Albert Solé-Ribalta’s research while affiliated with Universitat Oberta de Catalunya and other places

Air pollution is a pressing environmental risk to public health, particularly in cities where population density and pollution levels are high. Traditional methods for exposure analysis often rely on census data, but recent studies highlight the impact of daily mobility on individuals' exposure. Here, we develop a methodology to determine unprecedented pedestrian exposure estimates at the city scale by combining sidewalk pedestrian flows with high-resolution (25 m x 25 m) NO2 data from bias-corrected predictions of the air quality system CALIOPE-Urban. Applied to Barcelona (Spain) for the year 2019, we show that pedestrian flow and NO2 levels exhibit negligible temporal correlation. While short-term (hourly) exposure is driven by pedestrian mobility, long-term (monthly) exposure is dominated by NO2 patterns. We identify strong spatial gradients of exposure, highlighting the importance for high-resolution solutions at the sidewalks scale. Finally, we determine that exposure mitigation strategies should consider different citizen subgroups based on their mobility and preferred routes, as significant differences were found between residential and pedestrian exposure. Our results provide exposure indicators designed for city planners and policymakers, helping to prioritize mitigation measures where and when they are most needed.

The International Institute of Intellectual Cooperation (IIIC) was an international organisation established post-World War I, aimed to foster intellectual relations for global peace. As this institution becomes centenary, the digitisation of archival records has democratised access, sparking renewed scholar interest and eventually enabling new research avenues. Here, we examine two letter collections from IIIC’s digitised funds, focusing on administrative and artistic/literary matters. By analysing recognisable sender-receiver pairs and their geographical origins, we construct networks revealing intricate international relationships. Notably, the community structures and roles differ between administrative and literary exchanges, suggesting distinct communication dynamics. Administrative matters depict a more egalitarian distribution. In contrast, relevant literary correspondents include Western European countries exclusively, reflecting the prominence of certain geocultural areas as well as potential geopolitical influence attempts, challenging the established historical narratives on centres and peripheries, inviting a revaluation of the IIIC’s geographical organisation and intellectual cooperation during the interwar period.

The discourse surrounding the structural organization of mutualistic interactions mostly revolves around modularity and nestedness. The former is known to enhance the stability of communities, while the latter is related to their feasibility, albeit compromising the stability. However, it has recently been shown that the joint emergence of these structures poses challenges that can eventually lead to limitations in the dynamic properties of mutualistic communities. We hypothesize that considering compound arrangements –modules with internal nested organization– can offer valuable insights in this debate. We analyze the temporal structural dynamics of 20 plant-pollinator interaction networks and observe large structural variability throughout the year. Compound structures are particularly prevalent during the peak of the pollination season, often coexisting with nested and modular arrangements in varying degrees. Motivated by these empirical findings, we synthetically investigate the dynamics of the structural patterns across two control parameters –community size and connectance levels– mimicking the progression of the pollination season. Our analysis reveals contrasting impacts on the stability and feasibility of these mutualistic communities. We characterize the consistent relationship between network structure and stability, which follows a monotonic pattern. But, in terms of feasibility, we observe non-linear relationships. Compound structures exhibit a favorable balance between stability and feasibility, particularly in mid-sized ecological communities, suggesting they may effectively navigate the simultaneous requirements of stability and feasibility. These findings may indicate that the assembly process of mutualistic communities is driven by a delicate balance among multiple properties, rather than the dominance of a single one.

In recent years, the design (and re-design) of cities to encourage walkability has taken on new urgency as part of a wider campaign for sustainable urban development. Complementary to other approaches like infrastructure improvements, increases in residential density, or traffic calming measures, here, we show how planning for walkability can be augmented by the adaptation of tools and approaches from the study of urban networks, by privileging the pedestrian perspective of short-distance access over the car (and rapid transit) perspective of flow and efficiency. Using a recently developed sidewalk network model that moves towards a more realistic representation of the pedestrian environment, we propose a framework for assessing multi-factor walkability using percolation theory and insights into pedestrian behavior. We apply our framework to the city of Barcelona, and show how it can be used to optimize service location and access for vulnerable populations (the elderly and young).

In recent years, research on network analysis applied to MRI data has advanced significantly. However, the majority of the studies are limited to single networks obtained from resting-state fMRI, diffusion MRI, or grey matter probability maps derived from T1 images. Although a limited number of previous studies have combined two of these networks, none have introduced a framework to combine morphological, structural and functional brain connectivity networks. The aim of this study was to combine the morphological, structural and functional information, thus defining a new multilayer network perspective. This has proved advantageous when jointly analysing multiple types of relational data from the same objects simultaneously using graph-mining techniques. The main contribution of this research is the design, development and validation of a framework that merges these three layers of information into one multilayer network that links and relates the integrity of white matter connections with grey matter probability maps and resting-state fMRI. To validate our framework, several metrics from graph theory are expanded and adapted to our specific domain characteristics. This proof of concept was applied to a cohort of people with MS, and results show that several brain regions with a synchronised connectivity deterioration could be identified.

Increased interaction between and among pedestrians and vehicles in the crowded urban environments of today gives rise to a negative side-effect: a growth in traffic accidents, with pedestrians being the most vulnerable elements. Recent work has shown that Convolutional Neural Networks are able to accurately predict accident rates exploiting Street View imagery along urban roads. The promising results point to the plausibility of aided design of safe urban landscapes, for both pedestrians and vehicles. In this paper, by considering historical accident data and Street View images, we detail how to automatically predict the impact (increase or decrease) of urban interventions on accident incidence. The results are positive, rendering an accuracies ranging from 60 to 80%. We additionally provide an interpretability analysis to unveil which specific categories of urban features impact accident rates positively or negatively. Considering the transportation network substrates (sidewalk and road networks) and their demand, we integrate these results to a complex network framework, to estimate the effective impact of urban change on the safety of pedestrians and vehicles. Results show that public authorities may leverage on machine learning tools to prioritize targeted interventions, since our analysis show that limited improvement is obtained with current tools. Further, our findings have a wider application range such as the design of safe urban routes for pedestrians or to the field of driver-assistance technologies.

Understanding human interactions in online communications is of paramount importance for our society. Alarming phenomena such as the spreading of fake news or the formation of echo-chambers can emerge in unhealthy communication environments and, ultimately, undermine the democratic discourse. In this context, unveiling the individual drivers that give rise to collective attention can help to conserve the health of our information ecosystems. Here, following a recently proposed analogy between natural and information ecosystems, we explore how competition for attention in online social networks and the strategies adopted by the users to maximize their visibility shape our communication dynamics. Specifically, by analyzing large-scale datasets from the micro-blogging platform Twitter and performing numerical modeling of the system dynamics, we are able to measure the amount of competition for attention experienced by users and how it changes when exogenous events captivate collective attention. The work relies on topic modeling to extract users' interests and memes context from the data and a framework based on ecological niche theory to quantify the strength of negative (competitive) and positive (mutualistic) interactions for both users and memes. Interestingly, our findings show two different behaviors. While memes undergo a sharp increase in competition during exceptional events that can lead to their extinction, users perceive a decrease in effective competition due to a stronger effect of mutualistic interaction, explaining the focus of collective attention around specific topics. Finally, to confirm our results we reproduce the observed shifts with a data-driven model of species dynamics.