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This thesis provides an analysis of Growth Optimal Portfolio (GOP) in discrete time. Growth Optimal Portfolio is a portfolio optimization method that aims to maximize expected long-term growth. One of the main properties of GOP is that, as time horizon increases, it outperforms all other trading strategies almost surely. Therefore, when compared with the other common methods of portfolio construction, GOP performs well in the long-term but might provide riskier allocations in the short-term. The first half of the thesis considers GOP from a theoretical perspective. Connections to the other concepts (numeraire portfolio, arbitrage freedom) are examined and derivations of optimal properties are given. Several examples where GOP has explicit solutions are provided and sufficiency and necessity conditions for growth optimality are derived. Yet, the main focus of this thesis is on the practical aspects of GOP construction. The iterative algorithm for finding GOP weights in the case of independently log-normally distributed growth rates of underlying assets is proposed. Following that, the algorithm is extended to the case with non-diagonal covariance structure and the case with the presence of a risk-free asset on the market. Finally, it is shown how GOP can be implemented as a trading strategy on the market when underlying assets are modelled by ARMA or VAR models. The simulations with assets from the real market are provided for the time period 2014-2019. Overall, a practical step-by-step procedure for constructing GOP strategies with data from the real market is developed. Given the simplicity of the procedure and appealing properties of GOP, it can be used in practice as well as other common models such as Markowitz or Black-Litterman model for constructing portfolios.

Urban vegetation has traditionally been mapped through traditional ways of remote sensing like laser scanning and aerial photography. However, it has been stated that the bird view examination of vegetation cannot fully represent the amount of green vegetation that the citizens observe on street level. Recent studies have raised human perspective methods like street view images and measuring of green view next to more traditional ways of mapping vegetation. Green view index states the percentage of green vegetation in street view on certain location. The purpose for this study was to create a green view dataset of Helsinki city through street view imagery and to reveal the differences between human perspective and aerial perspective in vegetation mapping. Street view imagery of Helsinki was downloaded from Google street view application interface. The spatial extent of the data was limited by the availability of street view images of summer months. Several green view maps of Helsinki were created based on the green view values calculated on the street view images. In order to understand the differences between human perspective and the aerial view, the green view values were compared with the regional land cover dataset of Helsinki trough linear regression. Areas with big differences between the datasets were examined visually through the street view imagery. Helsinki green view was also compared internationally with other cities with same kind of data available. It appealed that the green view of Helsinki was divided unequally across the city area. The lowest green view values were found in downtown, industrial areas and the business centers of the suburbs. Highest values were located at the housing suburbs. When compared with the land cover, it was found that the green view has a weak correlation with low vegetation and relatively high correlation with taller vegetation such as trees. Differences between the datasets were mainly concentrated on areas where the vegetation was not visible from the street by several reasons. Main sources of errors were the oldest street view images and the flaws in image classification caused by other green objects and shadows. Even though Helsinki has many parks and other green spaces, the greenery visible to the streets isn’t always that high. The green view dataset created in this study helps to understand the spatial distribution of street greenery and brings human perspective next to more traditional ways of mapping city vegetation. When combined with previous city greenery datasets, the green view dataset can help to build up more holistic understanding of the city greenery in Helsinki

We study how higher-order gravity affects Higgs inflation in the Palatini formulation. We first review the metric and Palatini formulations in comparative manner and discuss their differences. Next cosmic inflation driven by a scalar field and inflationary observables are discussed. After this we review the Higgs inflation and compute the inflationary observables both in the metric and Palatini formulations. We then consider adding higher-order terms of the curvature to the action. We derive the equations of motion for the most general action quadratic in the curvature that does not violate parity in both the metric and Palatini formulations. Finally we present a new result. We analyse Higgs inflation in the Palatini formulation with higher-order curvature terms. We consider a simplified scenario where only terms constructed from the symmetric part of the Ricci tensor are added to the action. This implies that there are no new gravitational degrees of freedom, which makes the analysis easier. As a new result we found out that the scalar perturbation spectrum is unchanged, but the tensor perturbation spectrum is suppressed by the higher-order curvature couplings.

