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This study focused on detecting horizontal and vertical collusion within Indonesian government procurement processes, leveraging data-driven techniques and statistical methods. Regarding horizontal collusion, we applied clustering techniques to categorize companies based on their supply patterns, revealing clusters with similar bidding practices that may indicate potential collusion. Additionally, we identified patterns where specific supplier groups consistently won procurements, raising questions about potential competitive advantages or strategic practices that need further examination for collusion. For vertical collusion, we examined the frequency of associations between specific government employees and winning companies. While high-frequency collaborations were observed, it is essential to interpret these results with caution as they do not definitively indicate collusion, and legitimate factors might justify such associations. Despite revealing important patterns, the study acknowledges its limitations, including the representativeness of the dataset and the reliance on quantitative methods. Nevertheless, our findings carry substantial implications for enhancing procurement monitoring, strengthening anti-collusion regulations, and promoting transparency in Indonesian government procurement processes. Future research could enrich these findings by incorporating qualitative methods, exploring additional indicators of collusion, and leveraging machine learning techniques to detect collusion.

This thesis presents a scalable method for identifying anomalous periods of non-activity in short periodic event sequences. The method is tested with real world point-of-sale (POS) data from grocery retail setting. However, the method can be applied also to other problem domains which produce similar sequential data. The proposed method models the underlying event sequence as a non-homogeneous Poisson process with a piecewise constant rate function. The rate function for the piecewise homogeneous Poisson process can be estimated with a change point detection algorithm that minimises a cost function consisting of the negative Poisson log-likelihood and a penalty term that is linear to the number of change points. The resulting model can be queried for anomalously long periods of time with no events, i.e., waiting times, by defining a threshold below which the waiting time observations are deemed anomalies. The first experimental part of the thesis focuses on model selection, i.e., in finding a penalty value that results in the change point detection algorithm detecting the true changes in the intensity of the arrivals of the events while not reacting to random fluctuations in the data. In the second experimental part the performance of the anomaly detection methodology is measured against stock-out data, which gives an approximate ground truth for the termination of a POS event sequence. The performance of the anomaly detector is found to be subpar in terms of precision and recall, i.e., the true positive rate and the positive predictive value. The number of false positives remains high even with small threshold values. This needs to be taken into account when considering applying the anomaly detection procedure in practice. Nevertheless, the methodology may have practical value in the retail setting, e.g., in guiding the store personnel where to focus their resources in ensuring the availability of the products.

Arctic ecosystems face drastic changes in community structure due to warming, shrubification, permafrost loss, and other environmental changes. Due to the spatial heterogeneity of these ecosystems, understanding such changes on a local scale requires high-resolution data. Earth observation using satellite imagery and aerial photography has become a staple in mapping large areas and general patterns. Advances in sensor technology, the proliferation of unmanned aerial vehicles (UAVs), and increases in processing capacity enable the use of higher spatial and spectral resolutions. As a result, more detailed ecological observations can be made using remote sensing methods. In this thesis, I assess how increased spectral resolution affects the remote-sensing based modelling of plant communities in low-growth oroarctic tundra heaths. Based on a large field observation dataset, I estimate biomass, leaf area index, species richness, Shannon's biodiversity index, and fuzzy community clusters. I then build random forest models of these with image data of varying spectral, spatial, and temporal specifications and topographical data. Finally, I create maps of the vegetation. Leaf area index and biomass are best estimated of the response variables, with R2 values of 0.64 and 0.59, respectively, with multispectral data proving the most important explanatory dataset. Biodiversity metrics are best estimated with R2 values of 0.40–0.50 with the most important explanatory variables being topographical and hyperspectral, and community cluster with R2 values of 0.27–0.53, with the importance of various explanatory variables depending on the cluster being estimated. These results can help choose a suitable high-resolution remote sensing approach for modelling plant communities in similar conditions.

