Background: The most common major surgical procedure performed worldwide is the caesarean section (CS). Effective pain management is a priority for women undergoing this procedure, to reduce the incidence of persistent pain, (a risk factor for postpartum depression), as well as optimize maternal-neonatal bonding and the successful establishment of breastfeeding. Multimodal analgesia is the gold standard for post-caesarean section analgesia. At present, no perioperative pain management protocols could be identified for the management of patients presenting for CS at regional hospitals in South Africa. This audit aimed to review the folders of patients who underwent CS, with reference to perioperative pain management guidelines for CS. Methods: A descriptive, retrospective, cross-sectional audit was conducted. Three hundred folders (10% of the annual number of caesarean procedures performed) from New Somerset Hospital, a regional hospital in Cape Town, South Africa were reviewed. Results: The women were a mean age of 30 years (SD 6.2). Median gravidity was 3 (IQR 2-3) and parity was 1 (IQR 1-2); 52% had previously undergone a CS. In 93.3%, spinal anaesthesia was employed for CS. Pain assessment was poor, with only 55 (18%) patients having their pain assessed on the day of the operation. Analgesia was prescribed in over 98% of the patients, however, medication was only administered as prescribed in 32.6%. Non-steroidal anti inflammatory drugs (NSAIDs) were prescribed in < 1.67% of cases. None of the patients received a patient-controlled analgesia (PCA), transversus abdominis plane (TAP) block, or wound infusion catheter as supplementary strategies. Conclusions: Pain management for post-CS patient at this hospital is lacking. There is the need for the implementation of a structured assessment tool to improve administration of analgesics in these patients. In addition, the reasons for the omission of NSAIDs from the analgesia regimen requires investigation. Hospitals require post-CS pain protocols to guide management especially in resource-limited settings.

<p>Research shows that there is a literacy crisis in many South African primary schools, especially in the Foundation and Intermediate Phases (Grades 1 &ndash / &nbsp / ). The latest Annual National Assessments (ANA) results released in 2011 indicate that learners performed below the acceptable literacy levels as&nbsp / the national pass rate for Grade 3 learners was 35% and was 28% for Grade 6 learners (ANA, 2011:6). Research on literacy focuses on reading and&nbsp / there is little known about how young learners develop writing skills. This qualitative ethnographic study investigated how writing skills are developed in Grade 1 learners by looking at the writing processes as well as the teaching methods used by teachers to develop learners&rsquo / writing skills. The research also&nbsp / analyzed the texts produced by Grade 1 learners and the languages used in their written texts. The sample group in this research was the Grade 1 learners&nbsp / to a multicultural school in Cape Town. Data were collected by means of classroom observations, interviews and document analysis. The thematic&nbsp / arrative approach was used to analyze data and the analysis was informed by the Writing Developmental Continuum model and the Multimodal&nbsp / Approach to literacy in order to gain a better understanding of how young learners use language and other forms of writing such as visuals and gestures to&nbsp / onstruct and convey meaning.&nbsp / The findings of this research show that Grade 1 learners make use of semiotic resources including the language(s)&nbsp / &nbsp / &nbsp / available in their immediate context to create multimodal texts that incorporate both visual and written features. This shows that young learners represent&nbsp / their world experiences through interpersonal and experiential meanings in language(s) exposed to them. The teacher has a big role to play in developing&nbsp / learners&rsquo / writing skills and has to employ a variety of pedagogical strategies that support learners to move through the different writing phases before they develop into early writers. The study concludes that writing is not a linear process but it is a gradual process which depends on a variety of resources and&nbsp / factors which build on learners&rsquo / prior experiences and creativity.</p>

