Cybernetic Modeling Revisited: A Method for Inferring the Cybernetic Variables u(i) from Experimental Data

The cybernetic modeling framework for the growth of microorganisms provides for an elegant methodology to account for the unknown regulatory phenomena through the use of cybernetic variables for enzyme induction and activity. In this paper, we revisit the assumption of limited resources for enzyme induction (Sigma u(i) = 1) used in the cybernetic modeling framework by presenting a methodology for inferring the individual cybernetic variables u(i) from experimental data. We use this methodology to infer u(i) during the simultaneous consumption of glycerol and lactose by Escherichia coli and then model the fitness trade-offs involved in the recently discovered predictive regulation strategy of microorganisms.

How modeling can reconcile apparently discrepant experimental results: The case of pacemaking in dopaminergic neurons

Midbrain dopaminergic neurons are endowed with endogenous slow pacemaking properties. In recent years, many different groups have studied the basis for this phenomenon, often with conflicting conclusions. In particular, the role of a slowly-inactivating L-type calcium channel in the depolarizing phase between spikes is controversial, and the analysis of slow oscillatory potential (SOP) recordings during the blockade of sodium channels has led to conflicting conclusions. Based on a minimal model of a dopaminergic neuron, our analysis suggests that the same experimental protocol may lead to drastically different observations in almost identical neurons. For example, complete L-type calcium channel blockade eliminates spontaneous firing or has almost no effect in two neurons differing by less than 1% in their maximal sodium conductance. The same prediction can be reproduced in a state of the art detailed model of a dopaminergic neuron. Some of these predictions are confirmed experimentally using single-cell recordings in brain slices. Our minimal model exhibits SOPs when sodium channels are blocked, these SOPs being uncorrelated with the spiking activity, as has been shown experimentally. We also show that block of a specific conductance (in this case, the SK conductance) can have a different effect on these two oscillatory behaviors (pacemaking and SOPs), despite the fact that they have the same initiating mechanism. These results highlight the fact that computational approaches, besides their well known confirmatory and predictive interests in neurophysiology, may also be useful to resolve apparent discrepancies between experimental results. © 2011 Drion et al.

Nonlinear piezoelectricity in electroelastic energy harvesters: Modeling and experimental identification

We propose and experimentally validate a first-principles based model for the nonlinear piezoelectric response of an electroelastic energy harvester. The analysis herein highlights the importance of modeling inherent piezoelectric nonlinearities that are not limited to higher order elastic effects but also include nonlinear coupling to a power harvesting circuit. Furthermore, a nonlinear damping mechanism is shown to accurately restrict the amplitude and bandwidth of the frequency response. The linear piezoelectric modeling framework widely accepted for theoretical investigations is demonstrated to be a weak presumption for near-resonant excitation amplitudes as low as 0.5 g in a prefabricated bimorph whose oscillation amplitudes remain geometrically linear for the full range of experimental tests performed (never exceeding 0.25% of the cantilever overhang length). Nonlinear coefficients are identified via a nonlinear least-squares optimization algorithm that utilizes an approximate analytic solution obtained by the method of harmonic balance. For lead zirconate titanate (PZT-5H), we obtained a fourth order elastic tensor component of c1111p =-3.6673× 1017 N/m2 and a fourth order electroelastic tensor value of e3111 =1.7212× 108 m/V. © 2010 American Institute of Physics.

Modeling non-harmonic behavior of materials from experimental inelastic neutron scattering and thermal expansion measurements

Based on thermodynamic principles, we derive expressions quantifying the non-harmonic vibrational behavior of materials, which are rigorous yet easily evaluated from experimentally available data for the thermal expansion coefficient and the phonon density of states. These experimentally- derived quantities are valuable to benchmark first-principles theoretical predictions of harmonic and non-harmonic thermal behaviors using perturbation theory, ab initio molecular-dynamics, or Monte-Carlo simulations. We illustrate this analysis by computing the harmonic, dilational, and anharmonic contributions to the entropy, internal energy, and free energy of elemental aluminum and the ordered compound FeSi over a wide range of temperature. Results agree well with previous data in the literature and provide an efficient approach to estimate anharmonic effects in materials.

