The University of Louisiana at Lafayette, or UL Lafayette, is a coeducational, public, research university located in Lafayette, in the U.S. state of Louisiana. It has the largest enrollment within the nine-campus University of Louisiana System and has the second largest enrollment in Louisiana.Founded in 1898 as an industrial school, the institution developed into a four-year university during the twentieth century and became known by its present name in 1999. Concurrently the university evolved into a national research and doctoral university as noted by its Carnegie categorization as a RU/H: research university . It offers Louisiana's only Ph.D. in francophone studies and Louisiana's only industrial design degree. The university has achieved several milestones in computer science, engineering and architecture. It is also home to a distinct College of the Arts. Wikipedia.
Katukuri J.R.,University of Louisiana at Lafayette
BMC genomics | Year: 2012
Computational approaches to generate hypotheses from biomedical literature have been studied intensively in recent years. Nevertheless, it still remains a challenge to automatically discover novel, cross-silo biomedical hypotheses from large-scale literature repositories. In order to address this challenge, we first model a biomedical literature repository as a comprehensive network of biomedical concepts and formulate hypotheses generation as a process of link discovery on the concept network. We extract the relevant information from the biomedical literature corpus and generate a concept network and concept-author map on a cluster using Map-Reduce frame-work. We extract a set of heterogeneous features such as random walk based features, neighborhood features and common author features. The potential number of links to consider for the possibility of link discovery is large in our concept network and to address the scalability problem, the features from a concept network are extracted using a cluster with Map-Reduce framework. We further model link discovery as a classification problem carried out on a training data set automatically extracted from two network snapshots taken in two consecutive time duration. A set of heterogeneous features, which cover both topological and semantic features derived from the concept network, have been studied with respect to their impacts on the accuracy of the proposed supervised link discovery process. A case study of hypotheses generation based on the proposed method has been presented in the paper. Source
Duke-Sylvester S.M.,University of Louisiana at Lafayette
Philosophical transactions of the Royal Society of London. Series B, Biological sciences | Year: 2013
RNA viruses account for numerous emerging and perennial infectious diseases, and are characterized by rapid rates of molecular evolution. The ecological dynamics of most emerging RNA viruses are still poorly understood and difficult to ascertain. The availability of genome sequence data for many RNA viruses, in principle, could be used to infer ecological dynamics if changes in population numbers produced a lasting signature within the pattern of genome evolution. As a result, the rapidly emerging phylogeographic structure of a pathogen, shaped by the rise and fall in the number of infections and their spatial distribution, could be used as a surrogate for direct ecological assessments. Based on rabies virus as our example, we use a model combining ecological and evolutionary processes to test whether variation in the rate of host movement results in predictive diagnostic patterns of pathogen genetic structure. We identify several linearizable relationships between host dispersal rate and measures of phylogenetic structure suggesting genetic information can be used to directly infer ecological process. We also find phylogenetic structure may be more revealing than demography for certain ecological processes. Our approach extends the reach of current analytic frameworks for infectious disease dynamics by linking phylogeography back to underlying ecological processes. Source
Schwarz C.,University of Louisiana at Lafayette
Information and Management | Year: 2014
Despite the pervasiveness of outsourcing, many outsourcing ventures have been unable to achieve success. I posit that one explanation for the elusiveness of the achievement of success relates to a lack of consensus regarding what constitutes "success." This study provides a more complete, multidimensional definition of outsourcing success, which will enable a better understanding of success with a more effective and consistent measure of outsourcing success. The results indicate that practitioners and academics agree on the top criteria used to define success. A quantitative study confirms the conceptualization of outsourcing success as a first-order construct with eight dimensions. © 2013 Published by Elsevier B.V. Source
Agency: NSF | Branch: Standard Grant | Program: | Phase: | Award Amount: 250.00K | Year: 2015
Big Data analytics is the process of mining useful knowledge in very large data sets, critical to the advancement of many research and application fields. With manufacturing technology downscaling coupled with increasing power densities, modern computer systems suffer from potential hardware and software failures, which can manifest themselves as errors. The errors are to happen when long-running Big Data analytics are executed on the systems, causing crash or worse, returning incorrect results silently. While numerous hardware-based resilience methods exist, they often come at the cost of excessive power efficiency reduction and substantial design complexity enlargement among others.
The project aims to reinforce popular Big Data analytics by embracing a host of comprehensive algorithmic resilience (CAR) software techniques that include concurrent error detection, coordinated checkpointing, and execution recovery, for high execution resilience. Upon detecting potential hardware and software errors concurrently during analytics, CAR enables execution recovery from detected errors without lofty overhead common to hardware-based resilience methods. Research activities of the project aim to achieve six main objectives that focus on addressing several technical challenges to realize CAR, based on investigators? encouraging preliminary results and prior work. The success of this project can benefit wide scientific and industrial applications due to its better support of Big Data analytics and processing. Research advances from this research are to be incorporated into undergraduate and graduate education, to be disseminated and shared broadly through technical presentations and by a website, and to inspire high school students for their STEM interest.
Agency: NSF | Branch: Continuing grant | Program: | Phase: Chemical Catalysis | Award Amount: 200.00K | Year: 2016
The Chemical Catalysis Program of the Chemistry Division supports the project by Professor Radhey S. Srivastava and Dr. Siva Murru. Prof. Radhey S. Srivastava is a faculty member in the Department of Chemistry at University of Louisiana at Lafayette. Prof. Srivastavas group is developing novel catalytic systems to convert simple hydrocarbons to value-added products. The main goal of the proposed research is to design and develop novel catalytic systems for the production of valuable molecules that are of industrial significance. The method uses copper catalysts with chiral ligands. The demand for chiral allyl amines has escalated sharply in recent years, driven by the demands in pharmaceuticals, agrochemicals, flavors, fragrances, and materials. In addition, these catalytic methods are potential useful for the total synthesis of bioactive molecules and chiral drugs. The researchers have introduced comprehensive educational and outreach programs associated with intellectual and economic development. Professor Srivastavas group has been working at the interface of organic, organometallics, and catalysis chemistry.
This research addresses the development of a direct catalytic asymmetric amination of simple (non-functionalized) allylic carbon-hydrogen (C-H) substrates using hydroxylamines as aminating agents. The prior art on this field used oxidative amination to make chiral N-hydroxy allyl amines that require additional methods to make chiral allyl amines. The main aim is to find suitable catalytic systems that would deliver the chiral allyl amines with high yields and enantioselectivities. This approach includes rational design and synthesis of new chiral ligands and complexes. The research project screens the catalysts under various reaction conditions while varying solvents, temperature, and additives. The next objective is to explore the synthetic applications to access valuable chemicals such as chiral beta-alkyl N-aryl Aza Baylis-Hillman (ABH) adducts, beta-amino esters and beta-lactams, as well as bioactive molecules such as hydroxymethyl docetaxel fragment, Ezetimibe, and Vigabatrin. Another objective is to address mechanistic aspects of the reaction and provide a better understanding of the reaction pathway and the catalytic activity. This objective helps to develop new catalysts and novel synthetic methods. Professor Srivastavas program enhances public awareness of the importance of chemical sciences. Undergraduates, K-12 students, and high school teachers are exposed to cutting-edge science related to chemical catalysis through summer research and teacher training workshops. The education outreach initiatives involve the inclusion of women and underrepresented groups and strengthen competitiveness by promoting enhancement programs such as LSAMP and McNair program to retain students.