A contribution for developing more efficient dynamic modelling algorithms of parallel robots

Parallel kinematic structures are considered very adequate architectures for positioning and orienti ng the tools of robotic mechanisms. However, developing dynamic models for this kind of systems is sometimes a difficult task. In fact, the direct application of traditional methods of robotics, for modelling and analysing such systems, usually does not lead to efficient and systematic algorithms. This work addre sses this issue: to present a modular approach to generate the dynamic model and through some convenient modifications, how we can make these methods more applicable to parallel structures as well. Kane’s formulati on to obtain the dynamic equations is shown to be one of the easiest ways to deal with redundant coordinates and kinematic constraints, so that a suitable c hoice of a set of coordinates allows the remaining of the modelling procedure to be computer aided. The advantages of this approach are discussed in the modelling of a 3-dof parallel asymmetric mechanisms.

The dual pathway in action: decoupling parallel routes for CO2 production during the oscillatory electro-oxidation of methanol

As in the case of most small organic molecules, the electro-oxidation of methanol to CO2 is believed to proceed through a so-called dual pathway mechanism. The direct pathway proceeds via reactive intermediates such as formaldehyde or formic acid, whereas the indirect pathway occurs in parallel, and proceeds via the formation of adsorbed carbon monoxide (COad). Despite the extensive literature on the electro-oxidation of methanol, no study to date distinguished the production of CO2 from direct and indirect pathways. Working under, far-from-equilibrium, oscillatory conditions, we were able to decouple, for the first time, the direct and indirect pathways that lead to CO2 during the oscillatory electro-oxidation of methanol on platinum. The CO2 production was followed by differential electrochemical mass spectrometry and the individual contributions of parallel pathways were identified by a combination of experiments and numerical simulations. We believe that our report opens some perspectives, particularly as a methodology to be used to identify the role played by surface modifiers in the relative weight of both pathways-a key issue to the effective development of catalysts for low temperature fuel cells.

New insight into the mechanisms associated with the rapid effect of T-3 on AT1R expression

The angiotensin II type 1 receptor (AT1R) is involved in the development of cardiac hypertrophy promoted by thyroid hormone. Recently, we demonstrated that triiodothyronine (T-3) rapidly increases AT1R mRNA and protein levels in cardiomyocyte cultures. However, the molecular mechanisms responsible for these rapid events are not yet known. In this study, we investigated the T-3 effect on AT1R mRNA polyadenylation in cultured cardiomyocytes as well as on the expression of microRNA-350 (miR-350), which targets AT1R mRNA. The transcriptional and translational actions mediated by T-3 on AT1R levels were also assessed. The total content of ubiquitinated proteins in cardiomyocytes treated with T-3 was investigated. Our data confirmed that T-3 rapidly raised AT1R mRNA and protein levels, as assessed by real-time PCR and western blotting respectively. The use of inhibitors of mRNA and protein synthesis prevented the rapid increase in AT1R protein levels mediated by T-3. In addition, T-3 rapidly increased the poly-A tail length of the AT1R mRNA, as determined by rapid amplification of cDNA ends poly-A test, and decreased the content of ubiquitinated proteins in cardiomyocytes. On the other hand, T-3 treatment increased miR-350 expression. In parallel with its transcriptional and translational effects on the AT1R, T-3 exerted a rapid posttranscriptional action on AT1R mRNA polyadenylation, which might be contributing to increase transcript stability, as well as on translational efficiency, resulting to the rapid increase in AT1R mRNA expression and protein levels. Finally, these results show, for the first time, that T-3 rapidly triggers distinct mechanisms, which might contribute to the regulation of AT1R levels in cardiomyocytes. Journal of Molecular Endocrinology (2012) 49, 11-20

Emergence of sensory selection mechanisms in Artificial Life simulations

Background The evolutionary advantages of selective attention are unclear. Since the study of selective attention began, it has been suggested that the nervous system only processes the most relevant stimuli because of its limited capacity [1]. An alternative proposal is that action planning requires the inhibition of irrelevant stimuli, which forces the nervous system to limit its processing [2]. An evolutionary approach might provide additional clues to clarify the role of selective attention. Methods We developed Artificial Life simulations wherein animals were repeatedly presented two objects, "left" and "right", each of which could be "food" or "non-food." The animals' neural networks (multilayer perceptrons) had two input nodes, one for each object, and two output nodes to determine if the animal ate each of the objects. The neural networks also had a variable number of hidden nodes, which determined whether or not it had enough capacity to process both stimuli (Table 1). The evolutionary relevance of the left and the right food objects could also vary depending on how much the animal's fitness was increased when ingesting them (Table 1). We compared sensory processing in animals with or without limited capacity, which evolved in simulations in which the objects had the same or different relevances. Table 1. Nine sets of simulations were performed, varying the values of food objects and the number of hidden nodes in the neural networks. The values of left and right food were swapped during the second half of the simulations. Non-food objects were always worth -3. The evolution of neural networks was simulated by a simple genetic algorithm. Fitness was a function of the number of food and non-food objects each animal ate and the chromosomes determined the node biases and synaptic weights. During each simulation, 10 populations of 20 individuals each evolved in parallel for 20,000 generations, then the relevance of food objects was swapped and the simulation was run again for another 20,000 generations. The neural networks were evaluated by their ability to identify the two objects correctly. The detectability (d') for the left and the right objects was calculated using Signal Detection Theory [3]. Results and conclusion When both stimuli were equally relevant, networks with two hidden nodes only processed one stimulus and ignored the other. With four or eight hidden nodes, they could correctly identify both stimuli. When the stimuli had different relevances, the d' for the most relevant stimulus was higher than the d' for the least relevant stimulus, even when the networks had four or eight hidden nodes. We conclude that selection mechanisms arose in our simulations depending not only on the size of the neuron networks but also on the stimuli's relevance for action.