Browsing by Title
Now showing items 61-80 of 4203
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(2016)The first order phase transition, the nucleation process, of a thermodynamic system is one of the basic physical phenomena and it has significant relevance on several scientific fields. Despite the importance of the nucleation process, the theoretical understanding is still imperfect. The emergence of a new phase, liquid or solid cluster, in the metastable gas phase is mainly treated with classical nucleation theory (CNT) by using known macroscopic thermodynamic properties of the studied substance, but the theory often fails in predicting the nucleation process adequately. The failure of describing the nucleation event by CNT has shifted the theoretical focus on molecular-level nucleation studies to improve the prediction and understanding of the origin of the failure. This thesis examines one of the key assumptions behind CNT, the constrained equilibrium hypothesis, by approaching it from statistical mechanics and thermodynamic point of view. The main tools in this work are computational: both Monte Carlo (MC) and molecular dynamics (MD) simulations have been used to simulate the homogeneous nucleation processes of Lennard-Jones argon. Two separate studies are presented: At first we compare the nucleation rates obtained by MC (based on thermodynamic equilibrium) and molecular dynamics simulations using the nonisothermal nucleation theory and then the constrained equilibrium hypothesis is invalidated by studying the kinetics of Lennad-Jones argon clusters from size of 4 up to 31 molecules at 50 K. In addition to the actual study, the thesis includes a systematic overview of the theoretical treatment of homogeneous nucleation from thermodynamic liquid drop model to applicable molecular-level simulation techniques.
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(2012)The thesis is written on the catalytic activation of carbon dioxide. It includes a literature part and an experimental part. In the literature part, a review on metal (salen) complexes in relation to their electronic and geometric properties is presented. Salicylidene-aminates are included considering similarity to the salens. Also included from literature is a selective review focusing on the mechanistic aspects in the carboxylation of epoxides by metal (salen) complexes. Some applications of iron (salen) complexes as catalyst are mentioned. In the experimental part, the bis(phenoxyiminato) chlorido iron(III)complexes are synthesized, characterized and applied on carbon dioxide/epoxide coupling reactions. Characterization is done by UV-vis, infra-red, nuclear magnetic resonance and electron impact mass spectroscopy and elemental analysis for C, H, and N. Thermogravimetric analysis for the complexes, DFT calculation for the most active species (L11)2Fe(III)Cl and X-ray for (L6)2Fe(III)Cl are also presented. X-ray crystallography reveals the space group of (L6)2Fe(III)Cl to be orthorhombic, Pbcn; a = 29.0038(14) Å, b = 8.6123(8) Å, c = 10.7843(9) Å; α = β = γ = 90 o. The ML2Cl complexes are observed to have M-O and M-N bonds involving the phenolic oxygen and azomethine nitrogen. Correlation study between spin state and the Fe-N bond length indicates a high-spin state for Fe(III) nucleus. The geometry around the metal nucleus is distorted square pyramidal. Reaction conditions for catalytic activity were fine-tuned envisaging the exclusive production of cyclic carbonates. Propylene and styrene oxides show high reactivity. The ketiminato complexes show better activity over the aldiminato complexes. Optimal result is obtained in dimethyl formamide at a temperature of 145 oC and carbon dioxide pressure of 10 bars in the presence of tetrabutylphosphonium bromide as co-catalyst. A TOF of 572/h is observed for propylene oxide. Three reaction mechanisms are proposed. Comparatively the Co(III) analogues are more active, and iodide as a halogen ligand produces a more active complex than chloride. Improving nucleophilicity of Fe(III), elimination of intramolecular H-bond and improving on solubility could yield a more active complex. Iron is a cheap and environmentally benign metal. The use of iron complexes is an attractive alternative to other transition metals which are expensive and/or toxic. The complexes are robust and show high thermal stability. Surprisingly, oligomers of styrene carbonate were noticed at the reaction temperature and pressure known to favor exclusive production of cyclic carbonates. These observations suggest the complexes for a promising study and application in future research for copolymerization. Such copolymers may have useful charateristics for diverse applications.
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(2020)We present Active Faceted Search, a technique which allows a user to iteratively refine search results by selecting from a set of facets that is dynamically refined with each iteration. The facets presented are selected using a contextual multi armed bandit model. We first describe the computational model of a system which implements Active Faceted Search. We also create a web application to demonstrate an example of a system that can use an active faceted search component along with more traditional search elements such as a typed query and sidebar component. We perform simulations to compare the performance of the system under different parameters. Finally, we present a user experiment in which users are instructed to perform tasks in order to compare Active Faceted Search to traditional search techniques.
