Why do they separate it or not? Attitudes and behaviors towards organic waste separation

This article shows the results of an exploratory study related to the separation of organic waste in order to offer suggestions for the improvement of waste disposal communication campaigns. The overall objective is to analyze attitude and behavior of those who do and those who do not separate organic waste, related to a specific promotional campaign carried out in two neighborhoods, in the municipality of Badalona (Spain), within the framework of the study of proenvironmental attitudes and behaviors and based on the Psychosocial Four Spheres Model. 1,010 interviews were conducted and data was analyzed using Chi-Squared Automatic Interaction Detector (CHAID). Waste separation behavior was used as a dependent variable. The reasons given to explain why people do or do not separate organic waste and sociodemographic variables, have been introduced as independent variables. In accordance with the Four Spheres Model, results show significant differences in waste separation. Based on the profiles obtained, we find some predictive variables that facilitate the development of communication campaigns according to the requirements of each community.

Effect of Tocopherol Extract, Staphylococcus carnosus Culture, and Celery Concentrate Addition on Quality Parameters of Organic and Conventional Dry-Cured Sausages

The effects of the addition to sausage mix of tocopherols (200 mg/kg), a conventional starter culture with or without Staphylococcus carnosus, celery concentrate (CP) (0.23% and 0.46%), and two doses of nitrate (70 and 140 mg/kg expressed as NaNO(3)) on residual nitrate and nitrite amounts, instrumental CIE Lab color, tocol content, oxidative stability, and overall acceptability were studied in fermented dry-cured sausages after ripening and after storage. Nitrate doses were provided by nitrate-rich CP or a chemical grade source. The lower dose complies with the EU requirements governing the maximum for ingoing amounts in organic meat products. Tocopherol addition protected against oxidation, whereas the nitrate dose, nitrate source, or starter culture had little influence on secondary oxidation values. The residual nitrate and nitrite amounts found in the sausages with the lower nitrate dose were within EU-permitted limits for organic meat products and residual nitrate can be further reduced by the presence of the S. carnosus culture. Color measurements were not affected by the CP dose. Product consumer acceptability was not affected negatively by any of the factors studied. As the two nitrate sources behaved similarly for the parameters studied, CP is a useful alternative to chemical ingredients for organic dry-cured sausage production.

Image-Based Coral Reef Classification and Thematic Mapping

This paper presents a novel image classification scheme for benthic coral reef images that can be applied to both single image and composite mosaic datasets. The proposed method can be configured to the characteristics (e.g., the size of the dataset, number of classes, resolution of the samples, color information availability, class types, etc.) of individual datasets. The proposed method uses completed local binary pattern (CLBP), grey level co-occurrence matrix (GLCM), Gabor filter response, and opponent angle and hue channel color histograms as feature descriptors. For classification, either k-nearest neighbor (KNN), neural network (NN), support vector machine (SVM) or probability density weighted mean distance (PDWMD) is used. The combination of features and classifiers that attains the best results is presented together with the guidelines for selection. The accuracy and efficiency of our proposed method are compared with other state-of-the-art techniques using three benthic and three texture datasets. The proposed method achieves the highest overall classification accuracy of any of the tested methods and has moderate execution time. Finally, the proposed classification scheme is applied to a large-scale image mosaic of the Red Sea to create a completely classified thematic map of the reef benthos

Self-Organising Maps and Correlation Analysis as a Tool to Explore Patterns in Excitation-Emission Matrix Data Sets and to Discriminate Dissolved Organic Matter Fluorescence Components

Dissolved organic matter (DOM) is a complex mixture of organic compounds, ubiquitous in marine and freshwater systems. Fluorescence spectroscopy, by means of Excitation-Emission Matrices (EEM), has become an indispensable tool to study DOM sources, transport and fate in aquatic ecosystems. However the statistical treatment of large and heterogeneous EEM data sets still represents an important challenge for biogeochemists. Recently, Self-Organising Maps (SOM) has been proposed as a tool to explore patterns in large EEM data sets. SOM is a pattern recognition method which clusterizes and reduces the dimensionality of input EEMs without relying on any assumption about the data structure. In this paper, we show how SOM, coupled with a correlation analysis of the component planes, can be used both to explore patterns among samples, as well as to identify individual fluorescence components. We analysed a large and heterogeneous EEM data set, including samples from a river catchment collected under a range of hydrological conditions, along a 60-km downstream gradient, and under the influence of different degrees of anthropogenic impact. According to our results, chemical industry effluents appeared to have unique and distinctive spectral characteristics. On the other hand, river samples collected under flash flood conditions showed homogeneous EEM shapes. The correlation analysis of the component planes suggested the presence of four fluorescence components, consistent with DOM components previously described in the literature. A remarkable strength of this methodology was that outlier samples appeared naturally integrated in the analysis. We conclude that SOM coupled with a correlation analysis procedure is a promising tool for studying large and heterogeneous EEM data sets.

