Effectiveness of catalytic archipelagos—I Regular arrays of regular islands

The interplay of the adsorption and desorption of reactants onto a catalytically active surface and their diffusion in that surface is an important process in catalysis. A surface with regularly distributed circular or rectangular catalyst islands in a plane has been investigated here under the assumption that the adsorbed reactant diffuses to the boundaries of the islands and disappears there by a first-order reaction. With the aid of the finite element method, effectiveness factors of the model systems have been calculated in terms of two dimensionless parameters ψ and μ, one measuring the ratio of the adsorption and desorption rates to that of surface diffusion and the other the rate of reaction to the rate of surface diffusion. It is found that the effectiveness factor for arrays whose symmetry elements are not too elongate can be approximated closely by that of an equivalent annulus whose inner and outer radii are calculated in terms of the geometrical parameters of the various systems. The equivalent annulus approximation is fairly accurate for these arrays in the parameter range 0.01 ?ψ, μ?10. Furthermore, outside this parameter range, asymptotic formulas are given which are accurate for all systems considered.     

Nop5p is a small nucleolar ribonucleoprotein component required for pre-18 S rRNA processing in yeast

We have identified a novel nucleolar protein, Nop5p, that is essential for growth in Saccharomyces cerevisiae. Monoclonal antibodies B47 and 37C12 recognize Nop5p, which has a predicted size of 57 kDa and possesses a KKX repeat motif at its carboxyl terminus. Truncations that removed the KKX motif were functional and localized to the nucleolus, but conferred slow growth at 37 degreesC. Nop5p shows significant sequence homology with yeast Sik1p/Nop56p, and putative homologues in archaebacteria, plants, and human. Depletion of Nop5p in a GAL-NOP5 strain lengthened the doubling time about 5-fold, and selectively reduced steady-state levels of 40 S ribosomal subunits and 18 S rRNA relative to levels of free 60 S subunits and 25 S rRNA. Northern blotting and primer extension analyses showed that Nop5p depletion impairs processing of 35 S pre-rRNA at the A0 and A2 cleavage sites. Nop5p is associated with the small nucleolar RNAs U3, snR13, U14, and U18. Depletion of Nop5p caused the nucleolar protein Nop1p (yeast fibrillarin) to be localized to the nucleus and cytosol. Also, 37C12 co-immunoprecipitated Nop1p. These results suggest that Nop5p functions with Nop1p in the execution of early pre-rRNA processing steps that lead to formation of 18 S rRNA.     

Contribution for optimal sizing of grid-connected PV-systems using PSO

Particle Swarm Optimization (PSO) is an optimization algorithm considered to be highly efficient for the solution of complicated problems. This paper presents the application of this method for the design optimization of photovoltaic grid-connected systems (PVGCSs). The purpose of the proposed methodology is to locate the optimal number of system devices and the optimal values of the PV module installation details, such that the total net economic benefit achieved during the system operational lifetime period is maximized. The optimization's decision variables are the optimal number of the PV modules, the PV modules optimal tilt angle, the optimal placement of the PV modules within the available installation area and the optimal distribution of the PV modules among the DC/AC converters. The objective function of the proposed optimization process is the lifetime system's total net profit which is calculated according to the method of the Net Present Value (NPV). The methodology's resulting system structures are economically evaluated through the methods of the discounted payback time and the Internal Rate of Return (IRR). The PSO algorithm is compared to the application of Genetic Algorithms (GAs) in terms of efficiency for the current problem.     

Assessing Disclosure Risk and Data Utility Trade-off in Transaction Data Anonymization

Organizations and businesses, including financial institutions and healthcare providers, are increasingly collecting and disseminating information about individuals in the form of transactions. A transaction associates an individual with a set of items, each representing a potentially confidential activity, such as the purchase of a stock or the diagnosis of a disease. Thus, transaction data need to be shared in a way that preserves individuals'' privacy, while remaining useful in intended tasks. While algorithms for anonymizing transaction data have been developed, the issue of how to achieve a "desired" balance between disclosure risk and data utility has not been investigated. In this paper, we assess the balance offered by popular algorithms using the R-U confidentiality map. Our analysis and experiments shed light on how the joint impact on disclosure risk and data utility can be examined, which allows the production of high-quality anonymization solutions.     

Parameter and state estimation of the activated sludge process-II. Applications

In a companion article (Kabouris and Georgakakos, 1996) the Linearized Maximum Likelihood (LML) method for the simultaneous estimation of activated sludge process states and parameters from noisy process measurements is presented. The present article describes the application of the LML method to the identification of a nitrifying activated sludge process, modelled by the IAWPRC Activated Sludge Model 1. It is concluded that identification of nine of the model parameters from process measurements is only possible when a sequential estimation scheme is applied and only influential measurements are considered.     

An artificial immune network for multiobjective optimization problems

Multiobjective optimization is an important problem of great complexity and evolutionary algorithms have been established as a dominant approach in the field. This article suggests a method for approximating the Pareto front of a given function based on artificial immune networks. The proposed method uses cloning and mutation on a population of antibodies to create local subsets of the Pareto front. Elements of these local fronts are combined, in a way that maximizes diversity, to form the complete Pareto front of the function. The method is tested on a number of well-known benchmark problems, as well as an engineering problem. Its performance is compared against state-of-the-art algorithms, yielding promising results.     

A Bayesian approach to design of adaptive multi-model inferential sensors with application in oil sand industry

In the context of process industries, online monitoring of quality variables is often restricted by inadequacy of measurement techniques or low reliability of measuring devices. Therefore, there has been a growing interest in the development of inferential sensors to provide frequent online estimates of key process variables on the basis of their correlation with real-time process measurements. Representation of multi-modal processes is one of the challenging issues that may arise in the design of inferential sensors. In this paper, Bayesian procedures for the development and implementation of adaptive multi-model inferential sensors are presented. It is shown that the application of a Bayesian scheme allows for accommodating the overlapping operating modes and facilitating the inclusion of prior knowledge. The effectiveness of the proposed procedures are first demonstrated through a simulation case study. The efficacy of the method is further highlighted by a successful industrial application of an adaptive multi-model inferential sensor designed for real-time monitoring of a key quality variable in an oil sands processing unit.     

Prediction of sand mass and organic matter distribution via in situ measured wet sediment bulk density profile

The objective of the study is to develop a spatial prediction model of sand mass and organic matter distribution in an urban stormwater holding pond using in situ measured wet sediment bulk density profile data to spatially distinguish the most likely contaminated sediment deposit areas. The wet bulk density profiles of deposited sediment at 25 locations in the Berembang (Malaysia) stormwater holding pond were measured using a single-probe nuclear density gauge. The sand and organic matter compositions of the surface sediment sample, 5 cm thickness from the bed surface, were determined. Discriminant analysis (DA) was conducted to generate two Fisher’s linear discriminant functions for the prediction of sand mass and organic matter composition areas, respectively. The linear discriminant functions generated better area classifications of surface organic matter composition compared to the sand mass distribution using wet sediment bulk density data measured at more than 15 cm depth levels.