Derivative expansion of quadratic operators in a general 't Hooft gauge

A derivative expansion technique is developed to compute functional determinants of quadratic operators, non diagonal in spacetime indices. This kind of operators arise in general 't Hooft gauge fixed Lagrangians. Elaborate applications of the developed derivative expansion are presented.Comment: 40 pages, to appear in Phys. Rev.

Measuring sub-mm structural displacements using QDaedalus: a digital clip-on measuring system developed for total stations

The monitoring of rigid structures of modal frequencies greater than 5 Hz and sub-mm displacement is mainly based so far on relative quantities from accelerometers, strain gauges etc. Additionally geodetic techniques such as GPS and Robotic Total Stations (RTS) are constrained by their low accuracy (few mm) and their low sampling rates. In this study the application of QDaedalus is presented, which constitutes a measuring system developed at the Geodesy and Geodynamics Lab, ETH Zurich and consists of a small CCD camera and Total Station, for the monitoring of the oscillations of a rigid structure. In collaboration with the Institute of Structural Engineering of ETH Zurich and EMPA, the QDaedalus system was used for monitoring of the sub-mm displacement of a rigid prototype beam and the estimation of its modal frequencies up to 30 Hz. The results of the QDaedalus data analysis were compared to those of accelerometers and proved to hold sufficient accuracy and suitably supplementing the existing monitoring techniques

Aspects of early universe phase transitions

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Electroweak baryogenesis by primordial black holes in Brans-Dicke modified gravity

A successful baryogenesis mechanism is proposed in the cosmological framework of Brans-Dicke modified gravity. Primordial black holes with small mass are produced at the end of the Brans-Dicke field domination era. The Hawking radiation reheats a spherical region around every black hole to a high temperature and the electroweak symmetry is restored there. A domain wall is formed separating the region with the symmetric vacuum from the asymmetric region where electroweak baryogenesis takes place. First-order phase transition is not needed. In Brans-Dicke cosmologies, black hole accretion can be strong enough to result in cosmic black hole domination, an extension of the lifetime of black holes and enhanced baryogenesis. The analysis of the whole scenario provides very easily and without fine tuning the observed baryon number asymmetry for either small or big CP-violating angles in the finite temperature corrected effective potential of two-Higgs-doublet models. The advantage of our proposed scenario with Brans-Dicke modified gravity is that it naturally provides both black hole domination and efficient baryogenesis for smaller CP-violating angles compared to the same mechanism applied in a Friedmann-Robertson-Walker cosmological background. © 2021 American Physical Society

Probabilistic method for estimation of spinning reserves in multi-connected power systems with Bayesian network-based rescheduling algorithm

This study proposes a new stochastic spinning reserve estimation model applicable to multi-connected energy systems with reserve rescheduling algorithm based on Bayesian Networks. The general structure of the model is developed based on the probabilistic reserve estimation model that considers random generator outages as well as load and renewable energy forecast errors. The novelty of the present work concerns the additional Bayesian layer which is linked to the general model. It conducts reserve rescheduling based on the actual net demand realization and other reserve requirements. The results show that the proposed model improves estimation of reserve requirements by reducing the total cost of the system associated with reserve schedule. Copyright © 2019 by SCITEPRESS - Science and Technology Publications, Lda. All rights reserve

Smart building's elevator with intelligent control algorithm based on Bayesian networks

Implementation of the intelligent elevator control systems based on machine-learning algorithms should play an important role in our effort to improve the sustainability and convenience of multi-floor buildings. Traditional elevator control algorithms are not capable of operating efficiently in the presence of uncertainty caused by random flow of people. As opposed to conventional elevator control approach, the proposed algorithm utilizes the information about passenger group sizes and their waiting time, provided by the image acquisition and processing system. Next, this information is used by the probabilistic decision-making model to conduct Bayesian inference and update the variable parameters. The proposed algorithm utilizes the variable elimination technique to reduce the computational complexity associated with calculation of marginal and conditional probabilities, and Expectation- Maximization algorithm to ensure the completeness of the data sets. The proposed algorithm was evaluated by assessing the correspondence level of the resulting decisions with expected ones. Significant improvement in correspondence level was obtained by adjusting the probability distributions of the variables affecting the decision-making process. The aim was to construct a decision engine capable to control the elevators actions, in way that improves user's satisfaction. Both sensitivity analysis and evaluation study of the implemented model, according to several scenarios, are presented. The overall algorithm proved to exhibit the desired behavior, in 94% case of the scenarios tested. © 2013 The Science and Information (SAI) Organization

Efficient Bayesian Expert Models for Fever in Neutropenia and Fever in Neutropenia with Bacteremia

Bayesian expert models are very efficient solutions since they can encapsulate in a mathematical consistent way, certain and uncertain knowledge, as well as preferences strategies and policies. Furthermore, the Bayesian modelling framework is the only one that can inference about causal connections and suggest the structure of a reasonable probabilistic model from historic data. Two novel expert models have been developed for a medical issue concerning diagnosis of fever in neutropenia or fever in neutropenia with bacteremia. Supervised and unsupervised learning was used to construct these two the expert models. The best one of them exhibited 93% precision of prediction. © 2020, Springer Nature Switzerland AG

COVID-19 CLUSTERING

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