Estudo de efeitos ambientais que influem na forma da curva de lactação de cabras mestiças no estado da Paraíba

Conselho Nacional do Desenvolvimento Científico e Tecnológico (CNPq)

Thermodynamic properties applied to the sorption isotherms of kiwifruit

The net isosteric heat and entropy of water sorption were calculated for kiwifruit, based on sorption isotherms obtained by the static gravimetric method at different temperatures (20 to 70 degreesC). The Guggenheim-Anderson-deBoer equation was fitted to the experimental data, using direct non-linear regression analysis; the agreement between experimental and calculated values was satisfactory. The net isosteric heat of sorption was estimated from equilibrium sorption data, using the Clausius-Clapeyron equation. Isosteric heats of sorption were found to increase with increasing temperature and could be well adjusted by an exponential relationship. The enthalpy-entropy compensation theory was applied to sorption isotherms and plots of DeltaH versus DeltaS provided the isokinetic temperature, T-B = 450.9 +/- 7.7 K, indicating an enthalpy-controlled desorption process over the whole range of moisture content considered.

Eco-physiological aspects of melaleuca seed germination

The invasive behavior of melaleuca (Melaleuca quinquenervia) plants in wetlands is due to its aggressive regeneration strategy, which is based on its seeds germination performance. Understanding of the eco-physiological aspects of the seed germination in melaleuca plants may significantly contribute for the development of management strategies. The objective of this research was to learn how the germination of M. quinquenervia seeds are affected by light and temperature. Melaleuca seeds were placed on filter paper moistened with 12 ml of distilled water at temperatures between 10 and 45°C. Germination was evaluated in dark and light conditions. Seed germination, first count of seed germination (seven days), germination speed index and germination mean time were determined up to 40 days after seeding, when germination had ceased in most of the treatments. After that period, the seeds were transferred to conditions of 30°C and light, which was found to be ideal in the previous phase. Seed germination was daily evaluated up to 63 days when it was again observed no longer to occur. The treatment repetitions were distributed in the growth-chamber according to a completely randomized design in a factorial scheme (eight temperatures x two light conditions) and four repetitions. The data were submitted to analysis of variance with the F test and the means were adjusted to polynomial and non linear regression models. The highest seed germination performance was observed to take place under conditions of 27.3°C with light. The temperatures of 35 and 40°C in the dark induced thermal inhibition of seed germination. The temperature of 45°C was lethal to the seeds.

Estimativas da condutividade térmica dos minerais e rochas e influência de parâmetros térmicos e petrofísicos na resistividade aparente da formação

O presente estudo realiza estimativas da condutividade térmica dos principais minerais formadores de rochas, bem como estimativas da condutividade média da fase sólida de cinco litologias básicas (arenitos, calcários, dolomitos, anidritas e litologias argilosas). Alguns modelos térmicos foram comparados entre si, possibilitando a verificação daquele mais apropriado para representar o agregado de minerais e fluidos que compõem as rochas. Os resultados obtidos podem ser aplicados a modelamentos térmicos os mais variados. A metodologia empregada baseia-se em um algoritmo de regressão não-linear denominado de Busca Aleatória Controlada. O comportamento do algoritmo é avaliado para dados sintéticos antes de ser usado em dados reais. O modelo usado na regressão para obter a condutividade térmica dos minerais é o modelo geométrico médio. O método de regressão, usado em cada subconjunto litológico, forneceu os seguintes valores para a condutividade térmica média da fase sólida: arenitos 5,9 ± 1,33 W/mK, calcários 3.1 ± 0.12 W/mK, dolomitos 4.7 ± 0.56 W/mK, anidritas 6.3 ± 0.27 W/mK e para litologias argilosas 3.4 ± 0.48 W/mK. Na sequência, são fornecidas as bases para o estudo da difusão do calor em coordenadas cilíndricas, considerando o efeito de invasão do filtrado da lama na formação, através de uma adaptação da simulação de injeção de poços proveniente das teorias relativas à engenharia de reservatório. Com isto, estimam-se os erros relativos sobre a resistividade aparente assumindo como referência a temperatura original da formação. Nesta etapa do trabalho, faz-se uso do método de diferenças finitas para avaliar a distribuição de temperatura poço-formação. A simulação da invasão é realizada, em coordenadas cilíndricas, através da adaptação da equação de Buckley-Leverett em coordenadas cartesianas. Efeitos como o aparecimento do reboco de lama na parede do poço, gravidade e pressão capilar não são levados em consideração. A partir das distribuições de saturação e temperatura, obtém-se a distribuição radial de resistividade, a qual é convolvida com a resposta radial da ferramenta de indução (transmissor-receptor) resultando na resistividade aparente da formação. Admitindo como referência a temperatura original da formação, são obtidos os erros relativos da resistividade aparente. Através da variação de alguns parâmetros, verifica-se que a porosidade e a saturação original da formação podem ser responsáveis por enormes erros na obtenção da resistividade, principalmente se tais "leituras" forem realizadas logo após a perfuração (MWD). A diferença de temperatura entre poço e formação é a principal causadora de tais erros, indicando que em situações onde esta diferença de temperatura seja grande, perfilagens com ferramentas de indução devam ser realizadas de um a dois dias após a perfuração do poço.