Stratification of the severity of critically ill patients with classification trees

Background: Development of three classification trees (CT) based on the CART (Classification and Regression Trees), CHAID (Chi-Square Automatic Interaction Detection) and C4.5 methodologies for the calculation of probability of hospital mortality; the comparison of the results with the APACHE II, SAPS II and MPM II-24 scores, and with a model based on multiple logistic regression (LR). Methods: Retrospective study of 2864 patients. Random partition (70:30) into a Development Set (DS) n = 1808 and Validation Set (VS) n = 808. Their properties of discrimination are compared with the ROC curve (AUC CI 95%), Percent of correct classification (PCC CI 95%); and the calibration with the Calibration Curve and the Standardized Mortality Ratio (SMR CI 95%). Results: CTs are produced with a different selection of variables and decision rules: CART (5 variables and 8 decision rules), CHAID (7 variables and 15 rules) and C4.5 (6 variables and 10 rules). The common variables were: inotropic therapy, Glasgow, age, (A-a)O2 gradient and antecedent of chronic illness. In VS: all the models achieved acceptable discrimination with AUC above 0.7. CT: CART (0.75(0.71-0.81)), CHAID (0.76(0.72-0.79)) and C4.5 (0.76(0.73-0.80)). PCC: CART (72(69- 75)), CHAID (72(69-75)) and C4.5 (76(73-79)). Calibration (SMR) better in the CT: CART (1.04(0.95-1.31)), CHAID (1.06(0.97-1.15) and C4.5 (1.08(0.98-1.16)). Conclusion: With different methodologies of CTs, trees are generated with different selection of variables and decision rules. The CTs are easy to interpret, and they stratify the risk of hospital mortality. The CTs should be taken into account for the classification of the prognosis of critically ill patients.

Prediction of crude protein and classification of the growth stage of wheat plant samples from NIR spectra

Near-infrared spectroscopy (NIRS) was used to analyse the crude protein content of dried and milled samples of wheat and to discriminate samples according to their stage of growth. A calibration set of 72 samples from three growth stages of wheat (tillering, heading and harvest) and a validation set of 28 samples was collected for this purpose. Principal components analysis (PCA) of the calibration set discriminated groups of samples according to the growth stage of the wheat. Based on these differences, a classification procedure (SIMCA) showed a very accurate classification of the validation set samples : all of them were successfully classified in each group using this procedure when both the residual and the leverage were used in the classification criteria. Looking only at the residuals all the samples were also correctly classified except one of tillering stage that was assigned to both tillering and heading stages. Finally, the determination of the crude protein content of these samples was considered in two ways: building up a global model for all the growth stages, and building up local models for each stage, separately. The best prediction results for crude protein were obtained using a global model for samples in the two first growth stages (tillering and heading), and using a local model for the harvest stage samples.

Reliability of the density functional approximation to describe the charge transfer and electrostatic complexes involved in the modeling of organic conducting polymers

Both the intermolecular interaction energies and the geometries for M ̄ thiophene, M ̄ pyrrole, M n+ ̄ thiophene, and M n+ ̄ pyrrole ͑with M = Li, Na, K, Ca, and Mg; and M n+ = Li+ , Na+ , K+ , Ca2+, and Mg2+͒ have been estimated using four commonly used density functional theory ͑DFT͒ methods: B3LYP, B3PW91, PBE, and MPW1PW91. Results have been compared to those provided by HF, MP2, and MP4 conventional ab initio methods. The PBE and MPW1PW91 are the only DFT methods able to provide a reasonable description of the M ̄ complexes. Regarding M n+ ̄ ␲ complexes, the four DFT methods have been proven to be adequate in the prediction of these electrostatically stabilized systems, even though they tend to overestimate the interaction energies.

Integrating defeasible argumentation with fuzzy ART neural networks for pattern classification

Many classification systems rely on clustering techniques in which a collection of training examples is provided as an input, and a number of clusters c1,...cm modelling some concept C results as an output, such that every cluster ci is labelled as positive or negative. Given a new, unlabelled instance enew, the above classification is used to determine to which particular cluster ci this new instance belongs. In such a setting clusters can overlap, and a new unlabelled instance can be assigned to more than one cluster with conflicting labels. In the literature, such a case is usually solved non-deterministically by making a random choice. This paper presents a novel, hybrid approach to solve this situation by combining a neural network for classification along with a defeasible argumentation framework which models preference criteria for performing clustering.

