Characterization of water-soluble organic compounds in bioaerosols by liquid chromatography and fluorescence spectroscopy

In the last decades, the effects of the air pollution have been increasing, especially in the case of the human health diseases. In order to overcome this problem, scientists have been studying the components of the air. As a part of water-soluble organic compounds, amino acids are present in the atmospheric environment as components of diverse living organisms which can be responsible for spreading diseases through the air. Liquid chromatography is one technique capable of distinguish the different amino acids from each other. In this work, aiming at separating the amino acids found in the aerosols samples collected in Aveiro, the ability of four columns (Mixed-Mode WAX-1, Mixed-Mode HILIC-1, Luna HILIC and Luna C18) to separate four amino acids (aspartic acid, lysine, glycine and tryptophan) and the way the interaction of the stationary phases of the columns with the analytes is influenced by organic solvent concentration and presence/concentration of the buffer, are being assessed. In the Mixed-Mode WAX-1 column, the chromatograms of the distinct amino acids revealed the separation was not efficient, since the retention times were very similar. In the case of lysine, in the elution with 80% (V/V) MeOH, the peaks appeared during the volume void. In the Mixed-Mode HILIC-1 column, the variation of the organic solvent concentration did not affect the elution of the four studied amino acids. Considering the Luna HILIC column, the retention times of the amino acids were too close to each other to ensure a separation among each other. Lastly, the Luna C18 column revealed to be useful to separate amino acids in a gradient mode, being the variation of the mobile phase composition in the organic solvent concentration (ACN). Luna C18 was the column used to separate the amino acids in the real samples and the mobile phase had acidified water and ACN. The gradient consisted in the following program: 0 – 2 min: 5% (V/V) ACN, 2 – 8 min: 5 – 2 % (V/V) ACN, 8 – 16 min: 2% (V/V) ACN, 16 – 20 min: 2 – 20 % (V/V) ACN, 20 – 35 min: 20 – 35 % (V/V) ACN. The aerosols samples were collected by using three passive samplers placed in two different locations in Aveiro and each sampler had two filters - one faced up and the other faced down. After the sampling, the water-soluble organic compounds was extracted by dissolution in ultra-pure water, sonication bath and filtration. The resulting filtered solutions were diluted in acidified water for the chromatographic separation. The results from liquid chromatography revealed the presence of the amino acids, although it was not possible to identify each one of them individually. The chromatograms and the fluorescence spectra showed the existence of some patterns: the samples that correspond to the up filters had more intense peaks and signals, revealing that the up filters collected more organic matter.

Oil spill environmental risk assessment of stationary sources in offshore zones (Innovating a mathematical model)

Nowadays, risks arising from the rapid development of oil and gas industries are significantly increasing. As a result, one of the main concerns of either industrial or environmental managers is the identification and assessment of such risks in order to develop and maintain appropriate proactive measures. Oil spill from stationary sources in offshore zones is one of the accidents resulting in several adverse impacts on marine ecosystems. Considering a site's current situation and relevant requirements and standards, risk assessment process is not only capable of recognizing the probable causes of accidents but also of estimating the probability of occurrence and the severity of consequences. In this way, results of risk assessment would help managers and decision makers create and employ proper control methods. Most of the represented models for risk assessment of oil spills are achieved on the basis of accurate data bases and analysis of historical data, but unfortunately such data bases are not accessible in most of the zones, especially in developing countries, or else they are newly established and not applicable yet. This issue reveals the necessity of using Expert Systems and Fuzzy Set Theory. By using such systems it will be possible to formulize the specialty and experience of several experts and specialists who have been working in petroliferous areas for several years. On the other hand, in developing countries often the damages to environment and environmental resources are not considered as risk assessment priorities and they are approximately under-estimated. For this reason, the proposed model in this research is specially addressing the environmental risk of oil spills from stationary sources in offshore zones.

Industrialización y desarrollo en la Argentina pos-“Washington Consensus”: un abordaje crítico desde la perspectiva sectorial, actoral y espacial

Argentina, en 2001, luego de experimentar una de las crisis más profunda de su historia, consecuencia de la primacía de las políticas neoliberales durante la década de 1990 y de los resultados negativos en términos de crecimiento y desigualdad socio-espacial a que estas dieron lugar, comenzó a reconsiderar el rol del Estado en la economía. En dicho contexto se lo sindicó como un actor estratégico para dinamizar un crecimiento inclusivo que viabilice el desarrollo. La promoción estatal a la actividad industrial ocupó un lugar central en la consecución de dicho objetivo. Transcurrida ya más de una década, en el presente trabajo se demuestra, a través del análisis de estadísticas oficiales, cómo la re-intervención del Estado poco ha podido hacer para avanzar en la conformación de un tejido industrial complejo, dinámico y descentralizado –actoral y espacialmente–, que permita viabilizar el desarrollo.

