The validation of predictions concerning personnel and training requirements: a case study

"References": leaves 26-27.

Crash and economic depression again in our time.

Mode of access: Internet.

Interurbans. Street railway crash

Streetcars

Predictive vaccinology: Optimisation of predictions using support vector machine classifiers

Promiscuous human leukocyte antigen (HLA) binding peptides are ideal targets for vaccine development. Existing computational models for prediction of promiscuous peptides used hidden Markov models and artificial neural networks as prediction algorithms. We report a system based on support vector machines that outperforms previously published methods. Preliminary testing showed that it can predict peptides binding to HLA-A2 and -A3 super-type molecules with excellent accuracy, even for molecules where no binding data are currently available.

Boundary element method predictions of the influence of the electrolyte on the galvanic corrosion of AZ91D coupled to steel

This research investigated the galvanic corrosion of the magnesium alloy AZ91D coupled to steel. The galvanic current distribution was measured in 5% NaCl solution, corrosive water and an auto coolant. The experimental measurements were compared with predictions from a Boundary Element Method (BEM) model. The boundary condition, required as an input into the BEM model, needs to be a polarization curve that accurately reflects the corrosion process. Provided that the polarization curve does reflect steady state, the BEM model is expected to be able to reflect steady state galvanic corrosion.

Development of a quasi-chemical viscosity model for fully liquid slags in the Al2O3-CaO-'FeO'-MgO-SiO2 system. Part 2. A review of the experimental data and the model predictions for the Al2O3-CaO-MgO, CaO-MgO-SiO2 and Al2O3-MgO-SiO2 systems

A structurally-based quasi-chemical viscosity model has been developed for the Al2O3 CaO-'FeO'-MgO-SiO2 system. The model links the slag viscosity to the internal structure of melts through the concentrations of various anion/cation Si0.5O, Me2/nn+O and Me1/nn+Si0.25O viscous flow structural units. The concentrations of structural units are derived from the quasi-chemical thermodynamic model. The focus of the work described in the present paper is the analysis of experimental data and the viscosity models for fully liquid slags in the Al2O3-CaO-MgO, Al2O3 MgO-SiO2 and CaO-MgO-SiO2 systems.

Development of a quasi-chemical viscosity model for fully liquid slags in the Al2O3-CaO-'FeO'-MgO-SiO2 system. Part 3. Summary of the model predictions for the Al2O3-CaO-MgO-SiO2 system and its sub-systems

A structurally-based quasi-chemical viscosity model for fully liquid slags in the Al2O3 CaO-'FeO'-MgOSiO2 system has been developed. The focus of the work described in the present paper is the analysis of the experimental data and viscosity models in the quaternary system Al2O3 CaO-MgO-SiO2 and its subsystems. A review of the experimental data, viscometry methods used and viscosity models available in the Al2O3 CaO-MgO-SiO2 and its sub-systems is reported. The quasi-chemical viscosity model is shown to provide good agreement between experimental data and predictions over the whole compositional range.

Testing predictions of macroscopic binary diffusion coefficients using lattice models with site heterogeneity

Quantitatively predicting mass transport rates for chemical mixtures in porous materials is important in applications of materials such as adsorbents, membranes, and catalysts. Because directly assessing mixture transport experimentally is challenging, theoretical models that can predict mixture diffusion coefficients using Only single-component information would have many uses. One such model was proposed by Skoulidas, Sholl, and Krishna (Langmuir, 2003, 19, 7977), and applications of this model to a variety of chemical mixtures in nanoporous materials have yielded promising results. In this paper, the accuracy of this model for predicting mixture diffusion coefficients in materials that exhibit a heterogeneous distribution of local binding energies is examined. To examine this issue, single-component and binary mixture diffusion coefficients are computed using kinetic Monte Carlo for a two-dimensional lattice model over a wide range of lattice occupancies and compositions. The approach suggested by Skoulidas, Sholl, and Krishna is found to be accurate in situations where the spatial distribution of binding site energies is relatively homogeneous, but is considerably less accurate for strongly heterogeneous energy distributions.

