This thesis aims to focus on relational controversy about the causes behind the inconsistency in number of students who use a Web 2.0 application named "LibGuide" inside the Linnaeus University and the attempts of program coordinator to develop certain strategies to perform a better use of it in a particular department of Healthcare. The nature of the problem will be discussed, suggesting that how the these strategies maintained a high significant of use in that department. The research foregoes, as it uses a process of translation which are the moments of struggles by a program coordinator to impose itself by its role to the definition of the situation. The research suggests a network within which the series of processes and interrelation of the sociotechnical role of each member is drawn and allocated.

Abstrakt   Tv-branschen är en jätteindustri som styrs av ett fåtal stora sändningsnätverk. I Sverige är den största distributören Sveriges Television (SVT) och på grund av detta så är sannolikheten att personer som arbetar inom ljud i Sverige någon gång kommer att arbeta på projekt för dem väldigt hög. I min uppsats har jag utfört en Actor Network-analys av empiriska studier jag utfört på postproduktionsbolaget Europa Sound and Vision. Med hjälp av analysen vill jag undersöka vad begreppet ”god hörbarhet” består av och därmed kunna definiera uttrycket tydligare. Jag jämför sedan SVTs syn på hörbarhet med den jag fått fram genom dessa studier för att till slut ändra på den nuvarande synen och på det regelverk SVT har. / Abstract The television industry is a huge industry that is governed by a few big broadcasting networks. The biggest distributer and producer of television in Sweden is Sveriges Television (SVT) and everyone working with sound will, more likely than not, work for them in a project. In this study I’ve analyzed the results of two months of empirical work, I’ve identified the actors that make up the network “God hörbarhet” (good audibility) and the reason this is interesting is for determining a more solid definition for the term “good audibility”. I’ve then put this more solid definition up against SVT’s loose definition, this only to expand on their current system and delivery specifications.

Supply chain optimization is one of the key components for the effective management of a company with a complex manufacturing process and distribution network. Companies with a global presence in particular are motivated to optimize their distribution plans in order to keep their operating costs low and competitive. Changing condition in the global market and volatile energy prices increase the need for an automatic decision and optimization tool. In recent years, many techniques and applications have been proposed to address the problem of supply chain optimization. However, such techniques are often too problemspecific or too knowledge-intensive to be implemented as in-expensive, and easy-to-use computer system. The effort required to implement an optimization system for a new instance of the problem appears to be quite significant. The development process necessitates the involvement of expert personnel and the level of automation is low. The aim of this project is to develop a set of strategies capable of increasing the level of automation when developing a new optimization system. An increased level of automation is achieved by focusing on three areas: multi-objective optimization, optimization algorithm usability, and optimization model design. A literature review highlighted the great level of interest for the problem of multiobjective optimization in the research community. However, the review emphasized a lack of standardization in the area and insufficient understanding of the relationship between multi-objective strategies and problems. Experts in the area of optimization and artificial intelligence are interested in improving the usability of the most recent optimization algorithms. They stated the concern that the large number of variants and parameters, which characterizes such algorithms, affect their potential applicability in real-world environments. Such characteristics are seen as the root cause for the low success of the most recent optimization algorithms in industrial applications. Crucial task for the development of an optimization system is the design of the optimization model. Such task is one of the most complex in the development process, however, it is still performed mostly manually. The importance and the complexity of the task strongly suggest the development of tools to aid the design of optimization models. In order to address such challenges, first the problem of multi-objective optimization is considered and the most widely adopted techniques to solve it are identified. Such techniques are analyzed and described in details to increase the level of standardization in the area. Empirical evidences are highlighted to suggest what type of relationship exists between strategies and problem instances. Regarding the optimization algorithm, a classification method is proposed to improve its usability and computational requirement by automatically tuning one of its key parameters, the termination condition. The algorithm understands the problem complexity and automatically assigns the best termination condition to minimize runtime. The runtime of the optimization system has been reduced by more than 60%. Arguably, the usability of the algorithm has been improved as well, as one of the key configuration tasks can now be completed automatically. Finally, a system is presented to aid the definition of the optimization model through regression analysis. The purpose of the method is to gather as much knowledge about the problem as possible so that the task of the optimization model definition requires a lower user involvement. The application of the proposed algorithm is estimated that could have saved almost 1000 man-weeks to complete the project. The developed strategies have been applied to the problem of Caterpillar’s global supply chain optimization. This thesis describes also the process of developing an optimization system for Caterpillar and highlights the challenges and research opportunities identified while undertaking this work. This thesis describes the optimization model designed for Caterpillar’s supply chain and the implementation details of the Ant Colony System, the algorithm selected to optimize the supply chain. The system is now used to design the distribution plans of more than 7,000 products. The system improved Caterpillar’s marginal profit on such products by a factor of 4.6% on average.

