Dark matter cores in the Fornax and Sculptor dwarf galaxies: joining halo assembly and detailed star formation histories

We combine the detailed Star Formation Histories of the Fornax and Sculptor dwarf Spheroidals with the Mass Assembly History of their dark matter (DM) halo progenitors to estimate if the energy deposited by Supernova type II (SNeII) is sufficient to create a substantial DM core. Assuming the efficiency of energy injection of the SNeII into DM particles is $\epsilon_{\rm gc}=0.05$, we find that a single early episode, $z \gtrsim z_{\rm infall}$, that combines the energy of all SNeII due to explode over 0.5 Gyr, is sufficient to create a core of several hundred parsecs in both Sculptor and Fornax. Therefore, our results suggest that it is energetically plausible to form cores in Cold Dark Matter (CDM) halos via early episodic gas outflows triggered by SNeII. Furthermore, based on CDM merger rates and phase-space density considerations, we argue that the probability of a subsequent complete regeneration of the cusp is small for a substantial fraction of dwarf-size haloes.Comment: ApJL accepted versio

Model independent constraints on transition redshift

This paper aims to put constraints on the transition redshift $z_t$, which determines the onset of cosmic acceleration, in cosmological-model independent frameworks. In order to perform our analyses, we consider a flat universe and {assume} a parametrization for the comoving distance $D_C(z)$ up to third degree on $z$, a second degree parametrization for the Hubble parameter $H(z)$ and a linear parametrization for the deceleration parameter $q(z)$. For each case, we show that {type Ia supernovae} and $H(z)$ data complement each other on the parameter {space} and tighter constrains for the transition redshift are obtained. By {combining} the type Ia supernovae observations and Hubble parameter measurements it is possible to constrain the values of $z_t$, for each approach, as $0.806\pm 0.094$, $0.870\pm 0.063$ and $0.973\pm 0.058$ at 1$\sigma$ c.l., respectively. Then, such approaches provide cosmological-model independent estimates for this parameter.Comment: 24 pages, 8 figures, 1 table, text revise

Satellite remote sensing reveals a positive impact of living oyster reefs on microalgal biofilm development

Satellite remote sensing (RS) is routinely used for the large-scale monitoring of microphytobenthos (MPB) biomass in intertidal mudflats and has greatly improved our knowledge of MPB spatio-temporal variability and its potential drivers. Processes operating on smaller scales however, such as the impact of benthic macrofauna on MPB development, to date remain underinvestigated. In this study, we analysed the influence of wild Crassostrea gigas oyster reefs on MPB biofilm development using multispectral RS. A 30-year time series (1985-2015) combining high-resolution (30 m) Landsat and SPOT data was built in order to explore the relationship between C. gigas reefs and MPB spatial distribution and seasonal dynamics, using the normalized difference vegetation index (NDVI). Emphasis was placed on the analysis of a before-after control-impact (BACI) experiment designed to assess the effect of oyster killing on the surrounding MPB biofilms. Our RS data reveal that the presence of oyster reefs positively affects MPB biofilm development. Analysis of the historical time series first showed the presence of persistent, highly concentrated MPB patches around oyster reefs. This observation was supported by the BACI experiment which showed that killing the oysters (while leaving the physical reef structure, i.e. oyster shells, intact) negatively affected both MPB biofilm biomass and spatial stability around the reef. As such, our results are consistent with the hypothesis of nutrient input as an explanation for the MPB growth-promoting effect of oysters, whereby organic and inorganic matter released through oyster excretion and biodeposition stimulates MPB biomass accumulation. MPB also showed marked seasonal variations in biomass and patch shape, size and degree of aggregation around the oyster reefs. Seasonal variations in biomass, with higher NDVI during spring and autumn, were consistent with those observed on broader scales in other European mudflats. Our study provides the first multi-sensor RS satellite evidence of the promoting and structuring effect of oyster reefs on MPB biofilms

Emerging role of angiogenesis in adaptive and maladaptive right ventricular remodeling in pulmonary hypertension

