Improving product profitability control in continuous IT-services

The target of the thesis is to improve product profitability control in continuous IT-services. Accurate product cost accounting and correctly allocated revenues are a necessity for good product profitability control. The focus of the study is on costs and revenues that are not traced directly to services. The thesis is focused on revenue allocations as revenue allocation methods have not been used in the case company before. In order to achieve the target revenue allocation methods, which improve the product profitability accounting and control, are presented. The research methods used in the thesis are literature review and empirical case study. The research approach is constructive. The theoretical part is composed of literature and articles that create a base for the empirical part. Internal interviews describe the current situation in the company and based on it development actions are planned. The part of the empirical case study is seen mostly in the limitations as the research is limited to concern only one department in the company. Problems in the revenue tracing are caused by customer specific services and lack of service definitions because of which the revenues are not traced correctly. Methods to allocate revenues are presented in the thesis and stand-alone revenue allocation method is the most suitable one because it is fair and it can be modified. Approximate product profitability analysis is done in the thesis and the results of it indicate that some services are profitable and some unprofitable.

One chip solution for low-cost active magnetic bearing system

In this work the implementation of the active magnetic bearing control system in a single FPGA is studied. Requirements for the full magnetic bearing control system are reviewed. Different control methods for active magnetic bearings are described shortly. Flux and the current base controllers are implemented in a FPGA. Suitability of the con-trollers for a low-cost magnetic bearing application is studied. Floating-point arithmetic’s are used in the controllers to ease designing burden and improve calculation precision. Per-formance of the flux controller is verified with simulations.

Communication Solutions for LVDC Island Networks

Production and generation of electrical power is evolving to more environmental friendly technologies and schemes. Pushed by the increasing cost of fossil fuels, the operational costs of producing electrical power with fossil fuels and the effect in the environment, like pollution and global warming, renewable energy sources gain con-stant impulse into the global energy economy. In consequence, the introduction of distributed energy sources has brought a new complexity to the electrical networks. In the new concept of smart grids and decen-tralized power generation; control, protection and measurement are also distributed and requiring, among other things, a new scheme of communication to operate with each other in balance and improve performance. In this research, an analysis of different communication technologies (power line communication, Ethernet over unshielded twisted pair (UTP), optic fiber, Wi-Fi, Wi-MAX, and Long Term Evolution) and their respective characteristics will be carried out. With the objective of pointing out strengths and weaknesses from different points of view (technical, economical, deployment, etc.) to establish a richer context on which a decision for communication approach can be done depending on the specific application scenario of a new smart grid deployment. As a result, a description of possible optimal deployment solutions for communication will be shown considering different options for technologies, and a mention of different important considerations to be taken into account will be made for some of the possible network implementation scenarios.

Energy-efficient control strategies for variable speed driven parallel pumping systems based on pump operation point monitoring with frequency converters

The pumping processes requiring wide range of flow are often equipped with parallelconnected centrifugal pumps. In parallel pumping systems, the use of variable speed control allows that the required output for the process can be delivered with a varying number of operated pump units and selected rotational speed references. However, the optimization of the parallel-connected rotational speed controlled pump units often requires adaptive modelling of both parallel pump characteristics and the surrounding system in varying operation conditions. The available information required for the system modelling in typical parallel pumping applications such as waste water treatment and various cooling and water delivery pumping tasks can be limited, and the lack of real-time operation point monitoring often sets limits for accurate energy efficiency optimization. Hence, alternatives for easily implementable control strategies which can be adopted with minimum system data are necessary. This doctoral thesis concentrates on the methods that allow the energy efficient use of variable speed controlled parallel pumps in system scenarios in which the parallel pump units consist of a centrifugal pump, an electric motor, and a frequency converter. Firstly, the suitable operation conditions for variable speed controlled parallel pumps are studied. Secondly, methods for determining the output of each parallel pump unit using characteristic curve-based operation point estimation with frequency converter are discussed. Thirdly, the implementation of the control strategy based on real-time pump operation point estimation and sub-optimization of each parallel pump unit is studied. The findings of the thesis support the idea that the energy efficiency of the pumping can be increased without the installation of new, more efficient components in the systems by simply adopting suitable control strategies. An easily implementable and adaptive control strategy for variable speed controlled parallel pumping systems can be created by utilizing the pump operation point estimation available in modern frequency converters. Hence, additional real-time flow metering, start-up measurements, and detailed system model are unnecessary, and the pumping task can be fulfilled by determining a speed reference for each parallel-pump unit which suggests the energy efficient operation of the pumping system.

