Combining multispecies home range and distribution models aids assessment of MPA effectiveness

Marine protected areas (MPAs) are today's most important tools for the spatial management and conservation of marine species. Yet, the true protection that they provide to individual fish is unknown, leading to uncertainty associated with MPA effectiveness. In this study, conducted in a recently established coastal MPA in Portugal, we combined the results of individual home range estimation and population distribution models for 3 species of commercial importance and contrasting life histories to infer (1) the size of suitable areas where they would be fully protected and (2) the vulnerability to fishing mortality of each species. Results show that the relationship between MPA size and effective protection is strongly modulated by both the species' home range and the distribution of suitable habitat inside and outside the MPA. This approach provides a better insight into the true potential of MPAs in effectively protecting marine species, since it can reveal the size and location of the areas where protection is most effective and a clear, quantitative estimation of the vulnerability to fishing throughout an entire MPA.

Occurrence and abundance models of threatened plant species: Applications to mitigate the impact of hydroelectric power dams

Species occurrence and abundance models are important tools that can be used in biodiversity conservation, and can be applied to predict or plan actions needed to mitigate the environmental impacts of hydropower dams. In this study our objectives were: (i) to model the occurrence and abundance of threatened plant species, (ii) to verify the relationship between predicted occurrence and true abundance, and (iii) to assess whether models based on abundance are more effective in predicting species occurrence than those based on presence–absence data. Individual representatives of nine species were counted within 388 randomly georeferenced plots (10 m × 50 m) around the Barra Grande hydropower dam reservoir in southern Brazil. We modelled their relationship with 15 environmental variables using both occurrence (Generalised Linear Models) and abundance data (Hurdle and Zero-Inflated models). Overall, occurrence models were more accurate than abundance models. For all species, observed abundance was significantly, although not strongly, correlated with the probability of occurrence. This correlation lost significance when zero-abundance (absence) sites were excluded from analysis, but only when this entailed a substantial drop in sample size. The same occurred when analysing relationships between abundance and probability of occurrence from previously published studies on a range of different species, suggesting that future studies could potentially use probability of occurrence as an approximate indicator of abundance when the latter is not possible to obtain. This possibility might, however, depend on life history traits of the species in question, with some traits favouring a relationship between occurrence and abundance. Reconstructing species abundance patterns from occurrence could be an important tool for conservation planning and the management of threatened species, allowing scientists to indicate the best areas for collection and reintroduction of plant germplasm or choose conservation areas most likely to maintain viable populations.

A Review of the Arcuate Structures in the Iberian Variscides; Constraints and Genetical Models

In the Iberian Variscides several first order arcuate structures have been considered. In spite of being highly studied their characterization, formation mechanisms and even existence is still debatable. Themain Ibero-Armorican Arc (IAA) is essentially defined by a predominantNW–SE trend in the Iberian branch and an E–Wtrend in the Brittany one. However, in northern Spain it presents a 180° rotation, sometimes known as the Cantabrian Arc (CA). The relation between both arcs is controversial, being considered either as a single arc due to one tectonic event, or as the result of a polyphasic process. According to the last assumption, there is a later arcuate structure (CA), overlapping a previousmajor one (IAA). Whatever themodels, they must be able to explain the presence of a Variscan sinistral transpression in Iberia and a dextral one in Armorica, and a deformation spanning from the Devonian to the Upper Carboniferous. Another arcuate structure, in continuity with the CA, the Central-Iberian Arc (CIA) was recently proposed mainly based upon on magnetic anomalies, geometry of major folds and Ordovician paleocurrents. The critical review of the structural, stratigraphic and geophysical data supports both the IAA and the CA, but as independent structures. However, the presence of a CIA is highly questionable and could not be supported. The complex strain pattern of the IAA and the CA could be explained by a Devonian — Carboniferous polyphasic indentation of a Gondwana promontory. In thismodel the CA is essentially a thin-skinned arc,while the IAA has a more complex and longer evolution that has led to a thick-skinned first order structure. Nevertheless, both arcs are essentially the result of a lithospheric bending process during the Iberian Variscides.

