Evaluation of the thermal and mechanical properties of poly(ε‐caprolactone), low‐density polyethylene,and their blends

The thermal and mechanical properties of low‐density polyethylene (LDPE), poly(ε‐caprolactone) (PCL), and their blends were evaluated. Differential scanning calorimetry showed that increasing the PCL content of the blend did not change the LDPE melting temperature, but reduced the crystallinity by up to 16.8%. This behavior was related to interactions between the PCL chains and the crystalline phase of LDPE. Tensile strength and elongation at break values for the blends were lower than those for the pure polymers, which suggested an incompatibility between the polymers. The values for Young's modulus under tensile increased when PCL was added to LDPE. © 2004 Wiley Periodicals, Inc. J Appl Polym Sci 91:3909–3914, 2004     

Evaluation of the removal of n-butanol vapor by the poly(lactic acid)-zeolite-TiO2 composite and formation of by-products

The use of polymeric films incorporated with zeolite-TiO₂ composites associated with UV radiation can be an alternative in the removal of volatile organic compounds (VOCs) through the adsorption and photodegradation processes. This study produced poly(lactic acid) (PLA) films incorporated with 13× zeolite, TiO₂, and 13×-TiO₂ zeolite composite to remove n-butanol and evaluate the by-products generated in the process. The results showed that 13× zeolite and TiO₂ added individually or as a composite to PLA, gave the polymer matrix a significant increase in the removal capacity of n-butanol. The best performance was presented by the zeolite-TiO₂, composite, confirming a synergistic effect. However, the formation of CO and CO₂ exceeded the expected values, with the verification that the polymeric matrix underwent photodegradation action by TiO₂. The polymeric film only containing zeolite is the most suitable for the removal of VOCs, as it did not present degradation of the PLA, generating a lower concentration of by-products.     

Decision support system for classifying archaeological pottery profiles based on Mathematical Morphology

We present a decision support system to help archaeologists in classifying wheel-made pottery pieces by its profile. A novel shape characterization method, using Mathematical Morphology, is introduced for this purpose. Each profile is represented as a vector, obtained by sampling the so called morphological curves (erosion, dilation, opening and closing), and Euclidean Distance is used as a similarity measure. We show results of our method applied to a profile database of Iberian Pottery from the upper valley of Guadalquivir River (Spain).     

Variational Foundations and Generalized Unified Theory of RVE-Based Multiscale Models

A unified variational theory is proposed for a general class of multiscale models based on the concept of Representative Volume Element. The entire theory lies on three fundamental principles: (1) kinematical admissibility, whereby the macro- and micro-scale kinematics are defined and linked in a physically meaningful way; (2) duality, through which the natures of the force- and stress-like quantities are uniquely identified as the duals (power-conjugates) of the adopted kinematical variables; and (3) the Principle of Multiscale Virtual Power, a generalization of the well-known Hill-Mandel Principle of Macrohomogeneity, from which equilibrium equations and homogenization relations for the force- and stress-like quantities are unequivocally obtained by straightforward variational arguments. The proposed theory provides a clear, logically-structured framework within which existing formulations can be rationally justified and new, more general multiscale models can be rigorously derived in well-defined steps. Its generality allows the treatment of problems involving phenomena as diverse as dynamics, higher order strain effects, material failure with kinematical discontinuities, fluid mechanics and coupled multi-physics. This is illustrated in a number of examples where a range of models is systematically derived by following the same steps. Due to the variational basis of the theory, the format in which derived models are presented is naturally well suited for discretization by finite element-based or related methods of numerical approximation. Numerical examples illustrate the use of resulting models, including a non-conventional failure-oriented model with discontinuous kinematics, in practical computations.     

Programmable optical phase-mostly holograms with coupled-mode modulation liquid-crystal television

Commercial twisted nematic liquid-crystal television provides coupled phase and amplitude modulation. We propose a simple wedged shear plate interferometer for in situ measurement of its phase modulation and operating curve. For a given operating curve, the coupled-mode modulation holograms are designed with an iterative method. We adjust the operating curve by rotating the polarizer and analyzer to obtain the optimal operating curve. The phase-mostly holograms yield good-quality reconstructed images with the zero-order spot reduced to a minimum. Experimental results are shown.     

Numerical and experimental analysis of wrinkling during the cup drawing of an AA5042 aluminium alloy

The recent trend to reduce the thickness of metallic sheets used in forming processes strongly increases the likelihood of the occurrence of wrinkling. Thus, in order to obtain defect-free components, the prediction of this kind of defect becomes extremely important in the tool design and selection of process parameters. In this study, the sheet metal forming process proposed as a benchmark in the Numisheet 2014 conference is selected to analyse the influence of the tool geometry on wrinkling behaviour, as well as the reliability of the developed numerical model. The side-wall wrinkling during the deep drawing process of a cylindrical cup in AA5042 aluminium alloy is investigated through finite element simulation and experimental measurements. The material plastic anisotropy is modelled with an advanced yield criterion beyond the isotropic (von Mises) material behaviour. The results show that the shape of the wrinkles predicted by the numerical model is strongly affected by the finite element mesh used in the blank discretization. The accurate modelling of the plastic anisotropy of the aluminium alloy yields numerical results that are in good agreement with the experiments, particularly the shape and location of the wrinkles. The predicted punch force evolution is strongly influenced by the friction coefficient used in the model. Moreover, the two punch geometries provide drawn cups with different wrinkle waves, mainly differing in amplitude.     