<p>Research shows that there is a literacy crisis in many South African primary schools, especially in the Foundation and Intermediate Phases (Grades 1 &ndash / &nbsp / ). The latest Annual National Assessments (ANA) results released in 2011 indicate that learners performed below the acceptable literacy levels as&nbsp / the national pass rate for Grade 3 learners was 35% and was 28% for Grade 6 learners (ANA, 2011:6). Research on literacy focuses on reading and&nbsp / there is little known about how young learners develop writing skills. This qualitative ethnographic study investigated how writing skills are developed in Grade 1 learners by looking at the writing processes as well as the teaching methods used by teachers to develop learners&rsquo / writing skills. The research also&nbsp / analyzed the texts produced by Grade 1 learners and the languages used in their written texts. The sample group in this research was the Grade 1 learners&nbsp / to a multicultural school in Cape Town. Data were collected by means of classroom observations, interviews and document analysis. The thematic&nbsp / arrative approach was used to analyze data and the analysis was informed by the Writing Developmental Continuum model and the Multimodal&nbsp / Approach to literacy in order to gain a better understanding of how young learners use language and other forms of writing such as visuals and gestures to&nbsp / onstruct and convey meaning.&nbsp / The findings of this research show that Grade 1 learners make use of semiotic resources including the language(s)&nbsp / &nbsp / &nbsp / available in their immediate context to create multimodal texts that incorporate both visual and written features. This shows that young learners represent&nbsp / their world experiences through interpersonal and experiential meanings in language(s) exposed to them. The teacher has a big role to play in developing&nbsp / learners&rsquo / writing skills and has to employ a variety of pedagogical strategies that support learners to move through the different writing phases before they develop into early writers. The study concludes that writing is not a linear process but it is a gradual process which depends on a variety of resources and&nbsp / factors which build on learners&rsquo / prior experiences and creativity.</p>

Les réseaux réflecteurs sont une alternative prometteuse aux antennes à réflecteurs pour la réalisation de diagrammes de rayonnement directifs ou de couvertures formées, notamment dans le spatial ou dans l'aéronautique. Constitués d'un grand nombre de cellules unitaires dont il faut optimiser la géométrie individuellement, ils restent toutefois difficiles à concevoir. Cette thèse traite de la synthèse des antennes réseau à réflecteur. Aujourd'hui les méthodes utilisées pour les concevoir exploitent, pour la majorité, les logiciels de simulation électromagnétique. Elles sont très coûteuses en temps de calcul et requièrent au final des hypothèses simplificatrices. L'objectif de cette thèse est de répondre à la question suivante : est-il possible de synthétiser de manière purement·analytique un réseau réflecteur? Nous avons répondu à cette question en exploitant la méthode de modélisation multimodale. Elle consiste à représenter la cellule unitaire sous la forme d'un circuit équivalent. Ses propriétés remarquables permettent de prédire analytiquement la phase en réflexion en fonction des dimensions de la cellule unitaire. Un réflecteur composé de cellules unitaires à motif 1 D de;type ruban métallique a tout d'abord été étudié et évalué dans le cadre de la synthèse analytique d'une structure simple. Cette étude a permis de valider la méthode de modélisation en vue de son utilisation dans la synthèse analytique de réseaux réflecteurs. Trois réseaux réflecteurs composés de cellules unitaires 2D de types patch et grille ont ensuite·été synthétisés sur la base de trois spécifications différentes, ceci afin de tester la synthèse analytique dans des configurations de plus en plus contraignantes. Finalement il s'est révélé que la méthode de modélisation multimodale et prometteuse mais que sa précision doit encore être améliorée pour permettre une synthèse complète de réseau réflecteur. / Reflectarrays antennas are a promising alternative to reflector antennas in order to produce focused and contoured beams especially for aeronautics and space applications. A reflectarray antenna is made up of ah array of unit cell that provide a pre-adjusted phasing to form the desired beam.The synthesis of a reflectarray consists in fixing the geometrical dimensions of each unit cell to generate the desired phase law. This thesis focuses on the synthesis of reflectarray. The current methods are mostly based on full­wave analysis and so they are time consuming.The purpose of this thesis is to answer at the following question: a reflectarray can be fully analytically synthesized? We answer it by using the multimodal method. The unit cell is then represented by an equivalent circuit. Using its remarkable properties, the reflected phase can be analytically predicted according to the geometrical dimensions of the unit cell. We used it firstly to synthesize a reflector where the unit cell is composed or a metallic strip. This study has validated this method in order to synthesize reflectarray, Then three reflectarrays have been synthesized based on three specifications. Finally, the multimodal method is promising but the precision has to be improving in order to fully synthesize a reflectarray.

