Estimation of phenotypic diversity in field populations of Magnaporthe grisea from two upland rice cultivars

The phenotypic diversity of Magnaporthe grisea was evaluated based on leaf samples with blast lesions collected from eight commercial fields of the upland rice cultivars 'BRS Primavera' and 'BRS Bonança', during the growing seasons of 2001/2002 and 2002/2003, in Goias State. The number of M. grisea isolates from each field utilized for virulence testing varied from 28 to 47. Three different indices were used based on reaction type in the eight standard international differentials and eight Brazilian differentials. The M. grisea subpopulations of ´Primavera' and 'Bonança', as measured by Simpson, Shannon and Gleason indices, showed similar phenotypic diversities. The Simpson index was more sensitive relation than those of Shannon and Gleason for pathotype number and standard deviation utilizing Brazilian differentials. However, the Gleason index was sensitive to standard deviation for international differentials. The sample size did not significantly influence the diversity index. The two sets of differential cultivars used in this study distinguished phenotypic diversity in different ways in all of the eight subpopulations analyzed. The phenotypic diversity determined based on eight differential Brazilian cultivars was lower in commercial rice fields of 'Primavera' than in the fields of 'Bonança,' independent of the diversity index utilized, year and location. Considering the Brazilian differentials, the four subpopulations of 'BRS Primavera' did not show evenness in distribution and only one pathotype dominated in the populations. The even distribution of pathotype was observed in three subpopulations of 'BRS Bonança'. The pathotype diversity of M. grisea was determined with more precision using Brazilian differentials and Simpson index.

Centralized Motion Control of a Linear Tooth Belt Drive: Analysis of the Performance and Limitations

A centralized robust position control for an electrical driven tooth belt drive is designed in this doctoral thesis. Both a cascaded control structure and a PID based position controller are discussed. The performance and the limitations of the system are analyzed and design principles for the mechanical structure and the control design are given. These design principles are also suitable for most of the motion control applications, where mechanical resonance frequencies and control loop delays are present. One of the major challenges in the design of a controller for machinery applications is that the values of the parameters in the system model (parameter uncertainty) or the system model it self (non-parametric uncertainty) are seldom known accurately in advance. In this thesis a systematic analysis of the parameter uncertainty of the linear tooth beltdrive model is presented and the effect of the variation of a single parameter on the performance of the total system is shown. The total variation of the model parameters is taken into account in the control design phase using a Quantitative Feedback Theory (QFT). The thesis also introduces a new method to analyze reference feedforward controllers applying the QFT. The performance of the designed controllers is verified by experimentalmeasurements. The measurements confirm the control design principles that are given in this thesis.

