In today’s fast paced technological world learners are under a great deal of pressure to obtain their grade 12 certificates. These learners are also faced with a number of life events that add to the stress they experience during the preliminary and final grade 12 examinations. This stress or anxiety increases when they fail their preliminary grade 12 examinations. This study focuses on these learners and the stress and anxiety that they experience after failing their grade 12 preliminary examinations. Guidelines are suggested to support and prevent learners from failing their grade 12 preliminary examinations. The research design adopted for this study was qualitative, exploratory and descriptive in nature. Phenomenological interviews were used for data collection and field notes were made to support the interviews. The data were analysed and coded with verification from an independent coder. In phase one, three themes were identified, discussed and supported by a literature control. In phase two the findings were used to suggest guidelines for the grade 12 learners. The findings of this study showed that grade 12 learners, writing their examinations, experience stress and anxiety physically, psychologically and emotionally. They describe rather alarming symptoms of this stress and anxiety. The learners also appear to have ambivalent feelings towards their future orientation during this stressful time. The guidelines suggested are aimed at managing the stress and anxiety as well as learning to develop support through effective communication skills and relationship building. It is hoped that application of these guidelines will support learners and prevent failure during the preliminary examinations thereby alleviating much of the stress and anxiety that learners experience during their examinations. / Prof. C.P.H. Myburgh

No / Effective maintenance is a key for infrastructures’ high operational reliability. The integration of corrective repairs and schedule-based failure preventions has been a mainstream of modern maintenance, and an associated policy-making technique, delay-time modelling, is overviewed in this paper for optimising the maintenance cost-efficiency in different practical scenarios, including imperfect, opportunistic and nested maintenance. A few typical examples of its applications in minimising maintenance operating expenses are discussed in this paper and their results are explained to better demonstrate the benefits of the technique. This work aims to prepare for the future applications of the delay-time modelling in railway maintenance policy making.

Most engineering curricula in the United States include some form of major design project experiences for students, such as capstone courses or design-build-fly projects. Such courses are examples of project-based learning (PBL). Part of PBL is to prepare students—and future engineers—to deal with and prevent common project failures such as missing requirements, overspending, and schedule delays. <i>But how well are students performing once they join the workforce?</i> Unfortunately, despite our best efforts to prepare future engineers as best we can, the frequency of failures of complex projects shows no signs of decreasing. In 2020 only 53% of projects were on time, 59% within budget, and 69% met their goal, as reported by the Project Management Institute. If we want to improve success rates in industry projects, letting students get the most out of their PBL experience and be better prepared to deal with project failures before they join the workforce may be a viable starting point. <br><br>The overarching goal of this dissertation is to identify and suggest improvements to areas that PBL lacks when it comes to preparing students for failure, to investigate student behaviors that lead to project failures, and to improve these behaviors by providing helpful feedback to students. <br><br>To investigate the actions and behaviors that lead to events that cause failures in student projects, I introduced “crowd signals”, which are data collected directly from the students that are part of a project team. In total, I developed 49 survey questions that collect these crowd signals. To complete the first part of the dissertation, I conducted a first experiment with 28 student teams and their instructors in two aerospace engineering PBL courses at Purdue University. The student teams were working on aircraft designs or low-gravity experiments.<br><br><i>Does PBL provide sufficient opportunities for students to fail safely, and learn from the experience? How can we improve?</i> To identify areas that PBL may lack, I compared industry failure cause occurrence rates with similar rates from student teams in PBL courses, and then provided recommendations to PBL instructors. Failure causes refer to events that frequently preceded budget, schedule, or requirements failures in industry, and are identified from the literature. Through this analysis, I found that PBL does not prepare students sufficiently for situations where the failure cause missing a design aspect occurs. The failure cause is fundamentally linked to proper systems engineering: it represents a scenario where, for example, students failed to consider an important requirement during system development, or did not detect a design flaw, or component incompatibility. I provided four recommendations to instructors who want to give their students more opportunities to learn from this failure cause, so they are better prepared to tackle it as engineers. <br><br><i>Is crowdsourced information from project team members a good indicator of future failure occurrences in student projects?</i> I developed models that predict the occurrence of future budget, schedule, or requirements failures, using crowd signals and other information as inputs, and interpreted those models to get an insight on which student actions are likely to lead to project failures. The final models correctly predict, on average, 73.11±6.92% of budget outcomes, 75.27%±9.21% of schedule outcomes, and 76.71±6.90% of technical requirements outcomes. The previous status of the project is the only input variable that appeared to be important in all three final predictive models for all three metrics. Overall, crowdsourced information is a useful source of knowledge to assess likelihood of future failures in student projects. <br><br><i>Does targeted feedback that addresses the failure causes help reduce failures in student projects?</i> To improve student behaviors that lead to project failures, I used correlations between failure measures and the crowd signals as a guide to generate 35 feedback statements. To evaluate whether the feedback statements help reduce project failures in the student teams, I conducted a second experiment at Purdue University with 14 student teams and their instructors. The student teams were enrolled in aircraft design, satellite design, or propulsion DBT courses. The student teams were split in two treatment groups: teams that received targeted feedback (i.e., feedback that aimed to address the failure causes that the specific team is most prone to) and teams that received non-targeted feedback (i.e., feedback that is positive, but does not necessarily address the failure causes the specific team is most prone to). Through my analysis, I found that my targeted feedback does not reduce the failure occurrences in terms of any metrics, compared to the non-targeted feedback. However, qualitative evaluations from the students indicated that student teams who received targeted feedback made more changes to their behaviors and thought the feedback was more helpful, compared to the student teams who received non-targeted feedback.<br><br>

