S-SETA: Selective Software-Only Error-Detection Technique Using Assertions

Software-based techniques offer several advantages to increase the reliability of processor-based systems at very low cost, but they cause performance degradation and an increase of the code size. To meet constraints in performance and memory, we propose SETA, a new control-flow software-only technique that uses assertions to detect errors affecting the program flow. SETA is an independent technique, but it was conceived to work together with previously proposed data-flow techniques that aim at reducing performance and memory overheads. Thus, SETA is combined with such data-flow techniques and submitted to a fault injection campaign. Simulation and neutron induced SEE tests show high fault coverage at performance and memory overheads inferior to the state-of-the-art.

EmotiBlog: a model to learn subjective information detection in the new textual genres of the Web 2.0 -a multilingual and multi-genre approach-

Tesis doctoral con mención europea en procesamiento del lenguaje natural realizada en la Universidad de Alicante por Ester Boldrini bajo la dirección del Dr. Patricio Martínez-Barco. El acto de defensa de la tesis tuvo lugar en la Universidad de Alicante el 23 de enero de 2012 ante el tribunal formado por los doctores Manuel Palomar (Universidad de Alicante), Dr. Paloma Moreda (UA), Dr. Mariona Taulé (Universidad de Barcelona), Dr. Horacio Saggion (Universitat Pompeu Fabra) y Dr. Mike Thelwall (University of Wolverhampton). Calificación: Sobresaliente Cum Laude por unanimidad.

Determination of cyclic and linear siloxanes in wastewater samples by ultrasound-assisted dispersive liquid-liquid microextraction followed by gas chromatography-mass spectrometry

A fast, simple and environmentally friendly ultrasound-assisted dispersive liquid-liquid microextraction (USA-DLLME) procedure has been developed to preconcentrate eight cyclic and linear siloxanes from wastewater samples prior to quantification by gas chromatography-mass spectrometry (GC-MS). A two-stage multivariate optimization approach has been developed employing a Plackett-Burman design for screening and selecting the significant factors involved in the USA-DLLME procedure, which was later optimized by means of a circumscribed central composite design. The optimum conditions were: extractant solvent volume, 13 µL; solvent type, chlorobenzene; sample volume, 13 mL; centrifugation speed, 2300 rpm; centrifugation time, 5 min; and sonication time, 2 min. Under the optimized experimental conditions the method gave levels of repeatability with coefficients of variation between 10 and 24% (n=7). Limits of detection were between 0.002 and 1.4 µg L−1. Calculated calibration curves gave high levels of linearity with correlation coefficient values between 0.991 and 0.9997. Finally, the proposed method was applied for the analysis of wastewater samples. Relative recovery values ranged between 71–116% showing that the matrix had a negligible effect upon extraction. To our knowledge, this is the first time that combines LLME and GC-MS for the analysis of methylsiloxanes in wastewater samples.

Sentiment classification for early detection of health alerts in the chemical textile domain

In the chemical textile domain experts have to analyse chemical components and substances that might be harmful for their usage in clothing and textiles. Part of this analysis is performed searching opinions and reports people have expressed concerning these products in the Social Web. However, this type of information on the Internet is not as frequent for this domain as for others, so its detection and classification is difficult and time-consuming. Consequently, problems associated to the use of chemical substances in textiles may not be detected early enough, and could lead to health problems, such as allergies or burns. In this paper, we propose a framework able to detect, retrieve, and classify subjective sentences related to the chemical textile domain, that could be integrated into a wider health surveillance system. We also describe the creation of several datasets with opinions from this domain, the experiments performed using machine learning techniques and different lexical resources such as WordNet, and the evaluation focusing on the sentiment classification, and complaint detection (i.e., negativity). Despite the challenges involved in this domain, our approach obtains promising results with an F-score of 65% for polarity classification and 82% for complaint detection.

Screen-printed electrode-based electrochemical detector coupled with in-situ ionic-liquid-assisted dispersive liquid–liquid microextraction for determination of 2,4,6-trinitrotoluene

A novel method is reported, whereby screen-printed electrodes (SPELs) are combined with dispersive liquid–liquid microextraction. In-situ ionic liquid (IL) formation was used as an extractant phase in the microextraction technique and proved to be a simple, fast and inexpensive analytical method. This approach uses miniaturized systems both in sample preparation and in the detection stage, helping to develop environmentally friendly analytical methods and portable devices to enable rapid and onsite measurement. The microextraction method is based on a simple metathesis reaction, in which a water-immiscible IL (1-hexyl-3-methylimidazolium bis[(trifluoromethyl)sulfonyl]imide, [Hmim][NTf2]) is formed from a water-miscible IL (1-hexyl-3-methylimidazolium chloride, [Hmim][Cl]) and an ion-exchange reagent (lithium bis[(trifluoromethyl)sulfonyl]imide, LiNTf2) in sample solutions. The explosive 2,4,6-trinitrotoluene (TNT) was used as a model analyte to develop the method. The electrochemical behavior of TNT in [Hmim][NTf2] has been studied in SPELs. The extraction method was first optimized by use of a two-step multivariate optimization strategy, using Plackett–Burman and central composite designs. The method was then evaluated under optimum conditions and a good level of linearity was obtained, with a correlation coefficient of 0.9990. Limits of detection and quantification were 7 μg L−1 and 9 μg L−1, respectively. The repeatability of the proposed method was evaluated at two different spiking levels (20 and 50 μg L−1), and coefficients of variation of 7 % and 5 % (n = 5) were obtained. Tap water and industrial wastewater were selected as real-world water samples to assess the applicability of the method.

