Anelastic reorganisation of fibre-reinforced biological tissues

In this work, we contribute to the study of the structural reorganisation of biological tissues in response to mechanical stimuli. We specialise our investigation to a class of hydrated soft tissues, whose internal structure features reinforcing fibres. These are oriented statistically within the tissue, and their pattern of orientation is such that, at each material point, the tissue is anisotropic. From its natural, stress-free state, the tissue can be distorted anelastically into a global reference configuration, and then deformed under the action of external mechanical loads. The anelastic distortions are responsible for changing irreversibly the internal structure of the tissue, which, in the present context, occurs through both the rearrangement of the bonds among the tissue cells and the deformation-driven reorientation of the fibres. The anelastic strains, in addition, are assumed to model the onset and evolution of microcracks in the tissue, which may be triggered by the mechanical loads applied to the tissue in the case of traumatic events, or diseases. For our purposes, we formulate an anisotropic model of remodelling and we consider a fully isotropic model of structural reorganisation for comparison, with the aim to study if, how, and to what extent the evolution of anelastic distortions is influenced by the tissue’s anisotropy.

     

Autothermal reforming of JP8 on a Pt/Rh catalyst: Catalyst durability studies and effects of sulfur

Autothermal reforming (ATR) of commercial grade JP8 was performed on a Pt/Rh catalyst deposited on a monolith. This study investigated catalyst performance under three test conditions: (i) 120 startup and shutdown cycles, (ii) 80 h of continuous operation with sulfur-free fuel, and (iii) 370 h of testing with JP8 containing 125 ppm of sulfur. Axial reactor temperature profiles and gas composition data showed that startup and shutdown cycling had no impact on catalyst performance. When durability testing was done with fuel containing 125 ppm of sulfur, the catalyst deactivated initially, which was reflected by a decrease in H₂ concentration and decrease in fuel conversion. However, after 250 h of operation the activity stabilized at 66% fuel conversion and product concentrations were constant for the remaining 120 h of testing. The presence of sulfur resulted in higher CO selectivity, lower H₂ concentrations, and lower fuel conversions compared to data with sulfur-free fuel. The data suggests that the presence of sulfur primarily affects steam reforming reactions, and CO oxidation. Regeneration was attempted with air and with fuel-lean combustion but initial H₂ yields and carbon selectivity were not achieved.     

An automatic procedure for the synthesis and optimization of very low-frequency Gm–C integrators

Fully integrated low frequency filters are critical cells that should be carefully designed in order to avoid excessive area occupation. In this work we propose an automatic procedure capable of optimizing the design of G_m?CC integrators, which constitute the basis of a wide class of G_m?CC filters. The optimization target is minimizing the cell area with constraints on input range and low frequency noise. Lower and upper bounds can be fixed to most quantities and design parameters in order to avoid solutions that are not compatible with the physical limitations of the process. The program has been developed within the MATLAB^? platform, exploiting the optimization toolbox. The effect of several important design parameters on the optimization of low frequency integrators has been investigated using the proposed routine. The strong interaction between noise and low frequency constraints has been demonstrated, showing the impressive impact of strict noise specifications on the occupied area. The actual effectiveness of parameters such as the current division factor or approaches such as flicker noise rejection by means of chopper modulation has been investigated. Examples of integrator synthesis, performed using the proposed procedure configured with the parameters of a commercial CMOS process, are presented. The consistence between the characteristics of the cells and the initial specifications has been checked using electrical simulations showing a maximum discrepancy with the initial specifications of nearly 80%. A semi-manual method to refine the synthesized cells and improve the accuracy is proposed.     

A Matrix-Based Approach to the Image Moment Problem

An image can be seen as an element of a vector space and hence it can be expressed in as a linear combination of the elements of any non necessarily orthogonal basis of this space. After giving a matrix formulation of this well-known fact, this paper presents a reconstruction method of an image from its moments that sheds new light on this inverse problem. Two main contributions are presented: (a) the results using the standard approach based on the least squares approximation of the result using orthogonal polynomials can also be obtained using matrix pseudoinverses, which implies higher control on the numerical stability of the problem; and (b) it is possible to use basis functions in the reconstruction different from orthogonal polynomials, such as Fourier or Haar basis, allowing to introduce constraints relative to the bandwidth or the spatial resolution on the image to be reconstructed. Judit Martònez received the B.Sc. degree in 1993 and the PhD degree (with honors) in 1998, both in telecommunications engineering from the Technical University of Catalonia. She developed her research at the Institut de Robútica i Informütica Industrial of the Spanish High Council for Scientific Research. In 1999 she joined the Computer Vision Center, a R&D center founded by the Autonomous University of Barcelona and the Autonomous Government of Catalonia. She has been principal researcher of several industrial and research projects related to computer vision technologies. Her research interests include industrial applications of machine vision, efficient algorithms for low-level image processing, multiresolution mathematical models, statistical clustering, pattern classification and inverse problems. Josep M. Porta received the Engineer Degree in Computer Science in 1994 and the Ph.D. in Artificial Intelligence in 2001, both from the Technical University of Catalonia (UPC). After that, he joined the IAS group of the University of Amsterdam and currently, he holds a post-doc position at the Institut de Robútica i Informütica Industrial (CSIC-UPC) in Barcelona. He carried research in legged robots, machine learning, vision-based methods for autonomous robot localization, and computational kinematics. Federico Thomas is Research Professor at the Spanish Scientific Research Council (CSIC) and director of the Institut de Robútica i Informütica Industrial (CSIC-UPC), Barcelona, Spain. He received the telecommunications engineering degree in 1984, and the Ph.D. degree (with honors) in computer science in 1988, both from the Technical University of Catalonia (UPC). In 1991, he won a NATO postdoctoral scholarship at the University of Massachusetts with the late Prof. Robin Popplestone. In 1999, he was visiting professor, sponsored by the Autonomous Government of Catalonia, at the Oxford University Computing Laboratory with Prof. Stephen Cameron. He has been project leader of several national projects financed by the Spanish Committee for Science and Technology (CICYT), and by local companies such as ENHER, a power generation company now part of ENDESA. His current research interests are in Geometry and Kinematics with applications to Robotics, Computer Graphics and Computer Vision. Prof. Thomas is an Associate Editor of the IEEE Transactions on Robotics.     

Probabilistic methods for eigenvalue estimates

Some probabilistic techniques are discussed regarding their use in estimating the principal eigenvalue of an elliptic operator. Such estimates are useful when studying models for biochemical reactions governed by lateral diffusion.In this paper we summarize recent results on some mathematical techniques that were developed to study certain biological models.     

Phase retrieval in digital speckle pattern interferometry by use of a smoothed space-frequency distribution

We evaluate the use of a smoothed space-frequency distribution (SSFD) to retrieve optical phase maps in digital speckle pattern interferometry (DSPI). The performance of this method is tested by use of computer-simulated DSPI fringes. Phase gradients are found along a pixel path from a single DSPI image, and the phase map is finally determined by integration. This technique does not need the application of a phase unwrapping algorithm or the introduction of carrier fringes in the interferometer. It is shown that a Wigner-Ville distribution with a smoothing Gaussian kernel gives more-accurate results than methods based on the continuous wavelet transform. We also discuss the influence of filtering on smoothing of the DSPI fringes and some additional limitations that emerge when this technique is applied. The performance of the SSFD method for processing experimental data is then illustrated.