Mathematical modeling of level of anaesthesia from EEG measurements

Problem of modeling of anaesthesia depth level is studied in this Master Thesis. It applies analysis of EEG signals with nonlinear dynamics theory and further classification of obtained values. The main stages of this study are the following: data preprocessing; calculation of optimal embedding parameters for phase space reconstruction; obtaining reconstructed phase portraits of each EEG signal; formation of the feature set to characterise obtained phase portraits; classification of four different anaesthesia levels basing on previously estimated features. Classification was performed with: Linear and quadratic Discriminant Analysis, k Nearest Neighbours method and online clustering. In addition, this work provides overview of existing approaches to anaesthesia depth monitoring, description of basic concepts of nonlinear dynamics theory used in this Master Thesis and comparative analysis of several different classification methods.

A feature-based object-oriented expert system to model and support product design

In this paper a computer program to model and support product design is presented. The product is represented through a hierarchical structure that allows the user to navigate across the product’s components, and it aims at facilitating each step of the detail design process. A graphical interface was also developed, which shows visually to the user the contents of the product structure. Features are used as building blocks for the parts that compose the product, and object-oriented methodology was used as a means to implement the product structure. Finally, an expert system was also implemented, whose knowledge base rules help the user design a product that meets design and manufacturing requirements.

Lagrangian multipliers for coast-arcs of optimum space trajectories

Some properties of generalized canonical systems - special dynamical systems described by a Hamiltonian function linear in the adjoint variables - are applied in determining the solution of the two-dimensional coast-arc problem in an inverse-square gravity field. A complete closed-form solution for Lagrangian multipliers - adjoint variables - is obtained by means of such properties for elliptic, circular, parabolic and hyperbolic motions. Classic orbital elements are taken as constants of integration of this solution in the case of elliptic, parabolic and hyperbolic motions. For circular motion, a set of nonsingular orbital elements is introduced as constants of integration in order to eliminate the singularity of the solution.

Numerical and experimental investigation of thermal louvers for space applications

Thermal louvers, using movable or rotating shutters over a radiating surface, have gained a wide acceptance as highly efficient devices for controlling the temperature of a spacecraft. This paper presents a detailed analysis of the performance of a rectangular thermal louver with movable blades. The radiative capacity of the louver, determined by its effective emittance, is calculated for different values of the blades opening angle. Experimental results obtained with a prototype of a spacecraft thermal louver show good agreement with the theoretical values.

Design Space Exploration for MPSoC Architectures

Multiprocessor system-on-chip (MPSoC) designs utilize the available technology and communication architectures to meet the requirements of the upcoming applications. In MPSoC, the communication platform is both the key enabler, as well as the key differentiator for realizing efficient MPSoCs. It provides product differentiation to meet a diverse, multi-dimensional set of design constraints, including performance, power, energy, reconfigurability, scalability, cost, reliability and time-to-market. The communication resources of a single interconnection platform cannot be fully utilized by all kind of applications, such as the availability of higher communication bandwidth for computation but not data intensive applications is often unfeasible in the practical implementation. This thesis aims to perform the architecture-level design space exploration towards efficient and scalable resource utilization for MPSoC communication architecture. In order to meet the performance requirements within the design constraints, careful selection of MPSoC communication platform, resource aware partitioning and mapping of the application play important role. To enhance the utilization of communication resources, variety of techniques such as resource sharing, multicast to avoid re-transmission of identical data, and adaptive routing can be used. For implementation, these techniques should be customized according to the platform architecture. To address the resource utilization of MPSoC communication platforms, variety of architectures with different design parameters and performance levels, namely Segmented bus (SegBus), Network-on-Chip (NoC) and Three-Dimensional NoC (3D-NoC), are selected. Average packet latency and power consumption are the evaluation parameters for the proposed techniques. In conventional computing architectures, fault on a component makes the connected fault-free components inoperative. Resource sharing approach can utilize the fault-free components to retain the system performance by reducing the impact of faults. Design space exploration also guides to narrow down the selection of MPSoC architecture, which can meet the performance requirements with design constraints.