Right ventricular (RV) function is the primary prognostic factor for both morbidity and mortality in pulmonary hypertension (PH). RV hypertrophy is initially an adaptive physiological response to increased overload; however, with persistent and/or progressive afterload increase, this response frequently transitions to more pathological maladaptive remodeling. The mechanisms and disease processes underlying this transition are mostly unknown. Angiogenesis has recently emerged as a major modifier of RV adaptation in the setting of pressure overload. A novel paradigm has emerged that suggests that angiogenesis and angiogenic signaling are required for RV adaptation to afterload increases and that impaired and/or insufficient angiogenesis is a major driver of RV decompensation. Here, we summarize our current understanding of the concepts of maladaptive and adaptive RV remodeling, discuss the current literature on angiogenesis in the adapted and failing RV, and identify potential therapeutic approaches targeting angiogenesis in RV failure

On the Transferability of Knowledge among Vehicle Routing Problems by using Cellular Evolutionary Multitasking

Multitasking optimization is a recently introduced paradigm, focused on the simultaneous solving of multiple optimization problem instances (tasks). The goal of multitasking environments is to dynamically exploit existing complementarities and synergies among tasks, helping each other through the transfer of genetic material. More concretely, Evolutionary Multitasking (EM) regards to the resolution of multitasking scenarios using concepts inherited from Evolutionary Computation. EM approaches such as the well-known Multifactorial Evolutionary Algorithm (MFEA) are lately gaining a notable research momentum when facing with multiple optimization problems. This work is focused on the application of the recently proposed Multifactorial Cellular Genetic Algorithm (MFCGA) to the well-known Capacitated Vehicle Routing Problem (CVRP). In overall, 11 different multitasking setups have been built using 12 datasets. The contribution of this research is twofold. On the one hand, it is the first application of the MFCGA to the Vehicle Routing Problem family of problems. On the other hand, equally interesting is the second contribution, which is focused on the quantitative analysis of the positive genetic transferability among the problem instances. To do that, we provide an empirical demonstration of the synergies arisen between the different optimization tasks.Comment: 8 pages, 1 figure, paper accepted for presentation in the 23rd IEEE International Conference on Intelligent Transportation Systems 2020 (IEEE ITSC 2020

Stream Learning in Energy IoT Systems: A Case Study in Combined Cycle Power Plants

The prediction of electrical power produced in combined cycle power plants is a key challenge in the electrical power and energy systems field. This power production can vary depending on environmental variables, such as temperature, pressure, and humidity. Thus, the business problem is how to predict the power production as a function of these environmental conditions, in order to maximize the profit. The research community has solved this problem by applying Machine Learning techniques, and has managed to reduce the computational and time costs in comparison with the traditional thermodynamical analysis. Until now, this challenge has been tackled from a batch learning perspective, in which data is assumed to be at rest, and where models do not continuously integrate new information into already constructed models. We present an approach closer to the Big Data and Internet of Things paradigms, in which data are continuously arriving and where models learn incrementally, achieving significant enhancements in terms of data processing (time, memory and computational costs), and obtaining competitive performances. This work compares and examines the hourly electrical power prediction of several streaming regressors, and discusses about the best technique in terms of time processing and predictive performance to be applied on this streaming scenario.This work has been partially supported by the EU project iDev40. This project has received funding from the ECSEL Joint Undertaking (JU) under grant agreement No 783163. The JU receives support from the European Union’s Horizon 2020 research and innovation programme and Austria, Germany, Belgium, Italy, Spain, Romania. It has also been supported by the Basque Government (Spain) through the project VIRTUAL (KK-2018/00096), and by Ministerio de Economía y Competitividad of Spain (Grant Ref. TIN2017-85887-C2-2-P)

Erratum: Martins, M.S., et al. Wideband and wIde beam polyvinylidene difluoride (PVDF) acoustic transducer for broadband underwater. Sensors 2019, 19, 3991

The authors wish to make the following erratum to this paper [...].info:eu-repo/semantics/publishedVersio