In the past 20 years, three known disease emergence events of highly pathogenic coronaviruses have highlighted the importance of monitoring wildlife for the presence of these viruses. Their peculiar characteristics, like high mutation and recombination rate, have increased their potential for species adaptation and interspecies transmission. Understanding the diversity of these viruses in wildlife and increased surveillance might be key to predicting and preventing future spillovers and pandemics. Studies on wildlife coronaviruses commonly focus on the order Chiroptera, mainly in temperate and tropical regions of the Asian continent. Even though animals belonging to this order are considered the main reservoir, the importance of other small terrestrial mammals should not be overlooked. Rodents, for instance, are animals of great interest for many zoonoses, as they often host parasites, bacteria and other groups of viruses that cause diseases in humans. A recent description of several lineages of coronaviruses recovered from rodents from China highlighted and suggested the presence of these viruses in small terrestrial rodents. In this project, we aimed to investigate the presence of coronaviruses in small mammals from France. Samples were collected during spring 2021 in twelve different locations, within two regions of eastern France, Auvergne Rhône-Alpes and Franche Comté. A total of 448 rodents, 13 shrews and 416 bat samples were collected. The samples were screened and coronaviruses sequences were recovered in 20 different samples. Nine Betacoronavirus genus sequences were recovered from rodent colon samples, and one Alpha- and ten Betacoronavirus sequences from bat guano. These results confirmed previous evidence of these viruses’ presence in small mammals from France and provide the first evidence of betacoronaviruses circulating in wild French bats. The study covers two eastern regions that have not been surveilled in previously released studies therefore this highlights the need to increase the efforts in monitoring these viruses and their wildlife host

Histamine is an important neurotransmitter in the central nervous system (CNS). It is involved e.g. in the sleep-wake cycle, endocrine and energy homeostasis as well as in synaptic plasticity and learning. It is produced from L-histidine by histidine decarboxylase (HDC). Almost all species have histamine in their body although the amount varies between species. Histaminergic neurons are located in the tuberomamillary nucleus (TMN) of the posterior hypothalamus. There are four different histamine receptors in mammals and they are all metabotropic GPCR receptors. The first three (Hrh1, Hrh2 and Hrh3) are located in the brain while Hrh1 and Hrh2 along with Hrh4 that is mainly found in mast cells, are found in the periphery. Receptors have different functions e.g. Hrh1 regulates wakefulness and alertness while Hrh2 is involved in learning and memory. It is established that histaminergic neurons contain GABA-producing enzyme GAD1 and GABA itself. In the present study we aimed to evaluate GABAergic phenotype of the hypothalamic histaminergic neurons with double fluorescent in situ hybridization. Specifically, we were interested in co-existence of VGAT, which is responsible for vesicular release of GABA, and HDC mRNA. The animals used in this study were mouse and zebrafish. The percentage of mouse HDC-neurons that expressed GAD1 was 99.65% and co-expression for VGAT was also high (94.53%). This coexistence was verified also in the zebrafish model. Our data suggest that histaminergic neurons containing VGAT mRNA and are potentially able to release GABA. If GABA is released in a paracrine manner like histamine, it causes tonic inhibition that counterbalances the effects of histamine during wakefulness. The fact that VGAT mRNA was also found in zebrafish histaminergic neurons indicates that histamine-GABA system is preserved among species.

A global pandemic, COVID-19 began in December 2019 in Wuhan, China. Since then it has expanded all around the globe and was declared a global pandemic in early March by the World Health Organization (WHO). Ever since this pandemic started, the number of infections grew exponentially. Currently, there is a global rise in COVID-19 cases with 3.6 million new cases and new deaths with a weekly growth of 21%. The disease outbreak caused over 55.6 million infected cases and more than 1.34 million deaths worldwide since the beginning of this pandemic. Reverse transcription polymerase chain reaction (RT-PCR) test is the best protocol currently in use to detect COVID-19 positive patients. In a setup with low resources especially in developing countries with huge populations, RT-PCR test is not always a viable option for being expensive, time-consuming and it requires trained professionals. With the overwhelming number of infected cases, there is a significant need for a substitute that is cheaper, faster and accessible. In that regard, machine learning classification models are developed in this study to detect COVID-19 positive patients and predict the patient deterioration in the presence of missing data using a dataset published by hospital Israelita Albert Einstein, at São Paulo, Brazil. The dataset consists of 5644 anonymous patient samples who visited the hospital and tested for RT-PCR along with additional laboratory test results providing 111 clinical features. Additionally, there are more than 90% missing values in this dataset. To explore missing data analysis on COVID-19 clinical data, a comparison between a complete case analysis and imputed case analysis is reported in this study. It is established that the logistic regression model with multivariate imputations by chained equations (MICE) on the data, provides 91% and 85% sensitivity respectively for detecting COVID-19 positive patients and predicting the patient deterioration. The area under the receiver operating characteristics curve (AUC) score is reported at 93% and 89% for both tasks respectively. Sensitivity and AUC scores are selected for evaluating the model’s performance as false negatives are harmful for patient screening and triaging. The proposed pipeline is an alternative approach towards COVID-19 diagnosis and prognosis. Clinicians can employ this pipeline for early screening of COVID-19 suspected patients, triaging the medical procedures and as a secondary diagnostic tool for deciding patient’s priority for treatments by utilizing low-cost, readily available laboratory test results.

