Automatic on-line monitoring of atmospheric volatile organic compounds: gas chromatography-mass spectrometry and gas chromatography-flame ionization detection as complementary systems

Traditionally air quality networks have been carrying out the continuous, on-line measurement of volatile organic compounds (VOC) in ambient air with GC-FID. In this paper some identification and coelution problems observed while using this technique in long-term measurement campaigns are described. In order to solve these problems a GC-MS was set up and operated simultaneously with a GC-FID for C₂-C₁₁ VOCs measurement.There are few on-line, unattended, long term measurements of atmospheric VOCs performed with GC-MS. In this work such a system has been optimized for that purpose, achieving good repeatability, linearity, and detection limits of the order of the GC-FID ones, even smaller in some cases. VOC quantification has been made by using response factors, which is not frequent in on-line GC-MS. That way, the identification and coelution problems detected in the GC-FID, which may led to reporting erroneous data, could be corrected.The combination of GC-FID and GC-MS as complementary techniques for the measurement of speciated VOCs in ambient air at sub-ppbv levels is proposed. Some results of the measurements are presented, including concentration values for some compounds not found until now on public ambient air VOC databases, which were identified and quantified combining both techniques. Results may also help to correct previously published VOC data with wrongly identified compounds by reprocessing raw chromatographic data.     

Evaluating the performance of machine learning methods and variable selection methods for predicting difficult-to-measure traits in Holstein dairy cattle using milk infrared spectral data

Fourier-transform infrared (FTIR) spectroscopy is a powerful high-throughput phenotyping tool for predicting traits that are expensive and difficult to measure in dairy cattle. Calibration equations are often developed using standard methods, such as partial least squares (PLS) regression. Methods that employ penalization, rank-reduction, and variable selection, as well as being able to model the nonlinear relations between phenotype and FTIR, might offer improvements in predictive ability and model robustness. This study aimed to compare the predictive ability of 2 machine learning methods, namely random forest (RF) and gradient boosting machine (GBM), and penalized regression against PLS regression for predicting 3 phenotypes differing in terms of biological meaning and relationships with milk composition (i.e., phenotypes measurable directly and not directly in milk, reflecting different biological processes which can be captured using milk spectra) in Holstein-Friesian cattle under 2 cross-validation scenarios. The data set comprised phenotypic information from 471 Holstein-Friesian cows, and 3 target phenotypes were evaluated: (1) body condition score (BCS), (2) blood β-hydroxybutyrate (BHB, mmol/L), and (3) κ-casein expressed as a percentage of nitrogen (κ-CN, % N). The data set was split considering 2 cross-validation scenarios: samples-out random in which the population was randomly split into 10-folds (8-folds for training and 1-fold for validation and testing); and herd/date-out in which the population was randomly assigned to training (70% herd), validation (10%), and testing (20% herd) based on the herd and date in which the samples were collected. The random grid search was performed using the training subset for the hyperparameter optimization and the validation set was used for the generalization of prediction error. The trained model was then used to assess the final prediction in the testing subset. The grid search for penalized regression evidenced that the elastic net (EN) was the best regularization with increase in predictive ability of 5%. The performance of PLS (standard model) was compared against 2 machine learning techniques and penalized regression using 2 cross-validation scenarios. Machine learning methods showed a greater predictive ability for BCS (0.63 for GBM and 0.61 for RF), BHB (0.80 for GBM and 0.79 for RF), and κ-CN (0.81 for GBM and 0.80 for RF) in samples-out cross-validation. Considering a herd/date-out cross-validation these values were 0.58 (GBM and RF) for BCS, 0.73 (GBM and RF) for BHB, and 0.77 (GBM and RF) for κ-CN. The GBM model tended to outperform other methods in predictive ability around 4%, 1%, and 7% for EN, RF, and PLS, respectively. The prediction accuracies of the GBM and RF models were similar, and differed statistically from the PLS model in samples-out random cross-validation. Although, machine learning techniques outperformed PLS in herd/date-out cross-validation, no significant differences were observed in terms of predictive ability due to the large standard deviation observed for predictions. Overall, GBM achieved the highest accuracy of FTIR-based prediction of the different phenotypic traits across the cross-validation scenarios. These results indicate that GBM is a promising method for obtaining more accurate FTIR-based predictions for different phenotypes in dairy cattle.     

Platelets: Still a Therapeutical Target for Haemostatic Disorders

Platelets are cytoplasmatic fragments from bone marrow megakaryocytes present in blood. In this work, we review the basis of platelet mechanisms, their participation in syndromes and in arterial thrombosis, and their potential as a target for designing new antithrombotic agents. The option of new biotechnological sources is also explored.     

