Spatial and Temporal Trends in Sunshine Duration over Western Europe

This work analyzes sunshine duration variability in the western part of Europe (WEU) over the 1938– 2004 period. A principal component analysis is applied to cluster the original series from 79 sites into 6 regions, and then annual and seasonal mean series are constructed on regional and also for the whole WEU scales. Over the entire period studied here, the linear trend of annual sunshine duration is found to be nonsignificant. However, annual sunshine duration shows an overall decrease since the 1950s until the early 1980s, followed by a subsequent recovery during the last two decades. This behavior is in good agreement with the dimming and brightening phenomena described in previous literature. From the seasonal analysis, the most remarkable result is the similarity between spring and annual series, although the spring series has a negative trend; and the clear significant increase found for the whole WEU winter series, being especially large since the 1970s. The behavior of the major synoptic patterns for two seasons is investigated, resulting in some indications that sunshine duration evolution may be partially explained by changes in the frequency of some of them

The Quality of Paediatric Nursing Care - Children’s Perspective

Hoitotyön laatu - lasten näkökulma Tämän kolmivaiheisen tutkimuksen tarkoituksena oli kuvailla lasten odotuksia ja arviointeja lasten hoitotyön laadusta sekä kehittää mittari kouluikäisille sairaalassa oleville lapsille laadun arviointiin. Perimmäisenä tavoitteena oli lasten hoitotyön laadun kehittäminen sairaalassa. Ensimmäisessä vaiheessa 20 alle kouluikäistä (4-6v) sekä 20 kouluikäistä (7-11v) lasta kuvailivat odotuksiaan lasten hoitotyön laadusta. Aineisto kerättiin haastattelulla ja lasten piirustusten avulla, sekä analysoitiin sisällön analyysilla. Lasten odotukset lasten hoitotyön laadusta kohdistuivat hoitajaan, hoitotyön toimintoihin ja ympäristöön, fyysinen ympäristö korostui piirustuksissa. Ensimmäisen vaiheen tulosten, aikaisemman kirjallisuuden sekä Leino-Kilven “HYVÄ HOITO” mittarin pohjalta kehitettiin “Lasten Hoidon Laatu Sairaalassa” (LHLS) mittari ja testattiin sen psykometrisiä ominaisuuksia tutkimuksen toisessa vaiheessa. Mittaria kehitettiin ja testattiin kolmen vaiheen kautta. Aluksi asiantuntijapaneeli (n=7) arvioi mittarin sisältöä. Seuraavaksi mittari esitestattiin kahdesti kouluikäisillä sairaalassa olevilla lapsilla (n=41 ja n=16), samassa vaiheessa myös viiden lastenosaston hoitajat (n=19) yhdessä arvioivat mittarin sisältöä sekä 8 lasta. Lopuksi mittaria testattiin kouluikäisillä lapsilla (n=388) sairaalassa sekä hoitajat (n=198) arvioivat mittarin sisällön validiteettia. Mittarin kehittämisen aikana päälaatuluokkien: hoitajan ominaisuudet, hoitotyön toiminnot ja hoitotyön ympäristö Cronbachin alfa kertoimet paranivat. Pääkomponentti analyysi tuki mittarin hoitotyön toimintojen ja ympäristön alaluokkien teoreettista rakennetta. Kolmannessa vaiheessa “Lasten Hoidon Laatu Sairaalassa” (LHLS III, versio neljä) mittarilla kerättiin aineisto Suomen yliopistosairaaloiden lastenosastoilta kouluikäisiltä 7-11 -vuotiailta lapsilta (n=388). Mittarin lopussa lapsia pyydettiin lisäksi kuvailemaan kivointa ja ikävintä kokemustaan sairaalahoidon aikana lauseen täydennystehtävänä. Aineisto analysoitiin tilastollisesti sekä sisällön analyysilla. Lapset arvioivat fyysisen hoitoympäristön, hoitajien inhimillisyyden ja luotettavuuden sekä huolenpidon ja vuorovaikutustoiminnot kiitettäviksi. Lapset arvioivat hoitajien viihdyttämistoiminnot kaikkein alhaisimmiksi. Lapsen ikä ja sairaalantulotapa olivat yhteydessä lasten saamaan tiedon määrään. Lasten kivoimmat kokemukset liittyivät ihmisiin ja heidän ominaisuuksiinsa, toimintoihin, ympäristöön sekä lopputuloksiin. Ikävimmät kokemukset liittyivät potilaana oloon, tuntemuksiin sairauden oireista sekä erossaoloon, hoitotyön fyysisiin toimintoihin sekä ympäristöön. Tutkimuksen tulokset osoittavat lasten olevan kykeneviä arvioimaan omaa hoitoaan ja heidän näkökulmansa tulisi nähdä osana koko laadun kehittämisprosessia parannettaessa laatua käytännössä todella lapsilähtöisemmällä lähestymistavalla. “Lasten Hoidon Laatu Sairaalassa” (LHLS) mittari on mahdollinen väline saada tietoa lasten arvioinneista lasten hoitotyön laadusta, mutta mittarin testaamista tulisi jatkaa tulevaisuudessa