Molecular biology has created a new pathway for plant breeding in cut flower industry. It focuses on studying flower gene functions and provides a more direct and effective way of breeding new flower cultivars using genetic transformation. Besides flower color, disease resistance, quality and vase life, modification of flower architecture is an important target for flower breeding. Previous studies have showed that various transcription factors encoded by the corresponding genes are involved regulating flower development and flower architecture. The most studied are MADS domain and TCP domain transcription factors. For targeted breeding, it is crucial to study the functions of the corresponding genes in detail. For both MADS and TCP domain proteins, previous studies have indicated that protein-protein interactions are important for their function. GhCYC1, GhCYC2, GhCYC3 and GhCYC4, isolated from gerbera (Gerbera hybrida), are CYCLOIDEA –like genes affecting inflorescence development. The protein-protein interactions among these four genes have previously been studied by yeast two-hybrid system. The aim of this thesis was to verify the interactions in living plant cells, using both BiFC and split luciferase assays. Protoplast electroporation and agroinfiltration were used to introduce the genes in planta. The results from the two assays were compared in order to find an effective in planta method for detecting protein-protein interactions. The experiment also provided information about DNA transformation efficiency using protoplast electroporation and agroinfiltration. The results of the split luciferase assay showed that GhCYC1+GhCYC4, GhCYC3+GhCYC4 as well as GhCYC4+GhCYC4 interacted quite strongly in plant cells while GhCYC1+GhCYC1, GhCYC2+GhCYC2 as well as GhCYC4+GhCYC2 had almost no interactions. The interactions between GhCYC3+GhCYC4, and GhCYC4+ GhCYC4 were also shown in yeast two-hybrid, but the other results were different. According to the BiFC assay, no signals of interactions were detected from GhCYC2+GhCYC2, while strong signals were observed from GhCYC2+GhCYC3, and weak signals were seen from GhCYC2+GhCYC4. The interactions between GhCYC2+GhCYC3, GhCYC2+GhCYC4 were also observed in yeast two-hybrid, but the other results were unconfirmed. Large standard deviations were observed in the split luciferase assay and thereby reliable conclusions cannot be drawn from it. However, BiFC turned out to be a better method to detect the protein-protein interactions in planta and clear signals from interactions could be observed. Comparison of the transformation methods indicated that agroinfiltration is a better way of introducing DNA into plant cells than protoplast electroporation. For further study, BiFC assay still needs to be repeated to confirm the efficiency of this assay, and factors affecting the transformation efficiency in protoplast electroporation need to be optimized in the future studies.

Accurate and sensitive analysis of mono-, di-, and oligosaccharides is desired in several different scientific areas due to the wide appearance of saccharides. This work focuses on the detection of mono-, di-, and oligosaccharides utilizing capillary electrophoresis (CE). Saccharide analysis with CE is challenging due to the lack of UV-absorbing chromophores in the molecular structure. CE also requires that the analytes are in their charged form, which is demanding in the case of mono-, di-, and oligosaccharides due to their high pKa-values. The first part of this work presents several detection methods and procedures to succeed in saccharide analysis with CE. A selection of the scientific work published in this area is presented to highlight the different detection possibilities. Derivatization of the analytes is commonly used to transform the saccharides into UV absorbing species. Special compositions of the background electrolyte, e.g. borate buffers and copper(II) containing buffers can be exploited to form charged complexes with the saccharides, which enhance the separation. Indirect UV detection is not as sensitive as direct UV detection of saccharide derivatives, but it is fast and useful in applications where high sensitivity is not required. Electrochemical detection (pulsed amperometric detection and contactless conductivity detection) is especially useful in miniaturized and portable systems. An advantage of electrochemical detection is also that no sample pretreatment or special reagents are required. Mass spectrometry (MS) detection is a powerful tool when detailed information about oligosaccharide structures is required and when the sample amounts are small. MS detection is therefore especially suitable in biochemical applications. In the second part of this work, CE was utilized for the separation and quantification of five novel ionic liquids and the quantification of acetate and xylose in ionic liquid matrices. The internal standard method was used in the quantitative work. The novel ionic liquids were detected with direct UV detection and the limit of detection ranged from 2-5 µg/mL. Resolution and number of effective plates were calculated from the separation studies. In the quantitative work, calibration curves were obtained for four of the novel ionic liquids. CE with indirect UV detection was used for the quantification of acetate, which is a typical counter ion in ionic liquids. A calibration curve for acetate was obtained and the linearity ranged from 0.0025 to 0.2 mg/mL. The method was successfully applied to the determination of the concentration of acetate in a standard sample containing the ionic liquid [MTBDH][OAc]. In the last part of the work, solid phase extraction was utilized to extract ionic liquids from industrial samples. CE with direct and indirect detection was used to check if the extraction was complete and if saccharides were present in the extracts. A calibration curve for xylose was constructed and the linear range for xylose was 0.05 to 3 mg/mL. It was found that the developed method for xylose detection was not sensitive enough to detect possible saccharide residues in the extracts and the analytical procedure requires further development.

