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Machine Learning-Based CloudCost Estimation: Developing and Evaluating Predictive Models
No full textTheoretical characterization of NV-like defects in 4H-SiC using ADAQ with SCAN and r2SCAN meta-GGA functionals
No full textKohn–Sham density functional theory is widely used for screening color centers in semiconductors. While the Perdew–Burke–Ernzerhof (PBE) generalized gradient approximation functional is efficient, its accuracy in describing defects is often not sufficient. The Heyd–Scuseria–Ernzerhof (HSE) functional is more accurate but computationally expensive, making it impractical for large-scale screening. This study evaluates the strongly constrained and appropriately normed (SCAN) family of meta-GGA functionals as potential alternatives to PBE for characterizing NV-like color centers in 4H-SiC using the Automatic Defect Analysis and Qualification (ADAQ) framework. We examine nitrogen, oxygen, fluorine, sulfur, and chlorine vacancies in 4H-SiC, focusing on applications in quantum technology. Our results show that SCAN and r2SCAN achieve a greater accuracy than PBE, approaching HSE's precision at a lower computational cost. This suggests that the SCAN family offers a practical improvement for screening new color centers, with computational demands similar to PBE. Funding Agencies|Knut and Alice Wallenberg Foundation [2018.0071]; Strategic Research Area in Material Science on Functional Materials at Linkoeping University, SFO-Mat-LiU [2009 00971]; Swedish Research Council [2022-00276, 2022-06725, 2018-05973]; Wallenberg Scholar [KAW2018.0194]; European Union under Horizon Europe for the QUEST project [101156088]; [2020-05402]</p
Breaking the Shell of Stress : Buffering Early Stress in Commercially Hatched Laying Hens
No full textApproximately eight billion laying hens currently support the global demand for eggs. To sustain this growing worldwide commercial demand, a continuous supply of laying hens in their peak production stage is required. These hens begin their lives in large-scale commercial hatcheries, where, as newly hatched chicks, they undergo a highly efficient industrial process. Shortly after hatching in large incubators, chicks are transferred onto a conveyor belt system that moves them through several stations, each handling a specific task: separation from the eggshell, sex-sorting, vaccination, and machine-packing for transportation to the rearing farm. This process has previously been shown to elicit a stress response with both short- and long-term effects on behaviour, physiology, and affective states, which could lead to future welfare issues. The primary focus of this thesis revolves around mitigating the stress chicks have incurred at the commercial hatchery upon hatching. In paper I, we enriched chicks by providing them with a more complex environment and a stuffed mother hen on which the chicks were imprinted. Among hatchery-hatched chicks, those provided with enrichment exhibited a reduced stress response to a restraint; however, no other effects of enrichment were found. Overall, hatchery chicks showed more pessimism and higher stress levels when socially isolated, which is consistent with previous studies indicating that the commercial hatching conditions are stressful. Similarly, in paper II, we investigated whether enriching the early environment with natural-like elements could buffer hatchery stress. Enriched chicks completed a spatial memory test more quickly while making less immediate errors, were more optimistic in a cognitive judgment bias test, and had fewer feather fault bars, which indicate lower levels of acute stress. We concluded that the provided naturalistic enrichment helped reduce early stress and improved welfare. In paper III, we explored whether stimulating play behaviours in an arena with objects during the first weeks of life could buffer early stress sustained during the commercial hatching process. Play-stimulated chicks exhibited reduced fear in response to a novel object; however, contrary to our expectations, they also showed more pessimism in a cognitive bias test. No effects of play were observed in the other assays. Our results suggest that play stimulation can help chicks tackle future challenges, but its impact on emotional states requires further research. In the final paper, paper IV, we examined how the commercial hatchery process affected stress events that occur at later stages in a laying hen’s life, such as transportation and introductions to novel environments and new social groups. Control chicks exhibited reduced tonic immobility durations following transportation. Comb temperatures of hatchery-hatched chicks indicated both stress-induced hyperthermia after a regrouping procedure and a stronger