UiS Brage
Not a member yet
19747 research outputs found
Sort by
Hvordan kan sykepleiere, ved hjelp av kartleggingsverktøy, bidra til å oppdage tidlige symptomer på depresjon hos slagrammede pasienter innlagt på en nevrologisk avdeling?
Full text linkSammendrag
Bakgrunn
Hjerneslag er en akutt alvorlig tilstand som rammer 15000 nordmenn hvert år. Forskning viser at depresjon forekommer hos 20-40% av alle slagrammede pasienter. Gjennom den akutte fasen på sykehus befinner pasientene seg i et sårbart stadium, hvor emosjonelle symptomer oppstår, men fort blir oversett. Sykepleierne skal i denne fasen bidra med kartlegging slik at symptomene på depresjon oppdages så tidlig som mulig.
Hensikt
Hensikten med denne bacheloroppgaven er å belyse hvordan kartleggingsverktøy kan hjelpe sykepleiere til å oppdage tidlige symptomer på depresjon hos slagrammede pasienter på en nevrologisk avdeling. Fokuset vil da være hvilken effekt kartleggingen har, samtidig som vi vil trekke inn sykepleiernes erfaringer.
Metode
Vi har valgt å anvende en integrativ litteraturoversikt hvor fem artikler ble analysert. Artiklene som ble brukt, benyttet både kvalitativ og kvantitativ metode. For å komme frem til resultatene har vi anvendt Fribergs trinn i en litteraturoversikt. Oppgaven baseres på resultater fra de fem artiklene, i tillegg til relevant litteratur.
Resultater
Bruken av kartleggingsverktøy var godt egnet til å fange opp pasienter som var utsatt for å utvikle depresjon. Til tross for gode resultater, er det fremdeles en betydelig andel som forblir ubehandlet. Sykepleiere hadde ulike opplevelser ved bruken av verktøyet. På den ene siden var det erfart at verktøyet var godt egnet for arbeidspraksisen og økte bevisstheten rundt depresjon som en konsekvens av hjerneslag. Kartleggingsverktøyet ble til tider nedprioritert grunnet tidspress og andre yrkesgrupper sin manglende kunnskap og engasjement.
Nøkkelord: hjerneslag, depresjon, lidelse, sykepleie, kartleggingsverktøy, pasient
Integration of energy communities in distribution grids: Development paths for local energy coordination
Full text linkEnergy Communities (ECs) have received increasing interest in recent years, with diverse perceptions of what they are and how they are expected to interact with existing infrastructure. This is particularly important with regard to the distribution grid. ECs are expected to bring certain benefits in the transition towards a renewables-dominated electricity system, but that requires an awareness and coordination with the power system and particularly the distribution system operator (DSO). This paper intends to structure the discourse on EC integration in the distribution system, by identifying the two most critical factors in the interaction, namely the decentralisation of coordination responsibility, and advancement of active distribution system management. These two factors are used in a 2-by-2 scenario topology to identify four scenarios for local energy coordination that will govern the development of ECs; Reference, Competitive, Cooperative and Participatory. The scenarios imply different DSO-EC interactions and, thus, different conditions for the development of ECs. The scenario were active distribution system management and decentralisation of coordination responsibility are combined (Participatory scenario) is likely to bring most benefits. However, it is also the most complex and requires comprehensive and costly developments at both DSO and ECs. As a consequence, developments should happen step-wise, and the framework presented here can foster a structured discourse for this development.publishedVersio
Machine Learning Approaches for Heart Rate Variability Data Correction and Coronary Artery Calcification Classification
Full text linkPhD thesis in Information technologyCoronary artery disease (CAD) is one of the most common cardiovascular diseases and a major cause of death worldwide. Detection of coronary artery calcification (CAC) through coronary computed tomography angiography (CCTA) is normally used to diagnose CAD. Previous studies have demonstrated significant differences in the physiological response to exercise between individuals with and without CAC. This thesis aimed to apply machine learning (ML) methods on data measured during exercise to predict the presence of CAC, with a particular focus on the analysis of heart rate (HR) and heart rate variability (HRV) measured with HR chest straps. For this purpose, signal issues from the HR monitors must be handled appropriately.
Hemodynamic measures were collected from healthy participants before, during, and after a 91-km mountain bike race (the North Sea Race). The presence of CAC was determined by CCTA after the race. Several time series methods were applied to the HRV data to address data artifact correction. A statistical analysis of hemodynamic measures at the most challenging hill was conducted to determine physiological differences between individuals with and without CAC. Finally, various classification algorithms were used to predict the presence of CAC based on hemodynamic and HRV data.