Magister Educationis - MEd / Research shows that there is a literacy crisis in many South African primary schools, especially in the Foundation and Intermediate Phases (Grades 1 – ). The latest Annual National Assessments (ANA) results released in 2011 indicate that learners performed below the acceptable literacy levels as the national pass rate for Grade 3 learners was 35% and was 28% for Grade 6 learners (ANA, 2011:6). Research on literacy focuses on reading and there is little known about how young learners develop writing skills. This qualitative ethnographic study investigated how writing skills are developed in Grade 1 learners by looking at the writing processes as well as the teaching methods used by teachers to develop learners’ writing skills. The research also analyzed the texts produced by Grade 1 learners and the languages used in their written texts. The sample group in this research was the Grade 1 learners to a multicultural school in Cape Town. Data were collected by means of classroom observations, interviews and document analysis. The thematic arrative approach was used to analyze data and the analysis was informed by the Writing Developmental Continuum model and the Multimodal Approach to literacy in order to gain a better understanding of how young learners use language and other forms of writing such as visuals and gestures to onstruct and convey meaning. The findings of this research show that Grade 1 learners make use of semiotic resources including the language(s) available in their immediate context to create multimodal texts that incorporate both visual and written features. This shows that young learners represent their world experiences through interpersonal and experiential meanings in language(s) exposed to them. The teacher has a big role to play in developing learners’ writing skills and has to employ a variety of pedagogical strategies that support learners to move through the different writing phases before they develop into early writers. The study concludes that writing is not a linear process but it is a gradual process which depends on a variety of resources and factors which build on learners’ prior experiences and creativity. / South Africa

Notre travail s'inscrit dans le domaine de l'affective computing et plus précisément la modélisation, détection et annotation des émotions. L'objectif est d'étudier, d'identifier et de modéliser les émotions afin d'assurer l’échange entre applications multimodales. Notre contribution s'axe donc sur trois points. En premier lieu, nous présentons une nouvelle vision de la modélisation des états émotionnels basée sur un modèle générique pour la représentation et l'échange des émotions entre applications multimodales. Il s'agit d'un modèle de représentation hiérarchique composé de trois couches distinctes : la couche psychologique, la couche de calcul formel et la couche langage. Ce modèle permet la représentation d'une infinité d'émotions et la modélisation aussi bien des émotions de base comme la colère, la tristesse et la peur que les émotions complexes comme les émotions simulées et masquées. Le second point de notre contribution est axé sur une approche monomodale de reconnaissance des émotions fondée sur l'analyse des signaux physiologiques. L'algorithme de reconnaissance des émotions s'appuie à la fois sur l'application des techniques de traitement du signal, sur une classification par plus proche voisins et également sur notre modèle multidimensionnel de représentation des émotions. Notre troisième contribution porte sur une approche multimodale de reconnaissance des émotions. Cette approche de traitement des données conduit à une génération d'information de meilleure qualité et plus fiable que celle obtenue à partir d'une seule modalité. Les résultats expérimentaux montrent une amélioration significative des taux de reconnaissance des huit émotions par rapport aux résultats obtenus avec l'approche monomodale. Enfin nous avons intégré notre travail dans une application de détection de la dépression des personnes âgées dans un habitat intelligent. Nous avons utilisé les signaux physiologiques recueillis à partir de différents capteurs installés dans l'habitat pour estimer l'état affectif de la personne concernée. / This study focuses on affective computing in both fields of modeling and detecting emotions. Our contributions concern three points. First, we present a generic solution of emotional data exchange between heterogeneous multi-modal applications. This proposal is based on a new algebraic representation of emotions and is composed of three distinct layers : the psychological layer, the formal computational layer and the language layer. The first layer represents the psychological theory adopted in our approach which is the Plutchik's theory. The second layer is based on a formal multidimensional model. It matches the psychological approach of the previous layer. The final layer uses XML to generate the final emotional data to be transferred through the network. In this study