Quimiometria I: calibração multivariada, um tutorial

The aim of this work is to present a tutorial on Multivariate Calibration, a tool which is nowadays necessary in basically most laboratories but very often misused. The basic concepts of preprocessing, principal component analysis (PCA), principal component regression (PCR) and partial least squares (PLS) are given. The two basic steps on any calibration procedure: model building and validation are fully discussed. The concepts of cross validation (to determine the number of factors to be used in the model), leverage and studentized residuals (to detect outliers) for the validation step are given. The whole calibration procedure is illustrated using spectra recorded for ternary mixtures of 2,4,6 trinitrophenolate, 2,4 dinitrophenolate and 2,5 dinitrophenolate followed by the concentration prediction of these three chemical species during a diffusion experiment through a hydrophobic liquid membrane. MATLAB software is used for numerical calculations. Most of the commands for the analysis are provided in order to allow a non-specialist to follow step by step the analysis.

Avaliação do uso de métodos quimiométricos em análise de solos

One of the major interests in soil analysis is the evaluation of its chemical, physical and biological parameters, which are indicators of soil quality (the most important is the organic matter). Besides there is a great interest in the study of humic substances and on the assessment of pollutants, such as pesticides and heavy metals, in soils. Chemometrics is a powerful tool to deal with these problems and can help soil researchers to extract much more information from their data. In spite of this, the presence of these kinds of strategies in the literature has obtained projection only recently. The utilization of chemometric methods in soil analysis is evaluated in this article. The applications will be divided in four parts (with emphasis in the first two): (i) descriptive and exploratory methods based on Principal Component Analysis (PCA); (ii) multivariate calibration methods (MLR, PCR and PLS); (iii) methods such as Evolving Factor Analysis and SIMPLISMA; and (iv) artificial intelligence methods, such as Artificial Neural Networks.

Speed of travelling fronts: Two-dimensional and ballistic dispersal probability distributions

The speed of traveling fronts for a two-dimensional model of a delayed reactiondispersal process is derived analytically and from simulations of molecular dynamics. We show that the one-dimensional (1D) and two-dimensional (2D) versions of a given kernel do not yield always the same speed. It is also shown that the speeds of time-delayed fronts may be higher than those predicted by the corresponding non-delayed models. This result is shown for systems with peaked dispersal kernels which lead to ballistic transport

Análise multivariada de parâmetros físico-químicos em amostras de vinhos tintos comercializados na região metropolitana do Recife

The Brazilian legislation requires analysis of certain parameters to classify a wine and allow its commercialization. Some physico-chemical and some color parameters were determined in this work in samples of different red wines sold in the metropolitan area of Recife. Multivariate analysis comprising principal component analysis and hierarchical cluster analysis was employed to distinguish the analyzed wines. The results for pH, chloride concentration, color parameters and ammonium content were the most important variables for sample classification. It was also possible to classify the wines as soft or dry wines and amongst the soft wines we could determine two out of four winegrowing producers.

Advanced chemometric methods: Applicability on industrial data

Recent years have produced great advances in the instrumentation technology. The amount of available data has been increasing due to the simplicity, speed and accuracy of current spectroscopic instruments. Most of these data are, however, meaningless without a proper analysis. This has been one of the reasons for the overgrowing success of multivariate handling of such data. Industrial data is commonly not designed data; in other words, there is no exact experimental design, but rather the data have been collected as a routine procedure during an industrial process. This makes certain demands on the multivariate modeling, as the selection of samples and variables can have an enormous effect. Common approaches in the modeling of industrial data are PCA (principal component analysis) and PLS (projection to latent structures or partial least squares) but there are also other methods that should be considered. The more advanced methods include multi block modeling and nonlinear modeling. In this thesis it is shown that the results of data analysis vary according to the modeling approach used, thus making the selection of the modeling approach dependent on the purpose of the model. If the model is intended to provide accurate predictions, the approach should be different than in the case where the purpose of modeling is mostly to obtain information about the variables and the process. For industrial applicability it is essential that the methods are robust and sufficiently simple to apply. In this way the methods and the results can be compared and an approach selected that is suitable for the intended purpose. Differences in data analysis methods are compared with data from different fields of industry in this thesis. In the first two papers, the multi block method is considered for data originating from the oil and fertilizer industries. The results are compared to those from PLS and priority PLS. The third paper considers applicability of multivariate models to process control for a reactive crystallization process. In the fourth paper, nonlinear modeling is examined with a data set from the oil industry. The response has a nonlinear relation to the descriptor matrix, and the results are compared between linear modeling, polynomial PLS and nonlinear modeling using nonlinear score vectors.

