Clinical application of differential time to positivity of blood cultures as a diagnostic method for catheter-related bloodstream infection.

La sospita de bacterièmia relacionada a catèter (BRC) necessita la retirada d’aquest, confirmant-se a posteriori només en un 15-25%. La diferencia en el temps de positivització d´ hemocultius (DTP) ha demostrat ser un mètode fiable per el diagnòstic de BRC evitant la retirada del catèter. Amb la intenció de comprovar la utilitat clínica de la DTP, l’hem comparada amb un mètode diagnòstic estàndard. Hem inclòs 133 pacients ingressats a una unitat de cures intensives portadors de catèters venosos centrals. 56 pacients s’han aleatoritzats. No hem trobat diferències significatives en quant a morbi-mortalitat en els 2 grups havent evitat 70% de retirada innecessària de catèters en el grup de DTP.

On separation of a degenerate differential operator in Hilbert space

A coercive estimate for a solution of a degenerate second order di fferential equation is installed, and its applications to spectral problems for the corresponding dif ferential operator is demonstrated. The suffi cient conditions for existence of the solutions of one class of the nonlinear second order diff erential equations on the real axis are obtained.

The Value of Imperfection : the Wabi-Sabi Principle in Aesthetics and Learning

OER-based learning has the potential to overcome many shortcomings and problems of traditional education. It is not hampered by IP restrictions; can depend on collaborative, cumulative, iterative refinement of resources; and the digital form provides unprecedented flexibility with respect to configuration and delivery. The OER community is a progressive group of educators and learners with decades of learning research to draw from, who know that we must prepare learners for an evolving and diverse reality. Despite this OER tends to replicate the unsuccessful characteristics of traditional education. To remedy this we may need to remember the importance of imperfection, mistakes, problems, disagreement, and the incomplete for engaged learning, and relinquish our notions of perfection, acknowledging that learners learn differently and we need diverse learners. We must stretch our perceptions of quality and provide mechanisms for engaging the incredible pool of educators globally to fulfill the promise of inclusive education.

Differential epipolar constraint in mobile robot egomotion estimation

The estimation of camera egomotion is a well established problem in computer vision. Many approaches have been proposed based on both the discrete and the differential epipolar constraint. The discrete case is mainly used in self-calibrated stereoscopic systems, whereas the differential case deals with a unique moving camera. The article surveys several methods for mobile robot egomotion estimation covering more than 0.5 million samples using synthetic data. Results from real data are also given

Estimating Gross Value Added (GVA) across multiple scales: theoretical approach and empirical validation

This paper presents an application of the Multi-Scale Integrated Analysis of Societal and Ecosystem Metabolism (MuSIASEM) approach to the estimation of quantities of Gross Value Added (GVA) referring to economic entities defined at different scales of study. The method first estimates benchmark values of the pace of GVA generation per hour of labour across economic sectors. These values are estimated as intensive variables –e.g. €/hour– by dividing the various sectorial GVA of the country (expressed in € per year) by the hours of paid work in that same sector per year. This assessment is obtained using data referring to national statistics (top down information referring to the national level). Then, the approach uses bottom-up information (the number of hours of paid work in the various economic sectors of an economic entity –e.g. a city or a province– operating within the country) to estimate the amount of GVA produced by that entity. This estimate is obtained by multiplying the number of hours of work in each sector in the economic entity by the benchmark value of GVA generation per hour of work of that particular sector (national average). This method is applied and tested on two different socio-economic systems: (i) Catalonia (considered level n) and Barcelona (considered level n-1); and (ii) the region of Lima (considered level n) and Lima Metropolitan Area (considered level n-1). In both cases, the GVA per year of the local economic entity –Barcelona and Lima Metropolitan Area – is estimated and the resulting value is compared with GVA data provided by statistical offices. The empirical analysis seems to validate the approach, even though the case of Lima Metropolitan Area indicates a need for additional care when dealing with the estimate of GVA in primary sectors (agriculture and mining).

Nonparametric estimation of Value-at-Risk

A method to estimate an extreme quantile that requires no distributional assumptions is presented. The approach is based on transformed kernel estimation of the cumulative distribution function (cdf). The proposed method consists of a double transformation kernel estimation. We derive optimal bandwidth selection methods that have a direct expression for the smoothing parameter. The bandwidth can accommodate to the given quantile level. The procedure is useful for large data sets and improves quantile estimation compared to other methods in heavy tailed distributions. Implementation is straightforward and R programs are available.