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(2017)A Bayesian Network (BN) is a graphical model applying probability and Bayesian rule for its inference. BN consists of structure, that is a directed acyclic graph (DAG), and parameters. The structure can be obtained by learning from data. Finding an optimal BN structure is an NP-Hard problem. If an ordering is given, then the problem becomes simpler. Ordering means the order of variables (nodes) for building the structure. One of structure learning algorithms that uses variable ordering as the input is K2 algorithm. The ordering determines the quality of resulted network. In this work, we apply Cuckoo Search (CS) algorithm to find a good node ordering. Each node ordering is evaluated by K2 algorithm. Cuckoo Search is a nature-inspired metaheuristic algorithm that mimics the aggressive breeding behavior of Cuckoo birds with several simplifications. It has outperformed Genetic Algorithms and Particle Swarm Optimization algorithm in finding an optimal solution for continuous problems, e.g., functions of Michalewicz, Rosenbrock, Schwefel, Ackley, Rastrigin, and Griewank. We conducted experiments on 35 datasets to compare the performances of Cuckoo Search to GOBNILP that is a Bayesian network learning algorithm based on integer linear programming and it is well known to be used as benchmark. We compared the quality of obtained structures and the running times. In general, CS can find good networks although all the obtained networks are not the best. However, it sometimes finds only low-scoring networks, and the running times of CS are not always very fast. The results mostly show that GOBNILP is consistently faster and can find networks of better quality than CS. Based on the experiment results, we conclude that the approach is not able to guarantee obtaining an optimal Bayesian network structure. Other heuristic search algorithms are potentially better to be used for learning Bayesian network structures that we have not compared to our works, for example the ordering-search algorithm by Teyssier and Koller [41] that combines greedy local hill-climbing with random restarts, a tabu list, caching computations, and a heuristic pruning procedure.
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(2024)Indexes are data structures that are used for retrieving records from a database. They are used in database management systems (DBMS) to optimize queries. The abundance of available data has motivated the research of indexes to provide faster query times and to have smaller memory usage. With the advantages of machine learning, new variations of indexes have been created. These indexes utilize the data distribution for faster query times and smaller footprints in comparison to traditional indexes like B-tree and B+ tree. These indexes are known as the learned indexes. In this thesis, we study the effect the distribution change between normal distributions has on one-dimensional learned indexes and compare that to the B+ tree. We conduct an experiment where we simulate the distribution change and measure the insertion and query times. In this experiment, we include three learned indexes, which are ALEX, PGM, and LIPP.
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(2020)Nowadays the amount of data collected on individuals is massive. Making this data more available to data scientists could be tremendously beneficial in a wide range of fields. Sharing data is not a trivial matter as it may expose individuals to malicious attacks. The concept of differential privacy was first introduced in the seminal work by Cynthia Dwork (2006b). It offers solutions for tackling this problem. Applying random noise to the shared statistics protects the individuals while allowing data analysts to use the data to improve predictions. Input perturbation technique is a simple version of privatizing data, which adds noise to whole data. This thesis studies an output perturbation technique, where the calculations are done with real data, but only suffcient statistics are released. With this method smaller amount of noise is required making the analysis more accurate. Yu-Xiang Wang (2018) improves the model by introducing an adaptive AdaSSP algorithm to fix the instability issues of the previously used Sufficient Statistics Perturbation (SSP) algorithm. In this thesis we will verify the results shown by Yu-Xiang Wang (2018) and look in to the pre-processing steps more carefully. Yu-Xiang Wang has used some unusual normalization methods especially regarding the sensitivity bounds. We are able show that those had little effect on the results and the AdaSSP algorithm shows its superiority over SSP algorithm also when combined with more common data standardization methods. A small adjustment for the noise levels is suggested for the algorithm to guarantee privacy conditions set by classical Gaussian Mechanism. We will combine different pre-processing mechanisms with AdaSSP algorithm and show a comparative analysis between them. The results show that Robust private linear regression by Honkela et al. (2018) makes significant improvements in predictions with half of the data sets used for testing. The combination of AdaSSP algorithm with robust private linear regression often brings us closer to non-private solutions.
