The specification of vector autoregressive moving average models

In this paper we propose a new identification method based on the residual white noise autoregressive criterion (Pukkila et al. , 1990) to select the order of VARMA structures. Results from extensive simulation experiments based on different model structures with varying number of observations and number of component series are used to demonstrate the performance of this new procedure. We also use economic and business data to compare the model structures selected by this order selection method with those identified in other published studies.

Factors affecting human reliability in the chemical process industry

This research was concerned with identifying factors which may influence human reliability within chemical process plants - these factors are referred to as Performance Shaping Factors (PSFs). Following a period of familiarization within the industry, a number of case studies were undertaken covering a range of basic influencing factors. Plant records and site `lost time incident reports' were also used as supporting evidence for identifying and classifying PSFs. In parallel to the investigative research, the available literature appertaining to human reliability assessment and PSFs was considered in relation to the chemical process plan environment. As a direct result of this work, a PSF classification structure has been produced with an accompanying detailed listing. Phase two of the research considered the identification of important individual PSFs for specific situations. Based on the experience and data gained during phase one, it emerged that certain generic features of a task influenced PSF relevance. This led to the establishment of a finite set of generic task groups and response types. Similarly, certain PSFs influence some human errors more than others. The result was a set of error type key words, plus the identification and classification of error causes with their underlying error mechanisms. By linking all these aspects together, a comprehensive methodology has been forwarded as the basis of a computerized aid for system designers. To recapitulate, the major results of this research have been: One, the development of a comprehensive PSF listing specifically for the chemical process industries with a classification structure that facilitates future updates; and two, a model of identifying relevant SPFs and their order of priority. Future requirements are the evaluation of the PSF listing and the identification method. The latter must be considered both in terms of `useability' and its success as a design enhancer, in terms of an observable reduction in important human errors.

A smart sensing platform for the discrimination of human movement

This thesis documents the design, implementation and testing of a smart sensing platform that is able to discriminate between differences or small changes in a persons walking. The distributive tactile sensing method is used to monitor the deflection of the platform surface using just a small number of sensors and, through the use of neural networks, infer the characteristics of the object in contact with the surface. The thesis first describes the development of a mathematical model which uses a novel method to track the position of a moving load as it passes over the smart sensing surface. Experimental methods are then described for using the platform to track the position of swinging pendulum in three dimensions. It is demonstrated that the method can be extended to that of real-time measurement of balance and sway of a person during quiet standing. Current classification methods are then investigated for use in the classification of different gait patterns, in particular to identify individuals by their unique gait pattern. Based on these observations, a novel algorithm is developed that is able to discriminate between abnormal and affected gait. This algorithm, using the distributive tactile sensing method, was found to have greater accuracy than other methods investigated and was designed to be able to cope with any type of gait variation. The system developed in this thesis has applications in the area of medical diagnostics, either as an initial screening tool for detecting walking disorders or to be able to automatically detect changes in gait over time. The system could also be used as a discrete biometric identification method, for example identifying office workers as they pass over the surface.

Biodiversity of the surface microbial consortia from Limburger, Reblochon, Livarot, Tilsit, and Gubbeen cheeses