Sensor failures or oversupply in wireless sensor networks (WSNs), especially initial random deployment, create both spare sensors (whose area is fully covered by other sensors) and sensing holes. We envision a team of robots to relocate sensors and improve their area coverage. Existing algorithms, including centralized ones and the only localized G-R3S2, move only spare sensors and have limited improvement because non-spare sensors, with area coverage mostly overlapped by neighbour sensors, are not moved, and additional sensors are deployed to fill existing holes. We propose a localized algorithm, called Localized Ant-based Sensor Relocation Algorithm with Greedy Walk (LASR-G), where each robot may carry at most one sensor and makes decision that depends only on locally detected information. In LASR-G, each robot calculates corresponding pickup or dropping probability, and relocates sensor with currently low coverage contribution to another location where sensing hole would be significantly reduced. The basic algorithm optimizes only area coverage, while modified algorithm includes also the cost of robot movement. We compare LASR-G with G-R3S2, and examine both single robot and multi robots scenarios. The simulation results show the advantages of LASR-G over G-R3S2.

As estratégias de mapeamento utilizando múltiplos robôs móveis possuem uma série de vantagens quando comparadas àquelas estratégias baseadas em um único robô. As principais vantagens que podem ser elucidadas são: flexibilidade, ganho de informação e redução do tempo de construção do mapa do ambiente. No presente trabalho, um método de integração de mapas locais é proposto baseado em observações inter-robôs, considerando uma nova abordagem para a exploração do ambiente. Tal abordagem é conhecida como Sistema de Vigilância baseado na Modificação do Sistema Colônias de Formigas, ou IAS-SS. A estratégia IAS-SS é inspirada em mecanismos biológicos que definem a organização social de sistemas de enxames. Especificamente, esta estratégia é baseada em uma modificação do tradicional algoritmo de otimização por colônias de formiga. A principal contribuição do presente trabalho é a adaptação de um modelo de compartilhamento de informações utilizado em redes de sensores móveis, adaptando o mesmo para tarefas de mapeamento. Outra importante contribuição é a colaboração entre o método proposto de integração de mapas e a estratégia de coordenação de múltiplos robôs baseada na teoria de colônias de formigas. Tal colaboração permite o desenvolvimento de uma abordagem de exploração que emprega um mecanismo não físico para depósito e detecção de feromônios em ambientes reais por meio da elaboração do conceito de feromônios virtuais integrados. Resultados obtidos em simulação demonstram que o método de integração de mapas é eficiente, de modo que os ensaios experimentais foram realizados considerando-se um número variável de robôs móveis durante o processo de exploração de ambientes internos com diferentes formas e estruturas. Os resultados obtidos com os diversos experimentos realizados confirmam que o processo de integração é efetivo e adequado para executar o mapeamento do ambiente durante tarefas de exploração e vigilância do mesmo / The multiple robot map building strategies have several advantages when compared to strategies based on a single robot, in terms of flexibility, gain of information and reduction of map building time. In this work, a local map integration method is proposed based on the inter-robot observations, considering a recent approach for the environment exploration. This approach is based on the Inverse Ant System-Based Surveillance System strategy, called IASSS. The IAS-SS strategy is inspired on biological mechanisms that define the social organization of swarm systems. Specifically, it is based on a modified version of the known ant colony algorithm. The main contribution of this work is the fit of an information sharing model used in an mobile sensor network, adapting the method for mapping tasks. Another important contribution is the collaboration between the local map integration method and the multiple robot coordination strategy based on ant colony theory. Through this collaboration it is possible to develop an approach that uses a mechanism for controlling the access to pheromones in real environments. Such mechanism is based on the integrated virtual pheromones concept. Simulation results show that the map integration method is efficient, the trials are performed considering a variable number of robots and environments with different structures. Results obtained from several experiments confirm that the integration process is effective and suitable to execute mapping during the exploration task