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(2023)Cancer consists of heterogeneous cell populations that repeatedly undergo natural selection. These cell populations contest with each other for space and nutrients and try to generate phenotypes that maximize their ecological fitness. For achieving this, they evolve evolutionarily stable strategies. When an oncologist starts to treat cancer, another game emerges. While affected by the cellular evolution processes, modeling of this game owes to the results of the classical game theory. This thesis investigates the theoretical foundations of adaptive cancer treatment. It draws from two game theoretical approaches, evolutionary game theory and Stackelberg leader-follower game. The underlying hypothesis of adaptive regimen is that the patient's cancer burden can be administered by leveraging the resource competition between treatment-sensitive and treatment-resistant cells. The intercellular competition is mathematically modelled as an evolutionary game using the G function approach. The properties of the evolutionary stability, such as ESS, the ESS maximum principle, and convergence stability, that are relevant to tumorigenesis and intra-tumoral dynamics, are elaborated. To mitigate the patient's cancer burden, it is necessary to find an optimal modulation and frequency of treatment doses. The Stackelberg leader-follower game, adopted from the economic studies of duopoly, provides a promising framework to model the interplay between a rationally playing oncologist as a leader and the evolutionary evolving tumor as a follower. The two game types applied simultaneously to cancer therapy strategisizing can nourish each other and improve the planning of adaptive regimen. Hence, the characteristics of the Stackelberg game are mathematically studied and a preliminary dose-optimization function is presented. The applicability of the combination of the two games in the planning of cancer therapy strategies is tested with a theoretical case. The results are critically discussed from three perspectives: the biological veracity of the eco-evolutionary model, the applicability of the Stackelberg game, and the clinical relevance of the combination. The current limitations of the model are considered to invite further research on the subject.
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(2023)Simulating space plasma on a global scale is computationally demanding due to the system size involved. Modeling regions with variable resolution depending on physical behavior can save computational resources without compromising too much on simulation accuracy. This thesis examines adaptive mesh refinement as a method of optimizing Vlasiator, a global hybrid-Vlasov plasma simulation. Behavior of plasma near the Earth's magnetosphere and different characteristic scales that need to be considered in simulation are introduced. Kinetic models using statistical methods and fluid methods are examined. Modeling electrons kinetically requires resolutions orders of magnitude finer than ions, so in Vlasiator ions are modeled kinetically and electrons as a fluid. This allows for lighter simulation while preserving some kinetic effects. Mesh refinement used in Vlasiator is introduced as a method to save memory and computational work. Due to the structure of the magnetosphere, resolution isn't uniform in the simulation domain, with particularly the tail regions and magnetopause having rapid spatial changes compared to the relatively uniform solar wind. The region to refine is parametrized and static throughout a simulation run. Adaptive mesh refinement based on the simulation data is introduced as an evolution of this method. This provides several benefits: more rigorous optimization of refinement regions, easier reparametrization for different conditions, following dynamic structures and saving computation time in initialization. Refinement is done based on two indices measuring the spatial rate of change of relevant variables and reconnection respectively. The grid is re-refined at set intervals as the simulation runs. Tests similar to production runs show adaptive refinement to be an efficient replacement for static refinement. Refinement parameters produce results similar to the static method, while giving somewhat different refinement regions. Performance is in line with static refinement, and refinement overhead is minor. Further avenues of development are presented, including dynamic refinement intervals.
