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Integrating Emerging Technologies into Education and Training: Proceedings of the 2nd ETELT 2024 Workshop
Get PDFThis open access book compiles state-of-the-art research on the development and implementation of advanced learning technologies and AI-assisted tools, showing how they can transform the educational landscape and learning processes in various domains. Covering topics ranging from immersive learning environments to personalized education, it offers insights and practical applications that are shaping the future of classrooms and training programs. This book provides research insights on harnessing emerging technology’s potential to enhance education, and it is an essential reading for educators, researchers, and edu-tech professionals
Democratisation of the outdoors: how equipment lending is emerging in Norway’s sharing economy to provide sustainable consumption at the local scale
Get PDFOutdoor equipment lending is emerging as a collaborative consumption practice in Norway’s sharing economy, potentially providing a solution to the adverse climate and environmental impacts connected with the country’s outdoor culture. Little is known, however, of how to upscale such practices when owning equipment is culturally embedded. This article contributes to an emerging literature on how emotions, knowledge, and environmental values may contribute to participation in collaborative consumption and responds to calls for research on cultural and contextual elements of sharing practices. Through ethnographic fieldwork and interviews, we explore the outdoor equipment lending outlet TURBO in the city of Tromsø, Norway. We ask, how can collaborative consumption of equipment provide a sustainable solution to the societal goal of democratic participation in outdoor life. Building on practice theory, our analyses reveal a shift in focus from low-income and immigrant families to broader outreach and inclusion of many new user groups. In the encounters taking place at TURBO, emotions of trust and affinity, knowledge of how to access the outdoors, and environmental values are shared in dynamic interaction between lenders and borrowers. The direct effect on private consumption lies outside the scope of this research, but we argue that collaborative consumption of equipment provides potential climate and environmental benefits through the lending of donated equipment, by extending life cycles of equipment through reuse and repair, and when borrowing equipment supersedes traditional consumption. Moreover, the practices at TURBO destigmatise borrowing instead of buying and contribute to the democratisation of the outdoors
Reevaluating the Flynn effect, and the reversal: Temporal trends and measurement invariance in Norwegian armed forces intelligence scores
Get PDFSince 1954, the Norwegian Armed Forces have annually administered an unchanged general mental ability test to male cohorts, comprising figure matrices, word similarities, and mathematical reasoning tests. These stable and representative data have supported various claims about shifts in general mental ability (GMA) levels, notably the Flynn effect and its reversal, influencing extensive research linking these scores with health and other outcomes. This study examines whether observed temporal trends in scores reflect changes in latent intelligence or are confounded by evolving test characteristics and specific test-taking abilities in numerical reasoning, word comprehension, and figure matrices reasoning. Our findings, using multiple-group factor analysis and multiple indicator multiple cause (MIMIC) models, indicate that while there was a general upward trend in observed scores until 1993, this was predominantly driven by enhancements in the fluid intelligence task, specifically figure matrices reasoning. Notably, these gains do not uniformly translate to a rise in underlying GMA, suggesting the presence of domain-specific improvements and test characteristic changes over time. Conversely, the observed decline is primarily due to decreases in word comprehension and numerical reasoning tests, also reflecting specific abilities not attributable to changes in the latent GMA factor. Our findings further challenge the validity of claims that changes in the general factor drive the Flynn effect and its reversal. Furthermore, they caution against using these scores for longitudinal studies without accounting for changes in test characteristics
Tertiary lymphoid structures and inflammation in Systemic Lupus Erythematous and Sjögrens Disease
No full textSystemic lupus erythematosus (SLE) and Sjögren’s disease (SjD) are autoimmune diseases, characterized by immune responses against the body’s own cells and tissues. Chronic inflammation may induce the formation of tertiary lymphoid structure (TLS), which might drive the disease progression. Approximately half of the SLE patients develop lupus nephritis (LN), a renal manifestation where autoantibodies and immune complexes deposit in the kidneys, initiating an immune cascade that can lead to renal failure. This study investigated inflammatory responses in these autoimmune diseases, with a special focus on TLS development and function.
RNA sequencing was performed to compare the cellular and genetic properties of renal TLSs, lymph nodes and kidneys in NZBW-F1 mice. We explored the role of mesenchymal stromal cells (MSCs) as lymphoid tissue organizer (LTo) cells and CD4+ T cells as lymphoid tissue inducer cells. NZBW-F1 mice were topical treated with imiquimod, which are a TLR7 agonist. Additionally, we investigated macrophage dynamics in the salivary glands during virus-induced SjD in C67BL/6J mice.
