Chemical composition observed over the mid-Atlantic and the detection of pollution signatures far from source regions

The atmospheric composition of the central North Atlantic region has been sampled using the FAAM BAe146 instrumented aircraft during the Intercontinental Transport of Ozone and Precursors (ITOP) campaign, part of the wider International Consortium for Atmospheric Research on Transport and Transformation (ICARTT). This paper presents an overview of the ITOP campaign. Between late July and early August 2004, twelve flights comprising 72 hours of measurement were made in a region from approximately 20 to 40°W and 33 to 47°N centered on Faial Island, Azores, ranging in altitude from 50 to 9000 m. The vertical profiles of O3 and CO are consistent with previous observations made in this region during 1997 and our knowledge of the seasonal cycles within the region. A cluster analysis technique is used to partition the data set into air mass types with distinct chemical signatures. Six clusters provide a suitable balance between cluster generality and specificity. The clusters are labeled as biomass burning, low level outflow, upper level outflow, moist lower troposphere, marine and upper troposphere. During this summer, boreal forest fire emissions from Alaska and northern Canada were found to provide a major perturbation of tropospheric composition in CO, PAN, organic compounds and aerosol. Anthropogenic influenced air from the continental boundary layer of the USA was clearly observed running above the marine boundary layer right across the mid-Atlantic, retaining high pollution levels in VOCs and sulfate aerosol. Upper level outflow events were found to have far lower sulfate aerosol, resulting from washout on ascent, but much higher PAN associated with the colder temperatures. Lagrangian links with flights of other aircraft over the USA and Europe show that such signatures are maintained many days downwind of emission regions. Some other features of the data set are highlighted, including the strong perturbations to many VOCs and OVOCs in this remote region.

Perfluorodecalin: global warming potential and first detection in the atmosphere

Perfluorodecalin (C10F18) has a range of medical uses that have led to small releases. Recently, it has been proposed as a carrier of vaccines, which could lead to significantly larger emissions. Since its emissions are controlled under the Kyoto Protocol, it is important that values for the global warming potential (GWP) are available. For a 50:50 mixture of the two isomers of perfluorodecalin, laboratory measurements, supplemented by theoretical calculations, give an integrated absorption cross-section of 3.91 x 10(-16) cm(2) molecule(-1) cm(-1) over the spectral region 0-1500 cm(-1); calculations yield a radiative efficiency of 0.56 W m(-2) ppbv(-1) and a 100-year GWP, relative to carbon dioxide, of 7200 assuming a lifetime of 1000 years. We report the first atmospheric measurements of perfluorodecalin, at Bristol, UK and Mace Head, Ireland, where volume mixing ratios are about 1.5 x 10(-15). At these concentrations, it makes a trivial contribution to climate change, but on a per molecule basis it is a potent greenhouse gas, indicating the need for careful assessment of its possible future usage. (c) 2005 Elsevier Ltd. All rights reserved.

The sample autocorrelation function and the detection of long-memory processes

The detection of long-range dependence in time series analysis is an important task to which this paper contributes by showing that whilst the theoretical definition of a long-memory (or long-range dependent) process is based on the autocorrelation function, it is not possible for long memory to be identified using the sum of the sample autocorrelations, as usually defined. The reason for this is that the sample sum is a predetermined constant for any stationary time series; a result that is independent of the sample size. Diagnostic or estimation procedures, such as those in the frequency domain, that embed this sum are equally open to this criticism. We develop this result in the context of long memory, extending it to the implications for the spectral density function and the variance of partial sums of a stationary stochastic process. The results are further extended to higher order sample autocorrelations and the bispectral density. The corresponding result is that the sum of the third order sample (auto) bicorrelations at lags h,k≥1, is also a predetermined constant, different from that in the second order case, for any stationary time series of arbitrary length.

Classification and fault detection methods for fuel cell monitoring and quality control

In this paper, various types of fault detection methods for fuel cells are compared. For example, those that use a model based approach or a data driven approach or a combination of the two. The potential advantages and drawbacks of each method are discussed and comparisons between methods are made. In particular, classification algorithms are investigated, which separate a data set into classes or clusters based on some prior knowledge or measure of similarity. In particular, the application of classification methods to vectors of reconstructed currents by magnetic tomography or to vectors of magnetic field measurements directly is explored. Bases are simulated using the finite integration technique (FIT) and regularization techniques are employed to overcome ill-posedness. Fisher's linear discriminant is used to illustrate these concepts. Numerical experiments show that the ill-posedness of the magnetic tomography problem is a part of the classification problem on magnetic field measurements as well. This is independent of the particular working mode of the cell but influenced by the type of faulty behavior that is studied. The numerical results demonstrate the ill-posedness by the exponential decay behavior of the singular values for three examples of fault classes.