The transformed-stationary approach: a generic and simplified methodology for non-stationary extreme value analysis

Statistical approaches to study extreme events require, by definition, long time series of data. In many scientific disciplines, these series are often subject to variations at different temporal scales that affect the frequency and intensity of their extremes. Therefore, the assumption of stationarity is violated and alternative methods to conventional stationary extreme value analysis (EVA) must be adopted. Using the example of environmental variables subject to climate change, in this study we introduce the transformed-stationary (TS) methodology for non-stationary EVA. This approach consists of (i) transforming a non-stationary time series into a stationary one, to which the stationary EVA theory can be applied, and (ii) reverse transforming the result into a non-stationary extreme value distribution. As a transformation, we propose and discuss a simple time-varying normalization of the signal and show that it enables a comprehensive formulation of non-stationary generalized extreme value (GEV) and generalized Pareto distribution (GPD) models with a constant shape parameter. A validation of the methodology is carried out on time series of significant wave height, residual water level, and river discharge, which show varying degrees of long-term and seasonal variability. The results from the proposed approach are comparable with the results from (a) a stationary EVA on quasi-stationary slices of non-stationary series and (b) the established method for non-stationary EVA. However, the proposed technique comes with advantages in both cases. For example, in contrast to (a), the proposed technique uses the whole time horizon of the series for the estimation of the extremes, allowing for a more accurate estimation of large return levels. Furthermore, with respect to (b), it decouples the detection of non-stationary patterns from the fitting of the extreme value distribution. As a result, the steps of the analysis are simplified and intermediate diagnostics are possible. In particular, the transformation can be carried out by means of simple statistical techniques such as low-pass filters based on the running mean and the standard deviation, and the fitting procedure is a stationary one with a few degrees of freedom and is easy to implement and control. An open-source MAT-LAB toolbox has been developed to cover this methodology, which is available at https://github.com/menta78/tsEva/(Mentaschi et al., 2016).

Multiple solutions and numerical analysis to the dynamic and stationary models coupling a delayed energy balance model involving latent heat and discontinuous albedo with a deep ocean

We study a climatologically important interaction of two of the main components of the geophysical system by adding an energy balance model for the averaged atmospheric temperature as dynamic boundary condition to a diagnostic ocean model having an additional spatial dimension. In this work, we give deeper insight than previous papers in the literature, mainly with respect to the 1990 pioneering model by Watts and Morantine. We are taking into consideration the latent heat for the two phase ocean as well as a possible delayed term. Non-uniqueness for the initial boundary value problem, uniqueness under a non-degeneracy condition and the existence of multiple stationary solutions are proved here. These multiplicity results suggest that an S-shaped bifurcation diagram should be expected to occur in this class of models generalizing previous energy balance models. The numerical method applied to the model is based on a finite volume scheme with nonlinear weighted essentially non-oscillatory reconstruction and Runge–Kutta total variation diminishing for time integration.

Identifying stationary series in panels: A Monte Carlo evaluation of sequential panel selection methods

Sequential panel selection methods (spsms — procedures that sequentially use conventional panel unit root tests to identify I(0)I(0) time series in panels) are increasingly used in the empirical literature. We check the reliability of spsms by using Monte Carlo simulations based on generating directly the individual asymptotic pp values to be combined into the panel unit root tests, in this way isolating the classification abilities of the procedures from the small sample properties of the underlying univariate unit root tests. The simulations consider both independent and cross-dependent individual test statistics. Results suggest that spsms may offer advantages over time series tests only under special conditions.

