Algorithmic aspects of Suslin's proof of Serre's conjecture

LetF be a unimodularr×s matrix with entries beingn-variate polynomials over an infinite fieldK. Denote by deg(F) the maximum of the degrees of the entries ofF and letd=1+deg(F). We describe an algorithm which computes a unimodulars×s matrixM with deg(M)=(rd) ^O(n) such thatFM=[I _r ,O], where [I _r ,O] denotes ther×s matrix obtained by adding to ther×r unit matrixI _r s–r zero columns.We present the algorithm as an arithmetic network with inputs fromK, and we count field operations and comparisons as unit cost.The sequential complexity of our algorithm amounts to field operations and comparisons inK whereas its parallel complexity isO(n ⁴ r ⁴log²(srd)).The complexity bounds and the degree bound for deg(M) mentioned above are optimal in order. Our algorithm is inspired by Suslin's proof of Serre's Conjecture.     

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.     

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.     

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.     

Decomposition of carbon-containing compounds on solid catalysts for single-walled nanotube production

Different catalyst formulations and reaction conditions have been used to test the validity of a hypothesis that tries to elucidate the mechanism of single-walled nanotube (SWNT) formation by CO disproportionation over a highly selective cobalt-molybdenum catalyst. This model proposed an intrinsic dependence between the selectivity of the catalysts toward SWNT and the stabilization of Co species in a nonmetallic state, which in turn results from an interaction with Mo. The series of tests performed to examine this model include the doping of this highly selective catalyst with sodium, the substitution of molybdenum by tungsten in the original catalyst formulation, the variation on reaction temperature, and the introduction of hydrogen in the gas feedstock. All these modifications were carried out to modify the growth conditions in which the SWNTs are formed. The results are consistent with the hypothesis proposed by the authors.     

Evolving clusters in gene-expression data

Clustering is a useful exploratory tool for gene-expression data. Although successful applications of clustering techniques have been reported in the literature, there is no method of choice in the gene-expression analysis community. Moreover, there are only a few works that deal with the problem of automatically estimating the number of clusters in bioinformatics datasets. Most clustering methods require the number k of clusters to be either specified in advance or selected a posteriori from a set of clustering solutions over a range of k. In both cases, the user has to select the number of clusters. This paper proposes improvements to a clustering genetic algorithm that is capable of automatically discovering an optimal number of clusters and its corresponding optimal partition based upon numeric criteria. The proposed improvements are mainly designed to enhance the efficiency of the original clustering genetic algorithm, resulting in two new clustering genetic algorithms and an evolutionary algorithm for clustering (EAC). The original clustering genetic algorithm and its modified versions are evaluated in several runs using six gene-expression datasets in which the right clusters are known a priori. The results illustrate that all the proposed algorithms perform well in gene-expression data, although statistical comparisons in terms of the computational efficiency of each algorithm point out that EAC outperforms the others. Statistical evidence also shows that EAC is able to outperform a traditional method based on multiple runs of k-means over a range of k.