Syftet med denna pro gradu-avhandling är att granska hur ljuden och ljudlandskapen i Monika Fagerholms roman Den amerikanska flickan (2004) formar och beskriver karaktärerna och om gestaltningen av ljudlandskapen har en betydelse för handlingen. Detta analyseras utgående från de fyra centrala karaktärerna Doris Flinkenberg, Sandra Wärn, Bengt och Eddie de Wire. Som teoretisk utgångspunkt används begrepp från ljudlandskapsforskning ur Murray R. Schafers lexikon i The Soundscape: Our Sonic Environment and the Tuning of the World samt begrepp ur lexikonet i Huutoja Hiljaisuuteen. De utvalda begreppen har anpassats för att fungera som begreppsapparat för en analys av litterära ljudlandskap. För att avgöra vem som hör och vem som hörs har jag använt mig av det narratologiska begreppet fokalisering, myntat av Gérard Genette. Jag har närmat mig romanen genom närläsning, och har valt ut relevanta ljud genom att se vilka som upprepas och är närvarande under utmärkande händelser för de här fyra karaktärerna då de hör och hörs. Analysen visar att ljudlandskapen har en betydelse för hur karaktärerna formas, samt för hur de beskrivs. Doris härmar och beskriver andra karaktärer, Sandra tar till sig den amerikanska flickans röst och sång, Bengt blir stum av sorg och Eddie de Wire spelar in sin röst på skiva vilken senare kommer att fungera som språngbräda för en av Sandras och Doris’ lekar. Gestaltningen av ljudlandskap har en betydelse för romanens handling. Ljudet kan förklara sådant som inte kan beskrivas i ord. Ljuden är ändå inte helt och hållet meningsbärande i sig själv, utan fungerar i symbios med den övriga handlingen. Alla fyra karaktärer har sina egna ljudmärken, och samtliga är kopplade till deras röst och tonfall. Hur de säger det de säger är det som ljuder och har en mening. Några av karaktärernas ljudmärken förändras under berättelsens gång och markerar en skiftning i karaktären. Förändringen i en karaktärs ljudlandskap beskriver alltså också en förändring i karaktären. Flera av ljuden i romanen är ständigt återkommande, vilket resulterar i en ekande effekt. Även om endast ett ljud är beskrivet i ett avsnitt, kan där finnas ett underliggande större ljudlandskap. Det ekar med andra ord, bland sidorna i Den amerikanska flickan.

Carbon pricing is a cost-effective instrument of climate change mitigation policy. Its implementation is, however, limited by various political constraints. The goal of this thesis is to examine what factors empirically explain cross-country variation in carbon pricing policy. Understanding the political constraints limiting carbon pricing may have implications for policy design. Previous literature on the empirical determinants of carbon pricing policy has focused mostly on determinants based on political economy theory, such as variation in domestic interests, and been conducted with data only on explicit carbon pricing. Implicit carbon prices created by fuel excise taxes are, however, a significant part of the total price on emissions. This thesis contributes to existing literature by introducing two new determinants in public finance considerations and country-level social cost of carbon as well as utilizing broader carbon pricing data. Empirical methods used include regression based on maximum-likelihood estimation of censored data and multiple linear regression. The size of the public sector is found to have a statistically significant positive association with carbon pricing regardless of the model used. This supports the hypothesis that cross-country variation in carbon pricing is empirically explained by a need to finance public spending and by the double-dividend hypothesis. Other factors that are found to have a clear positive association with carbon pricing are level of democracy, administrative capacity, and GDP per capita. The results are somewhat mixed concerning the effect of other political institutions related factors as well as factors related to carbon intensity. The hypothesis that country-level social cost of carbon positively affects carbon pricing is clearly rebuked which suggests that a competitive game does not describe national-level policymakers’ decision-making. The results of the thesis should not, however, be interpreted as causal because of omitted variable bias, reverse causality, and a lack of time-series data.