Development of new processes to protect zinc against corrosion,suitable for on-site use

Protection against corrosion of metals is well known as an important issue in numerous fields. In all cases, the improvement of durability of these metals has to be connected to the development of environmentally friendly processes. Sol–gel protective coatings have shown excellent chemical stability and enhanced corrosion resistance for zinc substrates. Further, the sol–gel method, used as technique of surface protection, showed the potential for the replacement of toxic pre-treatments. This paper highlights the recent developments and applications of silane based sol–gel coatings on zinc substrates. Then, the challenges for industrial transfer of the developed process are also discussed because this process presents a disadvantage for on-site use, which is the too time-consuming thermal treatment. So, the goal of this study was to determine the convenient experimental conditions to reduce the duration of heat treatment of the hybrid sol–gel layer, compatible with the severe industrial requirements, without reducing the protection against corrosion. To reach this objective, a correlation between the results of chemical analyses and the protection against corrosion efficiency was established.     

Targeting a polyketide synthase gene for Aspergillus carbonarius quantification and ochratoxin A assessment in grapes using real-time PCR

Aspergillus carbonarius is an ochratoxin producing fungus that has been considered to be responsible of the ochratoxin A (OTA) contamination in grapes and wine. In order to monitor and quantify A. carbonarius, a specific primer pair Ac12RL_OTAF/Ac12RL_OTAR has been designed from the acyltransferase (AT) domain of the polyketide synthase sequence Ac12RL3 to amplify 141 bp PCR product. Among the mycotoxigenic fungi tested, only A. carbonarius gave a positive result. This specific primer pair was also successfully employed in real-time PCR conjugated with SYBR Green I dye for the direct quantification of this fungus in grape samples. A positive correlation (R(2)=0.81) was found between A. carbonarius DNA content and OTA concentration in 72 grape samples, allowing for the estimation of the potential risk from OTA contamination. Consequently, this work offers a quick alternative to conventional methods of OTA quantification and mycological detection and quantification of A. carbonarius in grapes.     

Transient and steady states of Gd2Zr2O7 and 2ZrO2∙Y2O3 (ss) interactions with calcium magnesium aluminium silicates

Reactions between calcium magnesium aluminium silicates (CMAS) and Gd₂Zr₂O₇ or 2ZrO₂?Y₂O₃ (ss) are investigated within a temperature range of 1200–1300 °C and for durations of 1 h–100 h. The evolution of CMAS penetration depth in Gd₂Zr₂O₇ and 2ZrO₂?Y₂O₃ (ss) pellets varies considerably depending on the interaction time. A quantitative analysis of the nature and composition of phases observed in stationary conditions (powder/powder interaction) is performed by SEM-FEG coupled with WDS analyses using micro-agglomerated nanoparticles of Gd₂Zr₂O₇ and 2ZrO₂?Y₂O₃. Faster kinetics of the gadolinium-based system are illustrated through an analysis of the morphology of the reaction area and of the resulting CMAS tightness of reaction products. The compositions and quantities of reaction products observed at equilibrium are very similar for the two systems, but transient states are significantly different.     

Multivariate Analysis of Multiple Datasets: a Practical Guide for Chemical Ecology

Chemical ecology has strong links with metabolomics, the large-scale study of all metabolites detectable in a biological sample. Consequently, chemical ecologists are often challenged by the statistical analyses of such large datasets. This holds especially true when the purpose is to integrate multiple datasets to obtain a holistic view and a better understanding of a biological system under study. The present article provides a comprehensive resource to analyze such complex datasets using multivariate methods. It starts from the necessary pre-treatment of data including data transformations and distance calculations, to the application of both gold standard and novel multivariate methods for the integration of different omics data. We illustrate the process of analysis along with detailed results interpretations for six issues representative of the different types of biological questions encountered by chemical ecologists. We provide the necessary knowledge and tools with reproducible R codes and chemical-ecological datasets to practice and teach multivariate methods.     

Novel optimized tree-based stack-type architecture for 2n-bit comparator at nanoscale with energy dissipation analysis

Comparator is an essential building block in many digital circuits such as biometric authentication, data sorting, and exponents comparison in floating-point architectures among others. Quantum-dot Cellular Automata (QCA) is a latest nanotechnology that overcomes the drawbacks of Complementary Metal Oxide Semiconductor (CMOS) technology. In this paper, novel area optimized 2n-bit comparator architecture is proposed. To achieve the objective, 1-bit stack-type and 4-bit tree-based stack-type (TB-ST) comparators are proposed using QCA. Then, two tree-based architectures of 4-bit comparators are arranged in two layers to optimize the number of quantum cells and area of an 8-bit comparator. Thus, this design can be extended to any 2n-bit comparator. Simulation results of 4-bit and 8-bit comparators using QCADesigner 2.0.3 show that there is a significant improvement in the number of quantum cells and area occupancy. The proposed TB-ST 8-bit comparator uses 2.5 clock cycles and 622 quantum cells with area occupancy of 0.49 µm² which is an improvement by 10.5% and 38%, respectively, compared to existing designs. Scaling it to a 32-bit comparator, the proposed architecture requires only 2675 quantum cells in an area of 2.05 µm² with a delay of 3.5 clock cycles, indicating 9.35% and 28.8% improvements, respectively, demonstrating the merit of the proposed architecture. Besides, energy dissipation analysis of the proposed TB-ST 8-bit comparator is simulated on QCADesigner-E tool, indicating average energy dissipation reduction of 17.3% compared to existing works.