The European Union and Assembling Biofuel Development – topological investigations concerning the associations between law, policy and space

Biofuels for transport are a renewable source of energy that were once heralded as a solution to multiple problems associated with poor urban air quality, the overproduction of agricultural commodities, the energy security of the European Union (EU) and climate change. It was only after the Union had implemented an incentivizing framework of legal and political instruments for the production, trade and consumption of biofuels that the problems of weakening food security, environmental degradation and increasing greenhouse gases through land-use changes began to unfold. In other words, the difference between political aims for why biofuels are promoted and their consequences has grown – which is also recognized by the EU policy-makers. Therefore, the global networks of producing, trading and consuming biofuels may face a complete restructure if the European Commission accomplishes its pursuit to sideline crop-based biofuels after 2020. My aim with this dissertation is not only to trace the manifold evolutions of the instruments used by the Union to govern biofuels but also to reveal how this evolution has influenced the dynamics of biofuel development. Therefore, I study the ways the EU’s legal and political instruments of steering biofuels are coconstitutive with the globalized spaces of biofuel development. My analytical strategy can be outlined through three concepts. I use the term ‘assemblage’ to approach the operations of the loose entity of actors and non-human elements that are the constituents of multi-scalar and -sectorial biofuel development. ‘Topology’ refers to the spatiality of this European biofuel assemblage and its parts whose evolving relations are treated as the active constituents of space, instead of simply being located in space. I apply the concept of ‘nomosphere’ to characterize the framework of policies, laws and other instruments that the EU applies and construes while attempting to govern biofuels. Even though both the materials and methods vary in the independent articles, these three concepts characterize my analytical strategy that allows me to study law, policy and space associated with each other. The results of my examinations underscore the importance of the instruments of governance of the EU constituting and stabilizing the spaces of producing and, on the other hand, how topological ruptures in biofuel development have enforced the need to reform policies. This analysis maps the vast scope of actors that are influenced by the mechanism of EU biofuel governance and, what is more, shows how they are actively engaging in the Union’s institutional policy formulation. By examining the consequences of fast biofuel development that are spatially dislocated from the established spaces of producing, trading and consuming biofuels such as indirect land use changes, I unfold the processes not tackled by the instruments of the EU. Indeed, it is these spatially dislocated processes that have pushed the Commission construing a new type of governing biofuels: transferring the instruments of climate change mitigation to land-use policies. Although efficient in mitigating these dislocated consequences, these instruments have also created peculiar ontological scaffolding for governing biofuels. According to this mode of governance, the spatiality of biofuel development appears to be already determined and the agency that could dampen the negative consequences originating from land-use practices is treated as irrelevant.

Dynamical and Chemical Structure of Galactic Halos

This doctoral dissertation presents studies of the formation and evolution of galaxies, through observations and simulations of galactic halos. The halo is the component of galaxies which hosts some of the oldest objects we know of in the cosmos; it is where clues to the history of galaxies are found, for example, by how the chemical structure is related to the dynamics of objects in the halo. The dynamical and chemical structure of halos, both in the Milky Way’s own halo, and in two elliptical galaxies, is the underlying theme in the research. I focus on the density falloff and chemistry of the two external halos, and on the dynamics, density falloff, and chemistry of the Milky Way halo. I first study galactic halos via computer simulations, to test the long- term stability of an anomalous feature recently found in kinematics of the Milky Way’s metal-poor stellar halo. I find that the feature is transient, making its origin unclear. I use a second set of simulations to test if an initially strong relation between the dynamics and chemistry of halo glob-ular clusters in a Milky Way-type galaxy is affected by a merging satellite galaxy, and find that the relation remains strong despite a merger in which the satellite is a third of the mass of the host galaxy. From simulations, I move to observing halos in nearby galaxies, a challenging procedure as most of the light from galaxies comes from the disk and bulge components as opposed to the halo. I use Hubble Space Tele scope observations of the halo of the galaxy M87 and, comparing to similar observations of NGC 5128, find that the chemical structure of the inner halo is similar for both of these giant elliptical galaxies. I use Very Large Telescope observations of the outer halo of NGC 5128 (Centaurus A) and, because of the difficultly in resolving dim extragalac- tic stellar halo populations, I introduce a new technique to subtract the contaminating background galaxies. A transition from a metal-rich stellar halo to a metal-poor has previously been discovered in two different types of galaxies, the disk galaxy M31 and the classic elliptical NGC 3379. Unexpectedly, I discover in this third type of galaxy, the merger remnant NGC 5128, that the density of metal-rich and metal-poor halo stars falls at the same rate within the galactocentric radii of 8 − 65 kpc, the limit of our observations. This thesis presents new results which open opportunities for future investigations.

