Crack redirection with thermal secondary loading

In the current paper crack redirection due to a strategic placing of a heat source in the vicinity of a crack tip is studied. Analysis suggests that for PMMA and considered temperature range the only factor responsible for the deviation of crack trajectory is thermal stress. The simulation of crack growth in PMMA under external tension and secondary heat loading shows that a moving heat source in the vicinity of a crack tip can serve as a pointer for the crack trajectory. In highly conductive materials, redirection can be possibly effected with low-power thermal dipoles.     

Partial oxidation of methane towards hydrogen production over a promising class of catalysts: Rh supported on Ti-modified MgO

The partial oxidation of methane over the supported Rh (0.8 wt.%) catalysts was investigated. Two kinds of supports were used, MgO and Ti-modified MgO (prepared by grafting technique). Among the Ti-modified MgO supports, two different compounds were used as source of Ti: inorganic (chloride) and organic (alkoxide). The catalytic performance of Rh-supported catalysts depends on the support and varies in the sequence: Ti-MgO/I > Ti-MgO/O > MgO. Ti-containing catalysts exhibited higher activity and selectivity compared to MgO, which is especially noticeable at low temperature. Possible explanations for the phenomena observed were proposed on the basis of characterization results.     

Burned area forecasting using past burned area records and Southern Oscillation Index for tropical Africa (1981-1999)

An 18-year time series of monthly NOAA-AVHRR Pathfinder Land burned area was analyzed for the region of tropical Africa, from July 1981 to June 1999. The transition period between NOAA-11 and NOAA-14 platforms from July 1993 to June 1995 was not included due to missing and outlier data. Stability of the time series was addressed for the input variables in the burned area algorithm, reflectance and temperature channels.A Seasonal AutoRegressive Integrated Moving-Average (SARIMA) model was developed for forecasting potential burned area. The SARIMA model identified an autoregressive regular term with 1-month lag and an autoregressive 12-month seasonal term with one season (12 months) component. A cross-correlation between Southern Oscillation Index (SOI) and burned area was statistically significant predictor variable in a time series with 20-month lag. Results show that the SARIMA model with this predictor improved both, fitting and forecasting, residual variance, by 4.1% and 5.6%, respectively, thereby, demonstrating potential relationship between SOI and burned area for the study region. Forecasting was estimated by considering only the first 16 years of the monthly burned area in the time series, from July 1981 to June 1997. The prediction for the following 24 months (from July 1997 to June 1999) was within the 95% confidence level indicating that the forecast was a valid characterization of the modeled process.     

Incremental wrapper-based gene selection from microarray data for cancer classification

Gene expression microarray is a rapidly maturing technology that provides the opportunity to assay the expression levels of thousands or tens of thousands of genes in a single experiment. We present a new heuristic to select relevant gene subsets in order to further use them for the classification task. Our method is based on the statistical significance of adding a gene from a ranked-list to the final subset. The efficiency and effectiveness of our technique is demonstrated through extensive comparisons with other representative heuristics. Our approach shows an excellent performance, not only at identifying relevant genes, but also with respect to the computational cost.     

The minimum dosage of praziquantel in the treatment of Taenia saginata, 1986-1993

Patients who have a unicornuate uterus with a noncommunicating rudimentary horn that contains an endometrial cavity are at risk for endometriosis and obstetric complications. As in this case, resection of the rudimentary horn can be performed laparoscopically without increased risk to the patient and with some potential benefit.     

Distributivity and conditional distributivity of a uninorm and a continuous t-conorm

The open problem recalled by Klement in the Linz2000 closing session, related to distributivity and conditional distributivity of a uninorm and a continuous t-conorm, is solved for the most usual known classes of uninorms. From the obtained results, it is deduced that distributivity and conditional distributivity are equivalent for these cases. It is remarkable that solutions appear involving not only strict t-conorms but also ordinal sums of the maximum with a strict t-conorm. Conversely, the distributivity of a t-conorm over a uninorm is also studied leading only to already known solutions. Moreover, the dual case of distributivity and conditional distributivity involving uninorms and continuous t-norms is also solved, proving again the equivalence of both kinds of distributivities.     