Comprehensive collaborative studies from our laboratories reveal the extensive biodiversity of the microflora of the surfaces of smear-ripened cheeses. Two thousand five hundred ninety-seven strains of bacteria and 2,446 strains of yeasts from the surface of the smear-ripened cheeses Limburger, Reblochon, Livarot, Tilsit, and Gubbeen, isolated at three or four times during ripening, were identified; 55 species of bacteria and 30 species of yeast were found. The microfloras of the five cheeses showed many similarities but also many differences and interbatch variation. Very few of the commercial smear microorganisms, deliberately inoculated onto the cheese surface, were reisolated and then mainly from the initial stages of ripening, implying that smear cheese production units must have an adventitious "house" flora. Limburger cheese had the simplest microflora, containing two yeasts, Debaryomyces hansenii and Geotrichum candidum, and two bacteria, Arthrobacter arilaitensis and Brevibacterium aurantiacum. The microflora of Livarot was the most complicated, comprising 10 yeasts and 38 bacteria, including many gram-negative organisms. Reblochon also had a very diverse microflora containing 8 yeasts and 13 bacteria (excluding gram-negative organisms which were not identified), while Gubbeen had 7 yeasts and 18 bacteria and Tilsit had 5 yeasts and 9 bacteria. D. hansenii was by far the dominant yeast, followed in order by G. candidum, Candida catenulata, and Kluyveromyces lactis. B. aurantiacum was the dominant bacterium and was found in every batch of the 5 cheeses. The next most common bacteria, in order, were Staphylococcus saprophyticus, A. arilaitensis, Corynebacterium casei, Corynebacterium variabile, and Microbacterium gubbeenense. S. saprophyticus was mainly found in Gubbeen, and A. arilaitensis was found in all cheeses but not in every batch. C. casei was found in most batches of Reblochon, Livarot, Tilsit, and Gubbeen. C. variabile was found in all batches of Gubbeen and Reblochon but in only one batch of Tilsit and in no batch of Limburger or Livarot. Other bacteria were isolated in low numbers from each of the cheeses, suggesting that each of the 5 cheeses has a unique microflora. In Gubbeen cheese, several different strains of the dominant bacteria were present, as determined by pulsed-field gel electrophoresis, and many of the less common bacteria were present as single clones. The culture-independent method, denaturing gradient gel electrophoresis, resulted in identification of several bacteria which were not found by the culture-dependent (isolation and rep-PCR identification) method. It was thus a useful complementary technique to identify other bacteria in the cheeses. The gross composition, the rate of increase in pH, and the indices of proteolysis were different in most of the cheeses.

Identifying DNA-binding proteins by combining support vector machine and PSSM distance transformation

Background: DNA-binding proteins play a pivotal role in various intra- and extra-cellular activities ranging from DNA replication to gene expression control. Identification of DNA-binding proteins is one of the major challenges in the field of genome annotation. There have been several computational methods proposed in the literature to deal with the DNA-binding protein identification. However, most of them can't provide an invaluable knowledge base for our understanding of DNA-protein interactions. Results: We firstly presented a new protein sequence encoding method called PSSM Distance Transformation, and then constructed a DNA-binding protein identification method (SVM-PSSM-DT) by combining PSSM Distance Transformation with support vector machine (SVM). First, the PSSM profiles are generated by using the PSI-BLAST program to search the non-redundant (NR) database. Next, the PSSM profiles are transformed into uniform numeric representations appropriately by distance transformation scheme. Lastly, the resulting uniform numeric representations are inputted into a SVM classifier for prediction. Thus whether a sequence can bind to DNA or not can be determined. In benchmark test on 525 DNA-binding and 550 non DNA-binding proteins using jackknife validation, the present model achieved an ACC of 79.96%, MCC of 0.622 and AUC of 86.50%. This performance is considerably better than most of the existing state-of-the-art predictive methods. When tested on a recently constructed independent dataset PDB186, SVM-PSSM-DT also achieved the best performance with ACC of 80.00%, MCC of 0.647 and AUC of 87.40%, and outperformed some existing state-of-the-art methods. Conclusions: The experiment results demonstrate that PSSM Distance Transformation is an available protein sequence encoding method and SVM-PSSM-DT is a useful tool for identifying the DNA-binding proteins. A user-friendly web-server of SVM-PSSM-DT was constructed, which is freely accessible to the public at the web-site on http://bioinformatics.hitsz.edu.cn/PSSM-DT/.

From traditional resource to global commodities: a comparison of Rhodiola species using NMR spectroscopy - metabolomics and HPTLC

