Microfiltration of water-based paint effluents

This paper deals with recovery of water from the effluent in a paint factory in Kuala Lumpur for reuse using microfiltration technique.

Mass transfer correlation in flotation of palm oil by colloidal gas aphrons

Colloidal gas aphrons (CGAs) are micron-sized bubbles, which are produced by stirring a dilute surfactant solution at a high speed. In this work, CGAs have been used to clarify oily wastewater by flotation technique. The CGAs sparging rate was a critical factor that governed the efficiency of the process. A model for the determination of the mass transfer coefficient is also developed for the purpose of process design.

A qualitative enquiry into the appropriation of mobile telephony at the bottom of the pyramid

Purpose: The paper aims to analyse Bottom of the Pyramid (BoP) customers’ (e.g. Bangladeshi farmers) use and appropriation of mobile telephony and to critically identify a suitable research strategy for such investigation.

Design/methodology/approach: Concentrated ethnographic immersion was combined with both methodological and investigator triangulation during a four-month period of fieldwork conducted in Bangladeshi villages to obtain more robust findings. Concentrated immersion was required to achieve relatively speedier engagement owing to the difficulty in engaging with respondents on a long-term basis.

Findings: The farmers’ use of mobile telephony went beyond the initial adoption, as they appropriated it through social and institutional support, inventive means and/or changes in their own lifestyle. The paper argues that technology appropriation, being a result of the mutual shaping of technology, human skills and abilities and macro-environmental factors, enables users to achieve desired outcomes which may not always be the ones envisaged by the original designers.

Research limitations/implications: The paper contributes to two major areas: first, it identifies technology appropriation as an important and emerging concept in international marketing research; second, it suggests a concentrated form of ethnographic engagement for studying technology appropriation in a developing country context.

Practical implications: A good understanding of the dynamic interplay between users’ skills and abilities, social contexts and technological artefacts/applications is required in order for businesses to serve BoP customers profitably.

Originality/value: The paper presents a dynamic model of technology appropriation based on findings collected through a pragmatic approach by combining concentrated ethnographic immersion with methodological and investigator triangulation

ER stress-mediated autophagy promotes Myc-dependent transformation and tumor growth

The proto-oncogene c-Myc paradoxically activates both proliferation and apoptosis. In the pathogenic state, c-Myc-induced apoptosis is bypassed via a critical, yet poorly understood escape mechanism that promotes cellular transformation and tumorigenesis. The accumulation of unfolded proteins in the ER initiates a cellular stress program termed the unfolded protein response (UPR) to support cell survival. Analysis of spontaneous mouse and human lymphomas demonstrated significantly higher levels of UPR activation compared with normal tissues. Using multiple genetic models, we demonstrated that c-Myc and N-Myc activated the PERK/eIF2α/ATF4 arm of the UPR, leading to increased cell survival via the induction of cytoprotective autophagy. Inhibition of PERK significantly reduced Myc-induced autophagy, colony formation, and tumor formation. Moreover, pharmacologic or genetic inhibition of autophagy resulted in increased Myc-dependent apoptosis. Mechanistically, we demonstrated an important link between Myc-dependent increases in protein synthesis and UPR activation. Specifically, by employing a mouse minute (L24+/-) mutant, which resulted in wild-type levels of protein synthesis and attenuation of Myc-induced lymphomagenesis, we showed that Myc-induced UPR activation was reversed. Our findings establish a role for UPR as an enhancer of c-Myc-induced transformation and suggest that UPR inhibition may be particularly effective against malignancies characterized by c-Myc overexpression.

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.

Fast Mining of Interesting Phrases from Subsets of Text Corpora

We address the problem of mining interesting phrases from subsets of a text corpus where the subset is specified using a set of features such as keywords that form a query. Previous algorithms for the problem have proposed solutions that involve sifting through a phrase dictionary based index or a document-based index where the solution is linear in either the phrase dictionary size or the size of the document subset. We propose the usage of an independence assumption between query keywords given the top correlated phrases, wherein the pre-processing could be reduced to discovering phrases from among the top phrases per each feature in the query. We then outline an indexing mechanism where per-keyword phrase lists are stored either in disk or memory, so that popular aggregation algorithms such as No Random Access and Sort-merge Join may be adapted to do the scoring at real-time to identify the top interesting phrases. Though such an approach is expected to be approximate, we empirically illustrate that very high accuracies (of over 90%) are achieved against the results of exact algorithms. Due to the simplified list-aggregation, we are also able to provide response times that are orders of magnitude better than state-of-the-art algorithms. Interestingly, our disk-based approach outperforms the in-memory baselines by up to hundred times and sometimes more, confirming the superiority of the proposed method.