Service Performance Indicators for Infrastructure Investment

Infrastructure systems serving modern economies are highly complex, highly interconnected, and often highly interactive. The result is increased complexity in investment decision-making, and increased challenges in prioritising that investment. However, this prioritisation is vital to developing a long-term, sound, robust and achievable pipeline of national infrastructure. One key to effective, objective and prudent investment prioritisation is understanding the real performance of infrastructure. Many metrics are employed to this end, and many are imposed by governments or regulators, but often these metrics relate only to inputs or outputs in a production process. Whilst these metrics may be useful for delivery agencies, they largely fail to address the real expectations or requirements of infrastructure users — quality of service, safety, reliability, and resilience. What is required is a set of metrics which address not outputs but outcomes — that is, how well does the infrastructure network meet service needs? This paper reports on a study undertaken at a national level, to identify service needs across a range of infrastructure sectors, to assess service performance metrics in use, and to show how they or other suitable metrics can be used to prioritise investment decisions across sectors and jurisdictions

Regression analysis with missing data and unknown colored noise: application to the MICROSCOPE space mission

The analysis of physical measurements often copes with highly correlated noises and interruptions caused by outliers, saturation events or transmission losses. We assess the impact of missing data on the performance of linear regression analysis involving the fit of modeled or measured time series. We show that data gaps can significantly alter the precision of the regression parameter estimation in the presence of colored noise, due to the frequency leakage of the noise power. We present a regression method which cancels this effect and estimates the parameters of interest with a precision comparable to the complete data case, even if the noise power spectral density (PSD) is not known a priori. The method is based on an autoregressive (AR) fit of the noise, which allows us to build an approximate generalized least squares estimator approaching the minimal variance bound. The method, which can be applied to any similar data processing, is tested on simulated measurements of the MICROSCOPE space mission, whose goal is to test the Weak Equivalence Principle (WEP) with a precision of $10^{-15}$. In this particular context the signal of interest is the WEP violation signal expected to be found around a well defined frequency. We test our method with different gap patterns and noise of known PSD and find that the results agree with the mission requirements, decreasing the uncertainty by a factor 60 with respect to ordinary least squares methods. We show that it also provides a test of significance to assess the uncertainty of the measurement.Comment: 12 pages, 4 figures, to be published in Phys. Rev.

Terrain Database Correlation Assessment Using an Open Source Tool

Configuring networked simulators for training military teams in a distributed environment requires the usage of a set of terrain databases to represent the same training area. The results of simulation exercises can be degraded if the terrain databases are poorly correlated. A number of methodologies for determining the correlation between terrain databaHowever, there are few computational tools for this task and most of them were developed to address government needs, have limited availability, and handle specific digital formats. The goal of this paper is thus to present a novel open source tool developed as part of an academic research project.Comment: 12 pages, I/ITSEC 201

Superconducting charge qubits from a microscopic many-body perspective

The quantised Josephson junction equation that underpins the behaviour of charge qubits and other tunnel devices is usually derived through cannonical quantisation of the classical macroscopic Josephson relations. However, this approach may neglect effects due to the fact that the charge qubit consists of a superconducting island of finite size connected to a large superconductor. We show that the well known quantised Josephson equation can be derived directly and simply from a microscopic many-body Hamiltonian. By choosing the appropriate strong coupling limit we produce a highly simplified Hamiltonian that nevertheless allows us to go beyond the mean field limit and predict further finite-size terms in addition to the basic equation.Comment: Accepted for J Phys Condensed Matte

Towards a knowledge-based system to assist the Brazilian data-collecting system operation

A study is reported which was carried out to show how a knowledge-based approach would lead to a flexible tool to assist the operation task in a satellite-based environmental data collection system. Some characteristics of a hypothesized system comprised of a satellite and a network of Interrogable Data Collecting Platforms (IDCPs) are pointed out. The Knowledge-Based Planning Assistant System (KBPAS) and some aspects about how knowledge is organized in the IDCP's domain are briefly described

Vascular risks in external sinus lifts: an anatomical approach

Abstract in proceedings of the Fourth International Congress of CiiEM: Health, Well-Being and Ageing in the 21st Century, held at Egas Moniz’ University Campus in Monte de Caparica, Almada, from 3–5 June 2019.This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.info:eu-repo/semantics/publishedVersio