Design of intelligent distributed control systems: a dependability point of view

The use of fieldbuses and the emergence of intelligent sensors and actuators are opening up new possibilities for distributed control systems, but are also introducing additional constraints in terms of achieving dependability objectives. The type of production environment will greatly determine the predominant criterion for an automatic control system, i.e. reliability, availability, maintainability, safety, etc. On the other hand, the choice of a fieldbus system will also depend on factors such as application size, data throughput, and integration of time considerations. Other important criteria include cost, confidentiality, and compatibility with existing equipment. Therefore, it appears essential that designers be given the means to assess dependability at each design step by integrating feedback from experience. Assessing dependability is too often limited to an evaluation at the end of the design process, which often involves reselecting previous choices. The main topic of this paper is to focus on the communication function which is a pivotal of intelligent distributed control systems. So this article is a synthesis of different aspects linked to the design of fieldbus based applications thanks to the contributors, who come from various fields. Consequently it highlights the main problem and give some ways to solve them.     

A time-discrete model for dynamic fracture based on crack regularization

We propose a discrete time model for dynamic fracture based on crack regularization. The advantages of our approach are threefold: first, our regularization of the crack set has been rigorously shown to converge to the correct sharp-interface energy Ambrosio and Tortorelli (Comm. Pure Appl. Math., 43(8): 999–1036 (1990); Boll. Un. Mat. Ital. B (7), 6(1):105–123, 1992); second, our condition for crack growth, based on Griffith’s criterion, matches that of quasi-static settings Bourdin (Interfaces Free Bound 9(3): 411–430, 2007) where Griffith originally stated his criterion; third, solutions to our model converge, as the time-step tends to zero, to solutions of the correct continuous time model Larsen (Math Models Methods Appl Sci 20:1021–1048, 2010). Furthermore, in implementing this model, we naturally recover several features, such as the elastic wave speed as an upper bound on crack speed, and crack branching for sufficiently rapid boundary displacements. We conclude by comparing our approach to so-called “phase-field” ones. In particular, we explain why phase-field approaches are good for approximating free boundaries, but not the free discontinuity sets that model fracture.     

Metabolomics-on-a-chip and predictive systems toxicology in microfluidic bioartificial organs

The world faces complex challenges for chemical hazard assessment. Microfluidic bioartificial organs enable the spatial and temporal control of cell growth and biochemistry, critical for organ-specific metabolic functions and particularly relevant to testing the metabolic dose-response signatures associated with both pharmaceutical and environmental toxicity. Here we present an approach combining a microfluidic system with (1)H NMR-based metabolomic footprinting, as a high-throughput small-molecule screening approach. We characterized the toxicity of several molecules: ammonia (NH(3)), an environmental pollutant leading to metabolic acidosis and liver and kidney toxicity; dimethylsulfoxide (DMSO), a free radical-scavenging solvent; and N-acetyl-para-aminophenol (APAP, or paracetamol), a hepatotoxic analgesic drug. We report organ-specific NH(3) dose-dependent metabolic responses in several microfluidic bioartificial organs (liver, kidney, and cocultures), as well as predictive (99% accuracy for NH(3) and 94% for APAP) compound-specific signatures. Our integration of microtechnology, cell culture in microfluidic biochips, and metabolic profiling opens the development of so-called "metabolomics-on-a-chip" assays in pharmaceutical and environmental toxicology.     

Mathematical modelling of the thin layer solar drying of banana, mango and cassava

The main objectives of this paper are firstly to investigate the behaviour of the thin layer drying of plantain banana, mango and cassava experimentally in a direct solar dryer and secondly to perform mathematical modelling by using thin layer drying models encountered in literature. The variation of the moisture content of the products studied and principal drying parameters are analysed. Seven statistical models, which are empirical or semi-empirical, are tested to validate the experimental data. A non-linear regression analysis using a statistical computer program is used to evaluate the constants of the models. The Henderson and Pabis drying model is found to be the most suitable for describing the solar drying curves of plantain banana, mango and cassava. The drying data of these products have been analysed to obtain the values of the effective diffusivity during the falling drying rate phase.     

