Synthesis of mechanically strong waterborne poly(urethane-urea)s capable of self-healing at elevated temperatures

Although various chemistries have been introduced into polyurethanes in order to obtain self-healing abilities, implementing these materials in applications requiring high strength is challenging as strong materials imply a limited molecular motion, but without movement of polymer chains self-healing is not possible. Here, waterborne poly(urethane-urea)s (PU(U)s) based on aromatic disulfide compounds are developed which balance these contradictory requirements by presenting good mechanical properties at room temperature, while showing the mobility necessary for healing when moderately heated. The influence of hard monomers on the stability and mobility of the materials is investigated by scratch closure, cut healing and rheological measurements, so that the limits of the readily available aromatic disulfide compounds, bis(4-aminophenyl)- and bis(4-hydroxyphenyl)disulfide, can be determined. Subsequently, a modified aromatic disulfide compound, bis[4-(3'-hydroxypropoxy)phenyl]disulfide, with increased reactivity, solubility and flexibility is synthesized and incorporated into the PU backbone, so that materials with more attractive mechanical properties, reaching ultimate tensile strengths up to 23 MPa, and self-healing abilities at elevated temperatures could be obtained.The European Union’s Horizon 2020 research and innovation programme is accredited for the financial support through Project TRACKWAY-ITN 642514 under the Marie Sklodowska-Curie grant agreement. N.B. acknowledges the financial support obtained through the Post-Doctoral fellowship Juan de la Cierva - Incorporación (IJCI-2016-28442), from the Ministry of Economy and Competitiveness of Spai

Improved Bounds on Information Dissemination by Manhattan Random Waypoint Model

With the popularity of portable wireless devices it is important to model and predict how information or contagions spread by natural human mobility -- for understanding the spreading of deadly infectious diseases and for improving delay tolerant communication schemes. Formally, we model this problem by considering $M$ moving agents, where each agent initially carries a \emph{distinct} bit of information. When two agents are at the same location or in close proximity to one another, they share all their information with each other. We would like to know the time it takes until all bits of information reach all agents, called the \textit{flood time}, and how it depends on the way agents move, the size and shape of the network and the number of agents moving in the network. We provide rigorous analysis for the \MRWP model (which takes paths with minimum number of turns), a convenient model used previously to analyze mobile agents, and find that with high probability the flood time is bounded by $O\big(N\log M\lceil(N/M) \log(NM)\rceil\big)$, where $M$ agents move on an $N\times N$ grid. In addition to extensive simulations, we use a data set of taxi trajectories to show that our method can successfully predict flood times in both experimental settings and the real world.Comment: 10 pages, ACM SIGSPATIAL 2018, Seattle, U