Enantiomer analysis of chiral lactones in foods by on-line coupled reversed-phase liquid chromatography-gas chromatography

A new application is proposed for the on-line coupling of reversed-phase liquid chromatography to gas chromatography (RPLC-GC) that allows the GC chirospecific analysis of gamma-lactones in fruits and commercially available fruit-containing products. The use of a programmed temperature vaporizer as an interface with the system makes the transfer of large volume fractions (i.e., 2520 microL) of aqueous eluents from LC to GC possible (speed of sample transfer, 1800 microL/min). Relative standard deviations obtained for the investigated lactones under the experimental conditions vary from 7 to 14%. The described system enlarges the LC-GC application field and overcomes the limitations reported thus far concerning the use of typical normal-phase eluents (i.e., the transfer of rather small volume fractions at low speeds of sample introduction).     

Macrophage formation of angiostatin during inflammation. A byproduct of the activation of plasminogen

Angiostatin is a potent inhibitor of tumor angiogenesis and the growth of metastatic foci. Recent studies have indicated that neoplastic cells can generate angiostatin directly or in cooperation with tumor-associated macrophages. In studies reported here, we determined whether angiostatin is generated in mice under non-neoplastic settings. Utilizing murine RAW264.7 macrophages and thioglycollate-elicited peritoneal macrophages, we demonstrate that angiostatin-like fragments are generated as a byproduct of the proteolytic regulation of membrane-bound plasmin. Plasmin proteolysis and subsequent loss in membrane-bound plasmin activity requires active plasmin but was unaffected by inhibitors of metalloproteinases. Lysine binding fragments of plasmin, isolated from macrophage-conditioned media utilizing affinity chromatography, appeared as a major (48 kDa) and two minor bands (42 and 50 kDa) in SDS-polyacrylamide gel electrophoresis and were immunoreactive with anti-kringle 1-3 IgG. Each peptide begins with Lys77 and contains the entire sequence of angiostatin. The affinity isolated plasmin fragments inhibited bFGF-induced endothelial cell proliferation. Lavage fluid recovered from the peritoneal cavities of mice previously injected with thioglycollate contained angiostatin-like plasmin fragments similar to those generated in vitro. This is the first demonstration that angiostatin-like plasmin fragments are generated in a non-neoplastic inflammatory setting. Thus, in addition to regulating pericellular plasmin activity, proteolysis of plasmin generates inactive kringle-containing fragments expressing angiostatic properties.     

Evolutionary Generalized Radial Basis Function neural networks for improving prediction accuracy in gene classification using feature selection

Radial Basis Function Neural Networks (RBFNNs) have been successfully employed in several function approximation and pattern recognition problems. The use of different RBFs in RBFNN has been reported in the literature and here the study centres on the use of the Generalized Radial Basis Function Neural Networks (GRBFNNs). An interesting property of the GRBF is that it can continuously and smoothly reproduce different RBFs by changing a real parameter τ. In addition, the mixed use of different RBF shapes in only one RBFNN is allowed. Generalized Radial Basis Function (GRBF) is based on Generalized Gaussian Distribution (GGD), which adds a shape parameter, τ, to standard Gaussian Distribution. Moreover, this paper describes a hybrid approach, Hybrid Algorithm (HA), which combines evolutionary and gradient-based learning methods to estimate the architecture, weights and node topology of GRBFNN classifiers. The feasibility and benefits of the approach are demonstrated by means of six gene microarray classification problems taken from bioinformatic and biomedical domains. Three filters were applied: Fast Correlation-Based Filter (FCBF), Best Incremental Ranked Subset (BIRS), and Best Agglomerative Ranked Subset (BARS); this was done in order to identify salient expression genes from among the thousands of genes in microarray data that can directly contribute to determining the class membership of each pattern. After different gene subsets were obtained, the proposed methodology was performed using the selected gene subsets as new input variables. The results confirm that the GRBFNN classifier leads to a promising improvement in accuracy.