A Spatial Analysis to Permanent Income as Deterrent of Homicides: the case of Medellin City

This paper studies the relationship between permanent income and homicides, estimating an income-crime elasticity. We assume that this elasticity varies across geographical areas. We estimate different specifications of Spatial Panel Models using information of urban areas in Medellin (Colombia), areas known as communes. Spatial Models consider the importance of location and the type of neighbors of each commune. We simulate an intervention over permanent income in order to estimate the income elasticity for each commune and the average elasticity of income-crime on the city. We provide evidence about spatial dependence between the homicides per commune and their neighbors, and about a relationship between homicides and neighbor’s income. In our case of study, the average estimated impact of 1% increase in permanent income in a specific commune produces a decrease in the homicide rate on average in 0.39%. Finally, permanent income plays a crime deterrent role, but also this effect of income on crime varies across the city, showing that some areas are strategically located to this kind of intervention.

Why is Asari (= Manila) clam Ruditapes philippinarum fitness poor in Arcachon Bay: a meta-analysis to answer?

Asari (= Manila) clam, Ruditapes philippinarum, is the second bivalve mollusc in terms of production in the world and, in many coastal areas, can beget important socio-economic issues. In Europe, this species was introduced after 1973. In Arcachon Bay, after a decade of aquaculture attempt, Asari clam rapidly constituted neo-naturalized population which is now fished. However, recent studies emphasized the decline of population and individual performances. In the framework of a national project (REPAMEP), some elements of fitness, stressors and responses in Arcachon bay were measured and compared to international data (41 publications, 9 countries). The condition index (CI=flesh weight/shell weight) was the lowest among all compared sites. Variation in average Chla concentration explained 30% of variation of CI among different areas. Among potential diseases, perkinsosis was particularly prevalent in Arcachon Bay, with high abundance, and Asari clams underwent Brown Muscle Disease, a pathology strictly restricted to this lagoon. Overall element contamination was relatively low, although arsenic, cobalt, nickel and chromium displayed higher values than in other ecosystems where Asari clam is exploited. Finally, total hemocyte count (THC) of Asari clam in Arcachon Bay, related to the immune system activity, exhibited values that were also under what is generally observed elsewhere. In conclusion, this study, with all reserves due to heterogeneity of available data, suggest that the particularly low fitness of Asari clam in Arcachon Bay is due to poor trophic condition, high prevalence and intensity of a disease (perkinsosis), moderate inorganic contamination, and poor efficiency of the immune system.

Spatial multicriteria decision analysis to potential beekeeping assessment. Case study: Montesinho Natural Park (Portugal)

The Multicriteria decision analysis is a tool to support decision-making in the identification of areas with the utmost beekeeping potential. This paper design a GIS multicriteria approach to assess the beekeeping potential. The development of a conceptual model structure requires the participation of stakeholders and experts in that process. The spatial Multicriteria Decision Analysis (MCDA) allowed defining the potential beekeeping map. The resulting maps can be used by the beekeepers associations to easily select the more suitable areas for the apiaries location or relocation and avoid prohibited areas by legal requirements.

Using Habitat Equivalency Analysis to Assess the Cost Effectiveness of Restoration Outcomes in Four Institutional Contexts

At the national level, with a fixed amount of resources available for public investment in the restoration of biodiversity, it is difficult to prioritize alternative restoration projects. One way to do this is to assess the level of ecosystem services delivered by these projects and to compare them with their costs. The challenge is to derive a common unit of measurement for ecosystem services in order to compare projects which are carried out in different institutional contexts having different goals (application of environmental laws, management of natural reserves, etc.). This paper assesses the use of habitat equivalency analysis (HEA) as a tool to evaluate ecosystem services provided by restoration projects developed in different institutional contexts. This tool was initially developed to quantify the level of ecosystem services required to compensate for non-market impacts coming from accidental pollution in the US. In this paper, HEA is used to assess the cost effectiveness of several restoration projects in relation to different environmental policies, using case studies based in France. Four case studies were used: the creation of a market for wetlands, public acceptance of a port development project, the rehabilitation of marshes to mitigate nitrate loading to the sea, and the restoration of streams in a protected area. Our main conclusion is that HEA can provide a simple tool to clarify the objectives of restoration projects, to compare the cost and effectiveness of these projects, and to carry out trade-offs, without requiring significant amounts of human or technical resources.

Using text-mining-assisted analysis to examine the applicability of unstructured data in the context of customer complaint management

Double Degree

Multiple solutions and numerical analysis to the dynamic and stationary models coupling a delayed energy balance model involving latent heat and discontinuous albedo with a deep ocean

We study a climatologically important interaction of two of the main components of the geophysical system by adding an energy balance model for the averaged atmospheric temperature as dynamic boundary condition to a diagnostic ocean model having an additional spatial dimension. In this work, we give deeper insight than previous papers in the literature, mainly with respect to the 1990 pioneering model by Watts and Morantine. We are taking into consideration the latent heat for the two phase ocean as well as a possible delayed term. Non-uniqueness for the initial boundary value problem, uniqueness under a non-degeneracy condition and the existence of multiple stationary solutions are proved here. These multiplicity results suggest that an S-shaped bifurcation diagram should be expected to occur in this class of models generalizing previous energy balance models. The numerical method applied to the model is based on a finite volume scheme with nonlinear weighted essentially non-oscillatory reconstruction and Runge–Kutta total variation diminishing for time integration.

