Viscoelastic properties of high pressure and heat induced tofu gels

Tofu gels were rheologically examined to determine their storage or elastic (G') and loss or viscous (G '') moduli as a function of frequency within their linear viscoelastic limits. The tofu gels were made using either glucono-delta-lactone (GDL) or calcium sulphate (CaSO4 center dot 2H(2)O), followed by either heat treatment (heated soymilk at >= 97 degrees C prior to coagulation and subsequently held at 70 degrees C for 60 min, HT) or high pressure treatment (400 MPa at 20 degrees C for 10 min, HP). The overall moduli values of the GDL gels and CaSO4 center dot 2H(2)O gels of both physical treatments were similar, each gave frequency profiles expected for weak viscoelastic materials. However, although both temperature and high pressure treatments could be used to produce tofu gels, the final products were not the same. Pressure formed gels, despite having a higher overall "consistency" (increasing values of their moduli), had a proportionately higher contribution from the loss modulus (increased tan delta). Differences could also be observed using confocal scanning laser microscopy. While such treatment may give rise to differing systems/structures, with new or modified organoleptic properties, the more "open" structures obtained by pressure treatment may well cause processing difficulties if subsequent reworking or moulding is required. (c) 2007 Elsevier Ltd. All rights reserved.

Mucoadhesive and elastic films based on blends of chitosan and hydroxyethylcellulose

Mucoadhesive polymeric films have been prepared based on blends of chitosan and hydroxyethylcellulose. The blends have been characterized by IR spectroscopy, DSC, WAXD, TGA, SEM, and mechanical testing. It is demonstrated that the mechanical properties of chitosan are improved significantly upon blending with hydroxyethylcellulose. An increase in hydroxyethylcellulose content in the blends makes the materials more elastic. The thermal treatment of the blends at 100 degrees C leads to partial cross-linking of the polymers and formation of water-insoluble but swellable materials. The adhesion of the films towards porcine buccal mucosa decreases with increasing hydroxyethylcellulose content in the blends.

Planning rigid body motions using elastic curves

This paper tackles the problem of computing smooth, optimal trajectories on the Euclidean group of motions SE(3). The problem is formulated as an optimal control problem where the cost function to be minimized is equal to the integral of the classical curvature squared. This problem is analogous to the elastic problem from differential geometry and thus the resulting rigid body motions will trace elastic curves. An application of the Maximum Principle to this optimal control problem shifts the emphasis to the language of symplectic geometry and to the associated Hamiltonian formalism. This results in a system of first order differential equations that yield coordinate free necessary conditions for optimality for these curves. From these necessary conditions we identify an integrable case and these particular set of curves are solved analytically. These analytic solutions provide interpolating curves between an initial given position and orientation and a desired position and orientation that would be useful in motion planning for systems such as robotic manipulators and autonomous-oriented vehicles.

OFDM joint data detection and phase noise cancellation for constant modulus modulations

This correspondence proposes a new algorithm for the OFDM joint data detection and phase noise (PHN) cancellation for constant modulus modulations. We highlight that it is important to address the overfitting problem since this is a major detrimental factor impairing the joint detection process. In order to attack the overfitting problem we propose an iterative approach based on minimum mean square prediction error (MMSPE) subject to the constraint that the estimated data symbols have constant power. The proposed constrained MMSPE algorithm (C-MMSPE) significantly improves the performance of existing approaches with little extra complexity being imposed. Simulation results are also given to verify the proposed algorithm.

Hydrogelation and self-assembly of Fmoc-tripeptides: unexpected influence of sequence on self-assembled fibril structure, and hydrogel modulus and anisotropy

The self-assembly and hydrogelation properties of two Fmoc-tripeptides [Fmoc = N-(fluorenyl-9-methoxycarbonyl)] are investigated, in borate buffer and other basic solutions. A remarkable difference in self-assembly properties is observed comparing Fmoc-VLK(Boc) with Fmoc-K(Boc)LV, both containing K protected by N(epsilon)-tert-butyloxycarbonate (Boc). In borate buffer, the former peptide forms highly anisotropic fibrils which show local alignment, and the hydrogels show flow-aligning properties. In contrast, Fmoc-K(Boc)LV forms highly branched fibrils that produce isotropic hydrogels with a much higher modulus (G' > 10(4) Pa), and lower concentration for hydrogel formation. The distinct self-assembled structures are ascribed to conformational differences, as revealed by secondary structure probes (CD, FTIR, Raman spectroscopy) and X-ray diffraction. Fmoc-VLK(Boc) forms well-defined beta-sheets with a cross-beta X-ray diffraction pattern, whereas Fmoc-KLV(Boc) forms unoriented assemblies with multiple stacked sheets. Interchange of the K and V residues when inverting the tripeptide sequence thus leads to substantial differences in self-assembled structures, suggesting a promising approach to control hydrogel properties.

