Non-linear modal analysis for beams subjected to axial loads: Analytical and finite-element solutions

A rigorous derivation of non-linear equations governing the dynamics of an axially loaded beam is given with a clear focus to develop robust low-dimensional models. Two important loading scenarios were considered, where a structure is subjected to a uniformly distributed axial and a thrust force. These loads are to mimic the main forces acting on an offshore riser, for which an analytical methodology has been developed and applied. In particular, non-linear normal modes (NNMs) and non-linear multi-modes (NMMs) have been constructed by using the method of multiple scales. This is to effectively analyse the transversal vibration responses by monitoring the modal responses and mode interactions. The developed analytical models have been crosschecked against the results from FEM simulation. The FEM model having 26 elements and 77 degrees-of-freedom gave similar results as the low-dimensional (one degree-of-freedom) non-linear oscillator, which was developed by constructing a so-called invariant manifold. The comparisons of the dynamical responses were made in terms of time histories, phase portraits and mode shapes. (C) 2008 Elsevier Ltd. All rights reserved.

Linear minimum mean square filter for discrete-time linear systems with Markov jumps and multiplicative noises

In this paper we obtain the linear minimum mean square estimator (LMMSE) for discrete-time linear systems subject to state and measurement multiplicative noises and Markov jumps on the parameters. It is assumed that the Markov chain is not available. By using geometric arguments we obtain a Kalman type filter conveniently implementable in a recurrence form. The stationary case is also studied and a proof for the convergence of the error covariance matrix of the LMMSE to a stationary value under the assumption of mean square stability of the system and ergodicity of the associated Markov chain is obtained. It is shown that there exists a unique positive semi-definite solution for the stationary Riccati-like filter equation and, moreover, this solution is the limit of the error covariance matrix of the LMMSE. The advantage of this scheme is that it is very easy to implement and all calculations can be performed offline. (c) 2011 Elsevier Ltd. All rights reserved.

Double generalized linear model for tissue culture proportion data: a Bayesian perspective

Joint generalized linear models and double generalized linear models (DGLMs) were designed to model outcomes for which the variability can be explained using factors and/or covariates. When such factors operate, the usual normal regression models, which inherently exhibit constant variance, will under-represent variation in the data and hence may lead to erroneous inferences. For count and proportion data, such noise factors can generate a so-called overdispersion effect, and the use of binomial and Poisson models underestimates the variability and, consequently, incorrectly indicate significant effects. In this manuscript, we propose a DGLM from a Bayesian perspective, focusing on the case of proportion data, where the overdispersion can be modeled using a random effect that depends on some noise factors. The posterior joint density function was sampled using Monte Carlo Markov Chain algorithms, allowing inferences over the model parameters. An application to a data set on apple tissue culture is presented, for which it is shown that the Bayesian approach is quite feasible, even when limited prior information is available, thereby generating valuable insight for the researcher about its experimental results.

ECONOMICAL OCCUPATION OF AGRICULTURAL AREA OF PIRACICABA (SAO PAULO STATE, BRAZIL), INCLUDING RISK THROUGH LINEAR PROGRAMMING

The economic occupation of an area of 500 ha for Piracicaba was studied with the irrigated cultures of maize, tomato, sugarcane and beans, having used models of deterministic linear programming and linear programming including risk for the Target-Motad model, where two situations had been analyzed. In the deterministic model the area was the restrictive factor and the water was not restrictive for none of the tested situations. For the first situation the gotten maximum income was of R$ 1,883,372.87 and for the second situation it was of R$ 1,821,772.40. In the model including risk a producer that accepts risk can in the first situation get the maximum income of R$ 1,883,372. 87 with a minimum risk of R$ 350 year(-1), and in the second situation R$ 1,821,772.40 with a minimum risk of R$ 40 year(-1). Already a producer averse to the risk can get in the first situation a maximum income of R$ 1,775,974.81 with null risk and for the second situation R$ 1.707.706, 26 with null risk, both without water restriction. These results stand out the importance of the inclusion of the risk in supplying alternative occupations to the producer, allowing to a producer taking of decision considered the risk aversion and the pretension of income.

