Tackling complexity in biological systems: Multi-scale approaches to tuberculosis infection

Tuberculosis is an ancient disease responsible for more than a million deaths per year worldwide, whose complex infection cycle involves dynamical processes that take place at different spatial and temporal scales, from single pathogenic cells to entire hosts' populations. In this thesis we study TB disease at different levels of description from the perspective of complex systems sciences. On the one hand, we use complex networks theory for the analysis of cell interactomes of the causative agent of the disease: the bacillus Mycobacterium tuberculosis. Here, we analyze the gene regulatory network of the bacterium, as well as its network of protein interactions and the way in which it is transformed as a consequence of gene expression adaptation to disparate environments. On the other hand, at the level of human societies, we develop new models for the description of TB spreading on complex populations. First, we develop mathematical models aimed at addressing, from a conceptual perspective, the interplay between complexity of hosts' populations and certain dynamical traits characteristic of TB spreading, like long latency periods and syndemic associations with other diseases. On the other hand, we develop a novel data-driven model for TB spreading with the objective of providing faithful impact evaluations for novel TB vaccines of different types

Machine learning approaches for determining effective seeds for k -means algorithm

In this study, I investigate and conduct an experiment on two-stage clustering procedures, hybrid models in simulated environments where conditions such as collinearity problems and cluster structures are controlled, and in real-life problems where conditions are not controlled. The first hybrid model (NK) is an integration between a neural network (NN) and the k-means algorithm (KM) where NN screens seeds and passes them to KM. The second hybrid (GK) uses a genetic algorithm (GA) instead of the neural network. Both NN and GA used in this study are in their simplest-possible forms. In the simulated data sets, I investigate two properties: clustering performance comparisons and effects of five factors (scale, sample size, density, number of clusters, and number of variables) on the five clustering approaches (KM, NN, NK, GA, GK). Density, number of clusters, and dimension influence the clustering performance of all five approaches. KM, NK, and GK classify well when all clusters contain a similar number of observations, while NK and GK perform better than the KM. NN performs well when one cluster contains more observations than any other cluster. The two hybrid models perform at least as well as KM, although the environments are in favor of the KM. The most crucial information, the true number of clusters, is provided to the KM only. In addition, the cluster structures are simple: the clusters are well separated; the variances and cluster sizes are uniform; the correlation between any pair of variables and collinearity problems are not significant; and the observations are normally distributed. Real-life problems consist of three problems with a known natural cluster structure and one problem with an unknown natural cluster structure. Overall results indicate that GK performs better than KM, while NK is the worst performing among the five approaches. The two machine learning approaches generate better results than KM in an environment that does not favor KM. GK has shown to be the best or among the best in a simulated environment and in real-life situations. Furthermore, the GK can detect firms with promising financial prospect such as acquisition targets and firms with “buy” recommendation, better than all other approaches

Integrated Reliability Assessment of Water Distribution Networks

The condition of current water infrastructure in North America is alarming due to deterioration of old water mains indicating the incompetence of existing water distribution to cope with increasing demands. ASCE (2013) report card graded the drinking water networks a score of D, representing “poor condition”. In contrast, the Canadian drinking water infrastructure scored better, indicating “Good: adequate for now”. Despite this, 15.4% of the water distributions are graded “fair” to “very poor” with a replacement value of CAD 25.9 billion. Delaying the investment can result in degrading water service, increasing service disruptions, and increasing expenditures for emergency repairs. Taking into account the deterioration severity and investment gap, water utilities need an asset management tool that can effectively prioritize the rehabilitation works. This research is aiming at developing an integrated reliability assessment model that can identify the crucial water main segments and prioritize their renewal at all hierarchical levels of the network. In this regard, the proposed research framework encompasses modeling aspects of mechanical and hydraulic condition of a WDN. The first model analyzes the mechanical condition of a WDN at the component, segment and network levels. This model utilizes reliability theory for assessing component and segment reliabilities, whereas minimum cut set approach is pursued for assessing mechanical reliability of a network. To facilitate this tedious process, it has been coded in MATLAB R2013b programming environment along with the utilization of Wolfram Mathematica 10.2 and Microsoft Excel 2013. The second model analyzes the hydraulic performance of a WDN in terms of hydraulic reliability. It involves the hydraulic simulation of a WDN in normal and failure conditions, which aid in obtaining the required as well as actual pressure head at demand nodes. The model is then formulated with pressure conditions to evaluate the available demand at demand nodes which in contrast to the required demand aids in predicting the hydraulic reliability of the network. Finally, an integrated reliability assessment model is presented to conclude the research methodology by determining the overall sub-network reliability. The developed methodology is worked out on two case studies from the cities of London, Ontario and Doha, Qatar. Also, it is implemented on two different sub-networks from the City of London (north phase and south phase) for drawing comparisons, which concluded that the mechanical reliability of a network encompassed by newer components with less number of failures is greater than that of other networks. Additionally, the results of model implementation revealed that structure/configuration of the network also played an important role in affecting the overall network reliability. It was found 0.82 for the north phase sub-network and 0.84 for the south phase indicating that the south phase sub-network is more reliable. Therefore, the north phase sub-network takes the priority when scheduling the rehabilitation. The resulting reliability indices of a WDN, helps the municipalities to effectively prioritize the rehabilitation works at respective hierarchical levels. Moreover, the outcomes of this research also aid in identifying the most critical water main segments that need to be monitored constantly in order to prevent the network failure