we demonstrate the effectiveness of our model to represent an in infinity of emotions and to model not only the basic emotions (e.g., anger, sadness, fear) but also complex emotions like simulated and masked emotions. Moreover, our proposal provides powerful mathematical tools for the analysis and the processing of these emotions and it enables the exchange of the emotional states regardless of the modalities and sensors used in the detection step. The second contribution consists on a new monomodal method of recognizing emotional states from physiological signals. The proposed method uses signal processing techniques to analyze physiological signals. It consists of two main steps : the training step and the detection step. In the First step, our algorithm extracts the features of emotion from the data to generate an emotion training data base. Then in the second step, we apply the k-nearest-neighbor classifier to assign the predefined classes to instances in the test set. The final result is defined as an eight components vector representing the felt emotion in multidimensional space. The third contribution is focused on multimodal approach for the emotion recognition that integrates information coming from different cues and modalities. It is based on our proposed formal multidimensional model. Experimental results show how the proposed approach increases the recognition rates in comparison with the unimodal approach. Finally, we integrated our study on an automatic tool for prevention and early detection of depression using physiological sensors. It consists of two main steps : the capture of physiological features and analysis of emotional information. The first step permits to detect emotions felt throughout the day. The second step consists on analyzing these emotional information to prevent depression.

Chronic Kidney Disease (CKD) is a common and dangerous health condition that requires early detection and treatment to be effective. Current diagnostic methods are time-consuming and expensive. In this research, we hope to construct a predictive model for CKD utilizing a combination of time series and static variables for early detection of CKD. In this study, we investigate the influence of multimodal approach by combining the predictions from multiple models that utilize different modalities. The ROCKET method is utilized for classification using time series features, whilst the Random Forest approach is employed for static data. XGBoost has been utilized to gain information about feature importance among labs and demographics-comorbidities data. In this study, we use the MIMIC-III database, adopting various strategies to handle data and class imbalance, such as stratification, balancing techniques, and backwards and forward fill for missing value imputation. The evaluation metrics for CKD and non-CKD class labels include precision, recall, F1, and accuracy. Our findings show that aggregating time series data produce contrasting results for labs compared to vitals data. We also addressed the significance of the different demographic, comorbidities and lab events features. The findings indicate that a multimodal approach did not show significant advantages over individual models when the individual models performed suboptimal. The study also found that Ethnicity is more significant than age and gender in predicting CKD. Furthermore, the study revealed some significant features from lab events and comorbidities. The study also provides some recommendations for future work to explore the potential of a multimodal approach further.

Devido ao aumento da população idosa ou com limitações físicas/mentais é possível observar o crescimento de estudos na área de Internet das Coisas com o objetivo de monitorar a saúde e auxiliar no gerenciamento e melhora na qualidade de vida dessa parte da população. Nesse sentido, a abordagem baseada em Internet das Coisas aplicada em ambientes médicos e casas inteligentes tem o objetivo de fornecer conectividade entre o paciente e o ambiente ao seu redor, provendo mecanismos para ajudar em diagnósticos e prevenção de acidentes e/ou doenças. Nesse contexto surge a oportunidade de explorar sistemas computacionais para identificar o estado físico e emocional, em tempo real, de indivíduos com alguma limitação para monitorar a saúde; por exemplo, identificar o comportamento da rotina do usuário e emitir alertas aos familiares e/ou equipe médica sobre algum evento anormal ou identificar indícios de distúrbios emocionais. Ainda, com base na Inteligência Artificial é possível que sistemas computacionais possam aprender e se adaptar ao contexto que se encontra, por exemplo aprender e se adaptar a quantidade de exercícios e/ou estado emocional do usuário em determinadas situações, combinando conceitos tanto de Internet of Things quanto de Inteligência Artificial. Assim, este projeto tem como objetivo desenvolver e avaliar um modelo que possa: i.) identificar o estado físico e emocional do usuário; ii.) prover um mecanismo que possa monitorar de