A presente dissertação aborda a modelagem física de sistemas marinho profundo em escala reduzida, uma metodologia que vem contribuindo no entendimento dos processos sedimentares atuantes neste ambiente, principalmente as correntes de turbidez, fluxo gravitacional subaquoso responsável pela formação dos turbiditos, importantes reservatórios de hidrocarbonetos da costa brasileira. A modelagem física 3D empregada neste trabalho aborda a influência da autogênese no comportamento espacial e evolutivo de depósitos análogos gerados por simulações de correntes de turbidez em duas séries de 10 experimentos com parâmetros de controle constantes (vazão, concentração volumétrica de sedimentos, tipo e granulometria das partículas sedimentares), onde em cada série foi utilizada uma concentração de sedimentos diferente: uma com maior concentração – HDTC (high-density turbidity currents) e outra com menor concentração – LDTC (low-density turbidity currents) onde se buscou observar o efeito desta propriedade na construção dos depósitos. Para caracterizar o comportamento geométrico dos depósitos, uma nova abordagem estatística é utilizada a partir de uma análise de variância. Os resultados obtidos apontam que processos autogênicos locais puderam alterar a configuração global dos depósitos. A concentração de sedimentos teve influência direta nas características morfológicas e evolutivas dos depósitos, sendo os experimentos de HDTC os que apresentam uma evolução mais complexa, onde ocorreu um processo de auto-confinamento das correntes, gerando uma morfologia mais diversa. / Autogenic / allogenic controls have been discussed widely because they represent an important parameter in the constructive and evolutionary process of a sedimentary system. To evaluate these controls in submarine fans and analyse its capacity of selforganizing and creating depositional patterns, this work performed fully controlled 3D physical simulations of turbidity currents under ideal autogenic controls (no external influence) with detailed data for the generated deposits. Two series of 10 experiments of high-density turbidity currents (HDTC) and low-density turbidity currents (LDTC) were run, keeping all other input parameters (discharge, volumetric concentration, type and grain size) constant. From statistical and qualitative approach were characterised the geometric elements and morphodynamic behaviour of the deposits (centroid, Length/Width ratio, morphodynamic evolution). The results indicate local autogenic processes change the global setting of the flow evolution and deposits of submarine fans. A morphodynamic evolution generated by HDTC showed complex stages of filling and stacking caused by two types of flow self-channelling. Type I is characterised by flow channelling due to the elevation of levees without lateral avulsion and more efficient sediment transport (longer deposits, with terminal lobes well developed), and Type II is characterised by flow channelling but allows lateral avulsions and involves less efficient sediment transport (shorter deposits with terminal lobes undeveloped). The HDTC deposits showed random behaviour for the length/width ratio and for the centroid of sedimentary bodies and distinct morphological elements (elongated central deposit, fringes and distal lobes). By contrast, the LDTC morphodynamics were simplified without any self-confining process or distinct morphological elements. Finally, the statistical approach showed that the HDTC deposits had a greater variance of geometrical elements in relation to LDTC deposits. The experiments provided evidence that high rates of sediment supply decisively influenced the geometry and morphodynamic of the deposits, as well as they self-organizing capacity.

