Mid Sweden University
Not a member yet
15329 research outputs found
Sort by
WATS up? What About Teacher Shortage? International Perspectives from Denmark, Germany, and Sweden
No full textThis article gives an insight into a research-based network with the intention to explore, explain and compare the state of the current situation of teacher shortage in Denmark, Germany and Sweden. The project is funded by a research grant for three years by the Swedish Research Council. The aim of the network is to share and transfer knowledge and insights, as well as to gain new international perspectives. A long-term goal is to expand the network. In this article, the current state of teacher shortage in the mentioned countries is described. Also, the research questions, the research process and methodological approach(es) of the first year as well as the first results are presented. All in all, teacher shortage is a multi-faceted and complex phenomenon. It is evident that more research has to be carried out to identify causes and measures on different levels and in different phases to highlight interdependencies
Alvar Aalto Route – 20th Century Architecture and Design : Independent Expert Report
No full textThis reports is an independent evaluation of the Alvar Aalto Route – 20th Century Architecture and Design for the certification cycle 2024-2025. This is a certified Cultural Route of the Council of Europe that connects destinations linked to the architectural and design legacy of Finnish architect Alvar Aalto. Alvar Aalto (1898– 1976) is renowned for his human-centric design, which harmonized functionalism with natural and cultural sensitivity. His works represent iconic contributions to modernist architecture and Scandinavian design. Overall, the Alvar Aalto Route has made significant progress since the last evaluation and successfully integrates cultural heritage, sustainable tourism, and modern innovation, ensuring that Aalto’s legacy remains a vibrant and influential force in contemporary architecture and design. Through collaboration, expansion, and education, the route exemplifies the values of the Council of Europe and contributes meaningfully to European cultural heritage.
Pseudo-Random Identification and Efficient Privacy-Preserving V2X Communication for IoV Networks
No full textThe advancement of Internet of Vehicles (IoV) technologies has significantly enhanced road safety and transportation efficiency through smart traffic management and precise control systems. With the advent of 5G and beyond, vehicles within the IoV ecosystem can seamlessly communicate with various smart entities (X) using V2X (Vehicle-to-Entity) communications. However, the openness of IoV networks and the exponential growth of V2X links have expanded potential attack surfaces, increasing the risk of security and privacy breaches. In response to these challenges, this article proposes a privacy-preserving and secure communication framework for IoV networks, addressing critical security challenges in V2X communication. By leveraging lightweight cryptographic mechanisms such as hash functions, quadratic residuosity, and Legendre symbols, the proposed scheme ensures secure authentication, group key sharing, and pseudonym management within IoV networks. The proposed scheme's security and privacy features, along with its correctness, have been rigorously validated against various security threats where other state-of-the-art schemes fail. Comprehensive performance analysis demonstrates that our scheme completes authentication in a fraction of a millisecond, significantly outperforming existing approaches. The design simplicity and efficiency of the proposed authentication structure make it highly suitable for real-world IoV applications.
Oppositional chaotic artificial hummingbird algorithm on engineering design optimization
No full textThis paper proposes an enhanced-search form of the newly designed artificial hummingbird algorithm (AHA), named oppositional chaotic artificial hummingbird algorithm. The proposed OCAHA methodology incorporates the oppositional learning (OBL) in the population-initialization and at the ending event of each iteration for a faster convergence, and the chaos-embedded sequences of Gauss/mouse map to replace the random sequences of the three population-updating iterative stages of AHA, viz. guided, territorial and migration foraging to employ more diverse population for more solutional accuracy. The effectiveness of the method has been evaluated in two phases. OCAHA, the four state of the art algorithms, namely, PSO, DE, GWO and WOA, their recently developed effective variants, namely, SLPSO, MTDE, SOGWO and EWOA, and the inspiring optimizer AHA have been implemented on the 29 unconstrained CEC 2017 benchmark functions in the first phase. In the second phase, OCAHA has been verified on 10 challenging engineering cases, and compared with the concerned leading performances. Comprehensive analysis of the simulated outcomes using various statistical metrics and of the convergence profiles demonstrates that, the optimization ability of OCAHA on CEC 2017 is superior to all the comparing algorithms except MTDE. For engineering cases, OCAHA provides better searching performance, solution precision, robustness and convergence rate than all competing designs, and, on average, it has lowered the computational cost by 57.5% and 22.63% in term of function evaluations and the fitness objective by 2.4% and 0.23% in comparison to AHA and the chaotic version CAHA, respectively.