maneira inteligente as atividades do cotidiano do usuário e; iii.) explorar a abordagem de integração de dados com a utilização de múltiplos sensores IoT para uma melhor interação entre dispositivos computacionais no ambiente Health Smart Homes. / Due to the increase in the elderly population or with physical/mental limitations it is possible to observe the growth of studies in the field of Internet of Things with the objective of monitoring health and assisting in the management and improvement of the quality of life of this part of the population . In this sense, the Internet of Things approach applied in medical environments and smart homes aims to provide connectivity between the patient and the environment around them, providing mechanisms to aid in diagnosis and prevention of accidents and/or diseases. In this context the opportunity arises to explore computational systems to identify the physical and emotional state, in real time, of individuals with some limitation to monitor health; for example, identifying the behavior of the users routine and issuing alerts to family members and/or medical staff about some abnormal event or identifying evidence of emotional disturbances. Also, based on Artificial Intelligence, it is possible for computational systems to learn and adapt to the context they are in, for example learning and adapting the amount of exercises and/or emotional state of the user in certain situations, combining concepts both of Internet of Things and of Artificial Intelligence. Thus, this project aims to develop and evaluate a model that can: i) identify the physical and emotional state of the user; ii) provide a mechanism that can intelligently monitor the daily activities of the user and; iii) explore the data integration approach with the use of multiple IoT sensors for better interaction between computing devices in the Health Smart Homes environment.

Ce travail de thèse s’inscrit dans le domaine de l’informatique affective et plus précisément de l’intelligence artificielle et de l’exploration d’architecture. L’objectif de ce travail est de concevoir un système complet de détection des émotions en utilisant des signaux physiologiques. Ce travail se place donc à l’intersection de l’informatique pour la définition d’algorithme de détection des émotions et de l’électronique pour l’élaboration d’une méthodologie d’exploration d’architecture et pour la conception de nœuds de capteurs. Dans un premier temps, des algorithmes de détection multimodale et instantanée des émotions ont été définis. Deux algorithmes de classification KNN puis SVM, ont été implémentés et ont permis d’obtenir un taux de reconnaissance des émotions supérieurs à 80%. Afin de concevoir un tel système alimenté sur pile, un modèle analytique d’estimation de la consommation à haut niveau d’abstraction a été proposé et validé sur une plateforme réelle. Afin de tenir compte des contraintes utilisateurs, un outil de conception et de simulation d’architecture d’objets connectés pour la santé a été développé, permettant ainsi d’évaluer les performances des systèmes avant leur conception. Une architecture logicielle/matérielle pour la collecte et le traitement des données satisfaisant les contraintes applicatives et utilisateurs a ainsi été proposée. Doté de cette architecture, des expérimentations ont été menées pour la Mnémothérapie musicale. EMOTICA est un système complet de détection des émotions utilisant des signaux physiologiques satisfaisant les contraintes d’architecture, d’application et de l’utilisateur. / This thesis work is part of the field of affective computing and more specifically artificial intelligence and architectural exploration. The goal of this work is to design a complete system of emotions detection using physiological signals. This work is therefore situated at the intersection of computer science for the definition of algorithm of detection of emotions and electronics for the development of an architecture exploration methodology for the design of sensor nodes. At first, algorithms for multimodal and instantaneous detection of emotions were defined. Two algorithms of classification KNN then SVM, were implemented and made it possible to obtain a recognition rate of the emotions higher than 80%. To design such a battery-powered system, an analytical model for estimating the power consumption at high level of abstraction has been proposed and validated on a real platform. To consider user constraints, a connected object architecture design and simulation tool for health has been developed, allowing the performance of systems to be evaluated prior to their design. Then, we used this tool to propose a hardware/software architecture for the collection and the processing of the data satisfying the architectural and applicative constraints. With this architecture, experiments have been conducted for musical Mnemotherapy. EMOTICA is a complete system for emotions detection using physiological signals satisfying the constraints of architecture, application and user.