Insulation System in an Integrated Motor Compressor

A high-speed and high-voltage solid-rotor induction machine provides beneficial features for natural gas compressor technology. The mechanical robustness of the machine enables its use in an integrated motor-compressor. The technology uses a centrifugal compressor, which is mounted on the same shaft with the high-speed electrical machine driving it. No gearbox is needed as the speed is determined by the frequency converter. The cooling is provided by the process gas, which flows through the motor and is capable of transferring the heat away from the motor. The technology has been used in the compressors in the natural gas supply chain in the central Europe. New areas of application include natural gas compressors working at the wellheads of the subsea gas reservoir. A key challenge for the design of such a motor is the resistance of the stator insulation to the raw natural gas from the well. The gas contains water and heavy hydrocarbon compounds and it is far harsher than the sales gas in the natural gas supply network. The objective of this doctoral thesis is to discuss the resistance of the insulation to the raw natural gas and the phenomena degrading the insulation. The presence of partial discharges is analyzed in this doctoral dissertation. The breakdown voltage of the gas is measured as a function of pressure and gap distance. The partial discharge activity is measured on small samples representing the windings of the machine. The electrical field behavior is also modeled by finite element methods. Based on the measurements it has been concluded that the discharges are expected to disappear at gas pressures above 4 – 5 bar. The disappearance of discharges is caused by the breakdown strength of the gas, which increases as the pressure increases. Based on the finite element analysis, the physical length of a discharge seen in the PD measurements at atmospheric pressure was approximated to be 40 – 120 m. The chemical aging of the insulation when exposed to raw natural gas is discussed based on a vast set of experimental tests with the gas mixture representing the real gas mixture at the wellhead. The mixture was created by mixing dry hydrocarbon gas, heavy hydrocarbon compounds, monoethylene glycol, and water. The mixture was chosen to be more aggressive by increasing the amount of liquid substances. Furthermore, the temperature and pressure were increased, which resulted in accelerated test conditions. The time required to detect severe degradation was thus decreased. The test program included a comparison of materials, an analysis of the e ects of di erent compounds in the gas mixture, namely water and heavy hydrocarbons, on the aging, an analysis of the e ects of temperature and exposure duration, and also an analysis on the e ect of sudden pressure changes on the degradation of the insulating materials. It was found in the tests that an insulation consisting of mica, glass, and epoxy resin can tolerate the raw natural gas, but it experiences some degradation. The key material in the composite insulation is the resin, which largely defines the performance of the insulation system. The degradation of the insulation is mostly determined by the amount of gas mixture di used into it. The di usion was seen to follow Fick’s second law, but the coe cients were not accurately defined. The di usion was not sensitive to temperature, but it was dependent upon the thermodynamic state of the gas mixture, in other words, the amounts of liquid components in the gas. The weight increase observed was mostly related to heavy hydrocarbon compounds, which act as plasticizers in the epoxy resin. The di usion of these compounds is determined by the crosslink density of the resin. Water causes slight changes in the chemical structure, but these changes do not significantly contribute to the aging phenomena. Sudden changes in pressure can lead to severe damages in the insulation, because the motion of the di used gas is able to create internal cracks in the insulation. Therefore, the di usion only reduces the mechanical strength of the insulation, but the ultimate breakdown can potentially be caused by a sudden drop in the pressure of the process gas.

Bayesian methods for estimation, optimization and experimental design

Mathematical models often contain parameters that need to be calibrated from measured data. The emergence of efficient Markov Chain Monte Carlo (MCMC) methods has made the Bayesian approach a standard tool in quantifying the uncertainty in the parameters. With MCMC, the parameter estimation problem can be solved in a fully statistical manner, and the whole distribution of the parameters can be explored, instead of obtaining point estimates and using, e.g., Gaussian approximations. In this thesis, MCMC methods are applied to parameter estimation problems in chemical reaction engineering, population ecology, and climate modeling. Motivated by the climate model experiments, the methods are developed further to make them more suitable for problems where the model is computationally intensive. After the parameters are estimated, one can start to use the model for various tasks. Two such tasks are studied in this thesis: optimal design of experiments, where the task is to design the next measurements so that the parameter uncertainty is minimized, and model-based optimization, where a model-based quantity, such as the product yield in a chemical reaction model, is optimized. In this thesis, novel ways to perform these tasks are developed, based on the output of MCMC parameter estimation. A separate topic is dynamical state estimation, where the task is to estimate the dynamically changing model state, instead of static parameters. For example, in numerical weather prediction, an estimate of the state of the atmosphere must constantly be updated based on the recently obtained measurements. In this thesis, a novel hybrid state estimation method is developed, which combines elements from deterministic and random sampling methods.

ESTIMATION OF CORK PRODUCTION USINGAERIAL IMAGERY1

ABSTRACT Inventory and prediction of cork harvest over time and space is important to forest managers who must plan and organize harvest logistics (transport, storage, etc.). Common field inventory methods including the stem density, diameter and height structure are costly and generally point (plot) based. Furthermore, the irregular horizontal structure of cork oak stands makes it difficult, if not impossible, to interpolate between points. We propose a new method to estimate cork production using digital multispectral aerial imagery. We study the spectral response of individual trees in visible and near infrared spectra and then correlate that response with cork production prior to harvest. We use ground measurements of individual trees production to evaluate the model’s predictive capacity. We propose 14 candidate variables to predict cork production based on crown size in combination with different NDVI index derivates. We use Akaike Information Criteria to choose the best among them. The best model is composed of combinations of different NDVI derivates that include red, green, and blue channels. The proposed model is 15% more accurate than a model that includes only a crown projection without any spectral information.

