Between 45-57% of South Africans are estimated to be engulfed by poverty. In an attempt to identify policy instruments that could help change this status quo, the various strategies that have been implemented in countries (e.g. China, Vietnam and Uganda) that are known to have been relatively successful in reducing poverty are reviewed. In the process, this dissertation discusses the literature regarding poverty, with a particular emphasis on the definition, measurement and determinants thereof. Furthermore, South Africa's anti-poverty strategies are discussed. It turns out that these have met limited success. This is largely due to insufficient pro-poor economic growth, weak implementation/administration at the municipal level, slow asset redistribution, high income/wealth inequality, low job generation rate by SMME's, high HIV/AIDS infection rate, public corruption and inadequate monitoring of poverty. Therefore, if meaningful progress towards poverty reduction is to be achieved, the government needs to deal with the foregoing constraints accordingly. / Economics / M.Comm. (Economics)

Between 45-57% of South Africans are estimated to be engulfed by poverty. In an attempt to identify policy instruments that could help change this status quo, the various strategies that have been implemented in countries (e.g. China, Vietnam and Uganda) that are known to have been relatively successful in reducing poverty are reviewed. In the process, this dissertation discusses the literature regarding poverty, with a particular emphasis on the definition, measurement and determinants thereof. Furthermore, South Africa's anti-poverty strategies are discussed. It turns out that these have met limited success. This is largely due to insufficient pro-poor economic growth, weak implementation/administration at the municipal level, slow asset redistribution, high income/wealth inequality, low job generation rate by SMME's, high HIV/AIDS infection rate, public corruption and inadequate monitoring of poverty. Therefore, if meaningful progress towards poverty reduction is to be achieved, the government needs to deal with the foregoing constraints accordingly. / Economics / M.Comm. (Economics)

This study uses a mixed-method approach to analyse the coverage of the 2011 Fukushima nuclear crisis in Japan’s two major English-language newspapers – The Japan Times and The Daily Yomiuri. Quantitative coding is combined with critical discourse analysis to determine whether the coverage was, overall, predominantly alarming, reassuring, or relatively balanced and neutral. This is done to ascertain whether the newspapers were sensationalising the crisis, echoing the official government and industry communication thereof, or reporting in a critical, responsible manner as the fourth estate. To answer the research question, key aspects of the coverage like foci, framing, sources, narratives, actors and agency, and criticisms are closely examined. It is revealed that the coverage was neither predominantly alarming nor reassuring, but was problematic in other ways. The implications of the complex findings, both for the Japanese media industry and international disaster reporting, are discussed. The study is situated in a broad literature framework that draws on agenda setting theory, research about the roles and responsibilities of the media, the field of risk communication and the reporting of radiation events in history. / Communication Science / M.A. (Communication)

Abstract of Master essay - 10 HP Izabel Lind Färnstrand Mentor: Emma Kihl Examinator: Sigrid Sandström How to Prepare for Death In this essay I dwell into the failures of our moral senses in relation to the concept of death. How does modern death culture affect our way of life and our ability to take responsibility for the life and death of others? These questions are formed by these current times and affect both my art practice and my everyday life. When facing death within my family it occur- red to me that my and my family’s relationship to death is failing us. I have become frustra- ted with the fear and silence that seems too natural to my surrounding. Not being able to talk about a part of life that is inevitable seems irrational. It became clear when the lack of under- standing and acceptance of death caused relatives an immense suffering. The struggle seemed unnessesary and urged me to try to understand more, based on their deaths. Seeing how very different the experience of dying can be made me wonder what makes a ”good” death possible for some and others not. From there my interest in the topic death culture and fear emerged, and this essay touches on this in a variety of aspects. I