The quantitative study of conflict management is concerned with finding models which are accurate and also capable of providing a causal interpretation of results. This dissertation applies computational intelligence methods to study interstate disputes. Both multilayer perceptron neural networks and Takagi-Sugeno neuro-fuzzy models are used to model interstate interactions. The multilayer perceptron neural network is trained in the Bayesian framework, using the Hybrid Monte Carlo method to sample from the posterior probabilities. It is found that the network is able to forecast conflict with an accuracy of 77.3%. A hybrid machine learning method using the neural network and the genetic algorithm is then presented as a method of suggesting how conflict can be brought under control. The automatic relevance determination approach and the sensitivity analysis are used as methods of extracting causal information from the neural network. The Takagi-Sugeno neuro-fuzzy model is optimised, using the Gustafson-Kessel clustering algorithm to partion the input space. It is found that the neuro-fuzzy model predicts conflict with an accuracy of 80.1%. The neuro-fuzzy model is also incorporated into the hybrid machine learning method to suggest how the identified conflict cases can be avoided. The casual interpretation is then formulated by a linguistic approximation of the fuzzy rules extracted from the neuro-fuzzy model. The major finding in this work is that the interpretations drawn from both the neural network and the neuro-fuzzy model are consistent.

Tese de mestrado integrado. Engenharia Electrotécnica e de Computadores (Major Automação). Faculdade de Engenharia. Universidade do Porto. 2009

Ce mémoire est réalisé sur base de l’analyse d’une décision de justice provenant des Etats-Unis, à travers la mobilisation de la théorie de l’acteur-réseau. L’objectif est d’étudier les manières dont les techniques de neuro-imageries produisent leurs effets, ainsi que les mécanismes utilisés par les acteurs neuroscientifiques en vue de mobiliser ces techniques et leurs effets devant la Cour. Une revue de littérature traitant des interactions déjà existantes entre, d’une part le secteur des neurosciences et des neuro-images, et d’autre part celui de la justice pénale, nous a permis de mettre en exergue plusieurs points d’intérêts liants ces différents domaines, afin de démontrer que des éléments neuroscientifiques sont déjà présents dans le cadre des procès pénaux. Au sein de notre analyse, pour répondre à notre question de recherche, nous avons tenté de mettre en évidence les manières dont les éléments de « la science en action » ont été exploités pour rendre les techniques de neuro-imageries et leurs effets plus légitimes devant la justice pénale.

<p>Color, Hygiene, and Body Politics: French Neo-Impressionist Theories of Vision and Volition, 1870-1905, explores the little studied "pragmatic" dimension of Neo-Impressionist theory and practice to reveal the full social and political import of Divisionist technique. Specifically, it examines how Neo-Impressionist painters such as Georges Seurat (1859-1891), Paul Signac (1863-1935), and Camille Pissarro (1830-1903), as well as their anarchist allies, applied artistic and political tenets to their daily practices, including hygienic habits and medical treatments. Neo-Impressionist Divisionism was based on a physiological awareness that the balanced use of three optical nerves generated a sense of harmony. By examining the ethical aspects of neuro-psychological color theories in nineteenth-century Europe, this research demonstrates that this awareness was not merely a matter of optics, but a part of the prevalent socio-ethical discourse of energy efficiency. </p><p>The first chapter, "Color Perception and Mental Labor: Divisionism and the Ethic of Nineteenth-Century Neuro-psychology," examines the history of nineteenth-century neuro-psychology to address how, in the fields of art and science, color perception was identified as an action. The chapter focuses on widespread neuro-psychological notions of "reflex theory," "nervous fatigue," and "homeostasis," all of which regulated the Neo-Impressionist concept of color harmony. Illuminating the Neo-Impressionist neural ethic, this chapter focuses on the behavioral phase of the Neo-Impressionist aesthetics neglected by previous studies. </p><p>The second chapter, "Neuro-psychological Space in Color and Dynamism," explores how this behavioral discourse was visualized in Neo-Impressionist painterly space. Contrary to Kantian a priori space, prominent theorists, such as Taine, Spencer, and Ribot who influenced the Neo-Impressionists, upheld the idea of dynamic space. As the raison d'être for this new space resided in the dynamic interaction between the self and the world, action became fundamental to its formation. Color in such new spaces was the perceptual bedrock, since optical nerves defined external objects chiefly as color. This chapter underscores the connection between dynamism and color in painterly space, through which the viewer could voluntarily engage with the world. </p><p>The third chapter, "Therapeutic Color and the Neo-Impressionists' Daily Practices," delves into the Neo-Impressionists' health-related pursuits including their commitment to hydrotherapy, color therapy, and homeopathy, all associated with an ecological concern for the equilibrium between the self and the environment. This comprehensive examination reveals an overlooked behavioral aspect of Neo-Impressionist theory, which was a critical dimension of their world-view that sought to merge art and life. The first section examines the artists' commitment to bathing and hydrotherapy through an analysis of the art and writings of Camille Pissarro and his anarchist allies. The second part examines the theory of color therapy developed by Dr. Paul Ferdinand Gachet, and his impact on the Neo-Impressionists. In the final section, I consider the broader implications of the Neo-Impressionist embrace of homeopathic practices with reference to a theory of ecological equilibrium.</p> / Dissertation