PCA 4 DCA: the application of principal component analysis to the Dendritic Cell Algorithm

As one of the newest members in the field of articial immune systems (AIS), the Dendritic Cell Algorithm (DCA) is based on behavioural models of natural dendritic cells (DCs). Unlike other AIS, the DCA does not rely on training data, instead domain or expert knowledge is required to predetermine the mapping between input signals from a particular instance to the three categories used by the DCA. This data preprocessing phase has received the criticism of having manually over-fitted the data to the algorithm, which is undesirable. Therefore, in this paper we have attempted to ascertain if it is possible to use principal component analysis (PCA) techniques to automatically categorise input data while still generating useful and accurate classication results. The integrated system is tested with a biometrics dataset for the stress recognition of automobile drivers. The experimental results have shown the application of PCA to the DCA for the purpose of automated data preprocessing is successful.

Application of stable isotope analysis to differentiate shrimp extracted by industrial fishing or produced through aquaculture practices.

Carbon and nitrogen stable isotope values were determined in Pacific white shrimp (Litopenaeus vannamei) with the objective of discriminating animals produced through aquaculture practices from those extracted from the wild. Farmed animals were collected at semi-intensive shrimp farms in Mexico and Ecuador. Fisheries-derived shrimps were caught in different fishing areas representing two estuarine systems and four open sea locations in Mexico and Ecuador. Carbon and nitrogen stable isotope values (13CVPDB and 15NAIR) allowed clear differentiation of wild from farmed animals. 13CVPDB and 15NAIR values in shrimps collected in the open sea were isotopically enriched (−16.99‰ and 11.57‰), indicating that these organisms belong to higher trophic levels than farmed animals. 13CVPDB and 15NAIR values of farmed animals (−19.72‰ and 7.85‰, respectively) partially overlapped with values measured in animals collected in estuaries (−18.46‰ and 5.38‰, respectively). Canonical discriminant analysis showed that when used separately and in conjunction, 13CVPDB and I5NAIR values were powerful discriminatory variables and demonstrate the viability of isotopic evaluations to distinguish wild-caught shrimps from aquaculture shrimps. Methodological improvements will define a verification tool to support shrimp traceability protocols.

Application of survival analysis to determine the persimmon cv. 'Rama-forte' sensory shelf-life.

2014

Applying Least Absolute Shrinkage Selection Operator and Akaike Information Criterion Analysis to Find the Best Multiple Linear Regression Models between Climate Indices and Components of Cow’s Milk

This study focuses on multiple linear regression models relating six climate indices (temperature humidity THI, environmental stress ESI, equivalent temperature index ETI, heat load HLI, modified HLI (HLI new), and respiratory rate predictor RRP) with three main components of cow’s milk (yield, fat, and protein) for cows in Iran. The least absolute shrinkage selection operator (LASSO) and the Akaike information criterion (AIC) techniques are applied to select the best model for milk predictands with the smallest number of climate predictors. Uncertainty estimation is employed by applying bootstrapping through resampling. Cross validation is used to avoid over-fitting. Climatic parameters are calculated from the NASA-MERRA global atmospheric reanalysis. Milk data for the months from April to September, 2002 to 2010 are used. The best linear regression models are found in spring between milk yield as the predictand and THI, ESI, ETI, HLI, and RRP as predictors with p-value < 0.001 and R2 (0.50, 0.49) respectively. In summer, milk yield with independent variables of THI, ETI, and ESI show the highest relation (p-value < 0.001) with R2 (0.69). For fat and protein the results are only marginal. This method is suggested for the impact studies of climate variability/change on agriculture and food science fields when short-time series or data with large uncertainty are available.

Tortoiseshell or Polymer? Spectroscopic Analysis to Redefine a Purported Tortoiseshell Box with Gold Decorations as a Plastic Box with Brass

The study and preservation of museum collections requires complete knowledge and understanding of constituent materials that can be natural, synthetic, or semi-synthetic polymers. In former times, objects were incorporated in museum collections and classified solely by their appearance. New studies, prompted by severe degradation processes or conservation-restoration actions, help shed light on the materiality of objects that can contradict the original information or assumptions. The selected case study presented here is of a box dating from the beginning of the 20th century that belongs to the Portuguese National Ancient Art Museum. Museum curators classified it as a tortoiseshell box decorated with gold applications solely on the basis of visual inspection and the information provided by the donor. This box has visible signs of degradation with white veils, initially assumed to be the result of biological degradation of a proteinaceous matrix. This paper presents the methodological rationale behind this study and proposes a totally non-invasive methodology for the identification of polymeric materials in museum artifacts. The analysis of surface leachates using 1H and 13C nuclear magnetic resonance (NMR) complemented by in situ attenuated total reflection infrared spectroscopy (ATR FT-IR) allowed for full characterization of the object s substratum. The NMR technique unequivocally identified a great number of additives and ATR FT-IR provided information about the polymer structure and while also confirming the presence of additives. The pressure applied during ATR FT-IR spectroscopy did not cause any physical change in the structure of the material at the level of the surface (e.g., color, texture, brightness, etc.). In this study, variable pressure scanning electron microscopy (VP-SEM-EDS) was also used to obtain the elemental composition of the metallic decorations. Additionally, microbiologic and enzymatic assays were performed in order to identify the possible biofilm composition and understand the role of microorganisms in the biodeterioration process. Using these methodologies, the box was correctly identified as being made of cellulose acetate plastic with brass decorations and the white film was identified as being composed mainly of polymer exudates, namely sulphonamides and triphenyl phosphate.

Phosphorus quantification in fertilizers using laser induced breakdown spectroscopy (LIBS): a methodology of analysis to correct physical matrix effcts.

2016