A program for terpenoid skeleton prediction based on botanical information

This paper describes a new program developed to the SISTEMAT expert system, the SISOCBOT program. This program employs the botanical data analysis and predicts, at the end of analysis, the probable skeleton of a compound based on the input of family or genus names. The SISOCBOT program was tested with 78 samples involving 302 substances, pertaining to 38 carbon skeletons, and showed a high hit index on skeleton prediction, thus emphasizing the potential importance of these data for structural determination of natural products. © 2002 Elsevier Science Ltd. All rights reserved.

Prediction of PA12 melt viscosity in Laser Sintering by a Time and Temperature dependent rheological model

Ein auf Basis von Prozessdaten kalibriertes Viskositätsmodell wird vorgeschlagen und zur Vorhersage der Viskosität einer Polyamid 12 (PA12) Kunststoffschmelze als Funktion von Zeit, Temperatur und Schergeschwindigkeit angewandt. Im ersten Schritt wurde das Viskositätsmodell aus experimentellen Daten abgeleitet. Es beruht hauptsächlich auf dem drei-parametrigen Ansatz von Carreau, wobei zwei zusätzliche Verschiebungsfaktoren eingesetzt werden. Die Temperaturabhängigkeit der Viskosität wird mithilfe des Verschiebungsfaktors aT von Arrhenius berücksichtigt. Ein weiterer Verschiebungsfaktor aSC (Structural Change) wird eingeführt, der die Strukturänderung von PA12 als Folge der Prozessbedingungen beim Lasersintern beschreibt. Beobachtet wurde die Strukturänderung in Form einer signifikanten Viskositätserhöhung. Es wurde geschlussfolgert, dass diese Viskositätserhöhung auf einen Molmassenaufbau zurückzuführen ist und als Nachkondensation verstanden werden kann. Abhängig von den Zeit- und Temperaturbedingungen wurde festgestellt, dass die Viskosität als Folge des Molmassenaufbaus exponentiell gegen eine irreversible Grenze strebt. Die Geschwindigkeit dieser Nachkondensation ist zeit- und temperaturabhängig. Es wird angenommen, dass die Pulverbetttemperatur einen Molmassenaufbau verursacht und es damit zur Kettenverlängerung kommt. Dieser fortschreitende Prozess der zunehmenden Kettenlängen setzt molekulare Beweglichkeit herab und unterbindet die weitere Nachkondensation. Der Verschiebungsfaktor aSC drückt diese physikalisch-chemische Modellvorstellung aus und beinhaltet zwei zusätzliche Parameter. Der Parameter aSC,UL entspricht der oberen Viskositätsgrenze, wohingegen k0 die Strukturänderungsrate angibt. Es wurde weiterhin festgestellt, dass es folglich nützlich ist zwischen einer Fließaktivierungsenergie und einer Strukturänderungsaktivierungsenergie für die Berechnung von aT und aSC zu unterscheiden. Die Optimierung der Modellparameter erfolgte mithilfe eines genetischen Algorithmus. Zwischen berechneten und gemessenen Viskositäten wurde eine gute Übereinstimmung gefunden, so dass das Viskositätsmodell in der Lage ist die Viskosität einer PA12 Kunststoffschmelze als Folge eines kombinierten Lasersinter Zeit- und Temperatureinflusses vorherzusagen. Das Modell wurde im zweiten Schritt angewandt, um die Viskosität während des Lasersinter-Prozesses in Abhängigkeit von der Energiedichte zu berechnen. Hierzu wurden Prozessdaten, wie Schmelzetemperatur und Belichtungszeit benutzt, die mithilfe einer High-Speed Thermografiekamera on-line gemessen wurden. Abschließend wurde der Einfluss der Strukturänderung auf das Viskositätsniveau im Prozess aufgezeigt.