Estimation of the potential evapotranspiration by a simplified penman method

The numerous methods for calculating the potential or reference evapotranspiration (ETo or ETP) almost always do for a 24-hour period, including values of climatic parameters throughout the nocturnal period (daily averages). These results have a nil effect on transpiration, constituting the main evaporative demand process in cases of localized irrigation. The aim of the current manuscript was to come up with a model rather simplified for the calculation of diurnal daily ETo. It deals with an alternative approach based on the theoretical background of the Penman method without having to consider values of aerodynamic conductance of latent and sensible heat fluxes, as well as data of wind speed and relative humidity of the air. The comparison between the diurnal values of ETo measured in weighing lysimeters with elevated precision and estimated by either the Penman-Monteith method or the Simplified-Penman approach in study also points out a fairly consistent agreement among the potential demand calculation criteria. The Simplified-Penman approach was a feasible alternative to estimate ETo under the local meteorological conditions of two field trials. With the availability of the input data required, such a method could be employed in other climatic regions for scheduling irrigation.

Estimation of transpiration of the 'Valencia' orange young plant using thermal dissipation probe method

Most studies on measures of transpiration of plants, especially woody fruit, relies on methods of heat supply in the trunk. This study aimed to calibrate the Thermal Dissipation Probe Method (TDP) to estimate the transpiration, study the effects of natural thermal gradients and determine the relation between outside diameter and area of xylem in 'Valencia' orange young plants. TDP were installed in 40 orange plants of 15 months old, planted in boxes of 500 L, in a greenhouse. It was tested the correction of the natural thermal differences (DTN) for the estimation based on two unheated probes. The area of the conductive section was related to the outside diameter of the stem by means of polynomial regression. The equation for estimation of sap flow was calibrated having as standard lysimeter measures of a representative plant. The angular coefficient of the equation for estimating sap flow was adjusted by minimizing the absolute deviation between the sap flow and daily transpiration measured by lysimeter. Based on these results, it was concluded that the method of TDP, adjusting the original calibration and correction of the DTN, was effective in transpiration assessment.

Detection of soybean planted areas through orbital images based on culture spectral dynamics

The soybean is important to the economy of Brazil, so the estimation of the planted area and the production with higher antecedence and reliability becomes essential. Techniques related to Remote Sensing may help to obtain this information at lower cost and less subjectivity in relation to traditional surveys. The aim of this study is to estimate the planted area with soybean culture in the crop of 2008/2009 in cities in the west of the state of Paraná, in Brazil, based on the spectral dynamics of the culture and through the use of the specific system of analysis for images of Landsat 5/TM satellite. The obtained results were satisfactory, because the classification supervised by Maximum Verisimilitude - MaxVer along with the techniques of the specific system of analysis for satellite images has allowed an estimate of soybean planted area (soybean mask), obtaining values ​​of the metrics of Global Accuracy with an average of 79.05% and Kappa Index over 63.50% in all cities. The monitoring of a reference area was of great importance for determining the vegetative phase in which the culture is more different from the other targets, facilitating the choice of training samples (ROIs) and avoiding misclassifications.

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.

