Depollution of atmospheric emissions of wood pyrolysis furnaces

The wood carbonization in Tunisia consists essentially of traditional activity using charcoaling stacks and pits characterized by high atmospheric pollution and poor energy conversion. Indeed, 70% of the initial mass of anhydrous wood are found in the vapor as aerosols, polluting and toxic gases and complex condensable organic compounds that can cause a substantial pollution of air, ground and water. Several processes of treatment and energy valorization of such effluents were proposed, but the incineration remains at present the most promising technique of depollution. The results show that the incineration, at about 1000°C, of wood carbonization smokes allows the destruction of 99% of the mass of pollutants except CO₂ and the reduction of polluting gas emission. The possible valorization of the smoke’s energy in the exit of the incinerator enhances the thermal efficiency of the process.     

A furostanol saponin from fruits of Balanites aegyptiaca

A new furostanol saponin was isolated from the mesocarp of Balanites aegyptiaca fruits and identified as 26-O-beta-D-glucopyranosyl-(25R)-furost-5-ene-3,22,26-triol 3-O-[[alpha-L-rhamnopyranosyl-(1-->2)]-[beta-D-xylopyranosyl-(1-->2)]-be ta- D-xylopyranosyl-(1-->2)]-beta-D-xylopyranoside (balanitesin).     

Influence of annealing on properties and structure of alumina

Alumina bodies were prepared from pure alumina powder (98.9% Al₂O₃ consisting of 82% > 53m). The powder was compacted by hot-pressing at 1200° C, Compacted bodies were annealed at 1300, 1400 and 1500° C. Annealing continued at each maximum temperature for 25, 50 and 100 h. Strong bodies were obtained with maximum bulk density of 2.32 g cm^–3 and minimum apparent porosity of 30.21%. The change in sintering parameters with annealing was correlated with developed structure.     

Creation of reactive centers on cotton. IV. Reaction of acrylamidomethylated cotton with some sulfur compounds

The addition of mercaptoethanol and hydrogen sulfide to the pendent double bonds of acrylamidomethylated cotton (AMC) has been investigated. The interaction of acrylonitrile with the modified celluloses so obtained (substrate I and II) and with AMC treated with ammonium hydroxide (substrate III) in the presence of Ce(IV) is studied. Substrate I shows higher initial grafting yields. than AMC; the opposite holds true for the maximum graft yields. The graft yields obtained with substrate II are lower than those of AMC. All modified cottons studied are less amenable to grafting compared with the unmodified cotton. The graft yields of AMC and substrate III are comparable due to the fact that both substrates are crosslinked. Probable reasons for the inferior reactivity of substrates I and II are also given.     

RF MEMS continuous reversible variable inductor based on a microfluidic network

In this work, RF MEMS continuous reversible variable inductor has been fabricated by using microelectronic technology and lamination process. We review, evaluate and compare this variable inductor with other work. The proposed inductor is a dual circular coil and has an inductance of few nH. The fundamental idea is to place a liquid droplet between the metal turns of a coil in order to modify the capacitive/resistive coupling between metal tracks and hence to change the stored magnetic energy. The SU-8 resin was used to realize the microfluidic channels and Au as metallic tracks. To prove the reversibility of the inductor, two cases were studied: filling and emptying of channels. The tuning range of the inductance is approximately 107 % at 1.6 GHz, making these devices very suitable as building blocks in many RF applications.     

New results on PWARX model identification based on clustering approach

This paper deals with the problem of piecewise auto regressive systems with exogenous input(PWARX) model identification based on clustering solution. This problem involves both the estimation of the parameters of the affine sub-models and the hyper planes defining the partitions of the state-input regression. The existing identification methods present three main drawbacks which limit its effectiveness. First, most of them may converge to local minima in the case of poor initializations because they are based on the optimization using nonlinear criteria. Second, they use simple and ineffective techniques to remove outliers. Third, most of them assume that the number of sub-models is known a priori. To overcome these drawbacks, we suggest the use of the density-based spatial clustering of applications with noise(DBSCAN) algorithm. The results presented in this paper illustrate the performance of our methods in comparison with the existing approach. An application of the developed approach to an olive oil esterification reactor is also proposed in order to validate the simulation results.     

