Improved Nonlinear PCA for Process Monitoring Using Support Vector Data Description

Nonlinear principal component analysis (PCA) based on neural networks has drawn significant attention as a monitoring tool for complex nonlinear processes, but there remains a difficulty with determining the optimal network topology. This paper exploits the advantages of the Fast Recursive Algorithm, where the number of nodes, the location of centres, and the weights between the hidden layer and the output layer can be identified simultaneously for the radial basis function (RBF) networks. The topology problem for the nonlinear PCA based on neural networks can thus be solved. Another problem with nonlinear PCA is that the derived nonlinear scores may not be statistically independent or follow a simple parametric distribution. This hinders its applications in process monitoring since the simplicity of applying predetermined probability distribution functions is lost. This paper proposes the use of a support vector data description and shows that transforming the nonlinear principal components into a feature space allows a simple statistical inference. Results from both simulated and industrial data confirm the efficacy of the proposed method for solving nonlinear principal component problems, compared with linear PCA and kernel PCA.

Statistical-mechanical description of classical test-particle dynamics in the presence of an external force field: modelling noise and damping from first principles

Aiming to establish a rigorous link between macroscopic random motion (described e.g. by Langevin-type theories) and microscopic dynamics, we have undertaken a kinetic-theoretical study of the dynamics of a classical test-particle weakly coupled to a large heat-bath in thermal equilibrium. Both subsystems are subject to an external force field. From the (time-non-local) generalized master equation a Fokker-Planck-type equation follows as a "quasi-Markovian" approximation. The kinetic operator thus defined is shown to be ill-defined; in specific, it does not preserve the positivity of the test-particle distribution function f(x, v; t). Adopting an alternative approach, previously introduced for quantum open systems, is proposed to lead to a correct kinetic operator, which yields all the expected properties. A set of explicit expressions for the diffusion and drift coefficients are obtained, allowing for modelling macroscopic diffusion and dynamical friction phenomena, in terms of an external field and intrinsic physical parameters.

Improved description of chemical barriers with generalized gradient approximations (GGAs) and meta-GGAs

The performance of exchange and correlation (xc) functionals of the generalized gradient approximation (GGA) type and of the meta-GGA type in the calculation of chemical reactions is related to topological features of the electron density which, in turn, are connected to the orbital structure of chemical bonds within the Kohn-Sham (KS) theory. Seventeen GGA and meta-GGA xc functionals are assessed for 15 hydrogen abstraction reactions and 3 symmetrical S(N)2 reactions. Systems that are problematic for standard GGAs characteristically have enhanced values of the dimensionless gradient argument s(sigma)(2) with local maxima in the bonding region. The origin of this topological feature is the occupation of valence KS orbitals with an antibonding or essentially nonbonding character. The local enhancement of s(sigma)(2) yields too negative exchange-correlation energies with standard GGAs for the transition state of the S(N)2 reaction, which leads to the reduced calculated reaction barriers. The unwarranted localization of the effective xc hole of the standard GGAs, i.e., the nondynamical correlation that is built into them but is spurious in this case, wields its effect by their s(sigma)(2) dependence. Barriers are improved for xc functionals with the exchange functional OPTX as x component, which has a modified dependence on s(sigma)(2). Standard GGAs also underestimate the barriers for the hydrogen abstraction reactions. In this case the barriers are improved by correlation functionals, such as the Laplacian-dependent (LAP3) functional, which has a modified dependence on the Coulomb correlation of the opposite- and like-spin electrons. The best overall performance is established for the combination OLAP3 of OPTX and LAP3.

Description and predictors of hospital costs of oesophageal cancer during the first year following diagnosis in Northern Ireland

The cost-effectiveness of novel interventions in the treatment of cancer is well researched; however, relatively little attention is paid to the cost of many aspects of routine care. Oesophageal cancer is the ninth most common cancer in the UK and sixth most common cause of cancer death. It usually presents late and has a poor prognosis. The hospital costs incurred by oesophageal cancer patients diagnosed in Northern Ireland in 2005 (n = 198) were determined by review of medical records. The average cost of hospital care per patient in the 12 months from presentation was £7847. Variations in total hospital costs by age at diagnosis, gender, cancer stage, histological type, mortality at 1 year, co-morbidity count and socio-economic status were analysed using multiple regression analyses. Higher costs were associated with earlier stages of cancer and cancer stage remained a significant predictor of costs after controlling for cancer type, patient age and mortality at 1 year. Thus, although early detection of cancer usually improves survival, this would mean increased costs in the first year. Deprivation achieved borderline significance with those from more deprived areas having lower resource consumption relative to the more affluent. © 2013 John Wiley & Sons Ltd.