Prediction of the thermal release of transactinide elements (112 ≤ Z ≤ 116) from metals

Metallic catcher foils have been investigated on their thermal release capabilities for future superheavy element studies. These catcher materials shall serve as connection between production and chemical investigation of superheavy elements (SHE) at vacuum conditions. The diffusion constants and activation energies of diffusion have been extrapolated for various catcher materials using an atomic volume based model. Release rates can now be estimated for predefined experimental conditions using the determined diffusion values. The potential release behavior of the volatile SHE Cn (E112), E113, Fl (E114), E115, and Lv (E116) from polycrystalline, metallic foils of Ni, Y, Zr, Nb, Mo, Hf, Ta, and W is predicted. Example calculations showed that Zr is the best suited material in terms of on-line release efficiency and long-term operation stability. If higher temperatures up to 2773 K are applicable, tungsten is suggested to be the material of choice for such experiments.

Disruption prediction at JET [Joint European Torus]

The sudden loss of the plasma magnetic confinement, known as disruption, is one of the major issue in a nuclear fusion machine as JET (Joint European Torus), Disruptions pose very serious problems to the safety of the machine. The energy stored in the plasma is released to the machine structure in few milliseconds resulting in forces that at JET reach several Mega Newtons. The problem is even more severe in the nuclear fusion power station where the forces are in the order of one hundred Mega Newtons. The events that occur during a disruption are still not well understood even if some mechanisms that can lead to a disruption have been identified and can be used to predict them. Unfortunately it is always a combination of these events that generates a disruption and therefore it is not possible to use simple algorithms to predict it. This thesis analyses the possibility of using neural network algorithms to predict plasma disruptions in real time. This involves the determination of plasma parameters every few milliseconds. A plasma boundary reconstruction algorithm, XLOC, has been developed in collaboration with Dr. D. Ollrien and Dr. J. Ellis capable of determining the plasma wall/distance every 2 milliseconds. The XLOC output has been used to develop a multilayer perceptron network to determine plasma parameters as ?i and q? with which a machine operational space has been experimentally defined. If the limits of this operational space are breached the disruption probability increases considerably. Another approach for prediction disruptions is to use neural network classification methods to define the JET operational space. Two methods have been studied. The first method uses a multilayer perceptron network with softmax activation function for the output layer. This method can be used for classifying the input patterns in various classes. In this case the plasma input patterns have been divided between disrupting and safe patterns, giving the possibility of assigning a disruption probability to every plasma input pattern. The second method determines the novelty of an input pattern by calculating the probability density distribution of successful plasma patterns that have been run at JET. The density distribution is represented as a mixture distribution, and its parameters arc determined using the Expectation-Maximisation method. If the dataset, used to determine the distribution parameters, covers sufficiently well the machine operational space. Then, the patterns flagged as novel can be regarded as patterns belonging to a disrupting plasma. Together with these methods, a network has been designed to predict the vertical forces, that a disruption can cause, in order to avoid that too dangerous plasma configurations are run. This network can be run before the pulse using the pre-programmed plasma configuration or on line becoming a tool that allows to stop dangerous plasma configuration. All these methods have been implemented in real time on a dual Pentium Pro based machine. The Disruption Prediction and Prevention System has shown that internal plasma parameters can be determined on-line with a good accuracy. Also the disruption detection algorithms showed promising results considering the fact that JET is an experimental machine where always new plasma configurations are tested trying to improve its performances.

The impact of spatial data redundancy on SOLAP query performance

Geographic Data Warehouses (GDW) are one of the main technologies used in decision-making processes and spatial analysis, and the literature proposes several conceptual and logical data models for GDW. However, little effort has been focused on studying how spatial data redundancy affects SOLAP (Spatial On-Line Analytical Processing) query performance over GDW. In this paper, we investigate this issue. Firstly, we compare redundant and non-redundant GDW schemas and conclude that redundancy is related to high performance losses. We also analyze the issue of indexing, aiming at improving SOLAP query performance on a redundant GDW. Comparisons of the SB-index approach, the star-join aided by R-tree and the star-join aided by GiST indicate that the SB-index significantly improves the elapsed time in query processing from 25% up to 99% with regard to SOLAP queries defined over the spatial predicates of intersection, enclosure and containment and applied to roll-up and drill-down operations. We also investigate the impact of the increase in data volume on the performance. The increase did not impair the performance of the SB-index, which highly improved the elapsed time in query processing. Performance tests also show that the SB-index is far more compact than the star-join, requiring only a small fraction of at most 0.20% of the volume. Moreover, we propose a specific enhancement of the SB-index to deal with spatial data redundancy. This enhancement improved performance from 80 to 91% for redundant GDW schemas.