Parametric correlation functions to model the structure of permanent environmental (co)variances in milk yield random regression models

The objective of the present study was to estimate milk yield genetic parameters applying random regression models and parametric correlation functions combined with a variance function to model animal permanent environmental effects. A total of 152,145 test-day milk yields from 7,317 first lactations of Holstein cows belonging to herds located in the southeastern region of Brazil were analyzed. Test-day milk yields were divided into 44 weekly classes of days in milk. Contemporary groups were defined by herd-test-day comprising a total of 2,539 classes. The model included direct additive genetic, permanent environmental, and residual random effects. The following fixed effects were considered: contemporary group, age of cow at calving (linear and quadratic regressions), and the population average lactation curve modeled by fourth-order orthogonal Legendre polynomial. Additive genetic effects were modeled by random regression on orthogonal Legendre polynomials of days in milk, whereas permanent environmental effects were estimated using a stationary or nonstationary parametric correlation function combined with a variance function of different orders. The structure of residual variances was modeled using a step function containing 6 variance classes. The genetic parameter estimates obtained with the model using a stationary correlation function associated with a variance function to model permanent environmental effects were similar to those obtained with models employing orthogonal Legendre polynomials for the same effect. A model using a sixth-order polynomial for additive effects and a stationary parametric correlation function associated with a seventh-order variance function to model permanent environmental effects would be sufficient for data fitting.

Random regression models to estimate test-day milk yield genetic parameters Holstein cows in Southeastern Brazil

A total of 152,145 weekly test-day milk yield records from 7317 first lactations of Holstein cows distributed in 93 herds in southeastern Brazil were analyzed. Test-day milk yields were classified into 44 weekly classes of DIM. The contemporary groups were defined as herd-year-week of test-day. The model included direct additive genetic, permanent environmental and residual effects as random and fixed effects of contemporary group and age of cow at calving as covariable, linear and quadratic effects. Mean trends were modeled by a cubic regression on orthogonal polynomials of DIM. Additive genetic and permanent environmental random effects were estimated by random regression on orthogonal Legendre polynomials. Residual variances were modeled using third to seventh-order variance functions or a step function with 1, 6,13,17 and 44 variance classes. Results from Akaike`s and Schwarz`s Bayesian information criterion suggested that a model considering a 7th-order Legendre polynomial for additive effect, a 12th-order polynomial for permanent environment effect and a step function with 6 classes for residual variances, fitted best. However, a parsimonious model, with a 6th-order Legendre polynomial for additive effects and a 7th-order polynomial for permanent environmental effects, yielded very similar genetic parameter estimates. (C) 2008 Elsevier B.V. All rights reserved.

Milk yield, milk composition, eating behavior, and lamb performance of ewes fed diets containing soybean hulls replacing coastcross (Cynodon species) hay

The objective of this experiment was to evaluate the effects of replacing coastcross hay NDF by soybean hull (SH) NDF on the lactation performance and eating behavior of ewes and also on the performance of their lambs. Fifty-six Santa Ines lactating ewes (56.1 +/- 6.8 kg of initial BW; mean +/- SD) were penned individually and used in a randomized complete block design with 14 blocks and 4 treatments. Diets were formulated to provide similar concentrations of NDF (56%) and CP (16%). The SH NDF replaced 33 (SH33), 67 (SH67), or 100% (SH100) of the NDF contributed by coastcross hay in a 70% forage-based diet (SH0), resulting in SH inclusion rates of 0, 25, 54, and 85% of the dietary DM. Once a week, from the second to the eighth week of lactation (weaning time), ewes were separated from their lambs, stimulated by a 6-IU i.v. oxytocin injection, and hand milked to empty the udder. After 3 h, milk production was obtained after the same procedure. Quadratic effect for milk production (142.4, 179.8, 212.6, and 202.9 g/3 h) and cubic effect for DMI (2.27, 2.69, 3.25, and 3.00 kg/d) were observed as SH inclusion increased from 0 to 85% of the dietary DM. Milk fat (7.59, 7.86, 7.59, and 7.74%), protein (4.53, 4.43, 4.40, and 4.55%), and total solids (18.24, 18.54, 18.39, and 18.64%) did not differ among the 70% forage-based diet and diets with SH NDF replacing 33, 67, or 100% of the NDF. A linear increase in lactose concentration was observed with SH inclusion. Ewe BW gain during the trial showed a cubic response (0.37, 0.03, 4.80, and 2.80 kg) with SH inclusion. The preweaning ADG of lambs increased linearly, and ADG of lambs after weaning decreased linearly with SH inclusion. Final BW of lambs (2 wk after weaning) did not differ among treatments. Eating behavior observations were conducted with 44 ewes. The same facilities, experimental design, dietary treatments, and feeding management were used. Observations were visually recorded every 5 min for a 24-h period when ewes were 46 +/- 6.8 d in milk. Eating time (min/d, min/g of DMI, and min/g of NDF intake) and time expended in rumination and chewing activities (min/g of DMI and min/g of NDF intake) decreased linearly with the addition of SH in the diets. The inclusion of SH improved DMI and milk production, also reflecting on the BW of lambs at weaning. Milk performance was not affected when SH NDF replaced 100% of hay NDF.

