Treatment of a PHC Source Zone using Land Application of Sulfate

This pilot-scale experiment was performed in the sand pit area at the University of Waterloo Groundwater Research Facility at CFB Borden located near Alliston, ON. A multicomponent PHC source zone (3 m x 3 m) was emplaced in 2012 between 1 and 3 m below ground surface inside a sheet pile walled experimental gate. Simulation tools were used to design an optimal sulfate dosage system that would satisfy the reagent delivery and remediation requirements. Three episodes of sulfate release (5 m3 of 5-20 g/L Na2SO4, and 0.3 g/L (NH4)2SO4) at the ground surface were conducted over an 8-month period. A host of multilevel monitoring wells in conjunction with a real-time resistivity data collection system was employed to continuously track sulfate patterns and migration. Treatment performance was evaluated based on changes in sulfate concentration in the plume and PHC mass discharge across a downgradient monitoring fence line. Results from compound specific isotope analysis (CSIA) and biomarker tools were combined with the conventional monitoring data to assess enhanced sulfate reduction of the PHCs. General sulfate migration pathway was captured during EC monitoring. These results demonstrated 5 g/L Na2SO4 did not provide sufficient infiltration, while 15-20 g/L Na2SO4 created strong density-dependent flow. EC results of sulfate monitoring showed the real-time resistivity system allowed the collection of high resolution data. PHC mass discharge results showed significant attenuation of benzene, toluene and xylene after the sulfate application. CSIA data showed the occurrence of PHCs biodegradation associated with sulfate reduction. The sulfate isotope data support the occurrence of sulfate reduction. The concentration and isotope patterns observed for DIC are also linked to PHCs biodegradation. The microbiological data showed the occurrence of biodegradation under both aerobic and anaerobic conditions in the PHC plume

Different response to 1-methylcyclopropene in two cultivars of Chinese pear fruit with contrasting softening characteristics

In this study, the change in softening and its related genes expression under influence of 500 nl L-1 1-methylcyclopropene (1-MCP) was assessed in the two Chinese pear fruit, ‘Jingbaili’ (Pyrus ussuriensis Maxim) and ‘Yali’ (Pyrus bretschneideri Rehd), which exhibit different softening characteristics. ‘Jingbaili’ pear fruit softened rapidly after harvest, and was strongly inhibited by 1-MCP. In contrast, there was no obvious change of firmness compared to the control after 1-MCP treatment in ‘Yali’ pear fruit. The respiration and ethylene production rates were reduced by 1-MCP at early storage in both two cultivars. ‘Jingbaili’ pear fruit exhibited dramatically increased expression levels of the softening-related genes, i.e., polygalacturonase1 (PG1), polygalacturonase2 (PG2), β-Galactosidase4 (GAL4), α-arabinofuranosidase1 (ARF1) and α-arabinofuranosidase2 (ARF2), and these genes’ expression levels were significantly decreased by 1-MCP treatment. In contrast, ‘Yali’ pear fruit showed lower expression levels of the above-mentioned genes, as well as a relatively smaller inhibition effect by 1-MCP treatment before day 27. These results suggest that ‘Jingbaili’ pear fruit are more sensitive to 1-MCP/ethylene than ‘Yali’ pear fruit during ripening

Infiltration of Sulfate to Enhance Sulfate Reduction of Petroleum Hydrocarbons

The lack of sufficient electron acceptors, particularly sulfate, can limit the rate of biodegradation of petroleum hydrocarbons (PHCs). Hence there is a growing interest by remediation practitioners to deliver sulfate to a PHC impacted saturated zone to enhance biodegradation. When shallow contamination is present in a relatively permeable aquifer and site constraints allow, a cost-effective approach is to apply sulfate on the ground surface. In this investigation a pilot-scale experiment was conducted to increase our understanding of the delivery of sulfate using a surface-based method and the resulting impact on a shallow PHC contaminated aquifer. A surficial infiltration pond positioned on the ground surface above a well-characterized residual PHC source zone was used to control sulfate dosing. A high-resolution network near the infiltration pond and downgradient of the source zone was employed to monitor relevant geochemical indicators and PHC concentrations. Compound specific isotope analysis (CSIA) was used to identify biodegradation patterns and to investigate the occurrence of microbial sulfate reduction. Selected metabolites and reverse-transcriptase quantitative polymerase chain reaction analyses of expressed biodegradation genes (as mRNA) were also used to characterize the response of indigenous microorganisms (especially sulfate reducing bacteria) to the added sulfate. Three sulfate application episodes (5000 L each) at various Na 2 SO 4 concentrations were allowed to infiltrate under a constant hydraulic head. Although the applied sulfate solution was impacted by density driven advection, detailed monitoring data indicated that the sulfate-enriched water mixed with up-gradient groundwater as it migrated downward through the residual PHC zone and formed a co-mingled downgradient plume with the dissolved PHC compounds. The enrichment of δ 34 S of sulfate in conjunction with a decrease in sulfate concentration showed the occurrence of sulfate reduction due to the applied sulfate. Increased dissolved inorganic carbon (DIC) concentrations associated with a shift toward more depleted values of δ 13 C of DIC was indicative of an input of isotopically depleted DIC from biodegradation of benzene, toluene and o-xylene (BTX). Despite fluctuations in the BTX concentrations, the CSIA data for BTX showed that these compounds were biodegraded. The biomarker data provided supporting evidence that toluene and o-xylene were undergoing anaerobic biodegradation due to sulfate reduction. This study provides insight into factors controlling surface-based delivery of sulfate to shallow PHC impacted groundwater systems, and the value of isotopic and molecular-biological procedures to augment conventional monitoring tools

