Publikationer från Uppsala Universitet
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Comparing environmental sustainability in the Smart Home between Open Source and Vendor Systems
No full textThe expansion of smart home technology has led to increasing concerns about environmental sustainability, particularly regarding CO₂ emissions from continuously operating smart home management systems (SHMS). While existing research has examined individual smart home components, a significant gap exists in understanding the carbon footprint of the central management systems that coordinate these ecosystems. This knowledge gap is crucial as these systems operate 24/7 and their environmental impact directly influences the overall sustainability of smart home adoption. This thesis addresses the problem of quantifying and comparing CO₂ emissions between vendor-based and open source smart home management systems. The primary objective is to develop a methodology for estimating both embodied and operational emissions, enabling evidence-based comparisons between different technological approaches to home automation. The approach involved establishing a controlled experimental environment using the Internet Microscope methodology for traffic monitoring and inline power meters for energy consumption measurement. Representative systems including Philips Hue Bridge and Amazon Echo Pop (vendor-based) versus Home Assistant Green and Voice PE (open source) were analyzed, examining idle consumption, voice control interactions, and software updates. Embodied emissions were estimated using an established weight-based methodology, while operational emissions incorporated both energy consumption and data traffic using the Sustainable Web Design model. The study's experiments demonstrate that SHMS consume between 1.5-1.9W during idle operation, with annual emissions ranging from 3.65-7.25 kg CO₂e per device. The open source voice assistant achieved 33% lower energy consumption and eliminated cloud-dependent data transmission compared to the vendor alternative, though with increased response times. Results indicate that operational emissions dominate over embodied emissions in long-term usage scenarios, with devices exceeding their manufacturing carbon footprint through idle consumption alone within approximately two years. These findings suggest that power efficiency should be prioritized in SHMS design and selection criteria. This work contributes to the field by providing the first systematic comparison of CO₂ emissions between vendor and open source smart home management approaches, establishing a foundation for environmentally informed decision-making in smart home technology adoption
Comparison of self-reported physical activity between survivors of out-of-hospital cardiac arrest and patients with myocardial infarction without cardiac arrest : a case-control study
No full textAims: To investigate whether out-of-hospital cardiac arrest (OHCA) survivors had lower levels of self-reported physical activity compared to a non-cardiac arrest control group with myocardial infarction (MI), and to explore if symptoms of anxiety, depression, kinesiophobia (fear of movement), and fatigue were associated with a low level of physical activity.Methods and results: Pre-defined case-control sub-study within the international Targeted Hypothermia versus Targeted Normothermia after Out-of-Hospital Cardiac Arrest (TTM2) trial. Out-of-hospital cardiac arrest survivors at 8 of 61 TTM2 sites in Sweden, Denmark, and the UK were invited. Participants were matched 1:1 to MI controls. Both OHCA survivors and MI controls answered two questions on self-reported physical activity, categorized as a low, moderate, or high level of physical activity, and questionnaires on anxiety and depression symptoms, kinesiophobia, and fatigue 7 months after the cardiac event. Overall, 106 of 184 (58%) eligible OHCA survivors were included and matched to 91 MI controls. In total, 25% of OHCA survivors and 20% of MI controls reported a low level of physical activity, with no significant difference (P = 0.13). Symptoms of kinesiophobia and fatigue were significantly associated with a low level of physical activity in both groups. Out-of-hospital cardiac arrest survivors had significantly more kinesiophobia compared to MI controls (18% vs. 9%, P = 0.04), while levels of anxiety and depression symptoms and fatigue were similar.Conclusion: Out-of-hospital cardiac arrest survivors had similar levels of physical activity compared to matched MI controls. High level of kinesiophobia and fatigue were associated with a low level of physical activity in both groups.Registration ClinicalTrials.gov: NCT0354333
Redefining Localization: Assessing Nationalization and Locally Led Approaches Through the Red Cross Red Crescent Movement : Insights from Pacific National Societies, Palang Merah Indonesia, and Papua New Guinea
