Blekinge Institute of Technology
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Numerical Study of Fluid Flow, Heat Transfer and Parameter Coupling in a Spider Web Microchannel Heat Sink
No full textThe microchannel heat sink is a commonly used structure in mechanical cooling systems for microelectronics. Based on bionics, a simplified heat sink with a spider-web design is proposed in this paper. Under the condition of bottom heat flux q = 100 W/cm2 and Reynolds number (Re) = 442-884, the influence of three parameters (main channel width, branch width and rib width) on the performance of a spider web microchannel heat sink (SW-MCHS) under different Re conditions was numerically analyzed by computational fluid dynamics. The results showed that the main channel had the greatest influence on the Nusselt number (Nu) and the Euler number (Eu); With the increase of main channel width, Nu increased by 46.97%, and Eu decreased by 31.74%. Rib width had the smallest influence on Nu and Eu; AWith the increase of rib width, Nu decreased by 7.18%, and Eu decreased by 12.00%. Based on the research results, the correlations for predicting Nu and Eu of the SW-MCHS were fitted; the Radj2 values for the two correlations were 0.9523 and 0.9246, respectively. These fitting correlations could be used to predict Nu and Eu for the SW-MCHS. The present study has contributed to advancing the applications of microchannel heat sinks and enhancing the cooling efficiency of mechanical microelectronics cooling systems
Insomni, ett problem bland äldre med hjärt- och kärlsjukdom : En kvantitativ studie baserad på SNAC-B
No full textBakgrund: Sömnstörningar såsom insomni påverkar i hög grad individens välbefinnande och är starkt kopplat till hjärt- och kärlsjukdomar och individer över 60 år. Obehandlade och långvariga sömnstörningar kan ge upphov till flera vanliga hälsoproblem med kopplingar till metabola förändringar och därmed medverka till förtidig död. Åldrandet som process är förknippad med en ökad förekomst av sömnstörning då ett normalt åldrande naturligt innebär förändringar i sömnmönstret. Samtidigt är livsförändringar en stor riskfaktor till sömnstörningar. Flera livsförändringar kan identifieras bland individer över 60 år såsom pension, sjukdomar och förlust av närstående. Sömnstörningar kan i sin tur både förvärras och uppstå av sådana faktorer. För individer över 60 år är sjukdom ofta orsaken till insomni. Syfte: Syftet var att beskriva förekomst av insomni bland individer 60+ i hemmet med hjärt- och kärlsjukdom i SNAC-B. Metod: Studien var en deskriptiv tvärsnittsstudie med kvantitativ ansats som utgått från data ur The Swedish National study of Care i Blekinge (SNAC-B). Resultat: Studien visade statistiskt signifikant skillnad mellan insomni och hjärtsvikt (p=0,005), mellan kön och insomni (p= <0,001) samt mellan åldersindelning och insomni (P= <0,001). Studien visade däremot ingen statistisk skillnad mellan Hjärt- och kärlsjukdom och insomni (p=0,069). Slutsats: Insomni visades vara ett vanligt och ofta förekommande problem bland individer över 60 år, både med och utan hjärt- och kärlsjukdom. Kvinnor var överrepresenterade bland individer som rapporterade insomni oavsett förekomst av hjärt- och kärlsjukdom och individer med hjärtsvikt identifierades som sjukdomen med högst förekomst av insomni
Clustering-based Adaptive Data Augmentation for Class-imbalance in Machine Learning (CADA) : Additive Manufacturing Use case
No full textLarge amount of data are generated from in-situ monitoring of additive manufacturing (AM) processes which is later used in prediction modelling for defect classification to speed up quality inspection of products. A high volume of this process data is defect-free (majority class) and a lower volume of this data has defects (minority class) which result in the class-imbalance issue. Using imbalanced datasets, classifiers often provide sub-optimal classification results i.e. better performance on the majority class than the minority class. However, it is important for process engineers that models classify defects more accurately than the class with no defects since this is crucial for quality inspection. Hence, we address the class-imbalance issue in manufacturing process data to support in-situ quality control of additive manufactured components. For this, we propose cluster-based adaptive data augmentation (CADA) for oversampling to address the class-imbalance problem. Quantitative experiments are conducted to evaluate the performance of the proposed method and to compare with other selected oversampling methods using AM datasets from an aerospace industry and a publicly available casting manufacturing dataset. The results show that CADA outperformed random oversampling and the SMOTE method and is similar to random data augmentation and cluster-based oversampling. Furthermore, the results of the statistical significance test show that there is a significant difference between the studied methods. As such, the CADA method can be considered as an alternative method for oversampling to improve the performance of models on the minority class.