autonomic response to an acute stressor. This demonstrates the long-lasting effects of commercial hatching on the hens’ abilities to cope with routine events encountered in rearing environments. In summary, these findings further illustrate the negative impact commercial hatching procedures have on both the stress response and welfare of laying hens, even affecting their ability to cope with stressors encountered later in life. Fortunately, enriching chicks’ lives with more complex environments and providing opportunities to enhance play can moderate stress sensitivity, reduce fearfulness, and improve positive affective states which in turn enhances welfare outcomes. Together, these papers bring support to the need to refine the early-life environments of hens to help buffer stress and in turn improve their long-term welfare.Cirka åtta miljarder värphöns tillgodoser för närvarande den globala efterfrågan på ägg. För att upprätthålla den växande globala kommersiella efterfrågan krävs en kontinuerlig tillgång till värphöns i deras mest effektiva produktionsfas. Dessa hönor börjar sina liv i storskaliga kommersiella kläckerier, där de som nykläckta kycklingar genomgår en mycket effektiv industriell process. Strax efter de har kläckts i stora inkubatorer, flyttas kycklingarna till ett transportbandssystem som för dem genom flera stationer, där varje station har en specifik uppgift: separation från äggskalet, könssortering, vaccinering och maskinell förpackning för transport till uppfödningsanläggningen. Denna process har tidigare visat sig framkalla en stressreaktion med både kortsiktiga och långsiktiga effekter på beteende, fysiologi och affektiva tillstånd, vilket kan leda till framtida välfärdsproblem. Det primära fokuset i denna avhandling kretsar kring att mildra den stress som kycklingar har utsatts för vid kläckningen på det kommersiella kläckeriet. I artikel I berikade vi kycklingarna genom att förse dem med en mer komplex miljö och en uppstoppad hönsmamma som kycklingarna präglades på. Bland kläckeri-kläckta kycklingar uppvisade de som fick berikning en minskad stressrespons på en fasthållning; inga andra effekter av berikning hittades dock. Sammantaget visade kläckerikycklingar mer pessimism och högre stressnivåer när de var socialt isolerade, vilket överensstämmer med tidigare studier som tyder på att de kommersiella kläckningsförhållandena är stressande. På samma sätt undersökte vi i artikel II om berikning av den tidiga miljön med naturliknande element kunde buffra kläckningsstress. Berikade kycklingar slutförde en spatial minnesuppgift snabbare samtidigt som de gjorde mindre omedelbara fel, var mer optimistiska i ett kognitivt bedömningsbias-test och hade färre fjäderfel, vilket indikerar lägre nivåer av akut stress. Vi drog slutsatsen att den naturalistiska berikningen bidrog till att minska den tidiga stressen och förbättra välfärden. I artikel III undersökte vi om stimulering av lekbeteenden i en arena med föremål under de första levnadsveckorna kunde buffra tidig stress som uppstod under den kommersiella kläckningsprocessen. Lekstimulerade kycklingar uppvisade minskad rädsla som svar på ett nytt objekt, men i motsats till våra förväntningar visade de också mer pessimism i ett kognitivt bias-test. Inga effekter av lek observerades i de andra analyserna. Våra resultat tyder på att lekstimulering kan hjälpa kycklingar att hantera framtida utmaningar, men dess inverkan på känslomässiga tillstånd kräver ytterligare forskning. I den sista artikeln, artikel IV, undersökte vi hur den kommersiella kläckeriprocessen påverkade stresshändelser som inträffar i senare skeden av en värphönas liv, såsom transport och introduktion till nya miljöer och nya sociala grupper. Kontrollkycklingar uppvisade minskad varaktighet av tonisk immobilitet efter transport. Kamtemperaturen hos kläckta kycklingar indikerade både stressinducerad hypertermi efter en omgrupperingsprocedur och ett starkare autonomt svar på en akut stressfaktor. Detta visar på de långvariga effekterna av kommersiell kläckning på hönornas förmåga att hantera rutinmässiga händelser i uppfödningsmiljön. Sammanfattningsvis illustrerar dessa resultat ytterligare den negativa inverkan som kommersiell kläckning har på både värphönsens stressrespons och välfärd, vilken till och med påverkar deras förmåga att hantera stressfaktorer som uppstår senare i livet. Lyckligtvis kan man genom att berika kycklingarnas liv med mer komplexa miljöer och ge dem möjlighet att leka mildra stresskänsligheten, minska rädslan och förbättra de positiva affektiva tillstånden, vilket i sin tur förbättrar välfärden. Tillsammans ger dessa artiklar stöd för behovet av att förbättra kycklingarnas miljöer tidigt i livet för att hjälpa till att buffra stress och i sin tur förbättra deras långsiktiga välfärd.
