Robot team coordination using dynamic role and positioning assignment and role based setplays

The coordination methodologies of CAMBADA, a robotic soccer team designed to participate in the RoboCup Middle-Size League (MSL), are presented in this paper. The approach, which relies on information sharing and integration within the team, is based on formations, flexible positionings and dynamic role and positioning assignment. Role assignment is carried out locally on each robot to increase its reactivity. Positioning assignment is carried out at a lower frequency by a coach agent following a new priority-based algorithm that maintains a competitive formation, covering the most important positionings when malfunctions lead to a reduction of the team size. Coordinated procedures for passing and setplays have also been implemented. With this design, CAMBADA reached the 1st place in RoboCup’2008 and the 3rd place in RoboCup’2009. Competition results and performance measures computed from logs and videos of real competition games are presented and discussed.     

Modeling of adsorption isotherm of a binary mixture with real adsorbed solution theory and nonrandom two‐liquid model

Gas‐phase adsorption equilibria of diluted mixtures of methyl‐ethyl‐ketone and isopropylanol on activated carbon were investigated. Experimental isotherms were determined by a constant volume method. Single‐component adsorption isotherms were fitted by the frequently used Toth model with good accuracy. Then adsorption isotherms were determined for different binary mixtures (with different initial ratio of the two components). Binary mixtures adsorption isotherms were calculated using the adsorbed solution theory. Ideal adsorbed solution theory (IAST) could not represent experimental data, but it was observed that increasing amount of MEK led to higher nonideality of the mixture. Then UNIversal QUAsi Chemical (UNIQUAC) and nonrandom two‐liquids (NRTL) models were considered to describe activity coefficients of the adsorbed phase. The fitted parameters of UNIQUAC model depend on the ratio of the two components, whereas the NRTL model is able to fit all experiments with the same parameters, whatever the initial ratio may be. © 2010 American Institute of Chemical Engineers AIChE J, 2010     

Further developments on the estimation of rigid body properties from experimental data

The evaluation of the rigid body properties of a structure is a very important issue for the study of its dynamic behaviour when the influence of the rigid body modes is considerably significant.Various approaches exist for this purpose. In the present work the authors explore a particular group of methods where ten rigid body properties are estimated from modal information extracted from Frequency Response Data (FRF) measured on structures. This kind of methods, usually known as modal methods, use the identified modal properties associated to the rigid body modes and their orthogonality properties. However, some difficulties have been reported in cases when a high degree of symmetry exists in the structure, when double modes appear or when it is not possible to excite all the six rigid body modes. New improvements and methodologies are proposed to help solving such situations.     

Antioxidant properties of anthocyanidins,anthocyanidin-3-glucosides and respective portisins

The study of the antioxidants properties of three classes of pigments, namely anthocyanidins (cyanidin-Cy, delphinidin-Dp and malvidin-Mv), anthocyanidin-3-glucosides (Cy-3-gluc, Dp-3-gluc and Mv-3-gluc), and portisins (Cy-pt, Dp-pt and Mv-pt) was carried out. The aim was to evaluate the relationship between the structure antioxidant properties of individual anthocyanins and respective derivative pigments. The ability of these compounds to inhibit lipid peroxidation in a liposome membrane system was examined by monitoring oxygen consumption and the antiradical and reducing capacities were determined using the DPPH and FRAP assay, respectively. All compounds tested showed antiradical and reducing properties. These features seemed to increase with the presence of catechol and pyrogallol groups in ring B of anthocyanidin-3-glucosides and respective aglycones. The results obtained for portisins are very likely to be related with their unique structural features. The flavanol moiety of the compounds structure seems to be crucial for the antiradical properties, whilst their reducing ability was only evident for the portisin derived from malvidin. This outcome could be due to some of the structural features of these pigments as they are complex structures and may have several different conformations in solution. The antioxidant protection towards lipid peroxidation increased with the overall hydrophobicity of the compounds. This feature is related to the location of the antioxidant on the liposome surface vs. water phase. Among the pigments tested, portisins showed the higher effect in preventing lipid peroxidation of soybean phosphatidylcholine liposomes, especially the portisin derived from Cy. This outcome could be due to the presence of two o-catechol groups in the structure of this compound.     

Damage detection and quantification using transmissibility

Structures experience various types of loads along their lifetime, which can be either static or dynamic and may be associated to phenomena of corrosion and chemical attack, among others. As a consequence, different types of structural damage can be produced; the deteriorated structure may have its capacity affected, leading to excessive vibration problems or even possible failure. It is very important to develop methods that are able to simultaneously detect the existence of damage and to quantify its extent. In this paper the authors propose a method to detect and quantify structural damage, using response transmissibilities measured along the structure. Some numerical simulations are presented and a comparison is made with results using frequency response functions. Experimental tests are also undertaken to validate the proposed technique.     