Adaptive Markov chain Monte Carlo and Bayesian filtering for state space models

This thesis is concerned with the state and parameter estimation in state space models. The estimation of states and parameters is an important task when mathematical modeling is applied to many different application areas such as the global positioning systems, target tracking, navigation, brain imaging, spread of infectious diseases, biological processes, telecommunications, audio signal processing, stochastic optimal control, machine learning, and physical systems. In Bayesian settings, the estimation of states or parameters amounts to computation of the posterior probability density function. Except for a very restricted number of models, it is impossible to compute this density function in a closed form. Hence, we need approximation methods. A state estimation problem involves estimating the states (latent variables) that are not directly observed in the output of the system. In this thesis, we use the Kalman filter, extended Kalman filter, Gauss–Hermite filters, and particle filters to estimate the states based on available measurements. Among these filters, particle filters are numerical methods for approximating the filtering distributions of non-linear non-Gaussian state space models via Monte Carlo. The performance of a particle filter heavily depends on the chosen importance distribution. For instance, inappropriate choice of the importance distribution can lead to the failure of convergence of the particle filter algorithm. In this thesis, we analyze the theoretical Lᵖ particle filter convergence with general importance distributions, where p ≥2 is an integer. A parameter estimation problem is considered with inferring the model parameters from measurements. For high-dimensional complex models, estimation of parameters can be done by Markov chain Monte Carlo (MCMC) methods. In its operation, the MCMC method requires the unnormalized posterior distribution of the parameters and a proposal distribution. In this thesis, we show how the posterior density function of the parameters of a state space model can be computed by filtering based methods, where the states are integrated out. This type of computation is then applied to estimate parameters of stochastic differential equations. Furthermore, we compute the partial derivatives of the log-posterior density function and use the hybrid Monte Carlo and scaled conjugate gradient methods to infer the parameters of stochastic differential equations. The computational efficiency of MCMC methods is highly depend on the chosen proposal distribution. A commonly used proposal distribution is Gaussian. In this kind of proposal, the covariance matrix must be well tuned. To tune it, adaptive MCMC methods can be used. In this thesis, we propose a new way of updating the covariance matrix using the variational Bayesian adaptive Kalman filter algorithm.

Amplification of 9q34 in childhood adrenocortical tumors: a specific feature unrelated to ethnic origin or living conditions

Adrenocortical tumors (ACT) in children under 15 years of age exhibit some clinical and biological features distinct from ACT in adults. Cell proliferation, hypertrophy and cell death in adrenal cortex during the last months of gestation and the immediate postnatal period seem to be critical for the origin of ACT in children. Studies with large numbers of patients with childhood ACT have indicated a median age at diagnosis of about 4 years. In our institution, the median age was 3 years and 5 months, while the median age for first signs and symptoms was 2 years and 5 months (N = 72). Using the comparative genomic hybridization technique, we have reported a high frequency of 9q34 amplification in adenomas and carcinomas. This finding has been confirmed more recently by investigators in England. The lower socioeconomic status, the distinctive ethnic groups and all the regional differences in Southern Brazil in relation to patients in England indicate that these differences are not important to determine 9q34 amplification. Candidate amplified genes mapped to this locus are currently being investigated and Southern blot results obtained so far have discarded amplification of the abl oncogene. Amplification of 9q34 has not been found to be related to tumor size, staging, or malignant histopathological features, nor does it seem to be responsible for the higher incidence of ACT observed in Southern Brazil, but could be related to an ACT from embryonic origin.

