This thesis describes an optimising compiler for translating from SMV to CLP(B). The optimisation is aimed at reducing the number of required variables in order to decrease the size of the resulting BDDs. Also a Partitioning of the transition relation is performed. The compiler uses an internal representation of a FSM that is built up from the SMV description. A number of rewrite steps are performed on the problem description such as encoding to a Boolean domain and performing the optimisations.

Procedural Content Generation innebär att spelinnehåll genereras automatiskt för att dels minska arbetsbelastningen hos designers men också för att öka variationen i spel. Ett användningsområde för detta är rumsbaserad bangenerering där banor byggs upp av fördefinierade rum. Målet med det här arbetet var att implementera och jämföra tre olika algoritmer som kan åstadkomma detta; Binary Space Partitioning, Shortest path och en evolutionär algoritm. De kriterier som algoritmerna utvärderades på var tidseffektivitet, variation och möjligheten att nå alla rum. Resultatet visade att Binary Space Partitioning och Shortest path var de två algoritmer som var bäst lämpade att användas i ett spel där genereringen ska ske under körtiden.

The main objective of this study is to divide a number of colorectal cancer cases into subgroups based on their molecular features using cluster analysis. The data used is supplied by a research group at Pathology, the Department of Medical Biosciences, Umeå University, and consists, after some preparation, of 455 observations which is a larger data set than many similar studies. The molecular variables that the clustering is based on are CIMP (CpG Island Methylator Phenotype), MSI (Micro Satellite Instability), BRAF- and KRAS-mutations. These are categorical variables and consequently the clustering method used is PAM (Partitioning Around Medoids) which is particularly useful with data on diverse variable level. The final analysis results in four subgroups that are represented by different combinations of attributes on the aforementioned variables.

The main objective of this study is to divide a number of colorectal cancer cases into subgroups based on their molecular features using cluster analysis. The data used is supplied by a research group at Pathology, the Department of Medical Biosciences, Umeå University, and consists, after some preparation, of 455 observations which is a larger data set than many similar studies. The molecular variables that the clustering is based on are CIMP (CpG Island Methylator Phenotype), MSI (Micro Satellite Instability), BRAF- and KRAS-mutations. These are categorical variables and consequently the clustering method used is PAM (Partitioning Around Medoids) which is particularly useful with data on diverse variable level. The final analysis results in four subgroups that are represented by different combinations of attributes on the aforementioned variables.

The main objective of this study is to divide a number of colorectal cancer cases into subgroups based on their molecular features using cluster analysis. The data used is supplied by a research group at Pathology, the Department of Medical Biosciences, Umeå University, and consists, after some preparation, of 455 observations which is a larger data set than many similar studies. The molecular variables that the clustering is based on are CIMP (CpG Island Methylator Phenotype), MSI (Micro Satellite Instability), BRAF- and KRAS-mutations. These are categorical variables and consequently the clustering method used is PAM (Partitioning Around Medoids) which is particularly useful with data on diverse variable level. The final analysis results in four subgroups that are represented by different combinations of attributes on the aforementioned variables.

The main objective of this study is to divide a number of colorectal cancer cases into subgroups based on their molecular features using cluster analysis. The data used is supplied by a research group at Pathology, the Department of Medical Biosciences, Umeå University, and consists, after some preparation, of 455 observations which is a larger data set than many similar studies. The molecular variables that the clustering is based on are CIMP (CpG Island Methylator Phenotype), MSI (Micro Satellite Instability), BRAF- and KRAS-mutations. These are categorical variables and consequently the clustering method used is PAM (Partitioning Around Medoids) which is particularly useful with data on diverse variable level. The final analysis results in four subgroups that are represented by different combinations of attributes on the aforementioned variables.

Parallel import in the European Union is the perfectly legal activity under Art. 28-30 EC of buying goods in a low-price country in order to ship and sell them in a high-price country. The pharmaceutical market in Europe is subject to the subsidiartiy principle of Art. 5 EC and heavily characterised by national regulations that lead to significant price differentials between – generally speaking – Northern and Southern Europe. This incites the activity of parallel traders in the first place, who act as arbitrageurs.

Parallel import in the European Union is the perfectly legal activity under Art. 28-30 EC of buying goods in a low-price country in order to ship and sell them in a high-price country. The pharmaceutical market in Europe is subject to the subsidiartiy principle of Art. 5 EC and heavily characterised by national regulations that lead to significant price differentials between ? generally speaking ? Northern and Southern Europe.