Structural damage identification via time domain response and Markov Chain Monte Carlo method

The present work addresses the problem of structural damage identification built on the statistical inversion approach. Here, the damage state of the structure is continuously described by a cohesion parameter, which is spatially discretized by the finite element method. The inverse problem of damage identification is then posed as the determination of the posterior probability densities of the nodal cohesion parameters. The Markov Chain Monte Carlo method, implemented with the Metropolis–Hastings algorithm, is considered in order to approximate the posterior probabilities by drawing samples from the desired joint posterior probability density function. With this approach, prior information on the sought parameters can be used and the uncertainty concerning the known values of the material properties can be quantified in the estimation of the cohesion parameters. The assessment of the proposed approach has been performed by means of numerical simulations on a simply supported Euler–Bernoulli beam. The damage identification and assessment are performed considering time domain response data. Different damage scenarios and noise levels were addressed, demonstrating the feasibility of the proposed approach.     

Transmembrane passage of cytokine-inducing bacterial products across new and reprocessed polysulfone dialyzers

The widespread use of bicarbonate dialysate and reprocessed high-efficiency and "high-flux" dialyzers has raised concerns about the increased risk of reverse-transfer of dialysate contaminants into the blood compartment. To evaluate this concern, the reverse-transfer of bacterial products from contaminated bicarbonate dialysate into the blood compartment was compared during in vitro dialysis with new or reprocessed high-flux polysulfone dialyzers. In vitro dialysis was carried out at 37 degrees C by use of a counter-current recirculating loop dialysis circuit with either new high-flux polysulfone dialyzers or dialyzers reprocessed once or 20 times with formaldehyde (0.75%) and bleach (< 1%) with an automated system. Heparinized whole blood from healthy volunteers was circulated through the blood compartment, and bicarbonate dialysate was circulated in the dialysate compartment. The dialysate was challenged sequentially by 1:1000 and 1:100 dilutions of a sterile Pseudomonas aeruginosa culture supernatant (bacterial challenge). Samples were drawn from the blood and dialysate compartments 1 h after each challenge. Peripheral blood mononuclear cells (PBMC) were harvested by Ficoll-Hypaque separation from whole blood in the blood compartment and a 5 x 10(6) PBMC/mL cell suspension was prepared. Likewise, dialysate samples (0.5 mL) were added to 0.5 mL suspension of 5 x 10(6) PBMC/mL drawn at baseline. All PBMC suspensions were incubated upright in a humidified atmosphere at 37 degrees C with 5% CO2 for 24 h, and total interleukin-1 alpha (IL-1 alpha) and tumor necrosis factor-alpha (TNF alpha) cytokine production (cell-associated and secreted) was measured by radioimmunoassay. Eight experiments were performed for each arm of the study with the same donor for each arm. One hour after contaminating the dialysate with a 1:1000 dilution of the bacterial challenge, IL-1 alpha production by PBMC harvested from the blood compartment was 160 +/- 0, 171 +/- 11, and 270 +/- 35 pg, respectively, for new dialyzers, dialyzers reprocessed once, and dialyzers reprocessed 20 times (P = 0.004). One hour after challenging the dialysate with 1:100 dilution, IL-1 alpha production by PBMC harvested from the blood compartment was 188 +/- 20, 228 +/- 35, and 427 +/- 67 pg, respectively, for new dialyzers, dialyzers reprocessed once, and dialyzers reprocessed 20 times (P = 0.006). IL-1 alpha production by PBMC from dialyzers reprocessed 20 times was significantly greater than both new and dialyzers reprocessed once. However, there were no significant differences between new dialyzers and dialyzers reprocessed once. Similarly, after the 1:1000 challenge, TNF alpha production by PBMC harvested from the blood compartment was 160 +/- 0, 160 +/- 0, and 213 +/- 22 pg, respectively, for new dialyzers, dialyzers reprocessed once, and dialyzers reprocessed 20 times (P = 0.008). After the 1:100 challenge, TNF alpha production was 168 +/- 8, 188 +/- 20, and 225 +/- 32 pg, respectively, for new dialyzers, dialyzers reprocessed once, and dialyzers reprocessed 20 times (P = 0.20). These results demonstrate that reprocessing of high-flux polysulfone dialyzers with bleach increases the risk of reverse-transfer of bacterial products from contaminated dialysate, and this risk appears to increase with the number of reuses. Consequently, units that reprocess membranes with bleach and have suboptimal water quality might subject their patients to a higher risk of cytokine-related morbidity.     

Constrained linear regression models for symbolic interval-valued variables

This paper introduces an approach to fitting a constrained linear regression model to interval-valued data. Each example of the learning set is described by a feature vector for which each feature value is an interval. The new approach fits a constrained linear regression model on the midpoints and range of the interval values assumed by the variables in the learning set. The prediction of the lower and upper boundaries of the interval value of the dependent variable is accomplished from its midpoint and range, which are estimated from the fitted linear regression models applied to the midpoint and range of each interval value of the independent variables. This new method shows the importance of range information in prediction performance as well as the use of inequality constraints to ensure mathematical coherence between the predicted values of the lower () and upper () boundaries of the interval. The authors also propose an expression for the goodness-of-fit measure denominated determination coefficient. The assessment of the proposed prediction method is based on the estimation of the average behavior of the root-mean-square error and square of the correlation coefficient in the framework of a Monte Carlo experiment with different data set configurations. Among other aspects, the synthetic data sets take into account the dependence, or lack thereof, between the midpoint and range of the intervals. The bias produced by the use of inequality constraints over the vector of parameters is also examined in terms of the mean-square error of the parameter estimates. Finally, the approaches proposed in this paper are applied to a real data set and performances are compared.