The MIS 5 palaeoenvironmental record in the SE Mediterranean coast of the Iberian Peninsula (Río Antas, Almería, Spain)

Se realiza un estudio detallado del episodio cálido MIS 5 en la zona sureste de la Península Ibérica. Se realiza la reconstrucción paleoambiental a partir del estudio polínico y biomarcadores de un sondeo perforado en la costa de Almería. La cronología se estableció a partir del método de racemizaciónd e aminoácidos.Landwards of a MIS5 bar, a borehole core (SRA) was analyzed to establish the relationship between the lagoonal record and the raised beach deposits in the surroundings of the Antas river mouth and to reconstruct the Pleistocene palaeoenvironmental evolution 5 of the southern Mediterranean coast of the Iberian Peninsula. 63 samples were recovered for amino acid racemization dating, 86 samples for sedimentological and paleontological determination, 37 samples for pollen identification and 54 for biomarker analysis. AAR revealed that the borehole record contains MIS11, MIS6 and MIS5 deposits, the latter extensively represented. During the end of MIS6 and MIS5, a sand 10 barrier developed and created a shallow lagoon with alternating terrestrial inputs this process being common in other Mediterranean realms. Litho- and biofacies allowed the identification of distinct paleoenvironments through time, with the presence of a lagoonal environment alternating with alluvial fan progradation. Biomarkers indicated constant input from terrestrial plants, together with variable development of aquatic 15 macrophytes. The palynological content allowed the reconstruction of the paleoclimatological conditions during MIS6 and 5, with evidence of seven scenarios characterized by alternating arid and relatively humid conditions

Categorical Missing Data Imputation Using Fuzzy Neural Networks with Numerical and Categorical Inputs

There are many situations where input feature vectors are incomplete and methods to tackle the problem have been studied for a long time. A commonly used procedure is to replace each missing value with an imputation. This paper presents a method to perform categorical missing data imputation from numerical and categorical variables. The imputations are based on Simpson’s fuzzy min-max neural networks where the input variables for learning and classification are just numerical. The proposed method extends the input to categorical variables by introducing new fuzzy sets, a new operation and a new architecture. The procedure is tested and compared with others using opinion poll data.

A review of the distribution of particulate trace elements in urban terrestrial environments and its application to considerations of risk

We review the evolution, state of the art and future lines of research on the sources, transport pathways, and sinks of particulate trace elements in urban terrestrial environments to include the atmosphere, soils, and street and indoor dusts. Such studies reveal reductions in the emissions of some elements of historical concern such as Pb, with interest consequently focusing on other toxic trace elements such as As, Cd, Hg, Zn, and Cu. While establishment of levels of these elements is important in assessing the potential impacts of human society on the urban environment, it is also necessary to apply this knowledge in conjunction with information on the toxicity of those trace elements and the degree of exposure of human receptors to an assessment of whether such contamination represents a real risk to the city’s inhabitants and therefore how this risk can be addressed.

Novel fast random search clustering approach for mixing matrix identification in MIMO linear blind inverse problems with sparse inputs

In this paper we propose a novel fast random search clustering (RSC) algorithm for mixing matrix identification in multiple input multiple output (MIMO) linear blind inverse problems with sparse inputs. The proposed approach is based on the clustering of the observations around the directions given by the columns of the mixing matrix that occurs typically for sparse inputs. Exploiting this fact, the RSC algorithm proceeds by parameterizing the mixing matrix using hyperspherical coordinates, randomly selecting candidate basis vectors (i.e. clustering directions) from the observations, and accepting or rejecting them according to a binary hypothesis test based on the Neyman–Pearson criterion. The RSC algorithm is not tailored to any specific distribution for the sources, can deal with an arbitrary number of inputs and outputs (thus solving the difficult under-determined problem), and is applicable to both instantaneous and convolutive mixtures. Extensive simulations for synthetic and real data with different number of inputs and outputs, data size, sparsity factors of the inputs and signal to noise ratios confirm the good performance of the proposed approach under moderate/high signal to noise ratios. RESUMEN. Método de separación ciega de fuentes para señales dispersas basado en la identificación de la matriz de mezcla mediante técnicas de "clustering" aleatorio.

