Moving window kernel PCA for adaptive monitoring of nonlinear processes

This paper discusses the monitoring of complex nonlinear and time-varying processes. Kernel principal component analysis (KPCA) has gained significant attention as a monitoring tool for nonlinear systems in recent years but relies on a fixed model that cannot be employed for time-varying systems. The contribution of this article is the development of a numerically efficient and memory saving moving window KPCA (MWKPCA) monitoring approach. The proposed technique incorporates an up- and downdating procedure to adapt (i) the data mean and covariance matrix in the feature space and (ii) approximates the eigenvalues and eigenvectors of the Gram matrix. The article shows that the proposed MWKPCA algorithm has a computation complexity of O(N2), whilst batch techniques, e.g. the Lanczos method, are of O(N3). Including the adaptation of the number of retained components and an l-step ahead application of the MWKPCA monitoring model, the paper finally demonstrates the utility of the proposed technique using a simulated nonlinear time-varying system and recorded data from an industrial distillation column.

Large-scale density functional calculations of the surface properties of the Wigner crystal

The surface properties of the jellium model have been investigated by large supercell computations in the density functional theory-local spin-density (DFT-LSD) approach for planar slabs with up to 1000 electrons. A wide interval of densities has been explored, extending into the stability range of the Wigner crystal. Most computations have been carried out on nominally paramagnetic samples with an equal number of spin-up and spin-down electrons. The results show that within DFT-LSD spontaneous spin polarization and charge localization start nearly simultaneously at the surface for r(s) similar to 20, then, with decreasing density, they progress toward the center of the slab. Electrons are fully localized and spin polarized at r(s) = 30. At this density the charge distribution is the superposition of disjoint charge blobs, each corresponding to one electron. The distribution of blobs displays both regularities and disorder, the first being represented by well-defined planes and simple in-plane geometries, and the latter by a variety of surface defects. The surface energy, surface dipole, electric polarisability, and magnetization pattern have been determined as a function of density. All these quantities display characteristic anomalies at the density of the localization transition. The analysis of the low-frequency electric conductivity shows that in the fluid paramagnetic regime the in-plane current preferentially flows in the central region of the slab and the two spin channels are equally conducting. In the charge localized, spin-polarized regime, conductivity is primarily a surface effect, and an apparent asymmetry is observed in the two spin currents.

Density-potential mapping in time-dependent density-functional theory

The key questions of uniqueness and existence in time-dependent density-functional theory are usually formulated only for potentials and densities that are analytic in time. Simple examples, standard in quantum mechanics, lead, however, to nonanalyticities. We reformulate these questions in terms of a nonlinear Schroedinger equation with a potential that depends nonlocally on the wave function.

Human odontoblasts express functional thermo-sensitive TRP channels: implications for dentin sensitivity

Odontoblasts form the outermost cellular layer of the dental pulp where they have been proposed to act as sensory receptor cells. Despite this suggestion, evidence supporting their direct role in mediating thermo-sensation and nociception is lacking. Transient receptor potential (TRP) ion channels directly mediate nociceptive functions, but their functional expression in human odontoblasts has yet to be elucidated. In the present study, we have examined the molecular and functional expression of thermo-sensitive TRP channels in cultured odontoblast-like cells and in native human odontoblasts obtained from healthy wisdom teeth. PCR and western blotting confirmed gene and protein expression of TRPV1, TRPA1 and TRPM8 channels. Immunohistochemistry revealed that these channels were localised to odontoblast-like cells as determined by double staining with dentin sialoprotein (DSP) antibody. In functional assays, agonists of TRPV1, TRPA1 and TRPM8 channels elicited [Ca2+]i transients that could be blocked by relevant antagonists. Application of hot and cold stimuli to the cells also evoked rises in [Ca2+]i which could be blocked by TRP-channel antagonists. Using a gene silencing approached we further confirmed a role for TRPA1 in mediating noxious cold responses in odontoblasts. We conclude that human odontoblasts express functional TRP channels that may play a crucial role in mediating thermal sensation in teeth. Cultured and native human odontoblasts express functional TRP channels that may play a crucial role in mediating thermal sensation in teeth.

Dual functional ionic liquids as plasticisers and antimicrobial agents for medical polymers

Contamination of medical devices with bacteria such as Meticillin resistant Staphylococcus aureus (MRSA) is of great clinical concern. Poly(vinyl chloride) is widely used in the production of medical devices, such as catheters. The flexibility of catheter tubing is derived from the addition of plasticisers. Here, we report the design of two dual functional ionic liquids, 1-ethylpyridinium docusate and tributyl(2-hydroxyethyl)phosphonium docusate, which uniquely provide a plasticising effect, and exhibit antimicrobial and antibiofilm-forming activity to a range of antibiotic resistant bacteria. The plasticisation of poly(vinyl chloride) was tailored as a function of ionic liquid concentration. The effective antimicrobial behaviour of both ionic liquids originates from the chemical structure of the anion or cation and is not limited to the length of the alkyl chain on the anion/cation. The design approach adopted will be useful in developing ionic liquids as multi-functional additives for polymers.

Evaluation of a multi-functional nanocarrier for targeted breast cancer iNOS gene therapy.

The present study determines whether the novel designer biomimetic vector (DBV) can condense anddeliver the cytotoxic iNOS gene to breast cancer cells to achieve a therapeutic effect. We have previouslyshown the benefits of iNOS for cancer gene therapy but the stumbling block to future development hasbeen the delivery system.The DBV was expressed, purified and complexed with the iNOS gene. The particle size and chargewere determined via dynamic light scattering techniques. The toxicity of the DBV/iNOS nanoparticleswas quantified using the cell toxicity and clonogenic assays. Over expression of iNOS was confirmed viaWestern blotting and Griess test.The DBV delivery system fully condensed the iNOS gene with nanoparticles less than 100 nm. Transfectionwith the DBV/iNOS nanoparticles resulted in a maximum of 62% cell killing and less than 20%clonogenicity. INOS overexpression was confirmed and total nitrite levels were in the range of 18M.We report for the first time that the DBV can successfully deliver iNOS and achieve a therapeuticeffect. There is significant cytotoxicity coupled with evidence of a bystander effect. We concludethat the success of the DBV fusion protein in the delivery of iNOS in vitro is worthy of future in vivo experiments.

Application of Nonlinear PCA for Fault Detection in Polymer Extrusion Processes

This paper describes the application of an improved nonlinear principal component analysis (PCA) to the detection of faults in polymer extrusion processes. Since the processes are complex in nature and nonlinear relationships exist between the recorded variables, an improved nonlinear PCA, which incorporates the radial basis function (RBF) networks and principal curves, is proposed. This algorithm comprises two stages. The first stage involves the use of the serial principal curve to obtain the nonlinear scores and approximated data. The second stage is to construct two RBF networks using a fast recursive algorithm to solve the topology problem in traditional nonlinear PCA. The benefits of this improvement are demonstrated in the practical application to a polymer extrusion process.

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.