Changes in mobility of toxic elements during the production of phosphoric acid in the fertilizer industry of Huelva (SW Spain) and environmental impact of phosphogypsum wastes

Presently, about 3 million tonnes of phosphogypsum are being generated annually in Spain as by-product from phosphoric acid in a fertilizer factory located in Huelva (southwestern Iberian Peninsula). Phosphate rock from Morocco is used as raw material in this process. Phosphogypsum wastes are stored in a stack containing 100Mt (approximately 1200ha of surface) over salt marshes of an estuary formed by the confluence of the Tinto and Odiel rivers, less than 1km away from the city centre. A very low proportion of this waste is used to improve fertility of agricultural soils in the area of the Guadalquivir river valley (Seville, SW Spain). The chemical speciation of potentially toxic elements (Ba, Cd, Cu, Ni, Sr, U and Zn) in phosphogypsum and phosphate rock was performed using the modified BCR-sequential extraction procedure, as described by the European Community Bureau of Reference (1999). This study has been done with the main of: (1) evaluate changes in the mobility of metals during the production of phosphoric acid; (2) estimate the amount of mobile metals that can affect the environmental surrounding; and (3) verify the environmentally safe use of phosphogypsum as an amendment to agricultural soils. The main environmental concern associated to phosphoric acid production is that Uranium, a radiotoxic element, is transferred from the non-mobile fraction in the phosphate rock to the bioavailable fraction in phosphogypsum in a rate of 23%. Around 21% of Ba, 6% of Cu and Sr, 5% of Cd and Ni, and 2% of Zn are also contained in the water-soluble phase of the final waste. Considering the total mass of phosphogypsum, the amount of metals easily soluble in water is approximately 6178, 3089, 1931, 579, 232, 193 and 77t for Sr, U, Ba, Zn, Ni, Cu and Cd, respectively. This gives an idea of the pollution potential of this waste.     

Food aroma mass transport properties in renewable hydrophilic polymers

The sorption and transport properties of gliadin and chitosan films with respect to four representative food aroma components (ethyl caproate, 1-hexanol, 2-nonanone and α-pinene) have been studied under dry and wet environmental conditions. The partition coefficients (K) of the selected volatiles were also obtained using isooctane and soybean oil as fatty food simulants. The results showed that gliadin and chitosan films have very low capacities for the sorption of volatile compounds, and these capacities are influenced by the nature of the sorbate, the environmental relative humidity and the presence of glycerol as a plasticizer in the polymeric matrix. The volatile compounds also present a low partitioning in the biopolymer film/food stimulant system. Given the low levels of interaction observed with the volatiles, gliadin and chitosan films are of potential interest for the packaging of foods in which aroma is one of the most important quality attributes.     

The Role of Si and Cu Alloying Elements on the Dendritic Growth and Microhardness in Horizontally Solidified Binary and Multicomponent Aluminum-Based Alloys

Horizontal directional solidification (HDS) experiments were carried out with Al-3wtpctCu, Al-3wtpctSi, and Al- 3wtpctCu-5.5wtpctSi alloys in order to analyze the interrelation between the secondary dendrite arm spacing (λ ₂) and microhardness (HV). A water-cooled horizontal directional solidification device was applied. Microstructural characterization has been carried out using traditional techniques of metallography, optical, and SEM microscopy. The ThermoCalc software was used to generate the phase equilibrium diagrams as a function of Cu and Si for the analyzed alloys. The effects of Si and Cu elements on the λ ₂ and HV evolution of the hypoeutectic binary Al-Cu and Al-Si alloys have been analyzed as well as the addition of Si in the formation of ternary Al-Cu-Si alloy. The secondary dendrite arm spacing was correlated with local solidification thermal parameters such as growth rate (V _L), cooling rate (T _R), and local solidification time (t _SL). This has allowed to observe that power experimental functions given by λ ₂ = Constant (V _L)^−2/3, λ ₂ = Constant (T _R)^−1/3 and λ ₂ = Constant (t _SL)^1/3 may represent growth laws of λ ₂ with corresponding thermal parameters for investigated alloys. Hall–Petch equations have also been used to characterize the dependence of HV with λ ₂. A comparative analysis is performed between λ ₂ experimental values obtained in this study for Al-3wtpctCu-5.5wtpctSi alloy and the only theoretical model from the literature that has been proposed to predict the λ ₂ growth in multicomponent alloys. Comparisons with literature results for upward directional solidification were also performed.