The development of an immobilized enzyme reactor containing glyceraldehyde-3-phosphate dehydrogenase from Trypanosoma cruzi: the effect of species' specific differences on the immobilization

Glyceraldehyde-3-phosphate dehydrogenase (GAPDH) plays an important role in the life cycle of the Trypanosoma cruzi, and an immobilized enzyme reactor (IMER) has been developed for use in the on-line screening for GAPDH inhibitors. An IMER containing human GAPDH has been previously reported; however, these conditions produced a T. cruzi GAPDH-IMER with poor activity and stability. The factors affecting the stability of the human and T. cruzi GAPDHs in the immobilization process and the influence of pH and buffer type on the stability and activity of the IMERs have been investigated. The resulting T. cruzi GAPDH-IMER was coupled to an analytical octyl column, which was used to achieve chromatographic separation of NAD+ from NADH. The production of NADH stimulated by D-glyceraldehyde-3-phosphate was used to investigate the activity and kinetic parameters of the immobilized T. cruzi GAPDH. The Michaelis-Menten constant (K-m) values determined for D-glyceraldehyde-3-phosphate and NAD(+) were K-m = 0.5 +/- 0.05 mM and 0.648 +/- 0.08 mM, respectively, which were consistent with the values obtained using the non-immobilized enzyme.

The Coupled Aerosol and Tracer Transport model to the Brazilian developments on the Regional Atmospheric Modeling System (CATT-BRAMS) - Part 1: Model description and evaluation

We introduce the Coupled Aerosol and Tracer Transport model to the Brazilian developments on the Regional Atmospheric Modeling System (CATT-BRAMS). CATT-BRAMS is an on-line transport model fully consistent with the simulated atmospheric dynamics. Emission sources from biomass burning and urban-industrial-vehicular activities for trace gases and from biomass burning aerosol particles are obtained from several published datasets and remote sensing information. The tracer and aerosol mass concentration prognostics include the effects of sub-grid scale turbulence in the planetary boundary layer, convective transport by shallow and deep moist convection, wet and dry deposition, and plume rise associated with vegetation fires in addition to the grid scale transport. The radiation parameterization takes into account the interaction between the simulated biomass burning aerosol particles and short and long wave radiation. The atmospheric model BRAMS is based on the Regional Atmospheric Modeling System (RAMS), with several improvements associated with cumulus convection representation, soil moisture initialization and surface scheme tuned for the tropics, among others. In this paper the CATT-BRAMS model is used to simulate carbon monoxide and particulate material (PM(2.5)) surface fluxes and atmospheric transport during the 2002 LBA field campaigns, conducted during the transition from the dry to wet season in the southwest Amazon Basin. Model evaluation is addressed with comparisons between model results and near surface, radiosondes and airborne measurements performed during the field campaign, as well as remote sensing derived products. We show the matching of emissions strengths to observed carbon monoxide in the LBA campaign. A relatively good comparison to the MOPITT data, in spite of the fact that MOPITT a priori assumptions imply several difficulties, is also obtained.