The primary purpose of the study was to explore the potential impact of voluntary remediation on success in ENC1101 and MAT1033 (gateway courses) and on minority or low-income students in Florida. The study was prompted in 2013 when the Florida Senate exempted most students entering the Florida College System from placement testing or developmental education regardless of their skill level. A quantitative design compared the gateway course success of 10,703 exempt students in 2014 and 2015 to the success of 8,644 students who would have been exempt had the law been in effect when they completed their gateway courses in 2012 and 2013. Data were collected from three FCS institutions. Using Astin and Astin's 1992 Input-Environment-Outcome model (Astin & Astin, 1992), independent variables included demographics, such as race and Pell grant eligibility, and prior academic performance, as well as enrollment status and remedial course decisions and perf ormance. The study found the policy to have a statistically significant (α = .05) negative effect on student success in the gateway courses. The voluntary remediation policy that was in part enacted to improve college completion rates threatens to have the opposite effect. The results show that fewer proportions of students were successful (grade of C or higher) in both courses once remediation became voluntary (12.8% decrease for English; 19.3% decrease in math). The study revealed a need for further research to investigate the degree of this impact on minority and low-income students. The results also suggested a need for more research to learn which students are likely to benefit, or not, by taking a remedial course. Of the students in the study who voluntarily took a placement test and scored below credit level, 11.3% chose remediation before taking ENC1101 and 24.5% chose remediation before taking MAT1033. Of those students, most who earned an A or B in the remedial course were successful in the credit courses; most who did not earn at least a B in the remedial course were unsuccessful at the credit level. Results were significant (α = .05), and effect sizes were moderate (.344 for English; .430 for math). / Includes bibliography. / Dissertation (Ph.D.)--Florida Atlantic University, 2015. / FAU Electronic Theses and Dissertations Collection

In this thesis, an evaluation of the existing control system for ABB: s HVDC Classic Links is performed in order to investigate whether a possible improvement to commutation failure prediction is possible and to be recommended. The thesis starts with a theoretical approach to the complexity of consequences of increasing the extinction angle (γ) in order to prevent commutation failure in inverter operation, which is later confirmed through using the simulation software PSCAD to evaluate coherence between simulation results and theory. Dynamic power studies are performed through simulations in the electromagnetic time domain transient tool PSCAD in order to establish a possible improvement to the existing commutation failure prediction today used in ABB control systems for HVDC applications.