No full textThis thesis critically examines the extent to which nationalization and locally led approaches offer more effective and contextually accurate pathways for achieving localization in humanitarian action, using the Red Cross Red Crescent (RCRC) Movement as a central case study. The study employs a qualitative approach, following an extensive literature review, seven key RCRC documents are analyzed through framework analysis to identify key themes related to localization, nationalization, and local leadership. Additionally, three operational contexts — Pacific National Societies, Palang Merah Indonesia (PMI), and Papua New Guinea (PNG) — provide context-specific insights into the operationalization of localization in varying humanitarian contexts. Findings reveal that while nationalization and locally led approaches offer viable pathways for advancing localization, their effectiveness is contingent upon targeted efforts by international actors to reconcile power dynamics, balance risk-sharing, and maintain financial and operational support. The RCRC's 2024 reframing of localization as 'locally led action' underscores the growing recognition of local agency in humanitarian response, yet the findings indicate that nationalization is a subset of the broader locally led approach rather than a comprehensive solution alone. Ultimately, the study argues that localization should be reframed as an ongoing, bottom-up process rooted in local agency, requiring strategic investment in locally led leadership, equitable partnerships, and long-term capacity building and financing to achieve sustainable humanitarian outcomes
The Role of Nanomaterials in the Wearable Electrochemical Glucose Biosensors for Diabetes Management
No full textThe increasing prevalence of diabetes mellitus necessitates the development of advanced glucose-monitoring systems that are non-invasive, reliable, and capable of real-time analysis. Wearable electrochemical biosensors have emerged as promising tools for continuous glucose monitoring (CGM), particularly through sweat-based platforms. This review highlights recent advancements in enzymatic and non-enzymatic wearable biosensors, with a specific focus on the pivotal role of nanomaterials in enhancing sensor performance. In enzymatic sensors, nanomaterials serve as high-surface-area supports for glucose oxidase (GOx) immobilization and facilitate direct electron transfer (DET), thereby improving sensitivity, selectivity, and miniaturization. Meanwhile, non-enzymatic sensors leverage metal and metal oxide nanostructures as catalytic sites to mimic enzymatic activity, offering improved stability and durability. Both categories benefit from the integration of carbon-based materials, metal nanoparticles, conductive polymers, and hybrid composites, enabling the development of flexible, skin-compatible biosensing systems with wireless communication capabilities. The review critically evaluates sensor performance parameters, including sensitivity, limit of detection, and linear range. Finally, current limitations and future perspectives are discussed. These include the development of multifunctional sensors, closed-loop therapeutic systems, and strategies for enhancing the stability and cost-efficiency of biosensors for broader clinical adoption
Anatomy of a Swedish population-scale network
No full textWith the increasing interest in large-scale social network analysis, recent research has expanded into nation-wide networks generated from administrative data. We construct a multilayer population-scale social network for Sweden using public register data from 2000 to 2017, covering approximately 8.3 million individuals aged 15 and older. The network models the social opportunity structure in Sweden across six layers: close family, extended family, household, school, neighbors, and work. We analyze the structure and connectivity patterns in the network, comparing our findings to a similar study of the Netherlands. The comparison reveals broadly similar degree distributions and small-world characteristics, but also discrepancies likely driven by differences in population density
Investigating the explanatory and predictive power of geodiversity metrics on Swedish insect diversity