Study and implementation of some quantitative trading models

Quantitative or algorithmic trading is the automatization of investments decisions obeying a fixed or dynamic sets of rules to determine trading orders. It has increasingly made its way up to 70% of the trading volume of one of the biggest financial markets such as the New York Stock Exchange (NYSE). However, there is not a signi cant amount of academic literature devoted to it due to the private nature of investment banks and hedge funds. This projects aims to review the literature and discuss the models available in a subject that publications are scarce and infrequently. We review the basic and fundamental mathematical concepts needed for modeling financial markets such as: stochastic processes, stochastic integration and basic models for prices and spreads dynamics necessary for building quantitative strategies. We also contrast these models with real market data with minutely sampling frequency from the Dow Jones Industrial Average (DJIA). Quantitative strategies try to exploit two types of behavior: trend following or mean reversion. The former is grouped in the so-called technical models and the later in the so-called pairs trading. Technical models have been discarded by financial theoreticians but we show that they can be properly cast into a well defined scientific predictor if the signal generated by them pass the test of being a Markov time. That is, we can tell if the signal has occurred or not by examining the information up to the current time; or more technically, if the event is F_t-measurable. On the other hand the concept of pairs trading or market neutral strategy is fairly simple. However it can be cast in a variety of mathematical models ranging from a method based on a simple euclidean distance, in a co-integration framework or involving stochastic differential equations such as the well-known Ornstein-Uhlenbeck mean reversal ODE and its variations. A model for forecasting any economic or financial magnitude could be properly defined with scientific rigor but it could also lack of any economical value and be considered useless from a practical point of view. This is why this project could not be complete without a backtesting of the mentioned strategies. Conducting a useful and realistic backtesting is by no means a trivial exercise since the \laws" that govern financial markets are constantly evolving in time. This is the reason because we make emphasis in the calibration process of the strategies' parameters to adapt the given market conditions. We find out that the parameters from technical models are more volatile than their counterpart form market neutral strategies and calibration must be done in a high-frequency sampling manner to constantly track the currently market situation. As a whole, the goal of this project is to provide an overview of a quantitative approach to investment reviewing basic strategies and illustrating them by means of a back-testing with real financial market data. The sources of the data used in this project are Bloomberg for intraday time series and Yahoo! for daily prices. All numeric computations and graphics used and shown in this project were implemented in MATLAB^R scratch from scratch as a part of this thesis. No other mathematical or statistical software was used.

The Strategic value of European renewable energy promotion policy

Encara falta per fer possible una transformació estratègica d'Europa del sistema d'energia, però el que és de la mateixa importància com a objectius a llarg termini de la FER i Reduccions de GEH són vinculants i forts objectius d'eficiència energètica, no només per 2020, però també per al 2030, 2040 i 2050, com aquesta força ajudaria a fixar l'augment de les energies renovables en el total d'energia consum i per reduir el total Emissions de GEH d'Europa en general, i les del sector de l'energia en particular, encara sent un dels majors emissors de gasos d'efecte hivernacle de tots els sectors. La refosa Directiva, prevista per 2011/12 ha de ser un bones finestres d'oportunitat per finalment establir objectius vinculants d'eficiència energètica, l'únic pilar que encara falta en la força energia interdependents i estratègia sobre el clima de la UE, basat en la reducció de gasos d'efecte hivernacle i i l'eficiència energètica.

Two-sided learning in new keynesian models: dynamics, (lack of) convergence and the value of information

This paper investigates the role of learning by private agents and the central bank (two-sided learning) in a New Keynesian framework in which both sides of the economy have asymmetric and imperfect knowledge about the true data generating process. We assume that all agents employ the data that they observe (which may be distinct for different sets of agents) to form beliefs about unknown aspects of the true model of the economy, use their beliefs to decide on actions, and revise these beliefs through a statistical learning algorithm as new information becomes available. We study the short-run dynamics of our model and derive its policy recommendations, particularly with respect to central bank communications. We demonstrate that two-sided learning can generate substantial increases in volatility and persistence, and alter the behavior of the variables in the model in a signifficant way. Our simulations do not converge to a symmetric rational expectations equilibrium and we highlight one source that invalidates the convergence results of Marcet and Sargent (1989). Finally, we identify a novel aspect of central bank communication in models of learning: communication can be harmful if the central bank's model is substantially mis-specified