Dynamic shear modulus of isotropic elastomers

We have investigated the dynamic mechanical behavior of two cross-linked polymer networks with very different topologies: one made of backbones randomly linked along their length; the other with fixed-length strands uniformly cross-linked at their ends. The samples were analyzed using oscillatory shear, at very small strains corresponding to the linear regime. This was carried out at a range of frequencies, and at temperatures ranging from the glass plateau, through the glass transition, and well into the rubbery region. Through the glass transition, the data obeyed the time-temperature superposition principle, and could be analyzed using WLF treatment. At higher temperatures, in the rubbery region, the storage modulus was found to deviate from this, taking a value that is independent of frequency. This value increased linearly with temperature, as expected for the entropic rubber elasticity, but with a substantial negative offset inconsistent with straightforward enthalpic effects. Conversely, the loss modulus continued to follow time-temperature superposition, decreasing with increasing temperature, and showing a power-law dependence on frequency.

Design of a biomimetic skin for an octopus-inspired robot - Part II: Development of the skin artefact

In order to develop skin artefact for an octopus-inspired robot arm, which is designed to be able to elongate 60% of its original length, silicone rubber and knitted nylon sheet were selected to manufacture an artificial skin, due to their higher elastic strain and high flexibility. Tensile and scissors cutting tests were conducted to characterise the matrix and reinforcing materials and the skin artefact. Material properties of the individual and the composite materials were compared with the measured properties of real octopus skin presented in Part I. The Young’s modulus of the skin should be below 20 MPa and the elastic strain range should be over 60%. The fracture toughness should be at least 0.9 kJ·m−2. Tubes made of the skin artefact filled with liquid were tested to study volume change under deformation. Finite element analysis model was developed to simulate the material and arm structure under tensile loading. Results show that the skin artefact developed has similar mechanical properties as the real octopus skin and satisfies all the design specifications of the OCTOPUS robot.

An elastic net orthogonal forward regression algorithm

In this paper we propose an efficient two-level model identification method for a large class of linear-in-the-parameters models from the observational data. A new elastic net orthogonal forward regression (ENOFR) algorithm is employed at the lower level to carry out simultaneous model selection and elastic net parameter estimation. The two regularization parameters in the elastic net are optimized using a particle swarm optimization (PSO) algorithm at the upper level by minimizing the leave one out (LOO) mean square error (LOOMSE). Illustrative examples are included to demonstrate the effectiveness of the new approaches.

The mechanical properties of amniotic membrane influence its effect as a biomaterial for ocular surface repair

The human amniotic membrane (AM) is a tissue of fetal origin and has proven to be clinically useful as a biomaterial in the management of various ocular surface disorders including corneal stem cell transplantation. However, its success rate displays a degree of clinical unpredictability. We suggest that the measured variability inAMstiffness offers an explanation for the poor clinical reproducibility when it is used as a substrate for stem cell expansion and transplantation. Corneal epithelial stem cells were expanded upon AM samples possessing different mechanical stiffness. To investigate further the importance of biological substrate stiffness on cell phenotype we replaced AM with type I collagen gels of known stiffness. Substrate stiffness was measured using shear rheometry and surface topography was characterized using scanning electron microscopy and atomic force microscopy. The differentiation status of epithelial cells was examined using RT-PCR, immunohistochemistry and Western blotting. The level of corneal stem cell differentiation was increased in cells expanded upon AM with a high dynamic elastic shear modulus and cell expansion on type I collagen gels confirmed that the level of corneal epithelial stem cell differentiation was related to the substrate’s mechanical properties. In this paper we provide evidence to show that the preparatory method of AM for clinical use can affect its mechanical properties and that these measured differences can influence the level of differentiation within expanded corneal epithelial stem cells.