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(2020)Aallokon mittaamiseen Itämerellä on vakiintunut käytettäväksi aaltopoiju, joka on ankkuroitava pintapoiju. Kyseinen havaintotapa aiheuttaa mittauskauden lyhenemistä jäätalven vuoksi. Jotta mittauskautta pystyttäisiin pidentämään, tarvitaan mittaussysteemi, jota ei tarvitse nostaa ylös vedestä ennen jäätalvea. Pintavirtauksia mitataan yleisesti pohjaan asennettavilla akustisilla Doppler virtaus profiilimittalaitteilla (ADCP), joissa ei ole reaaliaikaista tiedonsiirtoa. Viimeisten vuosien aikana lähes reaaliaikaista aineistoa lähettävään aaltopoijuun on lisätty pintavirtauksen havainnoinnin mahdollistavat anturit. Tässä tutkielmassa arvioidaan ADCP:n havaintojen luotettavuutta aallokon mittaamisessa verrattuna aaltopoijuun ja vertaillaan aaltopoijun ja ADCP:n virtaushavaintoja toisiinsa. Tässä tutkielmassa käytetty havaintoaineisto on saatu kahden eri vuoden, kesien 2017 ja 2018, aikana toteutetuista mittalaitevertailuista. Mittausjaksot tehtiin Suomenlahdella Hankoniemen itäpuolella. Havaintoaineistolle on tehty laadunvarmistusta ennen kuin niitä on vertailtu. Laaduntarkastuksen kriteerit on saatu mittalaitteiden valmistajien ilmoittamista raja-arvoista, suosituksista ja kirjallisuudessa olevista Suomenlahden aallokko-olosuhteiden raja-arvoista. Havaintoaineistoa on analysoitu ja verrattu toisiinsa aikasarjojen, hajontakuvaajien ja tilastollisten arvojen kautta. ADCP:n ja aaltopoijun merkitsevän aallonkorkeuden vastaavuus on hyvä, mutta ADCP ei pysty havaitsemaan alle 0,5 metrin aallokkoa luotettavasti. Syvemmälle asennettu ADCP aliarvioi suhteellisen systemaattisesti merkitsevää aallonkorkeutta verrattuna aaltopoijun havaintoihin. Aliarviointia on teoriassa mahdollista korjata ja näin parantaa mittauksien vastaavuutta, mutta käytännössä se ei ole järkevää koska se vaatisi uusien vertailujen tekemistä muuan muassa jokaiselle mittalaitteelle ja -paikalle. Huipun periodin ja aallokon suunnan vastaavuus ei ollut tilastollisesti merkittävää ja ADCP:n mittauksia näistä suureista voisi käyttää, tarvittaisiin tarkempaa spektrien analysointia. Aaltopoijun ja ADCP:n pintavirtaushavaintoja vertaillessa on vastaavuutta arvojen välillä, mutta aallokon kasvaessa erot mittauksissa kasvavat. Havaittuja eroja ei voi selittää pelkästään vertailtavien laitteiden mittaussyvyyden erolla, joka oli keskimäärin 1 metri.
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(2018)Ilmatieteen laitoksella on runsaasti hankekohtaisesti tehtyjä virtausmittauksia akustisella Dopplerilmiöön perustuvalla ADCP -laitteella. Tällaiset akustiset mittarit pystyvät muita virtausmittareita paremmin mittaamaan laajoja merialueita, joten ne ovatkin maailmanlaajuisesti yksi suosituimmista menetelmistä tarkkailla merien virtauksia. Tärkeimmät ehdot mittausten onnistumiselle on mitatun virtauksen horisontaalinen homogeenisuus, joka ei aina toteudu muun muassa vedessä olevien äänisignaalin sirottajien itsenäisen liikkeen seurauksena. Laite pyrkii jatkuvasti tarkistamaan olosuhteiden riittävän sopivuuden ja poistaa tehokkaasti esimerkiksi mittausalueelle osuneiden kalojen liikkeet. Mikäli laitteen sisäinen laaduntarkkailu on kuitenkin liian tiukka, se saattaa liian helposti hylätä poikkeuksellisempia ilmiöitä, joten tiukempi laaduntarkkailu jätetään usein erikseen tehtäväksi. Tässä tutkielmassa kehitin laaduntarkastusohjelmiston merenpohjaan ankkuroidulle ADCP:lle. Työssä keskitytään erityisesti Ilmatieteen laitoksen käyttämään Teledyne RD Instrument’s -valmistajan Workhorse Sentinel -laitteeseen. Kynnysarvot datan laadulle on määritelty erityisesti tämän valmistajan mittareille ja testit perustuvat laitteen tallentamaan tietoon mittausprosessista. Lähestymistapa perustuu oletukseen, että jos valtaosa virtausnopeuden määrittämisen yhteydessä tehdyistä mittauksista eivät olleet riittävän luotettavia, niin luultavasti loput näistä näennäisesti onnistuneista mittauksista eivät myöskään edusta todellista virtaustilannetta. Laaduntarkistusohjelmisto kehitettiin käyttämällä esimerkkimateriaalina Saaristomerellä Lövskärin risteyksessä vuonna2013 suoritettuja mittauksia. Lövskärin datasetti oli erittäin hyvälaatuista ja epähomogeenisuuden seurauksena datasetistä poistettiin noin 0,3 % mittauksista. Meren ylintä 5 metrin kerrosta ei pystytty mittaamaan voimakkaan sivukeilan aiheuttaman häiriön takia (13 % mittauksista). Datasetissä on huomattavissa selkeää mittausten epävarmuuden kasvua termokliinissä ja yöaikaan, mikä johtuu sirottajina toimivan eläinplanktonin aktiivisuudesta. Yleisesti alueen virtaukset olivat termokliinin seurauksena vahvasti kerrostuneet ja alueella ilmeni syksyllä lyhytkestoisia voimakkaita (lähes 50 cm/s) virtauksia.
