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In recent years, there has been a great interest in modelling financial markets using fractional Brownian motions. It has been noted in studies that ordinary diffusion based stochastic volatility models cannot reproduce certain stylized facts that are observed in financial markets, such as the fact that the at the money (ATM) volatility skew tends to infinity at short maturities. Rough stochastic volatility models, where the spot volatility process is driven by a fractional Brownian motion, can reproduce these effects. Although the use of long memory processes in finance has been advocated since the 1970s, it has taken until now for fractional Brownian motion to gain widespread attention. This thesis serves as an introduction to the subject. We begin by presenting the mathematical definition of fractional Brownian motion and its basic mathematical properties. Most importantly, we show that fractional Brownian motion is not a semimartingale, which means that the theory of Itô calculus cannot be applied to stochastic integrals with fractional Brownian motion as integrator. We also present important representations of fractional Brownian motion as moving average process of a Brownian motion. In the subsequent chapter, we show that we can define a Wiener integral with respect to fractional Brownian motion as a Wiener integral with respect to Brownian motion with transformed integrand. We also present divergence type integrals with respect to fractional Brownian motion and an Itô type formula for fractional Brownian motion. In the last chapter, we introduce rough volatility. We derive the so called rough Bergomi model model that can be seen as an extension of the Bergomi stochastic volatility model. We then show that for a general stochastic volatility model, there is an exact analytical expression for the ATM volatility skew, defined as the derivative of the volatility smile slope with respect to strike price evaluated at the money. We then present an expression for the short time limit of the ATM volatility skew under general assumptions which shows that in order to reproduce the observed short time limit of infinity, the volatility must be driven by a fractional process. We conclude the thesis by comparing the rough Bergomi model to SABR- and Heston stochastic volatility models.

In this thesis, Convolutional Neural Networks (CNN) and Inverse Mathematic methods will be discussed for automated defect detection in materials that are used for radiation detectors. The first part of the thesis is dedicated to the literature review on the methods that are used. These include a general overview of Neural Networks, computer vision algorithms and Inverse Mathematics methods, such as wavelet transformations, or total variation denoising. In the Materials and Methods section, how these methods can be utilized in this problem setting will be examined. Results and Discussions part will reveal the outcomes and takeaways from the experiments. A focus of this thesis is put on the CNN architecture that fits the task best, how to optimize that chosen CNN architecture and discuss, how selected inputs created by Inverse Mathematics influence the Neural Network and it's performance. The results of this research reveal that the initially chosen Retina-Net is well suited for the task and the Inverse Mathematics methods utilized in this thesis provided useful insights.

This study researched the rationale behind creative practitioners for utilising or not utilising Text-to-Image Generative (TTIG) AI in their creative process. In addition, it also researches how the workflow of creative practitioners who are utilising this technology. A Reddit analysis of 331 posts and their comments and an online questionnaire with 92 participants is performed. The result showed that the rationale for creative practitioners not using TTIG is varied, including personal reasons, impact on the artist, ethical issues surrounding it, the AI-generated art itself, and their own creative workflow. On the other hand, motivation for using TTIG is mostly driven by the playfulness and usefulness of the system. Amplified by the benefit felt by the users for example source of inspiration, helping idea generation and exploration of new creative possibilities. There are mainly four types of workflow incorporating TTIG: to use it as reference only, use it as is, use it as a base, and use it as parts. We further discuss the implications of these findings and the author highlights the urgency of policymakers to create regulations safeguarding creative and their creations. The author also proposes to develop the system collaboratively with creative practitioners and the inclusion of AI in art education curricula.

Along with the current studies on plasmonic photocatalysis that have emerged in the past years, we inquired into the catalytic performance of spherical gold and silver nanoparticles to further on investigate how their addition to doped molybdenum oxide (MoO3-x) could enhance its own plasmonic properties. Such studies were carried out synthesizing nanofilms on which the coupling oxidation of p-aminothiophenol (PATP) to p,p´-dimercaptoazobenzene (DMAB) was carried out using Surface Enhanced Raman Spectroscopy (SERS). The corresponding irradiation of 532 and 633nm light, observing satisfactory results with the nanoparticles and some conversion on the oxide when using the latter wavelength. Additional characterization of the different catalysts was performed using scanning electron microscopy (SEM) to observe their morphologies, confirming the spherical shape of the nanoparticles and the layered composition of the oxide. X-ray diffraction (XRD) was used to study the α-MoO3 structure of the semiconductor and the effects that the doping process had on it, showing how the distance between layers increased as we introduced more hydrides between them. These compounds were also analyzed with a spectrophotometer to study their performances when irradiated with light, showing a noteworthy increase in the absorbance after the reduction with the dopant. Finally, we tested the performance of these catalysts on the oxidation of benzyl alcohol when irradiated with 427 and 525 nm lamps, as well as with white xenon ones, not finding unfortunately any significant conversion during the time given though.

