Operational performance of the mechanized and semi-mechanized potato harvest

Potato is an important crop plant throughout the world. Harvesting is a fundamental step in its production system. Maybe, it is the most complex and expensive operation. Thus, the objective of this work was to compare the cost of the mechanized and semi-mechanized harvest, the operational capacity and the production losses during the potato harvest process. The work was accomplished in a commercial farming, cultivated under pivot system, in the municipal district of Perdizes - MG, Brazil. A completely randomized design with two treatments was used: mechanized and semi-mechanized harvest. The mechanized harvest used a self-propelled harvester. In the semi-automated harvest, a digger mounted on tractor was used and the potato was manually harvested. It was concluded that the cost of mechanized harvest was 49.03% lower than the cost of semi-mechanized harvest. On average, the harvester had a work for 23 workers in manual harvest. Mechanized harvest showed losses of 2.35% of potato yield, while the semi-mechanized harvest showed losses of 6.32%.

Regulated deficit irrigation and tommy atkins mango orchard productivity under microsprinkling in brazilian semi arid

This study aimed to test controlled levels of water deficiency in soil in mango trees, under microsprinkling irrigation, in semi-arid conditions, and to evaluate its effect in the productivity and fruits quality. The deficits were applied in the phases I, II and III of growth of the fruit, during the productive cycles of the mango tree in 2006 and 2007. The experiment in both cases was arranged in an entirely random design with 10 treatments and 3 repetitions, in the year I, and with 8 treatments and 3 repetitions in the year II. The values of soil water potential, of the treatments submitted to regulated deficit irrigation (RDI), were placed in the range of 0 to -0.011 MPa, showing that the soil humidity varied between the saturation and the field capacity, not characterizing deficit water condition. The average values of stem water potential (Ψstem) varied between -0.90 and -1.74 MPa, evidencing significant effect (p <0.05) just for T1 (without irrigation), T7 and T8 (RDI with 30% of the ETc in the phases II and III, respectively). Through the variance analysis, significant differences were not verified among productivity, number of fruits per plant and size of the fruit, in none of the experiments, what indicates the possibility of reduction of the water use in the irrigation of the mango tree without significant losses of productivity and fruit quality.

Broiler breeder hen performance and egg hatchability during rainy season in Brazilian semi-arid region

Henhouse thermal comfort and bird age influence broiler breeder hen productivity; this way, the present research aimed to assess thermal comfort indexes and broiler breeder hen performance during winter in North East semi-arid region from Brazil, using a Cobb Avian 48 broiler strain at the age of 38 and 50 weeks. We measured the following bioclimatic indexes: air temperature and relative humidity, black globe temperature and humidity, CO2 concentration and sound pressure levels at henhouse facility. Data analysis design was complete randomized in a 3 x 3 factorial scheme with three treatments (months) and three replications (week number of collection by month). A number of 25 climatic data were observed. Bioclimatic indexes at the most warm hours throughout the day promoted bird discomfort yet sound pressure and carbon dioxide levels were under the Brazilian standards. Even in thermal comfort zone at some hours, the productivity indexes were in compliance with those recommended by the strain creation guidebook.

Novel Methods for Error Modeling and Parameter Identification of a Redundant Serial-Parallel Hybrid Robot

To obtain the desirable accuracy of a robot, there are two techniques available. The first option would be to make the robot match the nominal mathematic model. In other words, the manufacturing and assembling tolerances of every part would be extremely tight so that all of the various parameters would match the “design” or “nominal” values as closely as possible. This method can satisfy most of the accuracy requirements， but the cost would increase dramatically as the accuracy requirement increases. Alternatively, a more cost-effective solution is to build a manipulator with relaxed manufacturing and assembling tolerances. By modifying the mathematical model in the controller, the actual errors of the robot can be compensated. This is the essence of robot calibration. Simply put, robot calibration is the process of defining an appropriate error model and then identifying the various parameter errors that make the error model match the robot as closely as possible. This work focuses on kinematic calibration of a 10 degree-of-freedom (DOF) redundant serial-parallel hybrid robot. The robot consists of a 4-DOF serial mechanism and a 6-DOF hexapod parallel manipulator. The redundant 4-DOF serial structure is used to enlarge workspace and the 6-DOF hexapod manipulator is used to provide high load capabilities and stiffness for the whole structure. The main objective of the study is to develop a suitable calibration method to improve the accuracy of the redundant serial-parallel hybrid robot. To this end, a Denavit–Hartenberg (DH) hybrid error model and a Product-of-Exponential (POE) error model are developed for error modeling of the proposed robot. Furthermore, two kinds of global optimization methods, i.e. the differential-evolution (DE) algorithm and the Markov Chain Monte Carlo (MCMC) algorithm, are employed to identify the parameter errors of the derived error model. A measurement method based on a 3-2-1 wire-based pose estimation system is proposed and implemented in a Solidworks environment to simulate the real experimental validations. Numerical simulations and Solidworks prototype-model validations are carried out on the hybrid robot to verify the effectiveness, accuracy and robustness of the calibration algorithms.