The fast developing international trade of products based on traditional knowledge and their value chains has become an important aspect of the ethnopharmacological debate. The structure and diversity of value chains and their impact on the phytochemical composition of herbal medicinal products has been overlooked in the debate about quality problems in transnational trade. Different government policies and regulations governing trade in herbal medicinal products impact on such value chains. Medicinal Rhodiola species, including Rhodiola rosea L. and Rhodiola crenulata (Hook.f. & Thomson) H.Ohba, have been used widely in Europe and Asia as traditional herbal medicines with numerous claims for their therapeutic effects. Faced with resource depletion and environment destruction, R. rosea and R. crenulata are becoming endangered, making them more economically valuable to collectors and middlemen, and also increasing the risk of adulteration and low quality. We compare the phytochemical differences among Rhodiola raw materials available on the market to provide a practical method for Rhodiola authentication and the detection of potential adulterant compounds. Samples were collected from Europe and Asia and nuclear magnetic resonance spectroscopy coupled with multivariate analysis software and high performance thin layer chromatography techniques were used to analyse the samples. A method was developed to quantify the amount of adulterant species contained within mixtures. We compared the phytochemical composition of collected Rhodiola samples to authenticated samples. Rosavin and rosarin were mainly present in R. rosea whereas crenulatin was only present in R. crenulata. 30% of the Rhodiola samples purchased from the Chinese market were adulterated by other Rhodiola spp. Moreover, 7 % of the raw-material samples were not labelled satifactorily. The utilisation of both 1H-NMR and HPTLC methods provided an integrated analysis of the phytochemical differences and novel identification method for R. rosea and R. crenulata. Using 1H-NMR spectroscopy it was possible to quantify the presence of R. crenulata in admixtures with R. rosea. This quantitative technique could be used in the future to assess a variety of herbal drugs and products. This project also highlights the need to further study the links between producers and consumers in national and trans-national trade.

Data-Driven Dynamic Modeling of Coupled Thermal and Electric Outputs of Microturbines

Microturbines are among the most successfully commercialized distributed energy resources, especially when they are used for combined heat and power generation. However, the interrelated thermal and electrical system dynamic behaviors have not been fully investigated. This is technically challenging due to the complex thermo-fluid-mechanical energy conversion processes which introduce multiple time-scale dynamics and strong nonlinearity into the analysis. To tackle this problem, this paper proposes a simplified model which can predict the coupled thermal and electric output dynamics of microturbines. Considering the time-scale difference of various dynamic processes occuring within microturbines, the electromechanical subsystem is treated as a fast quasi-linear process while the thermo-mechanical subsystem is treated as a slow process with high nonlinearity. A three-stage subspace identification method is utilized to capture the dominant dynamics and predict the electric power output. For the thermo-mechanical process, a radial basis function model trained by the particle swarm optimization method is employed to handle the strong nonlinear characteristics. Experimental tests on a Capstone C30 microturbine show that the proposed modeling method can well capture the system dynamics and produce a good prediction of the coupled thermal and electric outputs in various operating modes.

Identification of Microturbine Model for Long-term Dynamic Analysis of Distribution Networks

As one of the most successfully commercialized distributed energy resources, the long-term effects of microturbines (MTs) on the distribution network has not been fully investigated due to the complex thermo-fluid-mechanical energy conversion processes. This is further complicated by the fact that the parameter and internal data of MTs are not always available to the electric utility, due to different ownerships and confidentiality concerns. To address this issue, a general modeling approach for MTs is proposed in this paper, which allows for the long-term simulation of the distribution network with multiple MTs. First, the feasibility of deriving a simplified MT model for long-term dynamic analysis of the distribution network is discussed, based on the physical understanding of dynamic processes that occurred within MTs. Then a three-stage identification method is developed in order to obtain a piecewise MT model and predict electro-mechanical system behaviors with saturation. Next, assisted with the electric power flow calculation tool, a fast simulation methodology is proposed to evaluate the long-term impact of multiple MTs on the distribution network. Finally, the model is verified by using Capstone C30 microturbine experiments, and further applied to the dynamic simulation of a modified IEEE 37-node test feeder with promising results.

Data-Driven Dynamic Prediction of Interrelated Heat and Electric Outputs of Microturbines

Microturbines are among the most successfully commercialized distributed energy resources, especially when they are used for combined heat and power generation. However, the interrelated thermal and electrical system dynamic behaviors have not been fully investigated. This is technically challenging due to the complex thermo-fluid-mechanical energy conversion processes which introduce multiple time-scale dynamics and strong nonlinearity into the analysis. To tackle this problem, this paper proposes a simplified model which can predict the coupled thermal and electric output dynamics of microturbines. Considering the time-scale difference of various dynamic processes occuring within microturbines, the electromechanical subsystem is treated as a fast quasi-linear process while the thermo-mechanical subsystem is treated as a slow process with high nonlinearity. A three-stage subspace identification method is utilized to capture the dominant dynamics and predict the electric power output. For the thermo-mechanical process, a radial basis function model trained by the particle swarm optimization method is employed to handle the strong nonlinear characteristics. Experimental tests on a Capstone C30 microturbine show that the proposed modeling method can well capture the system dynamics and produce a good prediction of the coupled thermal and electric outputs in various operating modes.