Development of Cost Estimation with Feedback Information in EPCM Business

Target company of this study is a large machinery company, which is, inter alia, engaged in energy and pulp engineering, procurement and construction management (EPCM) supply business. The main objective of this study was to develop cost estimation of the target company by providing more accurate, reliable and up-to-date information through enterprise resource planning (ERP) system. Another objective was to find cost-effective methods to collect total cost of ownership information to support more informed supplier selection decision making. This study is primarily action-oriented, but also constructive, and it can be divided in two sections: theoretical literature review and empirical study on the abovementioned part of the target company’s business. Development of information collection is, in addition to literature review, based on nearly 30 qualitative interviews of employees at various organizational units, functions and levels at the target company. At the core of development was to make initial data more accurate, reliable and available, a necessary prerequisite for informed use of the information. Certain development suggestions and paths were presented in order to regain confidence in ERP system as information source by reorganizing work breakdown structure and by complementing mere cost information with quantitative, technical and scope information. Several methods to use the information ever more effectively were also discussed. While implementation of the development suggestions outreached the scope of this study, it was forwarded in test environment and interest groups.

Estimation of Fetal Weight during Labor: Still a Challenge

Objective To evaluate the accuracy of fetal weight prediction by ultrasonography labor employing a formula including the linear measurements of femur length (FL) and mid-thigh soft-tissue thickness (STT). Methods We conducted a prospective study involving singleton uncomplicated term pregnancies within 48 hours of delivery. Only pregnancies with a cephalic fetus admitted in the labor ward for elective cesarean section, induction of labor or spontaneous labor were included. We excluded all non-Caucasian women, the ones previously diagnosed with gestational diabetes and the ones with evidence of ruptured membranes. Fetal weight estimates were calculated using a previously proposed formula [estimated fetal weight = [1] 1687.47 + (54.1 x FL) + (76.68 x STT). The relationship between actual birth weight and estimated fetal weight was analyzed using Pearson's correlation. The formula's performance was assessed by calculating the signed and absolute errors. Mean weight difference and signed percentage error were calculated for birth weight divided into three subgroups: < 3000 g; 3000-4000g; and > 4000 g. Results We included for analysis 145 cases and found a significant, yet low, linear relationship between birth weight and estimated fetal weight (p < 0.001; R2 = 0.197) with an absolute mean error of 10.6%. The lowest mean percentage error (0.3%) corresponded to the subgroup with birth weight between 3000 g and 4000 g. Conclusions This study demonstrates a poor correlation between actual birth weight and the estimated fetal weight using a formula based on femur length and mid-thigh soft-tissue thickness, both linear parameters. Although avoidance of circumferential ultrasound measurements might prove to be beneficial, it is still yet to be found a fetal estimation formula that can be both accurate and simple to perform.

Estimation of aircraft aerodynamic derivatives using Extended Kalman Filter

Design of flight control laws, verification of performance predictions, and the implementation of flight simulations are tasks that require a mathematical model of the aircraft dynamics. The dynamical models are characterized by coefficients (aerodynamic derivatives) whose values must be determined from flight tests. This work outlines the use of the Extended Kalman Filter (EKF) in obtaining the aerodynamic derivatives of an aircraft. The EKF shows several advantages over the more traditional least-square method (LS). Among these the most important are: there are no restrictions on linearity or in the form which the parameters appears in the mathematical model describing the system, and it is not required that these parameters be time invariant. The EKF uses the statistical properties of the process and the observation noise, to produce estimates based on the mean square error of the estimates themselves. Differently, the LS minimizes a cost function based on the plant output behavior. Results for the estimation of some longitudinal aerodynamic derivatives from simulated data are presented.

