Purification,structural data and biological properties of polysaccharide from Prunus amygdalus gum

This work demonstrates the efficiency of almond gum polysaccharides (AGPs) as bioactive compounds. AGPs were first extracted using H₂O₂, in the presence of NaOH, at different times and temperatures. The optimal extraction conditions were 4% H₂O₂ and 2 N NaOH, for 7 h at 50 °C, leading to an extraction yield of 58.2% (w/w). After a purification step, the retained AGPs were characterised using high‐performance liquid chromatography showing a molecular weight of 99.3 kDa. The monosaccharide composition of AGPs were assessed using gas chromatography–mass spectrometry. AGPs were found to be a complex heteropolysaccharide with a repeating unit mainly composed of galactose, arabinose, xylose, mannose, rhamnose, and glucuronic acid with the respective ratios: 45:26:7:10:1:11. The acidic nature of the polysaccharide is due to the presence of glucuronic acid. Total antioxidant activity, free radical‐scavenging activity and reducing power assay of AGPs were investigated. The obtained results showed high antioxidant activities of AGPs. Furthermore, beyond 60 mg mL^?1, AGPs exhibited bacterial growth inhibition for five pathogenic strains: Escherichia coli, Staphylococcus aureus, Enterococcus feacalis, Pseudomonas aeruginosa and Salmonella typhimurium.     

Three-parameter vs. two-parameter Weibull distribution for pultruded composite material properties

The three-parameter and two-parameter Weibull distributions are compared using 26 mechanical property data sets of fiber-reinforced polymeric (FRP) composite materials manufactured by the pultrusion process. Both strength and stiffness properties were examined. The probability distributions were compared on the basis of goodness of fit, nominal design values, and allowable load to achieve uniform reliability. It is recommended that the two-parameter Weibull distribution be used to characterize FRP composite material properties. The primary basis for this recommendation is small differences in nominal design values and small differences in allowable loads between the two-parameter and three-parameter Weibull distributions. Other supporting reasons for the recommendation are similar observed significance levels in distribution fitting, computational efficiency, and the fact that the location parameter of the three-parameter Weibull distribution is near the first order statistic.     

Hepatitis C virus RNA in serum of blood donors with or without elevated transaminase levels

The pattern of hepatitis C virus (HCV) viremia in blood donors who are positive for antibody to HCV (anti-HCV) according to the level of transaminase activity is unclear. A polymerase chain reaction-based HCV RNA detection method was used to study two clearly defined groups of anti-HCV-positive blood donors with repeatedly normal (n = 27) and elevated (n = 17) alanine aminotransferase (ALT) levels. HCV RNA was detected in only 4 of 27 blood donors with normal ALT values and 15 of 17 with elevated ALT values. These results indicate that anti-HCV-positive blood donors with normal ALT levels constitute a heterogeneous group, as HCV viremia is detectable in only a small proportion of cases. Polymerase chain reaction should be useful in the surveillance of anti-HCV-positive blood donors with normal ALT levels, by identifying those who might benefit from further investigation and treatment.     

Reliability‐based estimation of the survival of Listeria monocytogenes in chorizos

Reliability analysis is especially important when critical decisions are to be made involving potentially severe adverse consequences such as foodborne illness. Owing to uncertainty associated with the parameters controlling survival of Listeria monocytogenes in chorizo (a Mexican‐style sausage), the time needed to reduce the count by a certain number (n) of logs (t_nD) is probabilistic. In this paper the first‐order second‐moment (FOSM) method based on Taylor series expansion is used to derive the expected value and standard deviation of t_nD as function of the operating conditions (random variables) affecting survival, namely initial water activity (a_w0) of the sausage batter, storage temperature (T) and airflow velocity (F), along with their uncertainties characterised by their means and coefficients of variation. For any given n the derived t_nD probability distribution enables one to determine an estimate of t_nD for any desired level of reliability or confidence level, such as 50% (median value), 95%, 99%, etc. Among the conclusions drawn were: (i) the variability associated with T and F has a minor effect on estimating uncertainty in t_nD, whereas the reliability of t_nD estimation is greatly influenced by the uncertainty in a_w0; and (ii) the uncertainty in a_w0 has the greatest impact when a_w0 of the sausage formulation exceeds 0.90. The approach used and discussed in this paper can be applied to any survival/inactivation study to incorporate the effect of uncertainty in the various extrinsic and intrinsic parameters on the survival kinetics of the pathogen in a food system under evaluation. Copyright © 2006 Society of Chemical Industry     

Eco-friendly Geopolymer Composite Based on Non-heat-treated Phosphate Sludge Reinforced With Polypropylene Fibers

Silicon - Geopolymers produced with metakaolin (MK) and thermally untreated phosphate sludge (PS) are beneficial and environmentally advantageous materials, but their fragility limits its...     

A dynamic video watermarking algorithm in fast motion areas in the wavelet domain

In this paper, we propose a video watermarking algorithm which embeds different parts of a single watermark into different shots of a video under the wavelet domain. Based on a Motion Activity Analysis, different regions of the original video are separated into perceptually distinct categories according to motion information and region complexity. Thus, the localizations of the watermark are adjusted adaptively in accordance with the human visual system and signal characteristics, which makes them perceptually invisible and less vulnerable to automated removal. In addition, contrary to traditional methods where the watermark remains at a fixed position on the screen, the watermark moves along with moving objects and thus motion artefacts can be avoid. The multi-frame based extraction strategy ensures that the watermark can be correctly recovered from a very short segment of video. Individual frames extracted from the video also contain watermark information. Experimental results show that the inserted watermark is not only less perceptible but also robust against common video processing attacks.     

Hybrid metaheuristics for scheduling of machines and transport robots in job shop environment

In real manufacturing environments, the control of some elements in systems based on robotic cells, such as transport robots has some difficulties when planning operations dynamically. The Job Shop scheduling Problem with Transportation times and Many Robots (JSPT-MR) is a generalization of the classical Job Shop scheduling Problem (JSP) where a set of jobs additionally have to be transported between machines by several transport robots. Hence, the JSPT-MR is more computationally difficult than the JSP presenting two NP-hard problems simultaneously: the job shop scheduling problem and the robot routing problem. This paper proposes a hybrid metaheuristic approach based on clustered holonic multiagent model for the JSPT-MR. Firstly, a scheduler agent applies a Neighborhood-based Genetic Algorithm (NGA) for a global exploration of the search space. Secondly, a set of cluster agents uses a tabu search technique to guide the research in promising regions. Computational results are presented using two sets of benchmark literature instances. New upper bounds are found, showing the effectiveness of the presented approach.     

A two-stage gene selection scheme utilizing MRMR filter and GA wrapper

Gene expression data usually contain a large number of genes, but a small number of samples. Feature selection for gene expression data aims at finding a set of genes that best discriminates biological samples of different types. In this paper, we propose a two-stage selection algorithm for genomic data by combining MRMR (Minimum Redundancy–Maximum Relevance) and GA (Genetic Algorithm). In the first stage, MRMR is used to filter noisy and redundant genes in high-dimensional microarray data. In the second stage, the GA uses the classifier accuracy as a fitness function to select the highly discriminating genes. The proposed method is tested for tumor classification on five open datasets: NCI, Lymphoma, Lung, Leukemia and Colon using Support Vector Machine (SVM) and Naïve Bayes (NB) classifiers. The comparison of the MRMR-GA with MRMR filter and GA wrapper shows that our method is able to find the smallest gene subset that gives the most classification accuracy in leave-one-out cross-validation (LOOCV).