This study focuses on the effects of mixing temperature, binder content, and dust content on the performance of Stone Matrix Asphalt (SMA). Mixes produced at different mixing temperatures and with or without added polymer modified binder (Pb) and/or bag house dust (BHD) were evaluated. Two different types of specimens: laboratory mixed laboratory compacted, and plant mixed laboratory compacted were evaluated by Cantabro Mass Loss (CML) testing in an unaged condition or after a laboratory conditioning protocol (CP) was applied. Additional Pb and BHD were added to plant produced mixes in the laboratory before mixing. Results showed that mixing temperature affected behavior in some cases. Specimens with lower air void (Va) levels performed better than those with higher Va levels. Additional polymer modified binder and BHD, when added together, meaningfully improved mixture results in terms of CML values.

American Indian tribes face the phenomenon known across the world as the brain drain. They invest millions of dollars in educating their members, only to have little return on their investments. Many nation members leave reservations to get postsecondary education but never return, contributing to the brain drain. Those who get education off the reservation and choose to return are the exceptions to this rule. Although there is an abundance of literature regarding the brain drain across the world, there has been little research done with American Indians. In order to begin to understand the brain drain phenomenon this study analyzed unstructured qualitative interviews of 17 Navajo Nation members who left their reservation, obtained a degree and returned to work on the reservation. Themes resulting from the hermeneutic analysis of texts that describe the reason why these individuals returned were (a) Family Support, (b) Cultural Identity, (c) Simple Lifestyle, (d) Community and (e) Reservation Economy. The analysis found that constant, lengthy, and meaningful relationships were motivating factors in drawing participants back to contribute to their reservations. Those principles and teachings in the home of these returnees prompts further research in identifying the reasons they were drawn back to their American Indian communities.

The “brain drain” phenomenon encompasses the mass movement of highly educated individuals. Highly-skilled and well-educated migrants are moving to more developed and urban settings, often in search of a higher standard of living and better wages. Since joining the European Union and the Schengen Agreement, Poland and Romania have experienced significant emigration which has subsequently affected their health care systems. Motivations for emigrating from these two countries and the effects emigration has had on patients and other doctors will be considered. The paper also seeks to compare policy responses to the mass medical emigration phenomenon in both countries, as well as the outcomes of such policies. The main methodology of study throughout this project entails a comparative assessment of the governmental policy responses to brain drain. An analysis of Poland’s and Romania’s health care systems will be performed initially. The analysis includes details on the causes and factors that bring about migration, the impact that emigration has had on patients, how doctors remaining in the sending country are affected, and what social upheavals and unrest result from such emigration. Following, the levels and flows of migration are considered for each country, looking at the type of people leaving, whether educated or not, and the range of professions, with a focus on health professionals that are migrating from both countries. After an analysis has been performed for both countries, the results will be compared to one another, paying special attention to any differences and potential reasons for these differences.

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Yes / In recent years, deep learning has been increasingly used for several applications such as object analysis, feature extraction and image classification. This paper explores the use of deep learning in a flood monitoring application in the context of an EC-funded project, Smart Cities and Open Data REuse (SCORE). IoT sensors for detecting blocked gullies and drainages are notoriously hard to build, hence we propose a novel technique to utilise deep learning for building an IoT-enabled smart camera to address this need. In our work, we apply deep leaning to classify drain blockage images to develop an effective image classification model for different severity of blockages. Using this model, an image can be analysed and classified in number of classes depending upon the context of the image. In building such model, we explored the use of filtering in terms of segmentation as one of the approaches to increase the accuracy of classification by concentrating only into the area of interest within the image. Segmentation is applied in data pre-processing stage in our application before the training. We used crowdsourced publicly available images to train and test our model. Our model with segmentation showed an improvement in the classification accuracy. / Research presented in this paper is funded by the European Commission Interreg project Smart Cities and Open Data REuse (SCORE).

Yes / Much attention has been paid to making 5G developments more en-ergy efficient, especially in view of the need for using high data rates with more complex modulation schemes within a limited bandwidth. The concept of the Doherty power amplifier for improving amplifier efficiency is explained in addi-tion to a case study of a 70W asymmetrical Doherty power Amplifier using two GaN HEMTs transistors with peak power ratings of 45W and 25W. The rationale for this choice of power ratio is discussed. The designed circuit works in the 3.4GHz frequency band with 200 MHz bandwidth. Rogers RO4350B substrate with dielectric constant εr=4.66 and thickness 0.035 mm is used. The perfor-mance analysis of the Doherty power amplifier is simulated using AWR MWO software. The simulated results showed that 54-64% drain efficiency has been achieved at 8 dB back-off within the specified bandwidth with an average gain of 10.7 dB.