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(2020)Prediction of the pathological T-stage (pT) in men undergoing radical prostatectomy (RP) is crucial for disease management as curative treatment is most likely when prostate cancer (PCa) is organ-confined (OC). Although multiparametric magnetic resonance imaging (MRI) has been shown to predict pT findings and the risk of biochemical recurrence (BCR), none of the currently used nomograms allow the inclusion of MRI variables. This study aims to assess the possible added benefit of MRI when compared to the Memorial Sloan Kettering, Partin table and CAPRA nomograms and a model built from available preoperative clinical variables. Logistic regression is used to assess the added benefit of MRI in the prediction of non-OC disease and Kaplan-Meier survival curves and Cox proportional hazards in the prediction of BCR. For the prediction of non-OC disease, all models with the MRI variables had significantly higher discrimination and net benefit than the models without the MRI variables. For the prediction of BCR, MRI prediction of non-OC disease separated the high-risk group of all nomograms into two groups with significantly different survival curves but in the Cox proportional hazards models the variable was not significantly associated with BCR. Based on the results, it can be concluded that MRI does offer added value to predicting non-OC disease and BCR, although the results for BCR are not as clear as for non-OC disease.
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(2023)Emulsion polymerization is used to make high molecular weight polymers with a fast reaction rate. In emulsion, the temperature is well controlled and the viscosity of the continuous phase remains constant since all polymer chains are inside colloidal particles. Colloid dispersions have the advantage of being used as they are without further purification, which is great for industrial purposes. Emulsion polymerization is also well-scalable to fit the standards of the industry. Adhesives serve an important role in the furniture and construction industry. Many adhesives used for such purposes are derived from non-renewable resources and are not reusable. Additionally, when such strong adhesives are being used in attaching wooden parts, they cannot be separated and once the lifetime of the product is finished, it ends in a landfill. The possibility to remove such strong adhesives from the wooden product would give the wood possibility to be used in other applications. Additionally, the possibility to reapply the adhesive would decrease the amount of adhesive needed to be produced and increase the lifetime of the glue product. In this thesis polyvinyl acetate (PVAc) adhesives are modified by introducing hydrogen bonding units to the polymer chain by copolymerization of vinyl acetate with monomers having urea and bis-urea hydrogen bonding motifs. Comonomers suitable for vinyl acetate are designed, synthesized and characterized.
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(2015)Managing a telecommunications network requires collecting and processing a large amount of data from the base stations. The current method used by the infrastructure providers is hierarchical and it has significant performance problems. As the amount of traffic within telecommunications networks is expected to continue increasing rapidly in the foreseeable future, these performance problems will become more and more severe. This thesis outlines a distributed publish/subscribe solution that is designed to replace the current method used by the infrastructure providers. In this thesis, we propose an intermediate layer between the base stations and the network management applications which will be built on top of Apache Kafka. The solution will be qualitatively evaluated from different aspects. ACM Computing Classification System (CCS): Networks -> Network management Networks -> Network architectures
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Adjusting contacts with observed infections: consequences on predictions about vaccine effectiveness (2023)Contacts between individuals play a central part in infectious disease modelling. Social or physical contacts are often determined through surveys. These types of contacts may not accurately represent the truly infectious contacts due to demographic differences in susceptibility and infectivity. In addition, surveyed data is prone to statistical biases and errors. For these reasons, a transmission model based on surveyed contact data may make predictions that are in conflict with real-life observations. The surveyed contact structure must be adjusted to improve the model and produce reliable predictions. The adjustment can be done in multiple different ways. We present five adjustment methods and study how the choice of method impacts a model’s predictions about vaccine effectiveness. The population is stratified into n groups. All five adjustment methods transform the surveyed contact matrix such that its normalised leading eigenvector (the model-predicted stable distribution of infections) matches the observed distribution of infections. The eigenvector method directly adjusts the leading eigenvector. It changes contacts antisymmetrically: if contacts from group i to group j increase, then contacts from j to i decrease, and vice versa. The susceptibility method adjusts the group-specific susceptibility of individuals. The changes in the contact matrix occur row-wise. Analogously, the infectivity method adjusts the group-specific infectivity; changes occur column-wise. The symmetric method adjusts susceptibility and infectivity in equal measure. It changes contacts symmetrically with respect to the main diagonal of the contact matrix. The parametrised weighting method uses a parameter 0 ≤ p ≤ 1 to weight the adjustment between susceptibility and infectivity. It is a generalisation of the susceptibility, infectivity and symmetric methods, which correspond to p = 0, p = 1 and p = 0.5, respectively. For demonstrative purposes, the adjustment methods were applied to a surveyed contact matrix and infection data from the COVID-19 epidemic in Finland. To measure the impact of the method on vaccination effectiveness predictions, the relative reduction of the basic reproduction number was computed for each method using Finnish COVID-19 vaccination data. We found that the eigenvector method has no impact on the relative reduction (compared to the unadjusted baseline case). As for the other methods, the predicted effectiveness of vaccination increased the more infectivity was weighted in the adjustment (that is, the larger the value of the parameter p). In conclusion, our study shows that the choice of adjustment method has an impact on model predictions, namely those about vaccination effectiveness. Thus, the choice should be considered when building infectious disease models. The susceptibility and symmetric methods seem the most natural choices in terms of contact structure. Choosing the ”optimal” method is a potential topic to explore in future research.
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(2023)Context: The Bank of Finland, as the national monetary and central bank of Finland, possesses an extensive repository of data that fulfills both the statistical needs of international organizations and the federal requirements. Data scientists within the bank are increasingly interested in investing in machine learning (ML) capabilities to develop predictive models. MLOps offers a set of practices that ensure the reliable and efficient maintenance and deployment of ML models. Objective: In this thesis, we focus on addressing how to implement an ML pipeline within an existing environment. The case study is explorative in nature, with the primary objective of gaining deeper insight into MLOps tools and their practical implementation within the organization. Method: We apply the design science research methodology to divide design and development into six tasks: problem identification, objective definition, design and development, demonstration, evaluation, and communication. Results: We select the tools for the MLOps based on the user requirements and the existing environment, and then we design and develop a simplified end-to-end ML pipeline utilizing the chosen tools. Lastly, we conduct an evaluation to measure the alignment between the selected tools and the initial user requirements.
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(2024)Agile software development and DevOps are both well studied methodologies in the field of computer science. Agile software development is an iterative development approach that focuses on collaboration, customer feedback and fast deliveries. DevOps on the other hand highlights the co-operation between the developers and IT operations personnel, in addition to describing how to continuously deploy working software with usage of tools and automation. Even though these two methodologies share similarities and DevOps as a concept can even be seen as a descendant of agile software development, the relationship between the two is not yet as explored as the effects of individual practices. In this thesis, a systematic literature review is conducted to examine the relationship between agile software development and DevOps. The aim was to find benefits and drawbacks of the combined implementation agile software development and DevOps in the field of software development, the key similarities and differences between the two and how the adoption of one methodology influences the implementation of the other. A systematic literature review was conducted to find information on how agile software development and DevOps are related and perform in combination. Results showed that agile software development and DevOps share a complex yet symbiotic relationship. The complementary role of each methodology enhances each other and in unison these methodologies address wider variety of aspects in software development lifecycle. This combination shows a wide array of promising benefits such as improvements in productivity, delivery speed and collaboration. It however presents challenges related to required culture shift and lack of knowledge, for example, that organizations need to be wary of and acknowledge.
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(2021)People spend more than 90% of time indoors. That has made the analysis of indoor air quality an subject of interest. There is a growing popularity of miniaturized sample extraction techniques utilizing solid adsorbent materials and thermal desorption allowing direct sample introduction for analysis. This approach is solvent free and there is possibility for reusing adsorbent materials depending of adsorbent properties. This thesis covers the basics of adsorption-desorption process and takes detailed look on different adsorbent materials such as activated carbon (AC), metal-organic framework (MOF) and carbon nanotubes (CNT) and evaluates the effect of surface functionality and pore size distribution for adsorption process. In experimental part, a self-made autosampler functionality and its injection parameters were optimized. The autosampler is able to independently inject up to six in-tube extraction (ITEX) needles with complete desorption. The ITEX was constructed during this experiment with TENAX-GR adsorbent and the repeatability of autosampler and ITEXs were tested and compared to commercial system with extraction of different amines. The effectiveness of this system was also demonstrated for indoor volatile organic compound (VOC) analysis.
