Self-Organising Maps and Correlation Analysis as a Tool to Explore Patterns in Excitation-Emission Matrix Data Sets and to Discriminate Dissolved Organic Matter Fluorescence Components

Dissolved organic matter (DOM) is a complex mixture of organic compounds, ubiquitous in marine and freshwater systems. Fluorescence spectroscopy, by means of Excitation-Emission Matrices (EEM), has become an indispensable tool to study DOM sources, transport and fate in aquatic ecosystems. However the statistical treatment of large and heterogeneous EEM data sets still represents an important challenge for biogeochemists. Recently, Self-Organising Maps (SOM) has been proposed as a tool to explore patterns in large EEM data sets. SOM is a pattern recognition method which clusterizes and reduces the dimensionality of input EEMs without relying on any assumption about the data structure. In this paper, we show how SOM, coupled with a correlation analysis of the component planes, can be used both to explore patterns among samples, as well as to identify individual fluorescence components. We analysed a large and heterogeneous EEM data set, including samples from a river catchment collected under a range of hydrological conditions, along a 60-km downstream gradient, and under the influence of different degrees of anthropogenic impact. According to our results, chemical industry effluents appeared to have unique and distinctive spectral characteristics. On the other hand, river samples collected under flash flood conditions showed homogeneous EEM shapes. The correlation analysis of the component planes suggested the presence of four fluorescence components, consistent with DOM components previously described in the literature. A remarkable strength of this methodology was that outlier samples appeared naturally integrated in the analysis. We conclude that SOM coupled with a correlation analysis procedure is a promising tool for studying large and heterogeneous EEM data sets.

Artificially Intelligent and Adaptive Methods for Prediction and Analysis of Superheater Fireside Corrosion in Fluidized Bed Boilers

Superheater corrosion causes vast annual losses for the power companies. With a reliable corrosion prediction method, the plants can be designed accordingly, and knowledge of fuel selection and determination of process conditions may be utilized to minimize superheater corrosion. Growing interest to use recycled fuels creates additional demands for the prediction of corrosion potential. Models depending on corrosion theories will fail, if relations between the inputs and the output are poorly known. A prediction model based on fuzzy logic and an artificial neural network is able to improve its performance as the amount of data increases. The corrosion rate of a superheater material can most reliably be detected with a test done in a test combustor or in a commercial boiler. The steel samples can be located in a special, temperature-controlled probe, and exposed to the corrosive environment for a desired time. These tests give information about the average corrosion potential in that environment. Samples may also be cut from superheaters during shutdowns. The analysis ofsamples taken from probes or superheaters after exposure to corrosive environment is a demanding task: if the corrosive contaminants can be reliably analyzed, the corrosion chemistry can be determined, and an estimate of the material lifetime can be given. In cases where the reason for corrosion is not clear, the determination of the corrosion chemistry and the lifetime estimation is more demanding. In order to provide a laboratory tool for the analysis and prediction, a newapproach was chosen. During this study, the following tools were generated: · Amodel for the prediction of superheater fireside corrosion, based on fuzzy logic and an artificial neural network, build upon a corrosion database developed offuel and bed material analyses, and measured corrosion data. The developed model predicts superheater corrosion with high accuracy at the early stages of a project. · An adaptive corrosion analysis tool based on image analysis, constructedas an expert system. This system utilizes implementation of user-defined algorithms, which allows the development of an artificially intelligent system for thetask. According to the results of the analyses, several new rules were developed for the determination of the degree and type of corrosion. By combining these two tools, a user-friendly expert system for the prediction and analyses of superheater fireside corrosion was developed. This tool may also be used for the minimization of corrosion risks by the design of fluidized bed boilers.

Analysis of vineyard differential management zones and relation to vine development, grape maturity and quality

The objective of research was to analyse the potential of Normalized Difference Vegetation Index (NDVI) maps from satellite images, yield maps and grapevine fertility and load variables to delineate zones with different wine grape properties for selective harvesting. Two vineyard blocks located in NE Spain (Cabernet Sauvignon and Syrah) were analysed. The NDVI was computed from a Quickbird-2 multi-spectral image at veraison (July 2005). Yield data was acquired by means of a yield monitor during September 2005. Other variables, such as the number of buds, number of shoots, number of wine grape clusters and weight of 100 berries were sampled in a 10 rows × 5 vines pattern and used as input variables, in combination with the NDVI, to define the clusters as alternative to yield maps. Two days prior to the harvesting, grape samples were taken. The analysed variables were probable alcoholic degree, pH of the juice, total acidity, total phenolics, colour, anthocyanins and tannins. The input variables, alone or in combination, were clustered (2 and 3 Clusters) by using the ISODATA algorithm, and an analysis of variance and a multiple rang test were performed. The results show that the zones derived from the NDVI maps are more effective to differentiate grape maturity and quality variables than the zones derived from the yield maps. The inclusion of other grapevine fertility and load variables did not improve the results.

