The prediction and representation of phase equilibria and physicochemical properties in complex slag systems

The development of new experimental techniques for the determination of phase equilibria in complex slag systems, chemical thermodynamic, and viscosity models is reported. The new experimental data, and new thermodynamic and viscosity models, have been combined in a custom-designed computer software package to produce limiting operability diagrams for slag systems. These diagrams are used to describe phase equilibria and physicochemical properties in complex slag systems. The approach is illustrated with calculations on the system FeO-Fe2O3-CaO-SiO-Al2O3 at metallic iron saturation, slags produced in coal slagging gasifiers, and in the reprocessing of nonferrous smelting slags. This article was presented at the Mills Symposium Molten Metals, Slags and Glasses-Characterisation of Properties and Phenomena held in London in August 2000.

Measurement and prediction of energy expenditure in males during household and garden tasks

Participation in at least 30 min of moderate intensity activity on most days is assumed to confer health benefits. This study accordingly determined whether the more vigorous household and garden tasks (sweeping, window cleaning, vacuuming and lawn mowing) are performed by middle-aged men at a moderate intensity of 3-6 metabolic equivalents (METs) in the laboratory and at home. Measured energy expenditure during self-perceived moderate-paced walking was used as a marker of exercise intensity. Energy expenditure was also predicted via indirect methods. Thirty-six males [Xmacr (SD): 40.0 (3.3) years; 179.5 (6.9) cm; 83.4 (14.0) kg] were measured for resting metabolic rate (RMR) and oxygen consumption (V.O-2) during the five activities using the Douglas bag method. Heart rate , respiratory frequency, CSA (Computer Science Applications) movement counts, Borg scale ratings of perceived exertion and Quetelet's index were also recorded as potential predictors of exercise intensity. Except for vacuuming in the laboratory, which was not significantly different from 3.0 METs (P=0.98), the MET means in the laboratory and home were all significantly greater than 3.0 (Pless than or equal to0.006). The sweeping and vacuuming MET means were significantly higher (P

Accurate prediction of scorpion toxin functional properties from primary structures

Scorpion toxins are common experimental tools for studies of biochemical and pharmacological properties of ion channels. The number of functionally annotated scorpion toxins is steadily growing, but the number of identified toxin sequences is increasing at much faster pace. With an estimated 100,000 different variants, bioinformatic analysis of scorpion toxins is becoming a necessary tool for their systematic functional analysis. Here, we report a bioinformatics-driven system involving scorpion toxin structural classification, functional annotation, database technology, sequence comparison, nearest neighbour analysis, and decision rules which produces highly accurate predictions of scorpion toxin functional properties. (c) 2005 Elsevier Inc. All rights reserved.

Prediction of subcellular localization using sequence-biased recurrent networks

Motivation: Targeting peptides direct nascent proteins to their specific subcellular compartment. Knowledge of targeting signals enables informed drug design and reliable annotation of gene products. However, due to the low similarity of such sequences and the dynamical nature of the sorting process, the computational prediction of subcellular localization of proteins is challenging. Results: We contrast the use of feed forward models as employed by the popular TargetP/SignalP predictors with a sequence-biased recurrent network model. The models are evaluated in terms of performance at the residue level and at the sequence level, and demonstrate that recurrent networks improve the overall prediction performance. Compared to the original results reported for TargetP, an ensemble of the tested models increases the accuracy by 6 and 5% on non-plant and plant data, respectively.

An Incremental EM-based Learning Approach for On-Line Prediction of Hospital Resource Utilization

Objective: Inpatient length of stay (LOS) is an important measure of hospital activity, health care resource consumption, and patient acuity. This research work aims at developing an incremental expectation maximization (EM) based learning approach on mixture of experts (ME) system for on-line prediction of LOS. The use of a batchmode learning process in most existing artificial neural networks to predict LOS is unrealistic, as the data become available over time and their pattern change dynamically. In contrast, an on-line process is capable of providing an output whenever a new datum becomes available. This on-the-spot information is therefore more useful and practical for making decisions, especially when one deals with a tremendous amount of data. Methods and material: The proposed approach is illustrated using a real example of gastroenteritis LOS data. The data set was extracted from a retrospective cohort study on all infants born in 1995-1997 and their subsequent admissions for gastroenteritis. The total number of admissions in this data set was n = 692. Linked hospitalization records of the cohort were retrieved retrospectively to derive the outcome measure, patient demographics, and associated co-morbidities information. A comparative study of the incremental learning and the batch-mode learning algorithms is considered. The performances of the learning algorithms are compared based on the mean absolute difference (MAD) between the predictions and the actual LOS, and the proportion of predictions with MAD < 1 day (Prop(MAD < 1)). The significance of the comparison is assessed through a regression analysis. Results: The incremental learning algorithm provides better on-line prediction of LOS when the system has gained sufficient training from more examples (MAD = 1.77 days and Prop(MAD < 1) = 54.3%), compared to that using the batch-mode learning. The regression analysis indicates a significant decrease of MAD (p-value = 0.063) and a significant (p-value = 0.044) increase of Prop(MAD

Multiagent realization of prediction-based diagnosis and loss prevention

A multiagent diagnostic system implemented in a Protege-JADE-JESS environment interfaced with a dynamic simulator and database services is described in this paper. The proposed system architecture enables the use of a combination of diagnostic methods from heterogeneous knowledge sources. The process ontology and the process agents are designed based on the structure of the process system, while the diagnostic agents implement the applied diagnostic methods. A specific completeness coordinator agent is implemented to coordinate the diagnostic agents based on different methods. The system is demonstrated on a case study for diagnosis of faults in a granulation process based on HAZOP and FMEA analysis.

Effects of graphene layer size on the adsorption of fluids on graphitized thermal carbon black. A computer simulation study

The adsorption of Lennard-Jones fluids (argon and nitrogen) onto a graphitized thermal carbon black surface was studied with a Grand Canonical Monte Carlo Simulation (GCMC). The surface was assumed to be finite in length and composed of three graphene layers. When the GCMC simulation was used to describe adsorption on a graphite surface, an over-prediction of the isotherm was consistently observed in the pressure regions where the first and second layers are formed. To remove this over-prediction, surface mediation was accounted for to reduce the fluid-fluid interaction. Do and co-workers have introduced the so-called surface-mediation damping factor to correct the over-prediction for the case of a graphite surface of infinite extent, and this approach has yielded a good description of the adsorption isotherm. In this paper, the effects of the finite size of the graphene layer on the adsorption isotherm and how these would affect the extent of the surface mediation were studied. It was found that this finite-surface model provides a better description of the experimental data for graphitized thermal carbon black of high surface area (i.e. small crystallite size) while the infinite- surface model describes data for carbon black of very low surface area (i.e. large crystallite size).

Wear-type rail corrugation prediction: Field study

Rail corrugation consists of undesirable periodic fluctuations in wear on railway track and costs the railway industry substantially for it's removal by regrinding. Much research has been performed on this problem, particularly over the past two decades, however, a reliable cure remains elusive for wear-type corrugations. Recently the growth behaviour of wear-type rail corrugation-has been investigated using theoretical and experimental models as part of the RailCRC Project (#18). A critical part of this work is the tuning and validation of these models via an extensive field testing program. Rail corrugations have been monitored for 2 years on sites throughout Australia. Measured rail surface profiles are used to determine corrugation growth rates on each site. Growth rates and other characteristics are compared with theoretical predictions from a computer model for validation. The results from several pertinent sites are presented and discussed.