Energy Recovery in Pressurized Hydraulic Networks

Water Resources Management - This paper aims to evaluate the possibility of using non-utilized hydraulic energy in urban water distribution systems. For this purpose, the viability and possible...     

Boron-doped diamond anodic treatment of landfill leachate: evaluation of operating variables and formation of oxidation by-products

Landfill leachate with a low BOD/COD ratio was electrochemically oxidized by means of a boron-doped diamond anode. In addition to organic matter removal, this study addressed the issue of formation of both chlorinated organic compounds and nitrate ions as a result of organic matter and ammonia and/or organic nitrogen electro-oxidation in the presence of chloride ions. A factorial design methodology was implemented to evaluate the statistically important operating variables: treatment time (1-4 h), pH (5-8), current intensity (6.3-8.4 A) and addition of chloride (2500-4500 mg L⁻¹). The process was evaluated on COD, total nitrogen (TN) and colour removal, as well as on the formation of nitrate, nitrite and chlorinated organics. Of the four variables studied, treatment time and pH had a considerable influence on COD and colour removal. On the contrary, none of the variables had a significant effect on the elimination of TN for which an average removal of 61 mg L⁻¹ was obtained. The studied variables exhibited different effects on the four groups of organo-chlorinated compounds considered in this study, namely trihalomethanes (THMs), haloacetonitriles (HANs), haloketons (HKs) and 1,2-dichloroethane (DCA). Further analysis at more intense conditions, i.e. current intensity up to 18 A and reaction time up to 8 h revealed that high levels of decolourization (84%) could be achieved followed by low COD (51%) and ammonia (32%) removals. Apart from DCA, the concentration of chlorinated organics increased continuously with treatment time reaching values as high as 1.9 mg L⁻¹, 753 μg L⁻¹ and 431 μg L⁻¹ of THMs, HANs and HKs, respectively.     

Mechanosynthesis of the multiferroic cubic spinel Co2MnO4: Influence of the calcination temperature

Multiferroic materials showing magnetoelectric coupling are required in various technological applications. Many synthetical approaches can be used to improve the magnetic and/or electrical properties, in particular when the materials exhibit cationic valence fluctuations, as in the Co₂MnO₄ cubic spinel. In this compound, Co and Mn ions are in competition at the tetrahedral and octahedral positions, depending on their various oxidation states. The Co₂MnO₄ was prepared following two techniques: by a soft chemical route based on a modified polymer precursor method, and by a mechanoactivation route. Both approaches yield polycrystalline powders, but their crystallites sizes and particles morphologies differ as a function of the calcination conditions. The magnetic characterization (ZFC/FC cycles, ordering temperatures, ferromagnetic coercive fields and saturation magnetizations) showed that the synthesis procedure influenced the physical properties of Co₂MnO₄ mainly through the size of the magnetic domains, which play an important role on the magnetic interactions between the Co/Mn cations.     

Changes in phytoplankton composition in a Mediterranean coastal lagoon in the Cullera Estany (Comunitat Valenciana,Spain)

The Cullera Estany is a coastal lagoon located in a highly intensified agriculture and tourist area in Valencia. This coastal lagoon has connections with the sea that produce marine intrusion and generate a freshwater interface. Four sampling campaigns were carried out during 2010 in order to analyse the phytoplankton composition and its relation to nutrient content through a Redundancy Analysis. Temperature, dissolved oxygen, nitrite and salinity are the main factors controlling the dynamics of phytoplankton community. During July and October, there is water column stratification; meanwhile in March, there is a well‐mixed water column. In addition, in May and July campaigns, hypoxia/anoxia conditions are detected at the bottom. The most abundant phytoplankton groups are Diatoms and Cryptophyceae. Diatoms and Cyanophyceae respond positively to temperature while Cryptophyceae, Prasinophyceae and Dinophyceae respond to high salinity and dissolved oxygen values. Furthermore, picoplankton is correlated inversely with nutrient concentrations.     