On state and parameter estimation in chaotic systems

State-of-the-art predictions of atmospheric states rely on large-scale numerical models of chaotic systems. This dissertation studies numerical methods for state and parameter estimation in such systems. The motivation comes from weather and climate models and a methodological perspective is adopted. The dissertation comprises three sections: state estimation, parameter estimation and chemical data assimilation with real atmospheric satellite data. In the state estimation part of this dissertation, a new filtering technique based on a combination of ensemble and variational Kalman filtering approaches, is presented, experimented and discussed. This new filter is developed for large-scale Kalman filtering applications. In the parameter estimation part, three different techniques for parameter estimation in chaotic systems are considered. The methods are studied using the parameterized Lorenz 95 system, which is a benchmark model for data assimilation. In addition, a dilemma related to the uniqueness of weather and climate model closure parameters is discussed. In the data-oriented part of this dissertation, data from the Global Ozone Monitoring by Occultation of Stars (GOMOS) satellite instrument are considered and an alternative algorithm to retrieve atmospheric parameters from the measurements is presented. The validation study presents first global comparisons between two unique satellite-borne datasets of vertical profiles of nitrogen trioxide (NO3), retrieved using GOMOS and Stratospheric Aerosol and Gas Experiment III (SAGE III) satellite instruments. The GOMOS NO3 observations are also considered in a chemical state estimation study in order to retrieve stratospheric temperature profiles. The main result of this dissertation is the consideration of likelihood calculations via Kalman filtering outputs. The concept has previously been used together with stochastic differential equations and in time series analysis. In this work, the concept is applied to chaotic dynamical systems and used together with Markov chain Monte Carlo (MCMC) methods for statistical analysis. In particular, this methodology is advocated for use in numerical weather prediction (NWP) and climate model applications. In addition, the concept is shown to be useful in estimating the filter-specific parameters related, e.g., to model error covariance matrix parameters.

Probabilistic position and orientation estimation of rigid objects using an RGB-D sensor

The recent emergence of low-cost RGB-D sensors has brought new opportunities for robotics by providing affordable devices that can provide synchronized images with both color and depth information. In this thesis, recent work on pose estimation utilizing RGBD sensors is reviewed. Also, a pose recognition system for rigid objects using RGB-D data is implemented. The implementation uses half-edge primitives extracted from the RGB-D images for pose estimation. The system is based on the probabilistic object representation framework by Detry et al., which utilizes Nonparametric Belief Propagation for pose inference. Experiments are performed on household objects to evaluate the performance and robustness of the system.

Greenhouse gas emissions from peat and biomass-derived fuels, electricity and heat — Estimation of various production chains by using LCA methodology

More discussion is required on how and which types of biomass should be used to achieve a significant reduction in the carbon load released into the atmosphere in the short term. The energy sector is one of the largest greenhouse gas (GHG) emitters and thus its role in climate change mitigation is important. Replacing fossil fuels with biomass has been a simple way to reduce carbon emissions because the carbon bonded to biomass is considered as carbon neutral. With this in mind, this thesis has the following objectives: (1) to study the significance of the different GHG emission sources related to energy production from peat and biomass, (2) to explore opportunities to develop more climate friendly biomass energy options and (3) to discuss the importance of biogenic emissions of biomass systems. The discussion on biogenic carbon and other GHG emissions comprises four case studies of which two consider peat utilization, one forest biomass and one cultivated biomasses. Various different biomass types (peat, pine logs and forest residues, palm oil, rapeseed oil and jatropha oil) are used as examples to demonstrate the importance of biogenic carbon to life cycle GHG emissions. The biogenic carbon emissions of biomass are defined as the difference in the carbon stock between the utilization and the non-utilization scenarios of biomass. Forestry-drained peatlands were studied by using the high emission values of the peatland types in question to discuss the emission reduction potential of the peatlands. The results are presented in terms of global warming potential (GWP) values. Based on the results, the climate impact of the peat production can be reduced by selecting high-emission-level peatlands for peat production. The comparison of the two different types of forest biomass in integrated ethanol production in pulp mill shows that the type of forest biomass impacts the biogenic carbon emissions of biofuel production. The assessment of cultivated biomasses demonstrates that several selections made in the production chain significantly affect the GHG emissions of biofuels. The emissions caused by biofuel can exceed the emissions from fossil-based fuels in the short term if biomass is in part consumed in the process itself and does not end up in the final product. Including biogenic carbon and other land use carbon emissions into the carbon footprint calculations of biofuel reveals the importance of the time frame and of the efficiency of biomass carbon content utilization. As regards the climate impact of biomass energy use, the net impact on carbon stocks (in organic matter of soils and biomass), compared to the impact of the replaced energy source, is the key issue. Promoting renewable biomass regardless of biogenic GHG emissions can increase GHG emissions in the short term and also possibly in the long term.