Digging Deep into the Data Mine with DataMiningGrid

The growing computerization in modern knowledge and technology sectors is generating huge volumes of electronically stored data. Data mining technology is often employed to make sense of these data. However, as modern data mining applications increase in complexity, so do their demands for resources. Grid computing is one of several emerging networked computing paradigms promising to meet the requirements of heterogeneous, large-scale and distributed data mining applications. Despite this promise, there are still too many issues to be resolved before grid technology is commonly applied to large-scale data mining tasks. To address some of these issues, we developed the DataMiningGrid system, which principally differs from similar systems by its ability to integrate a diverse set of programs and application scenarios within a single framework. The system's key features include high performance and scalability, sophisticated support for relevant standards, different user types, and flexible extensibility. The software is available as open source.     

A dynamic rescheduling algorithm for resource management in large scale dependable distributed systems

Scheduling is a key component for performance guarantees in the case of distributed applications running in large scale heterogeneous environments. Another function of the scheduler in such system is the implementation of resilience mechanisms to cope with possible faults. In this case resilience is best approached using dedicated rescheduling mechanisms. The performance of rescheduling is very important in the context of large scale distributed systems and dynamic behavior. The paper proposes a generic rescheduling algorithm. The algorithm can use a wide variety of scheduling heuristics that can be selected by users in advance, depending on the system’s structure. The rescheduling component is designed as a middleware service that aims to increase the dependability of large scale distributed systems. The system was evaluated in a real-world implementation for a Grid system. The proposed approach supports fault tolerance and offers an improved mechanism for resource management. The evaluation of the proposed rescheduling algorithm was performed using modeling and simulation. We present experimental results confirming the performance and capabilities of the proposed rescheduling algorithm.     

Electrocatalytic removal of dissolved oxygen from seawater in a packed-bed electrode

Oxygen dissolved in seawater is the source of severe corrosion issues in off-shore platforms for oil exploitation. The electrocatalytic reduction of dissolved oxygen from seawater was investigated at laboratory scale as an alternative to the current processes for the removal of oxygen. Due to the low concentration of dissolved oxygen and the very high conversion required before use of seawater in off-shore platforms, the reduction has been carried out in a packed–bed electrode consisting of carbon-based particles of various nature. The reduction involves the intermediate formation of peroxide species whose further reduction into hydroxyl ion is a slow process on Pt-free surfaces. A continuous laboratory packed-bed electrode was designed and tested in long-term operations for the assessment of the production and the reaction selectivity. The removal of oxygen from seawater can be inhibited by the formation of a solid CaCO₃–Mg(OH)₂ film at the carbon particles, which favours the accumulation of peroxide species. However, this electrode deactivation can be avoided at sufficient polarisation.     

In situ MRI of the structure and function of multiphase catalytic reactors

NMR imaging (MRI) was used to study the distribution of the liquid phase in an operating trickle bed reactor using hydrogenation of -methylstyrene or n-octene-1 as representative examples. In a single pellet reactor, the existence of oscillating regimes under unchanged external conditions was shown. The experiments with packed beds have demonstrated the non-uniform distribution of the liquid phase over the bed, the presence of partially liquid-filled or completely dry catalyst particles in the operating reactor, and the existence of liquid phase transport between liquid-filled and dry catalyst particles. Detection of spatially resolved NMR spectra was used to characterize chemical conversion variations within the operating reactor. Preliminary MRI results for an operating monolithic reactor were obtained. It was found that MRI can be used to directly image solid materials using NMR signal detection of nuclei other than ¹H. In particular, imaging of alumina using ²⁷Al NMR signal appears highly promising for the development of novel MRI applications in chemical engineering and catalysis, including spatially resolved NMR thermometry.     

Multiple reconstruction and dynamic modeling of 3D digital objects using a morphing approach

Organ segmentation and motion simulation of organs can be useful for many clinical purposes such as organ study, diagnostic aid, therapy planning or even tumor destruction. In this paper we present a full workflow starting from a CT-Scan resulting in kidney motion simulation and tumor tracking. Our method is divided into three major steps: kidney segmentation, surface reconstruction and animation. The segmentation is based on a semi-automatic region-growing approach that is refined to improve its results. The reconstruction is performed using the Poisson surface reconstruction and gives a manifold three-dimensional (3D) model of the kidney. Finally, the animation is accomplished using an automatic mesh morphing among the models previously obtained. Thus, the results are purely geometric because they are 3D animated models. Moreover, our method requires only a basic user interaction and is fast enough to be used in a medical environment, which satisfies our constraints. Finally, this method can be easily adapted to magnetic resonance imaging acquisition because only the segmentation part would require minor modifications.     

A multi-agent platform for auction-based allocation of loads in transportation logistics

This paper describes an agent-based platform for the allocation of loads in distributed transportation logistics, developed as a collaboration between CWI, Dutch National Center for Mathematics and Computer Science, Amsterdam and Vos Logistics Organizing, Nijmegen, The Netherlands.The platform follows a real business scenario proposed by Vos, and it involves a set of agents bidding for transportation loads to be distributed from a central depot in the Netherlands to different locations across Germany. The platform supports both human agents (i.e. transportation planners), who can bid through specialized planning and bidding interfaces, as well as automated, software agents. We exemplify how the proposed platform can be used to test both the bidding behaviour of human logistics planners, as well as the performance of automated auction bidding strategies, developed for such settings.The paper first introduces the business problem setting and then describes the architecture and main characteristics of our auction platform. We conclude with a preliminary discussion of our experience from a human bidding experiment, involving Vos planners competing for orders both against each other and against some (simple) automated strategies.     

Robust statistic for the one-way MANOVA

The Wilks’ Lambda Statistic (likelihood ratio test, LRT) is a commonly used tool for inference about the mean vectors of several multivariate normal populations. However, it is well known that the Wilks’ Lambda statistic which is based on the classical normal theory estimates of generalized dispersions, is extremely sensitive to the influence of outliers. A robust multivariate statistic for the one-way MANOVA based on the Minimum Covariance Determinant (MCD) estimator will be presented. The classical Wilks’ Lambda statistic is modified into a robust one through substituting the classical estimates by the highly robust and efficient reweighted MCD estimates. Monte Carlo simulations are used to evaluate the performance of the test statistic under various distributions in terms of the simulated significance levels, its power functions and robustness. The power of the robust and classical statistics is compared using size-power curves, for the construction of which no knowledge about the distribution of the statistics is necessary. As a real data application the mean vectors of an ecogeochemical data set are examined.     

Response of a thermoacoustic refrigerator to the variation of the driving frequency and loading

The performance of a thermoacoustic refrigerator subjected to variable loading was analyzed experimentally and the data were compared with those obtained using a computational model. The computational model relies on one-dimensional cross-section-averaged equations discretized using the network analogy. The thermoacoustic refrigerator was modeled by dividing it into 1 mm long slices in the direction of the acoustic axis. The hot heat exchanger of the thermoacoustic refrigerator was maintained at ambient temperature and the temperature of the cold heat exchanger was varied to achieve temperature differences of ΔT=0, 5 and 10 K along the stack. The cooling load was measured and calculated for these temperature differences while varying the driving frequency between 30 and 65 Hz. The contribution of the progressive and stationary waves and the losses on the thermoacoustic heat flow was computed and discussed.