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).     

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.     

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.     

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.     

Rheological characteristics of modified calcium grease with graphene nanosheets

The effect of graphene nanosheets (GNS) on the rheological characteristics and thermal conductivity of calcium grease used in marine applications, and water pumps for lower demanding applications, has been experimentally evaluated in this paper. Various volume fractions of GNS (0.5, 1, 2, 3, and 4% wt.) have been immersed in the grease aiming at finding the better percentages that improve the properties of nanogrease. The rheological characteristics and thermal conductivity were evaluated with a Brookfield Rheometer DV-III ULTRA and KD2 thermal analyzer, respectively. The results of the modified calcium grease indicated that the best concentration of GNS is 3% wt. The rheological characteristics of the grease with a different volume fraction of GNS indicated a non-Newtonian behavior. Thermal conductivity of nanogreas also increases with an increase of GNS volume fraction. Moreover, the apparent viscosity, shear stress and dropping point increase by 59%, 52%, and 65%, respectively, with increasing GNS volume fraction.     

EEG‐based brain source localization using visual stimuli

Electroencephalography (EEG) is widely used in variety of research and clinical applications which includes the localization of active brain sources. Brain source localization provides useful information to understand the brain's behavior and cognitive analysis. Various source localization algorithms have been developed to determine the exact locations of the active brain sources due to which electromagnetic activity is generated in brain. These algorithms are based on digital filtering, 3D imaging, array signal processing and Bayesian approaches. According to the spatial resolution provided, the algorithms are categorized as either low resolution methods or high resolution methods. In this research study, EEG data is collected by providing visual stimulus to healthy subjects. FDM is used for head modelling to solve forward problem. The low‐resolution brain electromagnetic tomography (LORETA) and standardized LORETA (sLORETA) have been used as inverse modelling methods to localize the active regions in the brain during the stimulus provided. The results are produced in the form of MRI images. The tables are also provided to describe the intensity levels for estimated current level for the inverse methods used. The higher current value or intensity level shows the higher electromagnetic activity for a particular source at certain time instant. Thus, the results obtained demonstrate that standardized method which is based on second order Laplacian (sLORETA) in conjunction with finite difference method (FDM) as head modelling technique outperforms other methods in terms of source estimation as it has higher current level and thus, current density (J) for an area as compared to others.     

Data mining-based materialized view and index selection in data warehouses

Materialized views and indexes are physical structures for accelerating data access that are casually used in data warehouses. However, these data structures generate some maintenance overhead. They also share the same storage space. Most existing studies about materialized view and index selection consider these structures separately. In this paper, we adopt the opposite stance and couple materialized view and index selection to take view–index interactions into account and achieve efficient storage space sharing. Candidate materialized views and indexes are selected through a data mining process. We also exploit cost models that evaluate the respective benefit of indexing and view materialization, and help select a relevant configuration of indexes and materialized views among the candidates. Experimental results show that our strategy performs better than an independent selection of materialized views and indexes.