Identification of tsunami-induced deposits using numerical modeling and rock magnetism techniques: Astudy case of the 1755 Lisbon tsunami in Algarve, Portugal (vol 182, pg 187, 2010)

Storm- and tsunami-deposits are generated by similar depositional mechanisms making their discrimination hard to establish using classic sedimentologic methods. Here we propose an original approach to identify tsunami-induced deposits by combining numerical simulation and rock magnetism. To test our method, we investigate the tsunami deposit of the Boca do Rio estuary generated by the 1755 earthquake in Lisbon which is well described in the literature. We first test the 1755 tsunami scenario using a numerical inundation model to provide physical parameters for the tsunami wave. Then we use concentration (MS. SIRM) and grain size (chi(ARM), ARM, B1/2, ARM/SIRM) sensitive magnetic proxies coupled with SEM microscopy to unravel the magnetic mineralogy of the tsunami-induced deposit and its associated depositional mechanisms. In order to study the connection between the tsunami deposit and the different sedimentologic units present in the estuary, magnetic data were processed by multivariate statistical analyses. Our numerical simulation show a large inundation of the estuary with flow depths varying from 0.5 to 6 m and run up of similar to 7 m. Magnetic data show a dominance of paramagnetic minerals (quartz) mixed with lesser amount of ferromagnetic minerals, namely titanomagnetite and titanohematite both of a detrital origin and reworked from the underlying units. Multivariate statistical analyses indicate a better connection between the tsunami-induced deposit and a mixture of Units C and D. All these results point to a scenario where the energy released by the tsunami wave was strong enough to overtop and erode important amount of sand from the littoral dune and mixed it with reworked materials from underlying layers at least 1 m in depth. The method tested here represents an original and promising tool to identify tsunami-induced deposits in similar embayed beach environments.

Non-selective optical wavelength-division multiplexing devices based on a-SiC:H multilayer heterostuctures

In this paper we present results on the optimization of multilayered a-SiC:H heterostructures for wavelength-division (de) multiplexing applications. The non selective WDM device is a double heterostructure in a glass/ITO/a-SiC:H (p-i-n) /a-SiC:H(-p) /a-Si:H(-i')/a-SiC:H (-n')/ITO configuration. The single or the multiple modulated wavelength channels are passed through the device, and absorbed accordingly to its wavelength, giving rise to a time dependent wavelength electrical field modulation across it. The effect of single or multiple input signals is converted to an electrical signal to regain the information (wavelength, intensity and frequency) of the incoming photogenerated carriers. Here, the (de) multiplexing of the channels is accomplished electronically, not optically. This approach offers advantages in terms of cost since several channels share the same optical components; and the electrical components are typically less expensive than the optical ones. An electrical model gives insight into the device operation.

Novel Scorpionate and Pyrazole Dioxovanadium Complexes, Catalysts for Carboxylation and Peroxidative Oxidation of Alkanes

The dioxovanadium(V) complexes [VO2(3,5-Me(2)Hpz)(3)][BF4] (1) (pz = pyrazolyl), [VO2{SO3C(pz)(3)}] (2), [VO2{HB(3,5-Me(2)pz)(3)}] (3) and [VO2{HC(pz)(3)}][BF4] (4), bearing pyrazole or scorpionate ligands, were obtained by reaction of triethyl vanadate [VO(OEt)(3)] with hydrotris(3,5-dimethyl-1-pyrazolyl)methane [HC(3,5-Me(2)pz)(3)] or 3,5-dimethylpyrazole (3,5-Me(2)Hpz; 1), lithium tris(1-pyrazolyl)methanesulfonate {Li[SO3C(pz)(3)], 2}, potassium hydrotris(3,5-dimethyl-1-pyrazolyl)borate {K[HB(3,5-Me(2)pz)(3)], 3} and hydrotris(1-pyrazolyl)methane [HC(pz)(3), 4], respectively. Treatment of [VO(OEt)(3)] with potassium hydrotris(1-pyrazolyl)borate {K[HB(pz)(3)]} led to the mixed eta(3)-tris(pyrazolyl)borate and eta(2)-bis(pyrazolyl)borate oxovanadium(IV) complex [VO{HB(pz)(3)}{H2B(pz)(2)}, 5]. The compounds were characterized by elemental analyses, IR, NMR and EPR spectroscopy, FAB and ESI mass spectrometry, cyclic voltammetry and, for 5, also by single crystal X-ray diffraction analysis. All complexes exhibit catalytic activity in the single-pot carboxylation [in trifluoroacetic acid/potassium peroxodisulfate (CF3COOH/K2S2O8)] of gaseous alkanes (methane and ethane) to carboxylic acids (yields up to 40%. TONs up to 157) and in the peroxidative oxidation [in water/acetonitrile (H2O/NCMe)] of liquid alkanes (cyclohexane and cyclopentane) to the corresponding alcohols and ketones (yields up to 24%, TONs up to 117), under mild conditions.

Does independent component analysis play a role in unmixing hyperspectral data?