The nature of dark matter (DM) is one of the outstanding problems of modern physics. The existence of dark matter implies physics beyond the Standard Model (SM), as the SM doesn’t contain any viable DM candidates. Dark matter manifests itself through various cosmological and astrophysical observations of the rotational speeds of galaxies, structure formation, measurements of the Cosmic Microwave Background (CMB) and gravitational lensing of galaxy clusters. An attractive explanation of the observed dark matter density is provided by the WIMP (Weakly Interacting Massive Particle) paradigm. In the following thesis I explore this idea within the well motivated Higgs portal framework. In particular, I explore three options for dark matter composition: a scalar field and U(1) and SU(2) hidden gauge Fields. I find that the WIMP paradigm is still consistent with the data. Even though it finds itself under pressure from direct detection experiments, it is not yet in crisis. Simple and well motivated WIMP models can fit the observed DM density without violating the collider and direct DM detection constraints.

A promising Cu-Ni-PGE containing sulphide ore deposit was discovered in 2009 by Anglo American and since the company has continued studies aiming towards utilisation of the deposit. The discovered deposit lies underneath a Natura 2000 protected mire complex, Viiankiaapa, in Sodankylä municipality in Finnish Lapland. The research and exploration activities in the area are performed with mitigation and preventing actions in order to minimize the deterioration impact to the delicate ecosystem. The more detailed understanding of the hydrogeochemistry of the mire environment in its current state can assist: in monitoring, mitigating and preventing of potential environmental effects due to future mining operations as well as planning the monitoring program. Hydrogeochemical studies, consisting of water and peat sampling at eight sampling points, were carried out along a 1.6 km long study line. Water samples were collected from the surface of the mire as well as within the peat layer and the bottom of the peat layer. Water samples were collected using a mini-piezometer. The analyses for the water samples involved: major components, trace elements and δ18O & δ2H. Groundwater influence in the different sampling points as well as different sections of the peat was investigated using the mentioned chemical and isotopic properties. Peat sampling focused on finding samples which would have different hydraulic properties in order to find the influence of peat in the hydrology in the mire. Hydraulic conductivity of peat samples was determined using rigid wall permeameter test setup. The chemical and physical methods were supplemented by a ground penetrating radar survey completed with 30 and 100 MHz antennas. Studies of peat showed that the hydraulic conductivity varies substantially even inside the rather small study area. Widely recognized correlation between hydraulic conductivity and depth was not observed statistically, but the sampling sites individually show a clear connection with depth and hydraulic conductivity. The influence of the hydraulic properties of peat on to the flow of water in the mire was observed to be significant. In cases where the hydraulic conductivity of peat was very low, water flow may be prevented altogether. This was confirmed with the use of chemical analyses. With higher hydraulic conductivity, groundwater influence was seen more or less throughout the peat profile.

Arctic peatlands are globally extensive and long-lasting storages of carbon and are therefore important ecosystems controlling global carbon cycling. Changes in climate affect peatlands’ ability to accumulate carbon through changes in hydrology and water table level, vegetation, soil temperature and permafrost thaw. As climate warming is projected mostly to northern and arctic regions, it may change the peatlands’ capacity to sequester and release carbon as carbon dioxide and methane. In this Master’s Thesis I studied how the past climate changes are reflected in carbon accumulation rates over the past millennia. Known climate anomalies, such as the Medieval Climate Anomaly, Little Ice Age and the last rapid warming starting from 1980, and their impact on average long-term apparent rate of carbon accumulation were studied from the peat proxies. 15 peat cores were collected from northern subarctic Swedish Lapland and from North-East European Russia. Cores were collected from the active peat layer above permafrost that is known to be sensitive to climate warming. Cores were dated with radiocarbon (14C) and lead (210Pb) methods and peat properties and accumulation patterns were calculated for one centimeter thick subsamples based on chronologies. The Little Ice Age and the last rapid warming affected the carbon accumulation rate considerably whereas for Medieval Climate Anomaly period the peat records did not show very distinctive response. During the Little Ice Age the carbon accumulation rates were low (median 10,5 g m-2v-1) but during the post-Little Ice Age and especially during the last warm decades after 1980 carbon accumulation rates have been high (median 48,5 g m-2v-1). Medieval Climate Anomaly had only a minor positive effect on accumulation rates. On average, the long-term apparent rate of carbon accumulation during the past millennia was 43,3 g m-2v-1 which is distinctly higher than the previously studied rate of 22,9 g m-2v-1 for northern peatlands (p-value 0,0003). Based on results it can be concluded that warm climate periods accelerated the carbon accumulation rate whereas during cold periods accumulation decelerated. Warm climate prolongs the growth period and accelerates the decomposition of peat; cold climate shortens the period of plant growth and thickens the permafrost layer in peatlands, respectively. However, peat layers that are formed after the Little Ice Age are incompletely decomposed which amplifies the carbon accumulation rate partly. Nevertheless, permafrost thawing has been shown to increase accumulation rates, as well. Studying past carbon accumulation rates helps to understand the peatland and carbon cycling dynamics better. Even though accumulation rates reveal a lot about carbon sequestration capabilities of peat, it does not indicate whether a peatland has been a carbon sink or a source.

