Bidirectional HMM-based Arabic POS tagging

In this work, we will present a new concept of POS tagging that will be implemented for the Arabic language. Indeed, we will see that in Arabic there are a numerous cases where the determination of the morpho-syntactic state of a word depends on the states of the subsequent words, which represents the theoretical foundation of the approach: how to consider, in addition of the past elements, the future ones. We will then demonstrate how the POS tagging in its statistical application: the HMM, is based mainly on the past elements, and how to combine both direct and reverse taggers to tag the same sequence of words in both senses. Thus, we will propose a hypothesis for the result selecting. In the practical part, we will present, in general, the used resource and the changes made on it. Then we will explain the experiment steps and the parameters collected and presented on graphics, that we will discuss later to lead to the final conclusion.     

Physiological and biochemical markers in the process of resistance and/or tolerance of heavy metals in the abandoned mining area of Sidi Kamber,Skikda, Algeria

This paper reports the biomonitoring of metallic pollution in the mine of Sidi Kamber Skikda town, to determine the resistance and/or tolerance to Zn, Pb, Cd and Cu in metalliferous plants. During the period of March–May, 2015, soil and plants samples were collected from three different stations in the study area. The total heavy metal fraction was determined by ICP/MS, whereas biochemical markers were determined by spectrophotometric techniques. The results obtained showed high levels of heavy metals in soil and plants compared to international standards. The translocation factor revealed that the studied plants can transfer and/or accumulate metals in their roots or their aerial parts as a function of characteristics of each metal, soil contents and physiological role in the plant. The contents of chlorophyll, proline, total sugars and total proteins are directly influenced by changes in bioavailable contents of heavy metals. It has been found that the plants studied and biomarkers are better suited for phytoremediation and biomonitoring of heavy metals pollution.     

Impact of morphological analysis and a large training corpus on the performances of Arabic diacritization

The absence of short vowels in Arabic texts is the source of some difficulties in several automatic processing systems of Arabic language. Several developed hybrid systems of automatic diacritization of the Arabic texts are presented and evaluated in this paper. All these approaches are based on three phases: a morphological step followed by statistical phases based on Hidden Markov Model at the word level and at the character level. The two versions of the morpho-syntactic analyzer Alkhalil were used and tested and the outputs of this stage are the different possible diacritizations of words. A lexical database containing the most frequent words in the Arabic language has been incorporated into some systems in order to make the system faster. The learning step was performed on a large Arabic corpus and the impact of the size of this learning corpus on the performance of the system was studied. The systems use smoothing techniques to circumvent the problem of missing transitions words and the Viterbi algorithm to select the optimal solution. Our proposed system that benefits from the wealth of morphological analysis and a large diacritized corpus presents interesting experimental results in comparison to other automatic diacritization systems known until now.     

Comparative ozonization of LDPE and HDPE and grafting of some monomers to elaborate new ion exchange membranes

A comparative study of the ozonization of low density polyethylene (LDPE) and high density polyethylene (HDPE) was carried out. A grafting study of acrylic acid (AA), N,N‐dimethylamino‐2‐ethylmethacrylate (MADAME) and vinyl phosphonic acid (VPA) on LDPE and HDPE was performed in mass and solution. The ozonized polyethylene and the grafting polymers were characterized by IR spectroscopy and elementary analysis. Ion exchange membranes were prepared from grafted copolymers and characterized by the exchange capacity and electrical resistance. © 2006 Wiley Periodicals, Inc. J Appl Polym Sci 101: 4423–4429, 2006     

Optimization of fluoride removal from brackish water by electrodialysis

The study showed that desired potable water can be easily obtained by electrodialysis from fluoride-rich brackish water. Studies have been performed to defluoridate brackish water containing 3000 ppm of total dissolved solids (TDS) and 3 ppm of fluoride using electrodialysis. The behaviour of water parameters (ion contents, TDS, pH, total hardness) with electrodialysis parameters (duration, flow rate, temperature, voltages) is followed. Optimal operational conditions for obtaining desired potable water have been proposed.     

Nd recovery from magnetic waste and its preliminary analysis by neutron activation analysis

The products of the new information and communication technologies (NTIC), whose life often not exceed three years, became consumables to the computer image and other waste that contains neodymium and praseodymium (rare earths); in this field of management, treatment, and recycling of waste, we propose the recovery of elements such as neodymium and praseodymium, from the super-magnet Nd–Fe–B. Our study provides a simple and inexpensive process. The implementation scheme of our project consists of a first step in developing a plan of experience that has been made specifically to perform the optimization of operating conditions of the precipitation process. This experimental design was inspired by a statistical technique advanced design of experiments known factorial design. The method begins by embrittlement in liquid nitrogen (?196°C), dissolution in an acidic solution and recovered by a unit operation of chemical engineering, we add an oxalic acid solution to precipitate Nd as neodymium oxalate, followed by purification of neodymium and praseodymium. For characterization we used the following techniques. The neutron radiography, the neutron activation analysis (NAA) and the scanning electron microscope coupled with EDX. The identification of the most intense peaks in the XRD spectrum shows the presence of a single compound which hydrated neodymium oxalate chemical formula Nd₂(C₂O₄)₃10H₂O. The counting of γ spectrum shows that the purity of the precipitate is higher than 99%. Then, the thermal decomposition transforms this powder to neodymium oxide. After the reduction we obtain pure neodymium, the analytical balance shows that this magnet contains 26% of neodymium.     