Submitted by Almir Azevedo (barbio1313@gmail.com) on 2018-06-07T16:27:53Z No. of bitstreams: 1 dissertação mestrado ien 2018 Filipe Santana Moreira do Desterro.PDF: 3379721 bytes, checksum: adf3227c935769d0deee6186bbb0daf6 (MD5) / Made available in DSpace on 2018-06-07T16:27:53Z (GMT). No. of bitstreams: 1 dissertação mestrado ien 2018 Filipe Santana Moreira do Desterro.PDF: 3379721 bytes, checksum: adf3227c935769d0deee6186bbb0daf6 (MD5) Previous issue date: 2018-03 / Recentemente, o uso de dispositivos móveis foi proposto para a medição da avaliação da dose durante acidentes nucleares. A ideia é apoiar equipes de campo, fornecendo uma estimativa aproximada do mapa de distribuição de dose na proximidade da usina de energia nuclear (UEN), sem a necessidade de se conectar aos sistemas da UEN. A fim de fornecer essa execução autônoma, um conjunto de redes neurais artificiais (RNA) é proposto em substituição aos tradicionais sistemas de dispersão atmosférica de radionuclídeo (DAR) que utilizam modelos físicos complexos que demandam um excessivo tempo de processamento. Uma limitação observada nessa abordagem é o treinamento muito demorado das RNAs. Além disso, se o número de parâmetros de entrada aumenta, o desempenho de RNAs tradicionais, como o Multilayer-Perceptron (MLP) com treinamento de backpropagation ou Redes Neurais de Regressão Geral (GRNN), é afetado, prejudicando sensivelmente a predição. Este trabalho centrase no estudo de tecnologias computacionais para melhoria das RNAs a serem usadas na aplicação móvel, bem como seus algoritmos de treinamento. Contudo, para refinar a aprendizagem e permitir melhores estimativas de dose, são necessárias arquiteturas de RNA mais complexas. As RNAs com muitas camadas (muito mais do que um número típico de camadas), às vezes referidas como Redes Neurais Profundas ou Deep Neural Networks (DNN), por exemplo, demonstraram obter melhores resultados. Por outro lado, o treinamento de tais RNAs é muito lento. Deste modo, com o objetivo de permitir o uso desses DNNs em um tempo de treinamento razoável. É proposta uma solução de programação paralela, usando a Unidade de Processamento Gráfico (GPU). Neste contexto, este trabalho utilizou o framework TensorFlow para desenvolver Redes Neurais Profundas com 9 camadas. Como resultado, speedups entre 50 e 100 vezes (dependendo das arquiteturas RNA comparadas) foram alcançadas no processo de treinamento, sem afetar a qualidade dos resultados obtidos (estimativas de dose). / Recently, the use of mobile devices has been proposed for the measurement of dose evaluation during nuclear accidents. The idea is to support field teams, providing a rough estimate of the dose distribution map in the vicinity of the nuclear power plant (NPP), without the need to connect to the NPP systems. In order to provide this autonomous execution, a set of artificial neural networks (ANNs) is proposed instead of the traditional atmospheric dispersion of radionuclides (ADR) systems that use complex physical models that require an excessive processing time. One limitation observed in this approach is the very time-consuming training of ANN. In addition, if the number of input parameters increases, the performance of standard ANNs, such as Multilayer-Perceptron (MLP) with backpropagation training or General Regression Neural Networks (GRNN), is affected, leading to an irrational prediction. Thus, work focuses on the study of computational technologies to improve the RNAs to be used in the mobile application, as well as their training algorithms. However, to refine learning and allow better dose estimates, more complex ANN architectures are required. Layer ANNs (much more than a typical number of layers), sometimes referred to as Deep Neural Networks (DNNs), for example, have been shown to perform better. On the other hand, the training of such ANNs is very slow. Thus, in order to allow the use of these DNNs in a reasonable training time. With this, a parallel programming solution is proposed, using the Graphics Processing Units (GPU). In this context, this work used the TensorFlow framework to develop deep neural networks with 9. As a result, speedups between 50 and 100 times (depending on the ANN architectures compared) were achieved in the training process, without affecting the quality of the results obtained dose).