In Papers 1 and 2, the autoregressive integrated moving average method was shown to outperform other artifact correction methods for HRV data, even with minimal training data and computational cost. Cubic interpolation, the most common artifact correction method, was found to be less effective and is therefore not recommended.
Paper 3 demonstrated that during prolonged high-intensity endurance exercise, diastolic blood pressure and HRV were the most important predictors of the presence of CAC. The level of physiological strain seems to be an essential factor in inducing these differences in otherwise healthy individuals.
In Paper 4, an ML approach combining dimensionality reduction with logistic regression achieved 84% accuracy for classifying individuals with and without CAC. This model’s most important input features were blood pressure, age, HRV, and body mass index. Overall, the results suggest that feature-based statistical analysis of HR and HRV data is more effective than raw-signal analysis, likely due to the high volatility of the signal data
Applied Transfer Learning in Drilling Engineering
Full text linkPhD thesis in Information technologyDrilling in the oil and gas industry generates multimodal data crucial for decision-making in both operational and administrative units. The sheer volume of data produced throughout the lifecycle of a well presents opportunities and challenges. Deep learning (DL) has made significant progress in computer vision and language modeling. However, its adoption in niche industries like oil and gas drilling lags due to practical constraints such as limited on-site computational resources, high costs of developing models, and large data requirements to capture meaningful relationships.
In the dissertation, we explore transfer learning to address the DL application bottlenecks. We cover two areas: sequential drilling data for rate of penetration (ROP) prediction and language modeling for efficient data retrieval. In the first part, we leverage simulated data from physics-based simulators as supplemental data. Then, we explore the idea and techniques of transferring knowledge from pre-trained models to adapt to specific wells. Second, we examine the capabilities of generic large language models for drilling text data. Subsequently, we adapt a generic language model in the drilling domain to improve a document retriever. We show that transfer learning enables DL applications in the drilling more accessible. Finally, we aim to foster the development of applications by sharing Our collated and generated datasets
Metaphors in Psychotherapy Research: A Meta-Narrative Review
Full text linkAbstract
Background: Theoretical and empirical literature on metaphors in psychotherapy has developed over several decades. Despite the broad interest within psychotherapy research, there is a need to synthesize how metaphors function and how they impact the therapeutic process. Aim: To bring awareness to the current state of knowledge on the role of both client-generated and therapist-generated metaphors, and to present implications for further research. Method: A meta-narrative review was conducted to synthesize findings across literature within psychotherapy research. The RAMSES publication standard was used as a guide throughout the research process. Literature was extracted from three electronic databases through a structured screening process based on inclusion and exclusion criteria. Results: A total of 21 documents were included in the analysis, and five meta-narratives were identified: 1) the role of imagination in metaphor production, 2) metaphors and emotional expression, 3) engagement in the therapeutic alliance, 4) externalizing personal experiences with metaphors, and 5) metaphors in the process of therapeutic change. Conclusion: Novel metaphors emerge through imaginative processes, helping clients articulate emotional distress and personal challenges. Therapists who engage with clients’ use of metaphors through collaboration can strengthen intersubjectivity and the therapeutic alliance, and several scholars encourage researchers and clinicians to cultivate greater awareness of metaphorical language.Abstract
Background: Theoretical and empirical literature on metaphors in psychotherapy has developed over several decades. Despite the broad interest within psychotherapy research, there is a need to synthesize how metaphors function and how they impact the therapeutic process. Aim: To bring awareness to the current state of knowledge on the role of both client-generated and therapist-generated metaphors, and to present implications for further research. Method: A meta-narrative review was conducted to synthesize findings across literature within psychotherapy research. The RAMSES publication standard was used as a guide throughout the research process. Literature was extracted from three electronic databases through a structured screening process based on inclusion and exclusion criteria. Results: A total of 21 documents were included in the analysis, and five meta-narratives were identified: 1) the role of imagination in metaphor production, 2) metaphors and emotional expression, 3) engagement in the therapeutic alliance, 4) externalizing personal experiences with metaphors, and 5) metaphors in the process of therapeutic change. Conclusion: Novel metaphors emerge through imaginative processes, helping clients articulate emotional distress and personal challenges. Therapists who engage with clients’ use of metaphors through collaboration can strengthen intersubjectivity and the therapeutic alliance, and several scholars encourage researchers and clinicians to cultivate greater awareness of metaphorical language
Old English Dithematic Names: The Name Element Swiϸ and its Semantic Relationship to the Adjective Swiϸ
Full text linkThis thesis explores the semantic and cultural dimensions of the Old English name element swiþ, particularly its recurrent use as a deuterotheme in women’s dithematic names. While the element also appears as a prototheme in male names, its prominence in female naming patterns raises questions about the meanings associated with the adjective swiþ “strong” and the reasons for its selection over near synonyms such as strang, which also denotes “strong” in Modern English. Drawing on name lists from William Searle (1897), Elisabeth Okasha (2011), and the Prosopography of Anglo-Saxon England database (PASE), this study investigates how swiþ functioned within the broader framework of Old English naming practices.
