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According to the celebrated singularity theorems, space-time singularities in general relativity are inevitable. However, it is generally believed that singularities do not exist in nature, and their existence suggests the necessity of a new theory of gravity. In this paper, we investigated a regular astrophysically viable space-time (regular in the sense that it is singularity-free) from the observational point of view using observations from the LIGO, Virgo, and KAGRA (LVK), and the event horizon telescope (EHT) collaborations. This black hole solution depends on a free parameter $\ell$ in addition to the mass, $M$, and the spin, $a$, violating, in this way, the non-hair theorem/conjecture. In the case of gravitational wave observations, we use the catalogs GWTC-1, 2, and 3 to constrain the free parameter. In the case of the EHT, we use the values of the angular diameter reported for SgrA* and M87*. We also investigated the photon ring structure by considering scenarios such as static spherical accretion, infalling spherical accretion, and thin accretion disk. Our results show that the EHT observations constrain the free parameter $\ell$ to the intervals $0\leq \ell \leq 0.148$ and $0\leq \ell \leq 0.212$ obtained for SgrA* and M87*, respectively. On the other hand, GW observations constrain the free parameter with values that satisfy the theoretical limit, particularly those events for which $\ell<<1$. Our results show that the most stringent constraints on $\ell$ correspond to the events GW191204-171526 ($\ell=0.041^{+0.106}_{-0.041}$) and GW190924-021846 ($\ell=0.050^{+0.165}_{-0.050}$) for the SEOB model., Comment: 25 pages, 15 figures

Black hole accretion disks are a fascinating topic in astrophysics, as they play a crucial role in several high-energy situations. This paper investigates the optical appearance of scalar hairy black holes (SHBHs) with asymmetric potential, a numerical solution obtained in Phys. Rev. D 73, 084002 (2006) and discussed in Phys.Rev.D 108 (2023) 4, 044020. Since the solution is spherically symmetric and surrounded by a thin accretion disk, we base our analysis on the work of J.~P. Lumininet (1979). We discuss the behavior of the effective potential for massive and massless particles, the innermost stable circular orbits (ISCO), and the photon sphere radius for different SHBHs. The study includes the plots of isoradial curves and spectral shifts arising from gravitational and Doppler shifts by considering direct and secondary images. Based on the work of Page and Thorne (1974), we also investigate the intrinsic intensity of radiation emitted by the disk at a given radius, which allows the calculation of the distribution of observed bolometric flux. We use the angular size of the shadow reported by the EHT for Sagittarius A* and M87* to constrain the SHBHs parameters., Comment: 21 pages, 16 figures

Preparing quantum many-body states on classical or quantum devices is a very challenging task that requires accounting for exponentially large Hilbert spaces. Although this complexity can be managed with exponential ans\"atze (such as in the coupled-cluster method), these approaches are often tailored to specific systems, which limits their universality. Recent work has shown that the contracted Schr\"odinger equation enables the construction of universal, formally exact exponential ans\"atze for quantum many-body physics. However, while the ansatz is capable of resolving arbitrary quantum systems, it still requires a full calculation of its parameters whenever the underlying Hamiltonian changes, even slightly. Here, inspired by recent progress in operator learning, we develop a surrogate neural network solver that generates the exponential ansatz parameters using the Hamiltonian parameters as inputs, eliminating the need for repetitive computations. We illustrate the effectiveness of this approach by training neural networks of several quantum many-body systems, including the Fermi-Hubbard model., Comment: 9 pages, 6 figures

Describing the dynamics of strong-laser driven open quantum systems is a very challenging task that requires the solution of highly involved equations of motion. While machine learning techniques are being applied with some success to simulate the time evolution of individual quantum states, their use to approximate time-dependent operators (that can evolve various states) remains largely unexplored. In this work, we develop driven neural quantum propagators (NQP), a universal neural network framework that solves driven-dissipative quantum dynamics by approximating propagators rather than wavefunctions or density matrices. NQP can handle arbitrary initial quantum states, adapt to various external fields, and simulate long-time dynamics, even when trained on far shorter time windows. Furthermore, by appropriately configuring the external fields, our trained NQP can be transferred to systems governed by different Hamiltonians. We demonstrate the effectiveness of our approach by studying the spin-boson and the three-state transition Gamma models., Comment: 7 pages, comment are welcome!

