Gas chromatography (GC) is an analytical chemistry tool used to determine the chemical composition of a gas sample by separating sample analytes as they travel through a GC column. Recent efforts have been made to understand and control gas chromatography separations with a negative thermal gradient on the column. The present work presents results from thermal gradient GC separations on two GC columns in different configurations (serpentine and radial) in a stainless-steel plate. Methods to fabricate the GC systems capable of isothermal, temperature programmed and thermal gradient separations are presented. Isothermal experimental data from the serpentine column were used to fit retention and dispersion parameters in a transport model that simulates GC separation for hydrocarbons C12-C14. Transport model simulated retention times and peak widths matched experimental values well for isothermal, temperature programmed and thermal gradient separations. The validated transport model was used to study the effect of static (not varying temporally) thermal gradients on GC separations with varying injection widths, injection band shapes and stationary phase thickness. Resolution results from different heating conditions were considered comparable if retention times for each analyte were within 5%. An optimal, static thermal gradient is shown to reduce analyte band spreading from axially-varying velocity gradients with resolution improvements over isothermal separations of up to 8% for analytes with similar retention factors. Static thermal gradients have a larger effect on fronting peak shape than tailing peak shape. Stationary phase distribution acts similar to a velocity gradient and can be corrected by a thermal gradient. Another transport model was created from isothermal experimental data on a commercial column for hydrocarbons C12-C20. An optimal, static thermal gradient does not improve resolution for all analyte pairs. An optimal, dynamic (varying tempo-rally) thermal gradient is created by uniformly increasing the temperature on an optimal, static thermal gradient. Improvements in resolution of up to 20% are achievable over temperature programmed GC separation. A dynamic thermal gradient can also correct for a poor sample injection by creating a temperature trap at the beginning of the column.

In this thesis, we set out to model the market risk exposure for 251 stocks in the S&P 500 index, during a ten-year period between 2011-04-30 and 2021-03-31. The study brings to light a model not often mentioned in the scientific literature focused on market risk estimation, the linear mixed model. The linear mixed model makes it possible to model a time-varying market risk, as well as adding structure to the idiosyncratic risk, which is often assumed to be a stationary process. The results show that the mixed model is able to produce more accurate estimates for the market risk, compared to the baseline, which is here defined as a CAPM model. The success of the mixed model, which we in the study will refer to as the ADAPT model (adaptive APT), most certainly lies in its ability to create a hierarchical regression model. This makes it possible to not just view the set of observations as a single population, but let us group the observations into different clusters and in such a way makes it possible to construct a time-varying exposure. In the last part of the thesis, we highlight possible improvements for future works, which could make the estimation even more accurate and also more efficient.

Spatial statistics is one of the youngest trends in the science of statistics. First, it has been applied in mining, during the fifth decade of the last century. In fifty years after this trend of science had been discovered, the circle of the scientists involved in it has grown drastically as well as areas of application. Also, a wide range of theoretical and practical material has been issued. Nowadays, spatial statistics methods are used in: ecology, quantity geology, image processing and analysis, epidemiology, studying global climate change and even cosmology. However, in Lithuania, the methodology of spatial data analysis has been studied only from the beginning of this Millennium. Since only few scientists (Dumbrauskas, A.; Kumetaitis, A.; Kumetaitienė, A. and others) are involved, it is very important to expand this area and develop the existing methods. Also it is essential to study the spatial dada modelling methods throughly and provide general spatial data modelling methodology. In order to apply the methods of spatial statistics, it is necessary to know the location of data in space, which is usually expressed in geographic coordinates. Thus, one of the main distinctions of spatial statistics which makes it different from the classical is the ability to model both spatial trend and spatial autocorrelation. One of the main objectives of spatial statistics is creating a mathematical model of spatial data, which can be used for interpolation (extrapolation) or for... [to full text] / Disertacijoje nagrinėjama erdvinių duomenų su stacionariomis klaidomis modeliavimo per semivariogramas ir tiesinio prognozavimo metodika. Erdvinių duomenų skiriamasis bruožas – jų išsidėstymas erdvėje, kuris dažniausiai aprašomas geografinėmis koordinatėmis. Tokių duomenų modeliavimas semivariogramomis, ir prognozavimas krigingu yra vienas iš svarbių geostatistikos mokslo uždavinių. Krigingas yra stochastinis prognozavimo metodas, kuris prie tam tikrų salygų pateikia geriausią tiesinę nepaslinktą prognozę. Krigingo rezultatų paklaidos priklauso nuo to kaip tiksliai erdvinių duomenų sklaida aprašoma kovariacine funkcija arba semivariograma. Darbe dėmesys skiriamas semivariogramoms, nes jos aprašo platesnę erdvinių procesų klasę. Pagrindinis disertacijos tikslas yra apibendrinti ir realizuoti vieningą erdvinių duomenų su stacionariomis klaidomis modeliavimo metodiką, pagrįstą semivariogramomis. Darbo objektai yra semivariogramos, jų modeliai, įvairūs erdvinių duomenų prognozavimo metodai bei erdvinių duomenų modeliavimo, prognozavimo etapai. Šių objektų analizė bei interpretacija prie tam tikrų sąlygų leidžia gauti geriausius erdvinių duomenų modeliavimo bei prognozavimo rezultatus. Taip pat disertaciniame darbe empiriniam Materon‘o semivariogramų įvertiniui MoM pateikta dispersijų-kovariacijų matricos išraiška per teorines semivariogramas stacionaraus Gauso duomenų modelio atvejui. Tiriami erdvinių duomenų vidurkio modelio parametrų bei semivariogramų vertinimo metodai... [toliau žr. visą tekstą]

