Structures of electromagnetic type on vector bundles

10 pages.Structures of electromagnetic type on a vector bundle are introduced and studied. The metric case is also defined and studied. The sets of compatible connections are determined and a canonical connection is defined.MICINN-CSIC.Peer reviewe

Enhanced fano resonance of organic material films deposited on arrays of asymmetric split-ring resonators (A-SRRs)

Depositing very thin organic films on the surface of arrays of asymmetric split-ring resonators (A-SRRs) produces a shift in their resonance spectra that can be utilized for sensitive analyte detection. Here we show that when poly-methyl-methacrylate (PMMA) is used as an organic probe (analyte) on top of the A-SRR array, the phase and amplitude of a characteristic molecular Fano resonance associated with a carbonyl bond changes according to the spectral positions of the trapped mode resonance of the A-SRRs and their plasmonic reflection peaks. Furthermore, we localize blocks of PMMA at different locations on the A-SRR array to determine the effectiveness of detection of very small amounts of non-uniformly distributed analyte

DNA Breathing Dynamics in the Presence of a Terahertz Field

We consider the influence of a terahertz field on the breathing dynamics of double-stranded DNA. We model the spontaneous formation of spatially localized openings of a damped and driven DNA chain, and find that linear instabilities lead to dynamic dimerization, while true local strand separations require a threshold amplitude mechanism. Based on our results we argue that a specific terahertz radiation exposure may significantly affect the natural dynamics of DNA, and thereby influence intricate molecular processes involved in gene expression and DNA replication

Quantifying the Simulation-Reality Gap for Deep Learning-Based Drone Detection

The detection of drones or unmanned aerial vehicles is a crucial component in protecting safety-critical infrastructures and maintaining privacy for individuals and organizations. The widespread use of optical sensors for perimeter surveillance has made optical sensors a popular choice for data collection in the context of drone detection. However, efficiently processing the obtained sensor data poses a significant challenge. Even though deep learning-based object detection models have shown promising results, their effectiveness depends on large amounts of annotated training data, which is time consuming and resource intensive to acquire. Therefore, this work investigates the applicability of synthetically generated data obtained through physically realistic simulations based on three-dimensional environments for deep learning-based drone detection. Specifically, we introduce a novel three-dimensional simulation approach built on Unreal Engine and Microsoft AirSim for generating synthetic drone data. Furthermore, we quantify the respective simulation-reality gap and evaluate established techniques for mitigating this gap by systematically exploring different compositions of real and synthetic data. Additionally, we analyze the adaptation of the simulation setup as part of a feedback loop-based training strategy and highlight the benefits of a simulation-based training setup for image-based drone detection, compared to a training strategy relying exclusively on real-world data

Estimating the super-spreading rate at workplaces using bluetooth technology

Workplaces deploy internal guidelines to remain operational during the ongoing COVID-19 pandemic. It is challenging to assess whether those interventions will prevent super-spreading events, where an infected individual transmits the disease to 10 or more secondary cases. Here we provide a model of infectious disease at the level of a workplace to address that problem. We take as input proximity contact records based on bluetooth technology and the infectious disease parameters from the literature. Using proximity contact data for a case-study workplace and an infection transmission model, we estimate the SARS-CoV-2 transmission rate as 0.014 per proximity contact, going up to 0.041 for the SARS-CoV-2 B.1.1.7 variant first detected in the UK. Defining super-spreading as events with 10 or more secondary infections, we obtain a super-spreading event rate of 2.3 per 1000 imported SARS-CoV-2 cases, rising up to 13.7 for SARS-CoV-2 B.1.1.7. This methodology provides the means for workplaces to determine their internal super-spreading rate or other infection related risks

Digital Twin conceptual framework for improving critical infrastructure resilience

Critical infrastructures are the backbone of our societies with increasingly complex and networked characteristics and high availability demands. This makes them vulnerable to a wide range of threats that can lead to major incidents. Resilience is a concept that describes a system’s ability to absorb and respond to disturbances, as well as to learn from the past and anticipate new threats. In this article, we apply the Digital Twin concept to the infrastructure domain to improve the system’s resilience capabilities. We conduct a comprehensive requirements analysis related to infrastructure characteristics, crisis management and resilience measures. As a result, we propose a Digital Twin Conceptual Framework for critical infrastructures. We conclude that the Digital Twin paradigm is well suited to enhance critical infrastructure resilience