No full textUnderstanding and predicting biodiversity patterns at fine spatial scales is a key challenge in biological modeling. Commonly used coarse-resolution variables often lack sufficient resolution for fine-scale modeling, or the grid size of fine-resolution variables does not align with the scale of biodiversity sampling. This study investigates whether geodiversity metrics—quantitative measures of abiotic heterogeneity—can improve both the explanatory and predictive performance of biodiversity models, using insect metabarcoding data collected across Sweden. Three major insect orders—Coleoptera, Diptera, and Hymenoptera—are analyzed based on 190 Malaise trap sampling sites from the Insect Biome Atlas Project. The study tests three geodiversity metrics: mean square roughness (MSR) for continuous environmental features (temperature, soil moisture, etc.), and landscape Shannon’s diversity index (SHDI) and landscape shape index (LSI) for categorical ones (soil type and forest presence). To examine spatial differences in explanatory power, the modeling area is divided into north and south Sweden, reflecting their contrasting climatic conditions and geomorphological structures. Model-based inference using AICc and Lasso regression is applied for assessing the explanatory and predictive power of individual features, and also for feature selection. Supportive vector regression (SVR) is applied to assess predictive accuracy. Results show that geodiversity variables improved the AICc optimized model in both north and south Sweden, and to a larger extent in the south. While AICc optimization for explanatory excludes many geodiversity variables in northern Sweden, Lasso regression that focuses on prediction tends to retain them, indicating their practical value in predictive modeling. With proper feature selection, geodiversity variables improve predictive performance. Models with geodiversity also better reflect observed biodiversity and ecological expectations than baseline environmental models. Biodiversity datasets are usually small due to their cost in systematic sampling; large datasets are hardly available, and feature dimensionality is limited. Assessment of feature explanatory power has shown that many redundant variables are commonly used, and they have both less explanatory and predictive power in comparison to geodiversity variables. This not only suggests that geodiversity variables are potentially good candidate variables but also highlights the importance of exploration for stronger features. In conclusion, this study demonstrates that integrating geodiversity into biodiversity models can significantly enhance their explanatory and predictive capabilities. These findings support the broader use of geodiversity in biodiversity modeling, especially for fine-scale ecological assessments and conservation planning
Optimizing Surface-Wave Methods for Quick-Clay Characterization
No full textQuick clays are a type of sensitive marine clay that pose a serious geohazard in many glaciated areas, including Sweden. Because they can rapidly lose their strength under stress, it is important to identify them early. Surface-wave methods are well suited for this task, since quick clays typically have low shear wave velocities. A key step in these methods is picking good dispersion curves from seismic data, which is often done manually. This is both time-consuming and prone to error. In this project, a convolutional neural network trained on hand-labeled data from a non–quick clay site in Denmark is tested on new data from a known quick clay site. The main purpose is to investigate how well the model generalizes to different geological conditions. The results show that the model works well overall, though it performs worse in cases with low signal-tonoise ratio or overlapping modes. If improved, this type of model could become a useful tool in the process of automating dispersion curve picking, which would in turn make surface wave methods more efficient in large-scale quick clay surveys.Kvicklera är en typ av känslig marin lera som utgör en allvarlig risk i många glaciärpåverkade områden, inklusive Sverige. Eftersom den snabbt kan förlora sin hållfasthet vid belastning är det viktigt att identifiera den tidigt. Ytvågmetoder lämpar sig väl för detta ändamål, eftersom kvicklera vanligtvis uppvisar låg skjuvvågshastighet. Ett viktigt steg i dessa metoder är att välja ut bra dispersionskurvor från seismiska data, vilket ofta görs manuellt. Detta är både tidskrävande och känsligt för fel. I detta projekt testas ett konvolutionellt neuralt nätverk, som har tränats på data som är märkt för hand från en plats utan kvicklera i Danmark, på nya data från en plats där det är känt att kvicklera finns. Syftet är att undersöka hur väl modellen generaliserar till olika geologiska förhållanden. Resultaten visar att modellen fungerar bra överlag, men presterar sämre i fall med låg signal-till-brus-nivå eller överlappande moder. Om den förbättras skulle denna typ av modell kunna bli ett användbart verktyg i processen att automatisera val av dispersionskurvor, vilket i sin tur skulle göra ytvågmetoder mer effektiva vid storskaliga kvickleraundersökningar
Asking about drug allergies : Managing antimicrobial medicines-related risks in primary care in England and Sweden
