Non-Markovianity and Quantum Correlations in Qubit-Systems

In this Thesis I discuss the exact dynamics of simple non-Markovian systems. I focus on fundamental questions at the core of non-Markovian theory and investigate the dynamics of quantum correlations under non-Markovian decoherence. In the first context I present the connection between two different non-Markovian approaches, and compare two distinct definitions of non-Markovianity. The general aim is to characterize in exemplary cases which part of the environment is responsible for the feedback of information typical of non- Markovian dynamics. I also show how such a feedback of information is not always described by certain types of master equations commonly used to tackle non-Markovian dynamics. In the second context I characterize the dynamics of two qubits in a common non-Markovian reservoir, and introduce a new dynamical effect in a wellknown model, i.e., two qubits under depolarizing channels. In the first model the exact solution of the dynamics is found, and the entanglement behavior is extensively studied. The non-Markovianity of the reservoir and reservoirmediated-interaction between the qubits cause non-trivial dynamical features. The dynamical interplay between different types of correlations is also investigated. In the second model the study of quantum and classical correlations demonstrates the existence of a new effect: the sudden transition between classical and quantum decoherence. This phenomenon involves the complete preservation of the initial quantum correlations for long intervals of time of the order of the relaxation time of the system.

Dialogue in the crisis of representation : realism and antirealism in the context of the conversation between theologians and quantum physicists in Göttingen 1949-1961

The aim of this study is to analyse the content of the interdisciplinary conversations in Göttingen between 1949 and 1961. The task is to compare models for describing reality presented by quantum physicists and theologians. Descriptions of reality indifferent disciplines are conditioned by the development of the concept of reality in philosophy, physics and theology. Our basic problem is stated in the question: How is it possible for the intramental image to match the external object?Cartesian knowledge presupposes clear and distinct ideas in the mind prior to observation resulting in a true correspondence between the observed object and the cogitative observing subject. The Kantian synthesis between rationalism and empiricism emphasises an extended character of representation. The human mind is not a passive receiver of external information, but is actively construing intramental representations of external reality in the epistemological process. Heidegger's aim was to reach a more primordial mode of understanding reality than what is possible in the Cartesian Subject-Object distinction. In Heidegger's philosophy, ontology as being-in-the-world is prior to knowledge concerning being. Ontology can be grasped only in the totality of being (Dasein), not only as an object of reflection and perception. According to Bohr, quantum mechanics introduces an irreducible loss in representation, which classically understood is a deficiency in knowledge. The conflicting aspects (particle and wave pictures) in our comprehension of physical reality, cannot be completely accommodated into an entire and coherent model of reality. What Bohr rejects is not realism, but the classical Einsteinian version of it. By the use of complementary descriptions, Bohr tries to save a fundamentally realistic position. The fundamental question in Barthian theology is the problem of God as an object of theological discourse. Dialectics is Barth¿s way to express knowledge of God avoiding a speculative theology and a human-centred religious self-consciousness. In Barthian theology, the human capacity for knowledge, independently of revelation, is insufficient to comprehend the being of God. Our knowledge of God is real knowledge in revelation and our words are made to correspond with the divine reality in an analogy of faith. The point of the Bultmannian demythologising programme was to claim the real existence of God beyond our faculties. We cannot simply define God as a human ideal of existence or a focus of values. The theological programme of Bultmann emphasised the notion that we can talk meaningfully of God only insofar as we have existential experience of his intervention. Common to all these twentieth century philosophical, physical and theological positions, is a form of anti-Cartesianism. Consequently, in regard to their epistemology, they can be labelled antirealist. This common insight also made it possible to find a common meeting point between the different disciplines. In this study, the different standpoints from all three areas and the conversations in Göttingen are analysed in the frameworkof realism/antirealism. One of the first tasks in the Göttingen conversations was to analyse the nature of the likeness between the complementary structures inquantum physics introduced by Niels Bohr and the dialectical forms in the Barthian doctrine of God. The reaction against epistemological Cartesianism, metaphysics of substance and deterministic description of reality was the common point of departure for theologians and physicists in the Göttingen discussions. In his complementarity, Bohr anticipated the crossing of traditional epistemic boundaries and the generalisation of epistemological strategies by introducing interpretative procedures across various disciplines.