We investigated the impact of various parameters on new particle formation rates predicted for the sulfuric acid - ammonia system using cluster distribution dynamics simulations, in our case ACDC (Atmospheric Cluster Dynamics Code). The predicted particle formation rates increase significantly if rotational symmetry number of monomers (sulfuric acid and ammonia molecules, and bisulfate and ammonium ions) are considered in the simulation. On the other hand, inclusion of the rotational symmetry number of the clusters only changes the results slightly, and only in conditions where charged clusters dominate the particle formation rate because most of the clusters stable enough to participate in new particle formation display no symmetry, therefore have a rotational symmetry number of one, and the few exceptions to this rule are positively charged. Further, we tested the influence of the application of a quasi-harmonic correction for low-frequency vibrational modes. Generally, this decreases predicted new particle formation rates, and significantly alters the shape of the formation rate curve plotted against the sulfuric acid concentration. We found that the impact of the maximum size of the clusters explicitly included in the simulations depends on the simulated conditions and the errors due to the limited set of clusters simulated generally increase with temperature, and decrease with vapor concentrations. The boundary conditions for clusters that are counted as formed particles (outgrowing clusters) have only a small influence on the results, provided that the definition is chemically reasonable and the set of simulated clusters is sufficiently large. We compared predicted particle formation rates with experimental data measured at the CLOUD (Cosmics Leaving OUtdoor Droplets) chamber. A cluster distribution dynamics model shows improved agreement with experiments when using our new input data and the proposed combination of symmetry and quasi-harmonic corrections., compared to an earlier study based on older quantum chemical data.

Cancer is a worldwide health problem; in 2018 9.6 million people died of cancer, meaning that about 1 in 6 deaths was caused by it. The challenge with cancer drug therapy has been the development of cancer drugs that are effective against cancer but are not harmful to the healthy cells. One of the solutions to this has been antibody-drug conjugates (ADCs), where a cytotoxic drug is bound to an antibody. The antibody binds to specific antigen present on the surface of the cancer cell, thus working as a vessel to carry the drug specifically to the cancer cells. Monomethyl auristatin E (MMAE) and monomethyl auristatin F (MMAF) are mitosis preventing cancer drugs. The auristatins are pentapeptides that were developed from dolastatin 10. MMAE consist of monomethyl valine (MeVal), valine (Val), dolaisoleiune (Dil), dolaproine (Dap) and norephedrine (PPA). MMAF has otherwise similar structure, but norephedrine is replaced by phenylalanine (Phe). They prevent cell division and cancer cell proliferation by binding to microtubules and are thus able to kill any kind of cell. By attaching the auristatin to an antibody that targets cancer cells, they can effectively be used in the treatment of cancer. MMAE and MMAF exist as two conformers in solution, namely as cis- and trans-conformers. The trans-conformer resembles the biologically active conformer. It was recently noted that in solution 50-60 % of the MMAE and MMAF-molecules exist in the biologically inactive cis-conformer. The molecule changes from one conformer to the other by the rotation of an amide bond. However, this takes several hours in body temperature. As the amount of the cis-conformer is significant, the efficacy of the drug is decreased, and the possibility of side effects is increased. It is possible that the molecule leaves the cancer cell in its inactive form, migrates to healthy cells and tissue, and transforms to the active form there, damaging the healthy cell. The goal of this study was to modify the structure of the auristatins so that the cis/trans-equilibrium would change to favor the biologically active trans-conformer. The modifications were done virtually, and the relative energies were computed using high-level quantum chemical methods, at density functional theory (DFT), 2nd order perturbation theory (MP2) and coupled cluster levels. Intramolecular interactions were analyzed computationally, employing symmetry-adapted perturbation theory and the non-covalent interactions analysis. The results suggest that simple halogenation of the benzene ring para-position is able to significantly shift the cis/trans-equilibrium to favor the trans-conformer. This is due to changes in intramolecular interactions that favor the trans-conformer after halogenation. For example, the NCI analysis shows that the halogen atom invokes stabilizing intramolecular interactions with the Dil amino acid; there is no such interaction between the para-position hydrogen and Dil in the original molecules. We also performed docking studies that show that the halogenated molecules can bind to microtubules, thus confirming that the modified structures have potential to be developed into new, more efficient and safe cancer drugs. The most promising drug candidates are Cl-MMAF, F-MMAF, and F-MMAE where 94, 90, and 79 % of the molecule is predicted to exist in the biologically active trans-conformer, respectively.