From Concepts to Conditions : Bridging the Gap in AI-Based Maintenance Systems
No full textThe importance of preventing machine failures and reducing costly unplanned downtime has led to extensive research aiming to develop methods that predict maintenance needs. In this context, data-driven and particularly Deep Learning (DL) based methods for anomaly detection, fault diagnosis, and health prognosis have been studied extensively because of their ability to handle the complexity of the sensor data describing the health state of machines. However, many challenging factors exist before it is possible to utilize these methods in practice. These primarily include the lack of labelled failure events, the heterogeneous nature of the data, and the occurrence of multi-component fault scenarios. Currently, most studies ignore these aspects and focus on scenarios limited to a laboratory environment, which means there is a need to develop methods that can be deployed in practice. Therefore, this thesis suggests methods for these challenges and gives insight, aiming to reduce the gap between research-defined scenarios and scenarios found in industrial environments. To achieve this, different areas in the context of DL for Predictive Maintenance (PdM) are examined, including multivariate anomaly detection, fault diagnosis, and Remaining Useful Life (RUL) prediction methods. One of the contributions is a threshold-setting procedure that optimizes anomaly detection models with the user's support and a novel separate scoring method, and outperforms state-of-the-art alternatives for deployments in industrial applications. A published dataset of bearing faults from an industrial environment is also described, which is beneficial when developing and evaluating methods. In addition, a novel DL method for fault diagnosis of bearings using vibration data constructed with knowledge enrichment, time-based contextual enrichment, and a transfer learning technique is suggested. This method can be deployed on any machine without historical faults and outperforms state-of-the-art methods. Lastly, the most significant contribution is a prognostic hybrid framework for multi-component fault scenarios in rotating machines using vibration data that utilizes advancements in methods for anomaly detection, fault diagnosis, and RUL prediction of machines. In summary, this thesis suggests novel methods for PdM adapted for industrial applications that can be used on a general basis and provides insights that lower the gap between research-defined scenarios and scenarios found in industrial environments. Vid tidpunkten för disputationen var följande delarbete opublicerat: delarbete 6 accepterat.At the time of the doctoral defence the following paper was unpublished: paper 6 accepted.</p
Exploring metal bioaccumulation ability of boreal white-rot fungi on fiberbank material
No full textFiberbanks are organic-rich sediment deposits in aquatic environments, primarily formed through historical pulp and paper mill activities. These deposits consist of wood-derived fibrous materials and are contaminated with potentially toxic elements (PTEs) such as vanadium, chromium, cobalt, nickel, copper, zinc, arsenic, cadmium, and lead. The leaching of these contaminants into surrounding waters poses significant environmental and health risks, impacting aquatic ecosystems and potentially entering the food chain. Effective remediation of fiberbanks is crucial, particularly in Sweden and other regions with extensive wood-pulping industries. This study aims to evaluate the bioaccumulation capacities of 26 native Swedish white-rot fungi (WRF) species for the remediation of PTEs in fiberbank material. Fiberbank samples were collected from Sundsvall’s Bay in the Baltic Sea, while the fungal species were isolated from boreal forests in Västernorrland, Sweden. The fungi were cultured on Hagem agar medium with sterilized fiberbank material as the substrate. After two months, fungal biomass was analyzed for PTE uptake using inductively coupled plasma-mass spectrometry (ICP-MS). The results revealed significant variability (p < 0.001) in PTE uptake among fungal species. Phlebia tremellosa consistently demonstrated the highest bioconcentration factors for analyzed elements, with values for V (0.39), Cr (0.10), Co (1.81), Cu (1.54), Pb (1.65), Ni (1.28), As (0.83), Zn (3.61), and Cd (5.56). Other species, including Laetiporus sulphureus (0.09–4.78), Hymenochaete tabacina (0.08–4.52), and Diplomitoporus crustulinus (0.08–4.48), also exhibited significant bioremediation potential. These findings highlight the potential of native WRF species for PTEs remediation in fiberbanks and provide a foundation for mycoremediation strategies in contaminated environments.
Distriktssköterskans erfarenhet av svårläkta sår inom primärvården : En intervjustudie med distriktssköterskor
