Thesis (M. Med.(Paediatrics and Child Health)) -- University of Limpopo, 2019 / Background: Congenital heart disease (CHD) is a significant contributor to Under 5 Mortality rate(U5MR) in Limpopo. Atrioventricular septal defect (AVSD) is the best ascertained lesion in Limpopo and is strongly associated with Down syndrome. Few children from Limpopo with CHD including AVSD access cardiac diagnostic and surgical services. Objectives: The study aimed to enumerate, describe syndromes associated with AVSD and outcomes of children with AVSD at Pietersburg hospital. Methods: This is a Retrospective study of all children (n=80) diagnosed with AVSD from 1 January 2010 to 31 December 2014 at Pietersburg hospital. Data were drawn from echocardiogram reports and patient records. District Health Information Software (DHIS) data was used to obtain the number of live births per district during the 5 years study period. Results: Eight hundred and sixty six (n=866) patients had a first diagnosis of CHD confirmed on echocardiography and 80 (9.2%) of these had AVSD (an estimated 31.5% of expected cases of AVSD). Eighty four per cent (84%) of AVSD patients were associated with Down syndrome. 42/67 (63%) AVSD patients were referred for surgical assessment and of those 15/42 (36%) had surgery. The median interval between diagnosis and surgery was 13 months. Seventy five percent (n=50/67) of patients defaulted follow up. Conclusion: The study confirmed that most children with AVSD had associated Down syndrome and that the majority of children with AVSD from Limpopo do no access surgery. There is under referral of children with Down syndrome for screening of CHD.

To summarize this entire thesis, MW-assisted spectroscopy has been proposed as a promising approach to investigate the optical properties of VSi and VV spin defects in 4H- and 6H-SiC. The MW-assisted spectroscopy has enabled to separate the spectrally overlapped contribution of different types of defects. From a PL spectrum containing no overlapping spectral contributions of other defects, the local vibrational mode of all measured VSi and VV in 4H- and 6H-SiC has been found along with the phonon energy and DW factor. The interaction of local vibrational modes with point defects has allowed to understand the spin, optical, mechanical, and thermal properties of these defects. In the investigation of V2 in 4H-SiC, a perfect agreement between the experimental data and theoretical calculation have been obtained. The MW-assisted spectra measured at different resonant frequencies associated with the same defect have been found to reveal the same vibrational mode and DW factor. Furthermore, some new ODMR lines to certain defects have been assigned, which have never been reported before. From the investigation of the V2 VSi in 6H-SiC, it has been found that the temperature does not have a clear influence on the DW factor, but high-fluence electron irradiation has been shown to decrease the DW factor. In the polarization investigation, it has been found that in 6H-SiC, V1 possesses no polarization, V2 shows a strong E||c-axis polarization, while V3 exhibits a strong E⊥c-axis polarization. It has also been demonstrated that the temperature and the orientation of the excitation laser have no influence on the photon polarization. In short, this thesis has demonstrated that MW-assisted spectroscopy is a powerful technique to investigate a large number of spin defects in wide-bandgap semiconducting materials.