In model selection, it is necessary to select a model from a set of candidate models based on some observed data. The model should fit the data well, but without being overly complex, since that would not allow the model to generalize well its predictions to unseen data. Information criteria are widely used model selection methods that select a model based on some criteria. Information criteria estimate a score for each candidate model, and use that score to make a selection. A common way of estimating such a score, rewards the candidate model for its goodness of fit on some observed data and penalizes for the model complexity. Many popular information criteria, such as Akaike's Information Criterion (AIC) and Bayesian Information Criterion (BIC) penalize model complexity by the feature dimension. However, in a non-standard setting with inherent dependencies, these criteria are prone to over-penalizing the complexity of the model. Motivated by how these commonly used criteria tend to over-penalize, we evaluate AIC and BIC on a multi-target setting with correlated features. We compare AIC and BIC, with the Fisher Information Criterion (FIC), a criterion that takes into consideration correlations amongst features and does not penalize model complexity solely by the feature dimension of the candidate model. We evaluate the feature selection and predictive performances of the three information criteria in a linear regression setting with correlated features. We evaluate the precision, recall and F1 score of the set of features each criterion selects, compared to the feature set of the generative model. Under this setting's assumptions, we find that FIC yields the best results, compared to AIC and BIC, both in the feature selection and predictive performance evaluation. Finally, using FIC's properties for feature selection, we derive a formulation that allows to approximate the effective feature dimension of models with correlated features, in linear regression settings.

Heating, ventilation, and air conditioning (HVAC) systems consume massive amounts of energy. Fortunately, by carefully controlling these systems, a significant amount of energy savings can be achieved. This requires detecting a presence or amount of people inside the building. Countless different sensors can be used for this purpose, most common being air quality sensors, passive infrared sensors, wireless devices, and cameras. A comprehensive review and comparison are done for these sensors in this thesis. Low-resolution infrared cameras in counting people are further researched in this thesis. The research is about how different infrared camera features influence counting accuracy. These features are resolution, frame rate and viewing angle. Two systems were designed: a versatile counting algorithm, and a testing system which modifies these camera features and tests the performance of the counting algorithm. The results prove that infrared cameras with resolution as low as 4x2 are as accurate as higher resolution cameras, and that frame rate above 5 frames per second does not bring any significant advantages in accuracy. Resolution of 2x2 is also sufficient in counting but requires higher frame rates. Viewing angles need to be carefully adjusted for best accuracy. In conclusion, this study proves that even the most primitive infrared cameras can be used for accurate counting. This puts infrared cameras in a new light since primitive cameras can be cheaper to manufacture. Therefore, infrared cameras used in occupancy counting become significantly more feasible and have potential for widespread adoption.