Adopting an interdisciplinary and qualitative approach grounded in onomastics and semantics, the thesis conducts a semasiological comparison of swiþ and strang based on their usage in Old English texts documented in Bosworth & Toller’s Anglo-Saxon Dictionary. Through the application of prototype theory, the study investigates how these adjectives were employed in different contexts and whether their meanings were interchangeable. The analysis reveals that while strang typically connotes external, physical strength, often in martial or tangible contexts, swiþ is more frequently aligned with abstract or spiritual strength, including endurance, divine power, and moral integrity.
The thesis argues that these semantic nuances influenced the selection of swiþ in personal names, particularly among women associated with religious authority or noble status. Names such as Ælfswiþ and Cyneswiþ suggest deliberate lexical choices intended to evoke spiritual resilience and virtuous strength. While it remains uncertain to what extent semantic meaning shaped Old English naming conventions more broadly, the findings suggest that in the case of swiþ, its association with abstract and spiritual strength likely influenced its use in women’s names, particularly those linked to religious or noble contexts.This thesis explores the semantic and cultural dimensions of the Old English name element swiþ, particularly its recurrent use as a deuterotheme in women’s dithematic names. While the element also appears as a prototheme in male names, its prominence in female naming patterns raises questions about the meanings associated with the adjective swiþ “strong” and the reasons for its selection over near synonyms such as strang, which also denotes “strong” in Modern English. Drawing on name lists from William Searle (1897), Elisabeth Okasha (2011), and the Prosopography of Anglo-Saxon England database (PASE), this study investigates how swiþ functioned within the broader framework of Old English naming practices.
Adopting an interdisciplinary and qualitative approach grounded in onomastics and semantics, the thesis conducts a semasiological comparison of swiþ and strang based on their usage in Old English texts documented in Bosworth & Toller’s Anglo-Saxon Dictionary. Through the application of prototype theory, the study investigates how these adjectives were employed in different contexts and whether their meanings were interchangeable. The analysis reveals that while strang typically connotes external, physical strength, often in martial or tangible contexts, swiþ is more frequently aligned with abstract or spiritual strength, including endurance, divine power, and moral integrity.
The thesis argues that these semantic nuances influenced the selection of swiþ in personal names, particularly among women associated with religious authority or noble status. Names such as Ælfswiþ and Cyneswiþ suggest deliberate lexical choices intended to evoke spiritual resilience and virtuous strength. While it remains uncertain to what extent semantic meaning shaped Old English naming conventions more broadly, the findings suggest that in the case of swiþ, its association with abstract and spiritual strength likely influenced its use in women’s names, particularly those linked to religious or noble contexts
Studenter og arbeid: en eksperimentell vignett-undersøkelse av studenters arbeidstilbud
Full text linkFormålet med dette prosjektet er å undersøke hva som kjennetegner norske studenters
arbeidstilbud og faktorer som påvirker studenters valg om å arbeide parallelt med studiene sine.
Det er gjennomført en spørreundersøkelse som innhenter både avslørte og uttrykte preferanser
for arbeidstimer. De avslørte preferansene analyseres gjennom en multippel lineær
regresjonsanalyse (OLS), mens de uttrykte preferansene analyseres gjennom en lineær
multinivåmodell (MLM). I disse modellene undersøkes det hvorvidt det er til stede et ikke
lineært forhold mellom ukentlige arbeidstimer og timelønnssats, samt effekten av
jobbattributtene relevans for studier og fremtidig karriere, fleksibel arbeidstid og sosialt
arbeidsmiljø på studenters arbeidstilbud. Effekten av disse jobbattributtene undersøkes for å
teste teori om kompenserende differensialer. Videre benyttes en logistisk regresjonsmodell for
å undersøke hvilke faktorer som motiverer studenter til å arbeide parallelt med studiene sine,
med fokus på økonomisk nødvendighet, studiebelastning og bosituasjon.
I
de uttrykte preferansene for arbeidstimer kan det observeres en bakoverbøyd
arbeidstilbudskurve med et punkt for bakoverbøying ved en timelønnssats på 445 kroner. Dette
impliserer at utvalgets studenter ønsker å redusere sitt ukentlige arbeidstilbud ved
timelønnssatser over 445 kroner, for å allokere mer tid til fritid og studier. En bakoverbøyd
arbeidstilbudskurve kan imidlertid ikke observeres i respondentenes avslørte preferanser for
arbeidstimer. I de uttrykte preferansene for arbeidstimer fremkommer det at alle tre
jobbattributter – relevans for studier og fremtidig karriere, fleksibel arbeidstid og sosialt
arbeidsmiljø – har en positiv og signifikant effekt på studentenes ukentlige arbeidstilbud.
