New quality criteria in wastewater reuse : The case of Gran Canaria

Wastewater reuse plays a key role in this vital cycle of water because it is able to reduce the wastewater spilled. Simultaneously, the supply of water for specific uses is increased. A new regulation (Royal Decree 1620/2007) came into force in Spain in December 2007 and regulates the basic conditions for the wastewater reuse and establishes the required criteria of quality to waters according to use.In the present paper, different kinds of tertiary treatments in reuse of wastewater are described. We focused on the applied tertiary treatments in most wastewater treatment plants of Gran Canaria Island. Particularly, we analysed Hoya del Pozo wastewater treatment plant due to its wide number of technologies and processes.Finally, we reviewed the quality criteria used in the regenerated waters, in agreement with the new regulations.     

Microstructure and densification of ZrB2–SiC composites prepared by spark plasma sintering

ZrB₂–SiC composites were prepared by spark plasma sintering (SPS) at temperatures of 1800–2100 °C for 180–300 s under a pressure of 20 MPa and at higher temperatures of above 2100 °C without a holding time under 10 MPa. Densification, microstructure and mechanical properties of ZrB₂–SiC composites were investigated. Fully dense ZrB₂–SiC composites containing 20–60 mass% SiC with a relative density of more than 99% were obtained at 2000 and 2100 °C for 180 s. Below 2120 °C, microstructures consisted of equiaxed ZrB₂ grains with a size of 2–5 μm and α-SiC grains with a size of 2–4 μm. Morphological change from equiaxed to elongated α-SiC grains was observed at higher temperatures. Vickers hardness of ZrB₂–SiC composites increased with increasing sintering temperature and SiC content up to 60 mass%, and ZrB₂–SiC composite containing 60 mass% SiC sintered at 2100 °C for 180 s had the highest value of 26.8 GPa. The highest fracture toughness was observed for ZrB₂–SiC composites containing 50 mass% SiC independent of sintering temperatures.     

Functional copolymer/organo-MMT nanoarchitectures: self-assembled core-shell morphology of poly(maleic anhydride-alt-α-olefin)/organo-MMT nanocomposites

This work describes the TEM morphology analysis of amphiphilic alternating copolymers of maleic anhydride (MA) with different α-olefins (1-hexene, 1-octene, 1-dodecene) synthesized by radical-initiated intercalative copolymerization of preintercalated MA···organoclay complexes in the presence of two types of organoclay such as reactive octadecyl amine-montmorillonite (ODA-MMT) and nonreactive dimethyldidodecyl ammonium-MMT (DMDA-MMT). TEM analyses of all the nanocomposites indicate the formation of symmetric or non-symmetric core-shell morphologies. Energy dispersive X-ray spectroscopy analyses were used to study the occurrence of the element in the poly(MA-alt-α-olefin)/organo-montmorillonite nanocomposites. A role of surfactant in these nanosystems insists on alternating copolymer segments with given hydrophobic/hydrophilic balance and surface organo-modifiers of MMT clays. Highly organized self-assembly with symmetric core-shell morphology were formed in nanosystems prepared in the presence of DMDA-MMT clay as compared with ODA-MMT clay-containing nanosystems. The relatively long branched olefin fragments significantly improve image parameters of core-shell morphology where some of the core structures contain accumulated and finely dispersed silicate particles at nano level around 2–25 nm. This observed phenomenon has brought a new approach for future utilization of these nanosystems in controlled drug delivery and anticancer applications with essentially prolonged activity.     

A new size distribution model by t-family curves for comminution of limestones in an impact crusher

In the evaluation of crushers, t-family curves obtained from single particle test methods are frequently used. It is known that there are many difficulties and problems in these tests. In this study the breakage behaviour of three different limestones in an impact crusher was investigated. A new size distribution model was developed by t-family value evaluation and Bond work index approach. As a result, the validity of the equation was proved by a high regression value (r² = 0.88).     

Electrospun polymeric nanofibrous composites containing TiO2 short nanofibers

In this study, polymeric nanofibrous composites containing anatase TiO₂ short nanofibers (TiO₂-SNF) were successfully produced via electrospinning. The fabrication of the nanofibrous composite structure includes two steps. First, anatase TiO₂ nanofibers were obtained by calcination of electrospun PVP/TiO₂ nanofibers and then crushed into short nanofibers ranging from few microns in length. Second, these TiO₂-SNF were dispersed into polymer solutions and then electrospun into nanofibrous composites. We obtained nanofibers containing TiO₂-SNF from different polymer types including PMMA, PAN, PET and PC. The SEM and TEM imaging indicated that some of the TiO₂-SNF were fully covered by the polymeric matrix whereas some TiO₂-SNF were partially covered and/or stick on the surface of the fibers. The photocatalytic activity of nanofibrous composites containing TiO₂-SNF was evaluated by monitoring the photocatalytic decomposition of a model dye (rhodamine-6G) under UV irradiation.     

A framework for rapid visual image search using single-trial brain evoked responses

We report the design and performance of a brain computer interface for single-trial detection of viewed images based on human dynamic brain response signatures in 32-channel electroencephalography (EEG) acquired during a rapid serial visual presentation. The system explores the feasibility of speeding up image analysis by tapping into split-second perceptual judgments of humans. We present an incremental learning system with less memory storage and computational cost for single-trial event-related potential (ERP) detection, which is trained using cross-session data. We demonstrate the efficacy of the method on the task of target image detection. We apply linear and nonlinear support vector machines (SVMs) and a linear logistic classifier (LLC) for single-trial ERP detection using data collected from image analysts and naive subjects. For our data the detection performance of the nonlinear SVM is better than the linear SVM and the LLC. We also show that our ERP-based target detection system is five-fold faster than the traditional image viewing paradigm.     

Failure Analysis of a Location Axle in Tracked Tractor

In this study, a failure analysis was performed on the location axle (brace axle) of a blade on a tracked tractor used in agricultural areas. The analysis included evaluation of the macrostructure of the damage and the microstructure of the material, as well as a chemical analysis of the material. A commercial finite element method software package was used to determine the behavior of the location axle under the defined boundary conditions. After all the examination steps were completed, the main reason for the damage was determined to be manufacture and material error despite the presence of fatigue tracks in the damaged area. In addition, a heat treatment was carried out with the specimens taken from the damaged area and microstructure was re-examined. The experimental and simulation evaluation results provide a technical basis for suggestions to prevent future damage to the location axle.     

Computer Forensics Investigations in a Corporate Environment

Most organizations today accept that it is a fundamental business requirement to protect electronic information assets, and are implementing an increasing number of information security counter_measures, such as intrusion detection systems (IDS), access control mechanisms, and anti_virus products to protect their IT systems from potential security threats.     

Gaussianization: An Efficient Multivariate Density Estimation Technique for Statistical Signal Processing

Multivariate density estimation is an important problem that is frequently encountered in statistical learning and signal processing. One of the most popular techniques is Parzen windowing, also referred to as kernel density estimation. Gaussianization is a procedure that allows one to estimate multivariate densities efficiently from the marginal densities of the individual random variables. In this paper, we present an optimal density estimation scheme that combines the desirable properties of Parzen windowing and Gaussianization, using minimum Kullback–Leibler divergence as the optimality criterion for selecting the kernel size in the Parzen windowing step. The utility of the estimate is illustrated in classifier design, independent components analysis, and Prices’ theorem.