We study the possibility of obtaining the Standard Model (SM) of particle physics as an effective theory of a more fundamental one, whose electroweak sector includes two non-universal local $U(1)$ gauge groups, with the chiral anomaly cancellation taking place through an interplay among families. As a result of the spontaneous symmetry breaking, a massive gauge boson $Z'$ arises, which couples differently to the third family of fermions (by assumption, we restrict ourselves to the scenario in which the $Z'$ couples in the same way to the first two families). Two Higgs doublets and one scalar singlet are necessary to generate the SM fermion masses and break the gauge symmetries. We show that in our model, the flavor-changing neutral currents (FCNC) of the Higgs sector are identically zero if each right-handed SM fermion is only coupled with a single Higgs doublet. This result represents a FCNC cancellation mechanism different from the usual procedure in Two-Higgs Doublet Models~(2HDM). The non-universal nature of our solutions requires the presence of three right-handed neutrino fields, one for each family. Our model generates all elements of the Dirac mass matrix for quarks and leptons, which is quite non-trivial for non-universal models. Thus, we can fit all the masses and mixing angles with two scalar doublets. Finally, we show the distribution of solutions for the scalar boson masses in our model by scanning well-motivated intervals for the model parameters. We consider two possibilities for the scalar potential and compare these results with the Higgs-like resonant signals recently reported by the ATLAS and CMS experiments at the LHC. Finally, we also report collider, electroweak, and flavor constraints on the model parameters., Comment: 27 pages, 3 figures

We study the stellar properties of a sample of simulated ultra-diffuse galaxies (UDGs) with stellar mass $M_\star=10^{7.5}$ - $10^{9} ~ \rm{M_{\odot}}$, selected from the TNG50 simulation, where UDGs form mainly in high-spin dwarf-mass halos. We divide our sample into star-forming and quenched UDGs, finding good agreement with the stellar assembly history measured in observations. Star-forming UDGs and quenched UDGs with $M_\star \geq 10^8\; \rm M_\odot$ in our sample are particularly inefficient at forming stars, having $2$ - $10$ times less stellar mass than non-UDGs for the same virial mass halo. These results are consistent with recent mass inferences in UDG samples and suggest that the most inefficient UDGs arise from a late assembly of the dark matter mass followed by a stellar growth that is comparatively slower (for star-forming UDGs) or that was interrupted due to environmental removal of the gas (for quenched UDGs). Regardless of efficiency, UDGs are $60\%$ poorer in [Fe/H] than the population of non-UDGs at a fixed stellar mass, with the most extreme objects having metal content consistent with the simulated mass-metallicity relation at $z \sim 2$. Quenched UDGs stop their star formation in shorter timescales than non-UDGs of similar mass and are, as a consequence, alpha-enhanced with respect to non-UDGs. We identify metallicity profiles in UDGs as a potential avenue to distinguish between different formation paths for these galaxies, where gentle formation as a result of high-spin halos would present well-defined declining metallicity radial profiles while powerful-outflows or tidal stripping formation models would lead to flatter or constant metallicity as a function of radius due, Comment: 19 pages, 11 figures, submitted to ApJ

We explored the composition dependence of a rather comprehensive set of properties of liquid water-ethanol mixtures by using the isobaric-isothermal molecular dynamics computer simulations. The united atom non-polarizable model from the TraPPE data basis for the ethanol molecule combined with the TIP4P-2005 and SPC/E water models is considered. We restrict our calculations to atmospheric pressure, 0.1013 MPa, and room temperature, 298.15 K. Composition trends of the behavior of density, excess mixing volume, apparent molar volumes are described. On the other hand, the excess mixing enthalpy and partial molar enthalpies of species are reported. Besides, we explore the coefficient of isobaric thermal expansion, isothermal heat capacity, adiabatic bulk modulus and heat capacity at constant pressure. In addition, the self-diffusion coefficients of species, the static dielectric constant and the surface tension are described. We intend to get insights into peculiarities of mixing of species in the mixture upon changes of ethanol molar fraction. The quality of predictions of the models is critically evaluated by detailed comparisons with experimental results. Then, necessary improvements of the modelling are discussed., Comment: 17 pages, 14 figures. arXiv admin note: text overlap with arXiv:2210.00487

From the implementation of the Grafcet programming language we obtain in this document the different ways of applying this method, the advantages it obtains with respect to efficiency, cost, quality and operational and time optimization. The behavior of a pump alternation process is analyzed using GRAFCET, which is easier to design, verify and implement sequential control systems. We will also have the different fields where this programming is applied., Comment: 8 PAGINAS, in Spanish language