The unique gene, pleiotrophin (ptn) was identified in the expressed sequence taq (EST) derived from the developing tilapia brain in our lab. The cDNA full length of ptn was cloned. ptn play a role in the differentiation of nerve cell. In this study, bioinformatics were searched for EE723939.1 (ptn), which is a gene with 1026 bp of cDNA sequence, open reading frame(ORF) is 483bp, and deduced as 160 amino acids. The protein of PTN was expressed in the prokaryotic system, BL21(E.coli), and purified with Ni-NTA affinity chromatography. In the present studies, ptn, cloned from tilapia, Oreochromis mossambicus. The influence of ptn on the proliferation of Neuro-2a cell was also investigated.The ORF of ptn was cloned, and the pEGFP-ptn plasmid was constructed. The distribution of ptn in the pEGFP-ptn transfected Neuro-2a cell was identified by fluorescence and laser confocal microscopy.

For some new multimedia applications using Mpeg-4 or Mpeg-7 video coding standards, it is important to find the main objects in a video frame. In this thesis, we propose a neuro-fuzzy modeling approach to the detection of human face and body. Firstly, a fuzzy clustering technique is performed to segment a video frame into clusters to generating several fuzzy rules. Secondly, chrominance and motion features are used to roughly classify the clusters into foreground and background, respectively. Finally, the fuzzy rules are refined by a fuzzy neural network, and the ambiguous regions between foreground and background are further distinguished by the fuzzy neural network. Our method improves the correctness of human face and body detection by getting training data more precisely. Besides, we can extract the VOs correctly even the VOs have no obvious motion in the video sequence.

