Magister Artium (Development Studies) - MA(DVS) / In this study, I explore recidivism, an ‘uncontrollable phenomenon’ in South Africa, within the context of rising crime and repeat offenders. Research has shown that the political economy of South Africa, inequality, persistent poverty within previously marginalised communities, high school drop-out rates among other factors, creates a breeding ground for criminal activity amongst adolescents, resulting in high convictions and prison sentences in the Western Cape. It is within this context that this study investigated recidivism and reasons for recurring trends, where first time offenders are more likely to return to prison after being released. Specifically, the study investigates why young men from Hanover Park are more likely to be repeat offenders and continuously incarcerated.

Indiana University-Purdue University Indianapolis (IUPUI) / Previous research has established that supportive work and family environments are critical in helping employees manage stressors that lead to work-family conflict. However, little is known about alternate ways that work-family conflict can be reduced in situations where support is insufficient. Drawing on Conservation of Resources theory, this study examines whether individual differences in personality, specifically core self-evaluations (CSE), can relieve work-family conflict when external sources of support (i.e., family-supportive organizational perceptions (FSOP), supervisor support, family support) are low. Results from 453 men and women in various industries and organizations suggest that FSOP and supervisor support reduce work-to-family conflict (WFC), and that family support reduces family-to-work conflict (FWC). In addition, work-family conflict mediated the negative relationships between social support and employee burnout. Contrary to predictions, however, instead of compensating for low FSOP, WFC was reduced especially for individuals, particularly men, who had both strong FSOP and high CSE. This implies that men who hold more positive views toward their self-worth and competence stand to gain more from family-supportive work environments than individuals who lack the same internal resources. CSE did not have this boosting influence for women. CSE also moderated the indirect relationship between FSOP and burnout through WFC, meaning that individuals with high CSE who also perceived their organization as family-supportive experienced significantly less burnout than those with low CSE.

In Canada, the natural birth rate is declining in part because of delayed childbirth (Canada 2016). As a result of their decision to postpone parenthood and their increasing age, more couples are turning to assisted reproductive treatment (ART) including in-vitro fertilization (IVF) to conceive. The risk of IVF failure, the detrimental mental health outcomes (i.e. depression) associated with infertility, and the strain on physical, financial, spiritual, and emotional resources contribute to heightened stress for IVF patients, and compel them to identify and leverage psychosocial supports. Since the quality of social support individuals receive depends on the nature of communication they share with the receiver, it is important to consider how disclosure builds social support. Common social support channels such as spouses, family, friends, counsellors, and support groups and their associated communication patterns have already been explored in the fertility literature whereas processes integral to peer relationships, a support that women have signaled as promising, remain uncovered. It remains important to understand the nature of peer relationships in the context of infertility. This dissertation explores how in-vitro fertilization (IVF) users approached their decision to disclose to a peer and carried out their communication. Guided by the Disclosure-Decision Making Model (DD-MM) it starts by showing how IVF patients assess their support needs, and peers as recipients before communication takes place. It then delineates how these women execute their communication with specific focus on the modalities of their process and the scope of the disclosures of fertility-related and non-fertility related information between them. A sample population of 23 first-time and recurring IVF patients were interviewed. Results show that prior to disclosing to a peer, women reflect on information about their condition and their personal support needs on the basis of the adequacy of their social circles and perceptions of stigma, the benefits and drawbacks of secrecy versus transparency, and their personal motivation to leverage peer support. They also assess their peers, considering diverse pathways of connection as well as desirable peer traits which include IVF experience, other common ground and transparency. Relating to their communication, patients showed a distinct capacity to communicate with their peers and meet their support needs. The IVF patient-peer communication process is characterized through immediate disclosure transitions and backwards introductions, a solid mutual understanding when it comes to engagement and disengagement boundaries, a preference for digital communication via texting and instant messaging, and the coverage of a broad range of fertility topics in reciprocal conversations. The findings also show however that the majority of women choose to distance themselves and limit their conversations during the post embryo transfer waiting period and refrain from discussing pregnancy testing as a form of self- preservation. IVF patients share a natural relationship with peers. This dissertation points to opportunities to facilitate patient-peer relationships and enhance the fertility-care experience overall by embracing: the transparency of patients, better coping resources for men, safe places to personally connect in clinic and support group settings, and roles for all IVF patients in social support regardless of their outcome. It also suggests that patient-peer support is a pragmatic and flexible support channel that when managed properly can respond to patients’ personal disclosure and communication needs and preferences.