TLSs were identified in the renal pelvis wall of autoantibody positive and proteinuric NZBW-F1 mice, showing a lymph node like cellular and genetic profile. Genes involved in immune cell activation, recruitment and antibody production were upregulated during disease progression. MSC-like cells in the renal pelvis wall, and stimulated MSCs induced CD4+ T cell proliferation and differentiation into Th2 and Th17 subsets in vitro, suggesting a role in early inflammation and as LTo cell candidates during TLS development. IMQ treatment accelerated SLE onset, by early production of autoantibodies and renal infiltration of lymphocytes, but not development of proteinuria. An increase in T regulatory cells was observed during disease progression, indicating more regulatory mechanisms. The presence of macrophages affiliated with TLS may indicate a multifaceted role in TLS dynamics, contributing to proinflammatory responses, TLS development, function, and tissue fibrosis.Systemisk lupus erythematosus (SLE) og Sjøgrens syndrom (SjD) er autoimmune sykdommer hvor kroppens immunsystem danner immunrespons mot seg selv og produserer autoantistoffer. Både i SLE og SjD kan kronisk inflammasjon føre til dannelse av tertiært lymfatisk vev (TLS). Omtrent halvparten av SLE pasientene får lupus nefritt (LN) som er en nyrebetennelse som oppstår på grunn av renal deponering av autoantistoffer og immunkomplekser. Dette fører til aktivering av immunresponser, som kan også føre til nyresvikt. TLS kan være involvert i å stimulere sykdomsutviklingen av både SLE og SjD. Vi har undersøkt inflammasjonsprosesser i sykdomsutviklingen i disse sykdommene, spesielt i forhold til TLS-dannelsen, samt dens mulige funksjon.
RNA-sekvensering ble brukt for å kunne sammenligne celle- og genprofilene i renale TLSer, lymfeknuter og nyrer i NZBW-F1 mus. Vi studerte rollen til mesenkymale stromale celler (MSCer) som mulige lymfoide vevsorganisator (LTo) celler og CD4+ T celler som lymfoide vevsinduser celler. NZBW-F1 mus ble behandlet topikalt med imiquimod, som er en TLR7 agonist. I tillegg, så ble makrofagdynamikken analysert i spyttkjertelen i en virusindusert modell av SjD i C57BL/6J mus.
TLSer ble identifisert i nyrebekkenet i autoantistoff-positive og i proteinuriske NZBW-F1 mus. Disse strukturene hadde liknende celle- og genprofil som lymfeknuter. Gener relatert til immuncelleaktivering, rekruttering og antistoffproduksjon var oppregulert under sykdomsforløpet av LN. MSC-lignende celler ble oppdaget i nyrebekkenveggen. Stimulerte MSCer førte til CD4+ T-celleproliferasjon og differensiering til Th2- og Th17 celler in vitro. Dette indikerer at MSC kan være med på å stimulere tidlig inflammasjon og fungere som LTo celle under TLS-dannelsen. Imiquimod-behandlingen akselererte sykdomsutvikling av SLE. Vi så en økning av antistoffproduksjon og lymfocyttinfiltrasjon inn i nyrene, men behandlingen førte ikke til proteinuri. Økning av T regulatoriske celler ble observert under sykdomsforløpet, noe som antyder økte regulatoriske mekanismer. Tilstedeværelsen av makrofager i forbindelse med TLS, kan indikere deres rolle både innenfor proinflammatoriske og antiinflammatoriske immunresponser under TLS-dannelsen og sykdomsutviklingen
Proximal-distal anatomical relationships between spermatocysts (Sertoli – germ cellunits) and eosinophilic immune system elements in the shark testis: a comparative study of the Greenland shark and thresher shark
Get PDFDespite lacking a voluminous testis-associated lymphomyeloid tissue (epigonal organ) in adulthood, the far
distally located non-spermatogenic tissue of the wild-captured Greenland shark (Somniosus microcephalus) houses
loosely circumscribed, granulocyte-liberating epigonal cell patches. These dynamic patches of epigonal cells are
coincident with the differentiation and subsequent distribution of two types of intravascular and extravascular
eosinophilic granulocytes in the expansive non-spermatogenic tissue, and in the interstitium of notably premeiotic and meiotic stages of spermatogenesis. Their sparse numbers, discreet occurrence, and as a rule within
the vicinity of a blood vessel in all examined tissue samples, including a 1960s archival collection of tissues, are
indicative of diapedesis whereupon they become ensconced in the tight interstitial spaces of spermatocysts
(stage-synchronized germ and Sertoli cell clones). By comparison, an anatomically robust and intimate testis –
epigonal association in the thresher shark does not correlate with any type of granulocyte in the testicular parenchyma nor its vasculature, despite that the most proximal eosinophilic epigonal cells nearly abut the basement membrane of some of its spermatozoal cysts (the only cyst stage to reveal this). These findings in Somniosus
are discussed in relation to the previously reported protracted phase of disordered internal cellular organization
of the Greenland shark’s spermatogonial cysts due to incompletely differentiated Sertoli supportive functions for
what should be well-advancing spermatogonial generations
Predicting Norwegian elderly hospitalizations using Machine Learning
Get PDFThis thesis focuses on developing a Machine Learning (ML) model to predict the first hospital admission in older Norwegian patients.