Experimental and modeling analysis of the reliability of the anisotropic conductive films

Experiments as well as computer modeling methods have been used to investigate the effect of the solder reflow process on the electrical characteristics and reliability of anisotropic conductive film (ACF) interconnections. In the experiments, the contact resistance of the ACF interconnections was found to increase after a subsequent reflow and the magnitude of this increase was strongly correlated to the peak temperature of the reflow profile. In fact, nearly 40 percent of the joints were opened (i.e. lifted away from the pad) after the reflow with a peak temperature of 260 OC while no openings was observed when the peak temperature was 210 "C. It is believed that the CTE mismatch between the polymer particle and the adhesive matrix is the main cause of this contact degradation. To understand this phenomenon better, a 3-D model of an ACF joint structure was built and Finite Element Analysis was used to predict the stress distrihution in the conductive particles, adhesive matrix and metal pads during the reflow process. The effects of the peak temperature, the CTE of the adhesive matrix and the bump height on the reliability of the ACF interconnections were discussed.

No-flow underfill flip chip assembly–an experimental and modeling analysis

In the flip-chip assembly process, no-flow underfill materials have a particular advantage over traditional underfill: the application and curing of the former can be undertaken before and during the reflow process. This advantage can be exploited to increase the flip-chip manufacturing throughput. However, adopting a no-flow underfill process may introduce reliability issues such as underfill entrapment, delamination at interfaces between underfill and other materials, and lower solder joint fatigue life. This paper presents an analysis on the assembly and the reliability of flip-chips with no-flow underfill. The methodology adopted in the work is a combination of experimental and computer-modeling methods. Two types of no-flow underfill materials have been used for the flip chips. The samples have been inspected with X-ray and scanning acoustic microscope inspection systems to find voids and other defects. Eleven samples for each type of underfill material have been subjected to thermal shock test and the number of cycles to failure for these flip chips have been found. In the computer modeling part of the work, a comprehensive parametric study has provided details on the relationship between the material properties and reliability, and on how underfill entrapment may affect the thermal–mechanical fatigue life of flip chips with no-flow underfill.

Liquid phase behavior of ionic liquids with alcohols: Experimental studies and modeling

Ionic liquids (ILs) have been suggested as potential

Switched Diversity Techniques for Indoor Off-body Communications Channels: An Experimental Analysis and Modeling

This paper investigates the potential improvement in signal reliability for indoor off-body communications channels operating at 5.8 GHz using switched diversity techniques. In particular we investigate the performance of switch-and-stay combining (SSC), switch-and-examine combining (SEC) and switch-and-examine combining with post-examining selection (SECps) schemes which utilize multiple spatially separated antennas at the base station. During the measurements a test subject, wearing an antenna on his chest, performed a number of walking movements towards and then away from a uniform linear array. It was found that all of the considered diversity schemes provided a worthwhile signal improvement. However, the performance of the diversity systems varied according to the switching threshold that was adopted. To model the fading envelope observed at the output of each of the combiners, we have applied diversity specific equations developed under the assumption of Nakagami-$m$ fading. As a measure of the goodness-of-fit, the Kullback-Leibler divergence between the empirical and theoretical probability density functions (PDFs) was calculated and found to be close to 0. To assist with the interpretation of the goodness-of-fit achieved in this study, the standard deviation, $\sigma$, of a zero-mean, $\sigma^2$ variance Gaussian PDF used to approximate a zero-mean, unit variance Gaussian PDF is also presented. These were generally quite close to 1 indicating that the theoretical models provided an adequate fit to the measured data.

Modeling Metal Protein Complexes from Experimental Extended X-ray Absorption Fine Structure using Computational Intelligence

Experimental Extended X-ray Absorption Fine Structure (EXAFS) spectra carry information about the chemical structure of metal protein complexes. However, pre- dicting the structure of such complexes from EXAFS spectra is not a simple task. Currently methods such as Monte Carlo optimization or simulated annealing are used in structure refinement of EXAFS. These methods have proven somewhat successful in structure refinement but have not been successful in finding the global minima. Multiple population based algorithms, including a genetic algorithm, a restarting ge- netic algorithm, differential evolution, and particle swarm optimization, are studied for their effectiveness in structure refinement of EXAFS. The oxygen-evolving com- plex in S1 is used as a benchmark for comparing the algorithms. These algorithms were successful in finding new atomic structures that produced improved calculated EXAFS spectra over atomic structures previously found.

Sparse kernel regression modeling using combined locally regularized orthogonal least squares and D-optimality experimental design

The note proposes an efficient nonlinear identification algorithm by combining a locally regularized orthogonal least squares (LROLS) model selection with a D-optimality experimental design. The proposed algorithm aims to achieve maximized model robustness and sparsity via two effective and complementary approaches. The LROLS method alone is capable of producing a very parsimonious model with excellent generalization performance. The D-optimality design criterion further enhances the model efficiency and robustness. An added advantage is that the user only needs to specify a weighting for the D-optimality cost in the combined model selecting criterion and the entire model construction procedure becomes automatic. The value of this weighting does not influence the model selection procedure critically and it can be chosen with ease from a wide range of values.