A nonlinear model for the long-term hydro-thermal generation scheduling problem over multiple areas with transmission constraints

This paper presents a nonlinear model with individual representation of plants for the centralized long-term hydrothermal scheduling problem over multiple areas. In addition to common aspects of long-term scheduling, this model takes transmission constraints into account. The ability to optimize hydropower exchange among multiple areas is important because it enables further minimization of complementary thermal generation costs. Also, by considering transmission constraints for long-term scheduling, a more precise coupling with shorter horizon schedules can be expected. This is an important characteristic from both operational and economic viewpoints. The proposed model is solved by a sequential quadratic programming approach in the form of a prototype system for different case studies. An analysis of the benefits provided by the model is also presented. ©2009 IEEE.

Dinâmica não linear, caos, e controle na microscopia de força atômica

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Modelagem da produção acumulada de biogás em biodigestores tipo batelada segundo a porcentagem de inóculo adicionada utilizando os modelos de regressão não-linear de Gompertz e expoencial

Pós-graduação em Agronomia (Energia na Agricultura) - FCA

Níveis de mercúrio, prolactina e interleucina 10 em mulheres em idade reprodutiva e puérperas dos municípios de Itaituba e Ananindeua, Pará

Ao mercúrio tem sido atribuída a capacidade de interferir nos sistemas orgânicos imunológico e hormonal, além dos sistemas nervoso e renal frequentemente atingidos por esse agente tóxico. Mulheres em idade fértil ou grávidas constituem um grupo vulnerável a esses efeitos, em relação a si mesmas e seus conceptos. Foi avaliada a exposição ao mercúrio (Hg) e os níveis de prolactina (PRL) e interleucina-10 (IL-10) em 144 mulheres (no pós-parto e cerca de um ano depois) de Itaituba, área sob impacto ambiental do mercúrio e em mulheres de municípios da área metropolitana de Belém, sobretudo Ananindeua, área sem impacto conhecido do mercúrio (156 puérperas e 156 não puérperas). As análises de mercúrio total (Hg-t) em sangue foram feitas por Espectrometria de Absorção Atômica por Vapor Frio. As análises séricas de PRL foram feitas por Ensaio Imunoenzimático com detecção final em fluorescência e as determinações de IL-10 foram realizadas por Ensaio Imunoenzimático de Fase Sólida. Dados demográficos e epidemiológicos foram obtidos através de questionário semi-estruturado. As puérperas de Itaituba apresentaram média de Hg-t, PRL e IL-10 de 13,93 μg/l, 276,20 ng/ml e 39,54 pg/ml, respectivamente. Nas puérperas de Ananindeua as respectivas médias foram 3,67 μg/l, 337,70 ng/ml e 4,90 pg/ml. As mulheres não puérperas de Itaituba apresentaram média de Hg-t de 12,68 μg/l, média de PRL de 30,75 ng/ml e média de IL-10 de 14,20 pg/ml. As médias de Hg-t, PRL e IL-10 das mulheres de Ananindeua foram 2,73 μg/l, 17,07 ng/ml e 1,49 pg/ml, respectivamente. Os níveis de Hg-t, PRL e IL-10 foram maiores em Itaituba (p<0,0001), exceto em relação à PRL das puérperas, maior em Ananindeua. Os níveis semelhantes de Hg-t nas duas avaliações das mulheres de Itaituba (p=0,7056) e a correlação moderada sugerem continuidade da exposição (r=0,4736, p<0,0001). A principal variável preditora dos níveis de mercúrio foi o consumo de peixe nos modelos de regressão múltipla linear e logística. A paridade e os níveis de IL-10 apresentaram associação positiva com a PRL nas puérperas de Itaituba e o peso do recém-nascido e a IL-10, associação positiva com a PRL em puérperas de Ananindeua. A IL-10 apresentou associação negativa com a PRL nas mulheres não puérperas de Itaituba (p=0,0270) e positiva nas mulheres de Ananindeua (p=0,0266). Os níveis de Hg-t estavam associados negativamente com a PRL nas puérperas (p=0,0460) e positivamente com o trabalho em garimpo (p=0,0173) (este também importante para as não puérperas) em Itaituba, segundo os modelos logísticos. A IL-10 esteve associada positivamente à morbidade recente nas puérperas de Itaituba (p=0,0210), negativamente ao consumo de bebida alcoólica (p=0,0178) e positivamente ao trabalho em garimpo nas mulheres não puérperas (p=0,0199). A exposição crônica ao Hg das mulheres de Itaituba, a diferença nos níveis dos fatores imunoendócrinos avaliados em relação às mulheres não expostas e a associação com variáveis epidemiológicas relevantes, sugerem a possibilidade de impactos da exposição no perfil imunoendócrino das mulheres de Itaituba, chamando atenção para a importância da vigilância da saúde dessa população e o possível uso de bioindicadores como a PRL em sua avaliação.