Quality of Life of Non-Self-Sufficient Immigrants : A Neighborhood Perspective
No full textThis paper explores the quality of life of non-self-sufficient immigrants in Sweden,focusing on their residential patterns while controlling for individual characteristics.We find large differences in both individual and locational attributes across incomelevels and ethnic backgrounds, illustrating that the status of ‘immigrant’ is far frombeing a homogenous group. The results show that non-self-sufficient individuals,especially those from Africa and the Middle East, may face limitations in their lifesatisfaction as these groups are most likely to reside in socioeconomically weakneighborhoods. Additionally, the results show that the motivation for residentialchoices varies across ethnic backgrounds. For example, ethnicity rather than incomelevel determines the neighborhood patterns of African immigrants, while non-self-sufficiencyis the major determinant for Nordic immigrants residing in poor, ethnicneighborhoods. We thus argue that the location of residence and differences acrossethnic backgrounds deserve more attention in research and policy discussions onquality-of-life issues.Integration är en process! Invandrares arbetsmarknadskarriär genom successiva ste
Wave : A Dynamic Physical-Based Metaheuristic Optimizer
No full textGlobal optimization is challenging, particularly in high-dimensional and multimodal search spaces characterized by complex landscapes and numerous local optima. This paper proposes Wave, a novel physical-Based metaheuristic optimizer, which combines wave-inspired oscillatory factors, Lévy-based random flights, and adaptive exploration and exploitation strategies to tackle global optimization problems. Inspired by the cyclical nature of wave phenomena, our approach exploits time-varying sinusoidal amplitudes that gradually reduce while maintaining oscillatory behavior, thus enhancing both population diversity and local search. However, in Wave, the random flights derived from heavy-tailed step distributions provide additional large jumps that aid in escaping local minima. Wave has been evaluated over CEC2022 benchmark functions; the results demonstrate that Wave exhibits a strong convergence performance and comparable results with several state-of-the-art metaheuristic optimizers. For example, Wave outperformed all compared optimizers in F1, F6, F11 and opined the first rank when solving the cantilver beam engineering design problem. The obtained results highlights the effectiveness of wave-driven exploration and targeted exploitation strategies, paving the way for broader applications in engineering design and other complex optimization problems.
Dynamic Ray Allocation for Aliasing Mitigation in DDGI through Importance Sampling : MS-DDGI: Multisampling Dynamic Diffuse Global Illumination
No full textBackground. Modern video games rely on advanced rendering techniques to simulate realistic lighting. Probe-based global illumination systems relying on ray tracing like DDGI, a modern and dynamic approach introduced by Z. Majercik et al., can suffer from aliasing artifacts, particularly in scenes with small, bright emissive light sources. These artifacts arise from inconsistent and insufficient sampling of the environment around probes, leading to jarring visual patterns that degrade image quality and artistic flexibility. Objectives. This thesis aims to address the aliasing artifacts in DDGI by developing a novel technique called Multisampling Dynamic Diffuse Global Illumination (MS-DDGI). The primary goals are to investigate existing methods for artifact mitigation, design a dynamic importance sampling strategy for ray allocation, and evaluate the proposed solution's effectiveness in terms of visual quality, performance, and robustness. Methods. The research combines a literature review of Monte Carlo importance sampling and related rendering techniques with the implementation of MS-DDGI in the proprietary Snowdrop engine. The proposed system dynamically redistributes probe rays based on light importance, using probability density functions (PDFs) and cumulative distribution functions (CDFs) to prioritize high-impact directions. The evaluation includes qualitative visual comparisons, quantitative error analysis against reference renders, and performance profiling. Results. MS-DDGI significantly reduces aliasing artifacts, producing smoother and more consistent lighting in scenes with small emissive sources. Quantitative tests demonstrate lower mean squared error (MSE) compared to baseline DDGI, and domain experts confirm its visual improvements. However, the technique introduces a modest performance overhead in frame time and memory usage, with temporal delay as a notable trade-off. Conclusions. The study confirms that importance sampling can mitigate aliasing artifacts effectively for DDGI, enhancing visual fidelity and artistic usability. While the implementation incurs performance costs, the benefits justify its application in real-time rendering pipelines. Future work could optimize performance further and address temporal responsiveness.Bakgrund. Moderna spel förlitar sig på avancerade renderingstekniker för att simulera realistisk belysning. System för global ljussättning som förlitar sig på strålspårning som DDGI, en modern och dynamisk teknik introducerad av Z. Majercik et al., kan lida av aliasingartefakter, särskilt i