Model development for circulating fluidized bed boiler operation

Comprehensive understanding of the heat transfer processes that take place during circulating fluidized bed (CFB) combustion is one of the most important issues in CFB technology development. This leads to possibility of predicting, evaluation and proper design of combustion and heat transfer mechanisms. The aim of this thesis is to develop a model for circulating fluidized bed boiler operation. Empirical correlations are used for determining heat transfer coefficients in each part of the furnace. The proposed model is used both in design and offdesign conditions. During off-design simulations fuel moisture content and boiler load effects on boiler operation have been investigated. In theoretical part of the thesis, fuel properties of most typical classes of biomass are widely reviewed. Various schemes of biomass utilization are presented and, especially, concerning circulating fluidized bed boilers. In addition, possible negative effects of biomass usage in boilers are briefly discussed.

Methods for construction and analysis of computational models in systems biology : applications to the modelling of the heat shock response and the self-assembly of intermediate filaments

Systems biology is a new, emerging and rapidly developing, multidisciplinary research field that aims to study biochemical and biological systems from a holistic perspective, with the goal of providing a comprehensive, system- level understanding of cellular behaviour. In this way, it addresses one of the greatest challenges faced by contemporary biology, which is to compre- hend the function of complex biological systems. Systems biology combines various methods that originate from scientific disciplines such as molecu- lar biology, chemistry, engineering sciences, mathematics, computer science and systems theory. Systems biology, unlike “traditional” biology, focuses on high-level concepts such as: network, component, robustness, efficiency, control, regulation, hierarchical design, synchronization, concurrency, and many others. The very terminology of systems biology is “foreign” to “tra- ditional” biology, marks its drastic shift in the research paradigm and it indicates close linkage of systems biology to computer science. One of the basic tools utilized in systems biology is the mathematical modelling of life processes tightly linked to experimental practice. The stud- ies contained in this thesis revolve around a number of challenges commonly encountered in the computational modelling in systems biology. The re- search comprises of the development and application of a broad range of methods originating in the fields of computer science and mathematics for construction and analysis of computational models in systems biology. In particular, the performed research is setup in the context of two biolog- ical phenomena chosen as modelling case studies: 1) the eukaryotic heat shock response and 2) the in vitro self-assembly of intermediate filaments, one of the main constituents of the cytoskeleton. The range of presented approaches spans from heuristic, through numerical and statistical to ana- lytical methods applied in the effort to formally describe and analyse the two biological processes. We notice however, that although applied to cer- tain case studies, the presented methods are not limited to them and can be utilized in the analysis of other biological mechanisms as well as com- plex systems in general. The full range of developed and applied modelling techniques as well as model analysis methodologies constitutes a rich mod- elling framework. Moreover, the presentation of the developed methods, their application to the two case studies and the discussions concerning their potentials and limitations point to the difficulties and challenges one encounters in computational modelling of biological systems. The problems of model identifiability, model comparison, model refinement, model inte- gration and extension, choice of the proper modelling framework and level of abstraction, or the choice of the proper scope of the model run through this thesis.

