Bioactivity of antifouling paints based on organotin toxicants

This paper discusses the bioactivity of antifouling paints based on organotin toxicants, such as tributyltin fluoride (TBTF) and triphenyltin fluoride (TPTF). These compounds were used alone or mixed as the main toxicant in formulations. The use of TPTF or mixtures of TPTF and TBTF, with vinyl binders, led to paints with satisfactory bioactivity during a 24 months immersion period. With the same binders, TBTF alone provided only 12 months of antifouling protection. The correlation coefficients calculated from the fouling attachment values and the rosin content in the binder showed that the bioactivity of the tested formulations depended on the solubility of the toxicant in sea water and on the composition and solubility of the binders.     

Integrating scheduling and control functions in computer integrated manufacturing using artificial intelligence

Proper integration of scheduling and control in Flexible Manufacturing Systems will make available the required level of decision-making capacity to provide a flexibly-automated, efficient, and quality manufacturing process. To achieve this level of integration, the developments in computer technology and sophisticated techniques of artificial intelligence (AI) should be applied to such FMS functions as scheduling. In this paper, we present an Intelligent Scheduling System for FMS under development that makes use of the integration of two AI technologies. These two AI technologies — Neural Networks and Expert Systems — provide the intelligence that the scheduling function requires in order to generate goodschedules within the restrictions imposed by real-time problems. Because the system has the ability to plan ahead and learn, it has a higher probability of success than conventional approaches. The adaptive behavior that will be achieved contribute to the integration of scheduling and control in FMS.     

A Study of Ozone Generation by Negative Corona Discharge Through Different Plasma Chemistry Models

The Steady radial distribution of chemical species in a wire‐to‐cylinder ozone generator filled with pure oxygen has been computed by applying four different plasma chemistry models of increasing complexity. The most complete model considers ten species (e, O₂ ⁺, O₂ ^?, O₃ ^?, O^?, O₂, O₂(¹Δ_g), O₂(¹∑_g ⁺), O and O₃) and 79 reactions, including ionization by electron impact, electron attachment and detachment, electron-ion recombination, charge transfer, etc. The chemical model is coupled with the electrical model through Poisson's equation. The spatially averaged ozone density has been computed as a function of the current intensity and compared with the experimental values obtained by UV spectroscopy.     

Beast methodology: An agile testing methodology for multi-agent systems based on behaviour driven development

This paper presents a testing methodology to apply Behaviour Driven Development (BDD) techniques while developing Multi-Agent Systems (MASs), termed BEhavioural Agent Simple Testing (BEAST) Methodology. This methodology is supported by the open source framework (BEAST Tool) which automatically generates test cases skeletons from BDD scenarios specifications. The developed framework allows the testing of MASs based on JADE or JADEX platforms. In addition, this framework offers a set of configurable Mock Agents with the aim of being able to execute tests while the MAS is under development. The BEAST Methodology presents transparent traceability from user requirements to test cases. Thus, the stakeholders can be aware of the project status. The methodology and the associated tool have been validated in the development of a MAS for fault diagnosis in FTTH (Fiber To The Home) networks. The results have been measured in quantifiable way obtaining a reduction of the tests implementation time.     

Variability for oil and fatty acid composition in castorbean varieties

Thirty-six castorbean varieties were surveyed for oil and fatty acid composition, in order to determine variability of these seed compounds. A large variability of seed oil percentage was observed, ranging from 39.6 to 59.5%. Concerning the fatty acids, little variability was observed for ricinoleic acid, which was the most abundant in the oil, ranging from 83.65 to 90.00%. The other fatty acids appeared in small concentrations and showed a small range: 0.87 to 2.35, 0.68 to 1.84, 2.96 to 5.64, 3.19 to 5.98, and 0.34 to 0.91%, for palmitic, stearic, oleic, linoleic, and linolenic acid, respectively. Non-significant correlations were observed between fatty acids and seed oil percentage. However, significant correlations were observed among fatty acid concentrations: positive and negative ones. These significant correlations could be associated with the biosynthetic pathways of the fatty acids, which are not fully elucidated. They suggest, however, that selection for a particular fatty acid will tend to increase those positively correlated, and decrease those negative ones. Selection and plant breeding techniques could then be applied to modify the oil content of the castorbean seeds, considering the variability observed. For the fatty acid composition, however, the variability was not large enough to make substantial changes in their concentrations by selection procedures. More varieties should be surveyed to find out if such variability is available.     

Numerical identification of a nonlinear diffusion coefficient by discrete mollification

The discrete mollification method is a convolution-based filtering procedure suitable for the regularization of ill-posed problems. Combined with explicit space-marching finite difference schemes, it provides stability and convergence for a variety of coefficient identification problems in linear parabolic equations. In this paper, we extend such a technique to identify some nonlinear diffusion coefficients depending on an unknown space dependent function in one dimensional parabolic models. For the coefficient recovery process, we present detailed error estimates and to illustrate the performance of the algorithms, several numerical examples are included.     

A protocol for the ranking of interpolation algorithms based on confidence levels

The choice of the best interpolation algorithm of data gathered at a finite number of locations has been a persistently relevant topic. Typical papers take a single data set, a single set of data points, and a handful of algorithms. The process considers a subset I of the data points as known, builds the interpolant with each algorithm, applies it to the points of another subset C, and evaluates the MAE (mean absolute error), the RMSE (root mean square error), or any other metric over such points. The less these statistics are, the better the algorithm is, so a deterministic ranking between methods (without confidence level) can be derived based upon it. Ties between methods are usually not considered. In this article a complete protocol is proposed in order to build, with a modest additional effort, a ranking with a confidence level. To illustrate this point, the results of two tests are shown. In the first one, a simple Monte Carlo experiment was devised using irregularly distributed points taken from a reference DEM (digital elevation model) in raster format. Different metrics led to different rankings, suggesting that the choice of the metric to define the ‘best interpolation algorithm’ would need a trade-off. The second experiment used mean daily radiation data from an international interpolation comparison exercise and RMSE as the metric of success. Only five simple interpolation methods were employed. The ranking using this protocol anticipated correctly the first and second place, afterwards confirmed employing independent control data.