Engineering proteins, subcloning and hyperexpressing oxidoreductase genes

A very efficient system for subcloning and studying proteinsequences, combining previously established elements for hyperexpression,replication and screening, was used to hyperproduce and characterizeseven different products. It expedited the cloning of genes,in a multipurpose recombinant DNA construct, for all the requirementsto study and engineer proteins with a strain of Escherichiacoli. Genes encoding six heme proteins and a flavoprotein havebeen subcloned and expressed to 13–30% of the total cellprotein, greatly facilitating purification and analyses. Threeof the heme proteins and the flavoprotein incorporated prostheticgroups in E.coli, and exhibited the expected activities. Fourof the enzymes have been purified to homogeneity and two ofthese crystallized for X-ray diffraction analysis. A rapid muta-genesisprotocol, based on polymerase chain reactions, was successfullyapplied to clone derivatives of one of these enzymes, cytochromec peroxidase. Thus, this system fulfills all criteria for engineeringproteins in an efficient and concerted manner.     

Anomalous codeposition of Co–Ni: alloys from gluconate baths

Thin films of cobalt–nickel alloys were galvanostatically deposited onto steel substrates from gluconate baths. Cathodic polarization curves were determined for the parent metals and Co–Ni alloy. The effects of bath composition, current density and temperature on cathodic current efficiency (CCE) and alloy composition were studied. The deposition of Co–Ni alloy is of anomalous type, in which the less noble metal (Co) is preferentially deposited. The CCE of codeposition is high and increases with increase in temperature and current density, but it decreases as the [Co²⁺]/[Ni²⁺] ratio in the bath increases. The percentage of Co in the deposit increases with increasing cathodic current density, temperature and increasing Co²⁺ ion concentration. The structure and surface morphology of the deposit were studied by XRD, ALSV and SEM. The results showed that the alloys consisted of a single solid solution phase with a hexagonal close packed structure.     

A cooperative recurrent neural network for solving L(1) estimation problems with general linear constraints

The constrained L(1) estimation is an attractive alternative to both the unconstrained L(1) estimation and the least square estimation. In this letter, we propose a cooperative recurrent neural network (CRNN) for solving L(1) estimation problems with general linear constraints. The proposed CRNN model combines four individual neural network models automatically and is suitable for parallel implementation. As a special case, the proposed CRNN includes two existing neural networks for solving unconstrained and constrained L(1) estimation problems, respectively. Unlike existing neural networks, with penalty parameters, for solving the constrained L(1) estimation problem, the proposed CRNN is guaranteed to converge globally to the exact optimal solution without any additional condition. Compared with conventional numerical algorithms, the proposed CRNN has a low computational complexity and can deal with the L(1) estimation problem with degeneracy. Several applied examples show that the proposed CRNN can obtain more accurate estimates than several existing algorithms.     

Hierarchically Distributed Peer-to-Peer Document Clustering and Cluster Summarization

In distributed data mining, adopting a flat node distribution model can affect scalability. To address the problem of modularity, flexibility and scalability, we propose a Hierarchically-distributed Peer-to-Peer (HP2PC) architecture and clustering algorithm. The architecture is based on a multi-layer overlay network of peer neighborhoods. Supernodes, which act as representatives of neighborhoods, are recursively grouped to form higher level neighborhoods. Within a certain level of the hierarchy, peers cooperate within their respective neighborhoods to perform P2P clustering. Using this model, we can partition the clustering problem in a modular way across neighborhoods, solve each part individually using a distributed K-means variant, then successively combine clusterings up the hierarchy where increasingly more global solutions are computed. In addition, for document clustering applications, we summarize the distributed document clusters using a distributed keyphrase extraction algorithm, thus providing interpretation of the clusters. Results show decent speedup, reaching 165 times faster than centralized clustering for a 250-node simulated network, with comparable clustering quality to the centralized approach. We also provide comparison to the P2P K-means algorithm and show that HP2PC accuracy is better for typical hierarchy heights. Results for distributed cluster summarization match those of their centralized counterparts with up to 88% accuracy.