Highly viable gastrointestinal Chlamydia trachomatis in women abstaining from receptive anal intercourse
No full textChlamydia trachomatis (CT) may employ persistence to evade antimicrobial clearance, possibly residing in the gastrointestinal tract. This study assessed the reliability of droplet digital PCR (ddPCR) in CT detection, its functionality in viability assessment, and predictions on CT transmission dynamics by combining viability PCR (vPCR) and clinical data from 52 infected women. The ddPCR showed 94% positive and 100% negative agreement with Abbott Alinity STI-M for endocervical samples, and 92% positive and 87% negative agreement in rectal samples. Viability was higher in endocervical samples (89.1%) than in rectal samples (69.4%). Samples from participants not engaging in anal intercourse, and with non-concordant multi-locus sequence typing between rectum and endocervix, had on average the highest viability in rectum, indicating a persistent population residing in the gastrointestinal tract. This study demonstrates the effectiveness of ddPCR in detecting CT, especially in samples with high inhibition or low bacterial load, suggesting its superiority over quantitative real-time PCR. These findings support that rectal CT infection can occur independently of anal intercourse, possibly through vaginorectal contamination or oral routes. High rectal CT viability, independent of endocervical infection, indicates potential gastrointestinal establishment. Understanding CT dynamics in various infection sites can provide insights into the epidemiology and pathogenesis of CT.Funding Agencies|Uppsala University.; Edvard Welander foundation [2020:3050]; Medical Research Council of Southeast Sweden [FORSS-859774, FORSS-930808]</p
Estimating 6D Pose from Depth and Color Images
No full textEstimating the 6D pose, i.e. the location and orientation, of an object is a central problem in many applications of computer vision. One of those is robotic bin-picking, an automated process where a robot picks an object from one location and moves it to another. As factories become more automated, able robotic bin-picking systems are becoming increasingly more in demand, and a robust pose estimation is a vital basis for the systems. There are many different approaches to solve the problem of 6D pose estimation, and in this study some of these approaches are assessed. A big part of this study is an extensive search to find state of the art methods. An initial reference point for this search is the BOP challenge. Numerous recent methods build upon machine learning approaches, and though the potential of these seems great, classical approaches should not be overlooked. Therefore, a classical point cloud registration method, TEASER, is compared to a machine learning-based algorithm, SAM-6D. To evaluate these two methods, large amounts of data are needed. Real RGBD (Red, Green, Blue, Depth) images are captured with a stereo camera that utilizes structured light to enhance the triangulation. To increase the size of the dataset, synthetic data are created using BlenderProc2, a procedural Blender pipeline, developed specifically for generating data for 6D pose estimation. In BlenderProc2, a physics engine is used to create realistic bin-picking scenes. Both methods (TEASER and SAM-6D) proved to be able to accurately solve the problem of 6D pose estimation for some objects within the scope of this project. However, both methods exhibited weaknesses, struggling to robustly estimate the pose of certain objects. Objects in heavily cluttered scenes, with large amounts of occlusion, proved more difficult to localize. Also, both methods proved to be slow, requiring an unbecoming amount of time to process a bin-picking scene and locate the objects in it
Applying Machine Learning in the Process Industry : A Quality Management Perspective
No full textIs there room for improvement in your production process? Would it not be great if we could assign an algorithm to solve all those problems? Unfortunately, production processes tend to be complex and no algorithm that potent has been identified within the scope of this thesis. But maybe there are ways to use algorithms so that they can provide a bit of help. Quality Management & Quality Technology is the research area that for decades has worked on the continuous improvement of industrial and other processes. A cornerstone of the methods from Quality Management & Quality Technology is to approach problems in these industrial and other processes in a scientific manner. A big part of this approach is to make decisions based on data: and thereby, statistical methods are an integrated part of the approach