Orientador: Joyce Maria Annichino-Bizzacchi / Dissertação (mestrado) - Universidade Estadual de Campinas, Faculdade de Ciências Médicas / Made available in DSpace on 2018-08-23T22:14:32Z (GMT). No. of bitstreams: 1 Prado_LucasBessa_M.pdf: 3008068 bytes, checksum: 3758f8a952dfe1f77c113246b8e2a0bd (MD5) Previous issue date: 2013 / Resumo: A Trombose Venosa Profunda (TVP) é definida como uma oclusão parcial ou total da circulação venosa profunda. A heparina é um fármaco com ação anticoagulante e antitrombótica utilizado desde 1930. O custo, a via de administração (endovenosa ou subcutânea) e as doses repetidas são algumas das limitações do seu uso. Assim, o desenvolvimento de um produto que possa ser administrado por via subcutânea ou oral em um menor número de aplicações, torna-se um importante desafio e de grande aplicabilidade clínica. Sistemas de liberação sustentada permitem que o fármaco seja encapsulado e liberado gradativamente. Este estudo constituiu na preparação, caracterização e avaliação in vivo de nanopartículas de poli (?-caprolactona) (PCL) e quitosana como carreadores de heparina de baixo peso molecular (enoxaparina). As nanopartículas foram preparadas pelo método de dupla emulsão água/óleo/água e evaporação do solvente. A caracterização das nanopartículas foi realizada por microscopia eletrônica de varredura (MEV), onde foram observadas partículas esféricas e homogêneas. O diâmetro médio das nanopartículas foi de 512,8 ± 13,8 nm e o potencial zeta foi de +30,9 ± 1,3 mV. A eficiência de encapsulamento, analisada pelo método Azure II foi de 99,04 ± 0,001 %. A atividade anticoagulante in vivo da enoxaparina encapsulada foi avaliada pela atividade anti-Xa plasmática, através de método colorimétrico. Quando a enoxaparina livre foi administrada por via subcutânea observou-se um pico de atividade (0,5 UI/mL) em 1 hora, com um decréscimo gradual até 6 horas. A atividade anticoagulante da enoxaparina encapsulada em nanopartículas manteve-se por até 14 horas, quando foi administrada por via subcutânea, sugerindo que as nanopartículas podem permitir que a enoxaparina seja liberada de forma gradual, podendo ser uma vantagem na prática clínica. Após a administração das nanopartículas por via oral não se observou nenhuma atividade em até 14 horas, sugerindo que as nanopartículas não tenham sido absorvidas ou a enoxaparina tenha sido degradada no trato gastrointestinal. Para avaliação do efeito antitrombótico foi padronizado o modelo de TVP por estase e hipercoagulabilidade em ratos. Após administração subcutânea, houve uma significativa diminuição do tamanho do trombo formado tanto com o emprego de enoxaparina livre (p= 0,002) como após encapsulamento em nanopartículas (p= 0,0411) em comparação ao grupo controle. Quando foram administradas nanopartículas por via oral, os resultados mostraram que não houve diferença estatística em comparação ao grupo controle (p= 0,9476) e a um grupo de nanopartículas vazias (p= 0,9372). Em resumo, o método de dupla emulsão a/o/a mostrou-se eficiente para o encapsulamento de enoxaparina, proporcionando a obtenção de nanopartículas esféricas e com alta eficiência de encapsulamento. Pelos estudos in vivo, a enoxaparina encapsulada mostrou uma atividade anticoagulante com liberação sustentada, por um período superior ao obtido com a enoxaparina livre, com excelente efeito antitrombótico quando administrada por via subcutânea. Contudo, não se observou nenhum efeito anticoagulante ou antitrombótico quando as nanopartículas foram administradas por via oral. Novos experimentos com quitosanas de diferentes massas molares serão necessários na tentativa de possibilitar a absorção oral dessas nanopartículas / Abstract: Deep vein thrombosis (DVT) is defined as partial or total occlusion of the deep venous circulation. Heparin is a drug with anticoagulant and antithrombotic action used since 1930. The costs, administration vias (intravenous or subcutaneous) and the repeated doses are some limitations of its use. Thus, the development of a product that could be administered subcutaneous or orally in a smaller number of applications becomes a major challenge with huge clinical applicability. Sustained release systems allow the medication to be gradually encapsulated and released. This study was based on the preparation, characterization and in vivo evaluation of nanoparticles of poly (?-caprolactone) (PCL) and chitosan as carriers of low molecular weight heparin (enoxaparin). The nanoparticles were prepared by the double emulsion water/oil/water method and solvent evaporation. The nanoparticles characterization was performed by scanning electron microscopy (SEM), in which were observed spherical and homogeneous particles. The average diameter of the nanoparticles was 512.8 ± 13.8 nm and the zeta potential was +30.9 ± 1.3 mV. The encapsulation efficiency, analyzed by Azure II method, was 99.04 ± 0.001%. The in vivo anticoagulant activity of the encapsulated enoxaparin was evaluated by plasmatic anti-Xa activity performed by colorimetric method. When the free enoxaparin was subcutaneously administered a peak of activity was observed (0.5 IU/mL) in 1 hour with a gradual