Influência da temperatura nos parâmetros de modelos bi-paramétricos que predizem isotermas de adsorção de umidade do guaraná (Paullinia cupana) em pó

Oito modelos matemáticos bi-paramétricos, existentes na literatura e com larga aplicação na predição de isotermas de adsorção foram submetidos à análise. O guaraná (Paullinia cupana) em pó objeto deste estudo foi obtido em "spray dryer", a partir de um extrato hidroalcoólico. Ajustaram-se os pontos experimentais das isotermas de adsorção de umidade do produto à 15°C, 25°C e 35°C, por análise de regressão não-linear. Para estudar o efeito da temperatura nos parâmetros dos modelos utilizaram-se regressões dos tipos: linear, exponencial, logarítmica e inversa. Utilizou-se para fazer os ajustes o aplicativo STATGRAPHICS 5.1. Entre os modelos testados os que apresentam melhores resultados foram as equações de Handerson, Oswin e Mizrahi.

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.

Muscular and pulmonary O2 uptake kinetics during moderate- and high-intensity sub-maximal knee-extensor exercise in humans

[EN] The purpose of this investigation was to determine the contribution of muscle O(2) consumption (mVO2) to pulmonary O(2) uptake (pVO2) during both low-intensity (LI) and high-intensity (HI) knee-extension exercise, and during subsequent recovery, in humans. Seven healthy male subjects (age 20-25 years) completed a series of LI and HI square-wave exercise tests in which mVO2 (direct Fick technique) and pVO2 (indirect calorimetry) were measured simultaneously. The mean blood transit time from the muscle capillaries to the lung (MTTc-l) was also estimated (based on measured blood transit times from femoral artery to vein and vein to artery). The kinetics of mVO2 and pVO2 were modelled using non-linear regression. The time constant (tau) describing the phase II pVO2 kinetics following the onset of exercise was not significantly different from the mean response time (initial time delay + tau) for mVO2 kinetics for LI (30 +/- 3 vs 30 +/- 3 s) but was slightly higher (P < 0.05) for HI (32 +/- 3 vs 29 +/- 4 s); the responses were closely correlated (r = 0.95 and r = 0.95; P < 0.01) for both intensities. In recovery, agreement between the responses was more limited both for LI (36 +/- 4 vs 18 +/- 4 s, P < 0.05; r = -0.01) and HI (33 +/- 3 vs 27 +/- 3 s, P > 0.05; r = -0.40). MTTc-l was approximately 17 s just before exercise and decreased to 12 and 10 s after 5 s of exercise for LI and HI, respectively. These data indicate that the phase II pVO2 kinetics reflect mVO2 kinetics during exercise but not during recovery where caution in data interpretation is advised. Increased mVO2 probably makes a small contribution to during the first 15-20 s of exercise.

