Self-potential signatures associated with an injection experiment at an in situ biological permeable reactive barrier

Strategies available to evaluate the performance of in situ permeable reactive barriers are currently not well developed and often rely on fluid and media sampling directly from the permeable reactive barrier (PRB). Here, we investigate the utility of the self-potential (SP) method as a technique to monitor in situ PRB performance. Our field study was conducted at in situ biological PRB in Portadown, Northern Ireland, UK, which was emplaced to assist in the remediation of groundwater contamination (e.g., hydrocarbons, ammonia) that resulted from the operations and waste disposal practices of a former gasworks. Borehole SP measurements were collected during the injection of contaminant groundwater slugs in an attempt to monitor/detect the response of the microbial activity associated with the breakdown of the added contaminants into the PRB. In addition, an uncontaminated groundwater slug was injected into a different portion of the PRB as a ‘control’ and SP measurements were collected for comparison to the SP response of the contaminant slugs. The results of the SP signals due to the contaminant injections show that the magnitude of the response was relatively small (<10 mV) yet showed a consistent decrease during both contaminant injections. The net decrease in SP recorded during the contaminant injections slowly rebounded to near background values through ~44 hours post-injection. The SP response during the uncontaminated injection showed a slight, albeit negligible (within the margin of error), 1 mV increase in the measured SP signals, in contrast to the contaminant injections. The results of the SP signals recorded from the uncontaminated groundwater injection also persisted through a period of ~47 hours after injection but show a net increase in SP relative to pre-injection values. Based on the difference in SP response between the contaminated and uncontaminated injections, we suggest that the responses are likely to be the result of differences in the chemistry of the injection types (contaminated versus uncontaminated) and in situ groundwater. We argue that the SP signals associated with the contaminated injections are dominated by diffusion (electrochemical) potential, possibly enhanced by a microbial effect. While the results of our investigation show a consistent SP response associated with the contaminant injections that is dominated by diffusional effects, further studies are required in order to better understand the effect of microbial activity on SP signals and the potential utility for the SP method to detect/monitor changes that may be indicative of biological PRB performance.

Clarification of rubber mill wastewater by a plant based biopolymer- comparison with common inorganic coagulants

Background: In this study, the efficiency of Guar gum as a biopolymer has been compared with two other widely used inorganic coagulants, ferric chloride (FeCl3) and aluminum chloride (AlCl3), for the treatment of effluent collected from the rubber-washing tanks of a rubber concentrate factory. Settling velocity distribution curves were plotted to demonstrate the flocculating effect of FeCl3, AlCl3 and Guar gum. FeCl3 and AlCl3 displayed better turbidity removal than Guar gum at all settling velocities.

Result: FeCl3, AlCl3 and Guar gum removed 92.8%, 88.2% and 88.1% turbidity, respectively, of raw wastewater at a settling velocity of 0.1 cm min-1, respectively. Scanning electron microscopic (SEM) study conducted on the flocs revealed that Guar gum and FeCl3produced strong intercoiled honeycomb patterned floc structure capable of entrapping suspended particulate matter. Statistical experimental design Response Surface Methodology (RSM) was used to design all experiments, where the type and dosage of flocculant, pH and mixing speed were taken as control factors and, an optimum operational setting was proposed.

Conclusion: Due to biodegradability issues, the use of Guar gum as a flocculating agent for wastewater treatment in industry is highly recommended.

Topology synthesis of three-legged spherical parallel manipulators employing Lie group theory

Driven by the requirements of the bionic joint or tracking equipment for the spherical parallel manipulators (SPMs) with three rotational degrees-of-freedom (DoFs), this paper carries out the topology synthesis of a class of three-legged SPMs employing Lie group theory. In order to achieve the intersection of the displacement subgroups, the subgroup characteristics and operation principles are defined in this paper. Mainly drawing on the Lie group theory, the topology synthesis procedure of three-legged SPMs including four stages and two functional blocks is proposed, in which the assembly principles of three legs are defined. By introducing the circular track, a novel class of three-legged SPMs is synthesized, which is the important complement to the existing SPMs. Finally, four typical examples are given to demonstrate the finite displacements of the synthesized three-legged SPMs.

A Bayesian method for detecting stellar flares

We present a Bayesian-odds-ratio-based algorithm for detecting stellar flares in light-curve data. We assume flares are described by a model in which there is a rapid rise with a half-Gaussian profile, followed by an exponential decay. Our signal model also contains a polynomial background model required to fit underlying light-curve variations in the data, which could otherwise partially mimic a flare. We characterize the false alarm probability and efficiency of this method under the assumption that any unmodelled noise in the data is Gaussian, and compare it with a simpler thresholding method based on that used in Walkowicz et al. We find our method has a significant increase in detection efficiency for low signal-to-noise ratio (S/N) flares. For a conservative false alarm probability our method can detect 95 per cent of flares with S/N less than 20, as compared to S/N of 25 for the simpler method. We also test how well the assumption of Gaussian noise holds by applying the method to a selection of 'quiet' Kepler stars. As an example we have applied our method to a selection of stars in Kepler Quarter 1 data. The method finds 687 flaring stars with a total of 1873 flares after vetos have been applied. For these flares we have made preliminary characterizations of their durations and and S/N.

Exploiting evidence from unstructured data to enhance master data management

Master data management (MDM) integrates data from multiple
structured data sources and builds a consolidated 360-
degree view of business entities such as customers and products.
Today’s MDM systems are not prepared to integrate
information from unstructured data sources, such as news
reports, emails, call-center transcripts, and chat logs. However,
those unstructured data sources may contain valuable
information about the same entities known to MDM from
the structured data sources. Integrating information from
unstructured data into MDM is challenging as textual references
to existing MDM entities are often incomplete and
imprecise and the additional entity information extracted
from text should not impact the trustworthiness of MDM
data.
In this paper, we present an architecture for making MDM
text-aware and showcase its implementation as IBM InfoSphere
MDM Extension for Unstructured Text Correlation,
an add-on to IBM InfoSphere Master Data Management
Standard Edition. We highlight how MDM benefits from
additional evidence found in documents when doing entity
resolution and relationship discovery. We experimentally
demonstrate the feasibility of integrating information from
unstructured data sources into MDM.