Quantum Monte Carlo (QMC) is an accurate but computationally expensive technique for simulating the electronic structure of solids, with its use as a simulation technique for modelling positron states and annihilation in solids relatively new. These simulations can support positron annihilation spectroscopy and help with defect characterisation in solids and vacancy identification by calculating the positron lifetime with increased accuracy and comparing them to experimental results. One method of reducing the computational cost of simulations whilst maintaining chemical accuracy is to employ pseudopotentials. Pseudopotentials are a method to approximate the interactions between the outer valence electrons of an atom and the inner core electrons, which are difficult to model. By replacing the core electrons of an atom with an effective potential, a level of accuracy can be maintained whilst reducing the computational cost. This work extends existing research with a new set of pseudopotentials with fewer core electrons replaced by an effective potential, leading to an increase in the number of core electrons in the simulation. With the inclusion of additional core electrons into the simulation, the corrections that need to be made to the positron lifetime may not be needed. Silicon is chosen as the element under study as the high electron count makes it difficult to model accurately for positron simulations. The suitability of these new pseudopotentials for QMC is shown by calculating the cohesive and relaxation energy with comparisons made to previously used pseudopotentials. The positron lifetime is calculated from QMC simulations and compared against experimental and theoretical values. The simulation method and challenges due to the inclusion of more core electrons are presented and discussed. The results show that these pseudopotentials are suitable for use in QMC studies, including positron lifetime studies. With the inclusion of more core electrons into the simulation a positron lifetime was calculated with similar accuracy to previous studies, without the need for corrections, proving the validity of the pseudopotentials for use in positron studies. The validation of these pseudopotentials enables future theoretical studies to better capture the annihilation characteristics in cases where core electrons are important. In achieving these results, it was found that energy minimisation rather than variance minimisation was needed for optimising the wavefunction with these pseudopotentials.

The chemoenzymatic approach has been utilized for several decades to overcome the challenges of conventional synthesis methods and work towards an environmentally benign, greener approach. Recently, the use of recombinant enzymes has spiked to expand the scope for synthesizing complex molecules. The synthesis of non-steroidal and anti-inflammatory drugs (NSAIDs) is an eminent field of research in pharmaceutical sciences to enhance therapeutic efficacy while minimizing adverse side effects. The experimental work outlined in this thesis aimed to establish a chemoenzymatic synthesis route for Polmacoxib. The study compared the chemoenzymatic pathway coupled with photooxidation to the conventional route described in the literature, with the goal of identifying the most efficient synthesis method. The integration of chemoenzymatic approaches and photocatalysis represents a promising and sustainable method for synthesizing key intermediates in small-molecule drug compounds. The focus of this thesis work was the successful synthesis of the fiuranone motif, a key intermediate in the synthesis of polmacoxib, using this innovative approach. As part of the research for this thesis, the reaction conditions for photooxidation were screened and reported, followed by a comparative study between the traditional route and the envisioned route. Notably, the study found that the wavelength of light used significantly impacts the optimization of reaction conditions.