Study of chemical modification of alkaline lignin by the glyoxalation reaction

In this study, glyoxalated alkaline lignins with a non-volatile and non-toxic aldehyde, which can be obtained from several natural resources, namely glyoxal, were prepared and characterized for its use in wood adhesives. The preparation method consisted of the reaction of lignin with glyoxal under an alkaline medium. The influence of reaction conditions such as the molar ratio of sodium hydroxide-to-lignin and reaction time were studied relative to the properties of the prepared adducts. The analytical techniques used were FTIR and 1H-NMR spectroscopies, gel permeation chromatography (GPC), differential scanning calorimetry (DSC), and thermogravimetric analysis (TGA). Results from both the FTIR and 1H-NMR spectroscopies showed that the amount of introduced aliphatic hydroxyl groups onto the lignin molecule increased with increasing reaction time and reached a maximum value at 10 h, and after they began to decrease. The molecular weights remained unchanged until 10 h of reaction time, and then started to increase, possibly due to the repolymerization reactions. DSC analysis showed that the glass transition temperature (Tg) decreased with the introduction of glyoxal onto the lignin molecule due to the increase in free volume of the lignin molecules. TGA analysis showed that the thermal stability of glyoxalated lignin is not influenced and remained suitable for wood adhesives. Compared to the original lignin, the improved lignin is reactive and a suitable raw material for adhesive formula

Methodology for studying the numerical speed of sound in finite differences schemes

The space and time discretization inherent to all FDTD schemesintroduce non-physical dispersion errors, i.e. deviations ofthe speed of sound from the theoretical value predicted bythe governing Euler differential equations. A generalmethodologyfor computing this dispersion error via straightforwardnumerical simulations of the FDTD schemes is presented.The method is shown to provide remarkable accuraciesof the order of 1/1000 in a wide variety of twodimensionalfinite difference schemes.

A short motif in Drosophila SECIS Binding Protein 2 provides differential binding affinity to SECIS RNA hairpins

Selenoproteins contain the amino acid selenocysteine which is encoded by a UGA Sec codon. Recoding UGA Sec requires a complex mechanism, comprising the cis-acting SECIS RNA hairpin in the 3′UTR of selenoprotein mRNAs, and trans-acting factors. Among these, the SECIS Binding Protein 2 (SBP2) is central to the mechanism. SBP2 has been so far functionally characterized only in rats and humans. In this work, we report the characterization of the Drosophila melanogaster SBP2 (dSBP2). Despite its shorter length, it retained the same selenoprotein synthesis-promoting capabilities as the mammalian counterpart. However, a major difference resides in the SECIS recognition pattern: while human SBP2 (hSBP2) binds the distinct form 1 and 2 SECIS RNAs with similar affinities, dSBP2 exhibits high affinity toward form 2 only. In addition, we report the identification of a K (lysine)-rich domain in all SBP2s, essential for SECIS and 60S ribosomal subunit binding, differing from the well-characterized L7Ae RNA-binding domain. Swapping only five amino acids between dSBP2 and hSBP2 in the K-rich domain conferred reversed SECIS-binding properties to the proteins, thus unveiling an important sequence for form 1 binding.

What is the value of joint processing of pilots and data in block-fading channels?

The spectral efficiency achievable with joint processing of pilot and data symbol observations is compared with that achievable through the conventional (separate) approach of first estimating the channel on the basis of the pilot symbols alone, and subsequently detecting the datasymbols. Studied on the basis of a mutual information lower bound, joint processing is found to provide a non-negligible advantage relative to separate processing, particularly for fast fading. It is shown that, regardless of the fading rate, only a very small number of pilot symbols (at most one per transmit antenna and per channel coherence interval) shouldbe transmitted if joint processing is allowed.

Book vs. Fair Value Accounting in Banking, and Intertemporal Smoothing

The aim of this paper is to examine the pros and cons of book and fair value accounting from the perspective of the theory of banking. We consider the implications of the two accounting methods in an overlapping generations environment. As observed by Allen and Gale(1997), in an overlapping generation model, banks have a role as intergenerational connectors as they allow for intertemporal smoothing. Our main result is that when dividends depend on profits, book value ex ante dominates fair value, as it provides better intertemporal smoothing. This is in contrast with the standard view that states that, fair value yields a better allocation as it reflects the real opportunity cost of assets. Banking regulation play an important role by providing the right incentives for banks to smooth intertemporal consumption whereas market discipline improves intratemporal efficiency.

Ecologies of Value in a Wall Street Trading Room

Our task in this paper is to analyze the organization of trading in the era of quantitative finance. To do so, we conduct an ethnography of arbitrage, the trading strategy that best exemplifies finance in the wake of the quantitative revolution. In contrast to value and momentum investing, we argue, arbitrage involves an art of association-the construction of equivalence (comparability) of properties across different assets. In place of essential or relational characteristics, the peculiar valuation that takes place in arbitrage is based on an operation that makes something the measure of something else-associating securities to each other. The process of recognizing opportunities and the practices of making novel associations are shaped by the specific socio-spatial and socio-technical configurations of the trading room. Calculation is distributed across persons and instruments as the trading room organizes interaction among diverse principles of valuation.