temperature Range (DTR). Thus, a more accurate weather stations’ grouping method has been evaluated, regardless of their geographic location. All the datasets were collected from 89 automated weather stations in the period 2000-2018 and pooled into two main regions (1 and 2). In each region, an iterative procedure has been carried out: 1) selection of all the stations except the candidate one for the training procedure and 2) test procedure using the candidate station. The results showed that Machine Learning models (ML) highly outperformed Hargreaves-Samani (HS) in most of the cases, being Multilayer Perceptron (MLP) the most accurate over Extreme Learning Machine models (ELM). On average, the results obtained by MLP using the best configuration in the first region were better than those obtained in the second region. Specifically, the first region got an Root Mean Square Error (RMSE) = 0.6572 mm/d, Nash–Sutcliffe Efficiency (NSE) = 0.8967, Coefficient of Determination (R 2) = 0.9306 and Mean Bias Error (MBE) = |0.04|mm/d while the second region obtained an RMSE = 0.7034 mm/d, NSE = 0.8665, R2 = 0.8968 and MBE = |0.045|mm/d. Regarding the seasonal performance, spring and autumn obtained the best NSE and R 2 results, whereas winter carried out the lowest RMSE values. This study provides a new and more accurate methodology to improve ET0 estimations on a regional basis and only using temperature data in the whole process

Detection of Cross-Correlation between Gravitational Lensing and γ Rays

Artículo escrito por un elevado número de autores, solo se referencian el que aparece en primer lugar, el nombre del grupo de colaboración, si le hubiere, y los autores pertenecientes a la UA

New machine learning approaches to improve reference evapotranspiration estimates using intra-daily temperature-based variables in a semi-arid region of Spain

The estimation of Reference Evapotranspiration (ET0) is crucial to estimate crop 9 water requirements, especially in developing countries and areas with scarce water resources. In these regions, the impossibility of collecting all the required data to compute FAO-56 Penman-Monteith equation (FAO56-PM) make scientists search new methodologies to accurately estimate ET0 with the minimum number of climatic parameters. In this work, several neural network approaches have been evaluated for estimating ET0 using datasets from five weather stations located in Southern Spain (semiarid region of Andalusia). The assessment of statistical performance (Root Mean Square Error -RMSE-, Mean Bias Error -MBE-, coefficient of determination -R 2 - and Nash-Sutcliffe model efficiency coefficient -NSE-) of models namely Multilayer perceptron (MLP), Generalized Regression Neural Network (GRNN), Extreme Learning Machine (ELM), Support Vector Machines (SVM), Random Forest (RF) and XGBoost were carried out using different input variables configurations. Only temperature-based data were used as inputs; the calculation of new variables called EnergyT (the integral of the half hourly temperature values of a day) andHourmin (the difference in hours between time sunset and the time when the maximum temperature occurs) had promising results for the most humid stations. The good results obtained with EnergyT when it is used as an input of the system demonstrated that the information contained on it gives detailed characterization of the daily thermic behavior at each location, resulting in a more efficient model than those using only daily maximum, minimum temperature and extraterrestrial radiation values. In general, the modelling results showed that no model firmly outperformed the others, although MLP and ELM were commonly the models that gave the best performances for all sites: mean values of R 2 >0.89, mean values of NSE >0.88, mean values of RMSE<0.67mm/day and mean values of MBE ranging from −0.17 to 0.30mm/day. Therefore, EnergyT and Hourmin can be used to estimate ET0 more accurately in stations where data acquisition is limited, like in developing countries or at low-cost weather stations that cannot collect all the required meteorological variables used in FAO56-PM. Overall, the use of ELM is recommended due to its high performance in terms of efficiency (NSE) for all the configurations and for all locations, especially using EnergyT as an input variable

Dark energy survey year 3 results: weak lensing shape catalogue

Artículo escrito por un elevado número de autores, solo se referencian el que aparece en primer lugar, el nombre del grupo de colaboración, si le hubiere, y los autores pertenecientes a la UAMWe present and characterize the galaxy shape catalogue from the first 3 yr of Dark Energy Survey (DES) observations, over an effective area of 4143 deg2 of the southern sky. We describe our data analysis process and our self-calibrating shear measurement pipeline metacalibration, which builds and improves upon the pipeline used in the DES Year 1 analysis in several aspects. The DES Year 3 weak-lensing shape catalogue consists of 100 204 026 galaxies, measured in the riz bands, resulting in a weighted source number density of neff = 5.59 gal arcmin-2 and corresponding shape noise σe = 0.261. We perform a battery of internal null tests on the catalogue, including tests on systematics related to the point spread function (PSF) modelling, spurious catalogue B-mode signals, catalogue contamination, and galaxy propertie

Assessing Machine Learning Models for Gap Filling Daily Rainfall Series in a Semiarid Region of Spain

The presence of missing data in hydrometeorological datasets is a common problem, usually due to sensor malfunction, deficiencies in records storage and transmission, or other recovery procedures issues. These missing values are the primary source of problems when analyzing and modeling their spatial and temporal variability. Thus, accurate gap-filling techniques for rainfall time series are necessary to have complete datasets, which is crucial in studying climate change evolution. In this work, several machine learning models have been assessed to gap-fill rainfall data, using different approaches and locations in the semiarid region of Andalusia (Southern Spain). Based on the obtained results, the use of neighbor data, located within a 50 km radius, highly outperformed the rest of the assessed approaches, with RMSE (root mean squared error) values up to 1.246 mm/day, MBE (mean bias error) values up to −0.001 mm/day, and R2 values up to 0.898. Besides, inland area results outperformed coastal area in most locations, arising the efficiency effects based on the distance to the sea (up to an improvement of 63.89% in terms of RMSE). Finally, machine learning (ML) models (especially MLP (multilayer perceptron)) notably outperformed simple linear regression estimations in the coastal sites, whereas in inland locations, the improvements were not such significant