ATM (Asynchronous Transfer Mode) networks were deemed the best choice for multimedia communication. The traditional mode was replaced because ATM can provide varied traffic types and QoS (quality of service). Maintaining QoS, however, requires a flexible traffic control, including call admission control and congestion control. Traditional approaches fail to estimate the required bandwidth and cell loss rate precisely. To alleviate these problems, we employ AI methods to improve the capability of estimated bandwidth and predicted cell loss rate. This thesis aims to apply neural network techniques to ATM traffic control and consists of two parts. The first part concerns a neural-based call admission control, while the second part presents an intelligent congestion control for ATM networks. In the first part, we focus on the improvement of RBF (Radial basis function) networks and the design of a neural-based call admission control. RBF networks have been widely used for modeling a function from given input-output patterns. However, two difficulties are encountered with traditional RBF networks. One is that the initial configuration of a RBF network needs to be determined by a trial-and-error method. The other is that the performance suffers from some difficulties when the desired output has abrupt changes or constant values in certain intervals. We propose a novel approach to overcome these difficulties. New kernel functions are used for hidden nodes, and the number of nodes is determined automatically by an ART-like algorithm. Parameters and weights are initialized appropriately, and then tuned and adjusted by the gradient descent method to improve the performance of the network. Then, we employ ART-RBF networks to design and implement a call admission control. Traditional approaches fail to estimate appropriately the required bandwidth, leading to a waste of bandwidth or a high cell loss rate. To alleviate the problem, we employ ART-RBF networks to estimate the required bandwidth, and thus a new connection request can then be accepted or rejected. Because of the more accurate estimation on the required bandwidth, the proposed method can provide a better control on quality of service for ATM networks. In the second part, we propose a neural-fuzzy rate-based feedback congestion control for ATM networks. Traditional methods perform congestion control by monitoring the queue length. The source rate is decreased by a fixed rate when the queue length is greater than a predefined threshold. However, it is difficult to get a suitable rate according to the degree of traffic congestion. We employ a neural-fuzzy mechanism to control the source rate. Through learning, cell loss can be predicted from the current value and the derivative of the queue length. Then an explicit rate is calculated and the source rate is controlled appropriately. In summary, we have proposed improvements on architecture and performance of neural networks, and applied neural networks to traffic control for ATM networks. We have developed some control mechanisms which, through simulations, have been shown to be more effective than traditional methods.

CNS-PNETs are rare, aggressive, paediatric embryonal brain tumours that are poorly studied. We recently identified an aggressive sub-group of CNS-PNETs characterized by the amplification of the C19MC microRNA cluster, however, little is known regarding the features of other CNS-PNET tumours. This study was designed to define additional molecular sub-groups of CNS-PNET by interrogating a large cohort of CNS-PNETs. To elucidate the features of CNS-PNET, we examined transcriptional and copy number profiles from primary hemispheric CNS-PNETs. We then validated and examined the clinical significance of novel sub-group markers in 123 primary CNS-PNETs. This thesis demonstrates that CNS-PNET can be categorized into three molecular sub-groups that are distinguished by distinct primitive neural, oligo-neural and mesenchymal lineage gene expression signatures and also correlated with distinct clinical features. Data from my thesis has generated a substantial body of knowledge to fuel both biological and clinical investigations of childhood CNS-PNETs.

CNS-PNETs are rare, aggressive, paediatric embryonal brain tumours that are poorly studied. We recently identified an aggressive sub-group of CNS-PNETs characterized by the amplification of the C19MC microRNA cluster, however, little is known regarding the features of other CNS-PNET tumours. This study was designed to define additional molecular sub-groups of CNS-PNET by interrogating a large cohort of CNS-PNETs. To elucidate the features of CNS-PNET, we examined transcriptional and copy number profiles from primary hemispheric CNS-PNETs. We then validated and examined the clinical significance of novel sub-group markers in 123 primary CNS-PNETs. This thesis demonstrates that CNS-PNET can be categorized into three molecular sub-groups that are distinguished by distinct primitive neural, oligo-neural and mesenchymal lineage gene expression signatures and also correlated with distinct clinical features. Data from my thesis has generated a substantial body of knowledge to fuel both biological and clinical investigations of childhood CNS-PNETs.

While researchers have explored many important aspects of living with childhood cancer, including the multitude of strains on family members and their reactions, very little is known about the experiences of children with brain tumours and their parents. Grounded theory methods were utilized to explore the unique and shared elements of the experiences of childhood brain tumours, from the perspectives of these children and their parents. Woven throughout their stories were expressions of grief and uncertainty related to the tumour and its effects on their lives. Children and parents tried to maintain a positive outlook and a sense of normalcy, in order to cope and to adapt to the struggles and the changes in their lives. A substantive theory of Balancing Grief and Survival was developed, offering a lens through which to view the children’s and parents’ complex experiences, struggles and coping strategies as integrated, dynamic processes.