'Herbal' but potentially hazardous: an analysis of the constituents and smoke emissions of tobacco-free waterpipe products and the air quality in the cafes where they are served

BACKGROUND: There are limited data on the composition and smoke emissions of 'herbal' shisha products and the air quality of establishments where they are smoked. METHODS: Three studies of 'herbal' shisha were conducted: (1) samples of 'herbal' shisha products were chemically analysed; (2) 'herbal' and tobacco shisha were burned in a waterpipe smoking machine and main and sidestream smoke analysed by standard methods and (3) the air quality of six waterpipe cafes was assessed by measurement of CO, particulate and nicotine vapour content. RESULTS: We found considerable variation in heavy metal content between the three products sampled, one being particularly high in lead, chromium, nickel and arsenic. A similar pattern emerged for polycyclic aromatic hydrocarbons. Smoke emission analyses indicated that toxic byproducts produced by the combustion of 'herbal' shisha were equivalent or greater than those produced by tobacco shisha. The results of our air quality assessment demonstrated that mean PM2.5 levels and CO content were significantly higher in waterpipe establishments compared to a casino where cigarette smoking was permitted. Nicotine vapour was detected in one of the waterpipe cafes. CONCLUSIONS: 'Herbal' shisha products tested contained toxic trace metals and PAHs levels equivalent to, or in excess of, that found in cigarettes. Their mainstream and sidestream smoke emissions contained carcinogens equivalent to, or in excess of, those of tobacco products. The content of the air in the waterpipe cafes tested was potentially hazardous. These data, in aggregate, suggest that smoking 'herbal' shisha may well be dangerous to health.

Using time and stochastic models for software reliability forecasting and analysis

Tämä työ luo katsauksen ajallisiin ja stokastisiin ohjelmien luotettavuus malleihin sekä tutkii muutamia malleja käytännössä. Työn teoriaosuus sisältää ohjelmien luotettavuuden kuvauksessa ja arvioinnissa käytetyt keskeiset määritelmät ja metriikan sekä varsinaiset mallien kuvaukset. Työssä esitellään kaksi ohjelmien luotettavuusryhmää. Ensimmäinen ryhmä ovat riskiin perustuvat mallit. Toinen ryhmä käsittää virheiden ”kylvöön” ja merkitsevyyteen perustuvat mallit. Työn empiirinen osa sisältää kokeiden kuvaukset ja tulokset. Kokeet suoritettiin käyttämällä kolmea ensimmäiseen ryhmään kuuluvaa mallia: Jelinski-Moranda mallia, ensimmäistä geometrista mallia sekä yksinkertaista eksponenttimallia. Kokeiden tarkoituksena oli tutkia, kuinka syötetyn datan distribuutio vaikuttaa mallien toimivuuteen sekä kuinka herkkiä mallit ovat syötetyn datan määrän muutoksille. Jelinski-Moranda malli osoittautui herkimmäksi distribuutiolle konvergaatio-ongelmien vuoksi, ensimmäinen geometrinen malli herkimmäksi datan määrän muutoksille.

Characteristics of 2D convective structures in Catalonia (NE Spain): an analysis using radar data and GIS

Flood simulation studies use spatial-temporal rainfall data input into distributed hydrological models. A correct description of rainfall in space and in time contributes to improvements on hydrological modelling and design. This work is focused on the analysis of 2-D convective structures (rain cells), whose contribution is especially significant in most flood events. The objective of this paper is to provide statistical descriptors and distribution functions for convective structure characteristics of precipitation systems producing floods in Catalonia (NE Spain). To achieve this purpose heavy rainfall events recorded between 1996 and 2000 have been analysed. By means of weather radar, and applying 2-D radar algorithms a distinction between convective and stratiform precipitation is made. These data are introduced and analyzed with a GIS. In a first step different groups of connected pixels with convective precipitation are identified. Only convective structures with an area greater than 32 km2 are selected. Then, geometric characteristics (area, perimeter, orientation and dimensions of the ellipse), and rainfall statistics (maximum, mean, minimum, range, standard deviation, and sum) of these structures are obtained and stored in a database. Finally, descriptive statistics for selected characteristics are calculated and statistical distributions are fitted to the observed frequency distributions. Statistical analyses reveal that the Generalized Pareto distribution for the area and the Generalized Extreme Value distribution for the perimeter, dimensions, orientation and mean areal precipitation are the statistical distributions that best fit the observed ones of these parameters. The statistical descriptors and the probability distribution functions obtained are of direct use as an input in spatial rainfall generators.