Reexposure and advection of C-14-depleted organic carbon from old deposits at the upper continental slope

Outcrops of old strata at the shelf edge resulting from erosive gravity-driven flows have been globally described on continental margins. The reexposure of old strata allows for the reintroduction of aged organic carbon (OC), sequestered in marine sediments for thousands of years, into the modern carbon cycle. This pool of reworked material represents an additional source of C-14-depleted organic carbon supplied to the ocean, in parallel with the weathering of fossil organic carbon delivered by rivers from land. To understand the dynamics and implications of this reexposure at the shelf edge, a biogeochemical study was carried out in the Gulf of Lions (Mediterranean Sea) where erosive processes, driven by shelf dense water cascading, are currently shaping the seafloor at the canyon heads. Mooring lines equipped with sediment traps and current meters were deployed during the cascading season in the southwestern canyon heads, whereas sediment cores were collected along the sediment dispersal system from the prodelta regions down to the canyon heads. Evidence from grain-size, X-radiographs and Pb-210 activity indicate the presence in the upper slope of a shelly-coarse surface stratum overlying a consolidated deposit. This erosive discontinuity was interpreted as being a result of dense water cascading that is able to generate sufficient shear stress at the canyon heads to mobilize the coarse surface layer, eroding the basal strata. As a result, a pool of aged organic carbon (Delta C-14 = -944.5 +/- 24.7%; mean age 23,650 +/- 3,321 ybp) outcrops at the modern seafloor and is reexposed to the contemporary carbon cycle. This basal deposit was found to have relatively high terrigenous organic carbon (lignin = 1.48 +/- 0.14 mg/100 mg OC), suggesting that this material was deposited during the last low sea-level stand. A few sediment trap samples showed anomalously depleted radiocarbon concentrations (Delta C-14 = -704.4 +/- 62.5%) relative to inner shelf (Delta C-14 = -293.4 +/- 134.0%), mid-shelf (Delta C-14 = -366.6 +/- 51.1%), and outer shelf (Delta C-14 = -384 +/- 47.8%) surface sediments. Therefore, although the major source of particulate material during the cascading season is resuspended shelf deposits, there is evidence that this aged pool of organic carbon can be eroded and laterally advected downslope.

Technical Note: Dissolved organic matter fluorescence a finite mixture approach to deconvolve excitation-emission matrices

The analysis of the shape of excitation-emission matrices (EEMs) is a relevant tool for exploring the origin, transport and fate of dissolved organic matter (DOM) in aquatic ecosystems. Within this context, the decomposition of EEMs is acquiring a notable relevance. A simple mathematical algorithm that automatically deconvolves individual EEMs is described, creating new possibilities for the comparison of DOM fluorescence properties and EEMs that are very different from each other. A mixture model approach is adopted to decompose complex surfaces into sub-peaks. The laplacian operator and the Nelder-Mead optimisation algorithm are implemented to individuate and automatically locate potential peaks in the EEM landscape. The EEMs of a simple artificial mixture of fluorophores and DOM samples collected in a Mediterranean river are used to describe the model application and to illustrate a strategy that optimises the search for the optimal output.

Genotypic classification of patients with Wolfram syndrome: insights into the natural history of the disease and correlation with phenotype

Purpose: Wolfram syndrome is a degenerative, recessive rare disease with an onset in childhood. It is caused by mutations in WFS1 or CISD2 genes. More than 200 different variations in WFS1 have been described in patients with Wolfram syndrome, which complicates the establishment of clear genotype-phenotype correlation. The purpose of this study was to elucidate the role of WFS1 mutations and update the natural history of the disease. Methods: This study analyzed clinical and genetic data of 412 patients with Wolfram syndrome published in the last 15 years. Results: (i) 15% of published patients do not fulfill the current ­inclusion criterion; (ii) genotypic prevalence differences may exist among countries; (iii) diabetes mellitus and optic atrophy might not be the first two clinical features in some patients; (iv) mutations are nonuniformly distributed in WFS1; (v) age at onset of diabetes mellitus, hearing defects, and diabetes insipidus may depend on the patient"s genotypic class; and (vi) disease progression rate might depend on genotypic class. Conclusion: New genotype-phenotype correlations were established, disease progression rate for the general population and for the genotypic classes has been calculated, and new diagnostic criteria have been proposed. The conclusions raised could be important for patient management and counseling as well as for the development of treatments for Wolfram syndrome.

Bioavailability of sinking organic matter in the Blanes canyon and the adjacent open slope (NW Mediterranean Sea)