Role and regulatory mechanisms of heat shock factor 2

Protein homeostasis is essential for cells to prosper and survive. Various forms of stress, such as elevated temperatures, oxidative stress, heavy metals or bacterial infections cause protein damage, which might lead to improper folding and formation of toxic protein aggregates. Protein aggregation is associated with serious pathological conditions such as Alzheimer’s and Huntington’s disease. The heat shock response is a defense mechanism that protects the cell against protein-damaging stress. Its ancient origin and high conservation among eukaryotes suggest that the response is crucial for survival. The main regulator of the heat shock response is the transcription factor heat shock factor 1 (HSF1), which induces transcription of genes encoding protective molecular chaperones. In vertebrates, a family of four HSFs exists (HSF1-4), with versatile functions not only in coping with acute stress, but also in development, longevity and cancer. Thus, knowledge of the HSFs will aid in our understanding on how cells survive suboptimal circumstances, but will also provide insights into normal physiological processes as well as diseaseassociated conditions. In this study, the function and regulation of HSF2 have been investigated. Earlier gene inactivation experiments in mice have revealed roles for HSF2 in development, particularly in corticogenesis and spermatogenesis. Here, we demonstrate that HSF2 holds a role also in the heat shock response and influences stress-induced expression of heat shock proteins. Intriguingly, DNA-binding activity of HSF2 upon stress was dependent on the presence of intact HSF1, suggesting functional interplay between HSF1 and HSF2. The underlying mechanism for this phenomenon could be configuration of heterotrimers between the two factors, a possibility that was experimentally verified. By changing the levels of HSF2, the expression of HSF1-HSF2 heterotrimer target genes was altered, implementing HSF2 as a modulator of HSF-mediated transcription. The results further indicate that HSF2 activity is dependent on its concentration, which led us to ask the question of how accurate HSF2 levels are achieved. Using mouse spermatogenesis as a model system, HSF2 was found to be under direct control of miR-18, a miRNA belonging to the miR-17~92 cluster/Oncomir-1 and whose physiological function had remained unclear. Investigations on spermatogenesis are severely hampered by the lack of cell systems that would mimic the complex differentiation processes that constitute male germ cell development. Therefore, to verify that HSF2 is regulated by miR-18 in spermatogenesis, a novel method named T-GIST (Transfection of Germ cells in Intact Seminiferous Tubules) was developed. Employing this method, the functional consequences of miR-18-mediated regulation in vivo were demonstrated; inhibition of miR- 18 led to increased expression of HSF2 and altered the expression of HSF2 target genes Ssty2 and Speer4a. Consequently, the results link miR-18 to HSF2-mediated processes such as germ cell maturation and quality control and provide miR-18 with a physiological role in gene expression during spermatogenesis.Taken together, this study presents compelling evidence that HSF2 is a transcriptional regulator in the heat shock response and establishes the concept of physical interplay between HSF2 and HSF1 and functional consequences thereof. This is also the first study describing miRNA-mediated regulation of an HSF.

Wood as a model material for medical biomaterials. In vivo and in vitro studies with bone and Betula pubescens Ehrh

Novel biomaterials are needed to fill the demand of tailored bone substitutes required by an ever‐expanding array of surgical procedures and techniques. Wood, a natural fiber composite, modified with heat treatment to alter its composition, may provide a novel approach to the further development of hierarchically structured biomaterials. The suitability of wood as a model biomaterial as well as the effects of heat treatment on the osteoconductivity of wood was studied by placing untreated and heat‐treated (at 220 C , 200 degrees and 140 degrees for 2 h) birch implants (size 4 x 7mm) into drill cavities in the distal femur of rabbits. The follow‐up period was 4, 8 and 20 weeks in all in vivo experiments. The flexural properties of wood as well as dimensional changes and hydroxyl apatite formation on the surface of wood (untreated, 140 degrees C and 200 degrees C heat‐treated wood) were tested using 3‐point bending and compression tests and immersion in simulated body fluid. The effect of premeasurement grinding and the effect of heat treatment on the surface roughness and contour of wood were tested with contact stylus and non‐contact profilometry. The effects of heat treatment of wood on its interactions with biological fluids was assessed using two different test media and real human blood in liquid penetration tests. The results of the in vivo experiments showed implanted wood to be well tolerated, with no implants rejected due to foreign body reactions. Heat treatment had significant effects on the biocompatibility of wood, allowing host bone to grow into tight contact with the implant, with occasional bone ingrowth into the channels of the wood implant. The results of the liquid immersion experiments showed hydroxyl apatite formation only in the most extensively heat‐treated wood specimens, which supported the results of the in vivo experiments. Parallel conclusions could be drawn based on the results of the liquid penetration test where human blood had the most favorable interaction with the most extensively heat‐treated wood of the compared materials (untreated, 140 degrees C and 200 degrees C heat‐treated wood). The increasing biocompatibility was inferred to result mainly from changes in the chemical composition of wood induced by the heat treatment, namely the altered arrangement and concentrations of functional chemical groups. However, the influence of microscopic changes in the cell walls, surface roughness and contour cannot be totally excluded. The heat treatment was hypothesized to produce a functional change in the liquid distribution within wood, which could have biological relevance. It was concluded that the highly evolved hierarchical anatomy of wood could yield information for the future development of bulk bone substitutes according to the ideology of bioinspiration. Furthermore, the results of the biomechanical tests established that heat treatment alters various biologically relevant mechanical properties of wood, thus expanding the possibilities of wood as a model material, which could include e.g. scaffold applications, bulk bone applications and serving as a tool for both mechanical testing and for further development of synthetic fiber reinforced composites.