Lesion detection and classification in breast cancer: evaluation of approaches based on morphological features, tracer kinetic modelling and semi-quantitative parameters in MR functional imaging (DCE-MRI)

The diagnosis, grading and classification of tumours has benefited considerably from the development of DCE-MRI which is now essential to the adequate clinical management of many tumour types due to its capability in detecting active angiogenesis. Several strategies have been proposed for DCE-MRI evaluation. Visual inspection of contrast agent concentration curves vs time is a very simple yet operator dependent procedure, therefore more objective approaches have been developed in order to facilitate comparison between studies. In so called model free approaches, descriptive or heuristic information extracted from time series raw data have been used for tissue classification. The main issue concerning these schemes is that they have not a direct interpretation in terms of physiological properties of the tissues. On the other hand, model based investigations typically involve compartmental tracer kinetic modelling and pixel-by-pixel estimation of kinetic parameters via non-linear regression applied on region of interests opportunely selected by the physician. This approach has the advantage to provide parameters directly related to the pathophysiological properties of the tissue such as vessel permeability, local regional blood flow, extraction fraction, concentration gradient between plasma and extravascular-extracellular space. Anyway, nonlinear modelling is computational demanding and the accuracy of the estimates can be affected by the signal-to-noise ratio and by the initial solutions. The principal aim of this thesis is investigate the use of semi-quantitative and quantitative parameters for segmentation and classification of breast lesion. The objectives can be subdivided as follow: describe the principal techniques to evaluate time intensity curve in DCE-MRI with focus on kinetic model proposed in literature; to evaluate the influence in parametrization choice for a classic bi-compartmental kinetic models; to evaluate the performance of a method for simultaneous tracer kinetic modelling and pixel classification; to evaluate performance of machine learning techniques training for segmentation and classification of breast lesion.

Anwendungen des Komplexe-Masse-Renormierungsschemas in effektiver Feldtheorie

Der erste Teil der vorliegenden Dissertation befasst sich mit der Untersuchung der perturbativen Unitarität im Komplexe-Masse-Renormierungsschema (CMS). Zu diesem Zweck wird eine Methode zur Berechnung der Imaginärteile von Einschleifenintegralen mit komplexen Massenparametern vorgestellt, die im Grenzfall stabiler Teilchen auf die herkömmlichen Cutkosky-Formeln führt. Anhand einer Modell-Lagrangedichte für die Wechselwirkung eines schweren Vektorbosons mit einem leichten Fermion wird demonstriert, dass durch Anwendung des CMS die Unitarität der zugrunde liegenden S-Matrix im störungstheoretischen Sinne erfüllt bleibt, sofern die renormierte Kopplungskonstante reell gewählt wird. Der zweite Teil der Arbeit beschäftigt sich mit verschiedenen Anwendungen des CMS in chiraler effektiver Feldtheorie (EFT). Im Einzelnen werden Masse und Breite der Deltaresonanz, die elastischen elektromagnetischen Formfaktoren der Roperresonanz, die elektromagnetischen Formfaktoren des Übergangs vom Nukleon zur Roperresonanz sowie Pion-Nukleon-Streuung und Photo- und Elektropionproduktion für Schwerpunktsenergien im Bereich der Roperresonanz berechnet. Die Wahl passender Renormierungsbedingungen ermöglicht das Aufstellen eines konsistenten chiralen Zählschemas für EFT in Anwesenheit verschiedener resonanter Freiheitsgrade, so dass die aufgeführten Prozesse in Form einer systematischen Entwicklung nach kleinen Parametern untersucht werden können. Die hier erzielten Resultate können für Extrapolationen von entsprechenden Gitter-QCD-Simulationen zum physikalischen Wert der Pionmasse genutzt werden. Deshalb wird neben der Abhängigkeit der Formfaktoren vom quadrierten Impulsübertrag auch die Pionmassenabhängigkeit des magnetischen Moments und der elektromagnetischen Radien der Roperresonanz untersucht. Im Rahmen der Pion-Nukleon-Streuung und der Photo- und Elektropionproduktion werden eine Partialwellenanalyse und eine Multipolzerlegung durchgeführt, wobei die P11-Partialwelle sowie die Multipole M1- und S1- mittels nichtlinearer Regression an empirische Daten angepasst werden.

DEVELOPMENT OF A PREDICTIVE COMBUSTION MODEL OF A SPARK IGNITED ENGINE WITH GASOLINE DIRECT INJECTION, VARIABLE VALVE TIMING, DURATION AND LIFT TECHNOLOGIES