decrease until 6 hours. The anticoagulant activity of the nanoparticles encapsulated enoxaparin was kept until 14 hours when it was administered subcutaneously, suggesting that nanoparticles may allow the enoxaparin release by a gradual way, what could be an advantage on clinical practice. After the oral administration of the nanoparticles, any activity could be observed in until 14 hours, suggesting that or the nanoparticles might be not absorbed or the enoxaparin might be degraded on the gastrointestinal tract. In order to evaluate its antithrombotic effect, it was standardized a model of DVT by stasis and hypercoagulability in rats. After subcutaneous administration, there was a significative reduction on the thrombus size both with free enoxaparin (p= 0.002) and after encapsulation (p= 0.0411) in comparison with control group. When nanoparticles were administered orally, the results showed no statistical difference compared to the control group (p = 0.9476) and to a group of empty nanoparticles (p = 0.9372). In summary, the double emulsion method w/o/w was efficient for the enoxaparin encapsulation, providing the obtainment of spherical nanoparticles with high encapsulation efficiency. For in vivo studies, the encapsulated enoxaparin showed a sustained release anticoagulant activity for a higher period than that obtained with free enoxaparin, with an excellent antithrombotic effect when administered subcutaneously. However, there was no anticoagulant or antithrombotic effect when the nanoparticles were administered orally. Further experiments with chitosans of different molecular weights will be needed on the attempt to allow the oral absorption of these nanoparticles / Mestrado / Medicina Experimental / Mestre em Fisiopatologia Médica

L’endométriose est une maladie gynécologique pour laquelle l’exposition à certains contaminants chimiques environnementaux est évoquée parmi les facteurs de risque associés. Les conclusions des études épidémiologiques existantes restent toutefois non convergentes. Leur hétérogénéité en termes de lésions décrites, de méthodologie et d’effectifs contribuent à ce constat, de même que l’étendue limitée des marqueurs d’exposition considérés dans ces études. Nous avons réalisé une étude cas-témoins appariés à partir d’une bio-collection de 113 patientes réunissant68 cas de patientes opérées d’endométriose profonde et 45 patientes témoins. Un ensemble unique de 78 polluants organiques persistants a été recherché, incluant dioxines, polychlorobiphényles, retardateurs de flamme polybromés, et pesticides organochlorés. Les niveaux d’exposition interne des sujets ont été mesurés à la fois dans les tissus adipeux pariétal et épiploïque ainsi que dans le sérum. La distribution de ces différents polluants au sein de ces trois compartiments a tout d’abord été caractérisée. Celle-ci a permis la prise en compte encore très rare de l’équilibre entre compartiments de stockage et compartiment circulant, ce rapport de concentration apparaissant comme un potentiel indicateur additionnel permettant d’affiner d’éventuels liens de causalité entre exposition chronique à des dangers chimiques et pathologie chez l’homme. Certains des contaminants ciblés sont ensuite apparus significativement associés à l’endométriose profonde, la stratification plus fine de notre population de cas indiquant un lien d’autant plus significatif en présence d’endométriome. Les mécanismes sous-jacents de cette association restent toutefois à élucider. / Endometriosis is a gynecological disease for whichexposure to some environmental chemicals is evocatedamong the associated risk factors. Epidemiological studies are however globally non convergent and finally fairly conclusive. Their heterogeneity in terms of lesion localization and sub-phenotype, methodology, size and nature of the populations studied, as well as the limited number of monitored markers of exposure contribute to this situation. We realized a matched case-control study based on a biocollection of 113 patients including 68 patients suffering of deep endometriosis and 45 controls. We characterized the internal exposure levels of an extended range of around 78 persistent organic pollutants (including dioxins, polychlorobiphenyls, brominated flame retardants and organochlorine pesticides). Internal level exposures were measured in three biological compartments (omental fat, subcutaneous fat and serum). First, the distribution of these chemicals was characterized within these compartments. These extended exposure data from deep infiltrating endometriosis patients are the first ones available for France and give a new insight about the equilibrium of chemicals between storage and circulating compartments that should be further considered as a potential indicator permitting to establish a possible association between a chronic exposure to chemical hazards and human pathology. Afterwards, some of the targeted chemicals appeared significantly associated with deep endometriosis. A sub-stratification of our case population indicated a more significant relationship with the presence of endometrioma. Underlying mechanisms remain to be determined.