Nonlinear dynamics and control strategies: On a energy harvester vibrating system with a linear form to non-ideal motor torque

In this paper, we deal with the research of a vibrating model of an energy harvester device, including the nonlinearities in the model of the piezoelectric coupling and the non-ideal excitation. We show, using numerical simulations, in the analysis of the dynamic responses, that the harvested power is influenced by non-linear vibrations of the structure. Chaotic behavior was also observed, causing of the loss of energy throughout the simulation time. Using a perturbation technique, we find an approximate analytical solution for the non-ideal system. Then, we apply both two control techniques, to keep the considered system, into a stable condition. Both the State Dependent Ricatti Equation (SDRE) control as the feedback control by changing the energy of the oscillator, were efficient in controlling of the considered non-ideal system.

The use of artificial neural networks in analysing the nutritional ecology of Chrysomya megacephala (F.) (Diptera: Calliphoridae), compared with a statistical model

Artificial neural networks (ANNs) have been widely applied to the resolution of complex biological problems. An important feature of neural models is that their implementation is not precluded by the theoretical distribution shape of the data used. Frequently, the performance of ANNs over linear or non-linear regression-based statistical methods is deemed to be significantly superior if suitable sample sizes are provided, especially in multidimensional and non-linear processes. The current work was aimed at utilising three well-known neural network methods in order to evaluate whether these models would be able to provide more accurate outcomes in relation to a conventional regression method in pupal weight predictions of Chrysomya megacephala, a species of blowfly (Diptera: Calliphoridae), using larval density (i.e. the initial number of larvae), amount of available food and pupal size as input data. It was possible to notice that the neural networks yielded more accurate performances in comparison with the statistical model (multiple regression). Assessing the three types of networks utilised (Multi-layer Perceptron, Radial Basis Function and Generalised Regression Neural Network), no considerable differences between these models were detected. The superiority of these neural models over a classical statistical method represents an important fact, because more accurate models may clarify several intricate aspects concerning the nutritional ecology of blowflies.

Previsão de demanda de veículos automotores através do modelo de regressão linear múltipla

This study aimed to model a equation for the demand of automobiles and light commercial vehicles, based on the data from February 2007 to July 2014, through a multiple regression analysis. The literature review consists of an information collection of the history of automotive industry, and it has contributed to the understanding of the current crisis that affects this market, which consequence was a large reduction in sales. The model developed was evaluated by a residual analysis and also was used an adhesion test - F test - with a significance level of 5%. In addition, a coefficient of determination (R2) of 0.8159 was determined, indicating that 81.59% of the demand for automobiles and light commercial vehicles can be explained by the regression variables: interest rate, unemployment rate, broad consumer price index (CPI), gross domestic product (GDP) and tax on industrialized products (IPI). Finally, other ten samples, from August 2014 to May 2015, were tested in the model in order to validate its forecasting quality. Finally, a Monte Carlo Simulation was run in order to obtain a distribution of probabilities of future demands. It was observed that the actual demand in the period after the sample was in the range that was most likely to occur, and that the GDP and the CPI are the variable that have the greatest influence on the developed model

Previsão de demanda de veículos automotores através do modelo de regressão linear múltipla

This study aimed to model a equation for the demand of automobiles and light commercial vehicles, based on the data from February 2007 to July 2014, through a multiple regression analysis. The literature review consists of an information collection of the history of automotive industry, and it has contributed to the understanding of the current crisis that affects this market, which consequence was a large reduction in sales. The model developed was evaluated by a residual analysis and also was used an adhesion test - F test - with a significance level of 5%. In addition, a coefficient of determination (R2) of 0.8159 was determined, indicating that 81.59% of the demand for automobiles and light commercial vehicles can be explained by the regression variables: interest rate, unemployment rate, broad consumer price index (CPI), gross domestic product (GDP) and tax on industrialized products (IPI). Finally, other ten samples, from August 2014 to May 2015, were tested in the model in order to validate its forecasting quality. Finally, a Monte Carlo Simulation was run in order to obtain a distribution of probabilities of future demands. It was observed that the actual demand in the period after the sample was in the range that was most likely to occur, and that the GDP and the CPI are the variable that have the greatest influence on the developed model