Clustering is actively used in several research fields, such as pattern recognition, machine learning and data mining. This dissertation focuses on clustering algorithms in the data mining area. Clustering algorithms can be applied to solve the unsupervised learning problem, which deals with finding clusters in unlabeled data. Most clustering algorithms require the number of cluster centers be known in advance. However, this is often not suitable for real world applications, since we do not know this information in most cases. Another question becomes, once clusters are found by the algorithms, do we believe the clusters are exactly the right ones or do there exist better ones? In this dissertation, we present two new Swarm Intelligence based approaches for data clustering to solve the above issues. Swarm based approaches to clustering have been shown to be able to skip local extrema by doing a form of global search, our two newly proposed ant clustering algorithms take advantage of this. The first algorithm is a kernel-based fuzzy ant clustering algorithm using the Xie-Beni partition validity metric, it is a two stage algorithm, in the first stage of the algorithm ants move the cluster centers in feature space, the cluster centers found by the ants are evaluated using a reformulated kernel-based Xie-Beni cluster validity metric. We found when provided with more clusters than exist in the data our new ant-based approach produces a partition with empty clusters and/or very lightly populated clusters. Then the second stage of this algorithm was applied to automatically detect the number of clusters for a data set by using threshold solutions. The second ant clustering algorithm, using chemical recognition of nestmates is a combination of an ant based algorithm and a consensus clustering algorithm. It is a two-stage algorithm without initial knowledge of the number of clusters. The main contributions of this work are to use the ability of an ant based clustering algorithm to determine the number of cluster centers and refine the cluster centers, then apply a consensus clustering algorithm to get a better quality final solution. We also introduced an ensemble ant clustering algorithm which is able to find a consistent number of clusters with appropriate parameters. We proposed a modified online ant clustering algorithm to handle clustering large data sets. To our knowledge, we are the first to use consensus to combine multiple ant partitions to obtain robust clustering solutions. Experiments were done with twelve data sets, some of which were benchmark data sets, two artificially generated data sets and two magnetic resonance image brain volumes. The results show how the ant clustering algorithms play an important role in finding the number of clusters and providing useful information for consensus clustering to locate the optimal clustering solutions. We conducted a wide range of comparative experiments that demonstrate the effectiveness of the new approaches.

A mobile ad hoc network is a collection of wireless mobile devices which dynamically form a temporary network, without using any existing network infrastructure or centralised administration. Each node in the network effectively becomes a router, and forwards packets towards the packet’s destination node. Ad hoc networks are characterized by frequently changing network topology, multi-hop wireless connections and the need for dynamic, efficient routing protocols. <p.This work considers the routing problem in a network of uniquely addressable sensors. These networks are encountered in many industrial applications, where the aim is to relay information from a collection of data gathering devices deployed over an area to central points. The routing problem in such networks are characterised by: <ul> <li>The overarching requirement for low power consumption, as battery powered sensors may be required to operate for years without battery replacement;</li> <li>An emphasis on reliable communication as opposed to real-time communication, it is more important for packets to arrive reliably than to arrive quickly; and</li> <li>Very scarce processing and memory resources, as these sensors are often implemented on small low-power microprocessors.</li> </ul> This work provides overviews of routing protocols in ad hoc networks, swarm intelligence, and swarm intelligence applied to ad hoc routing. Various mechanisms that are commonly encountered in ad hoc routing are experimentally evaluated under situations as close to real-life as possible. Where possible, enhancements to the mechanisms are suggested and evaluated. Finally, a routing protocol suitable for such low-power sensor networks is defined and benchmarked in various scenarios against the Ad hoc On-Demand Distance Vector (AODV) algorithm. / Dissertation (MSc)--University of Pretoria, 2005. / Computer Science / Unrestricted