     

Conjugate mixture models for clustering multimodal data

The problem of multimodal clustering arises whenever the data are gathered with several physically different sensors. Observations from different modalities are not necessarily aligned in the sense there there is no obvious way to associate or compare them in some common space. A solution may consist in considering multiple clustering tasks independently for each modality. The main difficulty with such an approach is to guarantee that the unimodal clusterings are mutually consistent. In this letter, we show that multimodal clustering can be addressed within a novel framework: conjugate mixture models. These models exploit the explicit transformations that are often available between an unobserved parameter space (objects) and each of the observation spaces (sensors). We formulate the problem as a likelihood maximization task and derive the associated conjugate expectation-maximization algorithm. The convergence properties of the proposed algorithm are thoroughly investigated. Several local and global optimization techniques are proposed in order to increase its convergence speed. Two initialization strategies are proposed and compared. A consistent model selection criterion is proposed. The algorithm and its variants are tested and evaluated within the task of 3D localization of several speakers using both auditory and visual data.     

An enhanced encryption algorithm for video based on multiple Huffman tables

Encryption is one of the fundamental technologies that is used in the security of multimedia data. Unlike ordinary computer applications, multimedia applications generate large amount of data that has to be processed in real time. This work investigates the problem of efficient multimedia data encryption. A scheme known as the Randomized Huffman Table scheme was recently proposed to achieve encryption along with compression. Though this scheme has several advantages it cannot overcome the chosen plaintext attack. An enhancement of this Huffman scheme is proposed in this work which essentially overcomes the attack and improves the security. The proposed encryption approach consists of two modules. The first module is the Randomized Huffman Table module, the output of which is fed to the second XOR module to enhance the performance. Security analysis shows that the proposed scheme can withstand the chosen plaintext attack. The efficiency and security of the proposed scheme makes it an ideal choice for real time secure multimedia applications.     

Performance-friendly rule extraction in large water data-sets with AOC posets and relational concept analysis

In this paper, we consider data analysis methods for knowledge extraction from large water data-sets. More specifically, we try to connect physico-chemical parameters and the characteristics of taxons living in sample sites. Among these data analysis methods, we consider formal concept analysis (FCA), which is a recognized tool for classification and rule discovery on object–attribute data. Relational concept analysis (RCA) relies on FCA and deals with sets of object–attribute data provided with relations. RCA produces more informative results but at the expense of an increase in complexity. Besides, in numerous applications of FCA, the partially ordered set of concepts introducing attributes or objects (AOC poset, for Attribute–Object–Concept poset) is used rather than the concept lattice in order to reduce combinatorial problems. AOC posets are much smaller and easier to compute than concept lattices and still contain the information needed to rebuild the initial data. This paper introduces a variant of the RCA process based on AOC posets rather than concept lattices. This approach is compared with RCA based on iceberg lattices. Experiments are performed with various scaling operators, and a specific operator is introduced to deal with noisy data. We show that using AOC poset on water data-sets provides a reasonable concept number and allows us to extract meaningful implication rules (association rules whose confidence is 1), whose semantics depends on the chosen scaling operator.     

Binary integer programming formulations for scheduling in market-driven foundries

This paper describes the development and solution of binary integer formulations for production scheduling problems in market-driven foundries. This industrial sector is comprised of small and mid-sized companies with little or no automation, working with diversified production, involving several different metal alloy specifications in small tailor-made product lots. The characteristics and constraints involved in a typical production environment at these industries challenge the formulation of mathematical programming models that can be computationally solved when considering real applications. However, despite the interest on the part of these industries in counting on effective methods for production scheduling, there are few studies available on the subject. The computational tests prove the robustness and feasibility of proposed models in situations analogous to those found in production scheduling at the analyzed industrial sector.     

A novel optimization-based bandwidth-aware minimum power multicast routing algorithm in green wireless networks

Multicast routing in wireless networks that possess the wireless multicast advantage could significantly reduce the power and energy consumption. However, this kind of multicast routing that only addresses the transmission radius coverage might not be able to meet the bandwidth requirement of the users. As a result, additional transmissions are required to incur more energy consumption and carbon dioxide emissions that make existing algorithms not applicable to bandwidth constrained applications. In this paper, for the first time, we address the bandwidth aware minimum power multicast routing problem in wireless networks where the objective function is to minimize the total power consumption subject to the users?? bandwidth requirements. This problem is a challenging cross-layer design problem that requires seamless and sophisticated integrated design in the network layer (multicast routing) and physical layer (bandwidth-aware wireless transmission and power control). We first formulate this problem as a mixed integer linear programming problem and then propose a Lagrangian relaxation based algorithm to solve this problem. Numerical results demonstrate that the proposed approach is a sound green networking algorithm that outperforms the existing power efficient multicast routing approaches under all tested cases, especially in large bandwidth request, fine radius granularity, large group size and sparse network.