Terminology matters! Why difference is not incompleteness and how early child bilinguals are heritage speakers

This paper integrates research on child simultaneous bilingual (2L1) acquisition more directly into the heritage language (HL) acquisition literature. The 2L1 literature mostly focuses on development in childhood, whereas heritage speakers (HSs) are often tested at an endstate in adulthood. However, insights from child 2L1 acquisition must be considered in HL acquisition theorizing precisely because many HSs are the adult outcomes of child 2L1 acquisition. Data from 2L1 acquisition raises serious questions for the construct of incomplete acquisition, a term broadly used in HL acquisition studies to describe almost any difference HSs display from baseline controls (usually monolinguals). We offer an epistemological discussion related to incomplete acquisition, highlighting the descriptive and theoretical inaccuracy of the term. We focus our discussion on two of several possible causal factors that contribute to variable competence outcomes in adult HSs, input (e.g., Sorace, 2004; Rothman, 2007; Pascual y Cabo & Rothman, 2012) and formal instruction (e.g., Kupisch, 2013; Kupisch et al., 2014) in the HL. We conclude by offering alternative terminology for HS outcomes.

Design and evaluation of intelligent classifier based intrusion detection system

We have discovered a novel approach of intrusion detection system using an intelligent data classifier based on a self organizing map (SOM). We have surveyed all other unsupervised intrusion detection methods, different alternative SOM based techniques and KDD winner IDS methods. This paper provides a robust designed and implemented intelligent data classifier technique based on a single large size (30x30) self organizing map (SOM) having the capability to detect all types of attacks given in the DARPA Archive 1999 the lowest false positive rate being 0.04 % and higher detection rate being 99.73% tested using full KDD data sets and 89.54% comparable detection rate and 0.18% lowest false positive rate tested using corrected data sets.

Resting tremor classification and detection in Parkinson's disease patients

Parkinson is a neurodegenerative disease, in which tremor is the main symptom. This paper investigates the use of different classification methods to identify tremors experienced by Parkinsonian patients.Some previous research has focussed tremor analysis on external body signals (e.g., electromyography, accelerometer signals, etc.). Our advantage is that we have access to sub-cortical data, which facilitates the applicability of the obtained results into real medical devices since we are dealing with brain signals directly. Local field potentials (LFP) were recorded in the subthalamic nucleus of 7 Parkinsonian patients through the implanted electrodes of a deep brain stimulation (DBS) device prior to its internalization. Measured LFP signals were preprocessed by means of splinting, down sampling, filtering, normalization and rec-tification. Then, feature extraction was conducted through a multi-level decomposition via a wavelettrans form. Finally, artificial intelligence techniques were applied to feature selection, clustering of tremor types, and tremor detection.The key contribution of this paper is to present initial results which indicate, to a high degree of certainty, that there appear to be two distinct subgroups of patients within the group-1 of patients according to the Consensus Statement of the Movement Disorder Society on Tremor. Such results may well lead to different resultant treatments for the patients involved, depending on how their tremor has been classified. Moreover, we propose a new approach for demand driven stimulation, in which tremor detection is also based on the subtype of tremor the patient has. Applying this knowledge to the tremor detection problem, it can be concluded that the results improve when patient clustering is applied prior to detection.

Combinatorial peptide ligand libraries and plant proteomics: a winning strategy at a price

The mechanism of action and properties of a solid-phase ligand library made of hexapeptides (combinatorial peptide ligand libraries or CPLL), for capturing the "hidden proteome", i.e. the low- and very low-abundance proteins constituting the vast majority of species in any proteome, as applied to plant tissues, are reviewed here. Plant tissues are notoriously recalcitrant to protein extraction and to proteome analysis. Firstly, rigid plant cell walls need to be mechanically disrupted to release the cell content and, in addition to their poor protein yield, plant tissues are rich in proteases and oxidative enzymes, contain phenolic compounds, starches, oils, pigments and secondary metabolites that massively contaminate protein extracts. In addition, complex matrices of polysaccharides, including large amount of anionic pectins, are present. All these species compete with the binding of proteins to the CPLL beads, impeding proper capture and identification / detection of low-abundance species. When properly pre-treated, plant tissue extracts are amenable to capture by the CPLL beads revealing thus many new species among them low-abundance proteins. Examples are given on the treatment of leaf proteins, of corn seed extracts and of exudate proteins (latex from Hevea brasiliensis). In all cases, the detection of unique gene products via CPLL capture is at least twice that of control, untreated sample.