Tämän pro gradu –tutkielman tarkoituksena on perehtyä ionisten nesteiden (IL) käyttöön ja niiden vaikutukseen yleisimmissä analyyttisen kemian erotusmenetelmissä kuten kaasukromatografiassa (GC), nestekromatografiassa (LC) ja kapillaarielektroforeesissa (CE). Lisäksi ionisten nesteiden käyttöä ohutkerroskromatografiassa (TLC) käsitellään. Ioniset nesteet ovat sulia suoloja, jotka on yleisesti määritelty ioneista koostuviksi nestemäisiksi elektrolyyteiksi. Ionisissa nesteissä anionien ja kationien välillä merkittävä vuorovaikutusvoima ovat vetysidokset. Ioniset nesteet ovat kokonaisvaraukseltaan neutraaleja, mutta sisältävät positiivisen ja negatiivisen osittaisvarauksen. Ionisten nesteiden käytön monipuolisuutta lisäävät niiden olematon höyrynpaine, mekaaniset ja elektrokemialliset ominaisuudet sekä hyvä lämmönkestävyys, viskositeettien muokattavuus sekä niiden kyky uuttaa monia orgaanisia yhdisteitä sekä metalli-ioneja. Tässä tutkielmassa perehdytään ionisten nesteiden käyttöön liikkuvaan faasiin lisättävinä modifioijina (LC, CE) sekä kapillaarin tai kolonnin stationäärifaasin pintaan (LC, CE, GC, sekä kiinteään matriisiin TLC:ssä) lisättävänä tai kapillaarin/kolonnin pintaan valmiiksi sidottuina kiinteinä faaseina. Näiden ionisilla nesteillä muokattujen erotusmenetelmien vaikutusta erilaisten analyyttien erotukseen tutkitaan kirjallisuudesta löytyvin esimerkein. Kirjallisuusosassa tarkastellaan ionisten nesteiden tarkempia vaikutusmekanismeja erotukseen liikkuvassa ja kiinteässä faasissa sekä mahdollisista yhteisvaikutuksista em. faaseissa. Näitä analyyttien ja ionisilla nesteillä muokattujen erotusfaasien välisistä vuorovaikutuksista on havaittu mm. vetysidokset, van der Waals –voimat, heikot ja vahvat ioniset vetovoimat, n-Ï€- jaπ-Ï€–vuorovaikutukset, elektrostaattiset ja hydrofobiset/hydrofiiliset vuorovaikutukset sekä esim. retention kasvaminen ionisen nesteen alkyyliketjun pituuden kasvaessa. Ionisia nesteitä on käytetty laajasti esim. sitomaan kasvihuonekaasuja, katalyytteinä, rakettimoottoreiden polttoaineena, biomateriaalien liuottimina ja voiteluaineina sekä monella teknologian alalla kuten metallurgiassa, ydinteknologiassa, sähkökemiassa sekä elektrolyytteinä vaihtoehtoisen energian sovelluksista. Ionisten nesteiden ihmisille ja ympäristölle yleensä varsin vaarattomien ja haitattomien ominaisuuksiensa vuoksi ionisten nesteiden käyttösovellukset tulevat todennäköisesti tulevaisuudessa kasvamaan ja korvamaan perinteisiä liuottimia sekä toisaalta olemaan potentiaalisia työkaluja kehitettäessä jatkossa ympäristön ja kestävän kehityksen kannalta olennaista 'vihreää kemiaa'. Kokeellisessa osassa tutkittiin CE-analyyseissa fosfaattipuskuriajoliuokseen lisättyjen IL:en (P14444OAc- ja P14444Cl-) vaikutusta erotukseen. Näiden ionisten nesteiden todettiin parantavan tutkittujen yhdisteiden (19 kpl) erottumista kapillaarissa. Bentsoaattien homologiselle sarjalle (6 kpl) saatiin selkeä erottuminen kaikille yhdisteille ja lähellä toisiaan migroituvat o-, p- ja m-ksyleenit saatiin erottumaan. Kokeellisen osan havainnot tukevat hyvin kirjallisuudessa esiintyviä tuloksia ionisten nesteiden ominaisuuksista kapillaarin ja mahdollisesti ajoliuoksen muokkaajina, sillä tässä työssä suoritettujen analyysien tulokset osoittavat ionisten nesteiden selvästi parantavan tutkittujen analyyttien erotusta CE:lla.