京都大学 / 0048 / 新制・課程博士 / 博士(農学) / 甲第22848号 / 農博第2431号 / 新制||農||1082(附属図書館) / 学位論文||R2||N5308(農学部図書室) / 京都大学大学院農学研究科応用生物科学専攻 / (主査)教授 松浦 健二, 教授 大門 高明, 教授 吉村 剛 / 学位規則第4条第1項該当 / Doctor of Agricultural Science / Kyoto University / DFAM

Masters of Science / It is well documented that cigarette smoking and nicotine exposure create widespread physiological disorders in humans and animals. The primary tobacco constituent that is responsible for the toxicological consequences associated with the effects of tobacco smoke is nicotine (Van Lancker 1977). After maternal nicotine exposure, the fetal gonads and lungs are the principle sites of nicotine damage (Szuts et al. 1978, Mosier & Jansons 1972). Whilst the fetal lung has received widespread attention in this regard (Maritz 1988), the testis has never been studied. Therefore, I have chosen to explore the effects of maternal nicotine exposure on the testis of male offspring by evaluating various aspects of the male reproductive tract. It is believed that, in adult male smokers (Rosenberg 1987, Handelsman et al. 1984) and sexually mature animals (Mattison 1982) that are exposed to nicotine, male fertility may be compromised. However, these studies provide conflicting data on single parameters. It was therefore my objective to identify the effect of nicotine exposure on the male reproductive tract and to establish possible sites through which these effects may be mediated in adult male rats. The influence of nicotine was then investigated in male offspring after maternal nicotine treatment (MNT), and in sexually mature adult males after direct adult nicotine treatment (ANT). In the former experiment (MNT), 7 day pregnant rats were exposed to Img nicotine/kg body weight/day. Therefore, these offspring were indirectly exposed to nicotine during fetal development and early neonatal development. After weaning the animals were divided into two groups. One group did not receive further treatment (withdrawn group), whilst the other group was continually treated till adulthood (nicotine group), after which both groups were sampled together with the control. In the latter experiment (ANT), the animals were treated daily for 3 weeks and were sampled as above (MNT animals). The fundamental parameter investigated in both experiments to assess reproductive status was sperm quality (motility and morphology). Thereafter, it was necessary to establish a possible site where the effects of nicotine would occur. Testicular growth, epididymal structure, and plasma testosterone content were measured as probable localities of nicotine's effect. The results signify that maternal nicotine exposure poses a greater threat to the male reproductive system than adult nicotine exposure. In the MNT experiment, progressive sperm motility of the nicotine and withdrawn groups were 1.7% and 3.4% respectively. The proportion of abnormal sperm was 72% in each of the above groups. The lower quality sperm that is evident after nicotine exposure implies that the fertilizing ability of the animals may be impaired during adulthood. The data on testicular growth indicates that nicotine exposure during early development results in slower testicular development until maturity. The epididymal lining of these animals also increased after nicotine exposure, indicating increased cellular activity. However, these results from testicular and epididymal studies are inconclusive and need further work. In the ANT experiment, progressive sperm motility of the nicotine group was 1.2%, whilst the proportion of abnormal sperm was 58%. No other parameter was affected after nicotine exposure to adult animals. From the above data it is evident that nicotine exposed animals were subject to greater nicotine damage after maternal nicotine exposure during early development. Moreover, within the maternal nicotine treated experiment, the withdrawal of nicotine after weaning did not appear to reverse the injurious effects of nicotine that were established during early development. These effects were evident since the nicotine and withdrawn groups showed similar levels of damage in all instances. The most profound effects after adult nicotine exposure and maternal nicotine exposure were on sperm quality. The probable site of sperm impairment appears to be via retarded testicular growth and possibly, structural status of the epididymis after maternal nicotine exposure. The results from adult nicotine exposure however, suggest that sperm cells may be directly affected by nicotine exposure. An epidemiological survey was included to validate the basic conclusions established in animal research when compared to clinical data from human patients. No statistically significant changes were observed in this study between the patient's spermiogram results versus his smoking habits, and, that of his mother. From the level of significance it was concluded that cigarette smoking does not appear to be a cause of impaired fertility in already infertile patients. However, the data does suggest that cigarette smoking may well be a precipitating agent in male infertility. Experimentally, nicotine exposure impairs the male reproductive system to some extent. The effects of which are irreversible after indirect exposure (MNT) during development and may begin with poor testicular development. The effects of adult nicotine exposure implies that nicotine exposure in mature animals (ANT) acts directly on sperm cells to incapacitate them. It is well advised that cigarette smoking should be curbed in pregnant women and in adult males to alleviate contributing effects to male infertility.