Inversion of ambient seismic noise HVSR to evaluate velocity and structural models of the Lower Tagus Basin, Portugal

During its history, several significant earthquakes have shaken the Lower Tagus Valley (Portugal). These earthquakes were destructive; some strong earthquakes were produced by large ruptures in offshore structures located southwest of the Portuguese coastline, and other moderate earthquakes were produced by local faults. In recent years, several studies have successfully obtained strong-ground motion syntheses for the Lower Tagus Valley using the finite difference method. To confirm the velocity model of this sedimentary basin obtained from geophysical and geological data, we analysed the ambient seismic noise measurements by applying the horizontal to vertical spectral ratio (HVSR) method. This study reveals the dependence of the frequency and amplitude of the low-frequency (HVSR) peaks (0.2–2 Hz) on the sediment thickness. We have obtained the depth of the Cenozoic basement along a profile transversal to the basin by the inversion of these ratios, imposing constraints from seismic reflection, boreholes, seismic sounding and gravimetric and magnetic potentials. This technique enables us to improve the existing three-dimensional model of the Lower Tagus Valley structure. The improved model will be decisive for the improvement of strong motion predictions in the earthquake hazard analysis of this highly populated basin. The methodology discussed can be applied to any other sedimentary basin.

Comparison of Photovoltaic panel’s standard and simplified models

Shockley diode equation is basic for single diode model equation, which is overly used for characterizing the photovoltaic cell output and behavior. In the standard equation, it includes series resistance (Rs) and shunt resistance (Rsh) with different types of parameters. Maximum simulation and modeling work done previously, related to single diode photovoltaic cell used this equation. However, there is another form of the standard equation which has not included Series Resistance (Rs) and Shunt Resistance (Rsh) yet, as the Shunt Resistance is much bigger than the load resistance and the load resistance is much bigger than the Series Resistance. For this phenomena, very small power loss occurs within a photovoltaic cell. This research focuses on the comparison of two forms of basic Shockley diode equation. This analysis describes a deep understanding of the photovoltaic cell, as well as gives understanding about Series Resistance (Rs) and Shunt Resistance (Rsh) behavior in the Photovoltaic cell. For making estimation of a real time photovoltaic system, faster calculation is needed. The equation without Series Resistance and Shunt Resistance is appropriate for the real time environment. Error function for both Series resistance (Rs) and Shunt resistances (Rsh) have been analyzed which shows that the total system is not affected by this two parameters' behavior.

The mesophotic zone of the Mediterranean Sea: spatial definition, biodiversity, and predictive models

The mesophotic zone is frequently defined as ranging between 30-40 and 150 m depth. However, these borders are necessarily imprecise due to variations in the penetration of light along the water column related to local factors. Moreover, density of data on mesophotic ecosystems vary along geographical distance, with temperate latitudes largely less explored than tropical situations. This is the case of the Mediterranean Sea, where information on mesophotic ecosystems is largely lower with respect to tropical situations. The lack of a clear definition of the borders of the mesophotic zone may represent a problem when information must be transferred to the policy that requires a coherent spatial definition to plan proper management and conservation measures. The present thesis aims at providing information on the spatial definition of the mesophotic zone in the Mediterranean Sea, its biodiversity and distribution of its ecosystems. The first chapter analyzes information on mesophotic ecosystems in the Mediterranean Sea to identify gaps in the literature and map the mesophotic zone in the Mediterranean Sea using light penetration estimated from satellite data. In the second chapter, different visual techniques to study mesophotic ecosystems are compared to identify the best analytical method to estimate diversity and habitat extension. In the third chapter, a set of Remotely Operated vehicles (ROV) surveys performed on mesophotic assemblages in the Mediterranean Sea are analyzed to describe their taxonomic and functional diversity and environmental factors influencing their structure. A Habitat Suitability Model is run in the fourth chapter to map the distribution of areas suitable for the presence of deep-water oyster reefs in the Adriatic-Ionian area. The fifth chapter explores the mesophotic zone in the northern Gulf of Mexico providing its spatial and vertical extension of the mesophotic zone and information on the diversity associated with mesophotic ecosystems.

