Change and Variation in the Trondheim Dialect

This thesis explores the status and production of four characteristics traditionally observed in the Trondheim dialect as ongoing trends observed in the dialect suggests that the dialect is undergoing a process of levelling with Standard Eastern Norwegian. The characteristics investigated are wh-words, diphthongs, apocope, and palatalisation. The data was collected by use of an experiment, and finally the data collected from ten of the twelve who participated in the study was included in the analysis of the data. The production of the traditional k-form on wh-words was compared to the production of hv-forms associated with Bokmål orthography and Eastern Norwegian dialects; diphthongs were analysed through production of optional diphthongs and the perceived quality of these; apocope was analysed by observing what words (and the amount of observations of these words) underwent the process; and finally, palatalisation was analysed by use of Centre of Gravity (COG), in which non-palatalised and possibly palatalised consonants were compared, and perception of the produced palatalised consonants

Towards Unsupervised Domain Adaptation for Diabetic Retinopathy Detection in the Tromsø Eye Study

Diabetic retinopathy (DR) is an eye disease which affects a third of the diabetic population. It is a preventable disease, but requires early detection for efficient treatment. While there has been increasing interest in applying deep learning techniques for DR detection in order to aid practitioners make more accurate diagnosis, these efforts are mainly focused on datasets that have been collected or created with ML in mind. In this thesis, however, we take a look at two particular datasets that have been collected at the University Hospital of North-Norway - UNN. These datasets have inherent problems that motivate the methodological choices in this work such as a variable number of input images and domain shift. We therefore contribute a multi-stream model for DR classification. The multi-stream model can model dependency across different images, can take in a variable of input of any size, is general in its detection such that the image processing is equal no matter which stream the image enters, and is compatible with the domain adaptation method ADDA, but we argue the model is compatible with many other methods. As a remedy for these problems, we propose a multi-stream deep learning architecture that is uniquely tailored to these datasets and illustrate how domain adaptation might be utilized within the framework to learn efficiently in the presence of domain shift. Our experiments demonstrates the models properties empirically, and shows it can deal with each of the presented problems. The model this paper contributes is a first step towards DR detection from these local datasets and, in the bigger picture, similar datasets worldwide

Modeling markups and its determinants: The case of Norwegian industries and regions

In this paper we use an innovative and nonstandard approach to model and estimate markups and market power. The approach uses a regression framework with determinants as well as a random component. We use this innovative tool to investigate the level of market power in Norwegian industries and regions. Norway is an interesting case study because in Norway prices on most consumer goods and services are higher than in similar countries. Given that many studies show substantial and an increasing trend in markup, it is naturally interesting to investigate the market power in different industries and different regions in Norway. We use an unbalanced panel collected by The Norwegian Tax Administration for the period 2000-2018 to address this issue. We find low and non-increasing market power in Norway, which is different from other countries. Further, we find that market powers decrease with firm-size, increase with geographical industrial concentration and decrease with rural location.publishedVersio

Life cycle analysis (LCA) and costs for energy storage in piles

This report is a part of the research project Sustainable energy from soils (BEAR). The BEAR project is a collaboration project between the industry, municipality and research institutions in mid Norway, funded by the regional research fund of Trøndelag (grant number 32116). The BEAR project involves designing and testing an energy concept that utilize the soil as a stable source of thermal energy for buildings, meanwhile also working as an integrated part of the building foundation, so called “energy piles”. The hypothesis is that integrating heat exchangers within the building foundations will enable and reduce the investment cost for the establishment of ground source heating systems in buildings that are situated on soils. The BEAR consortium consists of Malvik Municipality (project owner), NGI (project lead), Winns AS, Fundamentering AS, Noranergy AS and NTNU. BEAR comprises of four working packages, where this report summarizes the results and findings of work package 3 (WP3 - Evaluation)

Effects of strong and weak non-pharmaceutical interventions on stock market returns: A comparative analysis of Norway and Sweden during the initial phase of the COVID-19 pandemic

In this paper, we examine the behaviour of stock market returns in Norway and Sweden during the early days of the COVID-19 pandemic. We test how the different government interventions chosen in Norway and Sweden, including restrictions such as school closures and travel prohibitions along with economic support, affected equity markets in both countries. Our dataset comprises a panel of data for Norway and Sweden over 221 trading days during the period 1 January to 5 November 2020. The result show that while non-pharmaceutical interventions had few or no effects on Norwegian stock market returns, they positively affected the stock market in Sweden, although the strength of this effect weakened with the increasing number of confirmed COVID-19 cases.publishedVersio

First steps in the development of standardised processes for life cycle assessments of geotechnical works

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Robustness studies of the feedback linearization method

Thesis (M.S.)--Massachusetts Institute of Technology, Dept. of Aeronautics and Astronautics, 1992.Includes bibliographical references (leaf 93).by John Mikal Størdal.M.S

Restoration of seagrass habitats: Effects of artificial and natural sediments on the development of transplanted eelgrass (Zostera marina)

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Expired carbon dioxide during newborn resuscitation as predictor of outcome

Aim: To explore and compare expired CO2 (ECO2) and heart rate (HR), during newborn resuscitation with bag-mask ventilation, as predictors of 24-h outcome. Methods: Observational study from March 2013 to June 2017 in a rural Tanzanian hospital. Side-stream measures of ECO2, ventilation parameters, HR, clinical information, and 24-h outcome were recorded in live born bag-mask ventilated newborns with initial HR \u3c 120 bpm. We analysed the data using logistic regression models and compared areas under the receiver operating curves (AUC) for ECO2 and HR within three selected time intervals after onset of ventilation (0-30 s, 30.1-60 s and 60.1-300 s). Results: Among 434 included newborns (median birth weight 3100 g), 378 were alive at 24 h, 56 had died. Both ECO2 and HR were independently significant predictors of 24-h outcome, with no differences in AUCs. In the first 60 s of ventilation, ECO2 added extra predictive information compared to HR alone. After 60 s, ECO2 lost significance when adjusted for HR. In 70% of newborns with initial ECO2 Conclusions: Higher levels and a faster rise in ECO2 and HR during newborn resuscitation were independently associated with improved survival compared to persisting low values. ECO2 increased before HR and may serve as an earlier predictor of survival