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(2022)The continuously evolving cyber threat landscape has become a major concern because sophisticated attacks against systems connected to the Internet have become frequent. The concern is on particular threats that are known as Advanced Persistent Threats (APT). The thesis aims to introduce what APTs are and illustrate other topics under the scope, such as tools and methods attackers can use. Attack models will also be explained, providing example models proposed in the literature. The thesis also introduces which kind of operational objectives attacks can have, and for each objective, one example attack is given that characterizes the objective. In addition, the thesis also uncovers various countermeasures, including most essential security solutions, complemented with more advanced methods. The last countermeasure that the thesis introduces is attribution analysis.
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(2015)Software delivery has evolved notably over the years, starting from plan-driven methodologies and lately moving to principles and practises shaped by Agile and Lean ideologies. The emphasis has moved from thoroughly documenting software requirements to a more people-oriented approach of building software in collaboration with users and experimenting with different approaches. Customers are directly integrated into the process. Users cannot always identify software needs before interacting with actual implementations. Building software is not only about building products in the right way, but also about building the right products. Developers need to experiment with different approaches, directly and indirectly. Not only do users value practical software, but the development process must also emphasise on the quality of the product or service. Development processes have formed to support these ideologies. To enable a short feedback-cycle, features are deployed often to production. A software is primarily delivered through a pipeline consisting of tree stages: development, staging and production. Developers develop features by writing code, verify these by writing related tests, interact and test software in a production-like 'staging' environment, and finally deploy features to production. Many practises have formed to support this deployment pipeline, notably Continuous Integration, Deployment and Experimentation. These practises focus on improving the flow of how software is being developed, tested, deployed and experimented with. The Internet has provided a thriving environment for using new practises. Due to the distributed nature of the web, features can be deployed without the need of any interaction from users. Users might not even notice the change. Obviously, there are other environments where many of these practises are much harder to achieve. Embedded systems, which have a dedicated function within a larger mechanical or electrical system, require hardware to accompany the software. Related processes and environments have their limitations. Hardware development can only be iterative to a certain degree. Producing hardware takes up front design and time. Experimentation is more expensive. Many stringent contexts require processes with assurances and transparency - usually provided by documentation and long-testing phases. In this thesis, I explore how advances in streamlining software delivery on the web has influenced the development of embedded systems. I conducted six interviews with people working on embedded systems, to get their view and incite discussion about the development of embedded systems. Though many concerns and obstacles are presented, the field is struggling with the same issues that Agile and Lean development are trying to resolve. Plan-driven approaches are still used, but distinct features of iterative development can be observed. On the leading edge, organisations are actively working on streamlining software and hardware delivery for embedded systems. Many of the advances are based on how Agile and Lean development are being used for user-focused software, particularly on the web.
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(2022)The detection heuristic in contemporary machine learning Windows malware classifiers is typically based on the static properties of the sample. In contrast, simultaneous utilization of static and behavioral telemetry is vaguely explored. We propose a hybrid model that employs dynamic malware analysis techniques, contextual information as an executable filesystem path on the system, and static representations used in modern state-of-the-art detectors. It does not require an operating system virtualization platform. Instead, it relies on kernel emulation for dynamic analysis. Our model reports enhanced detection heuristic and identify malicious samples, even if none of the separate models express high confidence in categorizing the file as malevolent. For instance, given the $0.05\%$ false positive rate, individual static, dynamic, and contextual model detection rates are $18.04\%$, $37.20\%$, and $15.66\%$. However, we show that composite processing of all three achieves a detection rate of $96.54\%$, above the cumulative performance of individual components. Moreover, simultaneous use of distinct malware analysis techniques address independent unit weaknesses, minimizing false positives and increasing adversarial robustness. Our experiments show a decrease in contemporary adversarial attack evasion rates from $26.06\%$ to $0.35\%$ when behavioral and contextual representations of sample are employed in detection heuristic.
Now showing items 61-80 of 4203