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A Thesis submitted to the Faculty of Science, University of the Witwatersrand, Johannesburg, in fulfilment of the requirements for the degree of Doctor of Philosophy, 2017 / The utility of lectures is directly related to the engagement of the students therein. To ensure the value of lectures, one needs to be certain that they are engaging to students. In small classes experienced lecturers develop an intuition of how engaged the class is as a whole and can then react appropriately to remedy the situation through various strategies such as breaks or changes in style, pace and content. As both the number of students and size of the venue grow, this type of contingent teaching becomes increasingly difficult and less precise. Furthermore, relying on intuition alone gives no way to recall and analyse previous classes or to objectively investigate trends over time. To address these problems this thesis presents the WITS INTELLIGENT TEACHING SYSTEM (WITS) to highlight disengaged students during class. A web-based, mobile application called Engage was developed to try elicit anonymous engagement information directly from students. The majority of students were unwilling or unable to self-report their engagement levels during class. This stems from a number of cultural and practical issues related to social display rules, unreliable internet connections, data costs, and distractions. This result highlights the need for a non-intrusive system that does not require the active participation of students. A nonintrusive, computer vision and machine learning based approach is therefore proposed. To support the development thereof, a labelled video dataset of students was built by recording a number of first year lectures. Students were labelled across a number of affects – including boredom, frustration, confusion, and fatigue – but poor inter-rater reliability meant that these labels could not be used as ground truth. Based on manual coding methods identified in the literature, a number of actions, gestures, and postures were identified as proxies of behavioural engagement. These proxies are then used in an observational checklist to mark students as engaged or not. A Support Vector Machine (SVM) was trained on Histograms of Oriented Gradients (HOG) to classify the students based on the identified behaviours. The results suggest a high temporal correlation of a single subject’s video frames. This leads to extremely high accuracies on seen subjects. However, this approach generalised poorly to unseen subjects and more careful feature engineering is required. The use of Convolutional Neural Networks (CNNs) improved the classification accuracy substantially, both over a single subject and when generalising to unseen subjects. While more computationally expensive than the SVM, the CNN approach lends itself to parallelism using Graphics Processing Units (GPUs). With GPU hardware acceleration, the system is able to run in near real-time and with further optimisations a real-time classifier is feasible. The classifier provides engagement values, which can be displayed to the lecturer live during class. This information is displayed as an Interest Map which highlights spatial areas of disengagement. The lecturer can then make informed decisions about how to progress with the class, what teaching styles to employ, and on which students to focus. An Interest Map was presented to lecturers and professors at the University of the Witwatersrand yielding 131 responses. The vast majority of respondents indicated that they would like to receive live engagement feedback during class, that they found the Interest Map an intuitive visualisation tool, and that they would be interested in using such technology. Contributions of this thesis include the development of a labelled video dataset; the development of a web based system to allow students to self-report engagement; the development of cross-platform, open-source software for spatial, action and affect labelling; the application of Histogram of Oriented Gradient based Support Vector Machines, and Deep Convolutional Neural Networks to classify this data; the development of an Interest Map to intuitively display engagement information to presenters; and finally an analysis of acceptance of such a system by educators. / XL2017