in order to understand process behavior. The statistical approach has proven to be very effective, but the specific methods used were developed during the 20th century, for the industry context of that time and within the technical abilities that then existed. Machine learning (ML) is the concept of data-driven algorithms learning patterns to enable prediction, classification, and decision making. It has exploded as a research topic in recent years and, in the midst of the fourth industrial revolution, a belief is expressed that together with the rapidly growing amounts of industrial data, ML will drastically change industry by making the production processes increasingly efficient and supporting decision making, etc. The big issue is that it is hard to find how ML is to be applied in industry. Would it not make sense to combine the knowledge from the research area that has applied numerical methods and benefited industry for decades and combine that with the new type of numerical methods? In this thesis, the aim has been to do just that. In essence, how can you combine the traditional concepts and knowledge from Quality Management & Quality Technology together with the more novel algorithms from ML? This thesis advances concept generation by assessing the use of statistics in Quality Technology. It highlights the limitations of traditional methodologies in data-intensive environments and proposes ML algorithms as more effective solutions for managing large datasets. Within Root Cause Analysis (RCA), the thesis proposes a specific approach that is useful in paperboard manufacturing. With appropriate adaptations depending on the specific technical case, it is likely that this could also benefit other process industries. Additionally, it explores how ML can enhance statistical process control, offering insights into its potential to deliver more precise guidance and operate effectively across different organizational levels.Finns det utrymme för förbättring i er produktionsprocess? Skulle det inte vara fantastiskt om man kunde tilldela en algoritm uppgiften att lösa alla dessa problem? Tyvärr så är produktionsprocesser i regel komplexa och inom ramen för den här avhandlingen så har ingen så potent algoritm kunnat identifierats. Men, det kanske finns sätt att använda algoritmer på ett sätt så dom kan bidra till uppgiften? Forskningsområdet Kvalitetledning & Kvalitetsteknik har i decennier arbetat med ständiga förbättringar av processer, framför allt industriella processer men även andra processer. En hörnsten har varit att angripa de problem som funnits i processerna på ett vetenskapligt sätt. Detta har till stor del handlat om att bygga beslut på data, och därmed har statistiska metoder varit en integrerad del av forskningsområdet då man genom statistik kan skapa en större förståelse för processen. Detta angreppsätt är idag välbeprövat och har bevisats vara väldigt effektivt. Metoderna utvecklades dock under 1900-talet, och är därmed anpassade för den industriella kontext och de tekniska möjligheter som fanns under den tiden. Maskininlärning är datadrivna algoritmer lär sig mönster för prediktion, klassificering och beslut. Inom den fjärde industriella revolution, som pågår i skrivande stund, tror man att med Maskininlärning och den allt större mängden industriella data som finns tillgänglig drastiskt kommer att kunna förändra industrin genom att göra den mer effektiv, ska möjligheter till bättre beslut, osv. Maskininlärning är ett forskningsområde som har expanderat kraftigt under de senaste åren. Dock är den väldigt svårt att hitta någon som beskriver hur den ska appliceras i industrin. Skulle det då inte vara rimligt att försöka kombinera den kunskap som förvärvats från det forskningsområde som applicerat och skapat värde genom att använda numeriska metoder inom industrin och kombinera denna kunskap med de nya typerna av numeriska metoder? Att kombinera dessa två forskningsområden har därför varit målet med denna avhandling. Det vill säga, hur kan man kombinera de koncept och den kunskap som finns inom forskningsområdet Kvalitetledning & Kvalitetsteknik med de algoritmer som finns inom Maskininlärning? I denna avhandling skapas nya koncept genom att utvärdera de fundamentala idéerna från användandet av statistik inom Kvalitetsteknik. Den beskriver klassiska metoders begränsningar när de sätts i en mer dataintensiv miljö och föreslår maskininlärningsalgoritmer för mer effektiva lösningar i denna typ av större data-set. För Rotorsaksanalys föreslås ett specifikt tillvägagångsätt som framgångsrikt testats i kartongtillverkning. Det är rimligt att tro att den även kommer fungera på andra industrier, men beroende på de tekniska förutsättningarna kan metoden behöva anpassas. Utöver det utforskas hur maskininlärning kan utveckla användandet av statistisk processtyrning genom att leverera bättre guidning för användare samt att kunna sprida tillvägagångssättet högre upp inom organisationen
