HgCdTe molecular beam epitaxy material for microcavity light emitters: Application to gas detection in the 2–6 µm range

Most pollution gases, CO, CO₂, NO_x, SO₂, CH₄ …, have fundamental optical absorption in the near infrared range. We report here on microcavity light sources emitting at room temperature between 2 and 6 μm integrated in a gas detection system. HgCdTe has been chosen for this application, among several semiconductor materials. Molecular beam epitaxy (MBE) is very well adapted to grow the suitable HgCdTe heterostructure. The quality of involved HgCdTe layers has to be optimized in order to have a good photoluminescence response at 300 K. For this study, we used the knowledge we acquired in the field of MBE HgCdTe growth for infrared focal plane arrays (IRFPAs). Especially, we took advantage of the substrate preparation before growing and the flux control. We show subsequently several characterization results concerning our material quality. The compact emitting system is formed by this microcavity structure coupled to a 0.8-μm external pumping source. The Fabry-Perot type microcavity is obtained by using two evaporated YF₃/ZnS dielectric multilayered Bragg mirrors. We developed several devices exhibiting emission wavelengths at 3.3 μm, 4.26 μm, and 4.7 μm for CH₄, CO₂, and CO gas measurements, respectively, and 3.7 μm for the reference beam. We measured less than 200 ppm CH₄ in a 1 bar mixed gas along a 10-cm-long cell.     

Entropic Grid Scheduling

Computational Grids (CGs) are large scale dynamical networks of geographically distributed peer resource clusters. These clusters are independent but cooperating computing systems bound by a management framework for the provision of computing services, called Grid Services. In its basic form, the Grid scheduling problem consists in finding at least one cluster that has the capacity to handle, within the constraints of a specified quality of service, a user service request submitted to the CG. Since CGs span distinct management domains, the scheduling process has to be decentralized. Furthermore, it has to account for the ubiquitous uncertainty on the state of the CG. In this paper, we propose a scalable distributed Entropy-based scheduling approach that utilizes a Markov chain model to capture the dynamics of the service capacity state. An entropy-based quantification of the uncertainty on the service capacity information is developed and explicitly integrated within the proposed Grid scheduling approach. The performance of the proposed scheduling strategy is validated, through simulation, against a random delegation scheme and a load balancing-based scheduling strategy with respect to throughput, exploitation and convergence speed, respectively.     

Niclosamide,a Drug with Many (Re)purposes

Niclosamide is an anthelmintic drug that has been used for over 50 years mainly to treat tapeworm infections. However, with the increase in drug repurposing initiatives, niclosamide has emerged as a true hit in many screens against various diseases. Indeed, from being an anthelmintic drug, it has now shown potential in treating Parkinson's disease, diabetes, viral and microbial infections, as well as various cancers. Such diverse pharmacological activities are a result of niclosamide's ability to uncouple mitochondrial phosphorylation and modulate a selection of signaling pathways, such as Wnt/β‐catenin, mTOR and JAK/STAT3, which are implicated in many diseases. In this highlight, we discuss the plethora of diseases that niclosamide has shown promise in treating.     

Finite element method investigation of electrostatic precipitator performance

This paper aims at analysis of the monopolar ionized field in electrostatic precipitator. A numerical model for simulating precipitation of particles in electrostatic precipitator is discussed in this paper. It includes all essential phenomena affecting the process. An iterative finite‐element technique is used to solve Poison's equation. We proposed the introduction of a potential corresponding to the critical minimum ionization field directly in the finite element formulation as a Dirichlet condition. The theoretical migration velocity is obtained by balancing the drag force with the Coulomb force or electrostatic force acting on a particle. We used the model introduced by Cochet for predicting a particle charge. The model assumes that a particle of the same size attains an equivalent maximum amount of charge for a charging time equal to infinity. Particles influence the electrical field, flow field, electrical migration velocity, gas discharge, particle charge and collection efficiency. Copyright © 2014 John Wiley & Sons, Ltd.     

Statistical analysis of solar measurements in Algeria using beta distributions

A method of smoothing solar data by beta probability distributions is implemented in this paper. In the first step, this method has been used to process daily sunshine duration data recorded at thirty-three meteorological stations in Algeria for eleven year periods or more. In the second step, it has been applied to hourly global solar irradiation flux measured in Algiers during the 1987/89 period. For each location and each month of the year, beta probability density functions fitting the monthly frequency distributions of the daily sunshine duration measurements are obtained. Both the parameters characterising the resulting beta distributions are then mapped, enabling us to build the frequency distributions of sunshine duration for every site in Algeria. In the case of solar radiation for Algiers, the recorded data have been processed following two different ways. The first one consists in sorting the hourly global solar irradiation data into eight typical classes of the daily clearness index. The second one is based on the repartition of these data per month. The results of the first classification show that for each class of daily clearness index, the hourly data under consideration are modelled by only one beta distribution. When using the second classification, linear combinations of two beta distributions are found to fit the monthly frequency distributions of the hourly solar radiation data.     

Open writer identification from offline handwritten signatures by jointing the one-class symbolic data analysis classifier and feature-dissimilarities

Usually, a large number of reference signatures are required for building the writing style model from offline handwritten signatures (OHSs). Moreover, the existing writer identification systems from OHSs are generally closed systems that require a retraining process when a new writer is added. This paper proposes an open writer identification system from OHSs, based on a new scheme of the one-class symbolic data analysis (OC-SDA) classifier, using few reference signatures. For generating more data, intra-class feature-dissimilarities, generated from curvelet transform, are introduced for building the symbolic representation model (SRM) associated with each writer. Feature-dissimilarities allow capturing more efficiently the intra-personnel variability produced naturally by a writer and, thus, increase the inter-personnel variability. Instead of using the mean and the standard deviation for building the OC-SDA model, intra-class feature-dissimilarities generated for each writer are modeled through a new weighted membership function, inspired from the real probability distribution of training intra-class feature-dissimilarities. The comparative analysis against the state-of-the-art works shows that the proposed OC-SDA classifier outperforms the existing classifiers on three public signature datasets GPDS-300, CEDAR-55 and MCYT-75, using only five reference signatures, achieving 98.31%, 98.06% and 99.89%, respectively, even when a combination of multiple classifiers is performed or even using learned features. Moreover, the evaluation of the proposed writer identification system in front of skilled forgeries shows its ability to detect also possible forged signatures in addition to the genuine ones.