Self-Organising Maps and Correlation Analysis as a Tool to Explore Patterns in Excitation-Emission Matrix Data Sets and to Discriminate Dissolved Organic Matter Fluorescence Components

Dissolved organic matter (DOM) is a complex mixture of organic compounds, ubiquitous in marine and freshwater systems. Fluorescence spectroscopy, by means of Excitation-Emission Matrices (EEM), has become an indispensable tool to study DOM sources, transport and fate in aquatic ecosystems. However the statistical treatment of large and heterogeneous EEM data sets still represents an important challenge for biogeochemists. Recently, Self-Organising Maps (SOM) has been proposed as a tool to explore patterns in large EEM data sets. SOM is a pattern recognition method which clusterizes and reduces the dimensionality of input EEMs without relying on any assumption about the data structure. In this paper, we show how SOM, coupled with a correlation analysis of the component planes, can be used both to explore patterns among samples, as well as to identify individual fluorescence components. We analysed a large and heterogeneous EEM data set, including samples from a river catchment collected under a range of hydrological conditions, along a 60-km downstream gradient, and under the influence of different degrees of anthropogenic impact. According to our results, chemical industry effluents appeared to have unique and distinctive spectral characteristics. On the other hand, river samples collected under flash flood conditions showed homogeneous EEM shapes. The correlation analysis of the component planes suggested the presence of four fluorescence components, consistent with DOM components previously described in the literature. A remarkable strength of this methodology was that outlier samples appeared naturally integrated in the analysis. We conclude that SOM coupled with a correlation analysis procedure is a promising tool for studying large and heterogeneous EEM data sets.

The polymorhism of crime scene investigation: an exploratory analysis of the influence of crime and forensic intelligence on decisions made by crime scene examiners

A growing body of scientific literature recurrently indicates that crime and forensic intelligence influence how crime scene investigators make decisions in their practices. This study scrutinises further this intelligence-led crime scene examination view. It analyses results obtained from two questionnaires. Data have been collected from nine chiefs of Intelligence Units (IUs) and 73 Crime Scene Examiners (CSEs) working in forensic science units (FSUs) in the French speaking part of Switzerland (six cantonal police agencies). Four salient elements emerged: (1) the actual existence of communication channels between IUs and FSUs across the police agencies under consideration; (2) most CSEs take into account crime intelligence disseminated; (3) a differentiated, but significant use by CSEs in their daily practice of this kind of intelligence; (4) a probable deep influence of this kind of intelligence on the most concerned CSEs, specially in the selection of the type of material/trace to detect, collect, analyse and exploit. These results contribute to decipher the subtle dialectic articulating crime intelligence and crime scene investigation, and to express further the polymorph role of CSEs, beyond their most recognised input to the justice system. Indeed, they appear to be central, but implicit, stakeholders in intelligence-led style of policing.

Expression of the inducible NO synthase in human monocytic U937 cells allows high output nitric oxide production.

Nitric oxide (NO) produced by inducible NO synthase (iNOS, NOS-2) is an important component of the macrophage-mediated immune defense toward numerous pathogens. Murine macrophages produce NO after cytokine activation, whereas, under similar conditions, human macrophages produce low levels or no NO at all. Although human macrophages can express iNOS mRNA and protein on activation, whether they possess the complete machinery necessary for NO synthesis remains controversial. To define the conditions necessary for human monocytes/macrophages to synthesize NO when expressing a functional iNOS, the human monocytic U937 cell line was engineered to synthesize this enzyme, following infection with a retroviral expression vector containing human hepatic iNOS (DFGiNOS). Northern blot and Western blot analysis confirmed the expression of iNOS in transfected U937 cells both at the RNA and protein levels. NOS enzymatic activity was demonstrated in cell lysates by the conversion of L-[3H]arginine into L-[3H]citrulline and the production of NO by intact cells was measured by nitrite and nitrate accumulation in culture supernatants. When expressing functional iNOS, U937 cells were capable of releasing high levels of NO. NO production was strictly dependent on supplementation of the culture medium with tetrahydrobiopterin (BH4) and was not modified by stimulation of the cells with different cytokines. These observations suggest that (1) human monocytic U937 cells contain all the cofactors necessary for NO synthesis, except BH4 and (2) the failure to detect NO in cytokine-stimulated untransfected U937 cells is not due to the presence of a NO-scavenging molecule within these cells nor to the destabilization of iNOS protein. DFGiNOS U937 cells represent a valuable human model to study the role of NO in immunity toward tumors and pathogens.

