Lean i Norge : opplever norske bedrifter som er mer lean, større økning i kundetilfredshet enn de som er mindre lean?

Vi ønsker å måle i hvor stor grad norske bedrifter har implementert lean, og for å måle grad av lean har vi utviklet et rammeverk basert på lean teori. Hovedformålet med lean er å øke kundeverdi med minst mulig ressursbruk, anstrengelse, energi, utstyr, tid, plass, material og kapital (Womack, Roos and Jones 2007). Vi vil utforske om høyere grad av lean fører til mer tilfredse kunder enn de som har lavere grad av lean. Altså om hovedformålet med lean blir oppnådd hvis man implementerer lean i større grad. Vi har derfor utformet følgende hypotese som utgangspunkt for vår oppgave: ”Norske bedrifter som er mer lean, opplever større økning i kundetilfredshet enn de som er mindre lean.” Våre funn indikerer at norske bedrifter har implementert lean i varierende grad og vi har i vår undersøkelse sett en klar sammenheng mellom grad av lean og økning i kundetilfredshet. Dette kan tyde på at hovedformålet med lean, økt kundeverdi, blir oppnådd ved å implementere lean i stor grad i norske bedrifte

Discovery and Designation of Type Specimens of Chrysomelidae (Coleoptera) From Argentina Described by E. von Harold in 1875

Type specimens of 14 species of Chrysomelidae from Cordova, Argentina. collected by W. M. Davis and described by E. von Harold in 1875, were discovered in the collections of the Museum of Comparative Zoology (Harvard University). A few specimens from some other museums such as the Museum für Naturkunde der Humboldt-Universität (Berlin), The Natural History Museum (London), Institut Royal des Sciences Naturelles de Belgique (Brussels), Museo Nacional de Hungaria (Budapest) are also apparently from the original series. Lectotypes and paralectotypes are designated for all species

Implicit body representations and the conscious body image

Recent studies have revealed that somatosensory processing relies on a class of implicit body representations showing large distortions of size and shape. The relation between these representations and the conscious body image remains unclear. Dissociations have been reported in the clinical literature on eating disorders between different body image measures, with larger and more consistent distortions found with depictive measures, in which participants compare their body to a visual depiction of a body, than metric measures, in which participants compare their body to some non-body standard. Here, we compared implicit body representations underlying position sense to the body image measured with both depictive and metric methods. The body image was measured using both a depictive method (template matching) in which participants judged whether their hand was wider or more slender than a shown hand picture, and a metric method (line length) in which participants judged whether different parts of the their hand were shorter or longer than a presented line. Consistent with previous findings, characteristic distortions were found for the implicit body representation underlying position sense. These distortions were also found in attenuated form for metric – but not depictive – body image measures. While replicating the basic dissociation between implicit body representations and the conscious body image, these results demonstrate that this dissociation is not absolute and specific tasks may utilise both to varying degrees depending on task demands. Metric measures may not be pure measures of body image, but some combination of visual and somatosensory body representations

Automated Mapping and Change Detection of Rivers and Inland Water Bodies by Semantic Segmentation of SAR Imagery using Deep Learing