No full textBakgrund: Primärvården främjar hälsa genom tidig intervention och individanpassad vård, där distriktssköterskan spelar en viktig roll i det hälsofrämjande arbetet. Svårläkta sår innebär stora utmaningar för vården, en korrekt diagnos och anpassad behandling är avgörande för sårläkning. Distriktssköterskan lindrar lidande genom respekt och empati, vilket förbättrar sårläkningen, något omvårdnadsteoretikern Katie Eriksson betonar utifrån sin syn på lidande, vård och hälsa. Syfte: Syftet med studien var att belysa distriktssköterskans erfarenheter av att vårda patienter med svårläkta sår inom primärvården. Metod: Studien genomfördes med en kvalitativ metod och induktiv ansats, där elva legitimerade distriktssköterskor deltog. Data samlades in via semistrukturerade intervjuer, transkriberades och analyserades med manifest innehållsanalys. Resultat: Distriktssköterskornas erfarenheter sammanfattades i tre huvudkategorier: Utmaningar i sårvården, Vikten av information och undervisning, Behandling och diagnostik av svårläkta sår. Resultatet visade att de största utmaningarna inom sårvården var patientföljsamhet, organisatoriska hinder, brist på kontinuitet samt otillräcklig sårdiagnostik. För att förbättra vården var tydlig kommunikation, stärkta relationer, kontinuerlig utbildning och erfarenhetsutbyte avgörande faktorer. Diskussion: Distriktssköterskor i primärvården hade god sårvårdskompetens, men det fanns stora utmaningar som påverkade kvaliteten negativt och förlängde läkningsprocessen. Faktorer som patientens följsamhet och behov av individanpassad information var avgörande för lyckad sårläkning, i enlighet med teori och riktlinjer. Slutsats: Sammanfattningsvis står sårvården inom primärvården inför flera utmaningar. För att förbättra sårläkningsprocessen var det avgörande att öka patientens förståelse och motivation, stärka relationerna mellan distriktssköterskorna och patienter, samt förbättra organisatoriska hinder och kompetensutveckling. Tydlig information, sårdiagnos och kontinuitet var nyckelfaktorer för att främja snabbare sårläkning och minska patienternas lidande
Förutsättningar och hinder för suicidpreventivt omvårdnadsarbete : - Psykiatrisjuksköterskors upplevelser i primärvården
No full text3AbstraktBakgrund: Suicid utgör ett globalt folkhälsoproblem och är en av de vanligaste dödsorsakerna bland personer med psykisk ohälsa. Trots en övergripande minskning har suicid ökat bland unga, och majoriteten av de drabbade har haft kontakt med primärvården kort tid före dödsfallet. Primärvården är första linjens vård vid psykisk ohälsa och tidigare studier visar att primärvårdssköterskor upplever bristande kompetens vid suicidriskbedömningar och därför undviker att ställa frågor om suicid. Syfte: Syftet med detta examensarbete var att belysa psykiatrisjuksköterskors upplevelser och erfarenheter av suicidpreventivt omvårdnadsarbete inom primärvård. Metod: Examensarbetet genomfördes med en kvalitativ design och induktiv ansats. Åtta psykiatrisjuksköterskor intervjuades genom semistrukturerade intervjuer och analyserades med en kvalitativ innehållsanalys. Resultat: Suicidpreventivt arbete upplevdes som utmanande och komplext, men samtidigt meningsfullt och djupt engagerande. Tre huvudkategorier framkom och dessa var: Det meningsfulla och utmanande patientmötet, Nyckelroll i hälsofrämjande omvårdnad och Organisatoriska förutsättningar och hinder i omvårdnadsarbetet. Diskussion: I takt med att allt fler söker hjälp för psykisk ohälsa inom primärvården framträder psykiatrisjuksköterskans roll som central i det suicidpreventiva omvårdnadsarbetet. Resultatet av detta examensarbete visar att specialistkompetens och teamarbete upplevs som viktiga förutsättningar i arbetet med denna patientgrupp. Vidare visar kategorierna betydelsen av vårdrelationen, som enligt Watsons och Travelbees teorier kan bidra till att skapa mening och sammanhang i det psykiska lidandet. Slutsats: Genom att uppmärksamma psykiatrisjuksköterskors utmaningar och behov kan evidensbaserade omvårdnadsstrategier vidareutvecklas för att stärka det suicidpreventiva arbetet inom primärvården
Evaluating Locally Hosted Large Language Models for Scientific Report Processing
No full textDen ökande tillgängligheten av stora språkmodeller (LLM:er) har öppnat nya möjligheter för automatisering av textbehandling. Molnbaserade lösningar väcker dock ofta oro kring dataintegrietet. Denna kandidatuppsats undersöker hur väl lokalt installerade LLM:er presterar vid bearbetning av vetenskapliga rapporter samt vilka infrastrukturkrav som ställs, i syfte att hitta den mest lämpliga strategin för implementering. Två mindre, open-source modeller – Mistral 7B och Llama 3 8B – utvärderades med hjälp av ramverket Ollama på en bärbar laptop och en gaming dator. Projektet följde en vattenfallsmetod som inkluderade en teoretisk bakgrund, förstudie av modeller och hårdvara, praktisk implementering samt prestandamätningar. Noggrannhet och processtid samlades in genom två användningscenarier: sökning efter nyckelord och frågebaserad granskning. Resultaten visar att lokalt installerade LLM:er kan prestera tillräckligt bra för rapportbearbetning inom hårdvarubegränsingar och understryker vikten av att balansera modellstorlek, hårdvaruresurser och säkerhetskrav vid val av AI-baserade lösningar för industriellt bruk.The increasing availability of large language models (LLMs) has opened new possibilities for automating text processing. However, cloud-based solutions often raise concerns around data privacy. This thesis explores how well locally hosted LLMs perform in processing scientific reports and their infrastructural requirements to find the most suitable strategy for implementing them. Two smaller open-source models, Mistral 7B and Llama 3 8B, were evaluated using the Ollama framework on a laptop and a gaming PC. The project followed a waterfall method, including theoretical research, a pre-study of models and hardware, practical implementation and performance measurements. Accuracy and process time were collected through two use-case scenarios: keyword search and question-based querying. The results indicate that locally hosted LLMs can perform sufficiently for report processing within hardware limitations, highlighting the importance of balancing model size, hardware resources and security requirements when selecting AI-based solutions for industrial use