Direct numerical simulation of statistically stationary and homogeneous shear turbulence and its relation to other shear flows

Statistically stationary and homogeneous shear turbulence (SS-HST) is investigated by means of a new direct numerical simulation code, spectral in the two horizontal directions and compact-finite-differences in the direction of the shear. No remeshing is used to impose the shear-periodic boundary condition. The influence of the geometry of the computational box is explored. Since HST has no characteristic outer length scale and tends to fill the computational domain, long-term simulations of HST are “minimal” in the sense of containing on average only a few large-scale structures. It is found that the main limit is the spanwise box width, Lz, which sets the length and velocity scales of the turbulence, and that the two other box dimensions should be sufficiently large (Lx ≳ 2Lz, Ly ≳ Lz) to prevent other directions to be constrained as well. It is also found that very long boxes, Lx ≳ 2Ly, couple with the passing period of the shear-periodic boundary condition, and develop strong unphysical linearized bursts. Within those limits, the flow shows interesting similarities and differences with other shear flows, and in particular with the logarithmic layer of wall-bounded turbulence. They are explored in some detail. They include a self-sustaining process for large-scale streaks and quasi-periodic bursting. The bursting time scale is approximately universal, ∼20S−1, and the availability of two different bursting systems allows the growth of the bursts to be related with some confidence to the shearing of initially isotropic turbulence. It is concluded that SS-HST, conducted within the proper computational parameters, is a very promising system to study shear turbulence in general.

Nested Sampling and its Applications in Stable Compressive Covariance Estimation and Phase Retrieval with Near-Minimal Measurements

Compressed covariance sensing using quadratic samplers is gaining increasing interest in recent literature. Covariance matrix often plays the role of a sufficient statistic in many signal and information processing tasks. However, owing to the large dimension of the data, it may become necessary to obtain a compressed sketch of the high dimensional covariance matrix to reduce the associated storage and communication costs. Nested sampling has been proposed in the past as an efficient sub-Nyquist sampling strategy that enables perfect reconstruction of the autocorrelation sequence of Wide-Sense Stationary (WSS) signals, as though it was sampled at the Nyquist rate. The key idea behind nested sampling is to exploit properties of the difference set that naturally arises in quadratic measurement model associated with covariance compression. In this thesis, we will focus on developing novel versions of nested sampling for low rank Toeplitz covariance estimation, and phase retrieval, where the latter problem finds many applications in high resolution optical imaging, X-ray crystallography and molecular imaging. The problem of low rank compressive Toeplitz covariance estimation is first shown to be fundamentally related to that of line spectrum recovery. In absence if noise, this connection can be exploited to develop a particular kind of sampler called the Generalized Nested Sampler (GNS), that can achieve optimal compression rates. In presence of bounded noise, we develop a regularization-free algorithm that provably leads to stable recovery of the high dimensional Toeplitz matrix from its order-wise minimal sketch acquired using a GNS. Contrary to existing TV-norm and nuclear norm based reconstruction algorithms, our technique does not use any tuning parameters, which can be of great practical value. The idea of nested sampling idea also finds a surprising use in the problem of phase retrieval, which has been of great interest in recent times for its convex formulation via PhaseLift, By using another modified version of nested sampling, namely the Partial Nested Fourier Sampler (PNFS), we show that with probability one, it is possible to achieve a certain conjectured lower bound on the necessary measurement size. Moreover, for sparse data, an l1 minimization based algorithm is proposed that can lead to stable phase retrieval using order-wise minimal number of measurements.

Lineamientos de política sectorial par la cadena de leche: el caso de Cochabamba – Bolivia

Introducción El sector lechero en Bolivia es incipiente en el ámbito internacional, debido a que su desarrollo fue tardío, pues tiene sus antecedentes recién en los últimos años de los 60’s, cuando la FAO invita a Bolivia a participar del Plan Internacional de Coordinación de Fomento Lechero y a partir de ello surge en 1971 el Plan de Desarrollo Lechero Nacional, cuyo resultado es la apuesta d

PROLINE-BASED CHIRAL STATIONARY PHASES: INSIGHTS INTO THE MECHANISM OF CHIRAL SELECTIVITY

Proline (Pro) is a unique amino acid that has been examined previously as a potential chiral selector for high-performance liquid chromatography. In recent years, a new class of promising Pro based enantioselective stationary phases has been studied and the longer peptides were found to be competitive with commercial chiral stationary phases (CSPs). Here, we aim to perform a comprehensive examination of a t-butoxycarbonyl- (t-Boc-) terminated monoproline selector. This selector was grafted through an amide linkage to an aminopropyl siloxane-terminated Si (111) wafer and to a silicon atomic force microscopy tip. To ensure a flat, homogeneous overlayer of selectors suitable for force spectrometric measurements, the prepared surfaces were characterized using XPS, AFM and contact angle measurements. Chemical force spectrometry (CFS) has been used to examine the chiral discrimination in our monoproline CSP by measuring the interaction forces between two D- or L-monoproline monolayers in water and in the presence of a series of amino acids in solution to explore the degree to which binding of amino acids impacts self-selectivity. Chemical force titration (CFT) has been used to observe the influence of variations in pH on the binding interaction of proline modified chiral surfaces. Here we aim to explore the connection between side-chain hydrophobicity and differences in the nature of the binding between different ionic forms of amino acids and the t-Boc-Pro interface, and thereby to gain insight into the mechanism of chiral selectivity. The CFS results show several trends for different proline selector/amino acid combinations and indicate that the binding characteristics of amino acid to the proline surface is strongly dependent on the amino acid side chain where hydrophilic side chain amino acids exhibit a selectivity opposite to that seen for those with hydrophobic side chains. The CFT studies also provide valuable insights into interactions between the proline selector and the amino acids under a wide range of pH conditions, indicating that protonated amine groups of alanine and serine are closely involved in the binding mechanism to proline surfaces. On the other hand, the presence of the second carboxylic group in aspartic acid plays an important role while interacting with proline.