Comprehensive characterization of SDH-deficient GIST using NGS data and iPSC models

Gastrointestinal stromal tumors (GIST) are the most common di tumors of the gastrointestinal tract, arising from the interstitial cells of Cajal (ICCs) or their precursors. The vast majority of GISTs (75–85% of GIST) harbor KIT or PDGFRA mutations. A small percentage of GIST (about 10‐15%) do not harbor any of these driver mutations and have historically been called wild-type (WT). Among them, from 20% to 40% show loss of function of the succinate dehydrogenase complex (SDH), also defined as SDH‐deficient GIST. SDH-deficient GISTs display distinctive clinical and pathological features, and can be sporadic or associated with Carney triad or Carney-Stratakis syndrome. These tumors arise most frequently in the stomach with predilection to distal stomach and antrum, have a multi-nodular growth, display a histological epithelioid phenotype, and present frequent lympho-vascular invasion. Occurrence of lymph node metastases and indolent course are representative features of SDH-deficient GISTs. This subset of GIST is known for the immunohistochemical loss of succinate dehydrogenase subunit B (SDHB), which signals the loss of function of the entire SDH-complex. The overall aim of my PhD project consists of the comprehensive characterization of SDH deficient GIST. Throughout the project, clinical, molecular and cellular characterizations were performed using next-generation sequencing technologies (NGS), that has the potential to allow the identification of molecular patterns useful for the diagnosis and development of novel treatments. Moreover, while there are many different cell lines and preclinical models of KIT/PDGFRA mutant GIST, no reliable cell model of SDH-deficient GIST has currently been developed, which could be used for studies on tumor evolution and in vitro assessments of drug response. Therefore, another aim of this project was to develop a pre-clinical model of SDH deficient GIST using the novel technology of induced pluripotent stem cells (iPSC).

Variational and learning models for image and time series inverse problems

Inverse problems are at the core of many challenging applications. Variational and learning models provide estimated solutions of inverse problems as the outcome of specific reconstruction maps. In the variational approach, the result of the reconstruction map is the solution of a regularized minimization problem encoding information on the acquisition process and prior knowledge on the solution. In the learning approach, the reconstruction map is a parametric function whose parameters are identified by solving a minimization problem depending on a large set of data. In this thesis, we go beyond this apparent dichotomy between variational and learning models and we show they can be harmoniously merged in unified hybrid frameworks preserving their main advantages. We develop several highly efficient methods based on both these model-driven and data-driven strategies, for which we provide a detailed convergence analysis. The arising algorithms are applied to solve inverse problems involving images and time series. For each task, we show the proposed schemes improve the performances of many other existing methods in terms of both computational burden and quality of the solution. In the first part, we focus on gradient-based regularized variational models which are shown to be effective for segmentation purposes and thermal and medical image enhancement. We consider gradient sparsity-promoting regularized models for which we develop different strategies to estimate the regularization strength. Furthermore, we introduce a novel gradient-based Plug-and-Play convergent scheme considering a deep learning based denoiser trained on the gradient domain. In the second part, we address the tasks of natural image deblurring, image and video super resolution microscopy and positioning time series prediction, through deep learning based methods. We boost the performances of supervised, such as trained convolutional and recurrent networks, and unsupervised deep learning strategies, such as Deep Image Prior, by penalizing the losses with handcrafted regularization terms.

Graphene-based bioelectronic interfaces and devices and interpretative models targeting glial cells

Bioelectronic interfaces have significantly advanced in recent years, offering potential treatments for vision impairments, spinal cord injuries, and neurodegenerative diseases. However, the classical neurocentric vision drives the technological development toward neurons. Emerging evidence highlights the critical role of glial cells in the nervous system. Among them, astrocytes significantly influence neuronal networks throughout life and are implicated in several neuropathological states. Although they are incapable to fire action potentials, astrocytes communicate through diverse calcium (Ca2+) signalling pathways, crucial for cognitive functions and brain blood flow regulation. Current bioelectronic devices are primarily designed to interface neurons and are unsuitable for studying astrocytes. Graphene, with its unique electrical, mechanical and biocompatibility properties, has emerged as a promising neural interface material. However, its use as electrode interface to modulate astrocyte functionality remains unexplored. The aim of this PhD work was to exploit Graphene-oxide (GO) and reduced GO (rGO)-coated electrodes to control Ca2+ signalling in astrocytes by electrical stimulation. We discovered that distinct Ca2+dynamics in astrocytes can be evoked, in vitro and in brain slices, depending on the conductive/insulating properties of rGO/GO electrodes. Stimulation by rGO electrodes induces intracellular Ca2+ response with sharp peaks of oscillations (“P-type”), exclusively due to Ca2+ release from intracellular stores. Conversely, astrocytes stimulated by GO electrodes show slower and sustained Ca2+ response (“S-type”), largely mediated by external Ca2+ influx through specific ion channels. Astrocytes respond faster than neurons and activate distinct G-Protein Coupled Receptor intracellular signalling pathways. We propose a resistive/insulating model, hypothesizing that the different conductivity of the substrate influences the electric field at the cell/electrolyte or cell/material interfaces, favouring, respectively, the Ca2+ release from intracellular stores or the extracellular Ca2+ influx. This research provides a simple tool to selectively control distinct Ca2+ signals in brain astrocytes in neuroscience and bioelectronic medicine.