Higher alcohol synthesis (HAS) from syngas or biomass-derived syngas is an important process for the production of oxygenate fuels, fuel additives and other intermediates for valueded chemical feedstock to produce medicine, cosmetics, lubricants, detergents, and polyesters. Chapter I reviews biomass to liquid fuels technology, higher alcohols being used as alternative fuels and fuel additives, the historical perspective and commercial status of higher alcohols, the catalyst system and the reaction mechanism for HAS from syngas. Chapter II discusses the Zn-Mn promoted Cu-Fe based catalyst that was synthesized by the co-precipitation method. The reaction temperature has been tested to study the influence on the catalytic performance. The maximal CO conversion rate was 72%, and the yield of alcohol and hydrocarbon was also very high. Cu was the active site for alcohol synthesis, iron carbide was the active site for olefin and paraffin synthesis. The reaction mechanism of HAS from syngas over Zn-Mn promoted Cu-Fe based catalyst was proposed. Chapter III documents the three-dimensionally ordered macroporous (3DOM) Cu-Fe catalyst developed using a glyoxylate route colloidal crystal template method. The high intrinsic activity was ascribed to three factors. First, the unique ordered structure has a large pore size and interconnected macroporous tunnels of the catalyst with a large accessible surface area to improve the catalytic activity. Second, a high density of uniformly distributed defective Cu0 and Fe5C2 nanoparticles derived from the glyoxylate route helps to provide abundant, active, and stable dual sites. Third, atomic steps on the Cu surface, induced by planar defects and lattice strain, serve as high-activity oxygenation sites. Active Fe5C2 chain-growth sites intimately surround the defective and strained form of the Cu surface, which results in a synergetic effect between the active and stable Cu–FexCy dual site for HAS. In Chapter IV, in situ ambient pressure x-ray photoelectron spectroscopy and in situ synchrotron powder diffraction were applied to identify the active site of 3DOM Cu-Fe catalyst for HAS. The results show that after syngas pretreatment of the 3DOM Cu-Fe catalyst, Cu0 is the active oxygenation site for alcohol synthesis, and Fe5C2 is the active site for carbon chain growth.

Very often defects are present in rolled products. For wire rods, defects are very deleterious since the wire rods are generally used directly in various applications. For this reason, the market nowadays requires wire rods to be completely defect-free. Any wire with defects must be rejected as scrap which is very costly for the production mill. Thus, it is very important to study the formation and evolution of defects during wire rod rolling in order to better understand and minimize the problem, at the same time improving quality of the wire rods and reducing production costs. The present work is focused on the evolution of artificial defects during rolling. Longitudinal surface defects are studied during shape rolling of an AISI M2 high speed steel and a longitudinal central inner defect is studied in an AISI 304L austenitic stainless steel during ultra-high-speed wire rod rolling. Experimental studies are carried out by rolling short rods prepared with arteficial defects. The evolution of the defects is characterised and compared to numerical analyses. The comparison shows that surface defects generally reduce quicker in the experiments than predicted by the simulations whereas a good agreement is generally obtained for the central defect. / QC 20101105

High-pressure die casting of semi-solid aluminium-silicon alloys is used in the automotive industry to manufacture components, like housings, brackets, and bars. It is commonly known that during high-pressure die casting, defect bands may be created that follow the contour of the component surface. These bands consist mainly of a eutectic phase. This phenomenon is also observed in semi-solid metal slurry high-pressure die casting. These bands could lead to premature failure of the component in service. The origin of these bands is not fully understood and so this research focuses on investigating these bands and their origins further. A series of casting trials were conducted with varying plunger velocity. Subsequent investigation using optical and scanning electron microscopy showed that a change of the plunger velocity alters the number of bands present in the samples. Energy dispersive X-ray spectroscopy revealed that a measurable difference in aluminium quantity across the band was noticed and it was postulated that aluminium migrates towards the component centre. Therefore, different mechanisms responsible for particle migrations found in literature were investigated and assessed quantitatively using experimental data and information from published literature. It was found that the Saffman lift force and the Mukai-Lin-Laplace effect were the mechanisms that were most likely to cause such a migration of aluminium. Further experimental investigation is recommended to identify which of the two mechanisms is ultimately responsible for the migration and to optimise the high-pressure die casting procedure to minimise defect band formation. / Produktion av högtrycksgjutning av halvfasta aluminium-kisellegeringar används i fordonsindustrin för att tillverka komponenter, som exempel till kåpor, konsoler och stag. Det är allmänt känt att defektband kan formas under högtrycksgjutning som följer konturen av komponentytan. Dessa band består huvudsakligen av eutektisk fas. Detta fenomen har också observerats vid högtrycksgjutning produktion av halvfast slurry. Potentiellt kan dessa band leda till en försämring av komponentens mekaniska egenskaper och resultera i ett förtida brott. Ursprunget av dessa band är inte helt kartlagda och det är därför viktigt att fokusera ytterligare på denna forskning och att undersöka dessa band och deras ursprung. En serie med gjutningsförsök genomfördes med varierande kolvhastighet. Efterföljande undersökning med optisk- och svepelektronmikroskopi visade att en förändring av kolvhastigheten förändrar antalet band som finns i proverna. Energidispersiv röntgenspektroskopi avslöjade en mätbar skillnad i aluminiumkvantitet över bandet, och det antogs att aluminium migrerar mot centrum av komponenten. Därför undersöktes och utvärderades olika mekanismer som ansvarar för partikelmigrationer som finns att finna i litteratur med hjälp av experimentella data och information från publicerad litteratur. Det visade sig att Saffman lyftkraft och Mukai-Lin-Laplace effekten var de mekanismer som mest troligen orsakade migration av aluminium. Ytterligare experimentella försök rekommenderas för att identifiera vilken av dessa två mekanismerna som i slutändan är ansvarig för migrationen. Detta för att optimera gjutningsprocessen och därmed minimera uppkomsten av defektband.