Caractérisation des propriétés mécaniques de la pâte de carbone à 150°C dans le but d’optimiser la mise en forme des anodes utilisées dans les cuves Hall-Héroult

Les anodes de carbone sont des éléments consommables servant d’électrode dans la réaction électrochimique d’une cuve Hall-Héroult. Ces dernières sont produites massivement via une chaine de production dont la mise en forme est une des étapes critiques puisqu’elle définit une partie de leur qualité. Le procédé de mise en forme actuel n’est pas pleinement optimisé. Des gradients de densité importants à l’intérieur des anodes diminuent leur performance dans les cuves d’électrolyse. Encore aujourd’hui, les anodes de carbone sont produites avec comme seuls critères de qualité leur densité globale et leurs propriétés mécaniques finales. La manufacture d’anodes est optimisée de façon empirique directement sur la chaine de production. Cependant, la qualité d’une anode se résume en une conductivité électrique uniforme afin de minimiser les concentrations de courant qui ont plusieurs effets néfastes sur leur performance et sur les coûts de production d’aluminium. Cette thèse est basée sur l’hypothèse que la conductivité électrique de l’anode n’est influencée que par sa densité considérant une composition chimique uniforme. L’objectif est de caractériser les paramètres d’un modèle afin de nourrir une loi constitutive qui permettra de modéliser la mise en forme des blocs anodiques. L’utilisation de la modélisation numérique permet d’analyser le comportement de la pâte lors de sa mise en forme. Ainsi, il devient possible de prédire les gradients de densité à l’intérieur des anodes et d’optimiser les paramètres de mise en forme pour en améliorer leur qualité. Le modèle sélectionné est basé sur les propriétés mécaniques et tribologiques réelles de la pâte. La thèse débute avec une étude comportementale qui a pour objectif d’améliorer la compréhension des comportements constitutifs de la pâte observés lors d’essais de pressage préliminaires. Cette étude est basée sur des essais de pressage de pâte de carbone chaude produite dans un moule rigide et sur des essais de pressage d’agrégats secs à l’intérieur du même moule instrumenté d’un piézoélectrique permettant d’enregistrer les émissions acoustiques. Cette analyse a précédé la caractérisation des propriétés de la pâte afin de mieux interpréter son comportement mécanique étant donné la nature complexe de ce matériau carboné dont les propriétés mécaniques sont évolutives en fonction de la masse volumique. Un premier montage expérimental a été spécifiquement développé afin de caractériser le module de Young et le coefficient de Poisson de la pâte. Ce même montage a également servi dans la caractérisation de la viscosité (comportement temporel) de la pâte. Il n’existe aucun essai adapté pour caractériser ces propriétés pour ce type de matériau chauffé à 150°C. Un moule à paroi déformable instrumenté de jauges de déformation a été utilisé pour réaliser les essais. Un second montage a été développé pour caractériser les coefficients de friction statique et cinétique de la pâte aussi chauffée à 150°C. Le modèle a été exploité afin de caractériser les propriétés mécaniques de la pâte par identification inverse et pour simuler la mise en forme d’anodes de laboratoire. Les propriétés mécaniques de la pâte obtenues par la caractérisation expérimentale ont été comparées à celles obtenues par la méthode d’identification inverse. Les cartographies tirées des simulations ont également été comparées aux cartographies des anodes pressées en laboratoire. La tomodensitométrie a été utilisée pour produire ces dernières cartographies de densité. Les résultats des simulations confirment qu’il y a un potentiel majeur à l’utilisation de la modélisation numérique comme outil d’optimisation du procédé de mise en forme de la pâte de carbone. La modélisation numérique permet d’évaluer l’influence de chacun des paramètres de mise en forme sans interrompre la production et/ou d’implanter des changements coûteux dans la ligne de production. Cet outil permet donc d’explorer des avenues telles la modulation des paramètres fréquentiels, la modification de la distribution initiale de la pâte dans le moule, la possibilité de mouler l’anode inversée (upside down), etc. afin d’optimiser le processus de mise en forme et d’augmenter la qualité des anodes.