Combining spatially distributed predictions from neural networks

In this report we discuss the problem of combining spatially-distributed predictions from neural networks. An example of this problem is the prediction of a wind vector-field from remote-sensing data by combining bottom-up predictions (wind vector predictions on a pixel-by-pixel basis) with prior knowledge about wind-field configurations. This task can be achieved using the scaled-likelihood method, which has been used by Morgan and Bourlard (1995) and Smyth (1994), in the context of Hidden Markov modelling

Differences between theoretical predictions and operational performance in a stock control application

Over the past decade, several experienced Operational Researchers have advanced the view that the theoretical aspects of model building have raced ahead of the ability of people to use them. Consequently, the impact of Operational Research on commercial organisations and the public sector is limited, and many systems fail to achieve their anticipated benefits in full. The primary objective of this study is to examine a complex interactive Stock Control system, and identify the reasons for the differences between the theoretical expectations and the operational performance. The methodology used is to hypothesise all the possible factors which could cause a divergence between theory and practice, and to evaluate numerically the effect each of these factors has on two main control indices - Service Level and Average Stock Value. Both analytical and empirical methods are used, and simulation is employed extensively. The factors are divided into two main categories for analysis - theoretical imperfections in the model, and the usage of the system by Buyers. No evidence could be found in the literature of any previous attempts to place the differences between theory and practice in a system in quantitative perspective nor, more specifically, to study the effects of Buyer/computer interaction in a Stock Control system. The study reveals that, in general, the human factors influencing performance are of a much higher order of magnitude than the theoretical factors, thus providing objective evidence to support the original premise. The most important finding is that, by judicious intervention into an automatic stock control algorithm, it is possible for Buyers to produce results which not only attain but surpass the algorithmic predictions. However, the complexity and behavioural recalcitrance of these systems are such that an innately numerate, enquiring type of Buyer needs to be inducted to realise the performance potential of the overall man/computer system.

A comparison of sales response predictions from demand models applied to store-level versus panel data

In order to generate sales promotion response predictions, marketing analysts estimate demand models using either disaggregated (consumer-level) or aggregated (store-level) scanner data. Comparison of predictions from these demand models is complicated by the fact that models may accommodate different forms of consumer heterogeneity depending on the level of data aggregation. This study shows via simulation that demand models with various heterogeneity specifications do not produce more accurate sales response predictions than a homogeneous demand model applied to store-level data, with one major exception: a random coefficients model designed to capture within-store heterogeneity using store-level data produced significantly more accurate sales response predictions (as well as better fit) compared to other model specifications. An empirical application to the paper towel product category adds additional insights. This article has supplementary material online.

On the hydrophobicity of peptides:comparing empirical predictions of peptide log P values

Peptides are of great therapeutic potential as vaccines and drugs. Knowledge of physicochemical descriptors, including the partition coefficient logP, is useful for the development of predictive Quantitative Structure-Activity Relationships (QSARs). We have investigated the accuracy of available programs for the prediction of logP values for peptides with known experimental values obtained from the literature. Eight prediction programs were tested, of which seven programs were fragment-based methods: XLogP, LogKow, PLogP, ACDLogP, AlogP, Interactive Analysis's LogP and MlogP; and one program used a whole molecule approach: QikProp. The predictive accuracy of the programs was assessed using r(2) values, with ALogP being the most effective (r( 2) = 0.822) and MLogP the least (r(2) = 0.090). We also examined three distinct types of peptide structure: blocked, unblocked, and cyclic. For each study (all peptides, blocked, unblocked and cyclic peptides) the performance of programs rated from best to worse is as follows: all peptides - ALogP, QikProp, PLogP, XLogP, IALogP, LogKow, ACDLogP, and MlogP; blocked peptides - PLogP, XLogP, ACDLogP, IALogP, LogKow, QikProp, ALogP, and MLogP; unblocked peptides - QikProp, IALogP, ALogP, ACDLogP, MLogP, XLogP, LogKow and PLogP; cyclic peptides - LogKow, ALogP, XLogP, MLogP, QikProp, ACDLogP, IALogP. In summary, all programs gave better predictions for blocked peptides, while, in general, logP values for cyclic peptides were under-predicted and those of unblocked peptides were over-predicted.