Maintaining quality outcomes is an essential task for any manufacturing organization. Visual inspections have long been an avenue to detect defects in manufactured products, and recent advances within the field of deep learning has led to a surge of research in how technologies like convolutional neural networks can be used to perform these quality inspections automatically. An alternative to these often large and deep network structures is the modular neural network, which can instead divide a classification task into several sub-tasks to decrease the overall complexity of a problem. To investigate how these two approaches to image classification compare in a quality inspection task, a case study was performed at AR Packaging, a manufacturer of food containers. The many different colors, prints and geometries present in the AR Packaging product family served as a natural occurrence of complexity for the quality classification task. A modular network was designed, being formed by one routing module to classify variant type which is subsequently used to delegate the quality classification to an expert module trained for that specific variant. An image dataset was manually generated from within the production environment portraying a range of product variants in both defective and non-defective form. An image processing algorithm was developed to minimize image background and align the products in the pictures. To evaluate the adaptability of the two approaches, the networks were initially trained on same data from five variants, and then retrained with added data from a sixth variant. The modular networks were found to be overall less accurate and slower in their classification than the conventional single networks were. However, the modular networks were more than six times smaller and required less time to train initially, though the retraining times were roughly equivalent in both approaches. The retraining of the single network did also cause some fluctuation in the predictive accuracy, something which was not noted in the modular network. / <p>Det finns övrigt digitalt material (t.ex. film-, bild- eller ljudfiler) eller modeller/artefakter tillhörande examensarbetet som ska skickas till arkivet.</p>

Modeling and analysing materials with theoretical tools is of great use when finding new systems for applications, for example, semiconductors with point defects can be used for quantum applications, like single photon emitters. One important aspect to consider symmetry, which can yield useful information about the properties of a system. To perform symmetry analysis, a code was developed that takes the orbitals of atomic structures, as calculated with Density Functional Theory simulations, as input. Specifically, the orbitals of molecules, and defects in solids are in focus. The symmetry analysis code calculates overlap of orbitals and their symmetry transformed counterpart, maps these overlaps to characters, finds the irreducible representations, and also finds which optical transitions are allowed. The code was tested on CH4 and SF6 molecules, and the divacancy defect in 4H-SiC. The symmetry analysis is performed easily and produces results that coincide well with other theoretical results. Furthermore, symmetry matrices can be approximated to be integer matrices, and the wave functions can be approximated with less accurate plane wave expansions by reducing the cutoff energy, and thus reducing the number of plane waves. These approximations shorten the calculation time and do not compromise the accuracy of the overlap. The code automates the symmetry analysis and is intended to be used in a high-throughput manner. / <p>2021-10-12          </p><p>The student thesis was first published online. </p><p>2022-02-25          </p><p>The student thesis was updated with an errata list which is downloadable from the permanent link.</p>