Målet med denne oppgaven er å detektere utstrekninger av vannforekomster og elver i Norge ved semantisk segmentering av Syntetisk Apertur-Radar (SAR) satellittbilder. To dype nevrale nettverksarkitekturer er implementert og trent til å utføre segmenteringsoppgaven. Fokuset er å undersøke nøyaktigheten og nytteverdien til automatisk vannkartlegging for endringsdeteksjon i elver og vannforekomster. Prosjektet er knyttet til FNs femtende bærekraftsmål: Livet på Land, som har som mål å forhindre at konsekvenser av klimaendringer forårsaker landforringelse og tap av biologisk mangfold. Automatisk vannkartlegging er et viktig verktøy for å begrense mulige konsekvenser knyttet til klimadrevne flomhendelser, samt hjelpe til med katastrofehåndtering og beredskapsarbeid. Eksisterende maskinlæringsmodeller oppnår høy nøyaktighet hvis de trenes på svært nøyaktige data, men å skaffe og forberede slike data krever mye tid og manuelt arbeid. For å ta fatt på denne utfordringen er datasettet laget for dette prosjektet en samling Sentinel-1 SAR-bilder av ulike regioner i Norge, med etiketter fra Statens kartverk. Bilder og etiketter blir behandlet og delt i fliser, deretter delt inn i trenings- og valideringsdata med en fordeling på 85% til 15%. De to valgte dype segmenteringsarkitekturene er U-Net med en ResNet50-ryggrad forhåndsopplært på ImageNet-datasettet, og DeepLabV3+ med en Xception-ryggrad forhåndstrent på Cityscapes-datasettet. U-Net oppnår en samlet klassevektet Jaccard-indeks på 0,974, med en Jaccard-indeks på 0,846 og nøyaktighet på 0,886 spesifikt for vannklassen. DeepLabV3+ gir en vektet Jaccard- indeks på 0,976, og oppdager vann med en klassespesifikk Jaccard-indeks på 0,856 og en nøyaktighet på 0,892. Etiketter fra Statens kartverk er detaljerte, og inkluderer smale bekker og elver som ikke er mulig å detektere i SAR-bilder med oppløsning 20x22m. Derfor er ytelsesberegninger begrenset av forskjeller mellom de originale etikettene og bildene, og kan forbedres ved å bruke etiketter med tilsvarende nøyaktighet ved testing. Innhenting av SAR-bilder med høyere oppløsning kan øke modellens ytelse når det gjelder å oppdage svært små endringer i vannmengdene. De nåværende versjonene av modellene er sannsynligvis nøyaktige nok til å brukes som en del av et halvautomatisk varslingssystem for flomovervåking. Likevel er det nødvendig med mer nøyaktige data og testing for å fullt ut realisere potensialet til et pålitelig, helautomatisert system.The aim of this thesis is to detect extents of water bodies and rivers in Norway by semantic segmentation of Synthetic Aperture Radar (SAR) satellite images. Two deep neural network architectures are implemented and trained to perform the segmentation task. The focus is to examine the accuracy and usability of automatic water mapping for change detection in rivers and water bodies. The project is connected to the UN’s fifteenth sustainable development goal: Life on Land, which aims to prevent consequences of climate change from causing land degradation and biodiversity loss. Automatic water mapping is an important tool in limiting potential consequences related to climate driven flood events, aiding disaster management and emergency response efforts. Existing machine learning models obtain high accuracy if trained on highly accurate data, however, creation of such data is labor intensive, time consuming and requires manual work. To assess this challenge, the dataset created for this project is a collection of Sentinel-1 SAR images of different regions in Norway, with labels from the Norwegian Mapping Authorities. Images and labels are processed and tiled, then divided into training and validation data with an 85% to 15% split. The two chosen deep segmentation architectures are U-Net with a ResNet50 backbone, pre-trained on the ImageNet dataset, and DeepLabV3+ with an Xception backbone, pre-trained on the Cityscapes dataset. U-Net achieves an overall class-weighted Jaccard index of 0.974, with a Jaccard index of 0.846 and accuracy of 0.886 specific to the water class. DeepLabV3+ yields a weighted Jaccard index of 0.976, detecting water with a class specific Jaccard index of 0.856 and an accuracy of 0.892. Labels from the Norwegian Mapping Authorities are detailed, and includes narrow streams and rivers that are not detectable in SAR images with resolution 20x22m. As such, performance metrics are limited by differences between the original labels and images, and may be improved by providing labels with corresponding accuracy when testing. Obtaining SAR images with higher resolutions may increase model performance in terms of detecting very small changes in water extents. The current versions of the models are likely accurate enough to be used as part of a semi-automatic flood monitoring notification system. Yet, more accurate data and testing is needed to fully realize the potential of a reliable, fully automated system

The Transition to CSRD and ESRS

Abstract Purpose: This paper examines Norwegian companies’ perceptions of the transition to widespread mandatory sustainability reporting (CSRD) and related frameworks (ESRS) initiated by the EU. Their perceptions are investigated by elucidating the effect it will have on companies’ practices, while covering possible areas of resistance. Method: The paper is based on a cross-sectional interview study with 13 Norwegian companies, which are all defined as large undertakings according to Directive 2013/34/EU. The analysis follows an inductive approach and is conducted utilizing a thematic analysis. Findings: The CSRD and ESRS are considered comprehensive, but necessary to improve sustainability performance of affected companies. Norwegian companies describe a need for resources and competencies within sustainability reporting, more evident in smaller-sized companies. Respondents claimed, inter alia, that streamlining of processes would ensure compliance and optimal prioritization. The transition was regarded as feasible, given that resources, competencies, and systems facilitate it. Research limitations/implications: This study’s findings extend prior research on mandatory sustainability reporting, by examining sustainability reporting practices in light of this widespread mandate. In terms of limitations, varying knowledge among interviewees placed a heavier burden of interpretation on the interviewers. Value: This study contributes to the ongoing discussion of widespread mandatory sustainability reporting. In addition, we believe that our findings will provide useful insights that can aid in improving the quality of sustainability reports

Owning an overweight or underweight body: distinguishing the physical, experienced and virtual body