One of the most noticeable effects of solar–terrestrial physics is the aurora which regularly appears in the polar regions. This polar light is the result of the excitation of atmospheric species by charged particles originating from the solar wind and magnetosphere that enter the Earth’s atmosphere, which are called precipitating particles. We present the first results on auroral proton precipitation into the ionosphere using a global 3-dimensional simulation of near-Earth space plasma with the Vlasiator hybrid-Vlasov model, driven with a southward interplanetary magnetic field and steady solar wind parameters. The hybrid-Vlasov approach describes ions through their velocity distribution function in phase space (3-dimensional ordinary space and 3-dimensional velocity space), while electrons are represented by a massless charge-neutralizing fluid. Vlasiator is a global model describing the whole region of near-Earth space including the Earth’s magnetosphere (whole dayside and part of the magnetotail), the magnetosheath, as well as the foreshock region and some solar wind. The precipitating proton differential number fluxes for this run are determined from the proton phase-space density contained within the bounce loss-cone, which is set at a constant angle of 10 degrees everywhere. To determine the precipitation of particles at ionospheric altitudes (in this case a height of 110 km above the Earth’s surface), we trace magnetic field lines from the ionosphere to the inner boundary of the Vlasiator domain using the Tsyganenko model. With this, we obtain a magnetic local time–geomagnetic latitude map of differential number flux of precipitating protons in 9 energy bins between 0.5 and 50 keV. From the differential number flux, proton integral energy fluxes and mean energies can be obtained. The integral energy fluxes in the Vlasiator run are then compared to data of the Precipitation Electron/Proton Spectrometer (SSJ) instrument of the Defense Meteorological Satellite Program (DMSP) for several satellite overpasses during events with similar solar wind conditions as in the Vlasiator run. The SSJ instrument bins proton energies between 0.03 and 30 keV. Typical values of the total integral energy flux are between 5 · 10^6 and 5 · 10^7 keV cm−2 s−1 sr−1 in the cusp and between 1 · 10^6 and 3 · 10^7 keV cm−2 s−1 sr−1 in the evening sector for both Vlasiator and DMSP, although DMSP fluxes can locally be up to an order of magnitude higher. Additionally, global precipitation patterns in Vlasiator are compared to Ovation Prime, which is an empirical model based on data from DMSP which can be used to forecast precipitation of auroral electrons and protons. Although Ovation Prime shows a much wider cusp region compared to Vlasiator, both show similar maximum integral energy fluxes around 1 to 2 · 10^7 keV cm−2 s−1 sr−1 in the cusp region, and between 3 · 10^6 and 5 · 10^7 keV cm−2 s−1 sr−1 in the nightside oval.

Exponential growth of Internet of Things complicates the network management in terms of security and device troubleshooting due to the heterogeneity of IoT devices. In the absence of a proper device identification mechanism, network administrators are unable to limit unauthorized accesses, locate vulnerable/rogue devices or assess the security policies applicable to these devices. Hence identifying the devices connected to the network is essential as it provides important insights about the devices that enable proper application of security measures and improve the efficiency of device troubleshooting. Despite the fact that active device fingerprinting reveals in depth information about devices, passive device fingerprinting has gained focus as a consequence of the lack of cooperation of devices in active fingerprinting. We propose a passive, feature based device identification technique that extracts features from a sequence of packets during the initial startup of a device and then uses machine learning for classification. Proposed system improves the average device prediction F1-score up to 0.912 which is a 14% increase compared with the state-of-the-art technique. In addition, We have analyzed the impact of confidence threshold on device prediction accuracy when a previously unknown device is detected by the classifier. As future work we suggest a feature-based approach to detect anomalies in devices by comparing long-term device behaviors.

Multiple Sequence Alignment (MSA) is one of the essential methods in molecular biology. The accuracy of MSAs effects downstream analyses such as phylogenetic inference, protein structure prediction, and function prediction. Because of the importance of MSA, current methods try to search for the optimal alignment with different objective functions and heuristics. As a result, different methods perform differently on various tasks. For example, the sequence alignments from methods designed based on structure homology are likely to mislead the comparative and phylogenetic analyses, since these downstream analyses require alignments correctly represent the evolutionary homology. The phylogeny-aware alignment method PRANK by Löytynoja and Goldman has been demonstrated to have good performance in aligning protein-coding genes for the evolutionary and comparative analyses. One of the reasons is that the phylogenetic information is also considered during the alignment in order to distinguish insertions from deletions. It has become the method of choice in the comparative sequence analyses and for instance, in recently published tiger genome study, PRANK was used to align the orthologous genes. However, there are still some issues that need to be resolved in PRANK. First of all, it can be sensitive to errors in the guide phylogenetic tree and bias the resulting alignment. Second, the single-threaded design does not allow PRANK to take advantage of modern CPU architecture or computer clusters, which is a disadvantage when working with large volumes of data. In this thesis, iPRANK, an iterative alignment tool to PRANK, will be introduced. The proposed tool is able to utilize multiple cores and make alignment faster via a divide-and-merge approach which splits the data set into subsets according to a guide tree and then runs PRANK simultaneously on each subset. The iteration of alignment and tree inference allows to search for a good tree and get rid of errors in the initial guide tree to improve the resulting alignment. In this way, iPRANK can estimate the tree and the multiple sequence alignment simultaneously for a set of unaligned sequences. In addition to improved alignment of single data set, the developed tool is also capable of inferring phylogeny from data sets consisting of multiple genes via gene concatenation strategy. By performing extensive studies on a set of simulation data, it will demostrate that our developed tool can run PRANK on a large computer cluster and produce improved alignments and phylogenetic trees compared to other approaches.