Structural disturbances, such as galaxy mergers or instabilities, are key candidates for driving galaxy evolution, so it is important to detect and quantify galaxies hosting these disturbances spanning a range of masses, environments, and cosmic times. Traditionally, this is done by quantifying the asymmetry of a galaxy as part of the concentration-asymmetry-smoothness system, $A_{\rm{CAS}}$, and selecting galaxies above a certain threshold as merger candidates. However, in this work, we show that $A_{\rm{CAS}}$, is extremely dependent on imaging properties -- both resolution and depth -- and thus defining a single $A_{\rm{CAS}}$ threshold is impossible. We analyze an alternative root-mean-squared asymmetry, $A_{\rm{RMS}}$, and show that it is independent of noise down to the average SNR per pixel of 1. However, both metrics depend on the resolution. We argue that asymmetry is, by design, always a scale-dependent measurement, and it is essential to define an asymmetry at a given physical resolution, where the limit should be defined by the size of the smallest features one wishes to detect. We measure asymmetry of a set of $z\approx0.1$ galaxies observed with HST, HSC, and SDSS, and show that after matching the resolution of all images to 200 pc, we are able to obtain consistent $A_{\rm{RMS, 200pc}}$ measurements with all three instruments despite the vast differences in the original resolution or depth. We recommend that future studies use $A_{\rm{RMS, x pc}}$ measurement when evaluating asymmetry, where $x$ is defined by the physical size of the features of interest, and is kept consistent across the dataset, especially when the redshift or image properties of galaxies in the dataset vary., Comment: 26 pages, 10 figures. Accepted to the Open Journal for Astrophysics

The observationally-inferred size versus stellar-mass relationship (SMR) for low-mass galaxies provides an important test for galaxy formation models. However, the relationship relies on assumptions that relate observed luminosity profiles to underlying stellar mass profiles. Here we use the Feedback in Realistic Environments simulations of low-mass galaxies to explore how the predicted SMR changes depending on whether one uses star-particle counts directly or mock observations. We reproduce the SMR found in The Exploration of Local Volume Satellites survey remarkably well only when we infer stellar masses and sizes using mock observations. However, when we use star particles to directly infer stellar masses and half-mass radii, we find that our galaxies are too large and obey a SMR with too little scatter compared to observations. This discrepancy between the "true" galaxy size and mass and those derived in the mock observation approach is twofold. First, our simulated galaxies have higher and more varied MLRs at a fixed colour than those commonly-adopted, which tends to underestimate their stellar masses compared to their true, simulated values. Second, our galaxies have radially increasing MLR gradients therefore using a single MLR tends to under-predict the mass in the outer regions. Similarly, the true half-mass radius is larger than the half-light radius because the light is more concentrated than the mass. If our simulations are accurate representations of the real universe, then the relationship between galaxy size and stellar mass is even tighter for low-mass galaxies than is commonly inferred from observed relations., Comment: 11 pages, 7 figures, 1 table

Feature models have become a de facto standard for representing variability in software product lines. UVL (Universal Variability Language) is a language which expresses the features, dependencies, and constraints between them. This language is written in plain text and follows a syntactic structure that needs to be processed by a parser. This parser is software with specific syntactic rules that the language must comply with to be processed correctly. Researchers have datasets with numerous feature models. The language description form of these feature models is tied to a version of the parser language. When the parser is updated to support new features or correct previous ones, these feature models are often no longer compatible, generating incompatibilities and inconsistency within the dataset. In this paper, we present UVL Sentinel. This tool analyzes a dataset of feature models in UVL format, generating error analysis reports, describing those errors and, eventually, a syntactic processing that applies the most common solutions. This tool can detect the incompatibilities of the feature models of a dataset when the parser is updated and tries to correct the most common syntactic errors, facilitating the management of the dataset and the adaptation of their models to the new version of the parser. Our tool was evaluated using a dataset of 1,479 UVL models from different sources and helped semi-automatically fix 185 warnings and syntax errors., Comment: Presented at 6th International Workshop on Languages for Modelling Variability (MODEVAR'24) (arXiv:cs/2402.15511)

COVID-19 disease has affected almost every country in the world. The large number of infected people and the different mortality rates between countries has given rise to many hypotheses about the key points that make the virus so lethal in some places. In this study, the eating habits of 170 countries were evaluated in order to find correlations between these habits and mortality rates caused by COVID-19 using machine learning techniques that group the countries together according to the different distribution of fat, energy, and protein across 23 different types of food, as well as the amount ingested in kilograms. Results shown how obesity and the high consumption of fats appear in countries with the highest death rates, whereas countries with a lower rate have a higher level of cereal consumption accompanied by a lower total average intake of kilocalories.

