Title: Consequences of diversion – a qualitative interview study regarding illicit buprenorphine use. In year 1999 buprenorphine was introduced in Sweden and is used in treatment for opioid dependence. An issue concerning substitution treatment programmes is the occurrence of diversion and the following illegal sails of buprenorphine on a black market. The aim of this study was to explore how people who have used illicitly obtained buprenorphine describes such abuse. The study was conducted using qualitative method, where a total of five individuals were interviewed about past experiences of having used illicitly acquired buprenorphine. The findings indicate that polydrug use and intake by injection or through the nose are common amongst illicit users of buprenorphine. Buprenorphine was described as a possible substance of abuse. Moreover, it is discussed whether availability and attitudes concerning buprenorphine as a cleaner and/or safer substance than other drugs can contribute to increased abuse of opiates through buprenorphine amongst drug users who have not previously been in contact with opiates. The authors argue that further research is needed to explore the extent of diversion from domestic substitution treatment programs, and to widen the understanding of how buprenorphine function as a drug of abuse.

The oceans around the world have been heavily impacted by overfishing due to very intensive commercial fishing in recent times. A large number of fish stocks have already been fully exploited. Vessel Monitoring System has been put in place to regulate fishing vessels and enforce sustainable fisheries management. Data coming from such systems can be used for the detection of illegal, unregulated, and unreported fishing. In this thesis, we present various machine learning models for the classification of fishing trip trajectories. To train these models, we develop a trajectory segmentation algorithm to create trip trajectories out of raw data and design a graphical user interface for labeling the trip trajectories into fishing and non-fishing. We also examine the impact of the temporal resolution of the data. In conclusion, the CNN-Transformer network performed the best on the binary classification of two different fishing vessel types. During the project, we realized that segmentation of real trajectory data into trips poses many problems and presents the biggest obstacle. The experiment on the varying temporal resolution of the data showed that having a higher temporal resolution gives better modeling results but only to a certain point. / Haven runt om i världen har drabbats hårt av överfiske på grund av ett mycket intensivt kommersiellt fiske på senare tid. Ett stort antal fiskbestånd har redan utnyttjats fullt ut. Fartygsövervakningssystem har införts för att reglera fiskefartyg och upprätthålla hållbar fiskeförvaltning. Data som kommer från sådana system kan användas för att upptäcka olagligt, oreglerat och orapporterat fiske. I detta examensarbete presenterar vi olika maskininlärningsmodeller för klassificering av fisketursbanor. För att träna dessa modeller utvecklar vi en segmenteringsalgoritm för att skapa turbanor av rådata och designa ett grafiskt användargränssnitt för att märka resbanorna till fiske och icke-fiske. Vi undersöker också effekten av den tidsmässiga upplösningen av datan. Sammanfattningsvis presterade CNN-Transformer-nätverket bäst i den binära klassificeringen av två olika fiskefartygstyper. Under projektet insåg vi att segmentering av verkliga bandata till resor utgör många problem och utgör det största hindret. Experimentet på den varierande tidsupplösningen av data visade att en högre tidsupplösning ger bättre modelleringsresultat men bara till en viss punkt.

Bitcoin's decentralized nature enables reasonably anonymous exchange of money outside of the authorities' control. This has led to Bitcoin being popular for various illegal activities, including scams, ransomware attacks, money laundering, black markets, etc.  In this thesis, we characterize this landscape, providing insights into similarities and differences in the use of Bitcoin for such activities.  Our analysis and the derived insights contributes to the understanding of Bitcoin transactions associated with illegal activities through three main aspects. First, it offers a comprehensive characterization of money flows to and from Bitcoin addresses linked to different abuse categories, revealing variations in flow patterns and success rates. Second, a temporal analysis captures long-term trends and weekly patterns across categories. Finally, an analysis of outflow from reported addresses uncovers differences in graph properties and flow patterns among illicit addresses and between abuse categories. These findings provide valuable insights into the distribution, temporal dynamics, and interconnections within various categories of Bitcoin transactions related to illicit activities.