Geographical differences in first acquisition of Pseudomonas aeruginosa in cystic fibrosis

RATIONALE: Risk of infection with Pseudomonas aeruginosa in cystic fibrosis (CF) may be associated with environmental factors.

OBJECTIVES: To determine whether residential location is associated with risk of first acquisition of P. aeruginosa.

METHODS: We performed bronchoalveolar lavage and upper airway cultures in children newly diagnosed with CF to identify infection with P. aeruginosa during infancy and early childhood. Children were assessed according to their residence in a regional or metropolitan area. Multilocus sequence typing was used to determine P. aeruginosa genotype. An environmental questionnaire was also administered.

MEASUREMENTS AND MAIN RESULTS: A total of 105 of 120 (87.5%) infants diagnosed with CF were included in this study. Diagnosis in 65 infants (61.9%) followed newborn screening at mean age of 4.6 weeks. Sixty subjects (57.1%) were homozygous ΔF508, and 47 (44.8%) were female. Fifty-five (52.3%) infants were regional, of whom 26 (47.3%), compared with 9 of 50 (18.0%) metropolitan children, acquired infection with P. aeruginosa (odds ratio, 4.084; 95% confidence interval, 1.55-11.30). Age at acquisition was similar (regional: median, 2.31 yr; range, 0.27-5.96 yr; metropolitan: median, 3.10 yr, range, 0.89-3.70 yr). Strain typing identified P. aeruginosa genotypes often encountered in different ecological settings and little evidence of cross-infection. Ninety questionnaires (85.7%) were completed. Those who acquired P. aeruginosa were more likely to be living in a household that used water sprinkler systems (P = 0.032), but no differences were identified to explain increased risk of acquisition of P. aeruginosa in regional children.

CONCLUSIONS: Geographical difference in residence of children with CF was associated with increased risk of first acquisition of P. aeruginosa, usually with strains associated with the environment rather than with cross-infection.

Chloride ingress through alkali activated slag concretes

Alkali activated slag (AAS) is a credible alternative to Portland cement (PC) based binder systems. The superior strength gain and low embodied carbon make it a potential binder for next generation concretes. However there is little known about the long term durability of AAS systems, especially the chloride transport and subsequent corrosion of reinforcing steel.
In this study, chloride transport through 12 AAS concretes with different alkali concentrations (Na2O% of mass of slag) and different modulus (Ms) of sodium silicate solution activator was investigated. A non-steady state chloride diffusion test was used for this study due to its similarity to the real exposure environment in terms of chloride transport through concrete. The results showed that the chloride concentration at the surface (Cs) of AAS concretes was higher than that for PC concrete.
However, lower non-steady state chloride diffusion coefficient (Dnssd) was obtained for the AAS concretes. The Dnssd of the AAS concretes decreased with the increase of Na2O% and Ms of 1.50 gave the lowest Dnssd. The results are encouraging and it can be concluded that AAS concrete offers a superior performance in terms of chloride transport.

Influence of compressive loading on chloride ingress through concrete

Chloride-induced corrosion of steel in concrete is one of most important durability and safety concern for reinforced concrete structures. To study chloride ingress into concrete is thus very important. However, most of the researchers focus on the studying chloride ingress through concrete samples without any loading. In reality concrete structures are subjected to different kinds of loads and therefore studying the effect of such loads on chloride transport is critical. In this work, 28 different concrete mixes were subjected to three levels of compressive load (0%, 50% and 75% of compressive failure load – f) for 24 hours. Further to unloading, these samples were subjected to non-steady state chloride diffusion test as per NT Build 443. The results were compared against the diffusion coefficient obtained for concrete samples that had no previous loading. D value for concretes subjected to 75% f showed a significant increase compared to 0% loading condition, but the increase was insignificant for 50% f. The results indicate that the influence of concrete mixes variables on D is more significant than that of loading level. Surface chloride concentration also increased with the loading level, which might be due to the increased concrete surface area caused by micro cracking.

Kernel-based local order estimation of nonlinear nonparametric systems

We consider the local order estimation of nonlinear autoregressive systems with exogenous inputs (NARX), which may have different local dimensions at different points. By minimizing the kernel-based local information criterion introduced in this paper, the strongly consistent estimates for the local orders of the NARX system at points of interest are obtained. The modification of the criterion and a simple procedure of searching the minimum of the criterion, are also discussed. The theoretical results derived here are tested by simulation examples.

Hybrid probabilistic wind power forecasting using temporally local Gaussian process

The demand for sustainable development has resulted in a rapid growth in wind power worldwide. Despite various approaches have been proposed to improve the accuracy and to overcome the uncertainties associated with traditional methods, the stochastic and variable nature of wind still remains the most challenging issue in accurately forecasting wind power. This paper presents a hybrid deterministic-probabilistic method where a temporally local ‘moving window’ technique is used in Gaussian Process to examine estimated forecasting errors. This temporally local Gaussian Process employs less measurement data while faster and better predicts wind power at two wind farms, one in the USA and the other in Ireland. Statistical analysis on the results shows that the method can substantially reduce the forecasting error while more likely generate Gaussian-distributed residuals, particularly for short-term forecast horizons due to its capability to handle the time-varying characteristics of wind power.