Analysis of Non-Stationary Flows Using Eulerian and Lagrangian Frameworks

Recent developments have made researchers to reconsider Lagrangian measurement techniques as an alternative to their Eulerian counterpart when investigating non-stationary flows. This thesis advances the state-of-the-art of Lagrangian measurement techniques by pursuing three different objectives: (i) developing new Lagrangian measurement techniques for difficult-to-measure, in situ flow environments; (ii) developing new post-processing strategies designed for unstructured Lagrangian data, as well as providing guidelines towards their use; and (iii) presenting the advantages that the Lagrangian framework has over their Eulerian counterpart in various non-stationary flow problems. Towards the first objective, a large-scale particle tracking velocimetry apparatus is designed for atmospheric surface layer measurements. Towards the second objective, two techniques, one for identifying Lagrangian Coherent Structures (LCS) and the other for characterizing entrainment directly from unstructured Lagrangian data, are developed. Finally, towards the third objective, the advantages of Lagrangian-based measurements are showcased in two unsteady flow problems: the atmospheric surface layer, and entrainment in a non-stationary turbulent flow. Through developing new experimental and post-processing strategies for Lagrangian data, and through showcasing the advantages of Lagrangian data in various non-stationary flows, the thesis works to help investigators to more easily adopt Lagrangian-based measurement techniques.

An application of two techniques for the analysis of short, multivariate non-stationary time-series of Mauritanian trawl survey data

Min/max autocorrelation factor analysis (MAFA) and dynamic factor analysis (DFA) are complementary techniques for analysing short (> 15-25 y), non-stationary, multivariate data sets. We illustrate the two techniques using catch rate (cpue) time-series (1982-2001) for 17 species caught during trawl surveys off Mauritania, with the NAO index, an upwelling index, sea surface temperature, and an index of fishing effort as explanatory variables. Both techniques gave coherent results, the most important common trend being a decrease in cpue during the latter half of the time-series, and the next important being an increase during the first half. A DFA model with SST and UPW as explanatory variables and two common trends gave good fits to most of the cpue time-series. (c) 2004 International Council for the Exploration of the Sea. Published by Elsevier Ltd. All rights reserved.

Productividad sectorial y cambio estructural en América Latina

En este documento se analiza el proceso de transformación estructural para algunas economías de América Latina y su comparación con los milagros asiáticos. De igual forma, para el caso de Colombia y de Corea del Sur, se describe el comportamiento de la productividad agregada y su descomposición sectorial. Los resultados evidencian que, en los dos países, el sector de servicios ha ganado participación en las últimas décadas. Para el caso de Colombia este sector explica en gran medida la baja competitividad del país frente a los Estados Unidos. Por su parte, en Corea del Sur la brecha de productividad con los Estados Unidos se ha cerrado en los tres sectores (agricultura, industria y servicios). Finalmente, se adapta un modelo de cambio estructural para estas dos economías y se encuentra que para Colombia el modelo logra reproducir las tendencias observadas en los datos. Para Corea el modelo no ajusta durante la época de industrialización tardía, pero para las últimas décadas replica de manera cercana los datos.

Estimation of the Traffic Intensity in a Piecewise-stationary Mt/Gt/1 Queue with Probing

We use a probing strategy to estimate the time dependent traffic intensity in an Mt/Gt/1 queue, where the arrival rate and the general service-time distribution change from one time interval to another, and derive statistical properties of the proposed estimator. We present a method to detect a switch from a stationary interval to another using a sequence of probes to improve the estimation. At the end, we compare our results with two estimators proposed in the literature for the M/G/1 queue.