The thesis aimed at explaining why some Zimbabwean teachers have migrated to South Africa while others have not despite experiencing the same economic and political crisis. The focus was on external secondary brain drain, which is the movement of human resources from one country to another within the Southern African Development Community region (SIRDIC, 2008). It was premised on the theoretical argument that uneven development in the SADC region sustains the movement of human resources from the poorer countries to the richer or ‘core’ countries in the region particularly South Africa. The thesis reviewed literature on the Zimbabwean crisis and conducted a quantitative field survey, which was supplemented by a qualitative aspect, in order to analyse the determinants of teacher migration to South Africa. The field survey involved the self-administration of questionnaires by 200 Zimbabwean teachers, half of them teaching in South Africa and the other half in Zimbabwe, as well as collected life stories from five migrant teachers, interviewed four school heads, and perused circulars. The research found that Zimbabwe’s reversed economic growth and social development constituted the background on which teacher migration occurred. This brain drain, which mainly involved highly qualified and specialised mathematics and science teachers, coincided with the peak of the Zimbabwean crisis around 2008 indicating its survival significance. Teacher migration continued after 2008 due networks and teachers’ salaries that remained inadequate as they were close to the poverty line. Several recommendations were made including strategies for reducing the brain drain. / Development Studies / D. Litt. et Phil. (Development studies)

Zimbabwe is suffering from an acute shortage of teachers mainly as a result of teacher emigration to South Africa and abroad. The southern migration of Zimbabwean teachers has received little research attention which has mainly focused on the migration of medical personnel. The purpose of this study is to uncover the factors that drive the migration of teachers from Zimbabwe to South Africa and to explain how these factors function within a grounded theory approach to teacher migration. To achieve this, a review of literature and an empirical study of a small sample of migrant Zimbabwean teachers resident in South Africa were undertaken. A constructivist grounded theory design was used. A theoretical sampling method generated a sample group of thirteen participants who were all qualified Zimbabwean teachers who had migrated to South Africa and been in the country for between one and five years. Data generation and collection consisted of two phases: in the first phase the participants were asked to write a life history narrative or provide a verbal narrative of their life history focussing on their migration. In the second phase they participated in individual interviews to clarify or expand on issues raised in the first phase. Three stages of coding were used in the analysis of the data, namely initial, intermediate and advanced coding. This led to the generation of a grounded theory on teacher migration. The grounded theory indicated that Zimbabwean teachers see migration as the best way to attain an ideal status. An ideal status is conceived to be the ideal interplay between the work conditions, standard of living and social esteem which teachers believe befits members of their profession. Depending on various criteria, teachers fall into one of the following status categories: further diminished status, diminished status, ideal status or ideal status surpassed. Migration is a drastic and demanding way to improve one’s status and it is adopted by teachers only after other strategies to this end have been exhausted. Weighed against existing theories of migration, the grounded theory contributes to understanding teacher migration and retention through the innovative use of the core category status. / Educational Studies / D. Ed. (Socio-Education)

"L’industrie nano repousse constamment les limites de la miniaturisation. Pour les systèmes CMOS à mémoires non-volatiles, des phénomènes qui étaient négligeables autrefois sont à présent incontournables et nécessitent des modèles de plus en plus complexes pour décrire, analyser et prédire le comportement électrique de ces dispositifs.Le but de cette thèse est de répondre aux besoins de l’industriel, afin d’optimiser au mieux les performances des produits avant et après les étapes de production. Cette étude propose des solutions, comme des méthodes de test innovantes pour des technologies telles que les mémoires non-volatiles EEPROM embarquées.La première méthode proposée, consiste à extraire la densité de pièges (NiT) générée, au cours du cyclage, dans l’oxyde tunnel de cellules EEPROM, à partir d’une Macro cellule de test reprenant toutes les caractéristiques d’un produit fini. Les résultats expérimentaux sont ensuite injectés dans un modèle analytique décrivant le phénomène de SILC (Stress Induced Leakage Current) qui est le principal effet issu de ces pièges. La densité de pièges en fonction du nombre de cycles est ensuite extraite par interpolation entre les courbes expérimentales et les courbes simulées par le modèleLa seconde méthode propose une étude de corrélation statistique entre le test traditionnel de mise en rétention et le test de stress électrique aux bornes de l’oxyde tunnel, proposant des temps d’exécution bien plus courts. Cette étude se base sur les populations de cellules défaillantes à l’issue des deux tests. C’est en comparant les distributions sur ces populations qu’une loi de corrélation apparaît sur la tendance comportementale des cellules." / The nano industry constantly extends the size limits, especially for CMOS devices with embedded non-volatile memories. Each size reduction step always induces new challenges caused by phenomenon which were previously negligible. As a result, more complex models are required to describe, analyze and predict as well as possible the electrical behaviors. The main goal of this thesis is to propose solutions to the industry in term of test, to optimize the performances before and after the whole process steps. Thus, this study proposes two innovative methodologies dedicated to embedded non-volatile EEPROM memories based devices.The first of them consists in to extract the post-cycling generated tunnel oxide traps density (NiT), directly from a macro cell. The experimental results are then used to be compared with an analytical model calculation which perfectly describes the Stress Induced Current phenomena (SILC). This electrical current directly comes from the generated traps inside the cells tunnel oxide. An interpolation is then done between the model and the experimental resulting curves, to extract the tunnel oxide traps density.The second study proposes a method of statistical correlation between the traditional retention test and testing of electrical stress across the tunnel oxide which has shorter execution time. This study is based on cell populations after failing both tests. By comparing the distributions of these populations a correlation law appears between the cells behavioral tendencies. Following this study the replacement of long retention tests by shorter electrical stress tests may be considered.