     

Artificial intelligence based system to improve the inspection of plastic mould surfaces

Plastic industry is today in a constant growth, demanding several products from other segments, which includes the plastic moulds, used mainly in the injection moulding process. This paper presents a methodology for the surface evaluation of plastic moulds, aiming the automation of the polishing surface analysis. Provided that this type of analysis by traditional procedures can be slow and expensive, the development of automatic system could lead to considerable improvements regarding the speed and reliability of information. The starting point of the evaluation procedure is the image generated by the laser light scattered over the sample mould surface that could be captured and analysed by image processing and artificial intelligence techniques. The results showed that the proposed system is able to mapping and classifying several damages over the polished surface and could be an alternative to reduce efficiently the costs and the spending time in mould surface inspection tasks.     

Where is my puppy? Retrieving lost dogs by facial features

A pet that goes missing is among many people’s worst fears: a moment of distraction is enough for a dog or a cat wandering off from home. Some measures help matching lost animals to their owners; but automated visual recognition is one that — although convenient, highly available, and low-cost — is surprisingly overlooked. In this paper, we inaugurate that promising avenue by pursuing face recognition for dogs. We contrast four ready-to-use human facial recognizers (EigenFaces, FisherFaces, LBPH, and a Sparse method) to two original solutions based upon convolutional neural networks: BARK (inspired in architecture-optimized networks employed for human facial recognition) and WOOF (based upon off-the-shelf OverFeat features). Human facial recognizers perform poorly for dogs (up to 60.5 % accuracy), showing that dog facial recognition is not a trivial extension of human facial recognition. The convolutional network solutions work much better, with BARK attaining up to 81.1 % accuracy, and WOOF, 89.4 %. The tests were conducted in two datasets: Flickr-dog, with 42 dogs of two breeds (pugs and huskies); and Snoopybook, with 18 mongrel dogs.     

Efficient biometric palm-print matching on smart-cards for high security and privacy

Multimedia Tools and Applications - Smart control access to any service is at the very basis of any smart city project. Biometrics have been used as a solution for system access control, for many...     

Learning from Network Device Statistics

We estimate end-to-end service metrics from network device statistics. Our approach is based upon statistical, supervised learning, whereby the mapping from device-level to service-level metrics is learned from observations, i.e., through monitoring the system. The approach enables end-to-end performance prediction without requiring an explicit model of the system, which is different from traditional engineering techniques that use stochastic modeling and simulation. The fact that end-to-end service metrics can be estimated from local network statistics with good accuracy in the scenarios we consider suggests that service-level properties are “encoded” in network-level statistics. We show that the set of network statistics needed for estimation can be reduced to a set of measurements along the network path between client and service backend, with little loss in estimation accuracy. The reported work is largely experimental and its results have been obtained through testbed measurements from a video streaming service and a KV store over an OpenFlow network .     

Data-Based Models for the Prediction of Dam Behaviour: A Review and Some Methodological Considerations

Predictive models are an important element in dam safety analysis. They provide an estimate of the dam response faced with a given load combination, which can be compared with the actual measurements to draw conclusions about dam safety. In addition to numerical finite element models, statistical models based on monitoring data have been used for decades for this purpose. In particular, the hydrostatic-season-time method is fully implemented in engineering practice, although some limitations have been pointed out. In other fields of science, powerful tools such as neural networks and support vector machines have been developed, which make use of observed data for interpreting complex systems. This paper contains a review of statistical and machine-learning data-based predictive models, which have been applied to dam safety analysis. Some aspects to take into account when developing analysis of this kind, such as the selection of the input variables, its division into training and validation sets, and the error analysis, are discussed. Most of the papers reviewed deal with one specific output variable of a given dam typology and the majority also lack enough validation data. As a consequence, although results are promising, there is a need for further validation and assessment of generalisation capability. Future research should also focus on the development of criteria for data pre-processing and model application.