Political polarization can emerge on electoral campaigns where the population faces a dichotomous decision, with only two voting alternatives. In this paper, we analyze the Twitter conversation around the second round of the 2017 Chilean elections, where voters had to choose between the final two candidates. First, we have estimated the opinions of Twitter users obtaining a distribution of opinions for each day. Next, we have measured the resulting political polarization from the cited opinion distributions and track its evolution during a full week that includes the voting day. We found the conversation to be highly polarized, reaching its maximum during the previous day to the election and significantly decreasing the day after due to the presence of new users who only participated during that day.

Addressing climate change requires innovative, collective action and robust international collaboration. Through joint efforts, nations can significantly reduce greenhouse gas emissions, pioneer sustainable technologies, and implement effective adaptation measures. Science diplomacy and knowledge sharing hold the potential to bolster global stability and peace by directly confronting climate change challenges. Therefore, it becomes imperative to evaluate a country’s alignment of its scientific knowledge system (SKS) with international guidelines. This study delineates the global scientific discourse on climate change and juxtaposes the alignment between an individual nation’s research endeavors and United Nations resolutions concerning climate change and sustainable development. Our methodology integrates data extraction from scientific research databases with advanced textual analysis tools, highlighting this study’s unique focus on the intersection of climate change and UN resolutions. To deliver an empirical analysis, we leveraged complex network theory and advanced text-processing techniques. Our findings demonstrate the trajectory of global scientific output related to these themes, segmented by countries and coupled with CO₂ emissions data, key disciplines, and collaboration networks. These insights are instrumental for leaders, policymakers, and stakeholders, highlighting areas of convergence and divergence in national research initiatives essential for achieving global climate goals. Such knowledge is strategically useful for crafting purpose-driven public policies and honoring enduring multilateral pledges to address the climate crisis proactively.

In this paper we present a complex network model based on a heterogeneous preferential attachment scheme to quantify the structure of porous soils. Under this perspective pores are represented by nodes and the space for the flow of fluids between them is represented by links. Pore properties such as position and size are described by fixed states in a metric space, while an affinity function is introduced to bias the attachment probabilities of links according to these properties. We perform an analytical study of the degree distributions in the soil model and show that under reasonable conditions all the model variants yield a multiscaling behavior in the connectivity degrees, leaving a empirically testable signature of heterogeneity in the topology of pore networks. We also show that the power-law scaling in the degree distribution is a robust trait of the soil model and analyze the influence of the parameters on the scaling exponents. We perform a numerical analysis of the soil model for a combination of parameters corresponding to empirical samples with different properties, and show that the simulation results exhibit a good agreement with the analytical predictions.

This paper describes an investigation into the properties of spatially embedded complex networks representing the porous architecture of soil systems. We suggest an approach to quantify the complexity of soil pore structure based on the node-node link correlation properties of the networks. We show that the complexity depends on the strength of spatial embedding of the network and that this is related to the transition from a non-compact to compact phase of the network.