Linear matrix inequality-based robust model predictive control for time-delayed systems

This work addresses the solution to the problem of robust model predictive control (MPC) of systems with model uncertainty. The case of zone control of multi-variable stable systems with multiple time delays is considered. The usual approach of dealing with this kind of problem is through the inclusion of non-linear cost constraint in the control problem. The control action is then obtained at each sampling time as the solution to a non-linear programming (NLP) problem that for high-order systems can be computationally expensive. Here, the robust MPC problem is formulated as a linear matrix inequality problem that can be solved in real time with a fraction of the computer effort. The proposed approach is compared with the conventional robust MPC and tested through the simulation of a reactor system of the process industry.

Cognitive function in patients with chronic pain treated with opioids: characteristics and associated factors

Background The paucity of studies regarding cognitive function in patients with chronic pain, and growing evidence regarding the cognitive effects of pain and opioids on cognitive function prompted us to assess cognition via neuropsychological measurement in patients with chronic non-cancer pain treated with opioids. Methods In this cross-sectional study, 49 patients were assessed by Continuous Reaction Time, Finger Tapping, Digit Span, Trail Making Test-B and Mini-mental State Examination tests. Linear regressions were applied. Results Patients scored poorly in the Trail Making Test-B (mean?=?107.6?s, SD?=?61.0, cut-off?=?91?s); and adequately on all other tests. Several associations among independent variables and cognitive tests were observed. In the multiple regression analyses, the variables associated with statistically significant poor cognitive performance were female sex, higher age, lower annual income, lower schooling, anxiety, depression, tiredness, lower opioid dose, and more than 5?h of sleep the night before assessment (P?<?0.05). Conclusions Patients with chronic pain may have cognitive dysfunction related to some reversible factors, which can be optimized by therapeutic interventions.

The association between coronary artery calcification progression and loss of bone density in non-dialyzed CKD patients

Background: Coronary artery calcification (CAC) and low bone density are coexisting deleterious conditions commonly shared by chronic kidney disease (CKD) patients. In the present study, we aimed to investigate whether the progression of CAC was associated with overtime reduction in bone density in non-dialyzed CKD patients. Methods: This is a prospective study of 24 months including 72 non-dialyzed CKD patients Stages 2 - 4 (age 57.6 +/- 10.3 years, 62% male, 22% diabetics). CAC and vertebral bone density (VBD) were measured by computed tomography. Results: At baseline, 46% of the patients had CAC (calcified group) and calcification was not identified in 54% of the patients (non-calcified group). The calcified group was older, predominantly male, and had lower VBD in comparison to non-calcified group. CAC progression was observed only in the calcified group (91% of the patients increased calcium score). The multiple regression analysis revealed loss of VBD as the independent determinant of CAC progression in these patients. Conclusion: CAC progression was associated with loss of VBD in non-dialyzed CKD patients.

Multi-objective evolutionary algorithm for single and multiple fault service restoration in large-scale distribution systems

Network reconfiguration for service restoration (SR) in distribution systems is a complex optimization problem. For large-scale distribution systems, it is computationally hard to find adequate SR plans in real time since the problem is combinatorial and non-linear, involving several constraints and objectives. Two Multi-Objective Evolutionary Algorithms that use Node-Depth Encoding (NDE) have proved able to efficiently generate adequate SR plans for large distribution systems: (i) one of them is the hybridization of the Non-Dominated Sorting Genetic Algorithm-II (NSGA-II) with NDE, named NSGA-N; (ii) the other is a Multi-Objective Evolutionary Algorithm based on subpopulation tables that uses NDE, named MEAN. Further challenges are faced now, i.e. the design of SR plans for larger systems as good as those for relatively smaller ones and for multiple faults as good as those for one fault (single fault). In order to tackle both challenges, this paper proposes a method that results from the combination of NSGA-N, MEAN and a new heuristic. Such a heuristic focuses on the application of NDE operators to alarming network zones according to technical constraints. The method generates similar quality SR plans in distribution systems of significantly different sizes (from 3860 to 30,880 buses). Moreover, the number of switching operations required to implement the SR plans generated by the proposed method increases in a moderate way with the number of faults.