We present an exact ansatz for the eigenstate problem of mixed fermion-boson systems that can be implemented on quantum devices. Based on a generalization of the electronic contracted Schr\"odinger equation (CSE), our approach guides a trial wave function to the ground state of any arbitrary mixed Hamiltonian by directly measuring residuals of the mixed CSE on a quantum device. Unlike density-functional and coupled-cluster theories applied to electron-phonon or electron-photon systems, the accuracy of our approach is not limited by the unknown exchange-correlation functional or the uncontrolled form of the exponential ansatz. To test the performance of the method, we study the Tavis-Cummings model, commonly used in polaritonic quantum chemistry. Our results demonstrate that the CSE is a powerful tool in the development of quantum algorithms for solving general fermion-boson many-body problems.

Social Network Analysis (SNA) is a set of techniques developed in the field of social and behavioral sciences research, in order to characterize and study the social relationships that are established among a set of individuals. When building a social network for performing an SNA analysis, an initial process of data gathering is achieved in order to extract the characteristics of the individuals and their relationships. This is usually done by completing a questionnaire containing different types of questions that will be later used to obtain the SNA measures needed to perform the study. There are, then, a great number of different possible network generating questions and also many possibilities for mapping the responses to the corresponding characteristics and relationships. Many variations may be introduced into these questions (the way they are posed, the weights given to each of the responses, etc.) that may have an effect on the resulting networks. All these different variations are difficult to achieve manually, because the process is time-consuming and error prone. The tool described in this paper uses semantic knowledge representation techniques in order to facilitate this kind of sensitivity studies. The base of the tool is a conceptual structure, called "ontology" that is able to represent the different concepts and their definitions. The tool is compared to other similar ones, and the advantages of the approach are highlighted, giving some particular examples from an ongoing SNA study about alcohol consumption habits in adolescents.

Alcohol Use Disorder (AUD) is a major concern for public health organizations worldwide, especially as regards the adolescent population. The consumption of alcohol in adolescents is known to be influenced by seeing friends and even parents drinking alcohol. Building on this fact, a number of studies into alcohol consumption among adolescents have made use of Social Network Analysis (SNA) techniques to study the different social networks (peers, friends, family, etc.) with whom the adolescent is involved. These kinds of studies need an initial phase of data gathering by means of questionnaires and a subsequent analysis phase using the SNA techniques. The process involves a number of manual data handling stages that are time consuming and error-prone. The use of knowledge engineering techniques (including the construction of a domain ontology) to represent the information, allows the automation of all the activities, from the initial data collection to the results of the SNA study. This paper shows how a knowledge model is constructed, and compares the results obtained using the traditional method with this, fully automated model, detailing the main advantages of the latter. In the case of the SNA analysis, the validity of the results obtained with the knowledge engineering approach are compared to those obtained manually using the UCINET, Cytoscape, Pajek and Gephi to test the accuracy of the knowledge model.

There is a great concern nowadays regarding alcohol consumption and drug abuse, especially in young people. Analyzing the social environment where these adolescents are immersed, as well as a series of measures determining the alcohol abuse risk or personal situation and perception using a number of questionnaires like AUDIT, FAS, KIDSCREEN, and others, it is possible to gain insight into the current situation of a given individual regarding his/her consumption behavior. But this analysis, in order to be achieved, requires the use of tools that can ease the process of questionnaire creation, data gathering, curation and representation, and later analysis and visualization to the user. This research presents the design and construction of a web-based platform able to facilitate each of the mentioned processes by integrating the different phases into an intuitive system with a graphical user interface that hides the complexity underlying each of the questionnaires and techniques used and presenting the results in a flexible and visual way, avoiding any manual handling of data during the process. Advantages of this approach are shown and compared to the previous situation where some of the tasks were accomplished by time consuming and error prone manipulations of data.