Desenvolvimento do sistema do capital e teorias de transição em Trotski e Mészáros

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Wide bandwidth envelope trackers with reduced switching frequency for RF power amplifier

El requerimiento de proveer alta frecuencia de datos en los modernos sistema de comunicación inalámbricos resulta en complejas señales moduladas de radio-frequencia (RF) con un gran ancho de banda y alto ratio pico-promedio (PAPR). Para garantizar la linealidad del comportamiento, los amplificadores lineales de potencia comunes funcionan típicamente entre 4 y 10 dB de back-o_ desde la máxima potencia de salida, ocasionando una baja eficiencia del sistema. La eliminación y restauración de la evolvente (EER) y el seguimiento de la evolvente (ET) son dos prometedoras técnicas para resolver el problema de la eficiencia. Tanto en EER como en ET, es complicado diseñar un amplificador de potencia que sea eficiente para señales de RF de alto ancho de banda y alto PAPR. Una propuesta común para los amplificadores de potencia es incluir un convertidor de potencia de muy alta eficiencia operando a frecuencias más altas que el ancho de banda de la señal RF. En este caso, la potencia perdida del convertidor ocasionado por la alta frecuencia desaconseja su práctica cuando el ancho de banda es muy alto. La solución a este problema es el enfoque de esta disertación que presenta dos arquitecturas de amplificador evolvente: convertidor híbrido-serie con una técnica de evolvente lenta y un convertidor multinivel basado en un convertidor reductor multifase con control de tiempo mínimo. En la primera arquitectura, una topología híbrida está compuesta de una convertidor reductor conmutado y un regulador lineal en serie que trabajan juntos para ajustar la tensión de salida para seguir a la evolvente con precisión. Un algoritmo de generación de una evolvente lenta crea una forma de onda con una pendiente limitada que es menor que la pendiente máxima de la evolvente original. La salida del convertidor reductor sigue esa forma de onda en vez de la evolvente original usando una menor frecuencia de conmutación, porque la forma de onda no sólo tiene una pendiente reducida sino también un menor ancho de banda. De esta forma, el regulador lineal se usa para filtrar la forma de onda tiene una pérdida de potencia adicional. Dependiendo de cuánto se puede reducir la pendiente de la evolvente para producir la forma de onda, existe un trade-off entre la pérdida de potencia del convertidor reductor relacionada con la frecuencia de conmutación y el regulador lineal. El punto óptimo referido a la menor pérdida de potencia total del amplificador de evolvente es capaz de identificarse con la ayuda de modelo preciso de pérdidas que es una combinación de modelos comportamentales y analíticos de pérdidas. Además, se analiza el efecto en la respuesta del filtro de salida del convertidor reductor. Un filtro de dampeo paralelo extra es necesario para eliminar la oscilación resonante del filtro de salida porque el convertidor reductor opera en lazo abierto. La segunda arquitectura es un amplificador de evolvente de seguimiento de tensión multinivel. Al contrario que los convertidores que usan multi-fuentes, un convertidor reductor multifase se emplea para generar la tensión multinivel. En régimen permanente, el convertidor reductor opera en puntos del ciclo de trabajo con cancelación completa del rizado. El número de niveles de tensión es igual al número de fases de acuerdo a las características del entrelazamiento del convertidor reductor. En la transición, un control de tiempo mínimo (MTC) para convertidores multifase es novedosamente propuesto y desarrollado para cambiar la tensión de salida del convertidor reductor entre diferentes niveles. A diferencia de controles convencionales de tiempo mínimo para convertidores multifase con inductancia equivalente, el propuesto MTC considera el rizado de corriente por cada fase basado en un desfase fijo que resulta en diferentes esquemas de control entre las fases. La ventaja de este control es que todas las corrientes vuelven a su fase en régimen permanente después de la transición para que la siguiente transición pueda empezar muy pronto, lo que es muy favorable para la aplicación de seguimiento de tensión multinivel. Además, el control es independiente de la carga y no es afectado por corrientes de fase desbalanceadas. Al igual que en la primera arquitectura, hay una etapa lineal con la misma función, conectada en serie con el convertidor reductor multifase. Dado que tanto el régimen permanente como el estado de transición del convertidor no están fuertemente relacionados con la frecuencia de conmutación, la frecuencia de conmutación puede ser reducida para el alto ancho de banda de la evolvente, la cual es la principal consideración de esta arquitectura. La optimización de la segunda arquitectura para más alto anchos de banda de la evolvente es presentada incluyendo el diseño del filtro de salida, la frecuencia de conmutación y el número de fases. El área de diseño del filtro está restringido por la transición rápida y el mínimo pulso del hardware. La rápida transición necesita un filtro pequeño pero la limitación del pulso mínimo del hardware lleva el diseño en el sentido contrario. La frecuencia de conmutación del convertidor afecta principalmente a la limitación del mínimo pulso y a las pérdidas de potencia. Con una menor frecuencia de conmutación, el ancho de pulso en la transición es más pequeño. El número de fases relativo a la aplicación específica puede ser optimizado en términos de la eficiencia global. Otro aspecto de la optimización es mejorar la estrategia de control. La transición permite seguir algunas partes de la evolvente que son más rápidas de lo que el hardware puede soportar al precio de complejidad. El nuevo método de sincronización de la transición incrementa la frecuencia de la transición, permitiendo que la tensión multinivel esté más cerca de la evolvente. Ambas estrategias permiten que el convertidor pueda seguir una evolvente con un ancho de banda más alto que la limitación de la etapa de potencia. El modelo de pérdidas del amplificador de evolvente se ha detallado y validado mediante medidas. El mecanismo de pérdidas de potencia del