The Distribution of Active ingredients in Supported Catalysts Prepared by Impregnation

Supported catalysts, one of the commonest forms of heterogeneous catalysts in practical use, consist of small crystallites of a catalytically active component dispersed in a porous support of high surface area. Impregnation of the support with an aqueous solution of a compound containing the appropriate catalytic component is an important and frequently used method of preparing this type of catalyst. A nonaqueous solution should be used if the sup port surface is hydrophobic or if hydrolysis of the support surface is to be avoided. In its simplest form, this impregnation method involves three steps: (1) contacting the support with impregnating solution for a certain period of time, (2) drying the support to remove the imbibed liquid, and (3) activating the catalyst by calcination, reduction, or other appropriate treatment.     

Study of wind flow and pollutant dispersion by newly developed precision-improving methods

The paper presents a three-dimensional transient numerical model for atmospheric wind flow and industry and/or traffic pollutant dispersion over terrains having a complex topography. The model is based on a finite-volume integration of the equations governing mass, momentum, heat and pollutant transport within the earth's atmospheric boundary layer, using a collocated grid arrangement. The instability provoked by such a formulation was avoided by using a special pressure-velocity coupling. Local refinement of the grid was achieved via a domain decomposition method. The technique of “porosity” used to approximate curved three-dimensional boundaries is incorporated in the procedure thus avoiding the less accurate and more common approximation by a broken surface with segments parallel to the coordinate lines. The method was validated by simulating the flow over the Attica peninsula for which measurements of wind speed and pollutant emissions are available.     

Scattering of a spherical acoustic field from an eccentric spheroidal structure simulating the kidney-stone system

Summary.  We consider the acoustic scattering of time-harmonic spherical waves from an eccentric non coaxial spheroidal structure simulating the kidney-stone system. The proposed analysis is based on the application of the translational addition theorem for spheroidal wave functions. The resulting theoretical model is frequency-independent. Numerical results concerning the applicability of our approach are also presented. Received September 20, 2002 Published online: March 20, 2003     

Time-optimized contextual information forwarding in mobile sensor networks

We study on the forwarding of quality contextual information in mobile sensor networks (MSNs). Mobile nodes form ad-hoc distributed processing networks that produce accessible and quality-stamped information about the surrounding environment. Due to the dynamic network topology of such networks the context quality indicators seen by the nodes vary over time. A node delays the context forwarding decision until context of better quality is attained. Moreover, nodes have limited resources, thus, they have to balance between energy conservation and quality of context. We propose a time-optimized, distributed decision making model for forwarding context in a MSN based on the theory of optimal stopping. We compare our findings with certain context forwarding schemes found in the literature and pinpoint the advantages of the proposed model.     

CMMN evaluation: the modelers’ perceptions of the main notation elements

Software and Systems Modeling - Case Management Model and Notation (CMMN) has been introduced as a graphical modeling language targeting the modeling of human-centric processes. Despite its growing...     

New facets of mobile botnet: architecture and evaluation

It is without a doubt that botnets pose a growing threat to the Internet, with DDoS attacks of any kind carried out by botnets to be on the rise. Nowadays, botmasters rely on advanced Command and Control (C&C) infrastructures to achieve their goals and most importantly to remain undetected. This work introduces two novel botnet architectures that consist only of mobile devices and evaluates both their impact in terms of DNS amplification and TCP flooding attacks, and their cost pertaining to the maintenance of the C&C channel. The first one puts forward the idea of using a continually changing mobile HTTP proxy in front of the botherder, while the other capitalizes on DNS protocol as a covert channel for coordinating the botnet. That is, for the latter, the messages exchanged among the bots and the herder appear as legitimate DNS transactions. Also, a third architecture is described and assessed, which is basically an optimized variation of the first one. Namely, it utilizes a mixed layout where all the attacking bots are mobile, but the proxy machines are typical PCs not involved in the actual attack. For the DNS amplification attack, which is by nature more powerful, we report an amplification factor that fluctuates between 32.7 and 34.1. Also, regarding the imposed C&C cost, we assert that it is minimal (about 0.25 Mbps) per bot in the worst case happening momentarily when the bot learns about the parameters of the attack.     

A <Emphasis Type="Italic">k</Emphasis>-norm pruning algorithm for decision tree classifiers based on error rate estimation

Decision trees are well-known and established models for classification and regression. In this paper, we focus on the estimation and the minimization of the misclassification rate of decision tree classifiers. We apply Lidstone’s Law of Succession for the estimation of the class probabilities and error rates. In our work, we take into account not only the expected values of the error rate, which has been the norm in existing research, but also the corresponding reliability (measured by standard deviations) of the error rate. Based on this estimation, we propose an efficient pruning algorithm, called k-norm pruning, that has a clear theoretical interpretation, is easily implemented, and does not require a validation set. Our experiments show that our proposed pruning algorithm produces accurate trees quickly, and compares very favorably with two other well-known pruning algorithms, CCP of CART and EBP of C4.5. Editor: Hendrik Blockeel.     

Operator context scanning to support high segmentation rates for real time license plate recognition

Introducing high definition videos and images in object recognition has provided new possibilities in the field of intelligent image processing and pattern recognition. However, due to the large amount of information that needs to be processed, the computational costs are high, making the HD systems slow. To this end, a novel algorithm applied to sliding window analysis, namely Operator Context Scanning (OCS), is proposed and tested on the license plate detection module of a License Plate Recognition (LPR) system. In the LPR system, the OCS algorithm is applied on the Sliding Concentric Windows pixel operator and has been found to improve the LPR system’s performance in terms of speed by rapidly scanning input images focusing only on regions of interest, while at the same time it does not reduce the system effectiveness. Additionally, a novel characteristic is presented, namely, the context of the image based on a sliding windows operator. This characteristic helps to quickly categorize the environmental conditions upon which the input image was taken. The algorithm is tested on a data set that includes images of various resolutions, acquired under a variety of environmental conditions.     

A portfolio optimization model with three objectives and discrete variables

We formulate the portfolio selection as a tri-objective optimization problem so as to find tradeoffs between risk, return and the number of securities in the portfolio. Furthermore, quantity and class constraints are introduced into the model in order to limit the proportion of the portfolio invested in assets with common characteristics and to avoid very small holdings. Since the proposed portfolio selection model involves mixed integer decision variables and multiple objectives finding the exact efficient frontier may be very hard. Nevertheless, finding a good approximation of the efficient surface which provides the investor with a diverse set of portfolios capturing all possible tradeoffs between the objectives within limited computational time is usually acceptable. We experiment with the current state of the art evolutionary multiobjective optimization techniques, namely the Non-dominated Sorting Genetic Algorithm II (NSGA-II), Pareto Envelope-based Selection Algorithm (PESA) and Strength Pareto Evolutionary Algorithm 2 (SPEA2), for solving the mixed-integer multiobjective optimization problem and provide a performance comparison among them using metrics proposed by the community.