Sampling in chemical analysis

The uncertainty of any analytical determination depends on analysis and sampling. Uncertainty arising from sampling is usually not controlled and methods for its evaluation are still little known. Pierre Gy’s sampling theory is currently the most complete theory about samplingwhich also takes the design of the sampling equipment into account. Guides dealing with the practical issues of sampling also exist, published by international organizations such as EURACHEM, IUPAC (International Union of Pure and Applied Chemistry) and ISO (International Organization for Standardization). In this work Gy’s sampling theory was applied to several cases, including the analysis of chromite concentration estimated on SEM (Scanning Electron Microscope) images and estimation of the total uncertainty of a drug dissolution procedure. The results clearly show that Gy’s sampling theory can be utilized in both of the above-mentioned cases and that the uncertainties achieved are reliable. Variographic experiments introduced in Gy’s sampling theory are beneficially applied in analyzing the uncertainty of auto-correlated data sets such as industrial process data and environmental discharges. The periodic behaviour of these kinds of processes can be observed by variographic analysis as well as with fast Fourier transformation and auto-correlation functions. With variographic analysis, the uncertainties are estimated as a function of the sampling interval. This is advantageous when environmental data or process data are analyzed as it can be easily estimated how the sampling interval is affecting the overall uncertainty. If the sampling frequency is too high, unnecessary resources will be used. On the other hand, if a frequency is too low, the uncertainty of the determination may be unacceptably high. Variographic methods can also be utilized to estimate the uncertainty of spectral data produced by modern instruments. Since spectral data are multivariate, methods such as Principal Component Analysis (PCA) are needed when the data are analyzed. Optimization of a sampling plan increases the reliability of the analytical process which might at the end have beneficial effects on the economics of chemical analysis,

Análise por componentes principais de espectros nexafs na especiação do molibdênio em catalisadores de hidrotratamento

Bulk and supported molybdenum based catalysts, modified by nickel, phosphorous or tungsten were studied by NEXAFS spectroscopy at the Mo L III and L II edges. The techniques of principal component analysis (PCA) together with a linear combination analysis (LCA) allowed the detection and quantification of molybdenum atoms in two different coordination states in the oxide form of the catalysts, namely tetrahedral and octahedral coordination.

Variabilidade espacial e temporal de parâmetros físico-químicos nos rios Turvo, Preto e Grande no estado de São Paulo, Brasil

This work aims to study spatial and seasonal variability of some chemical-physical parameters in the Turvo/Grande watershed, São Paulo State, Brazil. Water samples were taken monthly, 2007/07-2008/11, from fourteen sampling stations sited along the Turvo, Preto and Grande Rivers and its main tributaries. The Principal Component Analysis and hierarchical cluster analysis showed two distinct groups in this watershed, the first one associated for the places more impacted by domestic effluent (lower levels of dissolved oxygen in the studied region). The sampling places located to downstream (Turvo and Grande rivers) were discriminate by diffuse source of pollutants from flooding and agriculture runoffs in a second group.

Uso de perfis cromatográficos de voláteis de cafés arábicas torrados para a diferenciação das amostras segundo o sabor, o aroma e a qualidade global da bebida

In this work, the volatile chromatographic profiles of roasted Arabica coffees, previously analyzed for their sensorial attributes, were explored by principal component analysis. The volatile extraction technique used was the solid phase microextraction. The correlation optimized warping algorithm was used to align the gas chromatographic profiles. Fifty four compounds were found to be related to the sensorial attributes investigated. The volatiles pyrrole, 1-methyl-pyrrole, cyclopentanone, dihydro-2-methyl-3-furanone, furfural, 2-ethyl-5-methyl-pyrazine, 2-etenyl-n-methyl-pyrazine, 5-methyl-2-propionyl-furan compounds were important for the differentiation of coffee beverage according to the flavour, cleanliness and overall quality. Two figures of merit, sensitivity and specificity (or selectivity), were used to interpret the sensory attributes studied.

Caracterização estrutural das substâncias húmicas aquáticas extraídas dos rios itapanhaú e ribeira de iguape

The knowledge of the structure characteristic of the Organic Matter is important for the understanding of the natural process. In this context aquatic humic substances (principal fraction) were isolated from water sample collected from the two distinct rivers, using procedure recommended for International Humic Substances Society and characterized by elemental analysis, electron paramagnetic resonance and nuclear magnetic resonance (13C NMR). The results were interpreted using principal component analysis (PCA) and the statistical analyses showed different in the structural characteristics of the aquatic humic substances studied.

Aplicação de métodos quimiométricos na otimização da extração de Ca, Mg, K, Fe, Zn, Cu e Mn em folhas de braquiária

This work applied a 2² factorial design to the optimization of the extraction of seven elements (calcium, magnesium, potassium, iron, zinc, copper and manganese) in brachiaria leaves, determined by flame atomic absorption spectrometry. The factors sample mass and digestion type were evaluated at two levels: 200/500 mg, and dry/wet, respectively. Principal component analysis allowed simultaneous discrimination of all the significant effects in one biplot. Wet digestion and mass of 200 mg were considered the best conditions. The decrease of 60% in sample mass allowed to save costs and reagents. The method was validated through the estimation of figures of merit.