Sound transmission through a poroelastic layered panel

Multi-layered panels are often used to improve the acoustics in cars, airplanes, rooms, etc. For such an application these panels include porous and/or fibrous layers. The proposed numerical method is an approach to simulate the acoustical behavior of such multi-layered panels. The model assumes plate-like structures and, hence, combines plate theories for the different layers. The poroelastic layer is modelled with a recently developed plate theory. This theory uses a series expansion in thickness direction with subsequent analytical integration in this direction to reduce the three dimensions to two. The same idea is used to model either air gaps or fibrous layers. The latter are modeled as equivalent fluid and can be handled like an air gap, i.e., a kind of ‘air plate’ is used. The coupling of the layers is done by using the series expansion to express the continuity conditions on the surfaces of the plates. The final system is solved with finite elements, where domain decomposition techniques in combination with preconditioned iterative solvers are applied to solve the final system of equations. In a large frequency range, the comparison with measurements shows very good agreement. From the numerical solution process it can be concluded that different preconditioners for the different layers are necessary. A reuse of the Krylov subspace of the iterative solvers pays if several excitations have to be computed but not that much in the loop over the frequencies.     

An ensemble of support vector machines for predicting virulent proteins

It is important to develop a reliable system for predicting bacterial virulent proteins for finding novel drug/vaccine and for understanding virulence mechanisms in pathogens.In this work we have proposed a bacterial virulent protein prediction method based on an ensemble of classifiers where the features are extracted directly from the amino acid sequence of a given protein. It is well known in the literature that the features extracted from the evolutionary information of a given protein are better than the features extracted from the amino acid sequence. Our method tries to fill the gap between the amino acid sequence based approaches and the evolutionary information based approaches.An extensive evaluation according to a blind testing protocol, where the parameters of the system are calculated using the training set and the system is validated in three different independent datasets, has demonstrated the validity of the proposed method.     

Microscopic polyangiitis: a case report]

Vertebral osteomyelitis arising from an enteric fistula in patients with inflammatory bowel disease is rare. We report on a patient with Crohn's disease who developed an enteric fistula, resulting in a presacral abscess and vertebral osteomyelitis involving the L4 and L5 vertebral bodies and related disc spaces. This was managed by a defunctioning colostomy with drainage of the pre-sacral abscess. The vertebral lesion was successfully managed non-operatively.     

Antimicrobial Combined Action of Graphene Oxide and Light Emitting Diodes for Chronic Wound Management

Innovative non-antibiotic compounds such as graphene oxide (GO) and light-emitting diodes (LEDs) may represent a valid strategy for managing chronic wound infections related to resistant pathogens. This study aimed to evaluate 630 nm LED and 880 nm LED ability to enhance the GO antimicrobial activity against Staphylococcus aureus- and Pseudomonas aeruginosa-resistant strains in a dual-species biofilm in the Lubbock chronic wound biofilm (LCWB) model. The effect of a 630 nm LED, alone or plus 5-aminolevulinic acid (ALAD)-mediated photodynamic therapy (PDT) (ALAD-PDT), or an 880 nm LED on the GO (50 mg/l) action was evaluated by determining the CFU/mg reductions, live/dead analysis, scanning electron microscope observation, and reactive oxygen species assay. Among the LCWBs, the best effect was obtained with GO irradiated with ALAD-PDT, with percentages of CFU/mg reduction up to 78.96% ± 0.21 and 95.17% ± 2.56 for S. aureus and P. aeruginosa, respectively. The microscope images showed a reduction in the cell number and viability when treated with GO + ALAD-PDT. In addition, increased ROS production was detected. No differences were recorded when GO was irradiated with an 880 nm LED versus GO alone. The obtained results suggest that treatment with GO irradiated with ALAD-PDT represents a valid, sustainable strategy to counteract the polymicrobial colonization of chronic wounds.     

New spectral vegetation indices based on the near-infrared shoulder wavelengths for remote detection of grassland phytomass

This article examines the possibility of exploiting ground reflectance in the near-infrared (NIR) for monitoring grassland phytomass on a temporal basis. Three new spectral vegetation indices (infrared slope index, ISI; normalized infrared difference index, NIDI; and normalized difference structural index, NDSI), which are based on the reflectance values in the H25 (863–881 nm) and the H18 (745–751 nm) Chris Proba (mode 5) bands, are proposed. Ground measurements of hyperspectral reflectance and phytomass were made at six grassland sites in the Italian and Austrian mountains using a hand-held spectroradiometer. At full canopy cover, strong saturation was observed for many traditional vegetation indices (normalized difference vegetation index (NDVI), modified simple ratio (MSR), enhanced vegetation index (EVI), enhanced vegetation index 2 (EVI 2), renormalized difference vegetation index (RDVI), wide dynamic range vegetation index (WDRVI)). Conversely, ISI and NDSI were linearly related to grassland phytomass with negligible inter-annual variability. The relationships between both ISI and NDSI and phytomass were however site specific. The WinSail model indicated that this was mostly due to grassland species composition and background reflectance. Further studies are needed to confirm the usefulness of these indices (e.g. using multispectral specific sensors) for monitoring vegetation structural biophysical variables in other ecosystem types and to test these relationships with aircraft and satellite sensors data. For grassland ecosystems, we conclude that ISI and NDSI hold great promise for non-destructively monitoring the temporal variability of grassland phytomass.     