Experimental poisoning by Senecio brasiliensis in calves: quantitative and semi-quantitative study on changes in the hepatic extracellular matrix and sinusoidal cells

Extracellular matrix plays an important role in chronic hepatic lesions and has been studied in experimental intoxication models. However in cattle, studies on chronic disease have focused on the hepatocellular damage and extracellular matrix (ECM) changes are usually overlooked. There are no specific studies on the hepatic ECM in either normal or chronically damaged bovine liver. Thus an experimental model of hepatic toxicity model using Senecio brasiliensis poisoned calves was designed. Senecio brasiliensis contains pyrrolizidine alkaloids which cause either acute or chronic progressive dose dependent liver damage. Five calves were orally fed with 0.38g of dry leaves of S. brasiliensis/kg/day for 24 days. Liver needle biopsy specimens were obtained every 15 days for 60 days. Clinical signs of digestive complications appeared at 3rd week. One calf died on 45th day and four were evaluated up to 60th day. Biopsy samples were processed for routine light microscopy, immuno-histochemistry and transmission electron microscopy. From 30th day on progressive liver damage characterized by hepatocellular ballooning, necrosis, apoptosis and megalocytosis, centrilobular, pericellular and portal fibrosis were seen by light microscopy. Quantitative and semi-quantitative measurements of hepatic ECM components were performed before and after the onset of lesions. Morphometric analysis of total collagen and elastic fiber system was conducted. Total collagen and I and III collagen types progressively increased in throughout the liver of affected calves. Changes in location, amount and disposition of the elastic fiber system were also observed. Then numbers of Kupffer cells were significantly increased at 30th day and total numbers of sinusoidal cells were significantly increased at 45th and 60th days. Liver damage was progressive and irreversible even after the exposure to the plant was discontinued. Severe fibrotic lesions occurred mainly in portal tracts, followed by veno-occlusive and pericellular fibrosis. Collagen types I and III s were present in every normal and damaged liver, with predominance of type I. In affected calves the increase of total collagen and elastic fibers system paralleled the number of total sinusoidal cells.

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.

Weed community and growth under the canopy of trees adapted to the brazilian semi-arid region

The objectives of this work were to evaluate the floristic composition and dry biomass of weeds under the canopy of seven perennial species adapted to the Semi-Arid region of Brazil, and correlate these characteristics with growth traits of the perennial species. The following perennial species were evaluated in two experiments (E1 and E2): mesquite (Prosopis juliflora), jucá (Caesalpinia ferrea), white popinac (Leucaena leucocephala), mofumbo (Combretum leprosum), neem (Azadirachata indica), sabiá (Mimosa caesalpiniaefolia) and tamarind (Tamarindus indica). In E1, the seven species were evaluated in a random block design with four replicates and nine plants per plot. In E2, evaluation comprised four species (mesquite, jucá, white popinac, and tamarind) in a random block design with eight replicates and nine plants per plot. A circle with an area of 1.77 m² was established around the trunk of each plant, two years after they were transplanted to the permanent location. The weeds collected within this circle were cut even with the ground, classified and weighed. At this time, plant height, and crown and stem diameters were evaluated in all trees of each plot. In E1 there were no differences between tree species as to weed frequency under their canopies; however, weed growth was smaller under the canopy of sabiá trees. Mesquite and sabiá had the greatest plant height and crown diameter means, but only sabiá had the greatest stem diameter. In E2, the perennial species were not different with regard to weed frequency and growth under their canopies, but mesquite had the greatest growth, as measured by plant height (with significant results for jucá as well) and crown and stem diameter.