Despite the impressive advancements in people detection and tracking, safety is still a key barrier to the deployment of autonomous vehicles in urban environments [1]. For example, in non-autonomous technology, there is an implicit communication between the people crossing the street and the driver to make sure they have communicated their intent to the driver. Therefore, it is crucial for the autonomous car to infer the future intent of the pedestrian quickly. We believe that human body orientation with respect to the camera can help the intelligent unit of the car to anticipate the future movement of the pedestrians. To further improve the safety of pedestrians, it is important to recognize whether they are distracted, carrying a baby, or pushing a shopping cart. Therefore, estimating the fine- grained 3D pose, i.e. (x,y,z)-coordinates of the body joints provides additional information for decision-making units of driverless cars. In this dissertation, we have proposed a deep learning-based solution to classify the categorized body orientation in still images. We have also proposed an efficient framework based on our body orientation classification scheme to estimate human 3D pose in monocular RGB images. Furthermore, we have utilized the dynamics of human motion to infer the body orientation in image sequences. To achieve this, we employ a recurrent neural network model to estimate continuous body orientation from the trajectories of body joints in the image plane. The proposed body orientation and 3D pose estimation framework are tested on the largest 3D pose estimation benchmark, Human3.6m (both in still images and video), and we have proved the efficacy of our approach by benchmarking it against the state-of-the-art approaches. Another critical feature of self-driving car is to avoid an obstacle. In the current prototypes the car either stops or changes its lane even if it causes other traffic disruptions. However, there are situations when it is preferable to collide with the object, for example a foam box, rather than take an action that could result in a much more serious accident than collision with the object. In this dissertation, for the first time, we have presented a novel method to discriminate between physical properties of these types of objects such as bounciness, elasticity, etc. based on their motion characteristics . The proposed algorithm is tested on synthetic data, and, as a proof of concept, its effectiveness on a limited set of real-world data is demonstrated.

>Magister Scientiae - MSc / In order to classify South African Sign Language as a signed gesture, five fundamental parameters need to be considered. These five parameters to be considered are: hand shape, hand orientation, hand motion, hand location and facial expressions. The research in this thesis will utilise Deep Learning techniques, specifically Convolutional Neural Networks, to recognise hand shapes in various hand orientations. The research will focus on two of the five fundamental parameters, i.e., recognising six South African Sign Language hand shapes for each of five different hand orientations. These hand shape and orientation combinations will be recognised by means of a video stream captured on a mobile device. The efficacy of Convolutional Neural Network for gesture recognition will be judged with respect to its classification accuracy and classification speed in both a desktop and embedded context. The research methodology employed to carry out the research was Design Science Research. Design Science Research refers to a set of analytical techniques and perspectives for performing research in the field of Information Systems and Computer Science. Design Science Research necessitates the design of an artefact and the analysis thereof in order to better understand its behaviour in the context of Information Systems or Computer Science. / National Research Foundation (NRF)

[pt] Os projetos de capacidade de suporte de estacas estão baseados em dados de ensaio de campo de maneira direta ou indireta. Devido a sua praticidade, os métodos empíricos são amplamente utilizados. No Brasil os métodos de Decourt e Quaresma (1978, 1982) e de Aoki e Velloso (1975) se destacam. Este trabalho procura avaliar o uso de dados de SPT e CPT para estimativa de parâmetros geotécnicos necessários na previsão de capacidade de suporte de estacas através de métodos teóricos. São apresentadas e avaliadas formulações empíricas de estimativa de parâmetros para materiais granulares e materiais argilosos. Outro elemento importante na previsão da capacidade de suporte é o estado de tensões atuante em torno da estaca que é analisado através de considerações a respeito do coeficiente de empuxo. Finalmente, são mostrados alguns resultados de provas de carga para a análise da seleção de parâmetros e do estado de tensões, além de uma avaliação dos métodos empíricos de previsão de capacidade de suporte. / [en] The main objective of this thesis is to discuss the applicability of in-situ tests like the Standard Penetration Test (SPT) and the Cone Penetration Test (CPT) to determine directly the design parameters to predict the bearing capacity of pile foundations. In case it will be considered the use of empirical correlation to indicate the mechanical properties of the soil in terms of shear resistance, and the application of these values directly in the classic formulation based on the theory of equilibrium limit to evaluate distinctly the shaft and the base resistance of piles. Adaptations of these values will be proceeded considering aspects related with the non-linear behavior of the soil; the mechanism of load transfer and the influence of the constructive aspects.The results obtained through this new methodology will be compared with experimental results, obtained from static and dynamic load tests and also with other empiric procedures that use the results obtained from in-situ tests to evaluate directly the bearing capacity of deep foundations.

