Heat Source Estimation with the Conjugate Gradient Method in Inverse Linear Diffusive Problems

In this work, we present the solution of a class of linear inverse heat conduction problems for the estimation of unknown heat source terms, with no prior information of the functional forms of timewise and spatial dependence of the source strength, using the conjugate gradient method with an adjoint problem. After describing the mathematical formulation of a general direct problem and the procedure for the solution of the inverse problem, we show applications to three transient heat transfer problems: a one-dimensional cylindrical problem; a two-dimensional cylindrical problem; and a one-dimensional problem with two plates.

Non-Linear Unsteady Aerodynamic Response Approximation Using Multi-Layer Functionals

Non-linear functional representation of the aerodynamic response provides a convenient mathematical model for motion-induced unsteady transonic aerodynamic loads response, that accounts for both complex non-linearities and time-history effects. A recent development, based on functional approximation theory, has established a novel functional form; namely, the multi-layer functional. For a large class of non-linear dynamic systems, such multi-layer functional representations can be realised via finite impulse response (FIR) neural networks. Identification of an appropriate FIR neural network model is facilitated by means of a supervised training process in which a limited sample of system input-output data sets is presented to the temporal neural network. The present work describes a procedure for the systematic identification of parameterised neural network models of motion-induced unsteady transonic aerodynamic loads response. The training process is based on a conventional genetic algorithm to optimise the network architecture, combined with a simplified random search algorithm to update weight and bias values. Application of the scheme to representative transonic aerodynamic loads response data for a bidimensional airfoil executing finite-amplitude motion in transonic flow is used to demonstrate the feasibility of the approach. The approach is shown to furnish a satisfactory generalisation property to different motion histories over a range of Mach numbers in the transonic regime.

Similarity among periphyton algal communities in a lentic-lotic gradient of the upper Paraná river floodplain, Brazil

Floristic comparison of periphyton communities from three systems with different hydrodynamic regimes (lentic, semilotic, and lotic) was carried out during high and low water periods on the Upper Paraná River floodplain. For each period and system, glass slides were sampled every two days during 18-day periods, and Eichhornia azurea Kunth petioles were sampled three times. A total of 228 species was collected, representing 12 classes, mainly diatoms and desmids. The highest species-richness was found in communities from lentic system and during high water. Species richness in the lotic system was more stable over succession and hydrological periods. Algal taxonomic structure in river community was clearly separated from the other two systems, with 43% of similarity level. The hydrological period was next in importance, followed last by the substratum type, with communities associated at 65-78% similarity levels, depending on system and hydrological period. The type of system, but not the water levels,was the main factor that influenced community richness, followed by disturbances caused by flood pulses and the operation of reservoirs upstream. The periphyton on artificial and natural substrata presented high degree of similarity.

Phytosociology of the remaining xerophytic woodland associated to an environmental gradient at the banks of the São Francisco river - Petrolina, Pernambuco, Brazil

Floristic and phytosociological surveys were carried out for 12 months in the Embrapa-SPSB, Petrolina, Pernambuco, Brazil. A transect was laid on starting at the river bank extending for 790 m away from the river and divided into 140 10 × 10 m contiguous plots. In each plot, all standing plants, alive or dead, with stem diameter at soil level > 3 cm and total height > 1 m were sampled. Along this transect, an elevation range of 9.40 m was registered and five topographical environments were identified: riverside (MR), dike (D), floodable depression (DI), boundary terrace (TL) - all of them belonging to the fluvial terrace with Fluvic Neosol and Haplic Cambisol both silty textured eutrophic soils - and the inlander tableland (TS), with medium sandy-textured Red-Yellow Argisols. Fourty-eight species/morphospecies, distributed into 39 genera and 21 families, were identified. Four phytogeoenvironments (MR, D + TL, DI + TL, and TS) were registered based on environmental variations and floristic similarities among plots using cluster analyses. The MR environment showed the largest total density, total basal area, maximum and medium heights and maximum diameter. Moreover, it had 8.1% of plants with heights above 8 m against 0.6% for D + TL, 0.2% for DI + TL, and 0% for TS. The species with the largest importance value were Inga vera subsp. affinis (DC.) T.D. Pennington in MR, Mimosa bimucronata Kunth in D + TL and DI + TL and M. tenuiflora (Willd.) Poir. in TS.