An algorithmic Friedman-Pippenger theorem on tree embeddings and applications

An (n, d)-expander is a graph G = (V, E) such that for every X subset of V with vertical bar X vertical bar <= 2n - 2 we have vertical bar Gamma(G)(X) vertical bar >= (d + 1) vertical bar X vertical bar. A tree T is small if it has at most n vertices and has maximum degree at most d. Friedman and Pippenger (1987) proved that any ( n; d)- expander contains every small tree. However, their elegant proof does not seem to yield an efficient algorithm for obtaining the tree. In this paper, we give an alternative result that does admit a polynomial time algorithm for finding the immersion of any small tree in subgraphs G of (N, D, lambda)-graphs Lambda, as long as G contains a positive fraction of the edges of Lambda and lambda/D is small enough. In several applications of the Friedman-Pippenger theorem, including the ones in the original paper of those authors, the (n, d)-expander G is a subgraph of an (N, D, lambda)-graph as above. Therefore, our result suffices to provide efficient algorithms for such previously non-constructive applications. As an example, we discuss a recent result of Alon, Krivelevich, and Sudakov (2007) concerning embedding nearly spanning bounded degree trees, the proof of which makes use of the Friedman-Pippenger theorem. We shall also show a construction inspired on Wigderson-Zuckerman expander graphs for which any sufficiently dense subgraph contains all trees of sizes and maximum degrees achieving essentially optimal parameters. Our algorithmic approach is based on a reduction of the tree embedding problem to a certain on-line matching problem for bipartite graphs, solved by Aggarwal et al. (1996).

Gait Pattern Adaptation for an Active Lower-Limb Orthosis Based on Neural Networks

This work deals with neural network (NN)-based gait pattern adaptation algorithms for an active lower-limb orthosis. Stable trajectories with different walking speeds are generated during an optimization process considering the zero-moment point (ZMP) criterion and the inverse dynamic of the orthosis-patient model. Additionally, a set of NNs is used to decrease the time-consuming analytical computation of the model and ZMP. The first NN approximates the inverse dynamics including the ZMP computation, while the second NN works in the optimization procedure, giving an adapted desired trajectory according to orthosis-patient interaction. This trajectory adaptation is added directly to the trajectory generator, also reproduced by a set of NNs. With this strategy, it is possible to adapt the trajectory during the walking cycle in an on-line procedure, instead of changing the trajectory parameter after each step. The dynamic model of the actual exoskeleton, with interaction forces included, is used to generate simulation results. Also, an experimental test is performed with an active ankle-foot orthosis, where the dynamic variables of this joint are replaced in the simulator by actual values provided by the device. It is shown that the final adapted trajectory follows the patient intention of increasing the walking speed, so changing the gait pattern. (C) Koninklijke Brill NV, Leiden, 2011

Special issue on advances in data mining and its applications

Data mining is the process to identify valid, implicit, previously unknown, potentially useful and understandable information from large databases. It is an important step in the process of knowledge discovery in databases, (Olaru & Wehenkel, 1999). In a data mining process, input data can be structured, seme-structured, or unstructured. Data can be in text, categorical or numerical values. One of the important characteristics of data mining is its ability to deal data with large volume, distributed, time variant, noisy, and high dimensionality. A large number of data mining algorithms have been developed for different applications. For example, association rules mining can be useful for market basket problems, clustering algorithms can be used to discover trends in unsupervised learning problems, classification algorithms can be applied in decision-making problems, and sequential and time series mining algorithms can be used in predicting events, fault detection, and other supervised learning problems (Vapnik, 1999). Classification is among the most important tasks in the data mining, particularly for data mining applications into engineering fields. Together with regression, classification is mainly for predictive modelling. So far, there have been a number of classification algorithms in practice. According to (Sebastiani, 2002), the main classification algorithms can be categorized as: decision tree and rule based approach such as C4.5 (Quinlan, 1996); probability methods such as Bayesian classifier (Lewis, 1998); on-line methods such as Winnow (Littlestone, 1988) and CVFDT (Hulten 2001), neural networks methods (Rumelhart, Hinton & Wiliams, 1986); example-based methods such as k-nearest neighbors (Duda & Hart, 1973), and SVM (Cortes & Vapnik, 1995). Other important techniques for classification tasks include Associative Classification (Liu et al, 1998) and Ensemble Classification (Tumer, 1996).