Time spent outdoors in childhood related to myopia among young adults in the Swedish ABIS cohort
No full textPurpose: Elucidate the prevalence of myopia among young adults from a birth cohort of Swedish children and its relationship to possible risk factors during their childhood. Methods: Five thousand two hundred young adults, mean 23.4 years and 58% females, participating in the prospective birth cohort All Babies in Southeast Sweden (ABIS) answered a questionnaire including questions regarding health and physical activity, spectacle use, myopia and age at first optical correction. Questionnaires at previous follow-ups at ages 2-3, 5-6 and 8 years included information on type of housing, time outdoors, screen time and hours of reading. Myopia prevalence and associations with potential risk factors were analysed in univariate and multivariate regression models with Bonferroni's correction of p-values. Results: In the ABIS Swedish birth cohort of young adults, the prevalence of myopia was 29%. A univariate logistic regression showed a higher odds ratio for myopia with female gender (OR 1.59; p &lt; 0.05) and a completed and started university education (OR 1.52; p &lt; 0.05). Significantly lower odds ratios were found for hours spent outdoors at 8 years of age (OR 0.82; p &lt; 0.05). Multivariate logistic regression showed a higher odds ratio for myopia in females (OR 1.52-1.57; p &lt; 0.05) and completed and started university education (OR 1.34-1.49; p &lt; 0.05) in all models. In a model including accommodative effort, measured in diopter hours at 8 years of age, hours spent outdoors were associated with a lower odds ratio for myopia (OR 0.86; p &lt; 0.05). No association could be detected between myopia and the type of housing or near work. Conclusion: The prevalence of myopia among young adults in a Swedish birth cohort was lower or unchanged compared to previous data. Female gender, higher education and less time spent outdoors in childhood were associated with an increased risk of developing myopia. Recommendations from child health services and schools should be given to stimulate children to spend enough time outdoors.Funding Agencies|Juvenile Diabetes Research Foundation</p
Biomarkers for monitoring disease activity and predicting disease progression in multiple sclerosis : Studies on body fluid and imaging biomarkers
No full textMultiple sclerosis (MS) is a chronic neuroinflammatory and neurodegenerative disease driven by complex pathophysiological mechanisms that contribute to neurologic impairment and disability progression. This thesis explores protein and metabolite biomarkers and employs machine learning models to predict disease trajectory in MS, aiming to improve diagnosis, prognosis, and treatment response. To address this objective, comprehensive proteomic profiling was conducted on cerebrospinal fluid (CSF) and plasma from individuals with early-stage MS and healthy controls. Differentially expressed CSF proteins were enriched in pathways related to B cell activation, and a linear regression model incorporating 11 of these proteins and age effectively predicted long-term disability progression for up to 13 years. Additionally, logistic regression models based on CSF proteins could distinguish MS from controls and predict short-term disease activity. Since disease progression in MS is influenced not only by baseline pathology but also by therapeutic interventions, further focus was placed on how dimethyl fumarate (DMF), a common oral treatment in MS, affects plasma and CSF proteomic profiles related to pathological mechanisms in MS. Longitudinal analysis revealed DMF-induced reductions in inflammatory proteins associated with T-helper 1 immunity, underscoring the drug’s ability to modulate this key pathologic pathway. Importantly, baseline levels of specific axonal, glial and myelination-related proteins differentiated responders from non-responders, suggesting a potential role for these biomarkers in guiding treatment selection and optimizing therapeutic strategies. Expanding the focus beyond proteomics, metabolic dysregulation in MS was examined through the analysis of CSF and normal-appearing white matter (NAWM) metabolites across different disease stages. Metabolites that were most strongly associated with clinical factors in MS were linked to mitochondrial dysfunction, axonal integrity, astrogliosis and demyelination. CSF