The life of work has undergone major changes. Globalization and liberalization is contributing to the fact that many companies often require flexibility regarding ability to work around the clock when they are hiring new staff. The fact that the health is poorer among evening and night shift workers compared to daytime workers is widely documented. The conditions for evening and night shift workers to maintain healthy habits are not considered to be good. The aim of the study was to investigate shift workers in the engineering industry’s experiences of what influences their food choices during the work shift. The study was based on a qualitative method with a phenomenological approach. Semi- structured interviews were used for collection of data. The result showed three main themes which affect the shift workers food choice. Within the themes were three factors identified: the conditions of the workplace, the ambition of balance between work and leisure and also physical and mental wellbeing. The result is not transferable to all shift workers, but the experiences that have emerged in the study are valuable to take into consideration in new sociological studies about food choice within the engineering industry. The individual at the workplace have a major responsibility for their food choice even though the design of the workplace has proven to be one of the major factors of influencing the food choice during working hours. Therefore should higher requirements be imposed on employers when it comes to health promotion linked to eating habits and the abilityof workers to make healthy food choices during work hours. / Arbetslivet har genomgått stora förändringar. Globalisering och avreglering bidrar till att företag ofta kräver arbetstidsmässig flexibilitet hos personal när de anställer. Att hälsan är sämre bland de skiftarbetare vilka  arbetar kvälls- eller nattskift jämfört med dagtidsarbetare är vida dokumenterat. Kvälls- och nattskiftsarbetarnas förutsättningar till hälsosamma vanor anses inte vara goda. Syftet med studien har varit att undersöka skiftarbetare inom verkstadsindustrins upplevelser av vad som påverkar deras matval under skiftarbetet. Studien utgick från en kvalitativ metod med fenomenologisk inriktning. Semi- strukturerade intervjuer användes som datainsamlingsmetod. Resultatet visade på tre övergripande teman som berör skiftarbetares matval. Ur dessa teman plockades tre faktorer fram: arbetsplatsens förutsättningar, strävan efter balans mellan arbete och fritid  samt fysiskt och psykiskt välmående. Resultatet är inte överförbart på alla skiftarbetare men upplevelserna vilka framkommit i studien är värdefulla att ta i beaktning i nya sociologiska studier kring matval inom verkstadsindustrin. Individen på arbetsplatsen har ett stort eget ansvar för sina matval trots att en av de största påverkansfaktorerna för matvalet visat sig vara arbetsplatsens utformning. Därför bör högre krav ställas på arbetsgivare när det kommer till hälsofrämjande arbete kopplat till matvanor och möjligheten för arbetstagare att göra hälsosamma matval under arbetstid.

published_or_final_version / Mathematics / Master / Master of Philosophy

Vena contracta width (VCW) and effective regurgitant orifice area (EROA) are well established methods for evaluating mitral regurgitation using transesophageal echocardiography (TEE). For color-flow Doppler (CF) measurements Nyquist limit of 50–60 cm/s is recommended. Aim of the study was to investigate the effectiveness of a baseline shift of the Nyquist limit for these measurements. After a comprehensive 2-dimensional (2D) TEE examination, the mitral regurgitation jet was acquired with a Nyquist limit of 50 cm/s (NL50) along with a baseline shift to 37.5 cm/s (NL37.5) using CF. Moreover a real time 3-dimensional (RT 3D) color complete volume dataset was stored with a Nyquist limit of 50 cm/s (NL50) and 37.5 cm/s (NL37.5). Vena contracta width (VCW) as well as Proximal Isovelocity Surface Area (PISA) derived EROA were measured based on 2D TEE and compared to RT 3D echo measurements for vena contracta area (VCA) using planimetry method. Correlation between VCA 3D NL50 and VCW NL50 was 0.29 (p<0.05) compared to 0.6 (p<0.05) using NL37.5. Correlation between VCA 3D NL50 and EROA 2D NL50 was 0.46 (p<0.05) vs. 0.6 (p<0.05) EROA 2D NL37.5. Correlation between VCA 3D NL37.5 and VCW NL50 was 0.45 (p<0.05) compared to 0.65 (p<0.05) using VCW NL37.5. Correlation between VCA 3D NL37.5 and EROA 2D NL50 was 0.41 (p<0.05) vs. 0.53 (p<0.05) using EROA 2D NL37.5. Baseline shift of the NL to 37.5 cm/s improves the correlation for VCW and EROA when compared to RT 3D NL50 planimetry of the vena contracta area. Baseline shift in RT 3D to a NL of 37.5 cm/s shows similar results like NL50.

