e-Infrastructures for e-Science: A Global View

In the last 10?years, a new way of doing science is spreading in the world thank to the development of virtual research communities across many geographic and administrative boundaries. A virtual research community is a widely dispersed group of researchers and associated scientific instruments working together in a common virtual environment. This new kind of scientific environment, usually addressed as a ??collaboratory??, is based on the availability of high-speed networks and broadband access, advanced virtual tools and Grid-middleware technologies which, altogether, are the elements of the e-Infrastructures. The European Commission has heavily invested in promoting this new way of collaboration among scientists funding several international projects with the aim of creating e-Infrastructures to enable the European Research Area and connect the European researchers with their colleagues based in Africa, Asia and Latin America. In this paper we describe the actual status of these e-Infrastructures and present a complete picture of the virtual research communities currently using them. Information on the scientific domains and on the applications supported are provided together with their geographic distribution.     

Discrimination between defective and non-defective roasted coffees by diffuse reflectance infrared Fourier transform spectroscopy

The objective of this work was to evaluate the feasibility of employing Diffuse Reflectance Infrared Fourier Transform Spectroscopy (DRIFTS) for discrimination between defective and non-defective coffees after roasting and grinding. Defective (black, immature and sour) and non-defective Arabica coffee beans were submitted to light, medium and dark roasts at 220, 235 and 250 °C. Principal Components Analysis of the DRIFTS spectra (normalized or not) and of the first derivatives of the spectra provided separation of the samples into four groups: non-defective, black, dark sour and light sour, with immature beans scattered among the sour samples. Classification models were developed based on Linear Discriminant Analysis and recognition and prediction abilities of these models ranged from 95 to 100%. Such results indicate that DRIFTS presents potential for the development of a fast and reliable analytical methodology for discrimination between defective and non-defective coffee after roasting and grinding.     

A knapsack problem as a tool to solve the production planning problem in small foundries

According to recent research carried out in the foundry sector, one of the most important concerns of the industries is to improve their production planning. A foundry production plan involves two dependent stages: (1) determining the alloys to be merged and (2) determining the lots that will be produced. The purpose of this study is to draw up plans of minimum production cost for the lot-sizing problem for small foundries. As suggested in the literature, the proposed heuristic addresses the problem stages in a hierarchical way. Firstly, the alloys are determined and, subsequently, the items that are produced from them. In this study, a knapsack problem as a tool to determine the items to be produced from furnace loading was proposed. Moreover, we proposed a genetic algorithm to explore some possible sets of alloys and to determine the production planning for a small foundry. Our method attempts to overcome the difficulties in finding good production planning presented by the method proposed in the literature. The computational experiments show that the proposed methods presented better results than the literature. Furthermore, the proposed methods do not need commercial software, which is favorable for small foundries.     

A self-organizing neural network using ideas from the immune system to solve the traveling salesman problem

Most combinatorial optimization problems belong to the NP-complete or NP-hard classes, which means that they may require an infeasible processing time to be solved by an exhaustive search method. Thus, less expensive heuristics in respect to the processing time are commonly used. These heuristics can obtain satisfactory solutions in short running times, but there is no guarantee that the optimal solution will be found. Artificial Neural Networks (ANNs) have been widely studied to solve combinatorial problems, presenting encouraging results. This paper proposes some modifications on RABNET-TSP, an immune-inspired self-organizing neural network, for the solution of the Traveling Salesman Problem (TSP). The modified algorithm is compared with other neural methods from the literature and the results obtained suggest that the proposed method is competitive in relation to the other ones, outperforming them in many cases with regards to the quality (cost) of the solutions found, though demanding a greater time for convergence in many cases.     

A liquid chromatography miniaturised system for agrofood applications

This work presents the realization of a Si-based miniaturised device for liquid chromatography (LC), targeted for agrofood applications and in particular for wine quality monitoring. The main modules of the system are: (1) a Si-based separation column, functionalized by n-octyltriethoxysilane (C8-TEOS), with inlet/outlet for fluidic connections; (2) a three-microelectrode voltammetric sensor. The system has been properly packaged and the capability of the sensing module to detect acetic acid concentration changes has been verified.     