Our bodies are the most intimately familiar objects we encounter in our perceptual environment. Virtual reality provides a unique method to allow us to experience having a very different body from our own, thereby providing a valuable method to explore the plasticity of body representation. In this paper, we show that women can experience ownership over a whole virtual body that is considerably smaller or larger than their physical body. In order to gain a better understanding of the mechanisms underlying body ownership, we use an embodiment questionnaire, and introduce two new behavioral response measures: an affordance estimation task (indirect measure of body size) and a body size estimation task (direct measure of body size). Interestingly, after viewing the virtual body from first person perspective, both the affordance and the body size estimation tasks indicate a change in the perception of the size of the participant’s experienced body. The change is biased by the size of the virtual body (overweight or underweight). Another novel aspect of our study is that we distinguish between the physical, experienced and virtual bodies, by asking participants to provide affordance and body size estimations for each of the three bodies separately. This methodological point is important for virtual reality experiments investigating body ownership of a virtual body, because it offers a better understanding of which cues (e.g. visual, proprioceptive, memory, or a combination thereof) influence body perception, and whether the impact of these cues can vary between different setups

Production planning and sales allocation in the salmon farming industry

In this thesis a multistage stochastic optimization model has been developed for production planning and sales allocation within the salmon farming industry. To the authors knowledge, an optimization model for this problem has not been developed before. The background for this thesis is a tactical planning problem spanning production and sales under uncertainty. The goal is to identify profitable solutions that are sufficiently flexible in accounting for this uncertainty. The model is inspired by the work done by Hæreid (2011), but addresses more aspects of the value chain. The most important innovations in the model is that processed products and inventory management is included. Furthermore, the scope are extended to include global operations. Additionally, the model offers a more detailed modeling of contract and transport decisions. The model also incorporate both market and product price dependencies. The model is implemented in two versions: one deterministic and one three-stage stochastic version. To illustrate how they can be used, the different models are tested on instances inspired of Marine Harvest's global production network. The input data used are however based on publicly available information. To model uncertainty in salmon price and sea water temperature, different seasonal ARIMA-models are used to model these uncertain time series. Based on these forecasts, residual scenarios are generated by using a copula-based scenario generation heuristic. The developed model acts according to the authors initial assumptions and gives reasonable results compared to how the industry operates as of today. The value of including stochastic programming constitutes 5 % savings, and tests indicate that the model holds in-sample stability

Talking the talk : an empirical investigation into the economic effects of strategy disclosure

Masteroppgave(MSc) in Master of Science in Business, Strategy - Handelshøyskolen BI, 2017What is the economic value of strategy? Although the extant literature in strategic management has explored many different theories of the firm, the research field has centered on an underlying consensus that strategy is an important driver of corporate performance, and thus holds significant economic value. By extension, if we assume efficient markets, the disclosure of such important information should be reflected in the firm market value. Building on these assumptions, our paper will attempt to identify the economic effects of strategy by examining the impact of strategy disclosure in annual reports on the firm market value. By performing an event study structured around the release date of corporate annual reports for Norwegian listed firms, this study aims to isolate the financial effects from changes in strategy disclosure quality, represented as the presence of abnormal returns in the event period. To test this relationship, we used a selfconstructed score to represent the quality of strategy disclosure by measuring the informational value across several important strategic dimensions presented in the corporate annual reports. Subsequently, we used the disclosure quality of prior years to establish the investor expectations for strategy disclosure, allowing us to investigate the impact of information “shocks” on security price returns. Our findings show that the disclosure of strategically important information indeed holds economic value, finding significant abnormal returns, and thus increased firm market value, for positive changes in strategy disclosure quality. Further testing of single dimension effects, however, were less conclusive. This can indicate that, while investors value revelations on corporate strategy overall, disclosure on single dimensions are less valuable due to their potential lack of context. Despite this, our results clearly show that there are substantial economic gains from increasing reporting quality on corporate strategy, encouraging further study of this important, yet partially neglected, area of research

Tumour-associated macrophages act as a slow-release reservoir of nano-therapeutic Pt(IV) pro-drug

Therapeutic nanoparticles (TNPs) aim to deliver drugs more safely and effectively to cancers, yet clinical results have been unpredictable owing to limited in vivo understanding. Here we use single-cell imaging of intratumoral TNP pharmacokinetics and pharmacodynamics to better comprehend their heterogeneous behaviour. Model TNPs comprising a fluorescent platinum(IV) pro-drug and a clinically tested polymer platform (PLGA-b-PEG) promote long drug circulation and alter accumulation by directing cellular uptake toward tumour-associated macrophages (TAMs). Simultaneous imaging of TNP vehicle, its drug payload and single-cell DNA damage response reveals that TAMs serve as a local drug depot that accumulates significant vehicle from which DNA-damaging Pt payload gradually releases to neighbouring tumour cells. Correspondingly, TAM depletion reduces intratumoral TNP accumulation and efficacy. Thus, nanotherapeutics co-opt TAMs for drug delivery, which has implications for TNP design and for selecting patients into trials.National Cancer Institute (U.S.) (Grant RO1-CA034992