Semi-automated and fully automated mammographic density measurement and breast cancer risk prediction

The task of breast density quantification is becoming increasingly relevant due to its association with breast cancer risk. In this work, a semi-automated and a fully automated tools to assess breast density from full-field digitized mammograms are presented. The first tool is based on a supervised interactive thresholding procedure for segmenting dense from fatty tissue and is used with a twofold goal: for assessing mammographic density (MD) in a more objective and accurate way than via visual-based methods and for labeling the mammograms that are later employed to train the fully automated tool. Although most automated methods rely on supervised approaches based on a global labeling of the mammogram, the proposed method relies on pixel-level labeling, allowing better tissue classification and density measurement on a continuous scale. The fully automated method presented combines a classification scheme based on local features and thresholding operations that improve the performance of the classifier. A dataset of 655 mammograms was used to test the concordance of both approaches in measuring MD. Three expert radiologists measured MD in each of the mammograms using the semi-automated tool (DM-Scan). It was then measured by the fully automated system and the correlation between both methods was computed. The relation between MD and breast cancer was then analyzed using a case–control dataset consisting of 230 mammograms. The Intraclass Correlation Coefficient (ICC) was used to compute reliability among raters and between techniques. The results obtained showed an average ICC = 0.922 among raters when using the semi-automated tool, whilst the average correlation between the semi-automated and automated measures was ICC = 0.838. In the case–control study, the results obtained showed Odds Ratios (OR) of 1.38 and 1.50 per 10% increase in MD when using the semi-automated and fully automated approaches respectively. It can therefore be concluded that the automated and semi-automated MD assessments present a good correlation. Both the methods also found an association between MD and breast cancer risk, which warrants the proposed tools for breast cancer risk prediction and clinical decision making. A full version of the DM-Scan is freely available.     

Health-promoting effects in the gut and influence on lipid metabolism of Himanthalia elongata and Gigartina pistillata in hypercholesterolaemic Wistar rats

The intake of Himanthalia elongata and Gigartina pistillata from the Spanish Atlantic coasts was evaluated in Wistar rats. Both seaweed diets showed higher (p < 0.001) faecal excretion. Colonic fermentation increased (p < 0.001) total short-chain fatty acids (SCFAs) in Himanthalia-fed rats due to the higher (p < 0.001) levels of acetic, propionic and butyric acids. The intake of Gigartina increased (p < 0.001) propionic acid and decreased (p < 0.001) butyric acid. The apparent absorption and true retention of calcium and magnesium enhanced (p < 0.05) with Himanthalia diet, while Gigartina produced no significant effect. The serum concentration of HDL-C increased (p < 0.01), triglycerides (TGL) decreased (p < 0.001) and bile acids diminished (p < 0.001) in faeces of Himanthalia-fed rats. The Gigartina diet produced a decrease (p < 0.001) in TGL, total cholesterol (p < 0.01) and LDL-C (p < 0.05) in serum and reduced TGL in liver (p < 0.001). Thus, both seaweeds improved the lipid profile, and Himanthalia increased SCFA production and the absorption and retention of Ca and Mg as a result of the gut fermentation.     

A smartphone-based system for detecting hand tremors in unconstrained environments

The detection of tremors can be crucial for the early diagnosis and proper treatment of some disorders such as Parkinson’s disease. A smartphone-based application has been developed for detecting hand tremors. This application runs in background and distinguishes hand tremors from common daily activities. This application can facilitate the continuous monitoring of patients or the early detection of this symptom. The evaluation analyzes 1770 accelerometer samples with cross-validation for assessing the ability of the system for processing unknown data, obtaining a sensitivity of 95.8 % and a specificity of 99.5 %. It also analyzes continuous data for some volunteers for several days, which corroborated its high performance.     

A Markovian–Bayesian Network for Risk Analysis of High Speed and Conventional Railway Lines Integrating Human Errors

The article provides a new Markovian–Bayesian network model to evaluate the probability of accident associated with the circulation of trains along a given high speed or conventional railway line with special consideration to human error. This probability increases as trains pass throughout the different elements encountered along the line. A Bayesian network, made up of a sequence of several connected Bayesian subnetworks, is used. A subnetwork is associated with each element in the line that implies a concentrated risk of accident or produces a change in the driver's attention, such as signals, tunnel, or viaduct entries or exits, etc. Bayesian subnetworks are also used to reproduce segments without signals where some elements add continuous risks, such as rolling stock failures, falling materials, slope slides in cuttings and embankments, etc. All subnetworks are connected with the previous one and some of them are multi‐connected because some consequences are dependent on previous errors. Because driver's attention plays a crucial role, its degradation with driving time and the changes due to seeing light signals or receiving acoustic signals is taken into consideration. The model updates the driver's attention level and accumulates the probability of accident associated with the different elements encountered along the line. This permits us to generate a continuously increasing risk graph that includes continuous and sudden changes indicating where the main risks appear and whether or not an action must be taken by the infrastructure manager. Sensitivity analysis allows the relevant and irrelevant parameters to be identified avoiding wastes of time and money by concentrating safety improvement actions only on the relevant ones. Finally, some examples are used to illustrate the model. In particular, the case of the Orense–Santiago de Compostela line, where a terrible accident took place in 2013.