Several machine learning applications use classifiers as a way of quantifying the prevalence of positive class labels in a target dataset, a task named quantification. For instance, a naive way of determining what proportion of positive reviews about given product in the Facebook with no labeled reviews is to (i) train a classifier based on Google Shopping reviews to predict whether a user likes a product given its review, and then (ii) apply this classifier to Facebook posts about that product. Unfortunately, it is well known that such a two-step approach, named Classify and Count, fails because of data set shift, and thus several improvements have been recently proposed under an assumption named prior shift. However, these methods only explore the relationship between the covariates and the response via classifiers and none of them take advantage of the fact that one often has access to a few labeled samples in the target set. Moreover, the literature lacks in approaches that can handle a target population that varies with another covariate; for instance: How to accurately estimate how the proportion of new posts or new webpages in favor of a political candidate varies in time? We propose novel methods that fill these important gaps and compare them using both real and artificial datasets. Finally, we provide a theoretical analysis of the methods. / Muitas aplicações de aprendizado de máquina usam classificadores para determinar a prevalência da classe positiva em um conjunto de dados de interesse, uma tarefa denominada quantificação. Por exemplo, uma maneira ingênua de determinar qual a proporção de postagens positivas sobre um determinado protuto no Facebook sem ter resenhas rotuladas é (i) treinar um classificador baseado em resenhas do Google Shopping para prever se um usuário gosta de um produto qualquer, e então (ii) aplicar esse classificador às postagens do Facebook relacionados ao produtos de interesse. Infelizmente, é sabido que essa técnica de dois passos, denominada classificar e contar, falha por não levar em conta a mudança de domínio. Assim, várias melhorias vêm sendo feitas recentemente sob uma suposição denominada prior shift. Entretanto, estes métodos exploram a relação entre as covariáveis apenas via classificadores e nenhum deles aproveitam o fato de que, em algumas situações, podemos rotular algumas amostras do conjunto de dados de interesse. Além disso, a literatura carece de abordagens que possam lidar com uma população-alvo que varia com outra covariável; por exemplo: Como estimar precisamente como a proporção de novas postagens ou páginas web a favor de um candidato político varia com o tempo? Nós propomos novos métodos que preenchem essas lacunas importantes e os comparamos utilizando conjuntos de dados reais e similados. Finalmente, nós fornecemos uma análise teórica dos métodos propostos.

O problema de n?corpos é um dos problemas mais importantes em Sistemas Dinâmicos. Nós estudamos o modelo do problema dos três corpos restrito introduzido por Sitnikov. Nesse modelo os corpos primários tem a mesma massa e o terceiro corpo é de massa muito pequena com respeito aos corpos primários. Usando os métodos de Alekseev, nós mostramos a existência de uma ?ferradura de Smale?como um subsistema da dinâmica do terceiro corpo e concluímos ricas conseqüências probabilísticas. Nós também estudamos o problema pelo método de Melnikov / The n?body problem is one of the most important problems in dynamical systems. We study the model introduced by Sitnikov of restricted three body problem. In this model the primaries are of equal mass and the third body is very small with respect to the primaries. Using methods of Alekseev, we show the existence of ?Smale horseshoe?as a subsystem of the dynamic of the third body and conclude rich probabilistic consequences. We also study the same problem by Melnikov?s method