Elektromagnetisk provning (ECT) har blivit en av de mest använda metoderna för attkvalitetssäkra produkter där icke-destruktiv testning av material krävs. Vid provning av ståltrådinteragerar utrustningen med det testade materialet och upptäcker ytdefekter och, i vissomfattning, om strukturen skiljer sig från det kalibrerade provet. Om produkten befinner sigutanför specifikationen, skrotas den eller ombearbetas. Denna Mastersuppsats undersökerkalibreringsförfarandet för elektromagnetisk provning som utförs av Suzuki Garphyttan, som är enav de största tillverkarna i världen av ventil- och transmissionsfjädrar för bilindustrin. Genom deslutsatser som framgår av denna rapport, som baseras på undersökningar gjorda i vetenskapligaartiklar och genom att analysera den data som inhämtats från produktionen, presenteras enstandardisering av kalibreringsförfarandet. Detta är nödvändigt för att säkerställa såväl testernastillförlitlighet, såväl som minimering av risken för att skrota ut material på grund av felaktigainställningar, till exempel på grund av otillräckligt signal-brus (S/N)-förhållande. Fokus ligger påsond-baserad, roterande testning, i denna avhandling kallad circografen, eftersom den är manuelltkalibrerad.Några av de konstaterade resultaten i rapporten är:● Standard Operating Procedures (SOP)-baserade instruktioner implementeras i företagetskvalitetssystem. Detta för att minska variationer i kvalitet mellan olika operatörer ochmaskiner.● Ett förslag på intervaller för värden (fasvinkel, förstärkning, korrigering av filter och såvidare) presenteras. Detta är baserat på insamlad unik produktionsdata från operatöreroch utförda test.● Fasvinklarna som används varierar inom specifika intervaller och bestäms av materialval iallmänhet och frekvensval i synnerhet.● Konduktivitets- och permeabilitetsvärdena för oljehärdad tråd, liksom penetrationsdjupetför tre olika frekvenser presenteras.● Härdningsfel kan inte detekteras i roterande provning● Ökat kolinnehåll minskar den elektriska ledningsförmågan och ger ökad resistivitet, vilketgör att fasen flyttar sig och resulterar i ett minskande gap mellan brussignal ochspricksignal. / Eddy current testing (ECT) has become one of the most extensively used methods to secure theproducts and constructions when non-destructive testing is required. In typical cases of steel wiretesting, the equipment interacts with the tested material and discovers surface defects and, to alimited extent, if the inner structure is differing from the calibration sample. If the product isfound to be outside specification, it is either scrapped or reworked. This master thesis investigatesthe Eddy current testing calibration procedures performed by steel wire manufacturer SuzukiGarphyttan, which is one of the largest producers in the world of valve and transmission springwire for the automotive industry. By the research shown in this thesis, based on the investigationmade in scientific papers and by analyzing data extracted from production, a standardization ofthe calibration procedure is being presented. This is to secure both the testing reliability, andminimizing the risk of scrapping material due to inaccurate settings, for example due toinsufficient signal to noise (S/N) ratio. The focus is on probe-based, rotating testing, in this thesiscalled the circograph, since it is manually calibrated.Some of the findings established in the report: Standard Operating Procedures (SOP) based instructions is being implemented in thecompany's Quality system. This is to decrease the process variations between differentoperators and machines. Suggestions of intervals for values (Phase angle, gain, filter correction and so forth) arepresented. These values are based on collected unique production data from operators andmachines, as well as performed tests. The phase angles used are ranging between specific value intervals, and set by materialchoice in general and choice of frequency in particular. The conductivity and permeability values for oil-tempered wire, as well as penetrationdepth for three different frequencies, are presented. Hardening error cannot be detected in the circograph. Increased carbon content is decreasing conductivity and increasing resistivity, causing thephase to move slightly and decreasing the gap between noise signal and crack signal.