convertidor reductor tiene que incluir las transiciones en tiempo real, lo cual es diferente del clásico modelos de pérdidas de un convertidor reductor síncrono. Este modelo estima la eficiencia del sistema y juega un papel muy importante en el proceso de optimización. Finalmente, la segunda arquitectura del amplificador de evolvente se integra con el amplificador de clase F. La medida del sistema EER prueba el ahorro de energía con el amplificador de evolvente propuesto sin perjudicar la linealidad del sistema. ABSTRACT The requirement of delivering high data rates in modern wireless communication systems results in complex modulated RF signals with wide bandwidth and high peak-to-average ratio (PAPR). In order to guarantee the linearity performance, the conventional linear power amplifiers typically work at 4 to 10 dB back-off from the maximum output power, leading to low system efficiency. The envelope elimination and restoration (EER) and envelope tracking (ET) are two promising techniques to overcome the efficiency problem. In both EER and ET, it is challenging to design efficient envelope amplifier for wide bandwidth and high PAPR RF signals. An usual approach for envelope amplifier includes a high-efficiency switching power converter operating at a frequency higher than the RF signal's bandwidth. In this case, the power loss of converter caused by high switching operation becomes unbearable for system efficiency when signal bandwidth is very wide. The solution of this problem is the focus of this dissertation that presents two architectures of envelope amplifier: a hybrid series converter with slow-envelope technique and a multilevel converter based on a multiphase buck converter with the minimum time control. In the first architecture, a hybrid topology is composed of a switched buck converter and a linear regulator in series that work together to adjust the output voltage to track the envelope with accuracy. A slow envelope generation algorithm yields a waveform with limited slew rate that is lower than the maximum slew rate of the original envelope. The buck converter's output follows this waveform instead of the original envelope using lower switching frequency, because the waveform has not only reduced slew rate but also reduced bandwidth. In this way, the linear regulator used to filter the waveform has additional power loss. Depending on how much reduction of the slew rate of envelope in order to obtain that waveform, there is a trade-off between the power loss of buck converter related to the switching frequency and the power loss of linear regulator. The optimal point referring to the lowest total power loss of this envelope amplifier is identified with the help of a precise power loss model that is a combination of behavioral and analytic loss model. In addition, the output filter's effect on the response is analyzed. An extra parallel damping filter is needed to eliminate the resonant oscillation of output filter L and C, because the buck converter operates in open loop. The second architecture is a multilevel voltage tracking envelope amplifier. Unlike the converters using multi-sources, a multiphase buck converter is employed to generate the multilevel voltage. In the steady state, the buck converter operates at complete ripple cancellation points of duty cycle. The number of the voltage levels is equal to the number of phases according the characteristics of interleaved buck converter. In the transition, a minimum time control (MTC) for multiphase converter is originally proposed and developed for changing the output voltage of buck converter between different levels. As opposed to conventional minimum time control for multiphase converter with equivalent inductance, the proposed MTC considers the current ripple of each phase based on the fixed phase shift resulting in different control schemes among the phases. The advantage of this control is that all the phase current return to the steady state after the transition so that the next transition can be triggered very soon, which is very favorable for the application of multilevel voltage tracking. Besides, the control is independent on the load condition and not affected by the unbalance of phase current. Like the first architecture, there is also a linear stage with the same function, connected in series with the multiphase buck converter. Since both steady state and transition state of the converter are not strongly related to the switching frequency, it can be reduced for wide bandwidth envelope which is the main consideration of this architecture. The optimization of the second architecture for wider bandwidth envelope is presented including the output filter design, switching frequency and the number of phases. The filter design area is restrained by fast transition and the minimum pulse of hardware. The fast transition needs small filter but the minimum pulse of hardware limitation pushes the filter in opposite way. The converter switching frequency mainly affects the minimum pulse limitation and the power loss. With lower switching frequency, the pulse width in the transition is smaller. The number of phases related to specific application can be optimized in terms of overall efficiency. Another aspect of optimization is improving control strategy. Transition shift allows tracking some parts of envelope that are faster than the hardware can support at the price of complexity. The new transition synchronization method increases the frequency of transition, allowing the multilevel voltage to be closer to the envelope. Both control strategies push the converter to track wider bandwidth envelope than the limitation of power stage. The power loss model of envelope amplifier is detailed and validated by measurements. The power loss mechanism of buck converter has to include the transitions in real time operation, which is different from classical power loss model of synchronous buck converter. This model estimates the system efficiency and play a very important role in optimization process. Finally, the second envelope amplifier architecture is integrated with a Class F amplifier. EER system measurement proves the power saving with the proposed envelope amplifier without disrupting the linearity performance.

