<p>This study explored the learning experiences of black working class women in the context of the National Skills Development Strategy. The research focused firstly, on how the National Skills Development Strategy facilitated women's learning and secondly, what has helped and hindered their learning and how their learning experiences related to the literature on women's learning.</p>

This work aims to improve the capability of accurate information extraction from high-dimensional data, with a specific neural learning paradigm, the Self-Organizing Map (SOM). The SOM is an unsupervised learning algorithm that can faithfully sense the manifold structure and support supervised learning of relevant information from the data. Yet open problems regarding SOM learning exist. We focus on the following two issues. (1) Evaluation of topology preservation. Topology preservation is essential for SOMs in faithful representation of manifold structure. However, in reality, topology violations are not unusual, especially when the data have complicated structure. Measures capable of accurately quantifying and informatively expressing topology violations are lacking. One contribution of this work is a new measure, the Weighted Differential Topographic Function ( WDTF ), which differentiates an existing measure, the Topographic Function ( TF ), and incorporates detailed data distribution as an importance weighting of violations to distinguish severe violations from insignificant ones. Another contribution is an interactive visual tool, TopoView, which facilitates the visual inspection of violations on the SOM lattice. We show the effectiveness of the combined use of the WDTF and TopoView through a simple two-dimensional data set and two hyperspectral images. (2) Learning multiple latent variables from high-dimensional data. We use an existing two-layer SOM-hybrid supervised architecture, which captures the manifold structure in its SOM hidden layer, and then, uses its output layer to perform the supervised learning of latent variables. In the customary way, the output layer only uses the strongest output of the SOM neurons. This severely limits the learning capability. We allow multiple, k , strongest responses of the SOM neurons for the supervised learning. Moreover, the fact that different latent variables can be best learned with different values of k motivates a new neural architecture, the Conjoined Twins, which extends the existing architecture with additional copies of the output layer, for preferential use of different values of k in the learning of different latent variables. We also automate the customization of k for different variables with the statistics derived from the SOM. The Conjoined Twins shows its effectiveness in the inference of two physical parameters from Near-Infrared spectra of planetary ices.

In the last decade, cellular networks have been characterized by an ever-growing user data demand. This caused increasing capacity shortfall and coverage issues, aggravated by inefficient fixed spectrum management policies and obsolete network structures. From a practical point of view, novel technical and architectural solutions have been proposed to frame next generation cellular networks, capable of meeting the identified target performance to satisfy the user data demands. Specifically, new spectrum management policies based on the so-called dynamic spectrum access (DSA), together with hierarchical approaches to network planning, where a tier of macro base stations is underlaid with a tier of massively deployed low-power small base stations, are seen as promising candidates to achieve this scope. The resulting two-tiered network layout may improve the capacity of current networks in several ways, thanks to a better average link quality between the devices, a more efficient usage of spectrum resources and a potentially higher spatial reuse. In this thesis, we focus on the challenging problem arising when the two tiers share the transmit band, to capitalize on the available spectrum and avoid possible inefficiencies. In this case, the coexistence of the two tiers is not feasible, if suitable interference management techniques are not designed to mitigate/cancel the mutual interference generated by the active transmitters in the network. This thesis is divided in three main parts, and proposes a rather exhaustive approach to the development of new DSA and interference management techniques, to go from the theoretical basis up to a proof-of-concept development.

A fascinating feature of bucket brigade assembly lines is that work load on workers is balanced spontaneously as workers follow some simple rules in the assembly process. This self-organizing property significantly reduces the management effort on an assembly line. We generalize this idea in several directions. These include an adapted bucket brigade protocol for complex assembly networks, a generalized model that permits chaotic behavior, and a more detailed model for a flow line in which jobs arrive arbitrarily in time and are introduced into the system at several points on the line.

The perceptual reality of the music theoretical relation between the Neapolitan chord and the dominant / and the secondary dominant chord and its diatonic associate was investigated within the chord priming paradigm. In Experiment 1, expectation towards the dominant chord after the Neapolitan chord was observed in Turkish musicians and non-musicians with piano timbre. In Experiment 2, expectation towards the dominant chord after the Neapolitan chord was observed in European musicians but not in European non-musicians. In Experiment 3, Turkish non-musicians were tested with Shepard tones / but it was not possible to observe any priming effects. To understand effects of cultural background on the difference between the results of Experiments 1 and 2 further studies are necessary. In Experiments 4-5, the perceptual reality of the relation between the secondary dominant chord and its diatonic associate was investigated in Turkish non-musicians. In Experiment 4, chord sequences that included secondary dominant chords were played with Shepard tones / and they were scrambled with 2by2 scrambling algorithm. Experiment 5 was identical with Experiment 4, except chord sequences were played with the piano timbre. Experiment 6 was identical with Experiment 5, except chord sequences were scrambled with 4by4. However, in Experiments 4-6 detrimental effects of scrambling sequences that include secondary dominant chords on the priming of chords were not observed. Turkish non-musicians did perceive the relation between the secondary dominant chord and its diatonic associate. In neural network simulations of this thesis it was shown that statistical learning from the musical environment with self-organization could be achieved without committing the questionable assumptions of previous studies.

