A four year study to determine the optimal harvesting period for Tunisian Chemlali olives

Due to the importance of the stage of maturity on several olive pomological parameters and oil quality indices, and to the interest that Tunisian olive oil production has recently received, the optimal harvesting period for the main Tunisian olive cultivar, Chemlali, was assessed. For the first time, a four‐season crops study, carried out in three representative geographical areas, was focused on both olive fruit pomological parameters and oil chemical composition at different stages of maturity. The stage of maturity was the factor showing the highest influence on the major part of these parameters, followed by the crop year. To guarantee a reasonable fat content and a good chemical quality of oil, in particular a harmonious acidic composition, acceptable UV absorbance, and a content high of antioxidants, unsaponifiable matter and sterols, the optimal harvesting period for Chemlali olives would appears to be between the end of November to the middle of December, which corresponds to a maturity index between 2.5 and 3.5.     

Newly designed polyacrylamide/dextran gels for electrophoresis protein separation: synthesis and characterization

Newly designed gels for electrophoresis protein separation were synthesized from acrylamide, N,N′‐methylenebis (acrylamide) and dextran mixtures. Radical polymerization was initiated by ammonium persulfate and N,N,N′,N′‐tetramethylethylenediamine. The time dependence of absorbance during polymerization was monitored using UV‐visible spectroscopy. The exothermic polymerization process exhibited a sharp rise of temperature reminiscent of the Trommsdorff effect. The swelling kinetics of the synthesized gels was examined in deionized water and buffer solutions. One of the challenges was to find an alternative to commercial products, sold as mixtures with no detailed chemical contents, commonly used in sodium dodecylsulfate polyacrylamide gel electrophoresis (SDS‐PAGE) for protein separation. For this reason, a systematic comparison was made of the properties of one of the most commonly used commercial gels, Duracryl^? from Genomics Solution Inc., and those of the synthesized polyacrylamide/dextran gels. Copyright © 2011 Society of Chemical Industry     

Fault Identification in System-Level Diagnosis: a Logic-Based Framework and an <Emphasis Type="Italic">O</Emphasis>(<Emphasis Type="Italic">n</Emphasis><Superscript>2</Superscript>\sqrt{τ}/{\log <Emphasis Type="Italic">n</Emphasis>}) Algorithm

Much research has been devoted to system-level diagnosis—SLD. Two issues have been addressed. The first of these is diagnosability, i.e., provide necessary and sufficient conditions for a system of n units to be diagnosable provided that the number of faulty units does not exceed τ . The second is the design of fault identification algorithms, assuming that the system being considered is diagnosable. This paper focuses on the second of these concerns, discussing several algorithms of which the most efficient runs in O(n ^2.5 ) . By considering a logical framework, this paper investigates the process of fault identification and proposes a fault identification algorithm which runs in O( n ² \sqrt τ / \sqrt log n ) , τ < n/2 . Received January 10, 2000; revised August 3, 2000.     

An evolutionary algorithm for generalized comparison-based self-diagnosis of multiprocessor systems

In this article, we consider the problem of self-diagnosis of multiprocessor and multicomputer systems under the generalized comparison model. In this approach, a system consists of a collection n independent heterogeneous processors (or units) interconnected via point-to-point communication links, and it is assumed that at most t of these processors are permanently faulty. For the purpose of diagnosis, system tasks are assigned to pairs of processors and the results are compared. The agreements and disagreements among units are the basis for identifying faulty processors. Such a system is said to be t-diagnosable if, given any complete collection of comparison results, the set of faulty processors can be unambiguously identified. We present an efficient fault identification method based on genetic algorithms. Analysis and simulations are provided, first, to evaluate the genetic parameters of the diagnosis algorithm; second, to show the efficiency of the genetic approach. The new strategy is shown to correctly identify the set of faulty processors, making it an attractive and viable addition or alternative to present fault diagnosis techniques.     

An evolutionary algorithm for abductive reasoning

Abductive reasoning (or abduction) is the process of inferring hypotheses from observed data using a certain ‘knowledge’ encoded in the form of inference rules (or causal relations). Many important kinds of intellectual tasks, including medical diagnosis, fault diagnosis, scientific discovery, legal reasoning, and natural language understanding have been characterised as abduction. Unfortunately, abduction is 𝒩𝒫-hard. Genetic algorithms and biologically motivated computational paradigms inspired by the natural evolution turned out to be efficient in solving many hard problems while other existing approaches failed to solve in general. In this article, we present a genetic algorithm called HAKIM, for solving abduction problems. We encode an explanation in a chromosome-like structure, where every gene models a possible single hypothesis. Thereafter, we develop a fitness function that characterises the overall ‘quality’ of a chromosome representing an explanation; and then use standard genetic operators to compute a set of hypotheses that best explains the observed data. Simulation results on large-scale medical problems reveal the good performance of our model HAKIM.     