A Log-Linear Regression Model for the Beta-Weibull Distribution

We introduce the log-beta Weibull regression model based on the beta Weibull distribution (Famoye et al., 2005; Lee et al., 2007). We derive expansions for the moment generating function which do not depend on complicated functions. The new regression model represents a parametric family of models that includes as sub-models several widely known regression models that can be applied to censored survival data. We employ a frequentist analysis, a jackknife estimator, and a parametric bootstrap for the parameters of the proposed model. We derive the appropriate matrices for assessing local influences on the parameter estimates under different perturbation schemes and present some ways to assess global influences. Further, for different parameter settings, sample sizes, and censoring percentages, several simulations are performed. In addition, the empirical distribution of some modified residuals are displayed and compared with the standard normal distribution. These studies suggest that the residual analysis usually performed in normal linear regression models can be extended to a modified deviance residual in the proposed regression model applied to censored data. We define martingale and deviance residuals to evaluate the model assumptions. The extended regression model is very useful for the analysis of real data and could give more realistic fits than other special regression models.

Potassium Soil Test Calibration for Lowland Rice on an Inceptisol

Potassium (K) plays an important role in many physiological and biochemical processes in plants and its adequate use is an important issue for sustainable economic crop production. Soil test-based K fertilizer recommendations are very limited for lowland rice (Oryza sativa L.) grown on Inceptisols. The objective of this study was to calibrate K soil testing for the response of lowland rice (cv. Ipagri 109) to added K. A field experiment was conducted in the farmers` field in the municipality of Lagoa da Confusao, State of Tocantins, central Brazil. The K rates used were 0, 125, 250, 375, 500, and 625 kg K ha-1 applied as broadcast and incorporated during sowing of the first rice crop. Rice responded significantly to K fertilization during 2 years of experimentation. Maximum grain yield of about 6,000 kg ha-1 was obtained with 57 mg K kg-1 soil in the first year and with 30 mg K kg-1 in the second year. This indicated that at low levels of K in the soil, nonexchangeable K was available for plant growth. Potassium use efficiency designated as agronomic efficiency (kg grain produced/kg K applied) decreased significantly in a quadratic fashion with increasing K level in the soil. Agronomic efficiency had a significantly linear association with grain yield. Hence, improving agronomic efficiency with management practices can improve rice yield.

Optimization of Pothomorphe umbellata (L.) Miquel topical formulations using experimental design

Pothomorphe umbellata is a native plant widely employed in the Brazilian popular medicine. This plant has been shown to exert a potent antioxidant activity on the skin and to delay the onset and reduce the incidence of UVB-induced skin damage and photoaging. The aim of this work was to optimize the appearance, the centrifuge stability and the permeation of emulsions containing R umbellata (0. 1% 4-nerolidylchatecol). Experimental design was used to study ternary mixtures models with constraints and graphical representation by phase diagrams. The constraints reduce the possible experimental domain, and for this reason, this methodology offers the maximum information while requiring the minimum investment. The results showed that the appearance follows a linear model, and that the aqueous phase was the principal factor affecting the appearance; the centrifuge stability parameter followed a mathernatic quadratic model and the interactions between factors produced the most stable emulsions; skin permeation was improved by the oil phase, following a linear model generated by data analysis. We propose as optimized P. umbellata formulation: 68.4% aqueous phase, 26.6% oil phase and 5.0% of self-emulsifying phase. This formulation displayed an acceptable compromise between factors and responses investigated. (c) 2007 Elsevier B.V. All rights reserved.