TRABAJOS DE MÉTODO ARQUEOLÓGICO [Material gráfico]

Copia digital. Madrid : Ministerio de Educación, Cultura y Deporte, 201

Método híbrido para categorización de texto basado en aprendizaje y reglas

En este artículo se presenta un nuevo método híbrido de categorización automática de texto, que combina un algoritmo de aprendizaje computacional, que permite construir un modelo base de clasificación sin mucho esfuerzo a partir de un corpus etiquetado, con un sistema basado en reglas en cascada que se emplea para filtrar y reordenar los resultados de dicho modelo base. El modelo puede afinarse añadiendo reglas específicas para aquellas categorías difíciles que no se han entrenado de forma satisfactoria. Se describe una implementación realizada mediante el algoritmo kNN y un lenguaje básico de reglas basado en listas de términos que aparecen en el texto a clasificar. El sistema se ha evaluado en diferentes escenarios incluyendo el corpus de noticias Reuters-21578 para comparación con otros enfoques, y los modelos IPTC y EUROVOC. Los resultados demuestran que el sistema obtiene una precisión y cobertura comparables con las de los mejores métodos del estado del arte

Critical thickness of phenolic resin-based carbon interfacial layer for improving long cycling stability of silicon nanoparticle anodes

Silicon has a high theoretical capacity, still limits its application on Si-based anodes due to the problems of low electric conductivity, large volume change, continuous formation of unstable solid electrolyte interphase layer, and easy fracture during lithiation and delithiation process. Despite various carbon coating approaches are developed to fabricate carbon coated silicon core-shell and yolk-shell nanocomposites with improved electrochemical performance, the challenges including poor long-term cyclability, low Si mass ratio, and scalability remains. To overcome these challenges, we design an interfacial microporous carbon coating strategy on silicon nanoparticles to form homogeneous coaxial core-shell nanostructures. This synthesis sol-gel approach is simple, easy to scale up, and direct growth phenolic resins on the surface with uniform and controllable thickness. Additionally, the fabricated carbon layers form the microporous structures and phenolic resin frameworks, thus enabling the fast lithium ion transport and formation of stable solid electrolyte interphase film. By finely controlling the thickness of this phenolic resin-based carbon of 10 nm, excellent protection of silicon nanoparticles as well as high electrochemical performance are achieved, delivering a high capacity of 1006 mA h g−1 and Coulombic efficiency of \u3e99.5% after 500 times at a current density of 500 mA g−1

A two-route CNN model for bank account classification with heterogeneous data.

Classifying bank accounts by using transaction data is encouraging in cracking down on illegal financial activities. However, few research simultaneously use heterogenous features, which are embedded in the time series data. In this paper, a two route convolution neural network TRHD-CNN model, fed with two types of heterogeneous feature matrices, is proposed for classifying the bank accounts. TRHD-CNN adopts divide and conquer strategy to extract characteristics from two types of data source independently. The strategy is proved able in mining complementary classification characteristics. We firstly transfer the original log data into a directed and dynamic transaction network. On the basis of that, two feature generation methods are devised for extracting information from local topological structure and time series transaction respectively. A DirectedWalk method is developed in this paper to learning the network vector of vertices used for embedding the neighbor relationship of bank account. The extensive experimental results, conducted on a real bank transaction dataset that contains illegal pyramid selling accounts, show the significant advantage of TRHD-CNN over the existing methods. TRHD-CNN can provide recall scores up to 5.15% higher than competing methods. In addition, the two-route architecture of TRHD-CNN is easy to extend to multi-route scenarios and other fields

Detecting Fraudulent Bank Account Based on Convolutional Neural Network with Heterogeneous Data

Detecting fraudulent accounts by using their transaction networks is helpful for proactively preventing illegal transactions in financial scenarios. In this paper, three convolutional neural network models, i.e., NTD-CNN, TTD-CNN, and HDF-CNN, are created to identify whether a bank account is fraudulent. The three models, same in model structure, are different in types of the input features. Firstly, we embed the bank accounts’ historical trading records into a general directed and weighted transaction network. And then, a DirectedWalk algorithm is proposed for learning an account’s network vector. DirectedWalk learns social representations of a network’s vertices, by modeling a stream of directed and time-related trading paths. The local topological feature, generating by accounts’ network vector, is taken as input of NTD-CNN, and TTD-CNN takes time series transaction feature as input. Finally, the two kinds of heterogeneous data, being integrated into a novel feature matrix, are fed into HDF-CNN for classifying bank accounts. The experimental results, conducted on a real bank transaction dataset, show the advantage of HDF-CNN over the existing methods