Methods and Models for Accelerating Dynamic Simulation of Fluid Power Circuits

The objective of this dissertation is to improve the dynamic simulation of fluid power circuits. A fluid power circuit is a typical way to implement power transmission in mobile working machines, e.g. cranes, excavators etc. Dynamic simulation is an essential tool in developing controllability and energy-efficient solutions for mobile machines. Efficient dynamic simulation is the basic requirement for the real-time simulation. In the real-time simulation of fluid power circuits there exist numerical problems due to the software and methods used for modelling and integration. A simulation model of a fluid power circuit is typically created using differential and algebraic equations. Efficient numerical methods are required since differential equations must be solved in real time. Unfortunately, simulation software packages offer only a limited selection of numerical solvers. Numerical problems cause noise to the results, which in many cases leads the simulation run to fail. Mathematically the fluid power circuit models are stiff systems of ordinary differential equations. Numerical solution of the stiff systems can be improved by two alternative approaches. The first is to develop numerical solvers suitable for solving stiff systems. The second is to decrease the model stiffness itself by introducing models and algorithms that either decrease the highest eigenvalues or neglect them by introducing steady-state solutions of the stiff parts of the models. The thesis proposes novel methods using the latter approach. The study aims to develop practical methods usable in dynamic simulation of fluid power circuits using explicit fixed-step integration algorithms. In this thesis, twomechanisms whichmake the systemstiff are studied. These are the pressure drop approaching zero in the turbulent orifice model and the volume approaching zero in the equation of pressure build-up. These are the critical areas to which alternative methods for modelling and numerical simulation are proposed. Generally, in hydraulic power transmission systems the orifice flow is clearly in the turbulent area. The flow becomes laminar as the pressure drop over the orifice approaches zero only in rare situations. These are e.g. when a valve is closed, or an actuator is driven against an end stopper, or external force makes actuator to switch its direction during operation. This means that in terms of accuracy, the description of laminar flow is not necessary. But, unfortunately, when a purely turbulent description of the orifice is used, numerical problems occur when the pressure drop comes close to zero since the first derivative of flow with respect to the pressure drop approaches infinity when the pressure drop approaches zero. Furthermore, the second derivative becomes discontinuous, which causes numerical noise and an infinitely small integration step when a variable step integrator is used. A numerically efficient model for the orifice flow is proposed using a cubic spline function to describe the flow in the laminar and transition areas. Parameters for the cubic spline function are selected such that its first derivative is equal to the first derivative of the pure turbulent orifice flow model in the boundary condition. In the dynamic simulation of fluid power circuits, a tradeoff exists between accuracy and calculation speed. This investigation is made for the two-regime flow orifice model. Especially inside of many types of valves, as well as between them, there exist very small volumes. The integration of pressures in small fluid volumes causes numerical problems in fluid power circuit simulation. Particularly in realtime simulation, these numerical problems are a great weakness. The system stiffness approaches infinity as the fluid volume approaches zero. If fixed step explicit algorithms for solving ordinary differential equations (ODE) are used, the system stability would easily be lost when integrating pressures in small volumes. To solve the problem caused by small fluid volumes, a pseudo-dynamic solver is proposed. Instead of integration of the pressure in a small volume, the pressure is solved as a steady-state pressure created in a separate cascade loop by numerical integration. The hydraulic capacitance V/Be of the parts of the circuit whose pressures are solved by the pseudo-dynamic method should be orders of magnitude smaller than that of those partswhose pressures are integrated. The key advantage of this novel method is that the numerical problems caused by the small volumes are completely avoided. Also, the method is freely applicable regardless of the integration routine applied. The superiority of both above-mentioned methods is that they are suited for use together with the semi-empirical modelling method which necessarily does not require any geometrical data of the valves and actuators to be modelled. In this modelling method, most of the needed component information can be taken from the manufacturer’s nominal graphs. This thesis introduces the methods and shows several numerical examples to demonstrate how the proposed methods improve the dynamic simulation of various hydraulic circuits.