Different Approaches for Digestion,Performance Assessment and Measurement Uncertainty for the Analysis of Cadmium and Lead in Feeds

Certain feedingstuffs are sources of contamination for animals with cadmium (Cd) and lead (Pb). Different approaches for sample digestion, performance assessment, and measurement uncertainty for the analysis of Pb and Cd by flame atomic absorption spectrometry were compared. Full method validation, including matrix effect and analytical limits, were assessed. Despite having a lower performance compared to the microwave digestion, dry ashing was a viable alternative for sample digestion, leading to lower operating costs. Interlaboratory validation proved to be a cheaper alternative, instead of the long traditional in-house approach. The method was suitable for routine analysis of several feed samples. There was a greater amount of quantifiable results for Pb, ranging from traces up to high concentrations (7430 ± 930 mg kg^-1). Cd was also quantified in some samples, ranging from 0.11 ± 0.01 to 8.71 ± 0.90 mg kg^?1. The higher concentrations of both metals were found in mineral mixtures and fish meal.     

Experimental characterization of Ti6Al4V T joints welded through linear friction welding technique: microstructure and NDE

Linear friction welding (LFW) is an innovative solid-state welding technique that allows to manufacture joints with high mechanical properties. This technology has various applications in the aerospace field; in particular it is used to weld massive structural components made of Ti6Al4V. This paper deals with the experimental study of Ti6Al4V T-joints welded through LFW, with particular focus on the effectiveness of ultrasonic control in detecting and distinguishing welding defects within the joints. Aiming to this scope, joints with different properties were manufactured and tested:some were free from defects but with different metallurgy, and some had different types of defects. The results obtained proved that the ultrasonic control was an effective method to detect and identify defects in linear friction welded titanium joints, moreover it was possible to get information regarding the microstructure and in particular the extension of the different metallurgical zones induced by the welding process.     

Adsorption of Cr(VI) from aqueous solution by hydrous zirconium oxide

A type of ZrO₂·nH₂O was synthesized and its Cr(VI) removal potential was investigated in this study. The kinetic study, adsorption isotherm, pH effect, thermodynamic study and desorption were examined in batch experiments. The kinetic process was described by a pseudo-second-order rate model very well. The Cr(VI) adsorption tended to increase with a decrease of pH. The adsorption data fitted well to the Langmuir model. The adsorption capacity increased from 61 to 66 mg g^?1 when the temperature was increased from 298 to 338 K. The positive values of both ΔH° and ΔS° suggest an endothermic reaction and increase in randomness at the solid–liquid interface during the adsorption. ΔG° values obtained were negative indicating a spontaneous adsorption process. The effective desorption of Cr(VI) on ZrO₂·nH₂O could be achieved using distilled water at pH 12.     

An efficient particle swarm approach for mixed-integer programming in reliability-redundancy optimization applications

The reliability-redundancy optimization problems can involve the selection of components with multiple choices and redundancy levels that produce maximum benefits, and are subject to the cost, weight, and volume constraints. Many classical mathematical methods have failed in handling nonconvexities and nonsmoothness in reliability-redundancy optimization problems. As an alternative to the classical optimization approaches, the meta-heuristics have been given much attention by many researchers due to their ability to find an almost global optimal solutions. One of these meta-heuristics is the particle swarm optimization (PSO). PSO is a population-based heuristic optimization technique inspired by social behavior of bird flocking and fish schooling. This paper presents an efficient PSO algorithm based on Gaussian distribution and chaotic sequence (PSO-GC) to solve the reliability-redundancy optimization problems. In this context, two examples in reliability-redundancy design problems are evaluated. Simulation results demonstrate that the proposed PSO-GC is a promising optimization technique. PSO-GC performs well for the two examples of mixed-integer programming in reliability-redundancy applications considered in this paper. The solutions obtained by the PSO-GC are better than the previously best-known solutions available in the recent literature.     

Improved quantum-inspired evolutionary algorithm with diversity information applied to economic dispatch problem with prohibited operating zones

The objective of the economic dispatch problem (EDP) of electric power generation, whose characteristics are complex and highly nonlinear, is to schedule the committed generating unit outputs so as to meet the required load demand at minimum operating cost while satisfying all unit and system equality and inequality constraints. Recently, as an alternative to the conventional mathematical approaches, modern meta-heuristic optimization techniques have been given much attention by many researchers due to their ability to find an almost global optimal solution in EDPs. Research on merging evolutionary computation and quantum computation has been started since late 1990. Inspired on the quantum computation, this paper presented an improved quantum-inspired evolutionary algorithm (IQEA) based on diversity information of population. A classical quantum-inspired evolutionary algorithm (QEA) and the IQEA were implemented and validated for a benchmark of EDP with 15 thermal generators with prohibited operating zones. From the results for the benchmark problem, it is observed that the proposed IQEA approach provides promising results when compared to various methods available in the literature.