39449 research outputs found
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Designing digital user interfaces for people with Parkinson's disease: A set of five design principles
No full textI en tid hvor samfunnet digitaliseres i et stadig raskere tempo, står enkelte brukergrupper i fare for å falle utenfor. En brukergruppe med høy risiko for digitalt utenforskap er personer med Parkinson. Motoriske og kognitive symptomer, i kombinasjon med lav digital kompetanse, gjør bruken av digitale brukergrensesnitt krevende og i verste fall utilgjengelig.
Gjennom et ettårig masterprosjekt har jeg og mine medstudenter gjort et redesign av Parkinson.no, i samarbeid med Norges Parkinsonforbund. På bakgrunn av innsiktsarbeid, faglig forankring innen interaksjonsdesign og universell utforming, utforsker denne oppgaven hvordan designere bedre kan møte personer med Parkinsons sammensatte behov. Oppgaven stiller følgende problemstilling: Hvordan kan man designe digitale brukergrensesnitt som imøtekommer personer med Parkinson sine sammensatte behov? Resultatet er et sett med fem praksisnære designprinsipper som bør ligge til grunn for utviklingen av digitale brukergrensesnitt for personer med Parkinson: 1) Tilrettelegging for fleksibilitet, 2) Skap en oversiktlig og intuitiv opplevelse, 3) Tilby klart og forståelig innhold, 4) Forebygg glipper og tilby feilhåndtering, og 5) Reduser fysisk belastning. Medfølgende designprinsippene presenteres tre designråd, som beskriver hvordan det kan brukes i praksis.Masteroppgave i medie- og interaksjonsdesignMIX350MASV-MI
A fishy gut feeling – current knowledge on gut microbiota in teleosts
Get PDFThe importance of the gastrointestinal microbiota (GM) in health and disease is widely recognized. Although less is known in fish than in mammals, advances in molecular techniques, such as 16S rRNA sequencing, have facilitated characterization of fish GM, comprising resident autochthonous and transient allochthonous bacteria. The microbial diversity and composition are strongly influenced by diet. High-protein diets, including alternative ingredients like plant and insect proteins, modify GM, impacting beneficial bacteria e.g. Cetobacterium. Lipids affect microbial metabolism and short-chain fatty acid (SCFA) production, while excessive carbohydrates can disrupt GM balance, causing enteritis. Dietary additives, including probiotics, prebiotics, and antibiotics, effectively modulate GM. Probiotics enhance immunity and growth, prebiotics support beneficial bacteria, and antibiotics, though effective against pathogens, disrupt microbial diversity and may promote antibiotic resistance. Environmental factors, such as temperature, salinity, and pollution, significantly influence GM. Elevated temperatures and salinity shifts alter microbial composition, and pollutants introduce toxins that compromise intestinal function and microbial diversity. Stress and pathogen infections further destabilize GM, often favoring pathogenic bacteria. GM communicates with the host via metabolites such as SCFAs, bile acids, and neurotransmitters, regulating appetite, energy metabolism, immunity, and neural functions. Additionally, GM influences the immune system by interacting with epithelial cells and stimulating immune responses. Despite recent advances, further research is needed to elucidate species-specific mechanisms underlying GM-host interactions, the ecological implications of GM diversity, and its applications in aquaculture to optimize fish health and performance.publishedVersio
Har tidlig barnehagegang en effekt på skoleprestasjoner?
Get PDFTidligere forskning viser at barnehage generelt har en positiv effekt på barns læring og utvikling. I tillegg påstår økonomisk teori at en investering i tidlig barndom gir høyere avkastning i humankapital enn en tilsvarende investering senere i livet. Med bakgrunn i dette undersøker jeg i denne oppgaven om tidlig barnehagegang påvirker barns skoleprestasjoner. I analysen brukes barnehagereformen fra 2003 som et naturlig eksperiment. Reformen førte til en betydelig økning i barnehagedekningen for 1-2 åringer i Norge. Data som benyttes i oppgaven er norske kommuners andel 1-2 åringer i barnehage fra årene 1998 – 2008, folketall fra kommunene og barnas resultater på nasjonale prøver som gjennomføres i 5.klasse på barneskolen.