No full textObjective Penicillins are the most common cause of drug-induced anaphylaxis worldwide, yet penicillin allergy status is seldom clinically tested and therefore reliant on patient report. Managing risk of harm is fraught with uncertainties: patients may not always be truly allergic and medical records may not be accurate. The aim of this study was to investigate how conversations about drug allergy risks unfold when prescribing for common infections. Method We screened 156 acute primary care consultations for adult patients presenting with upper respiratory concerns in England and Sweden to identify all cases where drug allergies were raised. Data are in British English and Swedish. We used conversation-analytic methods to make systematic observations on how the topic was initiated; the activity context; the patient’s response and any subsequent mention of drug allergy; identifying recurrent patterns within and across the two datasets. Results In both datasets, drug allergies were raised in just over one third of consultations most often via questions conveying a bias towards a ‘no allergy’ outcome. When asked during information-gathering, the question was sometimes misunderstood as asking about allergies in general. In the majority of cases, no allergies were reported, yet patients often qualified their ‘no allergy’ answers displaying uncertainty. Patients who did report allergies were seldom questioned about the nature of their symptoms. Where patient allergy status was contested or neglected by doctors and brought to the interactional surface, work was done by both parties to maintain neutrality or display cautiousness around different territories of knowledge. Conclusion Our analysis reveals common interactional problems faced by prescribing professionals when managing the risk of patient harm from drug allergies when recommending antimicrobials. Practice Implications This study has provided pre-intervention evidence for how drug allergy checking can be improved. Other simple changes may help to identify low-risk individuals for future testing
The Colonial Legacies of Aid : Exploring the Influence of Angola’s Colonial Past on Modern Humanitarian Intervention in the Country
No full textHumanitarian interventions are influenced by broader historical, political and cultural forces, which in turn shape the framing and delivery of aid. In Angola, the legacies of colonialism and post-conflict reconstruction continue to impact the strategies of both international and local humanitarian organisations. This qualitative study examines the manner in which colonial historical memory and developmental narratives influence the work of two actors operating within Angola: the Western-based organisation RISE International and the Angolan organisation ADPP Angola. The study draws on organisational documents and a semi-structured interview to compare how each organisation frames needs and implements programmes. The findings demonstrate that RISE frequently adopts a deficit-oriented approach, with a focus on infrastructural gaps and the prioritisation of technocratic solutions that are aligned with the agendas of international donors. In contrast, ADPP focuses on building local capacity, using participatory methodologies and promoting community-led development that uses local knowledge. The study reveals that while Western-influenced interventions often reproduce structural power imbalances linked to colonial histories, locally embedded organisations offer alternative models that resist these legacies through inclusive and context-sensitive approaches. The findings emphasise the necessity for humanitarian actors to engage in critical reflection on their frameworks and to engage more meaningfully with local voices and practices
Swedish Acquirers' Cross-Border M&A Performance : A comparison with domestic M&As and analysis of determinants in foreign M&As
No full textCross-border mergers and acquisitions (CBM&A) is a common phenomenon worldwide. In Sweden, approximately 63% of all M&As in 2024 were cross-border. However, there is an absence of a common understanding of acquirers’ performance in CBM&As. Our study thus aims to explore the impact of CBM&As on the performance of Swedish public acquirers. Firstly, we investigate if the acquirers’ performance differs in CBM&As and domestic M&As and secondly, we study which determinants influence the acquirers’ performance in CBM&As. Acquirers’ short-term performance is measured as the cumulative abnormal stock returns in the days surrounding the M&A announcement. Acquirers’ long-term performance is measured as the change in abnormal residual earnings post deal completion. Our findings reveal that although the immediate stock returns are higher in CBM&As than in domestic M&As, this difference diminish over time and is neither reflected in the long-term performance. Furthermore, we find that neither the national cultural distance between the merged firms, nor the acquirers’ prior M&A experience, influence acquirers’ CBM&A performance. Contrarily, related resource-profiles of the merged firms, cross-country variations in resources and capabilities and the risk for agency problems in the acquisition process, sporadically serve as determinants of acquirers’ performance in CBM&As.