Granular flow and segregation in a four-bladed mixer

In this study, we experimentally examine flow and segregation of granular material in a cylindrical mixer geometry agitated by four 45 pitched blades, which is representative of equipment such as high-shear granulators and filter-dryers. We observe that the free surface of the granular bed deforms, rising where the blades are present and falling between blades passes. Using particle image velocimetry (PIV), we measure the instantaneous, average, and fluctuating velocity fields at exposed surfaces (top surface and near the wall), for both near-monodisperse and polydisperse granular materials. The radial and axial point-velocity profiles indicate three-dimensional recirculation patterns indicative of avalanching and bed penetration. For polydisperse mixtures, we find that depending on the shear rate, different segregation mechanisms can take place. Under low shear, complex lobe and striation segregation patterns occur through stretching and folding due to surface avalanching. This leads to enhanced initial mixing rates in a manner consistent with spontaneous chaotic granular mixing. At high-shear rates, segregation is controlled by the rotation of the blades. As a result, coarse particles have a tendency to migrate both to the free surface and the outer wall independently of initial bed loading conditions.     

On the density profile within random and oriented wood-based composite panels: horizontal distribution

Wood composites are produced from the processing of discontinuous wood elements such as flakes and strands, with resin and other additives. Consolidation of the loose mat structure into a panel is achieved under heat and pressure, and the final product structure and properties are therefore governed not only by the properties of the wood elements but also by the forming and pressing processes. This paper deals with the density variation within the horizontal plane of flakeboards, in an approach that combines a structural model, based on the layout of the flakes, together with the consolidation model and mass transfer, both resulting from the hot-pressing process. The density distribution within the panels manufactured according to several possible combinations of random orientation and placement of flakes is analyzed, and the significant influence of the random character of the orientation of flakes during the forming process is highlighted.     

Influence of pH and ionic strength on the metal profile of impregnation catalysts

In this study the influence of pH and ionic strength on the final metal distribution is investigated. After impregnation, catalysts are usually dried at temperatures between 50°C and 200°C. During this process, a redistribution of the metal occurs, which is a complex function of the drying conditions, the properties of the impregnating solution and the support material. In our model, transport in the gas and liquid phase is described by the dusty gas model, and the Nernst-Plank equation, respectively. The metal adsorption on the porous support is described by the Revised Physical Adsorption model developed by Agashe and Regalbuto (J. Colloid Interface Sci. 185 (1997) 174). In their model, the adsorption constant is a function of the pH and the ionic strength of the liquid solution. The results show that for a positively charged metal complex, the impact of drying is strong when the initial pH of the liquid solution is below the point of zero charge (PZC). In such a case, the metal accumulates at the particle surface when the convective flow is strong (high temperature), and at the particle center when the solute-metal diffusivity is high. A comparison of our model with a drying model that assumes a constant adsorption equilibrium constant shows that the variations of pH and ionic strength cannot be ignored when the initial adsorption constant is low and the pH below the PZC. In such cases, the adsorption constant increases over several orders of magnitude when pH and ionic strength effects are accounted for.     

Machine Learning Techniques for Software Maintainability Prediction: Accuracy Analysis

Maintaining software once implemented on the end-user side is laborious and, over its lifetime, is most often considerably more expensive than the initial software development. The prediction of software maintainability has emerged as an important research topic to address industry expectations for reducing costs, in particular, maintenance costs. Researchers and practitioners have been working on proposing and identifying a variety of techniques ranging from statistical to machine learning (ML) for better prediction of software maintainability. This review has been carried out to analyze the empirical evidence on the accuracy of software product maintainability prediction (SPMP) using ML techniques. This paper analyzes and discusses the findings of 77 selected studies published from 2000 to 2018 according to the following criteria: maintainability prediction techniques, validation methods, accuracy criteria, overall accuracy of ML techniques, and the techniques offering the best performance. The review process followed the well-known systematic review process. The results show that ML techniques are frequently used in predicting maintainability. In particular, artificial neural network (ANN), support vector machine/regression (SVM/R), regression &; decision trees (DT), and fuzzy &; neuro fuzzy (FNF) techniques are more accurate in terms of PRED and MMRE. The N-fold and leave-one-out cross-validation methods, and the MMRE and PRED accuracy criteria are frequently used in empirical studies. In general, ML techniques outperformed non-machine learning techniques, e.g., regression analysis (RA) techniques, while FNF outperformed SVM/R, DT, and ANN in most experiments. However, while many techniques were reported superior, no specific one can be identified as the best.