Discriminating Crop, Weeds and Soil Surface with a Terrestrial LIDAR Sensor

In this study, the evaluation of the accuracy and performance of a light detection and ranging (LIDAR) sensor for vegetation using distance and reflection measurements aiming to detect and discriminate maize plants and weeds from soil surface was done. The study continues a previous work carried out in a maize field in Spain with a LIDAR sensor using exclusively one index, the height profile. The current system uses a combination of the two mentioned indexes. The experiment was carried out in a maize field at growth stage 12–14, at 16 different locations selected to represent the widest possible density of three weeds: Echinochloa crus-galli (L.) P.Beauv., Lamium purpureum L., Galium aparine L.and Veronica persica Poir.. A terrestrial LIDAR sensor was mounted on a tripod pointing to the inter-row area, with its horizontal axis and the field of view pointing vertically downwards to the ground, scanning a vertical plane with the potential presence of vegetation. Immediately after the LIDAR data acquisition (distances and reflection measurements), actual heights of plants were estimated using an appropriate methodology. For that purpose, digital images were taken of each sampled area. Data showed a high correlation between LIDAR measured height and actual plant heights (R 2 = 0.75). Binary logistic regression between weed presence/absence and the sensor readings (LIDAR height and reflection values) was used to validate the accuracy of the sensor. This permitted the discrimination of vegetation from the ground with an accuracy of up to 95%. In addition, a Canonical Discrimination Analysis (CDA) was able to discriminate mostly between soil and vegetation and, to a far lesser extent, between crop and weeds. The studied methodology arises as a good system for weed detection, which in combination with other principles, such as vision-based technologies, could improve the efficiency and accuracy of herbicide spraying.

Life cycle assessment of concentrated solar power (CSP) and the influence of hybridising with natural gas

Purpose Concentrating Solar Power (CSP) plants based on parabolic troughs utilize auxiliary fuels (usually natural gas) to facilitate start-up operations, avoid freezing of HTF and increase power output. This practice has a significant effect on the environmental performance of the technology. The aim of this paper is to quantify the sustainability of CSP and to analyse how this is affected by hybridisation with different natural gas (NG) inputs. Methods A complete Life Cycle (LC) inventory was gathered for a commercial wet-cooled 50 MWe CSP plant based on parabolic troughs. A sensitivity analysis was conducted to evaluate the environmental performance of the plant operating with different NG inputs (between 0 and 35% of gross electricity generation). ReCiPe Europe (H) was used as LCA methodology. CML 2 baseline 2000 World and ReCiPe Europe E were used for comparative purposes. Cumulative Energy Demands (CED) and Energy Payback Times (EPT) were also determined for each scenario. Results and discussion Operation of CSP using solar energy only produced the following environmental profile: climate change 26.6 kg CO2 eq/KWh, human toxicity 13.1 kg 1,4-DB eq/KWh, marine ecotoxicity 276 g 1,4-DB eq/KWh, natural land transformation 0.005 m2/KWh, eutrophication 10.1 g P eq/KWh, acidification 166 g SO2 eq/KWh. Most of these impacts are associated with extraction of raw materials and manufacturing of plant components. The utilization NG transformed the environmental profile of the technology, placing increasing weight on impacts related to its operation and maintenance. Significantly higher impacts were observed on categories like climate change (311 kg CO2 eq/MWh when using 35 % NG), natural land transformation, terrestrial acidification and fossil depletion. Despite its fossil nature, the use of NG had a beneficial effect on other impact categories (human and marine toxicity, freshwater eutrophication and natural land transformation) due to the higher electricity output achieved. The overall environmental performance of CSP significantly deteriorated with the use of NG (single score 3.52 pt in solar only operation compared to 36.1 pt when using 35 % NG). Other sustainability parameters like EPT and CED also increased substantially as a result of higher NG inputs. Quasilinear second-degree polynomial relationships were calculated between various environmental performance parameters and NG contributions. Conclusions Energy input from auxiliary NG determines the environmental profile of the CSP plant. Aggregated analysis shows a deleterious effect on the overall environmental performance of the technology as a result of NG utilization. This is due primarily to higher impacts on environmental categories like climate change, natural land transformation, fossil fuel depletion and terrestrial acidification. NG may be used in a more sustainable and cost-effective manner in combined cycle power plants, which achieve higher energy conversion efficiencies.

Deciduous tree reconstruction algorithm based on cylinder fitting from mobile terrestrial laser scanned point clouds

Vector reconstruction of objects from an unstructured point cloud obtained with a LiDAR-based system (light detection and ranging) is one of the most promising methods to build three dimensional models of orchards. The cylinder fitting method for woody structure reconstruction of leafless trees from point clouds obtained with a mobile terrestrial laser scanner (MTLS) has been analysed. The advantage of this method is that it performs reconstruction in a single step. The most time consuming part of the algorithm is generation of the cylinder direction, which must be recalculated at the inclusion of each point in the cylinder. The tree skeleton is obtained at the same time as the cluster of cylinders is formed. The method does not guarantee a unique convergence and the reconstruction parameter values must be carefully chosen. A balanced processing of clusters has also been defined which has proven to be very efficient in terms of processing time by following the hierarchy of branches, predecessors and successors. The algorithm was applied to simulated MTLS of virtual orchard models and to MTLS data of real orchards. The constraints applied in the method have been reviewed to ensure better convergence and simpler use of parameters. The results obtained show a correct reconstruction of the woody structure of the trees and the algorithm runs in linear logarithmic time