Approximately three rhinos are poached daily in South Africa. Rhino poaching is a serious problem that a ects not only the rhino population of South Africa, but also the rhino population of the world. South Africa has the largest rhino population and of those rhinos the largest number can be found in the Kruger National Park (KNP). The KNP has been hit the hardest by the poaching epidemic, losing 1,175 rhinos in 2015 alone. Two big challenges are the size of the park and the unknown locations of both the poachers and new poaching events. The KNP is the size of a small country and there are simply not enough rangers to patrol this area e ectively. A costly solution would be to employ more rangers, but a proposed alternative is to reduce the search space and thus ensure that the rangers are allocated to the high risk areas first. A mathematical model was developed in the form of a Bayesian network (BN) to infer the most important factors contributing to poaching events and to model the rhino poaching problem. This model can be used to predict the area in which a future poaching attack could take place and thereby reduce the search area of rangers. The model also serves as a vehicle to enhance the understanding of the problem and encourage reasoning and discussion amongst decision makers. The map of the KNP is divided into cells and each cell is given a poaching probability, based on the outcome of the BN. This probability map forms a heat map that can be shown to the control centre and rangers can then be sent to the respective hotspots on the map. This is a proactive approach, which is in stark contrast to the numerous reactive approaches attempted thus far. This is the first BN modelling approach to the rhino poaching problem, and it is also the first BN application to wildlife crime. Previous applications of BN have only gone so far as environmental modelling, but not wildlife crimes. In this study the rhino poaching problem was shifted from a complex, ill-structured space to a position where researchers can begin to address the underlying problems by using a causal model as the vehicle for understanding the complex interplay between the factors a ecting poaching events. / Ongeveer drie renosters word daagliks in Suid-Afrika gestroop. Renosterstroping is 'n ernstige probleem wat nie net die renosterbevolking van Suid-Afrika raak nie, maar ook die res van die wêreld. Suid-Afrika het die grootste renoster bevolking in die wêreld, en die grootste getal van dié renosters word in die Kruger Nasionale Park (KNP) aangetref. Die KNP word die ergste geraak deur die stropings epidemie en 1,175 renosters is in 2015 gestroop. Twee groot uitdagings is die grootte van die park, asook die onbekende posisies van beide die stropers en die nuwe stropingsaanvalle. Die KNP is die grootte van 'n klein land en daar is eenvoudig nie genoeg veldwagters om hierdie area e ektief te patrolleer nie. 'n Duur oplossing sou wees om meer veldwagters aan te stel, maar 'n alternatief is om die soekarea van die veldwagters te verklein en sodoende te verseker dat die veldwagters die hoë-risiko areas eerste, en meer gereeld, patrolleer. 'n Wiskundige model in die vorm van 'n Bayesiese netwerk (BN) is ontwikkel om die belangrikste faktore te bepaal wat bydra tot stropingsaanvalle en uiteindelik die probleem te modelleer. Hierdie model kan gebruik word om die area te voorspel waar 'n stropingsaanval moontlik kan plaasvind en die soekarea van die veldwagters te verminder. Dit dien ook as 'n kanaal om die begrip van die probleem te verbeter en redenasie en bespreking onder besluitnemers aan te moedig. Die kaart van die KNP word in selle verdeel en aan elke sel word 'n stropingswaarskynlikheid toegeken gebaseer op die uitkoms van die BN. Hierdie waarskynlikheidskaart vorm 'n "hittekaart" wat aan die kontrolesentrum gewys kan word, en veldwagters kan dan na die onderskeie responskolle op die kaart gestuur word. Hierdie pro-aktiewe benadering is in teenstelling met huidige reaktiewe benaderings. Hierdie is die eerste BN modellering benadering tot die renosterstropingsprobleem, en dit is ook die eerste BN toepassing tot natuurlewe-misdaad. Vorige toepassings van BNs het omgewingsmodellering aangespreek, maar nie natuurlewe-misdade nie. In hierdie studie word aangetoon hoe die renosterstropings probleem geskuif is vanaf 'n komplekse, swak gestruktureerde probleemruimte na 'n omgewing waar navorsers kan begin om die onderliggende probleme aan te spreek deur gebruik te maak van 'n kausale model as die voertuig van begrip om die komplekse wisselwerking tussen faktore wat 'n stropingsaanval beïnvloed, te verstaan. / Thesis (PhD)--University of Pretoria, 2017. / Electrical, Electronic and Computer Engineering / PhD / Unrestricted