Addressing the complexity of predicting all-cause hospitalizations required a comprehensive approach, beginning with a systematic review of existing work. The review highlighted some important gaps in the use of ML for predicting hospitalization such as challenges in representing Health Code Systems (HCSs), the quality of reporting, and individual clinical interpretations. These insights were starting points for the methodological and practical frameworks presented in this thesis.
To tackle the representation of HCSs, while taking into consideration the trade-off between model performance and meaningful clinical interpretations, we proposed a methodology based on Network Analysis (NA) modularity detection. The idea was to group these codes after their prevalence in the population. HCSs such as the Internation Classification of Disease (ICD), were modeled as a network, where nodes represent the codes and edges quantify co-occurrence among patients. The methodology demonstrated good predicting performance and several advantages over traditional grouping approaches. Building on that, and to validate the clinical relevance of this methodology, we demonstrated a framework for detecting and interpreting Multimorbidity Patterns (MPs) using data from the Norwegian elderly hospitalized population.
This thesis focuses on developing a Machine Learning (ML) model to predict the first hospital admission in older Norwegian patients.
Addressing the complexity of predicting all-cause hospitalizations required a comprehensive approach, beginning with a systematic review of existing work. The review highlighted some important gaps in the use of ML for predicting all-cause hospitalization such as challenges in representing high-dimensional Health Code Systems (HCSs), the quality of reporting, clinical interpretations on the individual patient level, and models’ deployment. These insights were the starting point for the methodological and practical framework presented in this thesis.
To tackle the representation of HCSs, while taking into consideration the trade-off between model performance and meaningful clinical interpretations, we proposed a methodology based on Network Analysis (NA) modularity detection. The idea was to group these codes after their prevalence in the population. HCSs such as the Internation Classification of Disease (ICD), were modeled as a network, where nodes represent the codes and edges quantify co-occurrence among patients. The methodology demonstrated good prediction performance and several advantages over traditional grouping approaches. Building on that, and to validate the clinical relevance of this methodology, we demonstrated a framework for detecting and interpreting Multimorbidity Patterns (MPs) using data from the Norwegian older patient hospitalized population.
We finally developed an ML model to predict all-cause somatic hospitalizations. We applied a pipeline to achieve good model performance and to find the most influential features for predicting hospitalizations. We also aimed to address some of the identified gaps in the literature and integrate the usage of the proposed methodology of representing HCSs. The model pipeline incorporated diverse data samples for model training, feature selection technique, and algorithm groups. The model was deployed as a web application to demonstrate the potential use of this work in practice. The thesis provides a clinically relevant framework for healthcare systems investigating similar outcomes and puts the foundation for future research on the Norwegian national level to refine predictive models, expand multimorbidity analyses, and address challenges in clinical deployment.Denne avhandlingen fokuserer på å utvikle en maskinlæringsmodell (ML) for å predikere den første sykehusinnleggelsen hos eldre norske pasienter.
Håndteringen av kompleksiteten av predikering alle typer sykehusinnleggelser krevde en omfattende tilnærming. Vi begynte med en systematisk gjennomgang av eksisterende arbeid. Gjennomgangen avdekket flere viktige svakheter i bruken av ML til å forutsi sykehusinnleggelser, blant annet utfordringer med representasjon av Health Code Systems (HCS), kvaliteten på rapportering og individuelle kliniske tolkninger. Disse funnene dannet utgangspunktet for de metodiske og praktiske rammene som presenteres i denne avhandlingen.
For å takle representasjon av HCS og balansere hensynet til både modellens ytelse og meningsfulle kliniske tolkninger, foreslo vi en metodikk basert på modulæritetsdeteksjon i nettverksanalyse (NA). Målet var å gruppere kodene ut fra hvor vanlige de er i befolkningen. HCS-er, som for eksempel International Classification of Disease (ICD), ble modellert som et nettverk der noder representerer kodene og kantene viser samforekomst blant pasienter. Metodikken ga god modellytelse og viste flere fordeler sammenlignet med tradisjonelle grupperingsmetoder. For å bekrefte den kliniske relevansen av denne tilnærmingen, lagde vi et rammeverk for å oppdage og tolke multimorbiditetsmønstre (MP-er) ved bruk av data fra eldre sykehuspasienter i Norge.