Key performance indicators for sustainable manufacturing evaluation in automotive companies

The automotive industry is regarded as one of the most important and strategic industry in manufacturing sector. It is the largest manufacturing enterprise in the world and one of the most resource intensive industries of all major industrial system. However, its products and processes are a significant source of environmental impact. Thus, there is a need to evaluate sustainable manufacturing performance in this industry. This paper proposes a set of initial key performance indicators (KPIs) for sustainable manufacturing evaluation believed to be appropriate to automotive companies, consisting of three factors divided into nine dimensions and a total of 41 sub-dimensions. A survey will be conducted to confirm the adaptability of the initial KPIs with the industry practices. Future research will focus on developing an evaluation tool to assess sustainable manufacturing performance in automotive companies

Negotiations between British and Dutch managers: cultural values, approaches to conflict management, and perceived negotiation.

The present research investigates cultural values, approaches to conflict management, and perceived negotiation satisfaction in manager samples from the UK and the Netherlands. Three studies (total N = 412) were conducted, of which Study 1 and 2 pertained to the development of the measure and Study 3 was used to conduct the main analysis. The research focus centres around the following main objectives: a) refinement of conflict management models and instruments; b) profile analyses of Dutch and British conflict management approaches using Schwartz’s (1992, 1994) Value Types to explain observed differences, and c) testing of a model describing interrelations between cultural values, approaches to conflict management, conflict context, and perceived negotiation satisfaction. Previous research on conflict management modeled conflict behaviour on the basis of a concern for self vs. concern for others matrix, which incorporated communication styles. The present research distinguishes between the underlying concerns, conflict management strategies, and communication styles to predict perceived negotiation satisfaction. Furthermore, conflict management dynamics are investigated by comparing the ratings of own vs. other team’s conflict management approach. In-group vs. out-group differentiation was dependent on the social desirability of the conflict management approach in question. Dutch managers associated themselves less and British managers more with a concern for Inconvenience, Avoiding, and Indirect communication, whereas British managers associated themselves more and Dutch managers less with these approaches. Results for cultural values showed that the main difference between Dutch and British managers concerned a higher score for Dutch managers on Self Transcendence and a higher score on Self Enhancement for British managers. Self Enhancement mediated the effect for nationality for Dominating strategy. Furthermore, Self Transcendence predicted a concern for Clarity, a Problem Solving strategy, and a Consultative communication style. Nationality as predictor of Concern for Inconvenience, Avoiding strategy, and Indirect communication style was not mediated by Value Types. Suggestions are made for future research exploring the role of Uncertainty Avoidance at the individual level. Success and Comfort were predicted by own and other team’s Conflict Management Approach, additional to cultural value types and contextual variables. National differences were observed for particular predictors of perceived negotiation satisfaction

Climate and Student Performance in Tennessee Middle Schools

The purpose of this study was to extend previous research by investigating the relationship between school climate and student performance (value-added gains) in selected middle schools. In order to confirm previous research with the selected instrument, school climate and student achievement were also compared. This study used a correlation analysis design. Forty middle schools from across Tennessee were used as a population sample. The data sources were the School Climate Inventory (SCI), and the State of Tennessee Report Card, 2005. Criterion referenced data and value-added gain data were collected from the Report Card. Aggregate achievement scores and aggregate performance (value-added) scores in language arts, math, science, and social studies were compared with school climate scores using the Pearson Product Moment Correlation Coefficient. There is a relationship between overall school climate and at least one aspect of student performance, that of language arts. Language arts performance scores correlated significantly with 5 of the 7 climate subscales. Significant correlations of science performance scores with the climate subscale of expectation, as well as the social studies performance scores with the climate subscale of order were also found. This study also affirmed previous research that showed a relationship between the academic emphasis of climate and student achievement