The spread of the Internet of Things (IoT) is demanding new, powerful architectures for handling the huge amounts of data produced by the IoT devices. In many scenarios, many existing isolated solutions applied to IoT devices use a set of rules to detect, report and mitigate malware activities or threats. This paper describes a development environment that allows the programming and debugging of such rule-based multi-agent solutions. The solution consists of the integration of a rule engine into the agent, the use of a specialized, wrapping agent class with a graphical user interface for programming and testing purposes, and a mechanism for the incremental composition of behaviors. Finally, a set of examples and a comparative study were accomplished to test the suitability and validity of the approach. The JADE multi-agent middleware has been used for the practical implementation of the approach., Comment: arXiv admin note: substantial text overlap with arXiv:2402.09499

The increasing number of connected devices and the complexity of Internet of Things (IoT) ecosystems are demanding new architectures for managing and securing these networked environments. Intrusion Detection Systems (IDS) are security solutions that help to detect and mitigate the threats that IoT systems face, but there is a need for new IDS strategies and architectures. This paper describes a development environment that allows the programming and debugging of distributed, rule-based multi-agent IDS solutions. The proposed solution consists in the integration of a rule engine into the agent, the use of a specialized, wrapping agent class with a graphical user interface for programming and debugging purposes, and a mechanism for the incremental composition of behaviors. A comparative study and an example IDS are used to test and show the suitability and validity of the approach. The JADE multi-agent middleware has been used for the practical implementations.

The COVID-19 pandemic has meant that young university students have had to adapt their learning and have a reduced relational context. Adversity contexts build models of human behaviour based on relationships. However, there is a lack of studies that analyse the behaviour of university students based on their social structure in the context of a pandemic. This information could be useful in making decisions on how to plan collective responses to adversities. The Social Network Analysis (SNA) method has been chosen to address this structural perspective. The aim of our research is to describe the structural behaviour of students in university residences during the COVID-19 pandemic with a more in-depth analysis of student leaders. A descriptive cross-sectional study was carried out at one Spanish Public University, Le\'on, from 23th October 2020 to 20th November 2020. The participation was of 93 students, from four halls of residence. The data were collected from a database created specifically at the university to "track" contacts in the COVID-19 pandemic, SiVeUle. We applied the SNA for the analysis of the data. The leadership on the university residence was measured using centrality measures. The top leaders were analyzed using the Egonetwork and an assessment of the key players. Students with higher social reputations experience higher levels of pandemic contagion in relation to COVID-19 infection. The results were statistically significant between the centrality in the network and the results of the COVID-19 infection. The most leading students showed a high degree of Betweenness, and three students had the key player structure in the network. Networking behaviour of university students in halls of residence could be related to contagion in the COVID-19 pandemic.

Cardiovascular diseases state as one of the greatest risks of death for the general population. Late detection in heart diseases highly conditions the chances of survival for patients. Age, sex, cholesterol level, sugar level, heart rate, among other factors, are known to have an influence on life-threatening heart problems, but, due to the high amount of variables, it is often difficult for an expert to evaluate each patient taking this information into account. In this manuscript, the authors propose using deep learning methods, combined with feature augmentation techniques for evaluating whether patients are at risk of suffering cardiovascular disease. The results of the proposed methods outperform other state of the art methods by 4.4%, leading to a precision of a 90%, which presents a significant improvement, even more so when it comes to an affliction that affects a large population.

Parkinson's disease is easy to diagnose when it is advanced, but it is very difficult to diagnose in its early stages. Early diagnosis is essential to be able to treat the symptoms. It impacts on daily activities and reduces the quality of life of both the patients and their families and it is also the second most prevalent neurodegenerative disorder after Alzheimer in people over the age of 60. Most current studies on the prediction of Parkinson's severity are carried out in advanced stages of the disease. In this work, the study analyzes a set of variables that can be easily extracted from voice analysis, making it a very non-intrusive technique. In this paper, a method based on different deep learning techniques is proposed with two purposes. On the one hand, to find out if a person has severe or non-severe Parkinson's disease, and on the other hand, to determine by means of regression techniques the degree of evolution of the disease in a given patient. The UPDRS (Unified Parkinson's Disease Rating Scale) has been used by taking into account both the motor and total labels, and the best results have been obtained using a mixed multi-layer perceptron (MLP) that classifies and regresses at the same time and the most important features of the data obtained are taken as input, using an autoencoder. A success rate of 99.15% has been achieved in the problem of predicting whether a person suffers from severe Parkinson's disease or non-severe Parkinson's disease. In the degree of disease involvement prediction problem case, a MSE (Mean Squared Error) of 0.15 has been obtained. Using a full deep learning pipeline for data preprocessing and classification has proven to be very promising in the field Parkinson's outperforming the state-of-the-art proposals.