use my personal experiences in combina- tion with thoughts of others to talk of layers of these issues through my artistic practice. This personal method is my way of trying to structure a thinking – in a way that I can use and make sense of it – with a varied level of success. I feel it is important to note that I don’t claim to have any answes. This essay is more an attempt to pose questions around human behaviour. Even though many of these ques- tions have been asked over and over again, throughout different times, I believe it is impor- tant to ask them again and again. As long as the Human is part and violently effecting this suffering world. Many of the thoughts in this essay are based on fragments of ideas by Judith Butler, espe- cielly from her book Frames of War: When is Life Grievable? (2009). I also reference Caitlin Doughty’s From Here to Eternity: Traveling the World to Find the Good Death (2017) and and Sogyal Rinpoche The Tibetan Book of Living and Dying (1992). The themes gathered that I try to make use of in this essay and in my artistic practice are purpose, fear, death, health, happiness, narcissim and resposibility. / Abstract of Artistic work How to Prepare for Death @ Galleri Mejan, Exercisplan 3, october 2019 Media: Spatial installation with a performance (1 h) Materials: Clay, plaster, metal & red plastic film How to Prepare for Death is a spatial installation in one of the gallery rooms of Galleri Mejan. The work includes the whole space of the area, such as the floor and the walls. You step into an altered reality, where the floor is covered with clay that is cracking increasingly over time and windows that are tinted red so that the air you breath seems red. When you enter your eyes need to adjust and after a while it is rather the outside that seems colored, neon green -  the complementary color of red. From the clay there are metal rods sticking out vertically, with plaster sculptures at the end. These sculptures are broken, and resemble body parts with a medical aesthetics. Similar sculptures come out from the walls, like fragile fragments of something that used to be. When you walk around the sculptures the clay crackle under your feet, and crumble into smaller pieces and dust. It is constructed as an ambivalent experience of nothingness, emptiness, ”afterness” and a sanctuary of thoughts. My questions about life and death drive me to investigate how to create spaces for these subjects to feel present, so that we can face our fears.

Tesis por compendio / [ES] Edge Computing es un modelo de computación emergente basado en acercar el procesamiento a los dispositivos de captura de datos en las infraestructuras Internet of things (IoT). Edge computing mejora, entre otras cosas, los tiempos de respuesta, ahorra anchos de banda, incrementa la seguridad de los servicios y oculta las caídas transitorias de la red. Este paradigma actúa en contraposición a la ejecución de servicios en entornos cloud y es muy útil cuando se desea desarrollar soluciones de inteligencia artificial (AI) que aborden problemas en entornos de desastres naturales, como pueden ser inundaciones, incendios u otros eventos derivados del cambio climático. La cobertura de estos escenarios puede resultar especialmente difícil debido a la escasez de infraestructuras disponibles, lo que a menudo impide un análisis de los datos basado en la nube en tiempo real. Por lo tanto, es fundamental habilitar técnicas de IA que no dependan de sistemas de cómputo externos y que puedan ser embebidas en dispositivos de móviles como vehículos aéreos no tripulados (VANT), para que puedan captar y procesar información que permita inferir posibles situaciones de emergencia y determinar así el curso de acción más adecuado de manera autónoma. Históricamente, se hacía frente a este tipo de problemas utilizando los VANT como dispositivos de recogida de datos con el fin de, posteriormente, enviar esta información a la nube donde se dispone de servidores capacitados para analizar esta ingente cantidad de información. Este nuevo enfoque pretende realizar todo el procesamiento y la obtención de resultados en el VANT o en un dispositivo local complementario. Esta aproximación permite eliminar la dependencia de un centro de cómputo remoto que añade complejidad a la infraestructura y que no es una opción en escenarios específicos, donde las conexiones inalámbricas no cumplen los requisitos de transferencia de datos o son entornos en los que la información tiene que obtenerse en ese preciso momento, por requisitos de seguridad o inmediatez. Esta tesis doctoral está compuesta de tres propuestas principales. En primer lugar se plantea un sistema de despegue