Weed control via intercropping with gliricidia: I. cotton crop

The majority of cotton grown commercially in the world has white lint, but recently, there has been a growing interest in colored lint cotton in several countries, including Brazil. The use of naturally-colored fiber reduces chemical pollution. The objective of this paper was to evaluate cotton cultivar fiber yield in response to weed control via intercropping with gliricídia. Cultivars BRS-Verde (greenish fibers), BRS-Rubi (reddish brown fibers), BRS-Safira (brown fibers), and BRS-187 8H (white fibers) were submitted to the following treatments: no hoeing, two hoeings (at 20 and 40 days after transplanting), and cotton intercropped with gliricídia. In the intercropped treatment, gliricídia was planted between rows of cotton plants, using one seedling pit-1, in pits spaced 50.0 cm apart. Twelve weed species predominated in the experiment, many of them belonging to the Poaceae family. Weeds occurred at different frequencies and in a non-uniform manner in the experimental area. Cultivars did not influence weed dry matter. Intercropping with gliricídia reduced weed dry matter but did not prevent reductions in cotton fiber and seed cotton yield, which were higher in hoed plots. Cultivar BRS Safira had the highest fiber yield, but no differences were observed between cultivars regarding to seed cotton yield.

Planting density of gliricidia when intercropped with corn for weed control

Reduced use of herbicides that cause environmental pollution problems is of great interest in modern agriculture. Soil mulching with gliricidia (Gliricidia sepium) branches does not have an allelopathic effect on corn, but decreases weed populations. The objective of this study was to evaluate the effects of gliricidia planting density, when grown as an intercrop, on weed control and corn yield parameters. A randomized block design with split-plots and ten replicates was adopted. Corn cultivars AG 1051 and BM 3061 were grown without hoeing, with two hoes (at 24 and 44 days after planting), and intercropped with gliricidia (planted simultaneously with corn, between crop rows, using two seedlings/pit, spaced at 30, 40, or 50 cm). Twenty-one weed species were found in the experimental area. Increased gliricidia planting density reduced weed biomass, but no difference was found between weed biomass in the intercrop and weed biomass in non-hoed corn. Gliricidia intercropped with corn, planted at a row spacing of 30 cm, did not significantly differ from hoed corn in most characteristics considered to evaluate green corn yield, although mean values were smaller. As to the number and weight of marketable green ears, reductions of 5% and 13%, respectively, were observed. Intercropping caused a 17% reduction in grain yield, reducing the losses (36%) observed in non-hoed corn by more than 50%. The highest green ear yield and grain yield values were obtained with two hoeings, while the lowest values were observed for non-hoed corn. The cultivars did not differ regarding green ear yield and grain yield.

Flexible multibody dynamics and intelligent control of a hydraulically driven hybrid redundant robot machine