Samtidig impliserer de lineære regresjonsmodellene at disse jobbattributtene kan kompensere
for lav lønn i studentenes arbeidstilbud, som er i tråd med teori om kompenserende
differensialer. I den logistiske regresjonsmodellen finner vi at både økonomisk nødvendighet
og studiebelastning påvirker sannsynligheten for at en student er i betalt arbeid parallelt med
studiene sine
Språklig tvetydighet og kunstig intelligens: Leksikale versus syntaktiske problemer i ChatGPT og DeepSeek
No full textThis thesis investigates how two advanced large language models (LLMs), ChatGPT and DeepSeek, detect and interpret linguistic ambiguity, focusing on lexical ambiguity (homonymy and polysemy) and syntactic ambiguity. Using qualitative empirical analysis, the study reveals important differences in the models’ approaches and effectiveness. ChatGPT demonstrates moderate efficacy in recognizing ambiguity, excelling in syntactic ambiguity with high precision, yet exhibiting lower identify in lexical instances, particularly polysemy. DeepSeek exhibits superior ambiguity detection by utilizing formal syntactic parsing and rule-based grammatical frameworks to analyze ambiguous structures comprehensively.
The findings underscore the significance of prompt formulation and contextual data. Both models encounter difficulties in identifying all legitimate ambiguous interpretations in the absence of clear, well-organized input. ChatGPT prioritizes the most probable interpretation for conversational clarity, whereas DeepSeek provides more comprehensive, linguistically grounded explanations, demonstrating superior analytical depth. In contrast to previous expectations, both models demonstrate superior proficiency in addressing syntactic ambiguity over lexical ambiguity, thereby challenging established assumptions in AI language processing.
This study affirms that while LLMs such as ChatGPT and DeepSeek exhibit sophisticated abilities in ambiguity detection, their methodologies are fundamentally distinct from human language understanding. They depend on probabilistic pattern recognition instead of genuine semantic comprehension. The findings provide significant insights into the strengths and weaknesses of existing AI models, highlighting the essential importance of accurate prompts and context in enhancing performance. This research indicates future avenues for improving AI's capacity to manage intricate linguistic phenomena in a way similar to human comprehension.This thesis investigates how two advanced large language models (LLMs), ChatGPT and DeepSeek, detect and interpret linguistic ambiguity, focusing on lexical ambiguity (homonymy and polysemy) and syntactic ambiguity. Using qualitative empirical analysis, the study reveals important differences in the models’ approaches and effectiveness. ChatGPT demonstrates moderate efficacy in recognizing ambiguity, excelling in syntactic ambiguity with high precision, yet exhibiting lower identify in lexical instances, particularly polysemy. DeepSeek exhibits superior ambiguity detection by utilizing formal syntactic parsing and rule-based grammatical frameworks to analyze ambiguous structures comprehensively.
The findings underscore the significance of prompt formulation and contextual data. Both models encounter difficulties in identifying all legitimate ambiguous interpretations in the absence of clear, well-organized input. ChatGPT prioritizes the most probable interpretation for conversational clarity, whereas DeepSeek provides more comprehensive, linguistically grounded explanations, demonstrating superior analytical depth. In contrast to previous expectations, both models demonstrate superior proficiency in addressing syntactic ambiguity over lexical ambiguity, thereby challenging established assumptions in AI language processing.
This study affirms that while LLMs such as ChatGPT and DeepSeek exhibit sophisticated abilities in ambiguity detection, their methodologies are fundamentally distinct from human language understanding. They depend on probabilistic pattern recognition instead of genuine semantic comprehension. The findings provide significant insights into the strengths and weaknesses of existing AI models, highlighting the essential importance of accurate prompts and context in enhancing performance. This research indicates future avenues for improving AI's capacity to manage intricate linguistic phenomena in a way similar to human comprehension
Kan prehospital bruk av pediatrisk tidlig varslingsskår (PEWS) bidra til tidlig avdekking av alvorlig akutt sykdom hos barn?
Full text linkOppgaver som utfordrer: Matematikk for unge tenkere (12-18 år)
Full text linkEt oppgavehefte med temaer tilpasset kompetansemålene henholdsvis 8. trinn - 3. vgs. Oppgavene ble testet på noen elever hvor tankegangen og løsningsmåten deres ble beskrevet