In the dissertation there is investigated a class of Markov chains defined by iterations of a function possessing a property of asymptotical homogeneity. Two problems are solved: 1) there are established rather general conditions under which the chain has unique stationary distribution; 2) for the chains evolving in a real line there are established conditions under which the stationary distribution of the chain is heavy-tailed. / Disertacijoje tirta Markovo grandinių klasė, kurios iteracijos nusakomos atsitiktinėmis asimptotiškai homogeninėmis funkcijomis, ir išspręsti du uždaviniai: 1) surastos bendros sąlygos, kurios garantuoja vienintelio stacionaraus skirstinio egzistavimą; 2) vienmatėms grandinėms surastos sąlygos, kurioms esant stacionarus skirstinys turi "sunkias" uodegas.

Disertacijoje tirta Markovo grandinių klasė, kurios iteracijos nusakomos atsitiktinėmis asimptotiškai homogeninėmis funkcijomis, ir išspręsti du uždaviniai: 1) surastos bendros sąlygos, kurios garantuoja vienintelio stacionaraus skirstinio egzistavimą; 1) vienmatėms grandinėms surastos sąlygos, kurioms esant stacionarus skirstinys turi "sunkias" uodegas. / In the dissertation there is investigated a class of Markov chains defined by iterations of a function possessing a property of asymptotical homogeneity. Two problems are solved: 1) there are established rather general conditions under which the chain has unique stationary distribution; 2) for the chains evolving in a real line there are established conditions under which the stationary distribution of the chain is heavy-tailed.

Šiame darbe nagrinėta tam tikra asimptotiškai homogeninė Markovo grandinė ir rasta jos stacionaraus skirstinio uodegos asimptotika. Nagrinėta grandinė negali būti ištirta šiuo metu žinomais metodais, todėl darbas turi praktinę reikšmę. Spręstas uždavinys aktualus sunkių uodegų analizėje. / In this work we have investigated some asymptotically homogeneous Markov chain and found asymptotics of the stationary distribution tail. To our best knowledge, considered chain cannot be investigated by means of existing methods, hence obtained results have practical value. Solved problem is relevant in heavy tail analysis.

Concept drift refers to a problem that is caused by a change in the data distribution in data mining. This leads to reduction in the accuracy of the current model that is used to examine the underlying data distribution of the concept to be discovered. A number of techniques have been introduced to address this issue, in a supervised learning (or classification) setting. In a classification setting, the target concept (or class) to be learned is known. One of these techniques is called “Ensemble learning”, which refers to using multiple trained classifiers in order to get better predictions by using some voting scheme. In a traditional ensemble, the underlying base classifiers are all of the same type. Recent research extends the idea of ensemble learning to the idea of using committees, where a committee consists of diverse classifiers. This is the main difference between the regular ensemble classifiers and the committee learning algorithms. Committees are able to use diverse learning methods simultaneously and dynamically take advantage of the most accurate classifiers as the data change. In addition, some committees are able to replace their members when they perform poorly. This thesis presents two new algorithms that address concept drifts. The first algorithm has been designed to systematically introduce gradual and sudden concept drift scenarios into datasets. In order to save time and avoid memory consumption, the Concept Drift Introducer (CDI) algorithm divides the number of drift scenarios into phases. The main advantage of using phases is that it allows us to produce a highly scalable concept drift detector that evaluates each phase, instead of evaluating each individual drift scenario. We further designed a novel algorithm to handle concept drift. Our Dynamic Committee for Concept Drift (DCCD) algorithm uses a voted committee of hypotheses that vote on the best base classifier, based on its predictive accuracy. The novelty of DCCD lies in the fact that we employ diverse heterogeneous classifiers in one committee in an attempt to maximize diversity. DCCD detects concept drifts by using the accuracy and by weighing the committee members by adding one point to the most accurate member. The total loss in accuracy for each member is calculated at the end of each point of measurement, or phase. The performance of the committee members are evaluated to decide whether a member needs to be replaced or not. Moreover, DCCD detects the worst member in the committee and then eliminates this member by using a weighting mechanism. Our experimental evaluation centers on evaluating the performance of DCCD on various datasets of different sizes, with different levels of gradual and sudden concept drift. We further compare our algorithm to another state-of-the-art algorithm, namely the MultiScheme approach. The experiments indicate the effectiveness of our DCCD method under a number of diverse circumstances. The DCCD algorithm generally generates high performance results, especially when the number of concept drifts is large in a dataset. For the size of the datasets used, our results showed that DCCD produced a steady improvement in performance when applied to small datasets. Further, in large and medium datasets, our DCCD method has a comparable, and often slightly higher, performance than the MultiScheme technique. The experimental results also show that the DCCD algorithm limits the loss in accuracy over time, regardless of the size of the dataset.

No description available.

No description available.

Haa1 is a transcription factor that adapts Saccharomyces cerevisiae cells to weak organic acid stresses by activating the expression of various genes. How Haa1 is activated by weak acids is not clear. This study proposes that Hrr25 is an important regulator of cellular adaptation to weak acid stress by inhibiting Haa1 through phosphorylation. YRO2, one of the targets of Haa1, shows increase in expression during stationary phase. This increase is due to basal activity of Haa1 and another, unknown, transcription factor. This study proposes that Gsm1 is another transcription factor that regulates YRO2 expression in the stationary phase. Finally, the mechanism of regulation of YRO2 by Haa1 is largely unknown. This study identifies two possible Haa1-medated cis-acting elements in the YRO2 promoter.