de enjambres de VANTs basado en el algoritmo de Kuhn Munkres que resuelve el problema de asignación en tiempo polinómico. Nuestra evaluación estudia la complejidad de despegue de grandes enjambres y analiza el coste computacional y de calidad de nuestra propuesta. La segunda propuesta es la definición de una secuencia de procesamiento de imágenes de catástrofes naturales tomadas desde drones basada en Deep learning (DL). El objetivo es reducir el número de imágenes que deben procesar los servicios de emergencias en la catástrofe natural para poder tomar acciones sobre el terreno de una manera más rápida. Por último, se utiliza un conjunto de datos de imágenes obtenidas con VANTs y relativas a diferentes inundaciones, en concreto, de la DANA de 2019, cedidas por el Ayuntamiento de San Javier, ejecutando un modelo DL de segmentación semántica que determina automáticamente las regiones más afectadas por las lluvias (zonas inundadas). Entre los resultados obtenidos se destacan los siguientes: 1- la mejora drástica del rendimiento del despegue vertical coordinado de una red de VANTs. 2- La propuesta de un modelo no supervisado para la vigilancia de zonas desconocidas representa un avance para la exploración autónoma mediante VANTs. Esto permite una visión global de una zona concreta sin realizar un estudio detallado de la misma. 3- Por último, un modelo de segmentación semántica de las zonas inundadas, desplegado para el procesamiento de imágenes en el VANTs, permite la obtención de datos de inundaciones en tiempo real (respetando la privacidad) para una reconstrucción virtual fidedigna del evento. Esta tesis ofrece una propuesta para mejorar el despegue coordinado de drones y dotar de capacidad de procesamiento de algoritmos de deep learning a dispositivos edge, más concretamente UAVs autónomos. / [CA] Edge Computing és un model de computació emergent basat a acostar el processament als dispositius de captura de dades en les infraestructures Internet of things (IoT). Edge computing millora, entre altres coses, els temps de resposta, estalvia amplades de banda, incrementa la seguretat dels serveis i oculta les caigudes transitòries de la xarxa. Aquest paradigma actua en contraposició a l'execució de serveis en entorns cloud i és molt útil quan es desitja desenvolupar solucions d'intel·ligència artificial (AI) que aborden problemes en entorns de desastres naturals, com poden ser inundacions, incendis o altres esdeveniments derivats del canvi climàtic. La cobertura d'aquests escenaris pot resultar especialment difícil a causa de l'escassetat d'infraestructures disponibles, la qual cosa sovint impedeix una anàlisi de les dades basat en el núvol en temps real. Per tant, és fonamental habilitar tècniques de IA que no depenguen de sistemes de còmput externs i que puguen ser embegudes en dispositius de mòbils com a vehicles aeris no tripulats (VANT), perquè puguen captar i processar informació per a inferir possibles situacions d'emergència i determinar així el curs d'acció més adequat de manera autònoma. Històricament, es feia front a aquesta mena de problemes utilitzant els VANT com a dispositius de recollida de dades amb la finalitat de, posteriorment, enviar aquesta informació al núvol on es disposa de servidors capacitats per a analitzar aquesta ingent quantitat d'informació. Aquest nou enfocament pretén realitzar tot el processament i l'obtenció de resultats en el VANT o en un dispositiu local complementari. Aquesta aproximació permet eliminar la dependència d'un centre de còmput remot que afig complexitat a la infraestructura i que no és una opció en escenaris específics, on les connexions sense fils no compleixen els requisits de transferència de dades o són entorns en els quals la informació ha d'obtindre's en aqueix precís moment, per requisits de seguretat o immediatesa. Aquesta tesi doctoral està composta de tres propostes principals. En primer lloc es planteja un sistema d'enlairament d'eixams de VANTs basat en l'algorisme de Kuhn Munkres que resol el problema d'assignació en temps polinòmic. La nostra avaluació estudia la complexitat d'enlairament de grans eixams i analitza el cost computacional i de qualitat de la nostra proposta. La segona proposta és la definició d'una seqüència de processament d'imatges de catàstrofes naturals preses des de drons basada en Deep learning (DL).L'objectiu és reduir el nombre d'imatges que han de processar els serveis d'emergències en la catàstrofe natural per a poder prendre accions sobre el terreny d'una manera més ràpida. Finalment, s'utilitza un conjunt de dades d'imatges obtingudes amb VANTs i relatives a diferents inundacions, en concret, de la DANA de 2019, cedides per l'Ajuntament de San Javier, executant un model DL de segmentació semàntica que determina automàticament les regions més afectades per les pluges (zones inundades). Entre els resultats obtinguts es destaquen els següents: 1- la millora dràstica del rendiment de l'enlairament vertical coordinat d'una xarxa de VANTs. 