There is a need by engine manufactures for computationally efficient and accurate predictive combustion modeling tools for integration in engine simulation software for the assessment of combustion system hardware designs and early development of engine calibrations. This thesis discusses the process for the development and validation of a combustion modeling tool for Gasoline Direct Injected Spark Ignited Engine with variable valve timing, lift and duration valvetrain hardware from experimental data. Data was correlated and regressed from accepted methods for calculating the turbulent flow and flame propagation characteristics for an internal combustion engine. A non-linear regression modeling method was utilized to develop a combustion model to determine the fuel mass burn rate at multiple points during the combustion process. The computational fluid dynamic software Converge ©, was used to simulate and correlate the 3-D combustion system, port and piston geometry to the turbulent flow development within the cylinder to properly predict the experimental data turbulent flow parameters through the intake, compression and expansion processes. The engine simulation software GT-Power © is then used to determine the 1-D flow characteristics of the engine hardware being tested to correlate the regressed combustion modeling tool to experimental data to determine accuracy. The results of the combustion modeling tool show accurate trends capturing the combustion sensitivities to turbulent flow, thermodynamic and internal residual effects with changes in intake and exhaust valve timing, lift and duration.

Sampling-Design Effects on Properties of Species-Area Relationships - A Case Study from Estonian Dry Grassland Communities

Despite widespread use of species-area relationships (SARs), dispute remains over the most representative SAR model. Using data of small-scale SARs of Estonian dry grassland communities, we address three questions: (1) Which model describes these SARs best when known artifacts are excluded? (2) How do deviating sampling procedures (marginal instead of central position of the smaller plots in relation to the largest plot; single values instead of average values; randomly located subplots instead of nested subplots) influence the properties of the SARs? (3) Are those effects likely to bias the selection of the best model? Our general dataset consisted of 16 series of nested-plots (1 cm(2)-100 m(2), any-part system), each of which comprised five series of subplots located in the four corners and the centre of the 100-m(2) plot. Data for the three pairs of compared sampling designs were generated from this dataset by subsampling. Five function types (power, quadratic power, logarithmic, Michaelis-Menten, Lomolino) were fitted with non-linear regression. In some of the communities, we found extremely high species densities (including bryophytes and lichens), namely up to eight species in 1 cm(2) and up to 140 species in 100 m(2), which appear to be the highest documented values on these scales. For SARs constructed from nested-plot average-value data, the regular power function generally was the best model, closely followed by the quadratic power function, while the logarithmic and Michaelis-Menten functions performed poorly throughout. However, the relative fit of the latter two models increased significantly relative to the respective best model when the single-value or random-sampling method was applied, however, the power function normally remained far superior. These results confirm the hypothesis that both single-value and random-sampling approaches cause artifacts by increasing stochasticity in the data, which can lead to the selection of inappropriate models.

A multi-criteria optimization and decision-making approach for improvement of food engineering processes.

The objective of this study was to propose a multi-criteria optimization and decision-making technique to solve food engineering problems. This technique was demostrated using experimental data obtained on osmotic dehydratation of carrot cubes in a sodium chloride solution. The Aggregating Functions Approach, the Adaptive Random Search Algorithm, and the Penalty Functions Approach were used in this study to compute the initial set of non-dominated or Pareto-optimal solutions. Multiple non-linear regression analysis was performed on a set of experimental data in order to obtain particular multi-objective functions (responses), namely water loss, solute gain, rehydration ratio, three different colour criteria of rehydrated product, and sensory evaluation (organoleptic quality). Two multi-criteria decision-making approaches, the Analytic Hierarchy Process (AHP) and the Tabular Method (TM), were used simultaneously to choose the best alternative among the set of non-dominated solutions. The multi-criteria optimization and decision-making technique proposed in this study can facilitate the assessment of criteria weights, giving rise to a fairer, more consistent, and adequate final compromised solution or food process. This technique can be useful to food scientists in research and education, as well as to engineers involved in the improvement of a variety of food engineering processes.

Modelos não lineares resultantes da soma de regressões lineares ponderadas por funções distribuição acumulada