scener med små, kraftfulla emissiva ljuskällor. Dessa artefakter uppstår på grund av ojämn och otillräcklig sampling av data i omgivningen, vilket leder till oönskade visuella mönster som försämrar visuell kvalitet och begränsar konstnärlig flexibilitet. Syfte. Detta examensarbete syftar till att åtgärda aliasingartefakterna i DDGI genom utvecklingen av en ny teknik: Multisampling Dynamic Diffuse Global Illumination (MS-DDGI). De primära målen är att undersöka befintliga metoder i bruk för att åtgärda liknande artefakter, bygga en ny, mer dynamisk samplingsstrategi för strålspårning, samt utvärdera den nya lösningens effektivitet i mån av visuell kvalitet, prestanda, och stabilitet. Metod. Forskningen kombinerar en litteraturöversikt kring relaterade renderingstekniker samt Monte Carlo importance sampling med implementationen av MS-DDGI i den företagsägda Snowdrop-motorn. Våran teknik omfördelar strålresurser dynamiskt baserat på existerande ljusfördelning, med hjälp av sannolikhetstäthetsfunktioner (PDF) och kumulativa distributionsfunktioner (CDF) för att prioritera riktningar med större påverkan. Utvärderingen omfattar kvalitativa visuella jämförelser, kvantitativ felanalys mot referensrenderingar, och prestandamätningar. Resultat. MS-DDGI minskar avsevärt inverkan av aliasingartefakter, vilket ger jämnare och mer konsekvent belysning i scener med små emissiva ljuskällor. Kvantitativa tester visar lägre medelkvadratfel (MSE) jämfört med tidigare teknik, och domänexperter understryker de visuella förbättringarna. Tekniken medför dock en mätbar prestandakostnad i mån om beräkningstid och minnesanvändning, tillsammans med märkbar tidsmässig tröghet. Slutsatser. Studien bekräftar att importance sampling kan tillämpas för att åtgärda aliasingartefakter effektivt för DDGI, vilket förbättrar det visuella resultatet och breddar den konstnärliga användbarheten. Implementationen medför prestandakostnader, men fördelarna understryker samtidigt starkt dess användbarhet för modern grafik. Framtida arbete kan innefatta optimering av prestandan ytterligare, och åtgärder för tidsmässig responsivitet
GraphTrace : A Graph-Guided Hotspot Detection Method for CCTV Placement
No full textObjectives: This study introduces and evaluates GraphTrace, a graph-based method for identifying crime hotspots suitable for CCTV placement. The method addresses key limitations in traditional spatial crime analysis techniques, such as rigid spatial divisions and reliance on heuristics, by dynamically modeling crime clusters with guaranteed distance constraints. Methods: We evaluate GraphTrace using five years of official crime data (N = 125,512) from Malm & ouml;, Sweden, and compare its performance against four established spatial methods: Grid+KDE, K-Means, HDBScan, and Greedy PAI Maximization. Each method uses crime data from one year to identify high-crime locations used as suggested CCTV camera placements, which are then evaluated based on their ability to capture crimes occurring within a specified radius in the following year. For example, hotspots identified from 2019 data are assessed against 2020 crime data by counting how many crimes that fall within the radius of each location. Performance is measured using total crime counts and the Predictive Accuracy Index (PAI). Results: GraphTrace significantly outperforms all comparison methods (p<0.05) in terms of both crime capture and PAI. Effect sizes using Cohen's d range from 0.14 to 1.98, demonstrating up to very large improvements in PAI. Despite its performance, GraphTrace maintains feasible runtimes and scales well. Conclusions: GraphTrace balances precision and computational efficiency by avoiding exhaustive pairwise comparisons while preserving spatial flexibility. Unlike grid-based methods, it does not segment the study area arbitrarily, and unlike many clustering heuristics, it enforces strict distance constraints. This study presents an initial evaluation and open-source implementation of GraphTrace for hotspot detection and CCTV placement, showing strong promise for spatial crime analysis.Data-driven analys av polisens kamerabevakning - Effekter på brott, brottsuppklarning och otrygghe
Effects of Noisy Auditory Environments and Auditory Masking on Cognitive Performance Among Software Development Students : A Pilot Study
No full textAn increasing number of programmers choose to listen to music while performing programming related tasks. This study aims to examine how different auditory environments affect students’ cognitive performance during programming tasks. Specifically, it investigates the impact of office noise, music used to mask noise, and silence on concentration. The study was conducted as a quasi-experiment in which participants completed two programming tasks under each of the three sound conditions. The findings suggest a slight improvement in task performance when office noise was masked with self-selected music. In contrast, the silent environment was associated with the lowest performance. These results provide insight into how soundscapes may influence concentration in programming contexts
Exploring iPaaS Integration : A Deep Dive into Security Challenges and Solutions