The accurate (or even approximate) solution of the equations that govern the dynamics of dissipative quantum systems remains a challenging task for quantum science. While several algorithms have been designed to solve those equations with different degrees of flexibility, they rely mainly on highly expensive iterative schemes. Most recently, deep neural networks have been used for quantum dynamics but current architectures are highly dependent on the physics of the particular system and usually limited to population dynamics. Here we introduce an artificial-intelligence-based surrogate model that solves dissipative quantum dynamics by parameterizing quantum propagators as Fourier neural operators, which we train using both dataset and physics-informed loss functions. Compared with conventional algorithms, our quantum neural propagator avoids time-consuming iterations and provides a universal super-operator that can be used to evolve any initial quantum state for arbitrarily long times. To illustrate the wide applicability of the approach, we employ our quantum neural propagator to compute population dynamics and time-correlation functions of the Fenna-Matthews-Olson complex., Comment: 20 pages, 5 figures

The large number of sensors and actuators that make up the Internet of Things obliges these systems to use diverse technologies and protocols. This means that IoT networks are more heterogeneous than traditional networks. This gives rise to new challenges in cybersecurity to protect these systems and devices which are characterized by being connected continuously to the Internet. Intrusion detection systems (IDS) are used to protect IoT systems from the various anomalies and attacks at the network level. Intrusion Detection Systems (IDS) can be improved through machine learning techniques. Our work focuses on creating classification models that can feed an IDS using a dataset containing frames under attacks of an IoT system that uses the MQTT protocol. We have addressed two types of method for classifying the attacks, ensemble methods and deep learning models, more specifically recurrent networks with very satisfactory results.

Background: Technology has provided a new way of life for adolescents. Strategies aimed at improving health behaviors through digital platforms can offer promising results. However, since peers can modify behaviors related to food and exercise, studying digital interventions based on peer influence to improve weight status of adolescents is important. Objective: To assess an eHealth app's effectiveness in adolescent population improvements in age- and sex-adjusted BMI percentiles. The study also examined social relationships of adolescents pre- and postintervention, identifying group leaders to study their profiles, eating, physical activity habits, and app usage. Methods: BMI percentiles were calculated following World Health Organization guidelines. Participants' diets and physical activity levels were assessed using the KIDMED questionnaire and PAQ-A. Social network variables were analyzed using SNA methodology, with reciprocal friendships used to compute the "degree" measure for centrality. Results: The sample included 210 individuals in the intervention group and 91 in the control group, with a 60.1% participation rate. Adolescents in the intervention group modified their BMI toward the 50th percentile, improving diet and increasing social network postintervention. Group leaders were also leaders in physical activity and app usage.

One of the main challenges in simultaneous localization and mapping (SLAM) is real-time processing. High-computational loads linked to data acquisition and processing complicate this task. This article presents an efficient feature extraction approach for mapping structured environments. The proposed methodology, weighted conformal LiDAR-mapping (WCLM), is based on the extraction of polygonal profiles and propagation of uncertainties from raw measurement data. This is achieved using conformal M bius transformation. The algorithm has been validated experimentally using 2-D data obtained from a low-cost Light Detection and Ranging (LiDAR) range finder. The results obtained suggest that computational efficiency is significantly improved with reference to other state-of-the-art SLAM approaches.

Background and objective: Diabetes is a chronic pathology which is affecting more and more people over the years. It gives rise to a large number of deaths each year. Furthermore, many people living with the disease do not realize the seriousness of their health status early enough. Late diagnosis brings about numerous health problems and a large number of deaths each year so the development of methods for the early diagnosis of this pathology is essential. Methods: In this paper, a pipeline based on deep learning techniques is proposed to predict diabetic people. It includes data augmentation using a variational autoencoder (VAE), feature augmentation using an sparse autoencoder (SAE) and a convolutional neural network for classification. Pima Indians Diabetes Database, which takes into account information on the patients such as the number of pregnancies, glucose or insulin level, blood pressure or age, has been evaluated. Results: A 92.31% of accuracy was obtained when CNN classifier is trained jointly the SAE for featuring augmentation over a well balanced dataset. This means an increment of 3.17% of accuracy with respect the state-of-the-art. Conclusions: Using a full deep learning pipeline for data preprocessing and classification has demonstrate to be very promising in the diabetes detection field outperforming the state-of-the-art proposals.

