Low-cost carrier

A low-cost carrier or low-cost airline (occasionally referred to as no-frills, budget or discount carrier or airline, and abbreviated as LCC) is an airline that is operated with an especially high emphasis on minimizing operating costs and without some of the traditional services and amenities provided in the fare, resulting in lower fares and fewer comforts. To make up for revenue lost in decreased ticket prices, the airline may charge extra fees – such as for carry-on baggage. As of April 2020, the world's largest low-cost carrier is Southwest Airlines, which operates primarily in the United States, as well as in some surrounding areas. The term originated within the airline industry referring to airlines with a lower operating cost structure than their competitors. While the term is often applied to any carrier with low ticket prices and limited services, regardless of their operating models, low-cost carriers should not be confused with regional airlines that operate short flig

Control of Current in AC Induction Machines with Pronounced Slot Harmonics for Smooth Torque

Nicolas Lorétan

AC introduction motors have been used in traction applications for a long time. They have a good efficiency for a low cost and are very robust. Most of these motors have been developed for industrial applications and they have a torque ripple because of the presence of slot harmonics. The smoothness of the torque is important for traction applications ands the torque ripple should be reduced or even suppressed without loss of eficiency.

2011

On the use of applied machine learning and digital infrastructure to leverage social media data in health and epidemiology

Martin Mathias Müller

The quantification of population-level health behaviors is crucial for guiding public health policy. However, traditional methods for measuring such health behaviors have several short- comings. In recent years social media data has been successfully used to measure health behaviors and may be used as a low-cost and real-time addition to traditional data sources. Methods from the field of natural language processing are increasingly used to automatically process, filter and categorize the rapidly growing amount of publicly available social media data. However, a number of methodological challenges limit the rate at which we can generate insight from such data. In this work I will argue that long-term investment into digital infrastructure and open source tooling is required in order to overcome these challenges. In chapter 2 we introduce the Crowd- breaks platform which is the basis of this thesis. Crowdbreaks is an open source framework for real-time data collection, continuous crowdsourced annotation, and continuous re-training of machine learning classifiers. In contrast to traditional research workflows, projects on Crowdbreaks run over an extended period of time, allowing for the observation of health trends over multiple years while keeping algorithms up-to-date. In chapter 3 we quantify the occurrence of concept drift in vaccine-related Twitter data, which further validates the need for the Crowdbreaks platform. In chapter 4 we use the Crowdbreaks platform to trace sentiment towards the novel gene-editing technology CRISPR/Cas9 back to its first application in 2013 and investigate how public opinion may have been affected in context of recent scandals sur- rounding the technology. In chapter 5 we turn our attention to the COVID-19 pandemic and analyze who was speaking and who was heard in the early months of the pandemic. Chapter 6 builds on this work and explores the dynamics of Twitter communities during the COVID-19 pandemic. Lastly, in chapter 7 we introduce COVID-Twitter-BERT, a domain-specific language model which has been used in various downstream natural language processing applications on COVID-19-related Twitter data.

EPFL2021

On the use of applied machine learning and digital infrastructure to leverage social media data in health and epidemiology

Martin Mathias Müller

EPFL2021

Remote sensing of the nearshore zone using a rotary-wing UAV

Control of Current in AC Induction Machines with Pronounced Slot Harmonics for Smooth Torque

On the use of applied machine learning and digital infrastructure to leverage social media data in health and epidemiology

Remote sensing of the nearshore zone using a rotary-wing UAV

On the use of applied machine learning and digital infrastructure to leverage social media data in health and epidemiology

Control of Current in AC Induction Machines with Pronounced Slot Harmonics for Smooth Torque