A theoretical model of the relationship between the <Emphasis Type="Italic">h</Emphasis>-index and other simple citation indicators

In this study, we quantitatively compared the impact of mission-oriented research grants and curiosity-driven grants on the diversity of research subjects in Japan. First, we examined data for Japanese principal investigators receiving research funding between 2000 and 2010 in the field of nanotechnology and materials science, and identified groups of researchers whose publication performance was positively affected by the mission-oriented grant, CREST. We then compared the effect of CREST with that of the curiosity-driven grant, KAKENHI. The analysis uses both propensity score matching and difference in differences (PSM-DID) methodologies. Our results show that for participants in the CREST program there was an increase in number of publications of more than 10% per year, for periods of both 5 and 3 years after the funding ended, even though the observed average effect on citation was not statistically significant. Second, we evaluated the diversity of research subjects through analysis of the distribution of the classification codes applied to articles published between 1996 and 2013, utilizing the J-Global database, which has the finest granularity of category among existing bibliographic scientific publication databases. Research subjects were better conserved under the mission-oriented program than the curiosity-driven one, a finding contrary to predictions of conventional theory. We also found that under mission-oriented funding, there was an increase in diversity in the sense of marginal utility. These findings should be of use in the “diversity-aware” design of programs for the funding of fundamental research.     

Cognitive Radio as the Facilitator for Advanced Communications Electronic Warfare Solutions

Throughout the 1990s, Software Defined Radio (SDR) technology was viewed almost exclusively as a solution for interoperability problems between various military standards, waveforms and devices. In the meantime, Cognitive Radio (CR) – a novel communication paradigm which embodies SDR with intelligence and self-reconfigurability properties – has emerged. Intelligence and on-the-fly self-reconfiguration abilities of CRs constitute an important next step in the Communications Electronic Warfare, as they may enable the jamming entities with the capabilities of devising and deploying advanced jamming tactics. Similarly, they may also aid the development of the advanced intelligent self-reconfigurable systems for jamming mitigation. This work outlines the development and implementation of the Spectrum Intelligence algorithm for Radio Frequency (RF) interference mitigation. The developed system is built upon the ideas of obtaining relevant spectrum-related data by using wideband energy detectors, performing narrowband waveform identification, extracting the waveforms’ parameters and properly classifying the waveforms. All relevant spectrum activities are continuously monitored and stored. Coupled with the self-reconfigurability of various transmission-related parameters, Spectrum Intelligence is the facilitator for the advanced interference mitigation strategies. The implementation is done on the Cognitive Radio test bed architecture which consists of two military Software Defined Radio terminals, each interconnected with the computationally powerful System-on-Module.     

CFRP Tendons for the Repair of Posttensioned, Unbonded Concrete Buildings

The deterioration attributable to corrosion of concrete structures reinforced with unbonded, posttensioned tendons is a costly problem. Recent research has shown composite materials such as fiber-reinforced polymers (FRP) to be suitable alternatives to steel because they provide similar strength without susceptibility to electrochemical corrosion. Carbon-FRP (CFRP) in particular has great promise for prestressed applications because it shows resistance to corrosion in environments that might be encountered in concrete and experiences less relaxation than steel. This paper outlines the testing and implementation of a posttensioned system that uses CFRP tendons to replace corroded, unbonded posttensioned steel tendons. This system was then implemented in a parking garage in downtown Toronto. To the writers’ knowledge, this is the first example of an unbonded, posttensioned tendon replacement using FRP tendons. The system used split-wedge anchors designed specifically for CFRP tendons. The dead end was anchored by directly bonding the tendon to the concrete slab. The CFRP tendon was successfully inserted in the opening created by the removal of the corroded tendon and stressed. Although the system was shown to be feasible, the current anchorage configuration results in load losses of up to 60% during the transfer. Changing the orientation of the anchor was found to reduce the load loss to an acceptable range of 1–9%.     

Ion beam analysis of partial lithium extraction of LiMn2O4 by chemical delithiation

Lithium manganese oxide, LiMn₂O₄, has been studied by many research groups. This material is a great candidate to be used as positive electrode in rechargeable lithium-ion batteries because of its low cost, abundant precursors and non-toxicity. LiMn₂O₄ has a spinel Fd-3m structure and shows a reversible extraction and insertion of lithium ions that is one of the most important characteristic of positive electrodes in rechargeable batteries.In this work, LiMn₂O₄ samples were synthesized by solid state reaction. A partial lithium removal was performed on this system by chemical delithiation using HCl aqueous solutions at different concentrations. Six partial-extracted compounds were obtained and characterized by Ion beam analysis (IBA) in order to obtain the Li concentrations. X-ray diffraction (XRD) and Rietveld analyses were also performed. A rigorous study of lithium contents is critical to analyze the structure properties of these compounds and samples production parameters. The IBA method used in this work was the analysis of energy spectra of elastic backscattered (EBS) proton from Mn, O and Li nuclei and the α-particles energy from the ⁷Li(p,α)⁴He nuclear reaction (NR).     

Phase Equilibrium Measurements of Sacha Inchi Oil (<Emphasis Type="Italic">Plukenetia volubilis</Emphasis>) and CO<Subscript>2</Subscript> at High Pressures

New data on phase equilibria for Sacha inchi seed oil in carbon dioxide have been measured using a variable volume cell phase equilibria system at temperatures of 303, 313 and 323 K and at pressures ranging from 4.3 to 27.7 MPa. The CO₂ mole fraction varied from 0.7488 to 0.9997. At the studied concentrations, phase transitions of vapor-liquid, liquid-liquid-vapor and liquid-liquid were observed. Sacha inchi oil contains 47% of omega-3 fatty acids, with a ratio of 0.76:1 for omega-6:omega-3, which is good for human health. The Peng-Robinson equation of state was used to describe the experimental data. A qualitative agreement was obtained between experimental and calculated data for the binary system CO₂ and Sacha inchi seed oil.