Experimental investigations of high temperature industrial processes, for example the melting and smelting processes taking place inside furnaces, are complicated by the high temperatures and the chemically reactive environment in which they take place. Fortunately, mathematical models can be used in conjunction with the limited experimental results that are available to gain insight into these high temperature processes. However, mathematical models of high temperature processes require high temperature material properties, which are difficult to measure experimentally since container materials are often unable to withstand high enough temperatures, and sample contamination often occurs. These difficulties can be overcome by employing containerless processing techniques such as electromagnetic levitation melting to allow for characterisation of high temperature material properties. Efficient design of electromagnetic levitation cells is challenging since the effects of changes in coil design, sample size and sample material on levitation force and sample temperature are not yet well understood. In this work a numerical model of the electromagnetic levitation cell is implemented and used to investigate the sensitivity of levitation cell operation to variations in coil design, sample material and sample size. Various levitation cell modelling methods in literature are reviewed and a suitable model is chosen, adapted for the current application, and implemented in Python. The finite volume electromagnetic component of the model is derived from Maxwell’s equations, while heat transfer is modelled using a lumped parameter energy balance based on the first law of thermodynamics. The implemented model is verified for a simple case with a known analytical solution, and validated against published experimental results. It is found that a calibrated model can successfully predict the lifting force inside the levitation cell, as well as the sample temperature at low coil currents. The validated model is used to characterise the operation of a levitation cell for a number of different sample materials and sample sizes, and for variations in coil geometry and coil current. The model can be used in this way to investigate a variety of cases and hence to support experimental levitation cell design. Based on model results, a number of operating procedure recommendations are also made. / Dissertation (MEng)--University of Pretoria, 2016. / Mechanical and Aeronautical Engineering / MEng / Unrestricted

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A large, multi-basin reservoir (Garza-Little Elm Reservoir) in north central Texas was studied to determine the relative effects of various parameters on primary productivity. The basins were impounded several years apart, thus allowing the influence of age on water chemistry and biota to be considered. Another principal influence on water quality was secondary sewage effluent that entered one basin from a nearby source.

Sammanfattning Utformningen av en jobbannons och dess innehåll är ett viktigt verktyg för att att lyckas attrahera den rätta kandidaten för den utlysta tjänsten. Det finns få studier som syftar till att undersöka hur personen som läser en jobbannons tilltalas eller inte av dess innehåll. Denna kvantitativa studie är baserad på 100 enkätsvar som har analyserats för att besvara studiens syfte och frågeställningar om vad deltagarna finner tilltalande och inte i två olika jobbannonser samt vilka arbetsvärderingar som är viktiga i utvärderande av en jobbannons.  I studiens teoretiska referensram introduceras läsaren i ämnet genom en bakgrund och tillbakablick på rekryteringsprocessens historia. Därefter följer en redogörelse för teorierna ELM, Signalteorin och lack of fit-teorin. Slutligen redogör vi för arbetsvärderingar och idealtypiska attityder till arbete. I metodavsnittet redogörs för datainsamling, urvalet, enkätens utformning och hur datan har bearbetats.  I studiens resultat och analys redovisas vad deltagarna har markerat som mest och minst tilltalande i de båda jobbannonserna. Här redovisas även för vad deltagarna har markerat som viktiga arbetsvärderingar.  Resultatet visar att de finns likheter mellan könen så väl som studieinriktingarna men också olikheter.  Det var vanligare bland de manliga deltagarna att markera de alternativ som syftade till utmaning och utveckling som de mest tilltalande alternativen och vanligare bland de kvinnliga deltagarna att markera de alternativ som syftade till mellanmänskliga relationer. Liknande tendenser gick att se mellan de studieinriktningar som är mer uppgiftsorienterade och  de som är mer människoorienterade. Den sammanfattande diskussionen sammanställer slutsatserna för att besvara frågeställningarna och studiens syfte. Avslutningsvis förs en diskussion gällande studiens för- och nackdelar och hur fortsatta forskningsfrågor skulle kunna se ut.