Amino acid composition of parturient plasma, the intervillous space of the placenta and the umbilical vein of term newborn infants

The objective of the present study was to determine the levels of amino acids in maternal plasma, placental intervillous space and fetal umbilical vein in order to identify the similarities and differences in amino acid levels in these compartments of 15 term newborns from normal pregnancies and deliveries. All amino acids, except tryptophan, were present in at least 186% higher concentrations in the intervillous space than in maternal venous blood, with the difference being statistically significant. This result contradicted the initial hypothesis of the study that the plasma amino acid levels in the placental intervillous space should be similar to those of maternal plasma. When the maternal venous compartment was compared with the umbilical vein, we observed values 103% higher on the fetal side which is compatible with currently accepted mechanisms of active amino acid transport. Amino acid levels of the placental intervillous space were similar to the values of the umbilical vein except for proline, glycine and aspartic acid, whose levels were significantly higher than fetal umbilical vein levels (average 107% higher). The elevated levels of the intervillous space are compatible with syncytiotrophoblast activity, which maintain high concentrations of free amino acids inside syncytiotrophoblast cells, permitting asymmetric efflux or active transport from the trophoblast cells to the blood in the intervillous space. The plasma amino acid levels in the umbilical vein of term newborns probably may be used as a standard of local normality for clinical studies of amino acid profiles.

Analytic evaluation of three-phase short circuit demagnetization and hysteresis loss risk in rotor-surface-magnet Permanent-Magnet Synchronous Machines

Permanent magnet synchronous machines (PMSM) have become widely used in applications because of high efficiency compared to synchronous machines with exciting winding or to induction motors. This feature of PMSM is achieved through the using the permanent magnets (PM) as the main excitation source. The magnetic properties of the PM have significant influence on all the PMSM characteristics. Recent observations of the PM material properties when used in rotating machines revealed that in all PMSMs the magnets do not necessarily operate in the second quadrant of the demagnetization curve which makes the magnets prone to hysteresis losses. Moreover, still no good analytical approach has not been derived for the magnetic flux density distribution along the PM during the different short circuits faults. The main task of this thesis is to derive simple analytical tool which can predict magnetic flux density distribution along the rotor-surface mounted PM in two cases: during normal operating mode and in the worst moment of time from the PM’s point of view of the three phase symmetrical short circuit. The surface mounted PMSMs were selected because of their prevalence and relatively simple construction. The proposed model is based on the combination of two theories: the theory of the magnetic circuit and space vector theory. The comparison of the results in case of the normal operating mode obtained from finite element software with the results calculated with the proposed model shows good accuracy of model in the parts of the PM which are most of all prone to hysteresis losses. The comparison of the results for three phase symmetrical short circuit revealed significant inaccuracy of the proposed model compared with results from finite element software. The analysis of the inaccuracy reasons was provided. The impact on the model of the Carter factor theory and assumption that air have permeability of the PM were analyzed. The propositions for the further model development are presented.

Plasma amino acids in pregnancy, placental intervillous space and preterm newborn infants

Plasma amino acid levels have never been studied in the placental intervillous space of preterm gestations. Our objective was to determine the possible relationship between plasma amino acids of maternal venous blood (M), of the placental intervillous space (PIVS) and of the umbilical vein (UV) of preterm newborn infants. Plasma amino acid levels were analyzed by ion-exchange chromatography in M from 14 parturients and in the PIVS and UV of their preterm newborn infants. Mean gestational age was 34 ± 2 weeks, weight = 1827 ± 510 g, and all newborns were considered adequate for gestational age. The mean Apgar score was 8 and 9 at the first and fifth minutes. Plasma amino acid values were significantly lower in M than in PIVS (166%), except for aminobutyric acid. On average, plasma amino acid levels were significantly higher in UV than in M (107%) and were closer to PIVS than to M values, except for cystine and aminobutyric acid (P < 0.05). Comparison of the mean plasma amino acid concentrations in the UV of preterm to those of term newborn infants previously studied by our group showed no significant difference, except for proline (P < 0.05), preterm > term. These data suggest that the mechanisms of active amino acid transport are centralized in the syncytiotrophoblast, with their passage to the fetus being an active bidirectional process with asymmetric efflux. PIVS could be a reserve amino acid space for the protection of the fetal compartment from inadequate maternal amino acid variations.