Crack, a typical term most people know, is a common form of distress or damage in road pavements and railway sleepers. It poses significant challenges to their structural integrity, safety, and longevity. Over the years, researchers have developed various data-driven technologies for image-based crack detection in road and sleeper applications. The image-based crack detection has become a promising field.  Many researchers use ensemble learning to win the Road Damage Detection Challenge. The challenge provides a street view dataset from several countries from different perspectives. The version of the dataset is 2020, which contains images from Japan, India, and Czech. Thus, the dataset inherits a domain shift problem. Current solutions use ensemble learning to deal with such a problem. Those solutions require much computational power and challenge adaptability in real-time applications. To mitigate the problem, the thesis experiments with various data augmentation techniques that could improve the base model performance. The main focuses are erasing a crack from an image using generative AI (Erase), implementing road segmentation by using the Panoptic Segmentation (RS) and injecting a perspective-aware synthetic crack (InjectPa) into the segmented road surface in the image. The results show that compared to the base model, the Erase + RS techniques improve the model's F1 score when trained only on Japan in the dataset rather than when trained on three countries simultaneously. Moreover, the InjectPa technique does not help improve the base model in both scenarios. Then, the experiment moved to the SBB dataset containing close-up images of sleepers from cameras mounted in front of the diagnostic vehicle. This section follows the same techniques but changes the segmentation model to the Segment Anything Model (SAM) because the previous segmentation model was trained on a street view dataset, making it vulnerable to close-up images. The Erase + SAM techniques show improvement in bbox/AP and validation loss. Nevertheless, it does not improve the F1 score significantly compared to the base model.  This thesis also applies the explainable AI name D-RISE to determine which feature most influences the model decision. D-RISE shows that the augmentation model can pay attention to the damage type pothole for road pavements and defect type spalling for sleepers than other types. Finally, the thesis discusses the results and suggests a strategy for future study. / Sprickor, en typisk term som de flesta känner till, är en vänlig form av skador i vägbeläggningar och järnvägsslipers. Det innebär betydande utmaningar för strukturella integritet, säkerhet och livslängd. Under årens lopp har olika datadrivna tekniker utvecklats för bildbaserade sprickdetektering i vägbeläggningar och järnvägsslipers applikationer. Den bildbaserade sprickdetekteringen har blivit ett lovande område. Många forskare använder ensembleinlärningsmodeller för att vinna den Road Damage Detection Challenge (Vägbeläggningar Detektering Utmaning). Utmaningen ger en Gatuvy dataset från flera länder från olika perspektiv. Versionen av datasetet är 2020 som innehåller bilder från Japan, Indien och Tjeckien. Därför ärver datasetet  ett domänskiftproblem. Nuvarande lösningar använder ensembleinlärning för att hantera ett sådant problem. Dessa lösningar kräver mycket datorkraft och utmanar anpassningsförmågan i realtidsapplikationer. För att mildra problemet, denna avhandling prover många tekniker för dataökningar som kan förbättra basmodellens prestanda. Huvudfokusen är att radera en spricka från en bild via en generativ AI (Erase), implementera vägyta segmentering via den Panoptic Segmentation (RS), lägga en persective-aware syntetik spricka (InjectPa) till segmenterade vögytan in bilden. Resultaten visar att den Erase + RS ökningsteknikerna förbättrar modellens F1 score när den tränas på Japan i datasetet i stället för att tränas alla länder samtidigt. Dessutom förbättrar den InjectPa tekniken inte basmodellen på båda fallen.  Därefter flyttades experimentet till SBB-datasetet som innehåller närbilder av järnvägsslipers från kameror monterades framför ett diagnosfordon. Denna section följer de samma teknikerna men ändra segmentering modellen till den Segment Anything Model (SAM) eftersom förra segmentering modellen tränades på en Gatuvy dataset vilket gör den sårbar för närbilder. Den Erase + SAM ökningsteknikerna visar förbättringar på bbox/AP och validering. Ändå förbättrade den inte F1 score avsevört jämfört med basmodellen.  Denna avhandling tillämpar också Förklarbar AI-namnet D-RISE för att avgöra vilken funktion som mest påverkar modellbeslutet. D-RISE visar att modellen som har dataökning kan uppmärksamma skadetypen potthål för vägbeläggningar och defekttypen spjälkning för järnvägsslipers än andra typer. Slutligen diskuterar avhandlingen resultaten och föreslår en strategi för framtida arbetsinsatser.

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