Os controladores eletrônicos de pulverização visam minimizar a variação das taxas de insumos aplicadas no campo. Eles fazem parte de um sistema de controle, e permitem a compensação da variação de velocidade de deslocamento do pulverizador durante a operação. Há vários tipos de controladores eletrônicos de pulverização disponíveis no mercado e uma das formas de selecionar qual o mais eficiente nas mesmas condições, ou seja, em um mesmo sistema de controle, é quantificar o tempo de resposta do sistema para cada controlador específico. O objetivo desse trabalho foi estimar os tempos de resposta para mudanças de velocidade de um sistema eletrônico de pulverização via modelos de regressão não lineares, estes, resultantes da soma de regressões lineares ponderadas por funções distribuição acumulada. Os dados foram obtidos no Laboratório de Tecnologia de Aplicação, localizado no Departamento de Engenharia de Biossistemas da Escola Superior de Agricultura \"Luiz de Queiroz\", Universidade de São Paulo, no município de Piracicaba, São Paulo, Brasil. Os modelos utilizados foram o logístico e de Gompertz, que resultam de uma soma ponderada de duas regressões lineares constantes com peso dado pela função distribuição acumulada logística e Gumbell, respectivamente. Reparametrizações foram propostas para inclusão do tempo de resposta do sistema de controle nos modelos, com o objetivo de melhorar a interpretação e inferência estatística dos mesmos. Foi proposto também um modelo de regressão não linear difásico que resulta da soma ponderada de regressões lineares constantes com peso dado pela função distribuição acumulada Cauchy seno hiperbólico exponencial. Um estudo de simulação foi feito, utilizando a metodologia de Monte Carlo, para avaliar as estimativas de máxima verossimilhança dos parâmetros do modelo.

Towards Closed-loop Deep Brain Stimulation: Behavior Recognition from Human STN

Deep brain stimulation (DBS) provides significant therapeutic benefit for movement disorders such as Parkinson’s disease (PD). Current DBS devices lack real-time feedback (thus are open loop) and stimulation parameters are adjusted during scheduled visits with a clinician. A closed-loop DBS system may reduce power consumption and side effects by adjusting stimulation parameters based on patient’s behavior. Thus behavior detection is a major step in designing such systems. Various physiological signals can be used to recognize the behaviors. Subthalamic Nucleus (STN) Local field Potential (LFP) is a great candidate signal for the neural feedback, because it can be recorded from the stimulation lead and does not require additional sensors. This thesis proposes novel detection and classification techniques for behavior recognition based on deep brain LFP. Behavior detection from such signals is the vital step in developing the next generation of closed-loop DBS devices. LFP recordings from 13 subjects are utilized in this study to design and evaluate our method. Recordings were performed during the surgery and the subjects were asked to perform various behavioral tasks. Various techniques are used understand how the behaviors modulate the STN. One method studies the time-frequency patterns in the STN LFP during the tasks. Another method measures the temporal inter-hemispheric connectivity of the STN as well as the connectivity between STN and Pre-frontal Cortex (PFC). Experimental results demonstrate that different behaviors create different m odulation patterns in STN and it’s connectivity. We use these patterns as features to classify behaviors. A method for single trial recognition of the patient’s current task is proposed. This method uses wavelet coefficients as features and support vector machine (SVM) as the classifier for recognition of a selection of behaviors: speech, motor, and random. The proposed method is 82.4% accurate for the binary classification and 73.2% for classifying three tasks. As the next step, a practical behavior detection method which asynchronously detects behaviors is proposed. This method does not use any priori knowledge of behavior onsets and is capable of asynchronously detect the finger movements of PD patients. Our study indicates that there is a motor-modulated inter-hemispheric connectivity between LFP signals recorded bilaterally from STN. We utilize a non-linear regression method to measure this inter-hemispheric connectivity and to detect the finger movements. Our experimental results using STN LFP recorded from eight patients with PD demonstrate this is a promising approach for behavior detection and developing novel closed-loop DBS systems.

Role of β3-adrenergic receptors in the action of a tumour lipid mobilizing factor

Induction of lipolysis in murine white adipocytes, and stimulation of adenylate cyclase in adipocyte plasma membranes, by a tumour-produced lipid mobilizing factor, was attenuated by low concentrations (10-7-10-5M) of the specific β3-adrenoceptor antagonist SR59230A. Lipid mobilizing factor (250 nM) produced comparable increases in intracellular cyclic AMP in CHOKI cells transfected with the human β3-adrenoceptor to that obtained with isoprenaline (1 nM). In both cases cyclic AMP production was attenuated by SR59230A confirming that the effect is mediated through a β3-adrenoceptor. A non-linear regression analysis of binding of lipid mobilizing factor to the β3-adrenoceptor showed a high affinity binding site with a Kd value 78±45 nM and a Bmax value (282±1 fmole mg protein-1) comparable with that of other β3-adrenoceptor agonists. These results suggest that lipid mobilizing factor induces lipolysis through binding to a β3-adrenoceptor. © 2002 The Cancer Research Campaign.