The assembly and maintenance of the International Thermonuclear Experimental Reactor (ITER) vacuum vessel (VV) is highly challenging since the tasks performed by the robot involve welding, material handling, and machine cutting from inside the VV. The VV is made of stainless steel, which has poor machinability and tends to work harden very rapidly, and all the machining operations need to be carried out from inside of the ITER VV. A general industrial robot cannot be used due to its poor stiffness in the heavy duty machining process, and this will cause many problems, such as poor surface quality, tool damage, low accuracy. Therefore, one of the most suitable options should be a light weight mobile robot which is able to move around inside of the VV and perform different machining tasks by replacing different cutting tools. Reducing the mass of the robot manipulators offers many advantages: reduced material costs, reduced power consumption, the possibility of using smaller actuators, and a higher payload-to-robot weight ratio. Offsetting these advantages, the lighter weight robot is more flexible, which makes it more difficult to control. To achieve good machining surface quality, the tracking of the end effector must be accurate, and an accurate model for a more flexible robot must be constructed. This thesis studies the dynamics and control of a 10 degree-of-freedom (DOF) redundant hybrid robot (4-DOF serial mechanism and 6-DOF 6-UPS hexapod parallel mechanisms) hydraulically driven with flexible rods under the influence of machining forces. Firstly, the flexibility of the bodies is described using the floating frame of reference method (FFRF). A finite element model (FEM) provided the Craig-Bampton (CB) modes needed for the FFRF. A dynamic model of the system of six closed loop mechanisms was assembled using the constrained Lagrange equations and the Lagrange multiplier method. Subsequently, the reaction forces between the parallel and serial parts were used to study the dynamics of the serial robot. A PID control based on position predictions was implemented independently to control the hydraulic cylinders of the robot. Secondly, in machining, to achieve greater end effector trajectory tracking accuracy for surface quality, a robust control of the actuators for the flexible link has to be deduced. This thesis investigates the intelligent control of a hydraulically driven parallel robot part based on the dynamic model and two schemes of intelligent control for a hydraulically driven parallel mechanism based on the dynamic model: (1) a fuzzy-PID self-tuning controller composed of the conventional PID control and with fuzzy logic, and (2) adaptive neuro-fuzzy inference system-PID (ANFIS-PID) self-tuning of the gains of the PID controller, which are implemented independently to control each hydraulic cylinder of the parallel mechanism based on rod length predictions. The serial component of the hybrid robot can be analyzed using the equilibrium of reaction forces at the universal joint connections of the hexa-element. To achieve precise positional control of the end effector for maximum precision machining, the hydraulic cylinder should be controlled to hold the hexa-element. Thirdly, a finite element approach of multibody systems using the Special Euclidean group SE(3) framework is presented for a parallel mechanism with flexible piston rods under the influence of machining forces. The flexibility of the bodies is described using the nonlinear interpolation method with an exponential map. The equations of motion take the form of a differential algebraic equation on a Lie group, which is solved using a Lie group time integration scheme. The method relies on the local description of motions, so that it provides a singularity-free formulation, and no parameterization of the nodal variables needs to be introduced. The flexible slider constraint is formulated using a Lie group and used for modeling a flexible rod sliding inside a cylinder. The dynamic model of the system of six closed loop mechanisms was assembled using Hamilton’s principle and the Lagrange multiplier method. A linearized hydraulic control system based on rod length predictions was implemented independently to control the hydraulic cylinders. Consequently, the results of the simulations demonstrating the behavior of the robot machine are presented for each case study. In conclusion, this thesis studies the dynamic analysis of a special hybrid (serialparallel) robot for the above-mentioned special task involving the ITER and investigates different control algorithms that can significantly improve machining performance. These analyses and results provide valuable insight into the design and control of the parallel robot with flexible rods.

Adaptive value of ryegrass biotypes with low-level resistance and susceptible to the herbicide fluazifop and competitive ability with the wheat culture

Ryegrass is the main weed in wheat crop, causing yield loss due to competition by environmental resources. The objectives of this study were to estimate the fitness cost of ryegrass biotypes with low-level resistance and susceptible to fluazifop and to investigate the relative competitive ability of these biotypes between themselves and against the crop. Thus, fitness cost and competitive ability experiments were conducted under greenhouse conditions. For the fitness cost experiments, the low-level resistant ryegrass biotypes and those susceptible to fluazifop were used. For competitive ability, the treatments were arranged in a replacement series, with five proportions of the wheat cultivar FUNDACEP Horizonte and the low-level resistant and susceptible ryegrass biotypes 100:0, 75:25, 50:50, 25:75 and 0:100. Competitive analysis was carried out through diagrams applied to the replacement experiments and use of relative competitiveness indices. Variables evaluated were plant height, in the fitness cost experiment, and leaf area and shoot dry biomass in both experiments. The ryegrass biotypes show overall similar fitness cost and competitive ability. The wheat cultivar FUNDACEP Horizonte is superior in competitive ability to the ryegrass biotype with low-level resistance and equivalent to the susceptible biotype.

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.