Kernel-Based Ranking. Methods for Learning and Performance Estimation

Machine learning provides tools for automated construction of predictive models in data intensive areas of engineering and science. The family of regularized kernel methods have in the recent years become one of the mainstream approaches to machine learning, due to a number of advantages the methods share. The approach provides theoretically well-founded solutions to the problems of under- and overfitting, allows learning from structured data, and has been empirically demonstrated to yield high predictive performance on a wide range of application domains. Historically, the problems of classification and regression have gained the majority of attention in the field. In this thesis we focus on another type of learning problem, that of learning to rank. In learning to rank, the aim is from a set of past observations to learn a ranking function that can order new objects according to how well they match some underlying criterion of goodness. As an important special case of the setting, we can recover the bipartite ranking problem, corresponding to maximizing the area under the ROC curve (AUC) in binary classification. Ranking applications appear in a large variety of settings, examples encountered in this thesis include document retrieval in web search, recommender systems, information extraction and automated parsing of natural language. We consider the pairwise approach to learning to rank, where ranking models are learned by minimizing the expected probability of ranking any two randomly drawn test examples incorrectly. The development of computationally efficient kernel methods, based on this approach, has in the past proven to be challenging. Moreover, it is not clear what techniques for estimating the predictive performance of learned models are the most reliable in the ranking setting, and how the techniques can be implemented efficiently. The contributions of this thesis are as follows. First, we develop RankRLS, a computationally efficient kernel method for learning to rank, that is based on minimizing a regularized pairwise least-squares loss. In addition to training methods, we introduce a variety of algorithms for tasks such as model selection, multi-output learning, and cross-validation, based on computational shortcuts from matrix algebra. Second, we improve the fastest known training method for the linear version of the RankSVM algorithm, which is one of the most well established methods for learning to rank. Third, we study the combination of the empirical kernel map and reduced set approximation, which allows the large-scale training of kernel machines using linear solvers, and propose computationally efficient solutions to cross-validation when using the approach. Next, we explore the problem of reliable cross-validation when using AUC as a performance criterion, through an extensive simulation study. We demonstrate that the proposed leave-pair-out cross-validation approach leads to more reliable performance estimation than commonly used alternative approaches. Finally, we present a case study on applying machine learning to information extraction from biomedical literature, which combines several of the approaches considered in the thesis. The thesis is divided into two parts. Part I provides the background for the research work and summarizes the most central results, Part II consists of the five original research articles that are the main contribution of this thesis.

Non-Markovian Dynamics and the Quantum-To-Classical Transition for Quantum Brownian Motion

In this Thesis I discuss the dynamics of the quantum Brownian motion model in harmonic potential. This paradigmatic model has an exact solution, making it possible to consider also analytically the non-Markovian dynamics. The issues covered in this Thesis are themed around decoherence. First, I consider decoherence as the mediator of quantum-to-classical transition. I examine five different definitions for nonclassicality of quantum states, and show how each definition gives qualitatively different times for the onset of classicality. In particular I have found that all characterizations of nonclassicality, apart from one based on the interference term in the Wigner function, result in a finite, rather than asymptotic, time for the emergence of classicality. Second, I examine the diverse effects which coupling to a non-Markovian, structured reservoir, has on our system. By comparing different types of Ohmic reservoirs, I derive some general conclusions on the role of the reservoir spectrum in both the short-time and the thermalization dynamics. Finally, I apply these results to two schemes for decoherence control. Both of the methods are based on the non-Markovian properties of the dynamics.

