Assessing the feasibility of a global model for multi-temporal burned area mapping using SPOT-VEGETATION data

The possibility of using the Syst@me Probatoire de l'Observation de la Terre (SPOT)-VEGETATION (VGT) data for global burned area mapping with a single algorithm was investigated. Using VGT images from south-eastern Africa, the Iberian Peninsula and south-eastern Siberia/north-eastern China, we analysed the variability of the spectral signature of burned areas and its relationship with land cover, and performed the selection of the best variables for burned area mapping. The results show that in grasslands and croplands, near-infrared (NIR) and short-wave infrared (SWIR) reflectance always decreases as a result of fire. In forests and woodlands, there may occur a simultaneous decrease of SWIR and NIR or an increase of SWIR and a decrease of NIR. Burning of green vegetation (high values of the Normalized Difference Vegetation Index (NDVI)) tends to result in an increase of the SWIR. The best variables for burned area mapping are different in each region. Only the NIR allows a good discrimination of burned areas in all study areas. We derived a logistic regression model for multi-temporal burned area mapping in tropical, temperate and boreal regions, which handles the spectral variability of burned areas dependent on the type of vegetation. The results underline the feasibility of a single model for global burned area mapping.     

Characterizing landscape changes in central Rondonia using Landsat TM imagery

An analysis of landscape changes in a region of pioneer settlements in central Rondonia, western Brazilian Amazon, was derived from Landsat TM data. Total deforested area increased from 206 x 103 ha in 1977, to 565 x 103 ha in 1985 and to 1210 x 103 ha, or 35.5% of the region, in 1995. Eighty-one per cent of the total 1995 deforestation had occurred in regions within 12.5km from areas of pioneer colonization deforested by 1977. Deforested area exceeded 79% in regions within 12.5km from the region's first road.     

A diagnoser algorithm for anomaly detection in DEDS under partial and unreliable observations: characterization and inclusion in sensor configuration optimization

Complex engineering systems have to be carefully monitored to meet demanding performance requirements, including detecting anomalies in their operations. There are two major monitoring challenges for these systems. The first challenge is that information collected from the monitored system is often partial and/or unreliable, in the sense that some occurred events may not be reported and/or may be reported incorrectly (e.g., reported as another event). The second is that anomalies often consist of sequences of event patterns separated in space and time. This paper introduces and analyzes a diagnoser algorithm that meets these challenges for detecting and counting occurrences of anomalies in engineering systems. The proposed diagnoser algorithm assumes that models are available for characterizing plant operations (via stochastic automata) and sensors (via probabilistic mappings) used for reporting partial and unreliable information. Methods for analyzing the effects of model uncertainties on the diagnoser performance are also discussed. In order to select configurations that reduce sensor costs, while satisfying diagnoser performance requirements, a sensor configuration selection algorithm developed in previous work is then extended for the proposed diagnoser algorithm. The proposed algorithms and methods are then applied to a multi-unit-operation system, which is derived from an actual facility application. Results show that the proposed diagnoser algorithm is able to detect and count occurrences of anomalies accurately and that its performance is robust to model uncertainties. Furthermore, the sensor configuration selection algorithm is able to suggest optimal sensor configurations with significantly reduced costs, while still yielding acceptable performance for counting the occurrences of anomalies.     

Application of SVM-RFE on EEG signals for detecting the most relevant scalp regions linked to affective valence processing

In this work, event related potentials (ERPs) induced by visual stimuli categorized with different value of affective valence are studied. EEG signals are recorded during visualization of selected pictures belonging to International Affective Picture System (IAPS). A Morlet wavelet filter is used to transform the EEG input space to a topography-time–frequency feature space. Support vector machine-recursive feature elimination (SVM-RFE) is applied for detecting scalp spectral dynamics of interest (SSDOIs) in this feature space, allowing to identify the most relevant time intervals, frequency bands and EEG channels. This feature selection method has proven to outperform the classical t-test in the discrimination of brain cortex regions involved in affective valence processing. Furthermore, the presented combination of feature extraction and selection techniques can be applied as an alternative in other different clinical applications.     

Pretreatment predictors of dropout from cognitive behavioral therapy for PTSD in Iraq and Afghanistan war veterans.

Cognitive–behavioral therapies (CBTs) can be effective treatments for posttraumatic stress disorder (PTSD) but their effectiveness is limited by high rates of premature dropout. Few studies have compared pretreatment characteristics of treatment completers and dropouts, and only one has examined these factors in Operations Iraqi Freedom and Enduring Freedom (OIF/OEF) Veterans. This study analyzed archival clinical data from 117 OEF/OIF Veterans evaluated and treated through a Veterans Affairs PTSD clinic. High numbers dropped out of treatment (68%). Treatment dropouts (n = 79) and completers (n = 38) differed significantly on Minnesota Multiphasic Personality Inventory-2 (MMPI-2) scales, PTSD symptom severity, and age. Regression analyses identified one MMPI-2 scale, TRT (negative treatment indicators), and age as unique but modest predictors of dropout. (PsycINFO Database Record (c) 2011 APA, all rights reserved)     

Phase transition characteristics and dielectric properties of rare-earth (La,Pr, Nd,Gd) doped Ba(Zr0.09Ti0.91)O3 ceramics