This thesis studies a family of unsupervised learning algorithms called feature distribution learning and their extension to perform covariate shift adaptation. Unsupervised learning is one of the most active areas of research in machine learning, and a central challenge in this field is to develop simple and robust algorithms able to work in real-world scenarios. A traditional assumption of machine learning is the independence and identical distribution of data. Unfortunately, in realistic conditions this assumption is often unmet and the performances of traditional algorithms may be severely compromised. Covariate shift adaptation has then developed as a lively sub-field concerned with designing algorithms that can account for covariate shift, that is for a difference in the distribution of training and test samples. The first part of this dissertation focuses on the study of a family of unsupervised learning algorithms that has been recently proposed and has shown promise: feature distribution learning; in particular, sparse filtering, the most representative feature distribution learning algorithm, has commanded interest because of its simplicity and state-of-the-art performance. Despite its success and its frequent adoption, sparse filtering lacks any strong theoretical justification. This research questions how feature distribution learning can be rigorously formalized and how the dynamics of sparse filtering can be explained. These questions are answered by first putting forward a new definition of feature distribution learning based on concepts from information theory and optimization theory; relying on this, a theoretical analysis of sparse filtering is carried out, which is validated on both synthetic and real-world data sets. In the second part, the use of feature distribution learning algorithms to perform covariate shift adaptation is considered. Indeed, because of their definition and apparent insensitivity to the problem of modelling data distributions, feature distribution learning algorithms seems particularly fit to deal with covariate shift. This research questions whether and how feature distribution learning may be fruitfully employed to perform covariate shift adaptation. After making explicit the conditions of success for performing covariate shift adaptation, a theoretical analysis of sparse filtering and another novel algorithm, periodic sparse filtering, is carried out; this allows for the determination of the specific conditions under which these algorithms successfully work. Finally, a comparison of these sparse filtering-based algorithms against other traditional algorithms aimed at covariate shift adaptation is offered, showing that the novel algorithm is able to achieve competitive performance. In conclusion, this thesis provides a new rigorous framework to analyse and design feature distribution learning algorithms; it sheds light on the hidden assumptions behind sparse filtering, offering a clear understanding of its conditions of success; it uncovers the potential and the limitations of sparse filtering-based algorithm in performing covariate shift adaptation. These results are relevant both for researchers interested in furthering the understanding of unsupervised learning algorithms and for practitioners interested in deploying feature distribution learning in an informed way.

The energy density formalism (EDF) is used to investigate two problems. The EDF is a phenomenological method that incorporates as much knowledge of infinite nuclear matter as possible. In this formalism the energy of the nucleus is expressed as a functional of its density. The nucleus energy is obtained by minimizing the function, with respect to the density. In this report, the EDF is used to investigate the mercury isotope shift anomaly following the aforementioned suggestion. Specifically, nucleon densities with different degrees of central depression are generated. Energies corresponding to these densities are obtained. The density with the minimum energy is the preferred one. Based on the findings of the present work, it can be concluded that a central depression in the lighter mercury isotopes does not-appear- to be a possible explanation for the isotope shift anomaly. And the anomaly remains unresolved.

Aligning and fixing the fiber and laser device is an important work in butterfly laser module packaging. Assembling these two component by laser welding is to achieve reliable and stable weld joint. However, during the welding process, rapid solidification of the welded region and the associated material shrinkage causes the fiber position moved. The relative movement between fiber and laser is called post-weld-shift(PWS). A few micrometers PWS makes coupled power lost. Therefore, minimizing the PWS between fiber and laser is a key research topic in butterfly laser module packaging. We can correct the PWS minimal by fiber shift inspect. But it has a difficult of space limit in butterfly laser module packaging. In this study, a video camera with image acquisition system was used to measure the PWS. We also used a mirror image to solve the problem of space limited. The PWS inspection result has matched our simulation. This method can successfully inspect the PWS in butterfly laser module.

Thesis (M.S.)--University of Michigan, 1976.

Thesis (M. Phil.)--University of Hong Kong, 1997. / Includes bibliographical references (leaf 91-95).