Limestone Reactions in a Fluidized Bed Heat Exchanger of an Oxy-fuel Circulating Fluidized Bed Boiler

Oxy-fuel combustion in a circulating fluidized bed (CFB) boiler appears to be a promising option for capturing CO2 in power plants. Oxy-fuel combustion is based on burning of fuel in the mixture of oxygen and re-circulated flue gas instead of air. Limestone (CaCO3) is typically used for capturing of SO2 in CFB boilers where limestone calcines to calcium oxide (CaO). Because of high CO2 concentration in oxy-fuel combustion, calcination reaction may be hindered or carbonation, the reverse reaction of calcination, may occur. Carbonation of CaO particles can cause problems especially in the circulation loop of a CFB boiler where temperature level is lower than in the furnace. The aim of the thesis was to examine carbonation of CaO in a fluidized bed heat exchanger of a CFB boiler featuring oxy-fuel combustion. The calculations and analyzing were based on measurement data from an oxy-fuel pilot plant and on 0-dimensional (0D) gas balance of a fluidized bed heat exchanger. Additionally, the objective was to develop a 1-dimensional (1D) model of a fluidized bed heat exchanger by searching a suitable pre-exponential factor for a carbonation rate constant. On the basis of gas measurement data and the 0D gas balance, it was found that the amount of fluidization gas decreased as it flew through the fluidized bed heat exchanger. Most likely the reason for this was carbonation of CaO. It was discovered that temperature has a promoting effect on the reaction rate of carbonation. With the 1D model, a suitable pre-exponential factor for the equation of carbonation rate constant was found. However, during measurements there were several uncertainties, and in the calculations plenty of assumptions were made. Besides, the temperature level in the fluidized bed heat exchanger was relatively low during the measurements. Carbonation should be considered when fluidized bed heat exchangers and the capacity of related fans are designed for a CFB boiler with oxy-fuel combustion.

Heat transfer inside internal combustion engine: modelling and comparison with experimental data

The paper is devoted to study specific aspects of heat transfer in the combustion chamber of compression ignited reciprocating internal combustion engines and possibility to directly measure the heat flux by means of Gradient Heat Flux Sensors (GHFS). A one – dimensional single zone model proposed by Kyung Tae Yun et al. and implemented with the aid of Matlab, was used to obtain approximate picture of heat flux behavior in the combustion chamber with relation to the crank angle. The model’s numerical output was compared to the experimental results. The experiment was accomplished by A. Mityakov at four stroke diesel engine Indenor XL4D. Local heat fluxes on the surface of cylinder head were measured with fast – response, high – sensitive GHFS. The comparison of numerical data with experimental results has revealed a small deviation in obtained heat flux values throughout the cycle and different behavior of heat flux curve after Top Dead Center.

Heat flux measurement inside internal combustion engine with gradient heat flux sensor.

This master’s thesis is devoted to study different heat flux measurement techniques such as differential temperature sensors, semi-infinite surface temperature methods, calorimetric sensors and gradient heat flux sensors. The possibility to use Gradient Heat Flux Sensors (GHFS) to measure heat flux in the combustion chamber of compression ignited reciprocating internal combustion engines was considered in more detail. A. Mityakov conducted an experiment, where Gradient Heat Flux Sensor was placed in four stroke diesel engine Indenor XL4D to measure heat flux in the combustion chamber. The results which were obtained from the experiment were compared with model’s numerical output. This model (a one – dimensional single zone model) was implemented with help of MathCAD and the result of this implementation is graph of heat flux in combustion chamber in relation to the crank angle. The values of heat flux throughout the cycle obtained with aid of heat flux sensor and theoretically were sufficiently similar, but not identical. Such deviation is rather common for this type of experiment.

Non-Markovian dynamics and quantum information probes

This Thesis discusses the phenomenology of the dynamics of open quantum systems marked by non-Markovian memory effects. Non-Markovian open quantum systems are the focal point of a flurry of recent research aiming to answer, e.g., the following questions: What is the characteristic trait of non-Markovian dynamical processes that discriminates it from forgetful Markovian dynamics? What is the microscopic origin of memory in quantum dynamics, and how can it be controlled? Does the existence of memory effects open new avenues and enable accomplishments that cannot be achieved with Markovian processes? These questions are addressed in the publications forming the core of this Thesis with case studies of both prototypical and more exotic models of open quantum systems. In the first part of the Thesis several ways of characterizing and quantifying non-Markovian phenomena are introduced. Their differences are then explored using a driven, dissipative qubit model. The second part of the Thesis focuses on the dynamics of a purely dephasing qubit model, which is used to unveil the origin of non-Markovianity for a wide class of dynamical models. The emergence of memory is shown to be strongly intertwined with the structure of the spectral density function, as further demonstrated in a physical realization of the dephasing model using ultracold quantum gases. Finally, as an application of memory effects, it is shown that non- Markovian dynamical processes facilitate a novel phenomenon of timeinvariant discord, where the total quantum correlations of a system are frozen to their initial value. Non-Markovianity can also be exploited in the detection of phase transitions using quantum information probes, as shown using the physically interesting models of the Ising chain in a transverse field and a Coulomb chain undergoing a structural phase transition.