A new scheme for training Rectified Linear Unit (ReLU) based feedforward neural networks is examined in this thesis. The project starts with the row-by-row updating strategy designed for Single-hidden Layer Feedforward neural Networks (SLFNs). This strategy exploits the properties held by ReLUs and optimizes each row in the input weight matrix individually, under the common optimization scheme. Then the Direct Updating Strategy (DUS), which has two different versions: Vector-Based Method (VBM) and Matrix-Based Method (MBM), is proposed to optimize the input weight matrix as a whole. Finally DUS is extended to Multi-hidden Layer Feedforward neural Networks (MLFNs). Since the extension, for general ReLU-based MLFNs, faces an initialization dilemma, a special structure MLFN is presented. Verification experiments are conducted on six benchmark multi-class classification datasets. The results confirm that MBM algorithm for SLFNs improves the performance of neural networks, compared to its competitor, regularized extreme learning machine. For most datasets involved, MLFNs with the proposed special structure perform better when adding extra hidden layers. / Ett nytt schema för träning av rektifierad linjär enhet (ReLU)-baserade och framkopplade neurala nätverk undersöks i denna avhandling. Projektet börjar med en rad-för-rad-uppdateringsstrategi designad för framkopplade neurala nätverk med ett dolt lager (SLFNs). Denna strategi utnyttjar egenskaper i ReLUs och optimerar varje rad i inmatningsviktmatrisen individuellt, enligt en gemensam optimeringsmetod. Därefter föreslås den direkta uppdateringsstrategin (DUS), som har två olika versioner: vektorbaserad metod (VBM) respektive matrisbaserad metod (MBM), för att optimera ingångsviktmatrisen som helhet. Slutli- gen utvidgas DUS till framkopplade neurala nätverk med flera lager (MLFN). Eftersom utvidgningen för generella ReLU-baserade MLFN står inför ett initieringsdilemma presenteras därför en MLFN med en speciell struktur. Verifieringsexperiment utförs på sex datamängder för klassificering av flera klasser. Resultaten bekräftar att MBM-algoritmen för SLFN förbättrar prestanda hos neurala nätverk, jämfört med konkurrenten, den regulariserade extrema inlärningsmaskinen. För de flesta använda dataset, fungerar MLFNs med den föreslagna speciella strukturen bättre när man lägger till extra dolda lager.

In north Mississippi, I evaluated fuel loads in the late dormant season and after leaf fall in unburned areas and after a dormant season burn. Fuel loads beneath winged elm (Ulmus alata) were assessed to determine if this shade-tolerant species impacts fine fuel loads in the area immediately near its bole. I found leaf litter fuel loads are higher after leaf fall suggesting that burns conducted closer to leaf fall may have increased burn intensity. Burning reduced leaf litter fuels and exposed mineral soil which returned to pre-burn levels following leaf fall. Declines in duff layer fuels were not evident until after leaf fall. Leaf litter fuels underneath winged elms had higher mass and percentage of winged elm litter beneath them compared to areas away from them. These results will help determine appropriate time for restoring prescribed fire and the implications of encroachment by non-oak species into upland oak forests.

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Structure editors have been available for many decades, and for multiple programming languages. Historically, they have been recommended for teaching new programmers. Currently, they are recommended by advocates of Model Driven Development. However, they are not widely used, except for the special case of graphical structure editors commonly referred to as "block-based editors" such as Scratch. Although structure editors were first introduced for procedural languages, and they could be used for any type of language, current structure editors target object-oriented languages, almost exclusively, and build in many assumptions related to object orientation. The notable exception, Hazel, targets a functional language, exploits the strong typing typical of functional languages and emphasizes the use of typed holes. This thesis introduces ElmSr, a structure editor developed for teaching the Elm programming language to novice programmers. As with most structure editors, ElmSr allows the developer to directly edit the Abstract Syntax Tree, without the intermediary of a compiler. As with Hazel, ElmSr's AST is typed, and transformations preserve types. Also typical of tree editors written in functional languages, ElmSr uses a zipper data structure to encode both the tree and a cursor position, making for efficient tree edits. Like other structure editors, ElmSr is designed to make common tasks simple and efficient. In our case, common tasks match the steps students are taught in the "Algebraic Thinking" curriculum developed at McMaster University. Some steps are common to almost all programming, such as arithmetic expression entry and modification (which has been previously identified as a weak point for structure editors). Other steps, like definition integration, and support for function calls and control structures are specific to our curriculum. This aspect of usability was evaluated by comparing the number of keystrokes necessary for a benchmark task, using ElmSr, using VS Code following our structured development approach, and using VS Code solely for text entry. ElmSr was much more efficient than VS Code for structured editing, and still more efficient than linear code entry. / Thesis / Master of Science (MSc)

Have You Ever Had Broken Heart? is a collection of essays that interrogate memory, loss, and grief through the intersection of personal narrative, films, the actress Frances Farmer, and woman saints and mystics from the twelfth through seventeenth centuries who were punished for daring to speak to G-d. The essays engage with autotheory and include a myriad of forms, such as segmented, one sentence, and hybrid works. The films discussed range from the philosophical, such as Ingmar Bergman's Winter Light (1963), to Graeme Clifford's biopic, Frances (1982), to catechize the grief of the persona losing her mother and sister to a hit and run car wreck in June 2022. The persona traverses the realm of the mystics and saints, including Marguerite Porete, Sor Juana Inez De La Cruz, and Joan of Arc, examining their respective quests to experience the unseen and often silent divine, while questioning her longing for G-d, and simultaneously believing G-d cannot exist. Yet, within this confusion, she finds herself immersed in memories which carry the presence of her mother's love.