New developments on VCA unmixing algorithm

Hyperspectral sensors are being developed for remote sensing applications. These sensors produce huge data volumes which require faster processing and analysis tools. Vertex component analysis (VCA) has become a very useful tool to unmix hyperspectral data. It has been successfully used to determine endmembers and unmix large hyperspectral data sets without the use of any a priori knowledge of the constituent spectra. Compared with other geometric-based approaches VCA is an efficient method from the computational point of view. In this paper we introduce new developments for VCA: 1) a new signal subspace identification method (HySime) is applied to infer the signal subspace where the data set live. This step also infers the number of endmembers present in the data set; 2) after the projection of the data set onto the signal subspace, the algorithm iteratively projects the data set onto several directions orthogonal to the subspace spanned by the endmembers already determined. The new endmember signature corresponds to these extreme of the projections. The capability of VCA to unmix large hyperspectral scenes (real or simulated), with low computational complexity, is also illustrated.

Nuevo método de identificación usando la respuesta escalón para sistemas de fase no mínima de segundo orden

Este artículo presenta un nuevo método de identificación para sistemas de fase no mínima basado en la respuesta escalón. El enfoque propuesto provee un modelo aproximado de segundo orden evitando diseños experimentales complejos. El método propuesto es un algoritmo de identificación cerrado basado en puntos característicos de la respuesta escalón de sistemas de fase no mínima de segundo orden. Él es validado usando diferentes modelos lineales. Ellos tienen respuesta inversa entre 3,5% y 38% de la respuesta en régimen permanente. En simulaciones, ha sido demostrado que resultados satisfactorios pueden ser obtenidos usando el procedimiento de identificación propuesto, donde los parámetros identificados presentan errores relativos medios, menores que los obtenidos mediante el método de Balaguer.

An investigation into the suitability of time-of-flight mass spectrometry in forensic toxicology

Liquid chromatography coupled with mass spectrometry is one of the most powerful tools in the toxicologist’s arsenal to detect a wide variety of compounds from many different matrices. However, the huge number of potentially abused substances and new substances especially designed as intoxicants poses a problem in a forensic toxicology setting. Most methods are targeted and designed to cover a very specific drug or group of drugs while many other substances remain undetected. High resolution mass spectrometry, more specifically time-of-flight mass spectrometry, represents an extremely powerful tool in analysing a multitude of compounds not only simultaneously but also retroactively. The data obtained through the time-of-flight instrument contains all compounds made available from sample extraction and chromatography, which can be processed at a later time with an improved library to detect previously unrecognised compounds without having to analyse the respective sample again. The aim of this project was to determine the utility and limitations of time-of-flight mass spectrometry as a general and easily expandable screening method. The resolution of time-of-flight mass spectrometry allows for the separation of compounds with the same nominal mass but distinct exact masses without the need to separate them chromatographically. To simulate the wide variety of potentially encountered drugs in such a general screening method, seven drugs (morphine, cocaine, zolpidem, diazepam, amphetamine, MDEA and THC) were chosen to represent this variety in terms of mass, properties and functional groups. Consequently, several liquid-liquid and solid phase extractions were applied to urine samples to determine the most general suitable and unspecific extraction. Chromatography was optimised by investigating the parameters pH, concentration, organic solvent and gradient of the mobile phase to improve data obtained by the time-of-flight instrument. The resulting method was validated as a qualitative confirmation/identification method. Data processing was automated using the software TargetAnalysis, which provides excellent analyte recognition according to retention time, exact mass and isotope pattern. The recognition of isotope patterns allows excellent recognition of analytes even in interference rich mass spectra and proved to be a good positive indicator. Finally, the validated method was applied to samples received from the A& E Department of Glasgow Royal Infirmary in suspected drug abuse cases and samples received from the Scottish Prison Service, which we received from their own prevalence study targeting drugs of abuse in the prison population. The obtained data was processed with a library established in the course of this work.