Methyl syringate: An efficient phenolic mediator for bacterial and fungal laccases

The aim of the present work is to provide insight into the mechanism of laccase reactions using syringyl-type mediators. We studied the pH dependence and the kinetics of oxidation of syringyl-type phenolics using the low CotA and the high redox potential TvL laccases. Additionally, the efficiency of these compounds as redox mediators for the oxidation of non-phenolic lignin units was tested at different pH values and increasing mediator/non-phenolic ratios. Finally, the intermediates and products of reactions were identified by LC-MS and H-1 NMR. These approaches allow concluding on the (1) mechanism involved in the oxidation of phenolics by bacterial laccases, (2) importance of the chemical nature and properties of phenolic mediators, (3) apparent independence of the enzyme's properties on the yields of non-phenolics conversion, (4) competitive routes involved in the catalytic cycle of the laccase-mediator system with several new C-O coupling type structures being proposed.

Digital-domain self-calibration technique for video-rate pipeline A/D converters using Gaussian white noise

Electronics Letters Vol.38, nº 19

A random key based genetic algorithm for the resource constrained project scheduling problem

This paper presents a genetic algorithm for the Resource Constrained Project Scheduling Problem (RCPSP). The chromosome representation of the problem is based on random keys. The schedule is constructed using a heuristic priority rule in which the priorities of the activities are defined by the genetic algorithm. The heuristic generates parameterized active schedules. The approach was tested on a set of standard problems taken from the literature and compared with other approaches. The computational results validate the effectiveness of the proposed algorithm.

An optimization approach for the job shop scheduling problem

This paper presents an optimization approach for the job shop scheduling problem (JSSP). The JSSP is a difficult problem in combinatorial optimization for which extensive investigation has been devoted to the development of efficient algorithms. The proposed approach is based on a genetic algorithm technique. The scheduling rules such as SPT and MWKR are integrated into the process of genetic evolution. The chromosome representation of the problem is based on random keys. The schedules are constructed using a priority rule in which the priorities and delay times of the operations are defined by the genetic algorithm. Schedules are constructed using a procedure that generates parameterized active schedules. After a schedule is obtained a local search heuristic is applied to improve the solution. The approach is tested on a set of standard instances taken from the literature and compared with other approaches. The computation results validate the effectiveness of the proposed approach.

Trajectory planning of redundant manipulators using genetic algorithms

The trajectory planning of redundant robots is an important area of research and efficient optimization algorithms are needed. This paper presents a new technique that combines the closed-loop pseudoinverse method with genetic algorithms. The results are compared with a genetic algorithm that adopts the direct kinematics. In both cases the trajectory planning is formulated as an optimization problem with constraints.

Dynamical modelling of a genetic algorithm

This work addresses the signal propagation and the fractional-order dynamics during the evolution of a genetic algorithm (GA). In order to investigate the phenomena involved in the GA population evolution, the mutation is exposed to excitation perturbations during some generations and the corresponding fitness variations are evaluated. Three distinct fitness functions are used to study their influence in the GA dynamics. The input and output signals are studied revealing a fractional-order dynamic evolution, characteristic of a long-term system memory.

Towards high-available and energy-efficient virtual computing environments in the cloud

Empowered by virtualisation technology, cloud infrastructures enable the construction of flexi- ble and elastic computing environments, providing an opportunity for energy and resource cost optimisation while enhancing system availability and achieving high performance. A crucial re- quirement for effective consolidation is the ability to efficiently utilise system resources for high- availability computing and energy-efficiency optimisation to reduce operational costs and carbon footprints in the environment. Additionally, failures in highly networked computing systems can negatively impact system performance substantially, prohibiting the system from achieving its initial objectives. In this paper, we propose algorithms to dynamically construct and readjust vir- tual clusters to enable the execution of users’ jobs. Allied with an energy optimising mechanism to detect and mitigate energy inefficiencies, our decision-making algorithms leverage virtuali- sation tools to provide proactive fault-tolerance and energy-efficiency to virtual clusters. We conducted simulations by injecting random synthetic jobs and jobs using the latest version of the Google cloud tracelogs. The results indicate that our strategy improves the work per Joule ratio by approximately 12.9% and the working efficiency by almost 15.9% compared with other state-of-the-art algorithms.

