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Networks specifying who interacts with whom are crucial for mathematical models of epidemic spreading. In the context of emerging diseases, these networks have the potential to encode multiple interaction contexts where non-pharmaceutical interventions can be introduced, allowing for proper comparisons among different intervention strategies in a plethora of contexts. Consequently, a multilayer network describing interactions in a population and detailing their contexts in different layers constitutes an appropriate tool for such descriptions. These approaches however become challenging in large-scale systems such as cities, particularly in a framework where data protection policies are enhanced. In this work, we present a methodology to build such multilayer networks and make those corresponding to five Spanish cities available. Our work uses approaches informed by multiple available datasets to create realistic digital twins of the citizens and their interactions and provides a playground to explore different pandemic scenario in realistic settings for better preparedness.

Professional sports enhance interaction among athletes through training groups, sponsored events and competitions. Among these, the Olympic Games represent the largest competition with a global impact, providing the participants with a unique opportunity for interaction. We studied the following patterns among highly successful athletes to understand the structure of their interactions. We used the list of Olympic medallists in the Tokyo 2020 Games to extract their follower-followee network in Twitter, finding 7,326 connections among 964 athletes. The network displayed frequent connections to similar peers in terms of their features including sex, country and sport. We quantified the influence of these features in the followees choice through a gravity approach capturing the number of connections between homogeneous groups. Our research remarks the importance of datasets built from public exposure of professional athletes, serving as a proxy to investigate interesting aspects of many complex socio-cultural systems at different scales.

The reduction in sea ice cover with Arctic warming facilitates the transit of ships through routes that are remarkably shorter than the traditional shipping routes. Automatic Identification System (AIS), ideally designed to avoid vessel collisions, transmits on vessel navigation information (currently 27 types of messages) such as name, position or speed, is a powerful data source to monitor the progress of Arctic shipping as the ice cover decreases. Based on the analysis of an online platform collecting shipping AIS data, we quantified the spatial distribution of shipping through the Arctic Ocean, its intensity and the temporal evolution, in relation to the area released by the sea ice area. Shipping through the Arctic Ocean is distributed spatially following a heavy-tailed distribution, implying heavy traffic through a limited Arctic area, with an exponent that depends on the vessel category. Fishing is the category with the largest spatial spread, with the width of shipping routes correlated with the proximal sea ice area. The time evolution of these routes is characterized by increasing extended periods of shipping activity through the year. AIS data offers valuable information on the activity of the international fleet worldwide. In the context of the new international agreements, it is a valuable source to monitor shipping, fishing and the potential impact in marine life among other aspects. Here we have focused on the Arctic shipping in recent years, which is rapidly growing, particularly around the Northeastern and Northwest Passage coastal routes, providing an opportunity for the design of shorter shipping routes and reduced greenhouse gas emissions from transport of goods, but at a risk of impacts on the Arctic ecosystem.

In railway applications, it is common to use an LC filter connected between the catenary and the input port of the main converter of the auxiliary and traction systems. In addition, in the auxiliary systems, there is a converter operating as a battery charger, which requires a very low ripple in the output current and output voltage, so a postfilter may be placed at the output port of the converter. This article proposes a step-by-step methodology to extend the injected-absorbed-current (IAC) method in order to obtain transfer functions that consider the effects of the input filter, output postfilter, and some feedforward compensations. The proposed methodology allows reusing the characteristic coefficients of the DC-DC converter model derived from the existing IAC method. One of the advantages of the proposed methodology is that the transfer functions obtained in this article are valid for cases where both, one or none of the filters, are implemented. Finally, for the experimental validation of the proposed methodology, the phase-shifted full-bridge converter was selected as a convenient example. Furthermore, the experimental measurements have been performed on two prototypes., Comment: This work was supported in part by the European Regional Development Fund (FEDER), in part by the Ministry of Science, Innovation and Universities, and in part by the State Research Agency through the Research Project: Modeling and Control Strategies for the Stabilization of the Interconnection of Power Electronic Converters CONEXPOT-2 under Grant DPI2017-84572-C2-2-R (AEI/FEDER, UE)

In the unconventional superconductor Sr$_2$RuO$_4$, uniaxial stress along the $[100]$ direction tunes the Fermi level through a Van Hove singularity (VHS) in the density of states, causing a strong enhancement of the superconducting critical temperature $T_\textrm{c}$. Here, we report measurements of the London penetration depth $\lambda$ as this tuning is performed. We find that the zero-temperature superfluid density, here defined as $\lambda(0)^{-2}$, increases by $\sim$15%, with a peak that coincides with the peak in $T_\textrm{c}$. We also find that the low temperature form of $\lambda(T)$ is quadratic over the entire strain range. Using scanning tunneling microscopy, we find that the gap increases from $\Delta_0 \approx 350~\mu$eV in unstressed Sr$_2$RuO$_4$ to $\Delta_0 \approx 600~\mu$eV in a sample strained to near the peak in $T_c$. With a nodal order parameter, an increase in the superconducting gap could bring about an increase in the superfluid density through reduced sensitivity to defects and through reduced non-local effects in the Meissner screening. Our data indicate that tuning to the VHS increases the gap throughout the Brillouin zone, and that non-local effects are likely more important than reduced scattering.

We show that on the real $2$-dimensional Banach space $\ell_1^2$ there is an analytic function $f:B_{\ell_1^2}\rightarrow \mathbb{R}$ such that its power series at origin has radius of uniform convergence one, but for some $a\in B_{\ell_1^2}$ the power series centred at that point has radius of uniform convergence strictly less than $1-\|a\|$. This result highlights a fundamental distinction in real analytic functions (compared to complex analytic functions), where the radius of analyticity can differ from the radius of uniform convergence. Moreover, this example provides the first non-trivial upper bound for the constant of analyticity.