Analysis of cumulative effects of multiple stressors on saltmarshes and consideration of management options

Natural systems face pressures exerted by natural physical-chemical forcings and a myriad of co-occurring human stressors that may interact to cause larger than expected effects, thereby presenting a challenge to ecosystem management. This thesis aimed to develop new information that can contribute to reduce the existing knowledge gaps hampering the holistic management of multiple stressors. I undertook a review of the state-of-the-art methods to detect, quantify and predict stressor interactions, identifying techniques that could be applied in this thesis research. Then, I conducted a systematic review of saltmarsh multiple stressor studies in conjunction with a multiple stressor mapping exercise for the study system in order to infer potential important synergistic stressor interactions. This analysis identified key stressors that are affecting the study system, but also pointed to data gaps in terms of driver and pressure data and raised issues for potentially overlooked stressors. Using field mesocosms, I explored how a local stressor (nutrient availability) affects the responses of saltmarsh vegetation to a global stressor (increased inundation) in different soil types. Results indicate that saltmarsh vegetation would be more drastically affected by increased inundation in low than in medium organic matter soils, and especially in estuaries already under high nutrient availability. In another field experiment, I examined the challenges of managing co-occurring and potentially interacting local stressors on saltmarsh vegetation: recreational trampling and smothering by deposition of excess macroalgal wrack due to high nutrient loads. Trampling and wrack prevention had interacting effects, causing non-linear responses of the vegetation to simulated management of these stressors, such that vegetation recovered only in those treatments simulating the combined prevention of both stressors. During this research I detected, using molecular genetic methods, a widespread presence of S. anglica (and to a lesser extent S. townsendii), two previously unrecorded non-native Spartinas in the study areas.

Analysis of the erosive effect of different dietary substances and medications

Excessive consumption of acidic drinks and foods contributes to tooth erosion. The aims of the present in vitro study were twofold: (1) to assess the erosive potential of different dietary substances and medications; (2) to determine the chemical properties with an impact on the erosive potential. We selected sixty agents: soft drinks, an energy drink, sports drinks, alcoholic drinks, juice, fruit, mineral water, yogurt, tea, coffee, salad dressing and medications. The erosive potential of the tested agents was quantified as the changes in surface hardness (ΔSH) of enamel specimens within the first 2 min (ΔSH2-0 = SH2 min - SHbaseline) and the second 2 min exposure (ΔSH4-2 = SH4 min - SH2 min). To characterise these agents, various chemical properties, e.g. pH, concentrations of Ca, Pi and F, titratable acidity to pH 7·0 and buffering capacity at the original pH value (β), as well as degree of saturation (pK - pI) with respect to hydroxyapatite (HAP) and fluorapatite (FAP), were determined. Erosive challenge caused a statistically significant reduction in SH for all agents except for coffee, some medications and alcoholic drinks, and non-flavoured mineral waters, teas and yogurts (P < 0·01). By multiple linear regression analysis, 52 % of the variation in ΔSH after 2 min and 61 % after 4 min immersion were explained by pH, β and concentrations of F and Ca (P < 0·05). pH was the variable with the highest impact in multiple regression and bivariate correlation analyses. Furthermore, a high bivariate correlation was also obtained between (pK - pI)HAP, (pK - pI)FAP and ΔSH.

Gauss-Seidel Estimation of Generalized Linear Mixed Models with Application to Poisson Modeling of Spatially Varying Disease Rates

Generalized linear mixed models (GLMMs) provide an elegant framework for the analysis of correlated data. Due to the non-closed form of the likelihood, GLMMs are often fit by computational procedures like penalized quasi-likelihood (PQL). Special cases of these models are generalized linear models (GLMs), which are often fit using algorithms like iterative weighted least squares (IWLS). High computational costs and memory space constraints often make it difficult to apply these iterative procedures to data sets with very large number of cases. This paper proposes a computationally efficient strategy based on the Gauss-Seidel algorithm that iteratively fits sub-models of the GLMM to subsetted versions of the data. Additional gains in efficiency are achieved for Poisson models, commonly used in disease mapping problems, because of their special collapsibility property which allows data reduction through summaries. Convergence of the proposed iterative procedure is guaranteed for canonical link functions. The strategy is applied to investigate the relationship between ischemic heart disease, socioeconomic status and age/gender category in New South Wales, Australia, based on outcome data consisting of approximately 33 million records. A simulation study demonstrates the algorithm's reliability in analyzing a data set with 12 million records for a (non-collapsible) logistic regression model.