Inferring Mechanisms for Global Constitutional Progress

Constitutions help define domestic political orders, but are known to be influenced by two international mechanisms: one that reflects global temporal trends in legal development, and another that reflects international network dynamics such as shared colonial history. We introduce the provision space; the growing set of all legal provisions existing in the world's constitutions over time. Through this we uncover a third mechanism influencing constitutional change: hierarchical dependencies between legal provisions, under which the adoption of essential, fundamental provisions precedes more advanced provisions. This third mechanism appears to play an especially important role in the emergence of new political rights, and may therefore provide a useful roadmap for advocates of those rights. We further characterise each legal provision in terms of the strength of these mechanisms

Improving PWR core simulations by Monte Carlo uncertainty analysis and Bayesian inference

A Monte Carlo-based Bayesian inference model is applied to the prediction of reactor operation parameters of a PWR nuclear power plant. In this non-perturbative framework, high-dimensional covariance information describing the uncertainty of microscopic nuclear data is combined with measured reactor operation data in order to provide statistically sound, well founded uncertainty estimates of integral parameters, such as the boron letdown curve and the burnup-dependent reactor power distribution. The performance of this methodology is assessed in a blind test approach, where we use measurements of a given reactor cycle to improve the prediction of the subsequent cycle. As it turns out, the resulting improvement of the prediction quality is impressive. In particular, the prediction uncertainty of the boron letdown curve, which is of utmost importance for the planning of the reactor cycle length, can be reduced by one order of magnitude by including the boron concentration measurement information of the previous cycle in the analysis. Additionally, we present first results of non-perturbative nuclear-data updating and show that predictions obtained with the updated libraries are consistent with those induced by Bayesian inference applied directly to the integral observables.Comment: 10 pages, 11 figure

Grups de trenes, representació de Burau i categorificació de Khovanov-Seidel

Treballs Finals de Grau de Matemàtiques, Facultat de Matemàtiques, Universitat de Barcelona, Any: 2023, Director: Ricardo García López[en] The braid group, together with its representations, is a fascinating mathematical structure, studied from different fields, such as group theory, topology... Moreover, it is a theory that extends beyond itself, with relations that go from the theory of knots and their invariants to concepts of theoretical physics. The main objective of the paper is the introduction of the notion of the braid group, the Burau representation and a categorification of it. We will begin by presenting braids as a mathematical structure and the different ways of interpreting the group they form. Then, we introduce the non-reduced and reduced Burau representations. This family of representations is faithful for $n4$, it is unknown if it is faithful for $n=4$. In this work, the case $n=5$ is not studied. Finally, the Seidel-Khovanov categorification of the Burau representation is presented, which, curiously, is faithful for all $n$

Visual analytics of flight trajectories for uncovering decision making strategies

In air traffic management and control, movement data describing actual and planned flights are used for planning, monitoring and post-operation analysis purposes with the goal of increased efficient utilization of air space capacities (in terms of delay reduction or flight efficiency), without compromising the safety of passengers and cargo, nor timeliness of flights. From flight data, it is possible to extract valuable information concerning preferences and decision making of airlines (e.g. route choice) and air traffic managers and controllers (e.g. flight rerouting or optimizing flight times), features whose understanding is intended as a key driver for bringing operational performance benefits. In this paper, we propose a suite of visual analytics techniques for supporting assessment of flight data quality and data analysis workflows centred on revealing decision making preferences