PETS2009: dataset and challenge

This paper describes the crowd image analysis challenge that forms part of the PETS 2009 workshop. The aim of this challenge is to use new or existing systems for i) crowd count and density estimation, ii) tracking of individual(s) within a crowd, and iii) detection of separate flows and specific crowd events, in a real-world environment. The dataset scenarios were filmed from multiple cameras and involve multiple actors.

A change detection approach to flood mapping in urban areas using TerraSAR-X

Very high-resolution Synthetic Aperture Radar sensors represent an alternative to aerial photography for delineating floods in built-up environments where flood risk is highest. However, even with currently available SAR image resolutions of 3 m and higher, signal returns from man-made structures hamper the accurate mapping of flooded areas. Enhanced image processing algorithms and a better exploitation of image archives are required to facilitate the use of microwave remote sensing data for monitoring flood dynamics in urban areas. In this study a hybrid methodology combining radiometric thresholding, region growing and change detection is introduced as an approach enabling the automated, objective and reliable flood extent extraction from very high-resolution urban SAR images. The method is based on the calibration of a statistical distribution of “open water” backscatter values inferred from SAR images of floods. SAR images acquired during dry conditions enable the identification of areas i) that are not “visible” to the sensor (i.e. regions affected by ‘layover’ and ‘shadow’) and ii) that systematically behave as specular reflectors (e.g. smooth tarmac, permanent water bodies). Change detection with respect to a pre- or post flood reference image thereby reduces over-detection of inundated areas. A case study of the July 2007 Severn River flood (UK) observed by the very high-resolution SAR sensor on board TerraSAR-X as well as airborne photography highlights advantages and limitations of the proposed method. We conclude that even though the fully automated SAR-based flood mapping technique overcomes some limitations of previous methods, further technological and methodological improvements are necessary for SAR-based flood detection in urban areas to match the flood mapping capability of high quality aerial photography.

A new rapid detection threshold method for use with older adults: reducing fatigue whilst maintaining accuracy

Taste and smell detection threshold measurements are frequently time consuming especially when the method involves reversing the concentrations presented to replicate and improve accuracy of results. These multiple replications are likely to cause sensory and cognitive fatigue which may be more pronounced in elderly populations. A new rapid detection threshold methodology was developed that quickly located the likely position of each individuals sensory detection threshold then refined this by providing multiple concentrations around this point to determine their threshold. This study evaluates the reliability and validity of this method. Findings indicate that this new rapid detection threshold methodology was appropriate to identify differences in sensory detection thresholds between different populations and has positive benefits in providing a shorter assessment of detection thresholds. The results indicated that this method is appropriate at determining individual as well as group detection thresholds.

Routing protocols and quality of services for security based applications using wireless video sensor networks

Wireless video sensor networks have been a hot topic in recent years; the monitoring capability is the central feature of the services offered by a wireless video sensor network can be classified into three major categories: monitoring, alerting, and information on-demand. These features have been applied to a large number of applications related to the environment (agriculture, water, forest and fire detection), military, buildings, health (elderly people and home monitoring), disaster relief, area and industrial monitoring. Security applications oriented toward critical infrastructures and disaster relief are very important applications that many countries have identified as critical in the near future. This paper aims to design a cross layer based protocol to provide the required quality of services for security related applications using wireless video sensor networks. Energy saving, delay and reliability for the delivered data are crucial in the proposed application. Simulation results show that the proposed cross layer based protocol offers a good performance in term of providing the required quality of services for the proposed application.

Goal sketching with activity diagrams

Goal orientation is acknowledged as an important paradigm in requirements engineering. The structure of a goal-responsibility model provides opportunities for appraising the intention of a development. Creating a suitable model under agile constraints (time, incompleteness and catching up after an initial burst of creativity) can be challenging. Here we propose a marriage of UML activity diagrams with goal sketching in order to facilitate the production of goal responsibility models under these constraints.