Phosphogypsum: potential uses and problems – a review

Phosphogypsum (PG: CaSO₄·2H₂O) is a waste product generated by the phosphate industry. World production of this waste exceeds 200 million tonnes per year. PG, discharged into the sea, watercourses or in wilderness stocks, contains toxic elements harmful to ecosystems and human health, including heavy metals and radionuclides, and there is therefore a concern regarding environmental impacts. The concentrations of these elements vary between the regions and the processes used, all of which require particular and specific follow-up after the release of PG and during its use. Phosphogypsum is used in agriculture for soil amendment or as fertilizer, as well as in the brick and cement industry, and in road construction.     

Classification of moroccan olive cultivars by linear discriminant analysis applied to ATR–FTIR spectra of endocarps

The potential of FTIR combined with chemometrics was studied to classify five Moroccan varieties of olives by analysis on the endocarps. Attenuated total reflectance (ATR) enabled the samples to be examined directly in the solid state. The spectral data were subjected to a preliminary derivative elaboration based on the Norris gap algorithm to reduce the noise and extract larger analytical information. Linear discriminant analysis (LDA) was adopted as classification method, and Principle component analysis (PCA) was employed to compress the original data set into a reduced new set of variables before LDA. The calibration set was built by using the IR data from seventy‐five samples scanned in reflectance mode, and the ranges 3000–2400 and 2300–600 cm^?1 were selected because furnishing the most useful analytical information. PCA allowed clustering the samples in five classes by using the first two principal components with an explained variance of 98.16%. Application of LDA on an external test set of twenty‐five samples enabled to classify them into five variety groups with a correct classification of 92.0%.     

Load-balancing in MANET shortest-path routing protocols

Mobile ad hoc networks (MANET) are infrastructure-less networks, dynamically formed by an independent system of mobile nodes that are connected via wireless links. Because routing is performed by nodes with limited resources, load should be efficiently distributed through the network. Otherwise, heavily-loaded nodes may make up a bottleneck that lowers the network performances by congestion and larger delays. Regrettably, load-balancing is a critical deficiency in MANET shortest-path routing protocols, as nodes at the center of the network are much heavily-loaded than the others. Thus, we propose, in this paper, load-balancing mechanisms that push the traffic further from the center of the network. Basically, we provide novel routing metrics that take into account nodes degree of centrality, for both proactive and reactive routing protocols. Simulations show that the proposed mechanisms improve the load distribution and significantly enhance the network performances in terms of average delay and reliability.     

Computation of Minimal Siphons in Petri Nets Using Problem Partitioning Approaches

A large amount of research has shown the vitality of siphon enumeration in the analysis and control of deadlocks in various resource-allocation systems modeled by Petri nets(PNs).In this paper,we propose an algorithm for the enumeration of minimal siphons in PN based on problem decomposition.The proposed algorithm is an improved version of the global partitioning minimal-siphon enumeration(GPMSE)proposed by Cordone et al.(2005)in IEEE Transactions on Systems,Man,and Cybernetics-Part A:Systems and Humans,which is widely used in the literature to compute minimal siphons.The experimental results show that the proposed algorithm consumes lower computational time and memory compared with GPMSE,which becomes more evident when the size of the handled net grows.     

Detection of Olive Oil Adulteration Using FT-IR Spectroscopy and PLS with Variable Importance of Projection (VIP) Scores

Determination of adulteration and authenticity of extra virgin olive oil (EVOO) was investigated by means of infrared spectroscopy and chemometric methods. The study was focused on the detection and quantification of extra virgin olive oil adulteration by soybean (SB) and sunflower (SF) oils using FT-IR spectroscopy based on the use of PLS modeling and variable importance of projection (VIP) scores. A PLS model, using orthogonal signal correction and mean centering data pretreatments, and VIP scores variable preselection, was able to predict the concentration of sunflower and soybean oil adulterants in the 1–24 % weight ratio range with relative prediction errors lower than 3 % (w/w), for external validation samples. Moreover, the PLS-DA (discriminant analysis) model using the same preselected wavelengths was able to explain 99.9 % of variance and to predict with 100 % accuracy both classes of adulteration (EVOO–SB and EVOO–SF) in the external validation.