Is carbon lock-in blocking investments in the hydrogen economy? A survey of actors’ strategies

The difficulty of introducing hydrogen and fuel cells in the market stems from the fact that they are not an evolutionary innovation such as biofuels or hybrid cars. Instead they create a disruption in technological utilization. The domination of oil technologies sets a socio-economical context favoring actors involved in the current paradigm, and gives less opportunity to alternative fuels to develop and challenge the status quo. If this hypothesis is correct, then companies interested in the hydrogen economy would not become active because of an unstable context or contradictory interests concerning the replacement of the present system. A review of actions and announcements of main actors shows that technology readiness and the absence of infrastructure are the major justifications to delay investments. Some measures are discussed, which could be deployed in order to reduce uncertainties, such as regulation of carbon emissions from cars, technological subvention, and partnerships for infrastructure implementation.     

Bovine Milk Formula Based on Partial Hydrolysis of Caseins by Bromelain Enzyme: Better Digestibility and Angiotensin-Converting Enzyme-Inhibitory Properties

A new methodology was developed using bromelain as a proteolytic enzyme for the production of a partially hydrolyzed bovine milk formula, without changing the milk's organoleptic and nutritional properties. This study also revealed that the in vitro angiotensin-converting enzyme inhibition increased in the modified milk as compared with the control sample, showing the highest angiotensin-converting enzyme-inhibitory activity for fractions with molecular weights between 3 and 10 kg mol^?1. The Lineweaver-Burk plot revealed that the produced milk formula acted as a non-competitive inhibitor, as determined by high performance liquid chromatography.     

Non-toxic Salvia sclareoides Brot. extracts as a source of functional food ingredients: Phenolic profile,antioxidant activity and prion binding properties

Salvia sclareoides is an aromatic herb native to Portugal, of which phenolic content (Folin–Ciocalteau method), chemical profile (HPLC/DAD), antioxidant activity (DPPH, β-carotene/linoleic acid assays), acute toxicity (MTT method, adapted for non-adherent cells), genotoxicity (short-term chromosomal aberration assay) and prion binding properties were evaluated in the acetone, water, ethanol, methanol and n-butanol extracts. The latter presented the highest phenolic content and antioxidant activity (DPPH assay), and was the single one with the flavonoids (+)-catechin, kaempferol O-glucoside and quercetin. Vanillic acid was the major component of all extracts but gallic, gentisic, caffeic, syringic, coumaric and ferulic acids were also found in some extracts. Only the n-butanol extract had components binding to the cellular form of human prion protein detected by NMR which showed specificity for two regions of the folded domain and for the unstructured N-terminal region. Extracts were not cytotoxic nor genotoxic, reinforcing the potential of S. sclareoides for nutraceutical purposes.     

ASR-based exercises for listening comprehension practice in European Portuguese

Spoken European Portuguese (EP) is known to be difficult to understand for L2 learners, due to phenomena such as strong vowel reduction. In this paper, we present a method to automatically generate exercises aimed at improving listening comprehension skills in EP. Learners identify the words pronounced in real speech utterances. The exercises introduce two innovative aspects: using broadcast news videos for curriculum and automatically generating exercises with material updated on a daily basis. The videos are automatically transcribed by a speech recognition engine. A filtering chain, used to select appropriate sentences, was validated by a first survey comprised of both manually and automatically selected sentences. Both sets were assigned good to very good subjective quality scores. A second survey concerned the features of the exercise interface. Subjects with varying self-reported exposure to Portuguese as a second language tested several interfaces and functionalities and highlighted their preferred features. The results confirmed that the largest difficulty was the fast speech rate. All participants valued slowed-down audio and video documents, though this feature was more often used by the lowest proficiency subjects. The exercises were integrated into a Web platform where they are automatically updated daily. Though further evaluation is needed to find whether the platform affords skill acquisition, it is expected to be particularly valuable for distance learners who need opportunities to access authentic audio documents in EP.     

Counting people with low-level features and Bayesian regression

An approach to the problem of estimating the size of inhomogeneous crowds, which are composed of pedestrians that travel in different directions, without using explicit object segmentation or tracking is proposed. Instead, the crowd is segmented into components of homogeneous motion, using the mixture of dynamic-texture motion model. A set of holistic low-level features is extracted from each segmented region, and a function that maps features into estimates of the number of people per segment is learned with Bayesian regression. Two Bayesian regression models are examined. The first is a combination of Gaussian process regression with a compound kernel, which accounts for both the global and local trends of the count mapping but is limited by the real-valued outputs that do not match the discrete counts. We address this limitation with a second model, which is based on a Bayesian treatment of Poisson regression that introduces a prior distribution on the linear weights of the model. Since exact inference is analytically intractable, a closed-form approximation is derived that is computationally efficient and kernelizable, enabling the representation of nonlinear functions. An approximate marginal likelihood is also derived for kernel hyperparameter learning. The two regression-based crowd counting methods are evaluated on a large pedestrian data set, containing very distinct camera views, pedestrian traffic, and outliers, such as bikes or skateboarders. Experimental results show that regression-based counts are accurate regardless of the crowd size, outperforming the count estimates produced by state-of-the-art pedestrian detectors. Results on 2 h of video demonstrate the efficiency and robustness of the regression-based crowd size estimation over long periods of time.