The modified count and weigh method: an improved procedure for assessing weight loss in stored maize cobs

This paper describes a modification of the well-known count and weigh (gravimetric) method for assessing losses due to insect pests in stored maize cobs. The modification involves counting the destroyed grains on each cob and using an adjusted calculation. Data are presented to show that the precision of the modified method is similar to that of the conventional count and weigh method. No systematic bias was detected with the modified method, in contrast to the conventional method which is shown to underestimate true weight loss by up to half. The main justification given for the use of the modified method is that the conventional method underestimates loss to an unpredictable extent, depending on the degree to which pest-damaged grains retain a separate, countable identity. For example, it is shown that the degree to which the conventional method underestimated loss was much greater for a floury, small-grained variety, in which many insect-damaged grains were reduced to powder, than for a large-grained variety in which most damaged grains retained their identity. This implies that comparisons of weight losses based on the conventional method should be interpreted with great caution in studies with significant amounts of destroyed grains.     

Development of biodegradable coatings for covered horizontal bunker‐stored silage

The addition of different plasticizers, thickeners and hydrophobic compounds such as fatty acids in biodegradable coatings has been evaluated using pH values and dry matter content as criteria in silage during one month of storage at room temperature under laboratory conditions. A base coating solution was prepared with whey protein concentrate, calcium caseinate and carboxymethylcellulose. From the base coating solution, derivative coating solutions were developed to evaluate the effect of plasticizers by incorporating glycerol at different concentrations and different ratios of sorbitol and glycerol. Thickening agents were also evaluated by adding fine silage powder, coarse silage powder, hay powder, or cornstarch. Derivative coating solutions were studied to evaluate the effect of fat constituents. Canola oil, beef fat, shortening, bees wax or candellila wax were added to the base solution following various procedures. Results showed that the addition of glycerol (320 g kg^?1) in the coating formulation resulted in a pH value of 5.21 after 31 days of storage without visible spoilage under the coating. The presence of plasticizer compounds avoids the appearance of cracking in the coating during the early stage of conservation. The pH of silage coated with formulations containing coarse or fine silage powder was preserved below 5 until day 17 as compared to the other formulations where the pH was preserved below 5 only for 10 days. The incorporation of beef fat in the coating formulation resulted in the lowest pH as compared to all the other fat compounds evaluated, showing a value of 4.61 at the end of storage. The addition of glycerol, coarse silage and beef fat in the same coating formulation recovered by a hydrophobic layer of zein stabilized the pH to 4.47 for 28 days of storage with a final dry matter content at 344.3 g kg^?1 without visible spoilage under the coating during all of the storage at room temperature. Copyright © 2004 Society of Chemical Industry     

Impacts of pollution in feral Mya arenaria populations: The effects of clam bed distance from the shore

This study examined the relationships between population characteristics and the expression of physiological biomarkers of stress in an intertidal clam population under pollution at sites differing in thermal history and coastline distance. The clam population metrics were age distribution, growth, condition factor, distance of the clam beds from the shore, and gonad development. Physiological biomarkers comprised biomarkers of defence such as superoxide dismutase, labile IIb metals in tissues, redox status of metallothioneins and glutathione S-transferase, of tissue damage such as lipid peroxidation and DNA strand breaks, of reproduction as determined by vitellogenin-like proteins and gonadosomatic index and immunocompetence such as phagocytosis and hemocyte viability. Age-related pigments were also examined to compare the physiological age of the clams with their chronological age. The results showed that all the above biomarkers were significantly affected at one of the two polluted sites at least. Distance from the shore was significantly correlated with most (81%) of the biomarkers examined. Clams collected at one polluted site were physiologically older than clams from the corresponding reference site. Canonical and adaptive regression (artificial neural networks) analyses found that the biomarkers measured in this study were able to predict the ecologically relevant endpoints. Biomarkers implicated in defense mechanisms, tissue damage and age-related pigments were most closely related to the clam population characteristics. Sensitivity analysis of the learning algorithm found that the following physiological and biochemical markers were the most predictive, in decreasing order, of clam population characteristics: glutathione S-transferase, phagocytosis, age pigments, lipid peroxidation in the gills, labile IIb metals and total MT levels. These biomarkers were affected by the distance of the clam beds from the shore, site quality (pollution) and reproduction activity.