Along-scan radiometric gradient in Landsat images: understanding the relation between floristic and remote sensing information in tropical forests

The along-scan radiometric gradient causes severe interpretation problems in Landsat images of tropical forests. It creates a decreasing trend in pixel values with the column number of the image. In practical applications it has been corrected assuming the trend to be linear within structurally similar forests. This has improved the relation between floristic and remote sensing information, but just in some cases. I use 3 Landsat images and 105 floristic inventories to test the assumption of linearity, and to examine how the gradient and linear corrections affect the relation between floristic and Landsat data. Results suggest the gradient to be linear in infrared bands. Also, the relation between floristic and Landsat data could be conditioned by the distribution of the sampling sites and the direction in which images are mosaicked. Additionally, there seems to be a conjunction between the radiometric gradient and a natural east-west vegetation gradient common in Western Amazonia. This conjunction might have enhanced artificially correlations between field and remotely-sensed information in previous studies. Linear corrections may remove such artificial enhancement, but along with true and relevant spectral information about floristic patterns, because they can´t separate the radiometric gradient from a natural one.

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.

Modeling a Network of Heat Exchangers

This research work addresses the problem of building a mathematical model for the given system of heat exchangers and to determine the temperatures, pressures and velocities at the intermediate positions. Such model could be used in nding an optimal design for such a superstructure. To limit the size and computing time a reduced network model was used. The method can be generalized to larger network structures. A mathematical model which includes a system of non-linear equations has been built and solved according to the Newton-Raphson algorithm. The results obtained by the proposed mathematical model were compared with the results obtained by the Paterson approximation and Chen's Approximation. Results of this research work in collaboration with a current ongoing research at the department will optimize the valve positions and hence, minimize the pumping cost and maximize the heat transfer of the system of heat exchangers.

Tympanometry and Spectral Gradient Acoustic Reflectometry in the Diagnosis of Otitis Media in Young Children

Acute otitis media (AOM) is the most prevalent bacterial infection among children. Tympanometry and spectral gradient acoustic reflectometry (SG-AR) are adjunctive diagnostic tools to pneumatic otoscopy. The aim was to investigate the diagnostic accuracy and success rates of tympanometry and SG-AR performed by physicians and nurses. The study populations comprised 515 (I-II), 281 (III), and 156 (IV) outpatients (6-35 months). Physicians performed 4246 tympanometric (I) and SG-AR (II) examinations. Nurses performed 1782 (III) and 753 (IV) examinations at symptomatic and asymptomatic visits, respectively. Pneumatic otoscopy by the physician was the diagnostic standard. The accuracy of test results by physicians or nurses (I-IV) and the proportion of visits with accurate exclusive test results from both ears (III-IV) were analyzed. Type B tympanogram and SG-AR level 5 (<49˚) predicted middle ear effusion (MEE). At asymptomatic visits, type A and C1 tympanograms (peak pressure > -200 daPa) and SG-AR level 1 (>95˚) indicated healthy middle ear. Negative predictive values of type A and C1 tympanograms by nurses in excluding AOM at symptomatic and MEE at asymptomatic visits were 94% and 95%, respectively. Nurses obtained type A or C1 tympanogram from both ears at 94/459 (20%) and 81/196 (41%) of symptomatic and asymptomatic visits, respectively. SG-AR level 1 was rarely obtained from both ears. Type A and C1 tympanograms were accurate in excluding AOM at symptomatic and MEE at asymptomatic visits. However, nurses obtained these tympanograms from both ears only at one fifth of symptomatic visits and less than half of asymptomatic visits.