Mobility traces and spreading of COVID-19

We use human mobility models, for which we are experts, and attach a virus infection dynamics to it, for which we are not experts but have taken it from the literature, including recent publications. This results in a virus spreading dynamics model. The results should be verified, but because of the current time pressure, we publish them in their current state. Recommendations for improvement are welcome. We come to the following conclusions: 1. Complete lockdown works. About 10 days after lockdown, the infection dynamics dies down. This assumes that lockdown is complete, which can be guaranteed in the simulation, but not in reality. Still, it gives strong support to the argument that it is never too late for complete lockdown. 2. As a rule of thumb, we would suggest complete lockdown no later than once 10% of hospital capacities available for COVID-19 are in use, and possibly much earlier. This is based on the following insights: a. Even after lockdown, the infection dynamics continues at home, leading to another tripling of the cases before the dynamics is slowed. b. There will be many critical cases coming from people who were infected before lockdown. Because of the exponential growth dynamics, their number will be large. c. Researchers with more detailed disease progression models should improve upon these statements. 3. Our simulations say that complete removal of infections at child care, primary schools, workplaces and during leisure activities will not be enough to sufficiently slow down the infection dynamics. It would have been better, but still not sufficient, if initiated earlier. 4. Infections in public transport play an important role. In the simulations shown later, removing infections in the public transport system reduces the infection speed and the height of the peak by approximately 20%. Evidently, this depends on the infection parameters, which are not well known. – This does not point to reducing public transport capacities as a reaction to the reduced demand, but rather use it for lower densities of passengers and thus reduced infection rates. 5. In our simulations, removal of infections at child care, primary schools, workplaces, leisure activities, and in public transport may barely have been sufficient to control the infection dynamics if implemented early on. Now according to our simulations it is too late for this, and (even) harsher measures will have to be initiated until possibly a return to such a restrictive, but still somewhat functional regime will again be possible. Evidently, all of these results have to be taken with care. They are based on preliminary infection parameters taken from the literature, used inside a model that has more transport/movement details than all others that we are aware of but still not enough to describe all aspects of reality, and suffer from having to write computer code under time pressure. Optimally, they should be confirmed independently. Short of that, given current knowledge we believe that they provide justification for “complete lockdown” at the latest when about 10% of available hospital capacities for COVID-19 are in use (and possibly earlier; we are no experts of hospital capabilities). What was not investigated in detail in our simulations was contact tracing, i.e. tracking down the infection chains and moving all people along infection chains into quarantine. The case of Singapore has so far shown that this may be successful. Preliminary simulation of that tactic shows that it is difficult to implement for COVID-19, since the incubation time is rather long, people are contagious before they feel sick, or maybe never feel sufficiently sick at all. We will investigate in future work if and how contact tracing can be used together with a restrictive, but not totally locked down regime. When opening up after lockdown, it would be important to know the true fraction of people who are already immune, since that would slow down the infection dynamics by itself. For Wuhan, the currently available numbers report that only about 0.1% of the population was infected, which would be very far away from “herd immunity”. However, there have been and still may be many unknown infections (Frankfurter Allgemeine Zeitung GmbH 2020)

The Social Climbing Game

The structure of a society depends, to some extent, on the incentives of the individuals they are composed of. We study a stylized model of this interplay, that suggests that the more individuals aim at climbing the social hierarchy, the more society's hierarchy gets strong. Such a dependence is sharp, in the sense that a persistent hierarchical order emerges abruptly when the preference for social status gets larger than a threshold. This phase transition has its origin in the fact that the presence of a well defined hierarchy allows agents to climb it, thus reinforcing it, whereas in a "disordered" society it is harder for agents to find out whom they should connect to in order to become more central. Interestingly, a social order emerges when agents strive harder to climb society and it results in a state of reduced social mobility, as a consequence of ergodicity breaking, where climbing is more difficult.Comment: 14 pages, 9 figure

Generalized Regressive Motion: a Visual Cue to Collision

Brains and sensory systems evolved to guide motion. Central to this task is controlling the approach to stationary obstacles and detecting moving organisms. Looming has been proposed as the main monocular visual cue for detecting the approach of other animals and avoiding collisions with stationary obstacles. Elegant neural mechanisms for looming detection have been found in the brain of insects and vertebrates. However, looming has not been analyzed in the context of collisions between two moving animals. We propose an alternative strategy, Generalized Regressive Motion (GRM), which is consistent with recently observed behavior in fruit flies. Geometric analysis proves that GRM is a reliable cue to collision among conspecifics, whereas agent-based modeling suggests that GRM is a better cue than looming as a means to detect approach, prevent collisions and maintain mobility

MAGDA: A Mobile Agent based Grid Architecture

Mobile agents mean both a technology and a programming paradigm. They allow for a flexible approach which can alleviate a number of issues present in distributed and Grid-based systems, by means of features such as migration, cloning, messaging and other provided mechanisms. In this paper we describe an architecture (MAGDA – Mobile Agent based Grid Architecture) we have designed and we are currently developing to support programming and execution of mobile agent based application upon Grid systems