Influence of seaward boundary condition on contaminant transport in unconfined coastal aquifers

Contaminant transport in coastal aquifers is complicated partly due to the conditions at the seaward boundary including seawater intrusion and tidal variations of sea level. Their inclusion in modelling this system will be computationally expensive. Therefore, it will be instructive to investigate the consequence of simplifying the seaward boundary condition by neglecting the seawater density and tidal variations in numerical predictions of contaminant transport in this zone. This paper presents a comparison of numerical predictions for a simplified seaward boundary condition with experimental results for a corresponding realistic one including a saltwater interface and tidal variations. Different densities for contaminants are considered. The comparison suggests that the neglect of the seawater intrusion and tidal variations does not affect noticeably the overall migration rate of the plume before it reaches the saltwater interface. However, numerical prediction shows that a more dense contaminant travels further seaward and part of the solute mass exits under the sea if the seawater density is not included. This is not consistent with the experimental result, which shows that the contaminant travels upwards to the shoreline along the saltwater interface. Neglect of seawater density, therefore, will result in an underestimation of the exit rate of solute mass around the coastline and fictitious migration paths under the seabed. For a less dense contaminant, neglect of seawater density has little effect on numerical prediction of migration paths. (C) 2001 Elsevier Science B.V. All rights reserved.

Effects of slurry rheology on industrial grinding performance

To determine the effect of slurry rheology on industrial grinding performance, 45 surveys were conducted on 16 full-scale grinding mills in five sites. Four operating variables - mill throughput, slurry density, slurry viscosity and feed fines content-were investigated. The rheology of the mill discharge slurries was measured either on-line or off-line, and the data were processed using a standard procedure to obtain a full range of flow curves. Multi-linear regression was employed as a statistical analysis tool to determine whether or not rheological effects exert an influence on industrial grinding, and to assess the influence of the four mill operating conditions on mill performance in terms of the Grinding Index, a criterion describing the overall breakage of particles across the mill. The results show that slurry rheology does influence industrial grinding. The trends of these effects on Grinding Index depend upon the rheological nature of the slurry-whether the slurries are dilatant or pseudoplastic, and whether they exhibit a high or low yield stress. The interpretation of the regression results is discussed, the observed effects are summarised, and the potential for incorporating rheological principles into process control is considered, Guidelines are established to improve industrial grinding operations based on knowledge of the rheological effects. This study confirms some trends in the effect of slurry rheology on grinding reported in the literature, and extends these to a broader understanding of the relationship between slurry properties and rheology, and their effects on industrial milling performance. (C) 2002 Elsevier Science B.V. All rights reserved.

Investigation of the effects of chrome ball charge on slurry rheology and milling performance

Ten surveys of the ball milling circuit at the Mt Isa Mines (MIM) Copper Concentrator were conducted aiming to identify any changes in slurry theology caused by the use of chrome balls charge, and the associated effect on grinding performance. Slurry theology was measured using an on-line viscometer. The data were mass balanced and analysed with statistical tools. Comparison of the rheogram demonstrated that slurry density and fines content affected slurry rheology significantly, while the effect of the chrome ball charge being negligible. Statistical analysis showed the effects of mill throughput and cyclone efficiency on the Grinding Index (a term describing the overall breakage). There was no difference in the Grinding Index between using the chrome ball charge and the ordinary steel ball charge. (C) 2002 Elsevier Science Ltd. All rights reserved.

Percolation Phenomena in Micropore Adsorption: Influence on Single and Multicomponent Adsorption Equilibria

A novel and simple method for determination of micropore network connectivity of activated carbon using liquid phase adsorption is presented in this paper. The method is applied to three different commercial carbons with eight different liquid phase adsorptives as probes. The effect of the pore network connectivity on the prediction of multicomponent adsorption equilibria was also studied. For this purpose, the Ideal Adsorbed Solution Theory (IAST) was used in conjuction with the modified DR single component isotherm. The results of comparison with experimental data show that incorporation of the connectivity, and consideration of percolation processes associated with the different molecular sizes of the adsorptives in the mixture, can improve the performance of the IAST in predicting multicomponent adsorption equilibria.

A brief survey on online analysis of movement data

The increasing availability of mobility data and the awareness of its importance and value have been motivating many researchers to the development of models and tools for analyzing movement data. This paper presents a brief survey of significant research works about modeling, processing and visualization of data about moving objects. We identified some key research fields that will provide better features for online analysis of movement data. As result of the literature review, we suggest a generic multi-layer architecture for the development of an online analysis processing software tool, which will be used for the definition of the future work of our team.