biomarkers in linear regression models could distinguish MS from unspecific but similar neurological symptoms and differentiate between subtypes of the disease. A random forest model incorporating NAWM metabolites demonstrated high predictive power for long-term disability progression for up to 16 years, offering a promising non-invasive tool for MS prognosis. Together, these studies provide a comprehensive perspective on MS pathophysiology, presenting protein- and metabolite-based models for enhanced diagnosis, treatment response monitoring, and long-term disease progression assessment. The biomarkers suggested in this thesis lay the groundwork for future translational applications in clinical practice
The ontological politics of synthetic data: Normalities, outliers, and intersectional hallucinations
No full textSynthetic data is increasingly used as a substitute for real data due to ethical, legal, and logistical reasons. However, the rise of synthetic data also raises critical questions about its entanglement with the politics of classification and the reproduction of social norms and categories. This paper aims to problematize the use of synthetic data by examining how its production is intertwined with the maintenance of certain worldviews and classifications. We argue that synthetic data, like real data, is embedded with societal biases and power structures, leading to the reproduction of existing social inequalities. Through empirical examples, we demonstrate how synthetic data tends to highlight majority elements as the “normal” and minimize minority elements, and that the slight changes to the data structures that create synthetic data will also inevitably result in what we term “intersectional hallucinations.” These hallucinations are inherent to synthetic data and cannot be entirely eliminated without compromising the purpose of creating synthetic datasets. We contend that decisions about synthetic data involve determining which intersections are essential and which can be disregarded, a practice which will imbue these decisions with norms and values. Our study underscores the need for critical engagement with the mathematical and statistical choices in synthetic data production and advocates for careful consideration of the ontological and political implications of these choices during curatorial style production of synthetic structured data.Funding Agencies|WASP-HS (NetX)</p
Wash-free fluorescent tools based on organic molecules: Design principles and biomedical applications
No full textFluorescence-assisted tools based on organic molecules have been extensively applied to interrogate complex biological processes in a non-invasive manner with good sensitivity, high resolution, and rich contrast. However, the signal-to-noise ratio is an essential factor to be reckoned with during collecting images for high fidelity. In view of this, the wash-free strategy is proven as a promising and important approach to improve the signal-to-noise ratio, thus a thorough introduction is presented in the current review about wash-free fluorescent tools based on organic molecules. Firstly, generalization and summarization of the principles for designing wash-free molecular fluorescent tools (WFTs) are made. Subsequently, to make the thought of molecule design more legible, a wash-free strategy is highlighted in recent studies from four diverse but tightly binding aspects: (1) special chemical structures, (2) molecular interactions, (3) bio-orthogonal reactions, (4) abiotic reactions. Meanwhile, biomedical applications including bioimaging, biodetection, and therapy, are ready to be accompanied by. Finally, the prospects for WFTs are elaborated and discussed. This review is a timely conclusion about wash-free strategy in the fluorescence-guided biomedical applications, which may bring WFTs to the forefront and accelerate their extensive applications in biology and medicine. In this review, the design principles for wash-free molecular fluorescent tools are highlighted in four diverse details. Meanwhile, biomedical applications including wash-free fluorescent imaging of organelles, cellular microenvironment, biomacromolecules, and small species, as well as therapy and potential drug delivery, are also emphasized. This review may bring wash-free molecular fluorescent tools to the forefront, as well as expand the applications in biology and medicine. imageFunding Agencies|Science and Technology Development Fund, Macau SAR [0047/2023/RIB2, 0085/2020/A2]; Research Grant of the University of Macau [MYRG2020-00130-FHS, MYRG2022-00036-FHS]; Guangdong Basic and Applied Basic Research Foundation [2022A1515010616, 2023A1515012524]</p