Humans possess a rich semantic knowledge of words and concepts which captures the perceivable physical properties of their real-world referents and their relations. Encoding this knowledge or some of its aspects is the goal of computational models of semantic representation and has been the subject of considerable research in cognitive science, natural language processing, and related areas. Existing models have placed emphasis on different aspects of meaning, depending ultimately on the task at hand. Typically, such models have been used in tasks addressing the simulation of behavioural phenomena, e.g., lexical priming or categorisation, as well as in natural language applications, such as information retrieval, document classification, or semantic role labelling. A major strand of research popular across disciplines focuses on models which induce semantic representations from text corpora. These models are based on the hypothesis that the meaning of words is established by their distributional relation to other words (Harris, 1954). Despite their widespread use, distributional models of word meaning have been criticised as ‘disembodied’ in that they are not grounded in perception and action (Perfetti, 1998; Barsalou, 1999; Glenberg and Kaschak, 2002). This lack of grounding contrasts with many experimental studies suggesting that meaning is acquired not only from exposure to the linguistic environment but also from our interaction with the physical world (Landau et al., 1998; Bornstein et al., 2004). This criticism has led to the emergence of new models aiming at inducing perceptually grounded semantic representations. Essentially, existing approaches learn meaning representations from multiple views corresponding to different modalities, i.e. linguistic and perceptual input. To approximate the perceptual modality, previous work has relied largely on semantic attributes collected from humans (e.g., is round, is sour), or on automatically extracted image features. Semantic attributes have a long-standing tradition in cognitive science and are thought to represent salient psychological aspects of word meaning including multisensory information. However, their elicitation from human subjects limits the scope of computational models to a small number of concepts for which attributes are available. In this thesis, we present an approach which draws inspiration from the successful application of attribute classifiers in image classification, and represent images and the concepts depicted by them by automatically predicted visual attributes. To this end, we create a dataset comprising nearly 700K images and a taxonomy of 636 visual attributes and use it to train attribute classifiers. We show that their predictions can act as a substitute for human-produced attributes without any critical information loss. In line with the attribute-based approximation of the visual modality, we represent the linguistic modality by textual attributes which we obtain with an off-the-shelf distributional model. Having first established this core contribution of a novel modelling framework for grounded meaning representations based on semantic attributes, we show that these can be integrated into existing approaches to perceptually grounded representations. We then introduce a model which is formulated as a stacked autoencoder (a variant of multilayer neural networks), which learns higher-level meaning representations by mapping words and images, represented by attributes, into a common embedding space. In contrast to most previous approaches to multimodal learning using different variants of deep networks and data sources, our model is defined at a finer level of granularity—it computes representations for individual words and is unique in its use of attributes as a means of representing the textual and visual modalities. We evaluate the effectiveness of the representations learnt by our model by assessing its ability to account for human behaviour on three semantic tasks, namely word similarity, concept categorisation, and typicality of category members. With respect to the word similarity task, we focus on the model’s ability to capture similarity in both the meaning and appearance of the words’ referents. Since existing benchmark datasets on word similarity do not distinguish between these two dimensions and often contain abstract words, we create a new dataset in a large-scale experiment where participants are asked to give two ratings per word pair expressing their semantic and visual similarity, respectively. Experimental results show that our model learns meaningful representations which are more accurate than models based on individual modalities or different modality integration mechanisms. The presented model is furthermore able to predict textual attributes for new concepts given their visual attribute predictions only, which we demonstrate by comparing model output with human generated attributes. Finally, we show the model’s effectiveness in an image-based task on visual category learning, in which images are used as a stand-in for real-world objects.