Attentional bias modulation by reappraisal in patients with generalized anxiety disorder: an event-related potential study

Affective states influence subsequent attention allocation. We evaluated emotional negativity bias modulation by reappraisal in patients with generalized anxiety disorder (GAD) relative to normal controls. Event-related potential (ERP) recordings were obtained, and changes in P200 and P300 amplitudes in response to negative or neutral words were noted after decreasing negative emotion or establishing a neutral condition. We found that in GAD patients only, the mean P200 amplitude after negative word presentation was much higher than after the presentation of neutral words. In normal controls, after downregulation of negative emotion, the mean P300 amplitude in response to negative words was much lower than after neutral words, and this was significant in both the left and right regions. In GAD patients, the negative bias remained prominent and was not affected by reappraisal at the early stage. Reappraisal was observed to have a lateralized effect at the late stage.

THE GENERALIZED MAXIMUM LIKELIHOOD METHOD APPLIED TO HIGH PRESSURE PHASE EQUILIBRIUM

The generalized maximum likelihood method was used to determine binary interaction parameters between carbon dioxide and components of orange essential oil. Vapor-liquid equilibrium was modeled with Peng-Robinson and Soave-Redlich-Kwong equations, using a methodology proposed in 1979 by Asselineau, Bogdanic and Vidal. Experimental vapor-liquid equilibrium data on binary mixtures formed with carbon dioxide and compounds usually found in orange essential oil were used to test the model. These systems were chosen to demonstrate that the maximum likelihood method produces binary interaction parameters for cubic equations of state capable of satisfactorily describing phase equilibrium, even for a binary such as ethanol/CO2. Results corroborate that the Peng-Robinson, as well as the Soave-Redlich-Kwong, equation can be used to describe phase equilibrium for the following systems: components of essential oil of orange/CO2.

Anharmonic Helmholtz free energy to O(lambdaâ ´) in the non- leading term approximation /|nby Leslie Wilk. -- 260 St. Catharines, Ont. : [s. n.],

Expressions for the anharmonic Helmholtz free energy contributions up to o( f ) ,valid for all temperatures, have been obtained using perturbation theory for a c r ystal in which every atom is on a site of inversion symmetry. Numerical calculations have been carried out in the high temperature limit and in the non-leading term approximation for a monatomic facecentred cubic crystal with nearest neighbour c entralforce interactions. The numbers obtained were seen to vary by a s much as 47% from thos e obtai.ned in the leading term approximati.on,indicating that the latter approximati on is not in general very good. The convergence to oct) of the perturbation series in the high temperature limit appears satisfactory.

Port Dalhousie and Thorold Railway estimate of work done to date with an approximation of probable damage sustained by suspending the track

Port Dalhousie and Thorold Railway estimate of work done to date with an approximation of probable damage sustained by suspending the track, Aug. 22, 1854.

Some Additional Specification Tests for Generalized Method of Moments Estimators with Macro-Economic Applications Part I : Theory

In This Paper Several Additional Gmm Specification Tests Are Studied. a First Test Is a Chow-Type Test for Structural Parameter Stability of Gmm Estimates. the Test Is Inspired by the Fact That \"Taste and Technology\" Parameters Are Uncovered. the Second Set of Specification Tests Are Var Encompassing Tests. It Is Assumed That the Dgp Has a Finite Var Representation. the Moment Restrictions Which Are Suggested by Economic Theory and Exploited in the Gmm Procedure Represent One Possible Characterization of the Dgp. the Var Is a Different But Compatible Characterization of the Same Dgp. the Idea of the Var Encompassing Tests Is to Compare Parameter Estimates of the Euler Conditions and Var Representations of the Dgp Obtained Separately with Parameter Estimates of the Euler Conditions and Var Representations Obtained Jointly. There Are Several Ways to Construct Joint Systems Which Are Discussed in the Paper. Several Applications Are Also Discussed.

The FCLT with Dependent Errors: an Helicopter Tour of the Quality of the Approximation

This note investigates the adequacy of the finite-sample approximation provided by the Functional Central Limit Theorem (FCLT) when the errors are allowed to be dependent. We compare the distribution of the scaled partial sums of some data with the distribution of the Wiener process to which it converges. Our setup is purposely very simple in that it considers data generated from an ARMA(1,1) process. Yet, this is sufficient to bring out interesting conclusions about the particular elements which cause the approximations to be inadequate in even quite large sample sizes.