Vi utviklet til slutt en ML-modell for å predikere somatiske sykehusinnleggelser av alle årsaker. Hensikten var å fylle noen av kunnskapshullene i litteraturen og inkludere den foreslåtte metodikken for å representere HCS. Modellen ble bygget ved hjelp av ulike datahåndteringsteknikker, metoder for utvelgelse av funksjoner og flere algoritmegrupper. Den ble deretter gjort tilgjengelig som en nettapplikasjon for å illustrere hvordan den kan tas i bruk i praksis. Avhandlingen presenterer et klinisk relevant rammeverk for helsesektorer som ønsker å undersøke tilsvarende problemstillinger, og legger grunnlaget for videre forskning på nasjonalt nivå i Norge for å forbedre prediksjonsmodeller, utvide multimorbiditetsanalyser og adressere utfordringer ved klinisk implementering
Ideelle velferdsorganisasjoners særtrekk, merverdi og impact (SMI)
Get PDFSource at https://www.vid.no/.Denne forskningsrapporten presenterer funnene fra en omfattende studie av ideelle organisasjoners særtrekk, merverdi og impact (samfunnseffekt) innen helse- og sosialsektoren. Forskningsprosjektet har tatt utgangspunkt i to hovedspørsmål: Hva er ideelle tjenesteleverandører på helse- og sosialfeltet sine særtrekk, merverdi og impact? Hvordan forske på ideelle tjenesteleverandører på helse- og sosialfeltet sine særtrekk, merverdi og impact, og hvilke fordeler og ulemper har forskjellige målemetoder? Prosjektet har fremhevet utforsking av merverdi som et prioritert formål. Et bærende resonnement i studien er at merverdi forutsetter impact ettersom det ikke ses på som mulig å generere merverdi uten først å skape en form for impact. Impact måler samfunnseffekten en tjeneste har for sluttbrukere. Merverdi er verdien av en tjeneste som bygger på samfunnseffekten. Det er verdien av ideelle velferdsorganisasjoner tjenester som positivt overskyter verdien som tilbys av andre tjenester, organisasjoner eller sektorer, slik dette oppfattes av sentrale interessenter. Siden dette er et relativt nytt forskningsfelt har prosjektet utviklet og testet et spørsmålsbatteri som er egnet til å utforske ideelle velferdsaktørers merverdi. Dette spørsmålsbatteriet opptrer i denne studien i to former; en intervjuguide brukt i kvalitative studier og et spørreskjema brukt i en kvantitativ studie
Tree models for assessing covariate-dependent method agreement with an application to physical activity measurements
Get PDFMethod comparison studies assess agreement between different measurement methods. In the present
work, we are interested in comparing physical activity measurements using two different accelerometers.
However, a potential issue arises with the popular Bland–Altman analysis, as it assumes that differences
between measurements are identically distributed across all observational units. In the case of the physical
activity measurements, agreement might depend on sex, height, weight, or age of the person wearing the
accelerometers, among others. To capture this potential dependency, we introduce the concept of
conditional method agreement, which defines subgroups with heterogeneous agreement in dependence of
covariates. We propose several tree-based models that can detect such a dependency and incorporate it
into the model by splitting the data into subgroups, showing that the agreement of the activity
measurements is conditional on the participant’s age. Simulation studies also showed that all models were
able to detect subgroups with high accuracy as the sample size increased. We call the proposed modelling
approach conditional method agreement trees and make them publicly available through the R package coat
Improving decision transparency in autonomous maritime collision avoidance
Get PDFRecent advances in artificial intelligence (AI) have laid the foundation for developing a sophisticated collision avoidance
system for use in maritime autonomous surface ships, potentially enhancing maritime safety and decreasing the navigator’s
workload. Understanding the reasoning behind an AI system is inherently difficult. To help the human operator understand
what the AI system is doing and its reasoning, we employed a human-centered design approach to develop transparency
layers that visualize different aspects of an operation by displaying labels, diagrams, and simulations intended to improve the
user’s situation awareness (SA). The effectiveness and usability of the different layers were investigated through simulatorbased experiments involving nautical students and licensed navigators. The SA global assessment technique was utilized to
measure navigators’ SA. User satisfaction was also measured, and effective layers were identified. The results indicate that
the transparency layers that enhance SA Level 3 are preferred by participants, suggesting a potential for improving human–