M. Tech. / This randomised, comparative study was undertaken in order to evaluate whether a menthol-containing gel (Deep Heat® Rub) combined with Chiropractic adjustments on mechanical low back pain, over a three-week period, is more effective than Chiropractic adjustments and aqueous cream only.Thirty participants who conformed to the specified inclusion and exclusion criteria, were accepted to form part of the study. The thirty participants were placed randomly into two groups of fifteen participants each. The Experimental Group received lumbar spine and/or Sacroiliac adjustment(s) over the restricted joint(s) together with the application of Deep Heat® Gel, and the Control Group received lumbar spine and/or Sacroiliac adjustment(s) over the restricted joint(s) together with the application of non-medicated aqueous cream. All participants received a total of six treatments over a three-week period, i.e. two treatments per week. Each participant was requested to come in for a follow-up consultation one day after the last treatment session used for data capturing only. The subjective data was collected by means of the Revised Oswestry Low Back Pain Disability Questionnaire, taken prior to the first, fourth and at the seventh consultations. The objective data, in the form of lumbar range of motion, was obtained by means of a Digital Inclinometer, taken prior to every consultation. Data was statistically analysed by means of the Friedman and Wilcoxon Signed Rank tests for intra-group comparisons and the Mann-Whitney test for inter-group comparisons.The results indicated that the treatment protocols used in the Control and the Experimental Group were equally effective in reducing low back pain and disability as well as increasing the lumbar range of motion. The Control Group did, however, demonstrate statistically significant differences in three out of six ranges of motion, compared to two out of six for the Experimental Group, although no statistically significant differences were found.The results of this study suggest that Chiropractic adjustive therapy (with non-medicated aqueous cream) alone or when administered with Deep Heat® Rub are equally effective in terms of subjective pain perception and objective clinical findings in the treatment of mechanical low back pain. No statistically significant difference could be seen when participants received diversified spinal adjustive therapy and application of aqueous cream, compared to receiving diversified spinal adjustive therapy and application of Deep Heat® Rub.

M.Phil. (Chemistry) / This project concerns the study of , specifically, the incomplete fusion mechanism. The nuclear reaction 7Li + 176Yb at 50 MeV was therefore carried out using the AFRODITE and DIAMANT facility of iThemba LABS. A 7Li nuclide is considered suitable for the breakup fusion (incomplete fusion) reaction because of its well developed cluster structure of an -particle and triton which are weakly bound in this nucleus. One of the breakup fragments may be captured by the target while the other escapes at the beam velocity. Light charged-particles (alpha, tritons, deuterons and protons) were detected with the DIAMANT (CsI) array in co-incidence with gammarays detected by the AFRODITE (HPGe) spectrometer. The light particle detection in co-incidence with gamma detection was an important innovation that allowed exclusivity in the reconstruction of the mechanism by which specific residues were produced. Off-line data processing was used to produce charged-particle-gated gamma-gamma coincidence matrices which were analysed with the RADWARE software package. The level scheme exclusive to a particular channel for the production of the 178Hf was extracted. The relative cross-section for the various reaction channels could also therefore be extracted. In particular, the intensity ratios of gamma transitions as function of spin for proton to triton-gated matrices populating the 178Hf isotope were extracted. Insights could be developed into the incomplete fusion reaction mechanisms initiated by the breakup of the incident 7Li projectile.