Excessive functions, Appell polynomials and optimal stopping

The main topic of the thesis is optimal stopping. This is treated in two research articles. In the first article we introduce a new approach to optimal stopping of general strong Markov processes. The approach is based on the representation of excessive functions as expected suprema. We present a variety of examples, in particular, the Novikov-Shiryaev problem for Lévy processes. In the second article on optimal stopping we focus on differentiability of excessive functions of diffusions and apply these results to study the validity of the principle of smooth fit. As an example we discuss optimal stopping of sticky Brownian motion. The third research article offers a survey like discussion on Appell polynomials. The crucial role of Appell polynomials in optimal stopping of Lévy processes was noticed by Novikov and Shiryaev. They described the optimal rule in a large class of problems via these polynomials. We exploit the probabilistic approach to Appell polynomials and show that many classical results are obtained with ease in this framework. In the fourth article we derive a new relationship between the generalized Bernoulli polynomials and the generalized Euler polynomials.

Structure and dynamics of a woody plant community of a tropical semi-deciduous seasonal forest in the "Estação Ecológica do Panga", municipality of Uberlândia, Minas Gerais, Brazil

Studies of plant community dynamics are essential in understanding the demographic patterns of species since changes in demographic rates can affect the floristic composition and future structure. The purpose of the present study was to analyze the changes in the community structure and floristic composition of woody plants in a tropical semi-deciduous forest in Uberlândia in central Brazil, during a 10-years period. The data were collected in 1989 and in 2000 in 50 quadrats (10 m x 10 m) where all trees with a minimum circumference at breast height of 10 cm were sampled. In 1989, 93 species and 1103 individuals were registered. Over a period of 10 years, seven new species were added to the community, although eight disappeared. The main change that occurred during this period in the floristic composition was the replacement of savannah species occurring in forest gaps by those from the forest understory.

Photosynthetic responses of four tropical tree species grown under gap and understorey conditions in a semi-deciduous forest

Leaf CO2 assimilation (A) as a function of photosynthetic photon flux density (Q) or intercellular CO2 concentration (Ci) and chlorophyll fluorescence measurements were carried out on four tropical woody species growing in forest gap and understorey (Bauhinia forficata Link. and Guazuma ulmifolia Lam. as pioneers, and Hymenaea courbaril L. and Esenbeckia leiocarpa Engl. as non-pioneers). Chlorophyll fluorescence indicated similar acclimation capacities of photochemical apparatus to contrasting light environments irrespective to plant species. Maximum CO2 assimilation and quantum yield derived from A/Q curves indicated higher photosynthetic capacity in pioneer than in non-pioneer species in forest gap. However, the differences among species did not show a straightforward relation with their successional status regarding data derived from A/Q curves under understorey conditions. Both successional groups are able to sustain positive carbon balance under contrasting natural light availabilities, modifying photochemical and biochemical photosynthetic traits with similar phenotypic plasticity capacity.

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.

Optimization and Measuring Techniques for Collect-and-Place Machines in Printed Circuit Board Industry

This thesis considers optimization problems arising in printed circuit board assembly. Especially, the case in which the electronic components of a single circuit board are placed using a single placement machine is studied. Although there is a large number of different placement machines, the use of collect-and-place -type gantry machines is discussed because of their flexibility and increasing popularity in the industry. Instead of solving the entire control optimization problem of a collect-andplace machine with a single application, the problem is divided into multiple subproblems because of its hard combinatorial nature. This dividing technique is called hierarchical decomposition. All the subproblems of the one PCB - one machine -context are described, classified and reviewed. The derived subproblems are then either solved with exact methods or new heuristic algorithms are developed and applied. The exact methods include, for example, a greedy algorithm and a solution based on dynamic programming. Some of the proposed heuristics contain constructive parts while others utilize local search or are based on frequency calculations. For the heuristics, it is made sure with comprehensive experimental tests that they are applicable and feasible. A number of quality functions will be proposed for evaluation and applied to the subproblems. In the experimental tests, artificially generated data from Markov-models and data from real-world PCB production are used. The thesis consists of an introduction and of five publications where the developed and used solution methods are described in their full detail. For all the problems stated in this thesis, the methods proposed are efficient enough to be used in the PCB assembly production in practice and are readily applicable in the PCB manufacturing industry.