Model based analysis of the post-combustion calcium looping process for carbon dioxide capture

This thesis presents a one-dimensional, semi-empirical dynamic model for the simulation and analysis of a calcium looping process for post-combustion CO2 capture. Reduction of greenhouse emissions from fossil fuel power production requires rapid actions including the development of efficient carbon capture and sequestration technologies. The development of new carbon capture technologies can be expedited by using modelling tools. Techno-economical evaluation of new capture processes can be done quickly and cost-effectively with computational models before building expensive pilot plants. Post-combustion calcium looping is a developing carbon capture process which utilizes fluidized bed technology with lime as a sorbent. The main objective of this work was to analyse the technological feasibility of the calcium looping process at different scales with a computational model. A one-dimensional dynamic model was applied to the calcium looping process, simulating the behaviour of the interconnected circulating fluidized bed reactors. The model incorporates fundamental mass and energy balance solvers to semi-empirical models describing solid behaviour in a circulating fluidized bed and chemical reactions occurring in the calcium loop. In addition, fluidized bed combustion, heat transfer and core-wall layer effects were modelled. The calcium looping model framework was successfully applied to a 30 kWth laboratory scale and a pilot scale unit 1.7 MWth and used to design a conceptual 250 MWth industrial scale unit. Valuable information was gathered from the behaviour of a small scale laboratory device. In addition, the interconnected behaviour of pilot plant reactors and the effect of solid fluidization on the thermal and carbon dioxide balances of the system were analysed. The scale-up study provided practical information on the thermal design of an industrial sized unit, selection of particle size and operability in different load scenarios.

Modeling a Network of Heat Exchangers

This research work addresses the problem of building a mathematical model for the given system of heat exchangers and to determine the temperatures, pressures and velocities at the intermediate positions. Such model could be used in nding an optimal design for such a superstructure. To limit the size and computing time a reduced network model was used. The method can be generalized to larger network structures. A mathematical model which includes a system of non-linear equations has been built and solved according to the Newton-Raphson algorithm. The results obtained by the proposed mathematical model were compared with the results obtained by the Paterson approximation and Chen's Approximation. Results of this research work in collaboration with a current ongoing research at the department will optimize the valve positions and hence, minimize the pumping cost and maximize the heat transfer of the system of heat exchangers.

A Bayesian Approach for Forecasting Heat Load in a District Heating System

The growing population in cities increases the energy demand and affects the environment by increasing carbon emissions. Information and communications technology solutions which enable energy optimization are needed to address this growing energy demand in cities and to reduce carbon emissions. District heating systems optimize the energy production by reusing waste energy with combined heat and power plants. Forecasting the heat load demand in residential buildings assists in optimizing energy production and consumption in a district heating system. However, the presence of a large number of factors such as weather forecast, district heating operational parameters and user behavioural parameters, make heat load forecasting a challenging task. This thesis proposes a probabilistic machine learning model using a Naive Bayes classifier, to forecast the hourly heat load demand for three residential buildings in the city of Skellefteå, Sweden over a period of winter and spring seasons. The district heating data collected from the sensors equipped at the residential buildings in Skellefteå, is utilized to build the Bayesian network to forecast the heat load demand for horizons of 1, 2, 3, 6 and 24 hours. The proposed model is validated by using four cases to study the influence of various parameters on the heat load forecast by carrying out trace driven analysis in Weka and GeNIe. Results show that current heat load consumption and outdoor temperature forecast are the two parameters with most influence on the heat load forecast. The proposed model achieves average accuracies of 81.23 % and 76.74 % for a forecast horizon of 1 hour in the three buildings for winter and spring seasons respectively. The model also achieves an average accuracy of 77.97 % for three buildings across both seasons for the forecast horizon of 1 hour by utilizing only 10 % of the training data. The results indicate that even a simple model like Naive Bayes classifier can forecast the heat load demand by utilizing less training data.