An Artificial Network Simulating Cause-to-Effect Reasoning: Cancellation Interactions and Numerical Studies

During the last few decades, a variety of models have been proposed to address causal reasoning (known also as abduction); most of these dealt with a probabilistic or a logical framework. Recently, a few models have been proposed within a neural framework. The investigation of neural approaches is mainly motivated by the computational burden of the causal reasoning task and by the satisfactory results given by neural networks in solving hard problems in general. A particular class of causal reasoning that raises several difficulties is the cancellation class. From an abstract point of view, cancellation occurs when two causes (hypotheses) cancel each other's explanation capabilities with respect to a given effect (observation). The present work is twofold. First, we extend an existing neural model to handle cancellation interactions. Second, we test the model on a large database and propose objective criteria to quantitatively evaluate the scenarios (explanations) produced. Simulation results show good performance and stability of the model. Received 17 November 1999 / Revised 12 May 2000 / Accepted in revised form 16 June 2000     

Recent progress in the manufacture of alkanesulfonates: Photosulfochlorination of single-chain length (C12−C16) n-alkanes using SO2Cl2 at high conversion rates in pure phase and in the presence of solvent

Increasing concentration of sulfuryl chloride during the photosulfochlorination reaction under visible light shows that under these conditions n-alkanes react at high conversion rates instead of the conversion rate of 15% reported in the literature. This photosulfochlorination with sulfuryl chloride leads to better and more interesting results compared with those of photosulfochlorination by gas mixture. Indeed, nearly total conversion of n-alkanes, specifically n-heptane, n-dodecane, n-tetradecane, and n-hexadecane, occurred in pure phase, with a quantitative global yield, a sulfochlorination-to-chlorination molar ratio of about 1, and a relative reactivity of secondary to primary hydrogen of about 2.5, at a reaction temperature of 30°C and a reaction time of 120 min, using 2×10⁻² mol L⁻¹ of pyridine. Under these conditions, no polysulfochlorinated compounds are detected. These results are further improved using chlorobenzene as the solvent, instead of benzene. Indeed, the sulfochlorination of n-heptane at a conversion rate of 80% in the presence of chlorobenzene leads also to a quantitative reaction yield, a higher RSO₂Cl/RCl molar ratio, and, as expected, a high selectivity of secondary over primary hydrogen. Under these conditions, sulfochlorination of long-chain n-alkanes leads to the highest RSO₂Cl/RCl molar ratio for n-dodecane, n-tetradecane, and n-hexadecane belonging to detergent range, and the value of the molar ratio for these is between 1.45 and 1.7. The isomeric distribution of sulfochlorinated compounds obtained during sulfochlorination in the presence of solvent resembles that of secondary alkanesul-fonates produced by sulfoxidation reaction, whereas that obtained in pure phase has a similar conversion rate, is rich in primary isomer, and thus is different from that of classic radical reactions such as photochlorination or photosulfochlorination with gas mixture.     

Isolation and characterisation of lactic acid bacteria strains from raw camel milk for potential use in the production of fermented Tunisian dairy products

The aim of this work was to study the suitability of camel milk for the production of dairy products by lactic acid fermentation. Sixty strains of lactic acid bacteria (LAB) were isolated from camel milk. The strains were tested for their acidification activity, ability to use citrate, exopolysaccharide production, lipolytic, proteolytic activities and resistance to antibiotics. Ten strains were investigated for their ability to metabolize carbohydrates and that resulted in the identification of 5 Lactococcus lactis, 1 Lactobacillus pentosus, 2 Lactobacillus plantarum, 1 Lactobacillus brevis and 1 Pediococcus pentosaceus strains. Two strains of Lactococcus lactis SCC133 and SLch14 were selected to produce traditional Tunisian fermented dairy products (Lben, Raib, Jben cheese and Smen). These strains were chosen based on their acid production capacity and their ability to produce a high yield of biomass.     

Assessment of the Wind Field in the East Coast Algerian Regions for the Installation of Wind Farms

The available electricity generated by a wind power generation system depends on mean wind speed, standard deviation of wind speed and the location of installation. Since year-to-year variation on annual mean wind speed is hard to predict, wind speed variations during a year can be well characterized in terms of a probability distribution function, as well Weibull distribution has been one of the most commonly used, accepted and recommended distribution to determine wind energy potential. In this study, the two Weibull parameters of the wind speed distribution function （the shape parameter k （dimensionless） and the scale parameter c （m/s））, were computed from the wind speed data for Algerian east coastal regions, recording over a 1 l-year period （1995-2005）. It was found that the numerical values of both Weibull parameters （k and c） vary over a wide range. The yearly values ofk range from 1.20 to 1.94, while those of c are in the range from 4.31 to 1.94. To minimize the uncertainties of statistical calculation, a spatial representation indicating distribution and occurrence frequency the direction from which the wind comes, appears a very primordial step. Over the whole valid data during the study period, the compass shows that there is no dominant direction marked. However, we can identify a preferred wind direction. The statistical results correspond to the analysis of the rose compass.