Rheological properties and gel strength of Phaseolus lunatus protein/carboxymethylated flamboyant gum systems

The rheological behaviour and gel strength of hydrocolloid mixture systems (HMSs) of carboxymethylated flamboyant gum (CFG) with protein hydrolysates (PHs) of Phaseolus lunatus were examined to evaluate the influence of the protein/polysaccharide ratio (2:1 and 3:1), pH (3 and 9) and concentration of solids, according to a 2³ factorial design. The protein concentrate of P. lunatus was hydrolysed with pepsin–pancreatin enzymes. The flow curve results were fitted to the Ostwald–de Waele model. The flow behaviour index (0.66–0.78) for all conditions studied was indicative of the shear‐thinning behaviour. For the HMS, the consistency index (k) values ranged from 0.4 Pa sⁿ to 1.2 Pa sⁿ. The analyses of variance showed that the ratio of PH/CFG and pH were the main variables that had significant effect on k values (P < 0.05). Only PH system presented a weak gel‐like viscoelastic behaviour. Both functional properties were affected by the protein degree of hydrolysis (DH).     

Short‐term load forecasting based on support vector regression and load profiling

The article proposes a methodology to forecast the electric load for the 24 h of the following day based on support vector regression. The study considers 24 distinct models, one for each predicted hour, where each individual model is treated independently. Its objective is to find the optimal combination of support vector machine parameters that could generalize low forecasting errors, using simulated annealing as a metaheuristic. The adopted methodology is compared to concurrent methods based on neural networks when applied to a simulated load diagram (to illustrate a distribution feeder supplying a sample of 740 consumers). The results have proven its effectiveness with mean absolute percentage errors being less than 5% for testing samples. The study also focuses on evaluating the potential benefits of adopting load profiling information as input in support vector regression, giving a consistent proof of its importance. Copyright © 2013 John Wiley & Sons, Ltd.     

Review of the environmental policy instruments in the standardization of H2 for the decarbonization of Mexico

The objective of this work is to review the environmental and ecological policy instruments that Mexican legislation for existence of an Official Mexican Hydrogen Standard according to the premises motivates decarbonization. There is a worldwide interest in hydrogen (H₂) and its incorporation into legal systems. In Mexico there are large legal gaps that question the legality of H₂, because it is not recognized in a federal law and is only considered in technical standards of voluntary application. Also, Mexico has forest, agricultural, and livestock waste that, if properly used, can cover the energy demand of communities. The main findings of this study refer to the fact that, according to international standards for the management of H₂ and taking into account the regulatory limitations in Mexico, it is possible to promote, an Official Mexican Standard exclusively aimed at establishing the minimum requirements for the design, construction, operation and maintenance of H₂ storage, considering the areas of the opportunity offered by foreign experiences, according to the Mexican context. It is concluded that the parameters of legality exist in the instruments of environmental policy, to support the existence of an Official Mexican Standard of H₂.     

Dispatching heuristics for the single machine weighted quadratic tardiness scheduling problem

In this paper, we consider the single machine scheduling problem with weighted quadratic tardiness costs. Several efficient dispatching rules are proposed. These include existing heuristics for the linear problem, as well as procedures suitably adapted to the quadratic objective function. Also, both forward and backward scheduling procedures are considered.The computational results show that the heuristics that specifically take into account the quadratic objective significantly outperform their linear counterparts. Also, the backward scheduling approach proves to be superior, and the difference in performance is even more noticeable for the harder instances.The best of the backward scheduling heuristics is both quite efficient and effective. Indeed, this procedure can quickly generate a schedule even for large instances. Also, its relative deviation from the optimum is usually rather low, and it performs adequately even for the more difficult instances.     

On the regional incidence of highway investments in the USA

The objective of this paper was to investigate the regional incidence of the aggregate effects of investment in highways in the US taking into consideration the presence of regional spillovers. The empirical results are based on VAR estimates at both the aggregate and state levels using output, employment, and investment, as well as different measures of highway investments. Empirical results allow us to establish several stylized facts. First, investment in highways affects private sector variables positively at the aggregate level as well as in most states. Second, the regional spillover effects are very significant for all private sector variables. Third, these spillovers have a clear geographical pattern in that they tend to be more important in western states and the corridor between the Great Lakes and the Gulf Coast.     

New possibilistic method for discovering linear local behavior using hyper-Gaussian distributed membership function

This paper presents a method to find a model of a system based on the integration of a set of local models. Mainly, properties are sought for the local models: independence of clusters and interpretability of their validity. This has been achieved through the introduction of a possibilistic clustering for the first property and a pre-fixed shape of the membership functions for the second one. A new cost index for the clustering optimization problem has been defined consisting of two terms: one for global error and another for local errors. By giving higher importance to the local errors term, local models valid regionally can be found. To avoid local optima and numerical issues, the parameters of the models are found using global optimization. This new method has been applied to several data sets, and results show how the desired characteristics can be achieved in the resulting models.     

Improving object classification robustness in RGB-D using adaptive SVMs

Nowadays object recognition is a fundamental capability for an autonomous robot in interaction with the physical world. Taking advantage of new sensing technologies providing RGB-D data, the object recognition capabilities increase dramatically. Object recognition has been well studied, however, known object classifiers usually feature poor generality and, therefore, limited adaptivity to different application domains. Although some domain adaptation approaches have been presented for RGB data, little work has been done on understanding the effects of applying object classification algorithms using RGB-D for different domains. Addressing this problem, we propose and comprehensively investigate an approach for object recognition in RGB-D data that uses adaptive Support Vector Machines (aSVM) and, in this way, achieves an impressive robustness in cross-domain adaptivity. For evaluation, two datasets from different application domains were used. Moreover, a study of state-of-the-art RGB-D feature extraction techniques and object classification methods was performed to identify which combinations (object representation - classification algorithm) remain less affected in terms of performance while switching between different application domains.