In the last decades, there has been a growing interest towards new use cases in the Internet of Things (IoT) domain, such as extended reality glasses, unmanned aerial vehicles (UAVs), and autonomous driving. The technological advancement observed in such scenarios has also been enabled by the increasing capabilities of small form factor devices. Although such devices allow to achieve remarkable computing performance with relatively low energy consumption, these are often used in contexts in which the trade-offs between power consumption and application performance play a key role (e.g., battery powered systems). Furthermore, if such trade-offs are not carefully set, the performance degradation can lead to system failure. The work proposed in this thesis aims at investigating this type of problems, and to propose a runtime model and controller pair based on the joint optimization of the platform and application parameters to reduce the likelihood of system failure. The proposed architecture is evaluated in a UAV emulated environment, in which the used platform embeds hardware features comparable to the ones of a drone, while the localization and mapping application executed on such device makes use of real-world visual-inertial datasets. The proposed runtime model-controller solution relies on the monitoring of the platform CPU peaks for identifying application failure. It has also been empirically demonstrated that the model-controller can substantially decrease the number of failures and, in specific scenarios, improve localization accuracy and power consumption even compared to the optimal static parameter configurations. Moreover, the solution has been proven to be simple and generalizable in scenarios characterized by different levels of concurrency, and in the datasets tested. / Under de senaste decennierna har det funnits ett växande intresse för nya användningsfall som Extended Reality-glasögon, obemannade flygfarkoster (UAV) och autonom körning. De tekniska framstegen som observerats i sådana scenarier har också möjliggjorts av den ökande kapaciteten hos små formfaktorenheter. Även om sådana enheter gör det möjligt att uppnå anmärkningsvärd datorprestanda med relativt låg energiförbrukning, används dessa ofta i sammanhang där kompromisserna mellan strömförbrukning och applikationsprestanda spelar en nyckelroll (t.ex. batteridrivna system). Dessutom, om sådana avvägningar inte är noggrant inställda, kan prestandaförsämringen leda till systemfel. Arbetet som föreslås i denna avhandling syftar till att undersöka denna typ av problem, och att föreslå en körtid modellstyrenhet baserad på gemensam optimering av plattformen och applikationsparametrar för att minska systemfel. Den föreslagna arkitekturen utvärderas i en UAV-emulerad miljö, där den använda plattformen har hårdvarufunktioner som är motsvarar en drönare, medan lokaliserings- och kartläggningsapplikationen som körs på en sådan enhet använder verkliga visuella tröghetsdatauppsättningar. Den föreslagna runtime-modellstyrningslösningen förlitar sig på övervakning av plattformens CPU-toppar för att identifiera programfel. Det har också visat sig empiriskt att modellstyrenheten avsevärt kan minska antalet fel och, i specifika scenarier, förbättra lokaliseringsnoggrannheten och strömförbrukningen även jämfört med de optimala statiska parameterkonfigurationerna. Dessutom har lösningen visat sig vara enkel och generaliserbar i scenarier som kännetecknas av olika nivåer av samtidighet och i de testade datamängderna.

Inom svenska järnvägen skapas en stor mängd information om hur objekt så som växlar och spårledningar beter sig, i form av dataloggar. Dessa används i dagsläget mycket sparsamt, trots sin stora potential till analys. Detta arbete ämnar utforma mjukvara som kan förädla dessa loggar och extrahera användbar information om banobjektens nuvarande och framtida tillstånd. Detta kan höja säkerheten och förebygga fel och på så vis minimera oplanerade driftstopp och kostsamma byten eller nödreparationer av utrustning. Arbetet resulterade i en mjukvara framtagen i Python som läser in loggar av godtycklig storlek och detekterar atypiska beteenden i ett flertal kategorier av banobjekt. Mjukvaran fungerar även som ett verktyg för mer användarvänlig hantering av dessa loggar, varur användaren kan sammanställa och presentera information som annars vore svårtillgänglig. / In the Swedish rail network, a large amount of information is generated regarding the behaviours of various objects such as switches and track circuits, which is then stored in logs. Currently, these logs are severely underused despite their great potential for analysis. The purpose of this project is to develop a software which can refine these logs and extract useful information about the current and future states of the objects. This can increase operational safety and prevent faults from occurring, thereby minimizing unplanned downtime and costly replacements or reparations of equipment. The project results in a software developed in Python which reads logs of an arbitrary size and detects atypical behaviours in several different categories of objects. The software also acts as a tool for more user-friendly handling of these logs, offering the ability to compile and present information which would otherwise be difficult to access.