Independent component analysis (ICA) has recently been proposed as a tool to unmix hyperspectral data. ICA is founded on two assumptions: 1) the observed spectrum vector is a linear mixture of the constituent spectra (endmember spectra) weighted by the correspondent abundance fractions (sources); 2)sources are statistically independent. Independent factor analysis (IFA) extends ICA to linear mixtures of independent sources immersed in noise. Concerning hyperspectral data, the first assumption is valid whenever the multiple scattering among the distinct constituent substances (endmembers) is negligible, and the surface is partitioned according to the fractional abundances. The second assumption, however, is violated, since the sum of abundance fractions associated to each pixel is constant due to physical constraints in the data acquisition process. Thus, sources cannot be statistically independent, this compromising the performance of ICA/IFA algorithms in hyperspectral unmixing. This paper studies the impact of hyperspectral source statistical dependence on ICA and IFA performances. We conclude that the accuracy of these methods tends to improve with the increase of the signature variability, of the number of endmembers, and of the signal-to-noise ratio. In any case, there are always endmembers incorrectly unmixed. We arrive to this conclusion by minimizing the mutual information of simulated and real hyperspectral mixtures. The computation of mutual information is based on fitting mixtures of Gaussians to the observed data. A method to sort ICA and IFA estimates in terms of the likelihood of being correctly unmixed is proposed.

Fungal burden in waste industry: an occupational risk to be solved

High loads of fungi have been reported in different types of waste management plants. This study intends to assess fungal contamination in one waste-sorting plant before and after cleaning procedures in order to analyze their effectiveness. Air samples of 50 L were collected through an impaction method, while surface samples, taken at the same time, were collected by the swabbing method and subject to further macro- and microscopic observations. In addition, we collected air samples of 250 L using the impinger Coriolis μ air sampler (Bertin Technologies) at 300 L/min airflow rate in order to perform real-time quantitative PCR (qPCR) amplification of genes from specific fungal species, namely Aspergillus fumigatus and Aspergillus flavus complexes, as well as Stachybotrys chartarum species. Fungal quantification in the air ranged from 180 to 5,280 CFU m−3 before cleaning and from 220 to 2,460 CFU m−3 after cleaning procedures. Surfaces presented results that ranged from 29 × 104 to 109 × 104 CFU m−2 before cleaning and from 11 × 104 to 89 × 104 CFU m−2 after cleaning. Statistically significant differences regarding fungal load were not detected between before and after cleaning procedures. Toxigenic strains from A. flavus complex and S. chartarum were not detected by qPCR. Conversely, the A. fumigatus species was successfully detected by qPCR and interestingly it was amplified in two samples where no detection by conventional methods was observed. Overall, these results reveal the inefficacy of the cleaning procedures and that it is important to determine fungal burden in order to carry out risk assessment.

Neighbour list optimization for real LTE radio network

With the increasing complexity of current networks, it became evident the need for Self-Organizing Networks (SON), which aims to automate most of the associated radio planning and optimization tasks. Within SON, this paper aims to optimize the Neighbour Cell List (NCL) for Long Term Evolution (LTE) evolved NodeBs (eNBs). An algorithm composed by three decisions were were developed: distance-based, Radio Frequency (RF) measurement-based and Handover (HO) stats-based. The distance-based decision, proposes a new NCL taking account the eNB location and interference tiers, based in the quadrants method. The last two algorithms consider signal strength measurements and HO statistics, respectively; they also define a ranking to each eNB and neighbour relation addition/removal based on user defined constraints. The algorithms were developed and implemented over an already existent radio network optimization professional tool. Several case studies were produced using real data from a Portuguese LTE mobile operator. © 2014 IEEE.

Insulin resistance does not play a major role in lipid and apolipoprotein profile in obese women

Background: Obesity is associated with increased atherogenesis through alterations in lipids, among other potential factors. Some of those abnormalities might be mediated by insulin resistance (IR). Aims: To compare lipid and apolipoprotein profile between lean and obese women; to evaluate the influence of IR on lipid and apolipoprotein profile, in obese women. Methods: We studied 112 obese and 100 normal-weight premenopausal women without known cardiovascular disease. Both groups were characterized for anthropometrics and a fasting blood sample was collected for assessment of glucose, insulin, triglycerides, cholesterol (total, LDL and HDL), and apolipoproteins A-I, A-II, B, C-II, C-III, and E; IR was assessed by the homeostatic model assessment (HOMA-IR). We compared lipids between obese and lean women; we looked for correlation of those levels with anthropometrics and IR (independently from anthropometrics) in obese women. Results: Obese women were characterized by mean age=34.6±8.3 years, BMI=43.6±7.9 kg/m2, waist circumference (Wc)=117.5±15.1 cm, and HOMA-IR=4.28±3.5. Lean women (age=34.2±8.3 years, BMI=21.4±1.7 kg/m2, Wc=71.7±5.8 cm, and HOMA-IR=1.21±0.76) presented with significantly lower levels of total cholesterol (P=0.001), LDL-cholesterol (P<0.001), and triglycerides (P<0.001); they presented higher levels of HDL-cholesterol (P<0.001), Apo A-I (P<0.001) and Apo A-II (P=0.037). HOMA-IR showed no significant association with apolipoproteins. HOMA-IR was inversely associated with HDL-cholesterol (P=0.048; r=−0.187) but that association disappeared when we adjusted for waist circumference. Only triglycerides were directly associated with HOMA-IR (P<0.001; r=0.343) independently from anthropometrics. Conclusion: We confirm that obese women present worst lipid and apolipoprotein profile. However, with the exception for triglycerides, insulin resistance per se does not play a major role in lipid and apolipoprotein abnormalities observed in obese women.