Mathematical modeling and optimal control of malaria

Malaria continues to infect millions and kill hundreds of thousands of people worldwide each year, despite over a century of research and attempts to control and eliminate this infectious disease. Challenges such as the development and spread of drug resistant malaria parasites, insecticide resistance to mosquitoes, climate change, the presence of individuals with subpatent malaria infections which normally are asymptomatic and behavioral plasticity in the mosquito hinder the prospects of malaria control and elimination. In this thesis, mathematical models of malaria transmission and control that address the role of drug resistance, immunity, iron supplementation and anemia, immigration and visitation, and the presence of asymptomatic carriers in malaria transmission are developed. A within-host mathematical model of severe Plasmodium falciparum malaria is also developed. First, a deterministic mathematical model for transmission of antimalarial drug resistance parasites with superinfection is developed and analyzed. The possibility of increase in the risk of superinfection due to iron supplementation and fortification in malaria endemic areas is discussed. The model results calls upon stakeholders to weigh the pros and cons of iron supplementation to individuals living in malaria endemic regions. Second, a deterministic model of transmission of drug resistant malaria parasites, including the inflow of infective immigrants, is presented and analyzed. The optimal control theory is applied to this model to study the impact of various malaria and vector control strategies, such as screening of immigrants, treatment of drug-sensitive infections, treatment of drug-resistant infections, and the use of insecticide-treated bed nets and indoor spraying of mosquitoes. The results of the model emphasize the importance of using a combination of all four controls tools for effective malaria intervention. Next, a two-age-class mathematical model for malaria transmission with asymptomatic carriers is developed and analyzed. In development of this model, four possible control measures are analyzed: the use of long-lasting treated mosquito nets, indoor residual spraying, screening and treatment of symptomatic, and screening and treatment of asymptomatic individuals. The numerical results show that a disease-free equilibrium can be attained if all four control measures are used. A common pitfall for most epidemiological models is the absence of real data; model-based conclusions have to be drawn based on uncertain parameter values. In this thesis, an approach to study the robustness of optimal control solutions under such parameter uncertainty is presented. Numerical analysis of the optimal control problem in the presence of parameter uncertainty demonstrate the robustness of the optimal control approach that: when a comprehensive control strategy is used the main conclusions of the optimal control remain unchanged, even if inevitable variability remains in the control profiles. The results provide a promising framework for the design of cost-effective strategies for disease control with multiple interventions, even under considerable uncertainty of model parameters. Finally, a separate work modeling the within-host Plasmodium falciparum infection in humans is presented. The developed model allows re-infection of already-infected red blood cells. The model hypothesizes that in severe malaria due to parasite quest for survival and rapid multiplication, the Plasmodium falciparum can be absorbed in the already-infected red blood cells which accelerates the rupture rate and consequently cause anemia. Analysis of the model and parameter identifiability using Markov chain Monte Carlo methods is presented.

Effects of formaldehyde on the frog's mucociliary epithelium as a surrogate to evaluate air pollution effects on the respiratory epithelium

The increasing use of alcohol as an alternative fuel to gasoline or diesel can increase emission of formaldehyde, an organic gas that is irritant to the mucous membranes. The respiratory system is the major target of air pollutants and its major defense mechanism depends on the continuous activity of the cilia and the resulting constant transportation of mucous secretion. The present study was designed to evaluate the effects of formaldehyde on the ciliated epithelium through a relative large dose range around the threshold limit value adopted by the Brazilian legislation, namely 1.6 ppm (1.25 to 5 ppm). For this purpose, the isolated frog palate preparation was used as the target of toxic injury. Four groups of frog palates were exposed to diluted Ringer solution (control, N = 8) and formaldehyde diluted in Ringer solution at three different concentrations (1.25, 2.5 and 5.0 ppm, N = 10 for each group). Mucociliary clearance and ciliary beat frequency decreased significantly in contact with formaldehyde at the concentrations of 2.5 and 5.0 ppm after 60 min of exposure (P<0.05). We conclude that relatively low concentrations of formaldehyde, which is even below the Brazilian threshold limit value, are sufficient to cause short-term mucociliary impairment.

Influence of season and pollution on the antioxidant defenses of the cichlid fish acará (Geophagus brasiliensis)

The livers of Geophagus brasiliensis collected from both a non-polluted site and a polluted site were analyzed for different antioxidant defenses, O2 consumption, thiobarbituric acid-reactive substance (TBARS) levels, and histological damage. Compared to controls (116.6 ± 26.1 nmol g-1), TBARS levels were enhanced at the polluted site (284.2 ± 25.6 nmol g-1), as also was oxygen consumption (86.6 ± 11.3 and 128.5 ± 9.8 µmol O2 min-1 g-1, respectively). With respect to enzymatic antioxidants, increased catalase activities (8.7 ± 1.3 and 29.2 ± 2.4 mmol min-1 g-1, respectively), unchanged superoxide dismutase activities (767.2 ± 113.3 and 563.3 ± 70.2 U g-1, respectively), and diminished glutathione S-transferase activities (29.0 ± 3.2 and 14.9 ± 3.2 µmol min-1 g-1, respectively) were detected. Reduced glutathione (1.91 ± 0.17 and 1.37 ± 0.25 mM, respectively), oxidized glutathione (1.50 ± 0.20 and 0.73 ± 0.17 mM, respectively), and total glutathione (3.40 ± 0.26 and 2.07 ± 0.27 mM, respectively) concentrations were also below control values at the polluted site. Nevertheless, the observed ethoxyresorufin-O-deethylase activities (1.34 ± 0.11 and 16.7 ± 0.21 pmol min-1 mg-1, respectively) showed enhanced values at the polluted site. The main histological damage observed in the hepatocytes from fish collected at the polluted site was characterized by heavy lipid infiltration. Fish collected at the end of spring showed higher O2 consumption, higher superoxide dismutase and glutathione S-transferase activities, and higher total and oxidized glutathione concentrations compared to the beginning of autumn. No seasonal changes were observed in catalase activities, glutathione or TBARS levels. Fish chronically exposed to relatively high pollution levels seem to be unable to set up adequate antioxidant defenses, probably due to severe injury to their hepatocytes. The higher antioxidant defenses found at the end of spring are probably related to the enhanced activities during high temperature periods in thermoconforming organisms.

