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.     

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.     

A generalized least absolute deviation method for parameter estimation of autoregressive signals

This paper proposes a generalized least absolute deviation (GLAD) method for parameter estimation of autoregressive (AR) signals under non-Gaussian noise environments. The proposed GLAD method can improve the accuracy of the estimation of the conventional least absolute deviation (LAD) method by minimizing a new cost function with parameter variables and noise error variables. Compared with second- and high-order statistical methods, the proposed GLAD method can obtain robustly an optimal AR parameter estimation without requiring the measurement noise to be Gaussian. Moreover, the proposed GLAD method can be implemented by a cooperative neural network (NN) which is shown to converge globally to the optimal AR parameter estimation within a finite time. Simulation results show that the proposed GLAD method can obtain more accurate estimates than several well-known estimation methods in the presence of different noise distributions.     

Wavelet approximation-based affine invariant shape representation functions

In this paper, new wavelet-based affine invariant functions for shape representation are presented. Unlike the previous representation functions, only the approximation coefficients are used to obtain the proposed functions. One of the derived functions is computed by applying a single wavelet transform; the other function is calculated by applying two different wavelet transforms with two different wavelet families. One drawback of the previously derived detail-based invariant representation functions is that they are sensitive to noise at the finer scale levels, which limits the number of scale levels that can be used. The experimental results in this paper demonstrate that the proposed functions are more stable and less sensitive to noise than the detail-based functions.     

Autoregressive Wiener filtering in a scanning electron microscopy imaging system

In this paper, we propose to use the autoregressive (AR)-based interpolator with Wiener filter and apply the idea to scanning electron microscope (SEM) images. The concept for combining the AR-based interpolator with Wiener filtering comes from the essential requirement of Wiener filtering for accurate and consistent estimation of the power of the noise in images prior to filter implementation. The resultant filter is called AR-Wiener filter. The proposed filter is embedded onto the frame grabber card of the scanning electron microscope (SEM) for real-time image processing. Different images are captured using SEM and used to compare the performances of the conventional Wiener and the proposed AR-Wiener technique.