Special condition wind power forecasting based on Gaussian process and similar historical data

Due to the variability of wind power, it is imperative to accurately and timely forecast the wind generation to enhance the flexibility and reliability of the operation and control of real-time power. Special events such as ramps, spikes are hard to predict with traditional methods using solely recently measured data. In this paper, a new Gaussian Process model with hybrid training data taken from both the local time and historic dataset is proposed and applied to make short-term predictions from 10 minutes to one hour ahead. A key idea is that the similar pattern data in history are properly selected and embedded in Gaussian Process model to make predictions. The results of the proposed algorithms are compared to those of standard Gaussian Process model and the persistence model. It is shown that the proposed method not only reduces magnitude error but also phase error.

The 'Ordinariness of the EU's Foreign Policy Power: The Use of WTO Litigation as a Means to Export EU Norms, Values and Interests

The EU has historically been portrayed as a distinctive international actor both in terms of the norms and values it exports in context of its international relations and the manner in which it seeks to influence others. However, such claims to the EU’s distinctiveness are increasingly being questioned. This article joins this chorus of voices arguing the non-distinctiveness of the EU’s foreign policy power by focusing on a specific feature of the EU’s external trade policy, the role of World Trade Organization (WTO) dispute settlement in the EU’s attempts to promote its interests, values and norms.

The EU Deep Trade Agenda: Law and Policy

Providing the first comprehensive examination of the key regulatory disciplines included in the new generation of EU free trade agreements (FTAs), this book investigates the EU's supposed deep trade agenda through a legal analysis of these FTAs. In doing so, Billy A. Melo Araujo determines whether there is any substance behind the EU's foreign policy rhetoric regarding the need to introduce regulatory issues within the remit of international trade law.

At a time when the EU is busily negotiating so-called 'mega-FTAs', such as the Transatlantic Trade and Investment Partnership (TTIP) and the plurilateral Trade in Services Agreement (TISA), Melo Araujo offers a timely insight into the important questions raised by such FTAs, in particular concerning the future of the multilateral trade system, the loss of policy autonomy, and the democratic legitimacy of regulating through treaty-making. The book provides a detailed analysis of the regulatory disciplines included in the more recent EU FTAs and explores the possible implications of such disciplines. Offering a significant contribution to a wider debate, this is a must read for those interested in the legal dimension of the EU's deep trade agenda.

Two-Stage Orthogonal Least Squares Methods for Neural Network Construction

A number of neural networks can be formulated as the linear-in-the-parameters models. Training such networks can be transformed to a model selection problem where a compact model is selected from all the candidates using subset selection algorithms. Forward selection methods are popular fast subset selection approaches. However, they may only produce suboptimal models and can be trapped into a local minimum. More recently, a two-stage fast recursive algorithm (TSFRA) combining forward selection and backward model refinement has been proposed to improve the compactness and generalization performance of the model. This paper proposes unified two-stage orthogonal least squares methods instead of the fast recursive-based methods. In contrast to the TSFRA, this paper derives a new simplified relationship between the forward and the backward stages to avoid repetitive computations using the inherent orthogonal properties of the least squares methods. Furthermore, a new term exchanging scheme for backward model refinement is introduced to reduce computational demand. Finally, given the error reduction ratio criterion, effective and efficient forward and backward subset selection procedures are proposed. Extensive examples are presented to demonstrate the improved model compactness constructed by the proposed technique in comparison with some popular methods.

Kernel based approaches to local nonlinear non-parametric variable selection

In this paper, we consider the variable selection problem for a nonlinear non-parametric system. Two approaches are proposed, one top-down approach and one bottom-up approach. The top-down algorithm selects a variable by detecting if the corresponding partial derivative is zero or not at the point of interest. The algorithm is shown to have not only the parameter but also the set convergence. This is critical because the variable selection problem is binary, a variable is either selected or not selected. The bottom-up approach is based on the forward/backward stepwise selection which is designed to work if the data length is limited. Both approaches determine the most important variables locally and allow the unknown non-parametric nonlinear system to have different local dimensions at different points of interest. Further, two potential applications along with numerical simulations are provided to illustrate the usefulness of the proposed algorithms.

Accurate image capturing control of bottle caps based on iterative learning control and Kalman filtering

This paper investigates camera control for capturing bottle cap target images in the fault-detection system of an industrial production line. The main purpose is to identify the targeted bottle caps accurately in real time from the images. This is achieved by combining iterative learning control and Kalman filtering to reduce the effect of various disturbances introduced into the detection system. A mathematical model, together with a physical simulation platform is established based on the actual production requirements, and the convergence properties of the model are analyzed. It is shown that the proposed method enables accurate real-time control of the camera, and further, the gain range of the learning rule is also obtained. The numerical simulation and experimental results confirm that the proposed method can not only reduce the effect of repeatable disturbances but also non-repeatable ones.