Den empiriske metoden jeg benytter er i hovedsak en toveis fast effekt-modell. I tillegg utfører jeg analyser ved bruk av Pooled OLS-modell. Jeg utfører også en event-studie for å undersøke om barnehagereformen er velegnet som et naturlig eksperiment. I motsetning til tidligere studier og teori finner jeg ingen effekt av tidlig barnehagegang på skoleprestasjoner. Det ser imidlertid ut som det er en negativ sammenheng mellom tidlig barnehagegang og regneferdigheter. Resultatene fra event-studien indikerer at barnehagereformen har vært velegnet som et naturlig eksperiment i analysen min, men at det likevel ikke var mulig å finne en kausal effekt.
Verktøyene som benyttes for databehandling og analyser er Excel og Stata.MasteroppgaveECON391PROF-SØKMASV-SØ
Characteristics of Interprofessional Collaboration Between Educational Psychological Services and Schools: A Scoping Review
No full textInterprofessional collaboration is the preferred working approach for supporting children with special educational needs. Furthermore, educational psychology services (EPS) have been identified as a significant means of realizing the ideals of inclusive education for at-risk children. However, the literature on interprofessional collaboration is highly dominated by research in healthcare context with limited focus on educational settings. To address this gap, this scoping review provides an overview of research on interprofessional collaborations in educational settings, specifically between EPS and primary and lower secondary schools. A systematic search was conducted in the PsycINFO, Pro Quest, and Web of Science databases yielding 1212 references. After screening, 156 references were reviewed in full text and 46 records were included in the final sample. These records included a wide range of methodologies (qualitative, quantitative, and mixed methods) and document types (articles, doctoral theses, and handbook chapters). After extraction and analysis, our results revealed inconsistent terminology related to interprofessional collaboration and identified three levels of rationale for collaboration: individual, systemic, and a combination of both. Organizational issues were the most commonly reported challenges. Notably, few studies incorporated the perspectives of children and caregivers, and there was limited exploration of student outcomes. We conclude this review by providing implications for future research.publishedVersio
EU regulations and their normative impacts: Perspectives on the governance of digital economy.
No full textIn the current time of major investigations against US tech companies conducted under the relatively new EU digital governance regime, this thesis seeks to deploy the EU governance theories of Normative Power Europe, Market Power Europe and the Brussels effect to investigate the impact of EU regulations onto digital economy. Of special interest is the Digital Services Act, as it is set to prevent harmful online content and disinformation. As companies such as X, Facebook and TikTok operate under foreign regulatory regimes, much remains to be understood about how ideas of harmful online content and disinformation are interpreted.Master's Thesis in Politics and Governance of Global ChallengesGOV380MASV-GLGO
An Idealized Model for the Emergence of the North Icelandic Jet
No full textThe North Icelandic Jet (NIJ) is a significant contributor to the lower limb of the Atlantic Meridional Overturning Circulation, transporting dense overflow waters banked up along the slope north of Iceland equatorward and supplying up to half of the Denmark Strait overflow water, including the densest portion. Major uncertainties remain regarding what mechanisms contribute to the emergence of the NIJ northeast of Iceland. This study investigates previously proposed mechanisms using a novel setup with a high-resolution idealized model for the north Icelandic slope. We set up a channel model along the slope north of Iceland with differing slope geometry, no external forcing, and horizontally uniform initial and boundary conditions based on observations. We impose highly idealized inflows and outflows as boundary conditions in the west, emulating the North Icelandic Irminger Current (NIIC) inflow and dense NIJ outflow through Denmark Strait. The model consistently replicates key features of the NIJ, such as its mid-depth intensified core associated with diverging isopycnals away from the slope. Our results corroborate that a steeper slope, a stronger NIIC-like current, and stronger cross-slope density gradients promote instabilities closely related to the emerging NIJ-like current. Moreover, the simulated gradual eastward weakening of the NIJ-like current combined with enhanced eddy kinetic energy in the east is sufficient to facilitate an emergence. The variability of the model's NIJ-like current is largely associated with passing eddies, which might explain some of the observed occupations where the NIJ features a double-core structure.acceptedVersio