Bakgrund: Prostatacancer är en av de vanligaste cancerformerna och år 2020 drabbades 1,4 miljoner män runt om i världen. Det finns många olika behandlingsmetoder och varje metod har sina komplikationer som kan påverka mannens liv drastiskt. Det är betydelsefullt att sjuksköterskor förstår mäns upplevelser av livsstilsförändringar relaterade till medicinska behandlingar och komplikationer. Genom att öka denna kunskap kan sjuksköterskor erbjuda personcentrerad vård och omvårdnad som är anpassad efter varje patients unika behov. Sjuksköterskor spelar en avgörande roll för att hjälpa dessa män. Det är väsentligt med personcentrerad vård för att minska lidandet hos patienterna. Syftet: Syftet med denna litteraturstudie är att sammanställa patienternas erfarenheter att leva med prostatacancer. Metod: Litteraturstudien bygger på tolv vetenskapliga artiklar av kvalitativ ansats. Artiklarna hämtades från två databaser inom omvårdnad och medicin. Artiklarna analyserades genom en tre-stegs modell som innebär att identifiera resultatet som sedan delas upp och kodas om till huvud- och underkategorier. Artiklarna kvalitetsgranskats med hjälp av SBU:s granskningsmall. Resultat: Resultatet baserades på tolv vetenskapliga artiklar med kategorierna En förändrad självbild och Strategier för att hantera vardagen och tillhörande sexunderkategorier. Konklusion: Män som lever med prostatacancer lider fysiskt, psykiskt och emotionellt, vilket innebär drastiska livsförändringar. Sexualitet och kroppsliga förändringar gör att män med prostatacancer lever med ständig oro. Män upplever att de inte får tillräckligt med stöd från omgivningen. Sjuksköterska spelar en väsentlig roll genom att ge korrekt och tillräcklig information. / Background: Prostate Cancer is one of the most common cancers among men with over 1,4 million incidents in the year 2020 worldwide. It’s significant that nurses understand lifestyle changes related to medical treatment and complications. By increasing this knowledge nurses can offer person-centered care and customized care to each patient's unique needs. There are many treatment options, and every treatment has its complications that can affect a man's life severely. Nurses play a crucial role in helping those men. It's crucial with person-centered-care to reduce suffering in patients. Aim: The purpose of this literature review is to comprehensively map and synthesize the existing literature on men's experiences to live with prostate cancer Methods: This literature study is based on twelve scientific articles with aqualitative approach. The articles were retrieved from two databases that specify in nursing and were analyzed using three steps which involves identifying the result and breaking it down into different aims until themes and subcategories submerge. The articles were evaluated for quality using SBU review temple. Results: From the analysis two themes emerged: A changed self-image and Strategies to manage everyday life, and based on them six subcategories were identified. Conclusion: Men living with prostate cancer suffer physically, mentally, and emotionally. Sexuality and body image are common concerns for prostate cancer patients. Information is of great importance, where the nurses play a decisive role. Men feel like they don't get enough support from the environment.

Data generated by complex, computational models can provide highly accurate predictions of hydrological and hydrodynamic data in multiple dimensions. Unfortunately, however, for large data sets, running these models is often timeconsuming and computationally expensive. Thus, finding a way to reduce the running time of these models, while still producing comparable results, is of notable interest. The Inference Engine is a proposed system for doing just this. It takes previously generated model data and uses them to predict additional data. Its performance, both accuracy and running time, has been compared to the performance of the actual models, in increasingly difficult data prediction tasks, and it is able, with sufficient accuracy, to quickly predict unknown model data.

This study proposes a nitinol-based thin-walled arch wedge support (AWS) and validated its performance in shock absorbing by using finite element analysis (FEA) method. Five human movements, two boundary conditions, and three thicknesses were implemented in FEA models to systematically reveal how those parameters and factors affect the response of the AWS. Due to the lack of data, the FEA models were meshed with elements of different sizes and used for simulations until the results converged. The simulation results showed that the thin-walled nitinol AWS with the selected thicknesses can withstand different human movements under both boundary conditions. In another word, the AWS will retain its original shape give the force conditions with no permanent deformation. Based on the initial numerical results, the AWS design can be further optimized before experimentation and testing. The potential of replacing the plastic AWS with additive manufactured nitinol AWS is verified from this study.