Two methodologies are presented for analyzing the choice between centralized and decentralized energy infrastructures from a least-cost perspective. The first of these develops a novel minimum spanning tree network algorithm to approximate the shortest-length network that connects a given fraction of total system population. This algorithm is used to identify high priority locations for decentralized electrification in 150 countries. The second methodology utilizes a mixed-integer programming framework to determine the least-cost combination of centralized and decentralized electricity infrastructure that is capable of serving demand throughout a given system. This methodology is demonstrated through a case study of Rwanda. The centralized-decentralized electrification paradigm is also approached from an energy security perspective, incorporating stochastic events and probabilistic parameters into a simulation model that is used to compare different development paths. The impact of explicitly modeling stochastic events as opposed to utilizing a conventional formulation is also considered Finally, a subsidy-free lighting cost curve is developed and a model is presented to compare the costs and benefits of three different financial mechanisms that can be employed to make capital intensive energy systems more accessible to rural populations. The optimal contract is determined on the basis of utility-maximization for a range of costs to the providing agency and a comprehensive single and multi-factor sensitivity analysis is performed.

The vast and complex wealth of information available to researchers often leads to a systematic review, which involves a detailed and comprehensive plan and search strategy with the goal of identifying, appraising, and synthesizing all relevant studies on a particular topic. A meta–analysis, conducted ideally as part of a comprehensive systematic review, statistically synthesizes evidence from multiple independent studies to produce one overall conclusion. The increasingly widespread use of meta–analysis has led to growing interest in meta–analytic methods for rare events and sparse data. Conventional approaches tend to perform very poorly in such settings. Recent work in this area has provided options for sparse data, but these are still often hampered when heterogeneity across the available studies differs based on treatment group. Heterogeneity arises when participants in a study are more correlated than participants across studies, often stemming from differences in the administration of the treatment, study design, or measurement of the outcome. We propose several new exact methods that accommodate this common contingency, providing more reliable statistical tests when such patterns on heterogeneity are observed. First, we develop a permutation–based approach that can also be used as a basis for computing exact confidence intervals when estimating the effect size. Second, we extend the permutation–based approach to the network meta–analysis setting. Third, we develop a new exact confidence distribution approach for effect size estimation. We show these new methods perform markedly better than traditional methods when events are rare, and heterogeneity is present.

The objective of this study was to provide an adequate model of the k-factor for scientific radio planning in South Africa for terrestrial propagation. An extensive literature survey played an essential role in the research and provided verification and confirmation for the novelty of the research on historical grounds. The approach of the research was initially structured around theoretical analysis of existing data, which resulted from the work of J. W. Nel. The search for analytical models was extended further to empirical studies of primary data obtained from the South African Weather Service. The methodology of the research was based on software technology, which provided new tools and opportunities to process data effectively and to visualise the results in an innovative manner by a means of digital terrain maps (DTMs) and spreadsheet graphics. MINITAB / Thesis (PhD)--University of Pretoria, 2005. / Electrical, Electronic and Computer Engineering / unrestricted

Based on the fact that accessibility in rural areas relies heavily on car traffic, call-driven traffic has been used in different regions to improve public transport usage in rural areas, while it has been difficult to maintain due to high maintenance and long waiting time. Over the past decade, a new demand response transit (DRT) service came into light, which combines with new technologies to provide a more attractive and efficient transport service. Now the Public Transport Authorities have the vision to change this situation of call-driven traffic. In this project, Södertälje and Nykvarn rural area was chosen to be the pilot area of the new DRT service, where a network algorithm was designed to support the routing choices of the new mobility service. The objective of the network algorithm is to achieve an optimal route based on the cost function i.e. operational cost and passengers’ ride time. In addition, the network algorithm is able to test different scenarios, in which user-friendly and operator-friendly scenarios were tested. The result has shown that user-friendly scenarios provide a lower passenger ride time and fleet travel time with the same amount of requests. On top of that, several recommendations regarding improving the service design were proposed in order to optimize customer satisfaction and operation cost. / Baserat på faktumet att tillgängligheten på landsbygden är mer eller mindre byggd på biltrafik så har anropstyrd trafik använts i flera regioner för att förbättra kollektivtrafikanvändningen på landsbygden. Systemet är dock gammalt och lett till svårigheter för drift och underhåll samt långa väntetider. Under det senaste decennium har det utvecklats nya mobilitetslösningar som styrs när behovet uppstår (DRT-service), som kombineras med ny teknik för att ge en mer attraktiv och effektiv transport service. Nu har offentliga transportmyndigheter (PTA) visionen att förbättra denna anropstyrda trafik. I detta projektet valdes landsbygden i Södertälje och Nykvarn som pilotområde för den nya DRT-servicen, där nätverksalgoritmen utformades för att kunna stödja de olika val av vägar för nya mobilitetstjänsten. Målet med nätverksalgoritmen är att uppnå en optimal väg baserad på de kostnadsfunktioner t.ex driktkostnader och passagerarens körtid. Dessutom testas nätverksalgoritmen i olika scenarier, vilket är användarvänliga och driftvänlga scenarier. Resultatet visade att användarvänlga scenarier ger en mindre passagerartid och fordonets resetid gentemot samma mängd av förfrågningar. Dessutom gavs rekomendationer angående hur man kan förbättra tjänstedesignen för att optmiera kundnöjdhet och driftkostnad.

This thesis deals with the distributed and stochastic algorithms including testing their convergence in networks. The theoretical part briefly describes above mentioned algorithms, including their division, problems, advantages and disadvantages. Furthermore, two distributed algorithms and two stochastic algorithms are chosen. The practical part is done by comparing the speed of convergence on various network topologies in Matlab.

This thesis deals with the distributed and stochastic algorithms including testing their convergence in networks. The theoretical part briefly describes above mentioned algorithms, including their division, problems, advantages and disadvantages. Furthermore, two distributed algorithms and two stochastic algorithms are chosen. The practical part is done by comparing the speed of convergence on various network topologies in Matlab.