Understanding the causal architecture underlying complex systems biology has a great value in agriculture production for the development of optimal management strategies and selective breeding. So far, most studies in this area use only prior knowledge to propose causal networks and/or do not consider the possible genetic confounding factors on the structure search, which may hide important relationships among phenotypes and also bias the resulting inferred causal network. In this dissertation, we explore many structural learning algorithms and present a new one, called PolyMaGNet (Polygenic traits with Major Genes Network analysis), to search for recursive causal structures involving complex phenotypic traits with polygenic inheritance and also allowing the possibility of major genes affecting the traits. Briefly, a multiple-trait animal mixed model is fitted using a Bayesian approach considering major genes as covariates. Next, posterior samples of the residual covariance matrix are used as input for the Inductive Causation algorithm to search for putative causal structures, which are compared to each other using the Akaike information criterion. The performance of PolyMaGNet was evaluated and compared with another widely used approach in a simulated study considering a QTL mapping population. Results showed that, in the presence of major genes, our method recovered the true skeleton structure as well as the causal directions with a higher rate of true positives. The PolyMaGNet approach was also applied to a real dataset of an F2 Duroc × Pietrain pig resource population to recover the causal structure underlying on carcass, meat quality and chemical composition traits. Results corroborated with the literature regarding the cause-effect relationships between these traits and also provided new insights about phenotypic causal networks and its genetic architectures in complex systems biology. / Compreender a arquitetura causal subjacente à sistemas biológicos complexos é de grande valia na produção agrícola para o desenvolvimento de estratégias de manejo e seleção genética. Até o momento, a maior parte dos estudos neste contexto utiliza apenas conhecimento prévio para propor redes causais e/ou não considera fatores de confundimento genético na busca de estruturas, fato que pode ocultar relações importantes entre os fenótipos e viesar inferências sobre a rede causal. Nesta tese, exploramos alguns algoritmos de aprendizagem de estruturas e apresentamos um novo, chamado PolyMaGNet (do inglês, Polygenic traits with Major Genes Network analysis), para buscar estruturas causais recursivas entre características fenotípicas poligênicas complexas e permitindo, também, a possibilidade de efeitos de genes maiores que as afetam. Resumidamente, um modelo misto de múltiplas características é ajustado usando abordagem Bayesiana considerando os genes maiores como covariáveis no modelo. Em seguida, amostras posteriores da matriz de covariância residual são usadas como entrada para o algoritmo de causação indutiva para pesquisar estruturas causais putativas, as quais são comparadas usando o critério de informação de Akaike. O desempenho do PolyMaGNet foi avaliado e comparado com outra abordagem bastante utilizada por meio de um estudo simulado considerando uma população de mapeamento de QTL. Os resultados mostraram que, na presença de genes maiores, o método PolyMaGNet recuperou a verdadeira estrutura do esqueleto, bem como as direções causais, com uma taxa de efetividade maior. O método é ilustrado também utilizando-se um conjunto de dados reais de uma população de suínos F2 Duroc × Pietrain para recuperar a estrutura causal subjacente à características fenotípicas relacionadas a qualidade da carcaça, carne e composição química. Os resultados corroboraram com a literatura sobre as relações de causa-efeito entre os fenótipos e também forneceram novos conhecimentos sobre a rede fenotípica e sua arquitetura genética.

An experimental survey (N = 415) is used to evaluate fascist qualifications within party preference groups, regarding susceptibility to a neofascist communication style and gravitation toward fascist ideas. Testing the notion by fascism expert Roger Griffin, that the influence of the neofascist intellectual movement the New Right (la Nouvelle Droite) is successfully shaping the 21st century wave of right-wing populism, it is hypothesized that sympathizers of the Swedish right-wing populism equivalent (the Sweden Democrats) are more susceptible to a neofascist communication style and more preconditioned to agree with covertly fascist ideas (as based on the writings of the Nouvelle Droite). The results strongly support this hypothesis, although the potential for generalizability beyond the collected sample is limited. Using a causal networks approach, the failure to falsify the hypothesis is however considered a small but valid observation that bolsters its probability. The study contributes to the current research by further strengthening the bridge between the fields of populism and fascism.