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.

Bayesian analysis and constraints on kinematic models from union SNIa

The kinematic expansion history of the universe is investigated by using the 307 supernovae type Ia from the Union Compilation set. Three simple model parameterizations for the deceleration parameter ( constant, linear and abrupt transition) and two different models that are explicitly parametrized by the cosmic jerk parameter ( constant and variable) are considered. Likelihood and Bayesian analyses are employed to find best fit parameters and compare models among themselves and with the flat Lambda CDM model. Analytical expressions and estimates for the deceleration and cosmic jerk parameters today (q(0) and j(0)) and for the transition redshift (z(t)) between a past phase of cosmic deceleration to a current phase of acceleration are given. All models characterize an accelerated expansion for the universe today and largely indicate that it was decelerating in the past, having a transition redshift around 0.5. The cosmic jerk is not strongly constrained by the present supernovae data. For the most realistic kinematic models the 1 sigma confidence limits imply the following ranges of values: q(0) is an element of [-0.96, -0.46], j(0) is an element of [-3.2,-0.3] and z(t) is an element of [0.36, 0.84], which are compatible with the Lambda CDM predictions, q(0) = -0.57 +/- 0.04, j(0) = -1 and z(t) = 0.71 +/- 0.08. We find that even very simple kinematic models are equally good to describe the data compared to the concordance Lambda CDM model, and that the current observations are not powerful enough to discriminate among all of them.

Thermal Study in Solid State of Zn(II)-Diclofenac Complex: Behavior Kinetic of the Dehydration, Transition Phase and Thermal Decomposition

The dehydration, thermal decomposition and transition phase stage of Zn(II)-diclofenac compound were studied by simultaneous TG-DTA and DSC techniques. The TG and DSC curves of this compound were obtained with the mass of sample of 2 and 5 mg. Additionally, DSC curves were carried out in opened and closed alpha-alumina pans under static and nitrogen atmosphere. The DTA and DSC curves show that this compound possesses exothermic transition phase between 170-180 degrees C, which it is irreversible (monotropic reaction) The kinetics study of this transition phase stage was evaluated by DSC under non-isothermal conditions. The obtained data were evaluated with the isoconversional method, where the values of activation energy (E(a) / kJ mol(-1)) was plotted in function of the conversion degree (alpha). The results show that due to mass sample, different activation energies were obtained From these curves a tendency can be seen where the plots maintain the same profile for closed lids and almost run parallel to each other.