Processing optimization of probiotic yogurt containing glucose oxidase using response surface methodology

Exposure to oxygen may induce a lack of functionality of probiotic dairy foods because the anaerobic metabolism of probiotic bacteria compromises during storage the maintenance of their viability to provide benefits to consumer health. Glucose oxidase can constitute a potential alternative to increase the survival of probiotic bacteria in yogurt because it consumes the oxygen permeating to the inside of the pot during storage, thus making it possible to avoid the use of chemical additives. This research aimed to optimize the processing of probiotic yogurt supplemented with glucose oxidase using response surface methodology and to determine the levels of glucose and glucose oxidase that minimize the concentration of dissolved oxygen and maximize the Bifidobacterium longum count by the desirability function. Response surface methodology mathematical models adequately described the process, with adjusted determination coefficients of 83% for the oxygen and 94% for the B. longum. Linear and quadratic effects of the glucose oxidase were reported for the oxygen model, whereas for the B. longum count model an influence of the glucose oxidase at the linear level was observed followed by the quadratic influence of glucose and quadratic effect of glucose oxidase. The desirability function indicated that 62.32 ppm of glucose oxidase and 4.35 ppm of glucose was the best combination of these components for optimization of probiotic yogurt processing. An additional validation experiment was performed and results showed acceptable error between the predicted and experimental results.

Non-linear pathways of corporate environmental management: a survey of ISO 14001-certified companies in Brazil

A study was conducted to verify whether the theory on the evolution of corporate environmental management (CEM) is applicable to organizations located in Brazil. Some of the most important proposals pertaining to the evolution of CEM were evaluated in a systematic fashion and integrated into a typical theoretical framework containing three evolutionary stages: reactive, preventive and proactive. The validity of this framework was tested by surveying 94 companies located in Brazil with ISO 14001 certification. Results indicated that the evolution of CEM tends to occur in a manner that is counter to what has generally been described in the literature. Two evolutionary stages were identified: 1) synergy for eco-efficiency and 2) environmental legislation view, which combine variables that were initially categorized into different theoretical CEM stages. These data, obtained from a direct study of Brazilian companies, suggest that the evolution of environmental management in organizations tends to occur in a non-linear fashion, requiring a re-analysis of traditional perceptions of CEM development, as suggested by Kolk and Mauser (2002). (C) 2010 Elsevier Ltd. All rights reserved.

Modeling volatile organic compounds (voc`s) adsorption onto cup-stacked carbon nanotubes (cscnt) using the linear driving force model

Modeling volatile organic compounds (voc`s) adsorption onto cup-stacked carbon nanotubes (cscnt) using the linear driving force model. Volatile organic compounds (VOC`s) are an important category of air pollutants and adsorption has been employed in the treatment (or simply concentration) of these compounds. The current study used an ordinary analytical methodology to evaluate the properties of a cup-stacked nanotube (CSCNT), a stacking morphology of truncated conical graphene, with large amounts of open edges on the outer surface and empty central channels. This work used a Carbotrap bearing a cup-stacked structure (composite); for comparison, Carbotrap was used as reference (without the nanotube). The retention and saturation capacities of both adsorbents to each concentration used (1, 5, 20 and 35 ppm of toluene and phenol) were evaluated. The composite performance was greater than Carbotrap; the saturation capacities for the composite was 67% higher than Carbotrap (average values). The Langmuir isotherm model was used to fit equilibrium data for both adsorbents, and a linear driving force model (LDF) was used to quantify intraparticle adsorption kinetics. LDF was suitable to describe the curves.

The influence of oscillations on global existence for a class of semi-linear wave equations

The goal of this paper is to study the global existence of small data solutions to the Cauchy problem for the nonlinear wave equation u(tt) - a(t)(2) Delta u = u(t)(2) - a(t)(2)vertical bar del u vertical bar(2). In particular we are interested in statements for the 1D case. We will explain how the interplay between the increasing and oscillating behavior of the coefficient will influence global existence of small data solutions. Copyright c 2011 John Wiley & Sons, Ltd.

Assessment of the differential linear coherent scattering coefficient of biological samples

New differential linear coherent scattering coefficient, mu(CS), data for four biological tissue types (fat pork, tendon chicken, adipose and fibroglandular human breast tissues) covering a large momentum transfer interval (0.07 <= q <= 70.5 nm(-1)), resulted from combining WAXS and SAXS data, are presented in order to emphasize the need to update the default data-base by including the molecular interference and the large-scale arrangements effect. The results showed that the differential linear coherent scattering coefficient demonstrates influence of the large-scale arrangement, mainly due to collagen fibrils for tendon chicken and fibroglandular breast samples, and triacylglycerides for fat pork and adipose breast samples at low momentum transfer region. While, at high momentum transfer, the mu(CS) reflects effects of molecular interference related to water for tendon chicken and fibroglandular samples and, fatty acids for fat pork and adipose samples. (C) 2009 Elsevier B.V. All rights reserved.