An interactive web-GIS tool for risk analysis: a case study in the Fella River basin, Italy

This paper presents a prototype of an interactive web-GIS tool for risk analysis of natural hazards, in particular for floods and landslides, based on open-source geospatial software and technologies. The aim of the presented tool is to assist the experts (risk managers) in analysing the impacts and consequences of a certain hazard event in a considered region, providing an essential input to the decision-making process in the selection of risk management strategies by responsible authorities and decision makers. This tool is based on the Boundless (OpenGeo Suite) framework and its client-side environment for prototype development, and it is one of the main modules of a web-based collaborative decision support platform in risk management. Within this platform, the users can import necessary maps and information to analyse areas at risk. Based on provided information and parameters, loss scenarios (amount of damages and number of fatalities) of a hazard event are generated on the fly and visualized interactively within the web-GIS interface of the platform. The annualized risk is calculated based on the combination of resultant loss scenarios with different return periods of the hazard event. The application of this developed prototype is demonstrated using a regional data set from one of the case study sites, Fella River of northeastern Italy, of the Marie Curie ITN CHANGES project.

A correlation sensitivity analysis of non-life underwriting risk in solvency capital requirement estimation

This paper analyses the impact of using different correlation assumptions between lines of business when estimating the risk-based capital reserve, the Solvency Capital Requirement -SCR-, under Solvency II regulations. A case study is presented and the SCR is calculated according to the Standard Model approach. Alternatively, the requirement is then calculated using an Internal Model based on a Monte Carlo simulation of the net underwriting result at a one-year horizon, with copulas being used to model the dependence between lines of business. To address the impact of these model assumptions on the SCR we conduct a sensitivity analysis. We examine changes in the correlation matrix between lines of business and address the choice of copulas. Drawing on aggregate historical data from the Spanish non-life insurance market between 2000 and 2009, we conclude that modifications of the correlation and dependence assumptions have a significant impact on SCR estimation.

SSPBE: um programa para solução numérica da equação de Poisson-Boltzmann em simetria esférica com modelo de adsorção

A Fortran77 program, SSPBE, designed to solve the spherically symmetric Poisson-Boltzmann equation using cell model for ionic macromolecular aggregates or macroions is presented. The program includes an adsorption model for ions at the aggregate surface. The working algorithm solves the Poisson-Boltzmann equation in the integral representation using the Picard iteration method. Input parameters are introduced via an ASCII file, sspbe.txt. Output files yield the radial distances versus mean field potentials and average molar ion concentrations, the molar concentration of ions at the cell boundary, the self-consistent degree of ion adsorption from the surface and other related data. Ion binding to ionic, zwitterionic and reverse micelles are presented as representative examples of the applications of the SSPBE program.

Scale-up model obtained from the rheological analysis of highly concentrated emulsions prepared at three scales

We examine the scale invariants in the preparation of highly concentrated w/o emulsions at different scales and in varying conditions. The emulsions are characterized using rheological parameters, owing to their highly elastic behavior. We first construct and validate empirical models to describe the rheological properties. These models yield a reasonable prediction of experimental data. We then build an empirical scale-up model, to predict the preparation and composition conditions that have to be kept constant at each scale to prepare the same emulsion. For this purpose, three preparation scales with geometric similarity are used. The parameter N¿D^α, as a function of the stirring rate N, the scale (D, impeller diameter) and the exponent α (calculated empirically from the regression of all the experiments in the three scales), is defined as the scale invariant that needs to be optimized, once the dispersed phase of the emulsion, the surfactant concentration, and the dispersed phase addition time are set. As far as we know, no other study has obtained a scale invariant factor N¿Dα for the preparation of highly concentrated emulsions prepared at three different scales, which covers all three scales, different addition times and surfactant concentrations. The power law exponent obtained seems to indicate that the scale-up criterion for this system is the power input per unit volume (P/V).

An analysis of the determinants in economics and business publications by spanish universities between 1994 and 2004

The objective of this study consists, firstly, of quantifying differences between Spanish universities’ output (in terms of publications and citations), and secondly, analysing its determinants. The results obtained show that there are factors which have a positive influence on these indicators, such as having a third-cycle programme, with public financing obtained in competitive selection procedures, having a large number of full-time researchers or involvement in collaborations with international institutions. However, other factors which appear to have the opposite effect were also noted. These include a higher number of students per lecturer or a lower proportion of lecturers with recognised six-year periods.

NCT: pacote de tratamento de coordenadas normais (versão 7)

A new version of the normal coordinate analysis package NCT is presented. The upgrade was mainly devised to enable the NCT package to manipulate easily the Hessian matrix evaluated by quantum chemical calculations. Program codes were almost wholly rewritten to be more efficient with GNU Fortran77, or g77, and compiled under FreeBSD and MS-DOS with the DJGPP implementation. Three typical usages of the program package are presented by giving the related input and output files. Functionality of the programs was carefully and satisfactorily checked for some sample calculations.