A pattern language for parallelizing irregular algorithms

Dissertação apresentada na Faculdade de Ciências e Tecnologia da Universidade Nova de Lisboa para obtenção do grau de Mestre em Engenharia Informática

Power-and-area efficient 14-bit 1.5 MSample/s two-stage algorithmic ADC based on a mismatch-insensitive MDAC

IEEE International Symposium on Circuits and Systems, pp. 220 – 223, Seattle, EUA

Optimization of multi-stage amplifiers in deep-submicron CMOS using a distributed/parallel genetic algorithm

IEEE International Symposium on Circuits and Systems, pp. 724 – 727, Seattle, EUA

Toxoplasmosis serology: an efficient hemagglutination procedure to detect IgG and IgM antibodies

In search of an efficient but simple, low cost procedure for the serodiagnosis of Toxoplasmosis, especially suited for routine laboratories facing technical and budget limitations as in less developed countries, the diagnostic capability of Hematoxo® , an hemagglutination test for toxoplasmosis, was evaluated in relation to a battery of tests including IgG- and IgM-immunofluorescence tests, hemagglutination and an IgM-capture enzymatic assay. Detecting a little as 5 I.U. of IgG antitoxoplasma antibodies, Hematoxo® showed a straight agreement as to reactivity and non-reactivity for the 443 non-reactive and the 387 reactive serum samples, included in this study. In 23 cases presenting a serological pattern of acute toxoplasmosis and showing IgM antibodies, Hematoxo® could detect IgM antibodies in 18, indicated by negativation or a significant decrease in titers as a result of treating samples with 2-mercapto-ethanol. However, a neat increase in sensitivity for IgM specific antibodies could be achieved by previously removing IgG from the sample, as demonstrated in a series of acute toxoplasmosis sera. A simple procedure was developed for this purpose, by reconstituting a lyophilized suspension of Protein A - rich Staphylococcus with the lowest serum dilution to be tested. Of low cost and easy to perform, Hematoxo® affords not only a practical qualitative procedure for screening reactors and non-reactors, as in prenatal services, but also quantitative assays that permit to titrate antibodies as well as to identify IgM antibodies.

Including context in a routing algorithm for the internet of things

Dissertação apresentada na Faculdade de Ciências e Tecnologia da Universidade Nova de Lisboa para obtenção do grau de Mestre em Engenharia Informática

Detect and pointing algorithm's performance for a planar smart antenna array: a review

An adaptive antenna array combines the signal of each element, using some constraints to produce the radiation pattern of the antenna, while maximizing the performance of the system. Direction of arrival (DOA) algorithms are applied to determine the directions of impinging signals, whereas beamforming techniques are employed to determine the appropriate weights for the array elements, to create the desired pattern. In this paper, a detailed analysis of both categories of algorithms is made, when a planar antenna array is used. Several simulation results show that it is possible to point an antenna array in a desired direction based on the DOA estimation and on the beamforming algorithms. A comparison of the performance in terms of runtime and accuracy of the used algorithms is made. These characteristics are dependent on the SNR of the incoming signal.

Nanoparticles based Permeable Reactive Barriers as an Eco-efficient Technology for nitrate remediation in soil and groundwater

The need to increase agricultural yield led, among others, to an increase in the consumption of nitrogen based fertilizers. As a consequence, there are excessive concentrations of nitrates, the most abundant of the reactive nitrogen (Nr) species, in several areas of the world. The demographic changes and projected population growth for the next decades, and the economic shifts which are already shaping the near future are powerful drivers for a further intensification in the use of fertilizers, with a predicted increase of the nitrogen loads in soils. Nitrate easily diffuses in the subsurface environments, portraying high mobility in soils. Moreover, the presence of high nitrate loads in water has the potential to cause an array of health dysfunctions, such as methemoglobinemia and several cancers. Permeable Reactive Barriers (PRB) placed strategically relatively to the nitrate source constitute an effective technology to tackle nitrate pollution. Ergo, PRB avoid various adverse impacts resulting from the displacement of reactive nitrogen downstream along water bodies. A four stages literature review was carried out in 34 databases. Initially, a set of pertinent key words were identified to perform the initial databases searches. Then, the synonyms of those initial key words were used to carry out a second set of databases searches. The third stage comprised the identification of other additional relevant terms from the research papers identified in the previous two stages. Again, databases searches were performed with this third set of key words. The final step consisted of the identification of relevant papers from the bibliography of the relevant papers identified in the previous three stages of the literature review process. The set of papers identified as relevant for in-depth analysis were assessed considering a set of relevant characterization variables.

A physical packing sequence algorithm for the container loading problem with static mechanical equilibrium conditions

The container loading problem (CLP) is a combinatorial optimization problem for the spatial arrangement of cargo inside containers so as to maximize the usage of space. The algorithms for this problem are of limited practical applicability if real-world constraints are not considered, one of the most important of which is deemed to be stability. This paper addresses static stability, as opposed to dynamic stability, looking at the stability of the cargo during container loading. This paper proposes two algorithms. The first is a static stability algorithm based on static mechanical equilibrium conditions that can be used as a stability evaluation function embedded in CLP algorithms (e.g. constructive heuristics, metaheuristics). The second proposed algorithm is a physical packing sequence algorithm that, given a container loading arrangement, generates the actual sequence by which each box is placed inside the container, considering static stability and loading operation efficiency constraints.