A-site deficient rare-earth doped barium zirconate titanate (BZT) ceramics (Ba_1−yLn_2y/3)Zr_0.09Ti_0.91O₃ (Ln = La, Pr, Nd, Gd) are obtained by a modified solid-state reaction method. Perovskite-like single-phase compounds were confirmed from X-ray diffraction data. Morphological analysis on sintered samples shows that the addition of rare-earth ions inhibits the growth of the grain and remarkably changes the grain morphology. The effect of rare-earth addition to BZT on phase transition and dielectric properties is analyzed. A dramatic fall in the transition temperature occurs when BZT ceramic is doped with rare-earths. Moreover, diffusivity degree of the phase transition increases and a relaxor-type behaviour is induced due to both the increment of the lanthanide content and the increase of the ionic radius of the dopant element. High values of dielectric tunability are obtained for lanthanum doped BZT. A direct relation between transition temperature and tunability is discussed. Conclusively, low permittivity and high tunability materials can be obtained by the adequate substitution of rare-earths into BZT ceramics.     

Comparison of four test methods to measure damping properties of materials by using piezoelectric transducers

This article presents the experimental results of damping loss factor and Young’s modulus obtained for stiff and flexible materials through the use of four different methodologies: the Central Impedance Method, the Modified Oberst Method, the Seismic Response Method, and the simply supported beam method. The first three methods are based on the ASTM standard but using different experimental setting and different Frequency Response Functions. The fourth method corresponds to a non-resonant technique used in the characterization of materials at very low frequencies. In this work, the results of damping loss factor and Young’s modulus obtained through these four methods are compared, the variability of results is studied and the sensitivity of each technique when facing controlled temperature variations is verified.     

Lipid Damage Inhibition in Hake by Active Packaging Film with Natural Antioxidants

The capacity of the natural antioxidant to retard oxidation of polyunsaturated fatty acids in hake (Merluccius merluccius) muscle was investigated. Hake fillets were packaged in low‐density polyethylene with the natural antioxidant (sample C1 = 7 mg/dm² film and sample C2 = 24 mg/dm² film) or without the antioxidant (control sample) and frozen for 12 months at ?20°C. The results confirm the efficacy of an active packaging with a natural antioxidant derived from barley husks to slow the progress of lipid hydrolysis and increase oxidative stability in hake muscle. Determination of peroxide value, conjugated dienes, free fatty acids, thiobarbituric acid reactive substances (TBARS) and anisidine value (AV) proved suitable for studying lipid hydrolysis and primary and secondary lipid oxidation over time. After 6 months of frozen storage, the TBARS levels were lower in hake packed with low‐density polyethylene with the natural antioxidant (C1 and C2) than in the control sample; at month 12, the TBARS values of C1 and C2 were lower (16 and 21%, respectively) than the control samples. AVs observed in samples C1 and C2 at month 9 were lower than those observed in the control sample at month 12. Copyright © 2011 John Wiley & Sons, Ltd.     

Monitoring of ochratoxin A exposure of the Portuguese population through a nationwide urine survey — Winter 2007

Ochratoxin A (OTA) is a mycotoxin produced by a variety of fungi, such as Penicillium verrucosum and Aspergillium spp., which has been found to have a wide number of potentially deadly toxic effects, and can enter the human organism through a variety of means. It then finds its way into the bloodstream and, after a lengthy process, is eventually excreted through the urine. It can thus be detected in its original form not only in blood samples but also in this biological medium. As such, and in an attempt to evaluate the exposure of the Portuguese population to this mycotoxin, morning urine samples were collected during the Winter of 2007, from each of five geographically distinct Portuguese locations — Bragança, Porto, Coimbra, Alentejo, and Algarve — and subjected to extraction by immunoaffinity columns and to OTA quantification through liquid chromatography coupled with fluorescence detection. Prevalent incidence was higher than 95% with Coimbra being the exception (incidence of 73.3%). In nearly all locations, the OTA content of most samples was found to be above the limit of quantification (LOQ) of 0.008 ng/ml. Indeed, excluding Coimbra, with an OTA content level of 0.014 ng/ml, all regions featured content values over 0.021 ng/ml.     

Assessment of the presence and dynamics of fungi in drinking water sources using cultural and molecular methods

A comparison of different isolation techniques and culture media for detection of filamentous fungi and yeasts in the aquatic environment revealed that the use of membrane filtration with the media dichloran rose bengal chloramphenicol (DRBC) optimized fungi detection in terms of abundance and variety in three untreated water sources with very different characteristics (surface water, spring water, and groundwater). The diversity of the fungi population captured by direct DNA extraction of fungi collected by membrane filtration was compared with the isolates obtained after selective growth using different culture media through amplification of the internal transcribed spacer gene and denaturing gradient gel electrophoresis (DGGE). The Czapek-Dox agar, Sabouraud dextrose agar, and DRBC media showed closer similarities to those obtained by the uncultured biomass for the different water sources. Based on these data and the best enumeration results, DRBC is recommended for the assessment of fungi in water sources using culture-based methods. DGGE was also used to monitor temporal variations in the fungal population structure and showed that each water matrix possessed a distinct population profile as well as that changes in the fungal community can be expected in the different matrices throughout the year.