Determination of fat-soluble vitamins in vegetable oils through microwave-assisted high-performance liquid chromatography

In this manuscript, a study of the effect of microwave radiation on the high-performance liquid chromatography separation of tocopherols and vitamin K1 was conducted. The novelty of the application was the use of a relatively low polarity mobile phase in which the dielectric heating effect was minimized to evaluate the nonthermal effect of the microwave radiation over the separation process. Results obtained show that microwave-assisted high-performance liquid chromatography had a shorter analysis time from 31.5 to 13.3 min when the lowest microwave power was used. Moreover, narrower peaks were obtained; hence the separation was more efficient maintaining or even increasing the resolution between the peaks. This result confirms that the increase in mobile phase temperature is not the only variable for improving the separation process but also other nonthermal processes must intervene. Fluorescence detection demonstrated better signal-to-noise compared to photodiode arrayed detection mainly due to the independent effect of microwave pulses on the baseline noise, but photodiode array detection was finally chosen as it allowed a simultaneous detection of nonfluorescent compounds. Finally, a determination of the content of the vitamin E homologs was carried out in different vegetable oils. Results were coherent with those found in the literature.

New Approach for Correction of Error Associated With Keratometric Estimation of Corneal Power in Keratoconus

The aim of this study was to obtain the exact value of the keratometric index (nkexact) and to clinically validate a variable keratometric index (nkadj) that minimizes this error. Methods: The nkexact value was determined by obtaining differences (DPc) between keratometric corneal power (Pk) and Gaussian corneal power (PGauss c ) equal to 0. The nkexact was defined as the value associated with an equivalent difference in the magnitude of DPc for extreme values of posterior corneal radius (r2c) for each anterior corneal radius value (r1c). This nkadj was considered for the calculation of the adjusted corneal power (Pkadj). Values of r1c ∈ (4.2, 8.5) mm and r2c ∈ (3.1, 8.2) mm were considered. Differences of True Net Power with PGauss c , Pkadj, and Pk(1.3375) were calculated in a clinical sample of 44 eyes with keratoconus. Results: nkexact ranged from 1.3153 to 1.3396 and nkadj from 1.3190 to 1.3339 depending on the eye model analyzed. All the nkadj values adjusted perfectly to 8 linear algorithms. Differences between Pkadj and PGauss c did not exceed 60.7 D (Diopter). Clinically, nk = 1.3375 was not valid in any case. Pkadj and True Net Power and Pk(1.3375) and Pkadj were statistically different (P , 0.01), whereas no differences were found between PGauss c and Pkadj (P . 0.01). Conclusions: The use of a single value of nk for the calculation of the total corneal power in keratoconus has been shown to be imprecise, leading to inaccuracies in the detection and classification of this corneal condition. Furthermore, our study shows the relevance of corneal thickness in corneal power calculations in keratoconus.

An Electronic Reconfigurable Neural Architecture for Intrusion Detection

The explosive growth of the traffic in computer systems has made it clear that traditional control techniques are not adequate to provide the system users fast access to network resources and prevent unfair uses. In this paper, we present a reconfigurable digital hardware implementation of a specific neural model for intrusion detection. It uses a specific vector of characterization of the network packages (intrusion vector) which is starting from information obtained during the access intent. This vector will be treated by the system. Our approach is adaptative and to detecting these intrusions by using a complex artificial intelligence method known as multilayer perceptron. The implementation have been developed and tested into a reconfigurable hardware (FPGA) for embedded systems. Finally, the Intrusion detection system was tested in a real-world simulation to gauge its effectiveness and real-time response.

Mercury determination in urine samples by gold nanostructured screen-printed carbon electrodes after vortex-assisted ionic liquid dispersive liquid–liquid microextraction

A novel approach is presented to determine mercury in urine samples, employing vortex-assisted ionic liquid dispersive liquid–liquid microextraction and microvolume back-extraction to prepare samples, and screen-printed electrodes modified with gold nanoparticles for voltammetric analysis. Mercury was extracted directly from non-digested urine samples in a water-immiscible ionic liquid, being back-extracted into an acidic aqueous solution. Subsequently, it was determined using gold nanoparticle-modified screen-printed electrodes. Under optimized microextraction conditions, standard addition calibration was applied to urine samples containing 5, 10 and 15 μg L−1 of mercury. Standard addition calibration curves using standards between 0 and 20 μg L−1 gave a high level of linearity with correlation coefficients ranging from 0.990 to 0.999 (N = 5). The limit of detection was empirical and statistically evaluated, obtaining values that ranged from 0.5 to 1.5 μg L−1, and from 1.1 to 1.3 μg L−1, respectively, which are significantly lower than the threshold level established by the World Health Organization for normal mercury content in urine (i.e., 10–20 μg L−1). A certified reference material (REC-8848/Level II) was analyzed to assess method accuracy finding 87% and 3 μg L−1 as the recovery (trueness) and standard deviation values, respectively. Finally, the method was used to analyze spiked urine samples, obtaining good agreement between spiked and found concentrations (recovery ranged from 97 to 100%).