The self-organizing map (SOM) is an unsupervised learning algorithm which has been successfully applied to various applications. One of advantages of SOM is it maintains an incremental property to handle data on the fly. In the last several decades, there have been variants of SOM used in many application domains. In this dissertation, two new SOM algorithms are developed for image quantization and compression. The first algorithm is a sample-size adaptive SOM algorithm that can be used for color quantization of images to adapt to the variations of network parameters and training sample size. The sweep size of neighborhood function is modulated by the size of the training data. In addition, the minimax distortion principle which is modulated by training sample size is used to search the winning neuron. Based on the sample-size adaptive self-organizing map, we use the sampling ratio of training data, rather than the conventional weight change between adjacent sweeps, as a stop criterion. As a result, it can significantly speed up the learning process. Experimental results show that the proposed sample-size adaptive SOM achieves much better PSNR quality, and smaller PSNR variation under various combinations of network parameters and image size. The second algorithm is a novel classified SOM method for edge preserving quantization of images using an adaptive subcodebook and weighted learning rate. The subcodebook sizes of two classes are automatically adjusted in training iterations based on modified partial distortions that can be estimated incrementally. The proposed weighted learning rate updates the neuron efficiently no matter of how large the weighting factor is. Experimental results show that the proposed classified SOM method achieves better quality of reconstructed edge blocks and more spread out codebook and incurs a significantly less computational cost as compared to the competing methods.

We present methods for distributed self-assembly that utilize simple rule-of-thumb control and communication schemes providing probabilistic performance guarantees. These methods represents a staunch departure from existing approaches that require more sophisticated control and communication, but provide deterministic guarantees. In particular, we show that even under severe communication restrictions, any assembly described by an acyclic weighted graph can be assembled with a rule set that is linear in the number of nodes contained in the desired assembly graph. We introduce the concept of stochastic stability to the self-assembly problem and show that stochastic stability of desirable configurations can be exploited to provide probabilistic performance guarantees for the process. Relaxation of the communication restrictions allows simple approaches giving deterministic guarantees. We establish a clear relationship between availability of communication and convergence properties. We consider Self-assembly tasks for the cases of many and few agents as well as large and small assembly goals. We analyze sensitivity of the presented process to communication errors as well as ill-intentioned agents. We discuss convergence rates of the presented process and directions for improving them.

Despite efforts by clinicians and scientists world-wide, coronary artery disease remains to be the leading cause of morbidity and mortality in industrialized nations. Development of a tissue engineered coronary by-pass graft with low thrombogenicity and immune responses, suitable mechanical properties, and a capacity to remodel to their environment could have a significant impact on the treatment of coronary artery disease. While many methods for the tissue engineering of blood vessels have been developed, one promising approach is the self-assembly method. Using autologous cells that produce an endogenous extracellular matrix (ECM), the potential for therapeutic success is high due to biocompatibility. However, despite these advantages, improvements can be made which will give the grafts an even higher rate of patency. This dissertation presents a study of the characterization of the biaxial mechanical properties of self-assembly tissue engineered blood vessels (SA-TEBV), as well as developing a framework for fabrication strategies of SA-TEBV. Native arteries are exposed to multiaxial mechanical loads, including (a pulsatile) blood pressure that causes the vessel to cyclically distend circumferentially, blood flow that induces a shearing load along the luminal surface, and an axial extending load; the latter is relieved upon excision, causing the vessel to retract. These mechanical loads introduce intramural wall stresses and flow induced wall shear stresses that play a key role in mechano-biological signaling and tissue homeostasis. Until now, the mechanical properties of SA-TEBV have only been characterized in the circumferential direction (i.e. burst pressure and circumferential elastic modulus). The objective of this work is to characterize the biaxial mechanical properties of SA-TEBV to quantify their mechanical behavior and local intramural stresses under physiological loading. The work will show that while the global mechanical response of the SA-TEBV is similar to that of native arteries (and potentially sufficient), the local intramural stresses (using the current fabrication techniques) differ greatly from native coronary arteries. Therefore, a novel approach to fabricate the self-assembly derived tissue sheets is developed and tested which utilizes biaxial mechanical stimulation to alter the microstructure, thereby controlling their mechanical response.

Fullerene research has advanced to elevated levels in a short period of time due to the unique chemical and physical properties of the caged molecule that have been utilized in numerous applications. Due to the spherical shape of the fullerene molecule which allows for a hollow cavity, encapsulation of atoms or small molecules can occur within the ball structure. This encapsulation creates an endohedral component that is limited from interacting with other molecules which creates potential of control over electronic information of the isolated molecule. Endohedral fullerenes have the potential as serving as the base unit in a quantum computer if control over global alignment is attained. Thus, by using the inherent self-assembling capabilities of some organic materials, ordered endohedral fullerenes can be achieved. This dissertation investigates the ability to use self-assembling strategies to obtain alignment which include ordering within a morphologically controlled copolymer matrix, forming a supramolecular polymer complex with cyclodextrin, and encapsulation within the helical wrap of polymer chains. The ultimate goal is to understand the dynamics that control association and orientation of varying fullerene-based molecules in each strategy in order to maximize control over the final alignment of endohedral elements.

Thesis (Ph. D.)--University of Missouri-Columbia, 2000. / Typescript. Vita. Includes bibliographical references (leaves 104-106). Also available on the Internet.