Umbrella Review of Systematic Reviews and Meta-Analyses on Consumption of Different Food Groups and Risk of Type 2 Diabetes Mellitus and Metabolic Syndrome
No full textType 2 diabetes is a major contributor to the burden of chronic diseases globally. Most cases of type 2 diabetes are preventable through healthy lifestyle modifications in diet and physical activity. This systematic umbrella review presents a comprehensive overview of the evidence about the associations between risk of type 2 diabetes and metabolic syndrome with 13 food groups, including refined and whole grains, fruits, vegetables, nuts, legumes, fish and fish products, eggs, dairy/milk, sugar-sweetened beverages, processed meat, and unprocessed red and white meat. We present these relationships in per-serving and with high-versus-low comparisons. After doing a systematic search in MEDLINE, Embase, Web of Science, and Epistemonikos (registered with PROSPERO: CRD42024547606), we screened 5074 references published until May 15, 2024, and included 67 articles. This included 46 meta-analyses on risk of type 2 diabetes with half a million participants, 17 meta-analyses on risk of metabolic syndrome, and 4 meta-analyses on risk of diabetes-related mortality. Based on quality assessments using AMSTAR-2, 25 of the 67 studies were classified as high-quality studies, 8 as moderate, 12 as low, and 22 as critically low quality. Our results showed that a high intake of whole grains was associated with a lower risk of type 2 diabetes (metaevidence: moderate) and metabolic syndrome (metaevidence: low), with a similar tendency also for a high intake of fruits and vegetables (metaevidence: moderate). In contrast, the high intakes of processed meat (metaevidence: high), red meat (metaevidence: moderate), and sugar-sweetened beverages (metaevidence: moderate) were associated with a higher risk of type 2 diabetes. For the other food groups, the associations were generally neutral and not statistically significant. The heterogeneity was high for most food groups except fruits, indicating potential differences within each of the food groups in association with type 2 diabetes.publishedVersio
A Deep Reinforcement Learning Hyper-Heuristic for Municipal Home Health Care Routing and Scheduling
No full textRapid population aging and the accompanying surge in demand for home-based care have made nurse routing and scheduling critical for municipal health services. This thesis formalizes a Home Health Care Routing and Scheduling Problem (HHCRSP) that captures multi-skill matching, two-worker synchronization, soft time windows, worker-continuity preferences, and costly last-minute outsourcing. We address this issue using two metaheuristics: an Adaptive Large Neighborhood Search (ALNS) with 29 destroy/repair operators, and a Deep Reinforcement Learning Hyper-Heuristic (DRLH) that utilizes a PPO-trained actor-critic network to select from 56 operators.
Real operational data from Øygarden municipality, Norway, were cleaned, geocoded, and augmented into 153 synthetic day-shift instances for training. On an unseen test day (187 visits, 16 workers), DRLH reduced the total objective value by 10.4 % relative to the commercial planning tool, lowered late visits from 10.2 % to 3.7 %, and virtually eliminated severe delays. It also outperformed a hand-tuned ALNS by 17.1 % and exhibited lower run-to-run variance.
Stress tests reveal sharply non-linear cost escalation: one absent nurse (+6.3 % workload) raises costs by 32-59 %, while a 50 % service-time overrun drives costs more than six-fold. Conversely, maintaining one surplus worker, permitting a five-minute same-day re-optimization, and prioritizing punctuality over strict worker continuity yields robust, near-optimal schedules.