Fusion of airborne LiDAR and satellite multispectral data for the estimation of timber volume in the Southern Alps

Remote sensing can be considered a key instrument for studies related to forests and their dynamics. At present, the increasing availability of multisensor acquisitions over the same areas, offers the possibility to combine data from different sensors (e.g., optical, RADAR, LiDAR). This paper presents an analysis on the fusion of airborne LiDAR and satellite multispectral data (IRS 1C LISS III), for the prediction of forest stem volume at plot level in a complex mountain area (Province of Trento, Southern Italian Alps), characterized by different tree species, complex morphology (i.e. altitude ranges from 65 m to 3700 m above sea level), and a range of different climates (from the sub-Mediterranean to Alpine type). 799 sample plots were randomly distributed over the 3000 km² of the forested areas of the Trento Province. From each plot, a set of variables were extracted from both LiDAR and multispectral data. A regression analysis was carried out considering two data sources (LiDAR and multispectral) and their combination, and dividing the plot areas into groups according to their species composition, altitude and slope. Experimental results show that the combination of LiDAR and IRS 1C LISS III data, for the estimation of stem volume, is effective in all the experiments considered. The best developed models comprise variables extracted from both of these data sources. The RMSE% on an independent validation set for the stem volume estimation models ranges between 17.2% and 26.5%, considering macro sets of tree species (deciduous, evergreen and mixed), between 17.5% and 29.0%, considering dominant species plots, and between 15.5% and 21.3% considering altitude and slope sets.     

Data pre-processing through reward–punishment editing

The nearest neighbor (NN) classifier represents one of the most popular non-parametric classification approaches and has been successfully applied in several pattern recognition problems. The two main limitations of this technique are its computational complexity and its sensitivity to the presence of outliers in the training set. Though the first problem has been partially overcome thanks to the availability of inexpensive memory and high processing speeds, the second one still persists, and several editing and condensing techniques have been proposed, aimed at selecting a proper set of prototypes from the training set. In this work, an editing technique is proposed, based on the idea of rewarding the patterns that contribute to a correct classification and punishing those that provide a wrong one. The analysis is carried out both at local and at global level, by analyzing the training set at different scales. A score is calculated for each pattern, and the patterns whose score is lower than a predefined threshold are edited out. An extensive experimentation has been conducted on several classification problems both to evaluate the efficacy of the proposed technique with respect to other editing approaches and to investigate the advantage of using reward–punishment editing in combination with condensing techniques or as a pre-processing stage when classifiers different from the NN are adopted.     

Feasibility study for the remediation of groundwater contaminated by organolead compounds

The aim of this research was to assess the effectiveness of chemical oxidation, Advanced Oxidation Processes (AOPs) and adsorption on granular activated carbon (GAC) for the ex situ remediation of a groundwater contaminated by organolead compounds, including tetraethyl lead (TEL), triethyl lead (TREL) and diethyl lead (DEL). The groundwater of concern was collected from the site of a former tetraalkyllead producing company in Trento (Italy), and showed an average total organic lead (TOL) content about 95.1 microg/L (TEL 0.5 microg/L, TREL 86.4 microg/L, DEL 8.3 microg/L). The main target of the study was to find out which method was more effective in reducing the pollutant content. For this purpose, several laboratory tests were performed, including chemical oxidation tests with different reactants (hydrogen peroxide, modified Fenton's reagent, potassium permanganate, activated potassium persulfate, oxygen and combinations of potassium permanganate and modified Fenton's reagent), AOPs with ozone, UV radiation and hydrogen peroxide and filtration on granular activated carbon. A combination of chemical and physical treatments was also tested, with GAC filtration followed by chemical oxidation. According to the results achieved, the treatments which showed the best remediation performances were: chemical oxidation with modified Fenton's reagent, AOPs with hydrogen peroxide and ozone (perozone), AOPs with hydrogen peroxide and UV radiation, and the combined treatment with activated carbon filtration followed by chemical oxidation with perozone. All these treatments showed a 90% TOL removal, with excellent removals of both TEL and TREL, and final DEL concentrations below 5 microg/L.