Submarine canyons are sites of intense energy and material exchange between the shelf and the deep adjacent basins. To test the hypothesis that active submarine canyons represent preferential conduits of available food for the deep-sea benthos, two mooring lines were deployed at 1200 m depth from November 2008 to November 2009 inside the Blanes canyon and on the adjacent open slope (Catalan Margin, NW Mediterranean Sea). We investigated the fluxes, biochemical composition and food quality of sinking organic carbon (OC). OC fluxes in the canyon and the open slope varied among sampling periods, though not onsistently in the two sites. In particular, while in the open slope the highest OC fluxes were observed in August 2009, in the canyon the highest OC fluxes occurred in April-May 2009. For almost the entire study period, the OC fluxes in the canyon were significantly higher than those in the open slope, whereas OC contents of sinking particles collected in the open slope were consistently higher than those in the canyon. This result confirms that submarine canyons are effective conveyors of OC to the deep sea. Particles transferred to the deep sea floor through the canyons are predominantly of inorganic origin, significantly higher than that reaching the open slope at a similar water depth. Using multivariate statistical tests, two major clusters of sampling periods were identified: one in the canyon that grouped trap samples collected in December 2008, oncurrently with the occurrence of a major storm at the sea surface, and associated with increased fluxes of nutritionally available particles from the upper shelf. Another cluster grouped samples from both the canyon and the open slope collected in March 2009, concurrently with the occurrence of the seasonal phytoplankton bloom at the sea surface, and associated with increased fluxes of total phytopigments. Our results confirm the key ecological role of submarine canyons for the functioning of deep-sea ecosystems, and highlight the importance of canyons in linking episodic storms and primary production occurring at the sea surface to the deep sea floor.

Effects of inorganic nitrogen (NH4Cl) and biodegradable organic carbon (CH3COONa) additions on a pilot-scale seawater biofilter

Biofilters degrade only a small fraction of the natural organic matter (NOM) contained in seawater which is the leading cause of biofouling in downstream processes. This work studies the effects of chemical additions on NOM biodegradation by biofilters. In this work, biofiltration of seawater with an empty bed contact time (EBCT) of 6 min and a hydraulic loading rate of 10 m h-1 reduces the biological oxygen demand (BOD7) by 8%, the dissolved organic carbon (DOC) by 6% and the UV absorbance at 254 nm (A254) by 7%. Different amounts of ammonium chloride are added to the seawater (up to twice the total dissolved nitrogen in untreated seawater) to study its possible effect on the removal of NOM by a pilot-scale biofilter. Seawater is amended with different amounts of easily biodegradable dissolved organic carbon (BDOC) supplied as sodium acetate (up to twice the DOC) for the same purpose. The results of this work reveal that the ammonium chloride additions do not significantly affect NOM removal and the sodium acetate is completely consumed by the biofiltration process. For both types of chemical additions, the BOD7, DOC and A254 in the outlet stream of the biofilter are similar to the values for the untreated control. These results indicate that this biofilter easily removes the BDOC from the seawater when the EBCT is not above 6 min. Furthermore, nitrogen does not limit the NOM biodegradation in seawater under these experimental conditions.

Self-Organising Maps and Correlation Analysis as a Tool to Explore Patterns in Excitation-Emission Matrix Data Sets and to Discriminate Dissolved Organic Matter Fluorescence Components

Dissolved organic matter (DOM) is a complex mixture of organic compounds, ubiquitous in marine and freshwater systems. Fluorescence spectroscopy, by means of Excitation-Emission Matrices (EEM), has become an indispensable tool to study DOM sources, transport and fate in aquatic ecosystems. However the statistical treatment of large and heterogeneous EEM data sets still represents an important challenge for biogeochemists. Recently, Self-Organising Maps (SOM) has been proposed as a tool to explore patterns in large EEM data sets. SOM is a pattern recognition method which clusterizes and reduces the dimensionality of input EEMs without relying on any assumption about the data structure. In this paper, we show how SOM, coupled with a correlation analysis of the component planes, can be used both to explore patterns among samples, as well as to identify individual fluorescence components. We analysed a large and heterogeneous EEM data set, including samples from a river catchment collected under a range of hydrological conditions, along a 60-km downstream gradient, and under the influence of different degrees of anthropogenic impact. According to our results, chemical industry effluents appeared to have unique and distinctive spectral characteristics. On the other hand, river samples collected under flash flood conditions showed homogeneous EEM shapes. The correlation analysis of the component planes suggested the presence of four fluorescence components, consistent with DOM components previously described in the literature. A remarkable strength of this methodology was that outlier samples appeared naturally integrated in the analysis. We conclude that SOM coupled with a correlation analysis procedure is a promising tool for studying large and heterogeneous EEM data sets.

ICA as a preprocessing technique for classification

In this paper we propose the use of the independent component analysis (ICA) [1] technique for improving the classification rate of decision trees and multilayer perceptrons [2], [3]. The use of an ICA for the preprocessing stage, makes the structure of both classifiers simpler, and therefore improves the generalization properties. The hypothesis behind the proposed preprocessing is that an ICA analysis will transform the feature space into a space where the components are independent, and aligned to the axes and therefore will be more adapted to the way that a decision tree is constructed. Also the inference of the weights of a multilayer perceptron will be much easier because the gradient search in the weight space will follow independent trajectories. The result is that classifiers are less complex and on some databases the error rate is lower. This idea is also applicable to regression