Cardiovascular control in experimental diabetes

Several studies have reported impairment in cardiovascular function and control in diabetes. The studies cited in this review were carried out from a few days up to 3 months after streptozotocin administration and were concerned with the control of the circulation. We observed that early changes (5 days) in blood pressure control by different peripheral receptors were maintained for several months. Moreover, the impairment of reflex responses observed after baroreceptor and chemoreceptor stimulation was probably related to changes in the efferent limb of the reflex arc (sympathetic and parasympathetic), but changes also in the central nervous system could not be excluded. Changes in renal sympathetic nerve activity during volume expansion were blunted in streptozotocin-treated rats, indicating an adaptive natriuretic and diuretic response in the diabetic state. The improvement of diabetic cardiovascular dysfunction induced by exercise training seems to be related to changes in the autonomic nervous system. Complementary studies about the complex interaction between circulation control systems are clearly needed to adequately address the management of pathophysiological changes associated with diabetes.

Air pollution and neonatal deaths in São Paulo, Brazil

Air pollution has been associated with health effects on different age groups. The present study was designed to assess the impact of daily changes in air pollutants (NO2, SO2, CO, O3, and particle matter (PM10)) on total number of daily neonatal deaths (those that occur between the first and the 28th days of life) in São Paulo, from January 1998 to December 2000, since adverse outcomes such as neonatal deaths associated with air pollution in Brazil have not been evaluated before. Generalized additive Poisson regression models were used and nonparametric smooth functions (loess) were adopted to control long-term trend, temperature, humidity, and short-term trends. A linear term was used for holidays. The association between air pollutants and neonatal deaths showed a short time lag. Interquartile range increases in PM10 (23.3 µg/m³) and SO2 (9.2 µg/m³) were associated with increases of 4% (95% CI, 2-6) and 6% (95% CI, 4-8), respectively. Instead of adopting a two-pollutant model we created an index to represent PM10 and SO2 effects. For an interquartile range increase in the index an increase of 6.3% (95% CI, 6.1-6.5) in neonatal deaths was observed. These results agree with previous studies performed by our group showing the deleterious effects of air pollutants during the perinatal period. The method reported here represents an alternative approach to analyze the relationship between highly correlated pollutants and public health problems, reinforcing the idea of the synergic effects of air pollutants in public health.

Temperature, air pollution, and mortality from myocardial infarction in São Paulo, Brazil

An increase in daily mortality from myocardial infarction has been observed in association with meteorological factors and air pollution in several cities in the world, mainly in the northern hemisphere. The objective of the present study was to analyze the independent effects of environmental variables on daily counts of death from myocardial infarction in a subtropical region in South America. We used the robust Poisson regression to investigate associations between weather (temperature, humidity and barometric pressure), air pollution (sulfur dioxide, carbon monoxide, and inhalable particulate), and the daily death counts attributed to myocardial infarction in the city of São Paulo in Brazil, where 12,007 fatal events were observed from 1996 to 1998. The model was adjusted in a linear fashion for relative humidity and day-of-week, while nonparametric smoothing factors were used for seasonal trend and temperature. We found a significant association of daily temperature with deaths due to myocardial infarction (P < 0.001), with the lowest mortality being observed at temperatures between 21.6 and 22.6ºC. Relative humidity appeared to exert a protective effect. Sulfur dioxide concentrations correlated linearly with myocardial infarction deaths, increasing the number of fatal events by 3.4% (relative risk of 1.03; 95% confidence interval = 1.02-1.05) for each 10 µg/m³ increase. In conclusion, this study provides evidence of important associations between daily temperature and air pollution and mortality from myocardial infarction in a subtropical region, even after a comprehensive control for confounding factors.