Practical methods of tracking of nonstationary time series applied to real-world data

In this paper, we discuss some practical implications for implementing adaptable network algorithms applied to non-stationary time series problems. Two real world data sets, containing electricity load demands and foreign exchange market prices, are used to test several different methods, ranging from linear models with fixed parameters, to non-linear models which adapt both parameters and model order on-line. Training with the extended Kalman filter, we demonstrate that the dynamic model-order increment procedure of the resource allocating RBF network (RAN) is highly sensitive to the parameters of the novelty criterion. We investigate the use of system noise for increasing the plasticity of the Kalman filter training algorithm, and discuss the consequences for on-line model order selection. The results of our experiments show that there are advantages to be gained in tracking real world non-stationary data through the use of more complex adaptive models.

Quantitative prediction of mouse class I MHC peptide binding affinity using support vector machine regression (SVR) models

Background - The binding between peptide epitopes and major histocompatibility complex proteins (MHCs) is an important event in the cellular immune response. Accurate prediction of the binding between short peptides and the MHC molecules has long been a principal challenge for immunoinformatics. Recently, the modeling of MHC-peptide binding has come to emphasize quantitative predictions: instead of categorizing peptides as "binders" or "non-binders" or as "strong binders" and "weak binders", recent methods seek to make predictions about precise binding affinities. Results - We developed a quantitative support vector machine regression (SVR) approach, called SVRMHC, to model peptide-MHC binding affinities. As a non-linear method, SVRMHC was able to generate models that out-performed existing linear models, such as the "additive method". By adopting a new "11-factor encoding" scheme, SVRMHC takes into account similarities in the physicochemical properties of the amino acids constituting the input peptides. When applied to MHC-peptide binding data for three mouse class I MHC alleles, the SVRMHC models produced more accurate predictions than those produced previously. Furthermore, comparisons based on Receiver Operating Characteristic (ROC) analysis indicated that SVRMHC was able to out-perform several prominent methods in identifying strongly binding peptides. Conclusion - As a method with demonstrated performance in the quantitative modeling of MHC-peptide binding and in identifying strong binders, SVRMHC is a promising immunoinformatics tool with not inconsiderable future potential.

Linear Fresnel mirror solar concentrator with tracking

Solar energy is the most abundant, widely distributed and clean renewable energy resource. Since the insolation intensity is only in the range of 0.5 - 1.0 kW/m2, solar concentrators are required for attaining temperatures appropriate for medium and high temperature applications. The concentrated energy is transferred through an absorber to a thermal fluid such as air, water or other fluids for various uses. This paper describes design and development of a 'Linear Fresnel Mirror Solar Concentrator' (LFMSC) using long thin strips of mirrors to focus sunlight on to a fixed receiver located at a common focal line. Our LFMSC system comprises a reflector (concentrator), receiver (target) and an innovative solar tracking mechanism. Reflectors are mirror strips, mounted on tubes which are fixed to a base frame. The tubes can be rotated to align the strips to focus solar radiation on the receiver (target). The latter comprises a coated tube carrying water and covered by a glass plate. This is mounted at an elevation of few meters above the horizontal, parallel to the plane of the mirrors. The reflector is oriented along north-south axis. The most difficult task is tracking. This is achieved by single axis tracking using a four bar link mechanism. Thus tracking has been made simple and easy to operate. The LFMSC setup is used for generating steam for a variety of applications. © 2013 The Authors. Published by Elsevier Ltd.