2- La proposta d'un model no supervisat per a la vigilància de zones desconegudes representa un avanç per a l'exploració autònoma mitjançant VANTs. Això permet una visió global d'una zona concreta sense realitzar un estudi detallat d'aquesta. 3- Finalment, un model de segmentació semàntica de les zones inundades, desplegat per al processament d'imatges en el VANTs, permet l'obtenció de dades d'inundacions en temps real (respectant la privacitat) per a una reconstrucció virtual fidedigna de l'esdeveniment. / [EN] Edge Computing is an emerging computing model based on bringing data processing and storage closer to the location needed to improve response times and save bandwidth. This new paradigm acts as opposed to running services in cloud environments and is very useful in developing artificial intelligence (AI) solutions that address problems in natural disaster environments, such as floods, fires, or other events of an adverse nature. Coverage of these scenarios can be particularly challenging due to the lack of available infrastructure, which often precludes real-time cloud-based data analysis. Therefore, it is critical to enable AI techniques that do not rely on external computing systems and can be embedded in mobile devices such as unmanned aerial vehicles (UAVs) so that they can capture and process information to understand their context and determine the appropriate course of action independently. Historically, this problem was addressed by using UAVs as data collection devices to send this information to the cloud, where servers can process it. This new approach aims to do all the processing and get the results on the UAV or a complementary local device. This approach eliminates the dependency on a remote computing center that adds complexity to the infrastructure and is not an option in specific scenarios where wireless connections do not meet the data transfer requirements. It is also an option in environments where the information has to be obtained at that precise moment due to security or immediacy requirements. This study consists of three main proposals. First, we propose a UAV swarm takeoff system based on the Kuhn Munkres algorithm that solves the assignment problem in polynomial time. Our evaluation studies the takeoff complexity of large swarms and analyzes our proposal's computational and quality cost. The second proposal is the definition of a Deep learning (DL) based image processing sequence for natural disaster images taken from drones to reduce the number of images processed by the first responders in the natural disaster. Finally, a dataset of images obtained with UAVs and related to different floods is used to run a semantic segmentation DL model that automatically determines the regions most affected by the rains (flooded areas). The results are 1- The drastic improvement of the performance of the coordinated vertical take-off of a network of UAVs. 2- The proposal of an unsupervised model for the surveillance of unknown areas represents a breakthrough for autonomous exploration by UAVs. This allows a global view of a specific area without performing a detailed study. 3- Finally, a semantic segmentation model of flooded areas, deployed for image processing in the UAV, allows obtaining real-time flood data (respecting privacy) for a reliable virtual reconstruction of the event. This thesis offers a proposal to improve the coordinated take-off of drones, to provide edge devices with deep learning algorithms processing capacity, more specifically autonomous UAVs, in order to develop services for the surveillance of areas affected by natural disasters such as fire detection, segmentation of flooded areas or detection of people in danger. Thanks to this research, services can be developed that enable the coordination of large arrays of drones and allow image processing without needing additional devices. This flexibility makes our approach a bet for the future and thus provides a development path for anyone interested in deploying an autonomous drone-based surveillance and actuation system. / I would like to acknowledge the project Development of High-Performance IoT Infrastructures against Climate Change based on Artificial Intelligence (GLOBALoT). Funded by Ministerio de Ciencia e Innovación (RTC2019-007159-5), of which this thesis is part. / Hernández Vicente, D. (2023). Analysis Design and Implementation of Artificial Intelligence Techniques in Edge Computing Environments [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/192605 / Compendio