Regime-Switching in the Impact of Oil Price Shocks on Stock Market Volatility: Evidence from Oil-Importing and Oil-Exporting Countries

Research has highlighted the adequacy of Markov regime-switching model to address dynamic behavior in long term stock market movements. Employing a purposed Extended regime-switching GARCH(1,1) model, this thesis further investigates the regime dependent nonlinear relationship between changes in oil price and stock market volatility in Saudi Arabia, Norway and Singapore for the period of 2001-2014. Market selection is prioritized to national dependency on oil export or import, which also rationalizes the fitness of implied bivariate volatility model. Among two regimes identified by the mean model, high stock market return-low volatility regime reflects the stable economic growth periods. The other regime characterized by low stock market return-high volatility coincides with episodes of recession and downturn. Moreover, results of volatility model provide the evidence that shocks in stock markets are less persistent during the high volatility regime. While accelerated oil price rises the stock market volatility during recessions, it reduces the stock market risk during normal growth periods in Singapore. In contrast, oil price showed no significant notable impact on stock market volatility of target oil-exporting countries in either of the volatility regime. In light to these results, international investors and policy makers could benefit the risk management in relation to oil price fluctuation.

Manual and semi-automatic quantification of in vivo ¹H-MRS data for the classification of human primary brain tumors

In vivo proton magnetic resonance spectroscopy (¹H-MRS) is a technique capable of assessing biochemical content and pathways in normal and pathological tissue. In the brain, ¹H-MRS complements the information given by magnetic resonance images. The main goal of the present study was to assess the accuracy of ¹H-MRS for the classification of brain tumors in a pilot study comparing results obtained by manual and semi-automatic quantification of metabolites. In vivo single-voxel ¹H-MRS was performed in 24 control subjects and 26 patients with brain neoplasms that included meningiomas, high-grade neuroglial tumors and pilocytic astrocytomas. Seven metabolite groups (lactate, lipids, N-acetyl-aspartate, glutamate and glutamine group, total creatine, total choline, myo-inositol) were evaluated in all spectra by two methods: a manual one consisting of integration of manually defined peak areas, and the advanced method for accurate, robust and efficient spectral fitting (AMARES), a semi-automatic quantification method implemented in the jMRUI software. Statistical methods included discriminant analysis and the leave-one-out cross-validation method. Both manual and semi-automatic analyses detected differences in metabolite content between tumor groups and controls (P < 0.005). The classification accuracy obtained with the manual method was 75% for high-grade neuroglial tumors, 55% for meningiomas and 56% for pilocytic astrocytomas, while for the semi-automatic method it was 78, 70, and 98%, respectively. Both methods classified all control subjects correctly. The study demonstrated that ¹H-MRS accurately differentiated normal from tumoral brain tissue and confirmed the superiority of the semi-automatic quantification method.

Novel control, haptic and calibration methods for teleoperated electrohydraulic servo systems

The aim of this thesis is to propose a novel control method for teleoperated electrohydraulic servo systems that implements a reliable haptic sense between the human and manipulator interaction, and an ideal position control between the manipulator and the task environment interaction. The proposed method has the characteristics of a universal technique independent of the actual control algorithm and it can be applied with other suitable control methods as a real-time control strategy. The motivation to develop this control method is the necessity for a reliable real-time controller for teleoperated electrohydraulic servo systems that provides highly accurate position control based on joystick inputs with haptic capabilities. The contribution of the research is that the proposed control method combines a directed random search method and a real-time simulation to develop an intelligent controller in which each generation of parameters is tested on-line by the real-time simulator before being applied to the real process. The controller was evaluated on a hydraulic position servo system. The simulator of the hydraulic system was built based on Markov chain Monte Carlo (MCMC) method. A Particle Swarm Optimization algorithm combined with the foraging behavior of E. coli bacteria was utilized as the directed random search engine. The control strategy allows the operator to be plugged into the work environment dynamically and kinetically. This helps to ensure the system has haptic sense with high stability, without abstracting away the dynamics of the hydraulic system. The new control algorithm provides asymptotically exact tracking of both, the position and the contact force. In addition, this research proposes a novel method for re-calibration of multi-axis force/torque sensors. The method makes several improvements to traditional methods. It can be used without dismantling the sensor from its application and it requires smaller number of standard loads for calibration. It is also more cost efficient and faster in comparison to traditional calibration methods. The proposed method was developed in response to re-calibration issues with the force sensors utilized in teleoperated systems. The new approach aimed to avoid dismantling of the sensors from their applications for applying calibration. A major complication with many manipulators is the difficulty accessing them when they operate inside a non-accessible environment; especially if those environments are harsh; such as in radioactive areas. The proposed technique is based on design of experiment methodology. It has been successfully applied to different force/torque sensors and this research presents experimental validation of use of the calibration method with one of the force sensors which method has been applied to.