In this work, a sentiment analysis method that is capable of accepting audio of any length, without being fixed a priori, is proposed. Mel spectrogram and Mel Frequency Cepstral Coefficients are used as audio description methods and a Fully Convolutional Neural Network architecture is proposed as a classifier. The results have been validated using three well known datasets: EMODB, RAVDESS, and TESS. The results obtained were promising, outperforming the state-of-the-art methods. Also, thanks to the fact that the proposed method admits audios of any size, it allows a sentiment analysis to be made in near real time, which is very interesting for a wide range of fields such as call centers, medical consultations, or financial brokers.

The aim of this paper was the detection of pathologies through respiratory sounds. The ICBHI (International Conference on Biomedical and Health Informatics) Benchmark was used. This dataset is composed of 920 sounds of which 810 are of chronic diseases, 75 of non-chronic diseases and only 35 of healthy individuals. As more than 88% of the samples of the dataset are from the same class (Chronic), the use of a Variational Convolutional Autoencoder was proposed to generate new labeled data and other well known oversampling techniques after determining that the dataset classes are unbalanced. Once the preprocessing step was carried out, a Convolutional Neural Network (CNN) was used to classify the respiratory sounds into healthy, chronic, and non-chronic disease. In addition, we carried out a more challenging classification trying to distinguish between the different types of pathologies or healthy: URTI, COPD, Bronchiectasis, Pneumonia, and Bronchiolitis. We achieved results up to 0.993 F-Score in the three-label classification and 0.990 F-Score in the more challenging six-class classification.

The use of social network theory and methods of analysis have been applied to different domains in recent years, including public health. The complete procedure for carrying out a social network analysis (SNA) is a time-consuming task that entails a series of steps in which the expert in social network analysis could make mistakes. This research presents a multi-domain knowledge model capable of automatically gathering data and carrying out different social network analyses in different domains, without errors and obtaining the same conclusions that an expert in SNA would obtain. The model is represented in an ontology called OntoSNAQA, which is made up of classes, properties and rules representing the domains of People, Questionnaires and Social Network Analysis. Besides the ontology itself, different rules are represented by SWRL and SPARQL queries. A Knowledge Based System was created using OntoSNAQA and applied to a real case study in order to show the advantages of the approach. Finally, the results of an SNA analysis obtained through the model were compared to those obtained from some of the most widely used SNA applications: UCINET, Pajek, Cytoscape and Gephi, to test and confirm the validity of the model.

Large future sky surveys, such as the LSST, will provide optical photometry for billions of objects. This paper aims to construct a proxy for the far ultraviolet attenuation (AFUVp) from the optical data alone, enabling the rapid estimation of the star formation rate (SFR) for galaxies that lack UV or IR data. To mimic LSST observations, we use the deep panchromatic optical coverage of the SDSS Photometric Catalogue DR~12, complemented by the estimated physical properties for the SDSS galaxies from the GALEX-SDSS-WISE Legacy Catalog (GSWLC) and inclination information obtained from the SDSS DR7. We restricted our sample to the 0.025-0.1 z-spec range and investigated relations among surface brightness, colours, and dust attenuation in the far UV range for star-forming galaxies obtained from the spectral energy distribution (SED). {Dust attenuation is best correlated with (u-r) colour and the surface brightness in the u band ($\rm \mu_{u}$). We provide a dust attenuation proxy for galaxies on the star-forming main sequence, which can be used for the LSST or any other type of broadband optical survey. The mean ratio between the catalogue values of SFR and those estimated using optical-only SDSS data with the AFUVp prior calculated as $\Delta$SFR=log(SFR$_{\tiny{\mbox{this work}}}$/SFR$_{\tiny{}\texttt{GSWLC}}$) is found to be less than 0.1~dex, while runs without priors result in an SFR overestimation larger than 0.3~dex. The presence or absence of theAFUVp has a negligible influence on the stellar mass estimation (with $\Delta$M$_{star}$ in the range from 0 to $-0.15$ dex). Forthcoming deep optical observations of the LSST Deep Drilling Fields, which also have multi-wavelength data, will enable one to calibrate the obtained relation for higher redshift galaxies and, possibly, extend the study towards other types of galaxies, such as early-type galaxies off the main sequence., Comment: 18 pages, accepted for publication in Astronomy and Astrophysics