Recurring drought conditions have always been an endemic feature of climate in South Africa, limiting maize development and production. However, recent projections of the future climate by the Intergovernmental Panel on Climate Change suggest that due to an increase of atmospheric greenhouse gases, the frequency and severity of droughts will increase in drought-prone areas, mostly in subtropical climates. This has raised major concern for the agricultural sector, particularly the vulnerable small-scale farmers who merely rely on rain for crop production. Farmers in the Luvuvhu River catchment are not an exception, as this area is considered economically poor, whereby a significant number of people are dependent on rain-fed farming for subsistence. This study was therefore conducted in order to improve agricultural productivity in the area and thus help in the development of measures to secure livelihoods of those vulnerable small-scale farmers. Two drought indices viz. Standardized Precipitation Evapotranspiration Index (SPEI) and Water Requirement Satisfaction Index (WRSI) were used to quantify drought. A 120-day maturing maize crop was considered and three consecutive planting dates were staggered based on the average start of the rainy season. Frequencies and probabilities during each growing stage of maize were calculated based on the results of the two indices. Temporal variations of drought severity from 1975 to 2015 were evaluated and trends were analyzed using the non-parametric Spearman’s Rank Correlation test at α (0.05) significance level. For assessing climate change impact on droughts, SPEI and WRSI were computed using an output from downscaled projections of CSIRO Mark3.5 under the SRES A2 emission scenario for the period 1980/81 – 2099/100. The frequency of drought was calculated and the difference of SPEI and WRSI means between future climate periods and the base period were assessed using the independent t-test at α (0.10) significance level in STATISTICA software. The study revealed that planting a 120-day maturing maize crop in December would pose a high risk of frequent severe-extreme droughts during the flowering to the grain-filling stage at Levubu, Lwamondo, Thohoyandou, and Tshiombo; while planting in October could place crops at a lower risk of reduced yield and even total crop failure. In contrast, stations located in the low-lying plains of the catchment (Punda Maria, Sigonde, and Pafuri) were exposed to frequent moderate droughts following planting in October, with favorable conditions noted following the December planting date. Further analysis on the performance of the crop under various drought conditions revealed that WRSI values corresponding to more intense drought conditions were detected during the December planting date for all stations. Moreover, at Punda Maria, Sigonde and Pafuri, it was observed that extreme drought (WRSI <50) occurred once in five seasons, regardless of the planting date. Temporal analysis on historical droughts in the area indicated that there had been eight agricultural seasons subjected to extreme widespread droughts resulting in total crop failure i.e. 1983/84, 1988/89, 1991/92, 1993/94, 2001/02, 2002/03, 2004/05 and 2014/15. Results of Spearman’s rank correlation test revealed weak increasing drought trends at Thohoyandou (ρ = of 0.5 for WRSI) and at Levubu and Lwamondo (ρ = of 0.4 for SPEI), with no significant trends at the other stations. The study further revealed that climate change would enhance the severity of drought across the catchment. This was statistically significant (at 10% significance level) for the near-future and intermediate-future climates, relative to the base period. Drought remains a threat to rain-fed maize production in the Luvuvhu River catchment area of South Africa. In order to mitigate the possible effects of droughts under climate change, optimal planting dates were recommended for each region. The use of seasonal forecasts during drought seasons would also be useful for local rain-fed maize growers especially in regions where moisture is available for a short period during the growing season. It was further recommended that the Government ensure proper support such as effective early warning systems and inputs to the farmers. Moreover, essential communication between scientists, decision makers, and the farmers can help in planning and decision making ahead of and during the occurrence of droughts. / Agriculture, Animal Health and Human Ecology / M. Sc. (Agriculture)