Michael Lawrence

Mr. Michael Lawrence

University of Louisiana at Lafayette, Lafayette, California, USA
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Research Focus & Metrics

Academic Career Length

19Years (2008-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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12 October 2023 in Sustainability
Verified
Journal Article
Open access
PDF file
OOOlatunji Victor Oni
Olatunji Victor Oni
MLMichael Lawrence
Michael Lawrence
MZMark Zappi
Mark Zappi
William ChirdonWilliam Chirdon
William Chirdon
The health risks associated with formaldehyde have propelled relevant stakeholders to push for the production of non-toxic wood adhesives. Several countries including the USA, Japan, and Germany have implemented policies mandating manufacturers to reduce the emissions of formaldehyde to lower levels. Protein adhesives stand out due to their sustainability, renewable sources, and biodegradability. However, they are limited by poor wet strength and water resistance, which affect their wide acceptability in the marketplace. Researchers have developed multiple strategies to mitigate these issues to advance protein adhesives so they may compete more favorably with their petroleum-based counterparts. This review paper explores these strategies including cross-linking, modified fillers, and the removal of hydrophilic content while providing insights into the methodological approaches utilized in recent literature with a comparison of the resultant protein adhesives.

1 January 2008 in International Journal of Data Warehousing and Mining
Verified
Journal Article
Closed access
PDF file
MLMichael Lawrence
Michael Lawrence
ARAndrew Rau-Chaplin
Andrew Rau-Chaplin
In a data warehousing environment, aggregate views are often materialized in order to speed up aggregate queries of online analytical processing (OLAP). Due to the increasing size of data warehouses, it is often infeasible to materialize all views. View selection, the task of selecting a subset of views to materialize based on updates and expectations of the query load, is an important and challenging problem. In this article, we explore dynamic view selection in which the distribution of queries changes over time and the set of materialized views must be tuned by replacing some of the previously materialized views with new ones.