AI compatibility. However, the introduction of transparency layers does not uniformly enhance SA across all levels, and a
tendency toward passive decision-making was observed. The findings highlight the importance of balancing information
presentation with the user’s cognitive capabilities and suggest that further research is needed to refine transparency layers
for optimized human–AI compatibility in maritime navigation
Local-Scale Advanced Ship Predictor towards Enhanced Maritime Situation Awareness
No full textWith the introduction of autonomous and remotely-controlled ships, close vessel encounter can be associated with higher risk of collision or near-miss scenarios. To avoid these possible incidents, a higher level of situation awareness is required to support the respective decision-making process in maritime navigation. Advanced Ship Predictors (ASPs) are thus proposed to be a solution framework to enhance situation awareness for ship navigation as the main contribution of this thesis. The research presented in this thesis focuses on local-scale predictions conducted by the ASP, with the typical prediction horizons ranging from 10 to 90 seconds. The workflow of the local-scale ASP is divided into two main parts: vessel navigation state estimation and pivot point (PP)-based trajectory prediction. Kalman filter (KF)-based algorithms, combined with kinematic motion models, are used to estimate vessel navigation states. These estimated states are then employed to determine the PP of the vessel. The predicted trajectory is generated by leveraging the role of the PP. Given that the understanding and applications of the pivot point are widely adopted in maritime navigation education and training, the local-scale prediction is designed to integrate this understanding to the vessel trajectory prediction. The evaluation of the designed local-scale ASP begins with simulated maneuvers conducted in the UiT bridge simulator. It is then followed by sea trials on the UiT research vessel, Ymir RV. The performance of the local-scale ASP has been gradually improved through modifications after each evaluation. The evaluation based on simulated maneuvering data shows that the 90-second position prediction yields a median L2-norm error between 10m and 15m, while the median heading error at the 90th second ranges from 0 to -5 degrees. During sea trials conducted by the Ymir RV, the predictions are calculated over a 10-second horizon. After the vessel entered a steady state following the execution of a new rudder order, the position prediction revealed a maximum L2-norm error of approximately 8.7 meters, while the maximum heading error was about 13 degrees.Med innføringen av autonome og fjernstyrte skip kan nærkontakt mellom fartøy være forbundet med høyere risiko for kollisjoner eller nestenulykker. For å unngå mulige hendelser, som kollisjoner og nestenulykker, kreves et høyere nivå av situasjonsforståelse for å støtte beslutningsprosessen i skipsnavigasjon. Advanced Ship Predictors (ASPs) foreslås derfor som et løsningsrammeverk for å forbedre situasjonsforståelsen for skipsnavigasjon, som hovedbidraget i denne avhandlingen. Forskningen som presenteres i denne avhandlingen fokuserer på lokale prediksjoner utført av ASP, med typiske prediksjonshorisonter som varierer fra 10 til 90 sekunder. Arbeidsflyten for den lokale ASP-en er delt inn i to hoveddeler: estimering av fartøynavigasjonsstater og pivotpunkt (PP)-basert baneprediksjon. Kalman filter (KF)-baserte algoritmer, kombinert med kinematiske bevegelsesmodeller, brukes til å estimere fartøynavigasjonsstater. Disse estimerte tilstandene brukes deretter til å bestemme fartøyets PP. Den predikerte banen genereres ved å utnytte rollen til PP. Gitt at forståelsen og anvendelsen av pivotpunktet er allment akseptert i maritim navigasjonsutdanning og opplæring, er lokal prediksjon designet for å integrere denne forståelsen i fartøyets baneprediksjon. Evalueringen av den utformede lokale ASP-en begynner med simulerte manøvrer utført i UiT-simulator. Deretter følges det opp med sjøprøver på UiTs forskningsfartøy, Ymir RV. Ytelsen til den lokale ASP-en har gradvis blitt forbedret gjennom modifikasjoner etter hver evaluering. Evalueringen basert på simulerte manøverdata viser at 90-sekunders posisjonsforutsigelse gir en median L2-norm feil mellom 10m og 15m, mens median kursfeil etter 90 sekunder varierer fra 0 til -5 grader. Under sjøprøvene utført av Ymir RV ble prediksjonene beregnet over en 10-sekunders horisont. Etter at fartøyet hadde gått inn i en stabil tilstand etter utførelsen av en ny rorordre, avslørte posisjonsforutsigelsen en maksimal L2-norm feil på omtrent 8,7 meter, mens maksimal kursfeil var rundt 13 grader