These findings demonstrate that deep RL-guided hyper-heuristics can deliver significant efficiency and service-quality gains in complex, real-world home care logistics, offering actionable guidance for managers facing escalating demand.Masteroppgave i informatikkINF399MAMN-INFMAMN-PRO
Child immunization data quality in Rwanda: an assessment of routine health information system data
No full textBackground: Documentation and reporting of routine data by health workers is the backbone of the childhood immunization program. Immunization data from health management information systems (HMIS) in low-and middle-income countries (LMICs) are often incomplete and unreliable. In Rwanda, the immunization e-Tracker, an individual-level health management information system (HMIS) built on DHIS2 open-source software, has been implemented and scaled nationwide since 2019. The aim of this study was to assess the quality of the routine HMIS immunization data over time.
Method: Data were derived from four HMIS sources for January to December 2020 from 24 health facilities from four districts: health facility registers (paper-based), district aggregated reports (paper-based), national HMIS reports (electronic), and e-Tracker reports (electronic). We then obtained e-Tracker reports and national HMIS reports from 2022 for the same facilities and assessed changes over time. Data quality assessments were conducted for four selected childhood immunization indicators: Bacille Calmette-Guérin (BCG), Pentavalent 3 (Penta 3) and Measles & Rubella 1 (MR1). We calculated frequencies and percentage differences. Accuracy ratios were computed for HMIS reports against facility registers for 2020 and e-Tracker for 2022.
Results: In 2020, varying degrees of inconsistencies between facility registers and HMIS reports were observed, ranging from − 2.57 to 0.67% for BCG, -13.85% to -1.45% for Penta3, and − 8.30–2.00% for MR1. Only BCG data were entered in the e-Tracker in 2020. By 2022, e-Tracker completeness of Penta3 and MR1 had also increased substantially.
Conclusions: Data quality in the paper based HMIS was variable across districts and health facilities. Improvements in quality of e-Tracker data over time demonstrate increased uptake of e-Tracker use by health workers, possibly explained by the removal of paper documentation and reporting. Further improvements in data quality can be achieved by purposefully designed implementation strategies to support health workers with digital data entry.publishedVersio
Deep learning based image enhancement for dynamic non-Cartesian MRI: Application to “silent“ fMRI
No full textRadial based non-Cartesian sequences may be used for silent functional MRI examinations particularly in settings where scanner noise could pose issues. However, to achieve reasonable temporal resolution, under-sampled 3D radial k-space commonly results in reduced image quality. In recent years, deep learning models for improving image quality have emerged. In this study, we investigate the applicability of deep learning image enhancement methods with a focus on preserving dynamic temporal signal changes.
By utilizing high-resolution resting-state fMRI datasets from the Human Connectome Project (HCP) foundation, a ground-truth training set was constructed. The k-space trajectory coordinates of a so-called silent ‘Looping Star’ fMRI sequence was used to simulate non-Cartesian MRI data from the HCP datasets. Subsequently, these sparse resampled k-space were reconstructed, thereby generating pairs of simulated ‘Looping Star’ images and ground truth HCP images. The dataset served as the basis for training both 2D-UNet and 3D-UNet deep learning models for image enhancement. A comparative analysis was conducted, and the superior model was further fine-tuned. Evaluation of the final model's performance included standard image quality metrics as well as resting-state fMRI (rs-fMRI) analysis in the time-domain.
The 3D-UNet outperformed the 2D-UNet in the image enhancement task, resulting in a significant reduction in error between the network input and the ground truth. Specifically, the 3D-UNet achieved a 97 % reduction in the mean square error between the simulated Looping Star input and the HCP ground truth in the pre-processed dataset. Moreover, the 3D-UNet successfully preserved voxel variations, observed as the correlated activity in the posterior cingulate cortex (PCC) during rs-fMRI analysis while simultaneously mitigating noise in the time-series images.
In summary, image quality was improved and artifacts were effectively eliminated through the application of both 2D and 3D deep learning approaches. Comparative analysis of the networks indicated that the use of 3D convolutions is more advantageous than employing a deeper network with 2D convolutions, particularly in scenarios involving global artifacts. Furthermore by demonstrating that the trained neural network successfully preserved temporal characteristics in the BOLD signals, the results suggest applicability in fMRI studies.publishedVersio