This thesis presents research focused on developing statistical methods with emphasis on techniques that can be used for the analysis of data in imaging studies and sparse estimations for applications in high-dimensional data. The first contribution addresses the pixel/voxel-labeling problem for spatial hidden Markov models in image analysis. We formulate a Gaussian spatial mixture model with Potts model used as a prior for mixture allocations for the latent states in the model. Jointly estimating the model parameters, the discrete state variables and the number of states (number of mixture components) is recognized as a difficult combinatorial optimization. To overcome drawbacks associated with local algorithms, we implement and make comparisons between iterated conditional modes (ICM), simulated annealing (SA) and hybrid ICM with ant colony system (ACS-ICM) optimization for pixel labelling, parameter estimation and mixture component estimation. In the second contribution, we develop ACS-ICM algorithm for spatiotemporal modeling of combined MEG/EEG data for computing estimates of the neural source activity. We consider a Bayesian finite spatial mixture model with a Potts model as a spatial prior and implement the ACS-ICM for simultaneous point estimation and model selection for the number of mixture components. Our approach is evaluated using simulation studies and an application examining the visual response to scrambled faces. In addition, we develop a nonparametric bootstrap for interval estimation to account for uncertainty in the point estimates. In the third contribution, we present sparse estimation strategies in linear mixed model (LMM) for longitudinal data. We address the problem of estimating the fixed effects parameters of the LMM when the model is sparse and predictors are correlated. We propose and derive the asymptotic properties of the pretest and shrinkage estimation strategies. Simulation studies is performed to compare the numerical performance of the Lasso and adaptive Lasso estimators with the pretest and shrinkage ridge estimators. The methodology is evaluated through an application of a high-dimensional data examining effective brain connectivity and genetics. In the fourth and final contribution, we conduct an imaging genetics study to explore how effective brain connectivity in the default mode network (DMN) may be related to genetics within the context of Alzheimer’s disease. We develop an analysis of longitudinal resting-state functional magnetic resonance imaging (rs-fMRI) and genetic data obtained from a sample of 111 subjects with a total of 319 rs-fMRI scans from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database. A Dynamic Causal Model (DCM) is fit to the rs-fMRI scans to estimate effective brain connectivity within the DMN and related to a set of single nucleotide polymorphisms (SNPs) contained in an empirical disease-constrained set. We relate longitudinal effective brain connectivity estimated using spectral DCM to SNPs using both linear mixed effect (LME) models as well as function-on-scalar regression (FSR). / Graduate