Elastic-net prefiltering for two-class classification

A two-stage linear-in-the-parameter model construction algorithm is proposed aimed at noisy two-class classification problems. The purpose of the first stage is to produce a prefiltered signal that is used as the desired output for the second stage which constructs a sparse linear-in-the-parameter classifier. The prefiltering stage is a two-level process aimed at maximizing a model's generalization capability, in which a new elastic-net model identification algorithm using singular value decomposition is employed at the lower level, and then, two regularization parameters are optimized using a particle-swarm-optimization algorithm at the upper level by minimizing the leave-one-out (LOO) misclassification rate. It is shown that the LOO misclassification rate based on the resultant prefiltered signal can be analytically computed without splitting the data set, and the associated computational cost is minimal due to orthogonality. The second stage of sparse classifier construction is based on orthogonal forward regression with the D-optimality algorithm. Extensive simulations of this approach for noisy data sets illustrate the competitiveness of this approach to classification of noisy data problems.

Planning autonomous vehicles in the absence of speed lanes using an elastic strip

Planning of autonomous vehicles in the absence of speed lanes is a less-researched problem. However, it is an important step toward extending the possibility of autonomous vehicles to countries where speed lanes are not followed. The advantages of having nonlane-oriented traffic include larger traffic bandwidth and more overtaking, which are features that are highlighted when vehicles vary in terms of speed and size. In the most general case, the road would be filled with a complex grid of static obstacles and vehicles of varying speeds. The optimal travel plan consists of a set of maneuvers that enables a vehicle to avoid obstacles and to overtake vehicles in an optimal manner and, in turn, enable other vehicles to overtake. The desired characteristics of this planning scenario include near completeness and near optimality in real time with an unstructured environment, with vehicles essentially displaying a high degree of cooperation and enabling every possible(safe) overtaking procedure to be completed as soon as possible. Challenges addressed in this paper include a (fast) method for initial path generation using an elastic strip, (re-)defining the notion of completeness specific to the problem, and inducing the notion of cooperation in the elastic strip. Using this approach, vehicular behaviors of overtaking, cooperation, vehicle following,obstacle avoidance, etc., are demonstrated.

A time-invariant visco-elastic windkessel model relating blood flow and blood volume

The difference between the rate of change of cerebral blood volume (CBV) and cerebral blood flow (CBF) following stimulation is thought to be due to circumferential stress relaxation in veins (Mandeville, J.B., Marota, J.J.A., Ayata, C., Zaharchuk, G., Moskowitz, M.A., Rosen, B.R., Weisskoff, R.M., 1999. Evidence of a cerebrovascular postarteriole windkessel with delayed compliance. J. Cereb. Blood Flow Metab. 19, 679–689). In this paper we explore the visco-elastic properties of blood vessels, and present a dynamic model relating changes in CBF to changes in CBV. We refer to this model as the visco-elastic windkessel (VW) model. A novel feature of this model is that the parameter characterising the pressure–volume relationship of blood vessels is treated as a state variable dependent on the rate of change of CBV, producing hysteresis in the pressure–volume space during vessel dilation and contraction. The VW model is nonlinear time-invariant, and is able to predict the observed differences between the time series of CBV and that of CBF measurements following changes in neural activity. Like the windkessel model derived by Mandeville, J.B., Marota, J.J.A., Ayata, C., Zaharchuk, G., Moskowitz, M.A., Rosen, B.R., Weisskoff, R.M., 1999. Evidence of a cerebrovascular postarteriole windkessel with delayed compliance. J. Cereb. Blood Flow Metab. 19, 679–689, the VW model is primarily a model of haemodynamic changes in the venous compartment. The VW model is demonstrated to have the following characteristics typical of visco-elastic materials: (1) hysteresis, (2) creep, and (3) stress relaxation, hence it provides a unified model of the visco-elastic properties of the vasculature. The model will not only contribute to the interpretation of the Blood Oxygen Level Dependent (BOLD) signals from functional Magnetic Resonance Imaging (fMRI) experiments, but also find applications in the study and modelling of the brain vasculature and the haemodynamics of circulatory and cardiovascular systems.