No full textWith the increasing adoption of cloud-based services, businesses rely heavily on Integration Platform as a Service (iPaaS) solutions to connect diverse applications efficiently, departing from the traditional integration techniques. While iPaaS enhances operational agility and scalability, it may also introduce significant security concerns, such as exposing a greater attack surface, more complex access control, and advanced management of security practices over several systems, services, and users. This thesis aims to identify the primary security challenges associated with iPaaS integration, analyse methods for mitigating these risks, and evaluate the benefits that continue to drive organisations toward adopting iPaaS solutions, despite potential vulnerabilities. Specifically, it focuses on access control mechanisms, cryptographic practices, and policy management within iPaaS environments, where best practices can be established, showcasing good security throughout the iPaaS platform. To achieve these objectives, a combination of literature review and observation analysis was used. The literature review provided insights into existing security measures and highlighted gaps in current practices by studying existing works regarding the topic. Observation analysis was conducted to assess real-world implementations and validate findings deriving out of the theoretical research, as well as voices from the real world regarding both custom integration and iPaaS integration. The study reveals that while iPaaS platforms offer substantial benefits in terms of system integration and cost efficiency, the lack of robust security measures can lead to vulnerabilities. However, adopting best practices such as strong access control methods, which include role-based access control (RBAC), attribute-based access control (ABAC), comprehensive cryptographic strategies, and continuous policy enforcement can significantly reduce these risks, allowing a heap of benefits for further continued and secure adoption. In conclusion, the research provides actionable recommendations for organisations to secure their iPaaS-based systems while maximising the advantages of seamless application integration
Optimering av produktpresentation inom e-handel : Utvärdering av kampanjstrategier och konsumentbeteende
No full textE-handelsföretag förlitar sig i allt högre grad på produktrekommendationer för att förbättra användarupplevelsen och öka försäljningen. Medan forskning tyder på att dessa system är avgörande för att öka engagemang och konverteringar, förblir deras specifika effektivitet i svenska e-handelssammanhang underutforskad. Denna studie undersökte hur olika typer av rekommendationer påverkar användares köpbeslut och fastställde vilken inverkan placeringen av rekommendationer har på deras effektivitet i en e-handelsmiljö. Åtta A/B-tester genomfördes i en e-handelsprototyp utvecklad tillsammans med Askås. Den experimentella metoden använde Google Analytics 4 för att spåra användarinteraktioner medan testdeltagare (10–16 per test) genomförde simulerade köp med förutbestämda budgetar. Både kvantitativa mätvärden och enkäter efter testerna analyserades. Säsongsrekommendationer uppnådde den högsta konverteringsgraden (61,9%), följt av bästsäljare och kampanjerbjudanden (50%). Startsidan visade sig vara den mest effektiva placeringen. Mobilanvändare visade konsekvent högre konverteringsgrader än datoranvändare. Anmärkningsvärt är att kontrollversionen utan rekommendationer uppnådde en konverteringsgrad på 52,9%, vilket överträffade flera testversioner. Både rekommendationstyp och placering påverkar e-handelsframgången betydligt. Säsongsinnehåll på startsidan och mobiloptimering bör prioriteras. Den starka prestandan hos kontrollversionen tyder på att strategisk implementering är avgörande, eftersom dåligt utförda rekommendationer kan vara mindre effektiva än inga alls. Dessa resultat ger praktiska riktlinjer för att optimera produktrekommendationer över olika enheter.E-commerce businesses increasingly rely on product recommendations to enhance user experienceand boost sales. While research suggests these systems are essential for increasing engagement andconversions, their specific effectiveness in Swedish e-commerce contexts remains under-explored. This study examined how different types of recommendations influence users' purchasing decisions and determined the impact of recommendation placement on their effectiveness in an e-commerce setting. Eight A/B tests were conducted in an e-commerce prototype developed with Askås. The experimental method employed Google Analytics 4 to track user interactions while test participants (10-16 per test) completed simulated purchases with predetermined budgets. Both quantitative metrics and post-test questionnaires were analyzed. Seasonal recommendations achieved the highest conversion rate (61.9%), followed by bestsellers and promotional campaigns (50%). The homepage proved the most effective placement. Mobile users consistently demonstrated higher conversion rates than desktop users. Notably, the control version without recommendations achieved a 52.9% conversion rate, outperforming several test versions. Both recommendation type and placement significantly impact e-commerce success. Seasonal content on the homepage and mobile optimization should be prioritized. The strong performance of the control version suggests that strategic implementation is crucial, as poorly executed recommendations may be less effective than none at all. These findings provide practical guidelines for optimizing product recommendations across different devices