Operant serial feature-positive conditional discrimination with composite features and different topography responses

In a serial feature-positive conditional discrimination procedure the properties of a target stimulus A are defined by the presence or not of a feature stimulus X preceding it. In the present experiment, composite features preceded targets associated with two different topography operant responses (right and left bar pressing); matching and non-matching-to-sample arrangements were also used. Five water-deprived Wistar rats were trained in 6 different trials: X-R®Ar and X-L®Al, in which X and A were same modality visual stimuli and the reinforcement was contingent to pressing either the right (r) or left (l) bar that had the light on during the feature (matching-to-sample); Y-R®Bl and Y-L®Br, in which Y and B were same modality auditory stimuli and the reinforcement was contingent to pressing the bar that had the light off during the feature (non-matching-to-sample); A- and B- alone. After 100 training sessions, the animals were submitted to transfer tests with the targets used plus a new one (auditory click). Average percentages of stimuli with a response were measured. Acquisition occurred completely only for Y-L®Br+; however, complex associations were established along training. Transfer was not complete during the tests since concurrent effects of extinction and response generalization also occurred. Results suggest the use of both simple conditioning and configurational strategies, favoring the most recent theories of conditional discrimination learning. The implications of the use of complex arrangements for discussing these theories are considered.

Novel retrograde puncture method to establish preperitoneal space for laparoscopic direct inguinal hernia repair with internal ring suturing

The aim of this study was to explore the clinical efficacy of a novel retrograde puncture approach to establish a preperitoneal space for laparoscopic direct inguinal hernia repair with inguinal ring suturing. Forty-two patients who underwent laparoscopic inguinal hernia repair with retrograde puncture for preperitoneal space establishment as well as inguinal ring suturing between August 2013 and March 2014 at our hospital were enrolled. Preperitoneal space was successfully established in all patients, with a mean establishment time of 6 min. Laparoscopic repairs were successful in all patients, with a mean surgical time of 26±15.1 min. Mean postoperative hospitalization duration was 3.0±0.7 days. Two patients suffered from postoperative local hematomas, which were relieved after puncturing and drainage. Four patients had short-term local pain. There were no cases of chronic pain. Patients were followed up for 6 months to 1 year, and no recurrence was observed. Our results demonstrate that preperitoneal space established by the retrograde puncture technique can be successfully used in adult laparoscopic hernioplasty to avoid intraoperative mesh fixation, and thus reduce medical costs.

Scalable Feature Selection Applications for Genome-Wide Association Studies of Complex Diseases

Personalized medicine will revolutionize our capabilities to combat disease. Working toward this goal, a fundamental task is the deciphering of geneticvariants that are predictive of complex diseases. Modern studies, in the formof genome-wide association studies (GWAS) have afforded researchers with the opportunity to reveal new genotype-phenotype relationships through the extensive scanning of genetic variants. These studies typically contain over half a million genetic features for thousands of individuals. Examining this with methods other than univariate statistics is a challenging task requiring advanced algorithms that are scalable to the genome-wide level. In the future, next-generation sequencing studies (NGS) will contain an even larger number of common and rare variants. Machine learning-based feature selection algorithms have been shown to have the ability to effectively create predictive models for various genotype-phenotype relationships. This work explores the problem of selecting genetic variant subsets that are the most predictive of complex disease phenotypes through various feature selection methodologies, including filter, wrapper and embedded algorithms. The examined machine learning algorithms were demonstrated to not only be effective at predicting the disease phenotypes, but also doing so efficiently through the use of computational shortcuts. While much of the work was able to be run on high-end desktops, some work was further extended so that it could be implemented on parallel computers helping to assure that they will also scale to the NGS data sets. Further, these studies analyzed the relationships between various feature selection methods and demonstrated the need for careful testing when selecting an algorithm. It was shown that there is no universally optimal algorithm for variant selection in GWAS, but rather methodologies need to be selected based on the desired outcome, such as the number of features to be included in the prediction model. It was also demonstrated that without proper model validation, for example using nested cross-validation, the models can result in overly-optimistic prediction accuracies and decreased generalization ability. It is through the implementation and application of machine learning methods that one can extract predictive genotype–phenotype relationships and biological insights from genetic data sets.