Challenging the status quo: women's experiences of opting for a home birth in Andalucia, Spain

Objective To explore the perceptions, beliefs and attitudes of women who opted for a home birth in Andalusia (Spain). Background Home birth is currently an unusual choice among Spanish women. It is not an option covered by the Spanish National Health Service and women who opt for a home birth have to pay for an independent midwife. Design A qualitative study with a phenomenological approach was adopted. All participants who took part in this study had chosen to have a home birth and given written consent to take part in the study. Methods Data collection was conducted in 2015–16. Face-to-face, semi-structured interviews were undertaken with women who chose a home birth in the last 5 years. Findings The sample consisted of thirteen women. Seven themes were created through analysis: 1. Getting informed about home birth; 2. Home birth as a choice, despite feeling unsupported; 3. The best way to have a personalized and a physiological birth; 4. Seeking a healing and empowering experience 5. The need for emotional safety, establishing a relationship and trusting the midwife; 6. Preparing for birth and working on fears; 7. Inequality of access (because of financial implications). Conclusions Women opted to plan birth at home because they wanted a personalised birth and control over their decision-making in labour, which they felt would not have been afforded to them in hospital settings. Andalusian maternity care leaders should strive to ensure that all pregnant women receive respectful and high-quality personalised care, by appropriately trained staff, both in the hospital and in the community

Interacting social processes on interconnected networks

We propose and study a model for the interplay between two different dynamical processes --one for opinion formation and the other for decision making-- on two interconnected networks $A$ and $B$. The opinion dynamics on network $A$ corresponds to that of the M-model, where the state of each agent can take one of four possible values ($S=-2,-1,1,2$), describing its level of agreement on a given issue. The likelihood to become an extremist ($S=\pm 2$) or a moderate ($S=\pm 1$) is controlled by a reinforcement parameter $r \ge 0$. The decision making dynamics on network $B$ is akin to that of the Abrams-Strogatz model, where agents can be either in favor ($S=+1$) or against ($S=-1$) the issue. The probability that an agent changes its state is proportional to the fraction of neighbors that hold the opposite state raised to a power $\beta$. Starting from a polarized case scenario in which all agents of network $A$ hold positive orientations while all agents of network $B$ have a negative orientation, we explore the conditions under which one of the dynamics prevails over the other, imposing its initial orientation. We find that, for a given value of $\beta$, the two-network system reaches a consensus in the positive state (initial state of network $A$) when the reinforcement overcomes a crossover value $r^*(\beta)$, while a negative consensus happens for $r<r^*(\beta)$. In the $r-\beta$ phase space, the system displays a transition at a critical threshold $\beta_c$, from a coexistence of both orientations for $\beta<\beta_c$ to a dominance of one orientation for $\beta>\beta_c$. We develop an analytical mean-field approach that gives an insight into these regimes and shows that both dynamics are equivalent along the crossover line $(r^*,\beta^*)$.Comment: 25 pages, 6 figure

Human brain distinctiveness based on EEG spectral coherence connectivity

The use of EEG biometrics, for the purpose of automatic people recognition, has received increasing attention in the recent years. Most of current analysis rely on the extraction of features characterizing the activity of single brain regions, like power-spectrum estimates, thus neglecting possible temporal dependencies between the generated EEG signals. However, important physiological information can be extracted from the way different brain regions are functionally coupled. In this study, we propose a novel approach that fuses spectral coherencebased connectivity between different brain regions as a possibly viable biometric feature. The proposed approach is tested on a large dataset of subjects (N=108) during eyes-closed (EC) and eyes-open (EO) resting state conditions. The obtained recognition performances show that using brain connectivity leads to higher distinctiveness with respect to power-spectrum measurements, in both the experimental conditions. Notably, a 100% recognition accuracy is obtained in EC and EO when integrating functional connectivity between regions in the frontal lobe, while a lower 97.41% is obtained in EC (96.26% in EO) when fusing power spectrum information from centro-parietal regions. Taken together, these results suggest that functional connectivity patterns represent effective features for improving EEG-based biometric systems.Comment: Key words: EEG, Resting state, Biometrics, Spectral coherence, Match score fusio

The influence of persuasion in opinion formation and polarization

We present a model that explores the influence of persuasion in a population of agents with positive and negative opinion orientations. The opinion of each agent is represented by an integer number $k$ that expresses its level of agreement on a given issue, from totally against $k=-M$ to totally in favor $k=M$. Same-orientation agents persuade each other with probability $p$, becoming more extreme, while opposite-orientation agents become more moderate as they reach a compromise with probability $q$. The population initially evolves to (a) a polarized state for $r=p/q>1$, where opinions' distribution is peaked at the extreme values $k=\pm M$, or (b) a centralized state for $r<1$, with most opinions around $k=\pm 1$. When $r \gg 1$, polarization lasts for a time that diverges as $r^M \ln N$, where $N$ is the population's size. Finally, an extremist consensus ($k=M$ or $-M$) is reached in a time that scales as $r^{-1}$ for $r \ll 1$

Fluctuations of a surface relaxation model in interacting scale free networks

Isolated complex networks have been studied deeply in the last decades due to the fact that many real systems can be modeled using these types of structures. However, it is well known that the behavior of a system not only depends on itself, but usually also depends on the dynamics of other structures. For this reason, interacting complex networks and the processes developed on them have been the focus of study of many researches in the last years. One of the most studied subjects in this type of structures is the Synchronization problem, which is important in a wide variety of processes in real systems. In this manuscript we study the synchronization of two interacting scale-free networks, in which each node has $ke$ dependency links with different nodes in the other network. We map the synchronization problem with an interface growth, by studying the fluctuations in the steady state of a scalar field defined in both networks. We find that as $ke$ slightly increases from $ke=0$, there is a really significant decreasing in the fluctuations of the system. However, this considerable improvement takes place mainly for small values of $ke$, when the interaction between networks becomes stronger there is only a slight change in the fluctuations. We characterize how the dispersion of the scalar field depends on the internal degree, and we show that a combination between the decreasing of this dispersion and the integer nature of our growth model are the responsible for the behavior of the fluctuations with $ke$.Comment: 11 pages, 4 figures and 1 tabl