Lafayette, LA, United States
Lafayette, LA, United States

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.


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Patent
University of Louisiana at Lafayette | Date: 2016-09-28

The disclosed invention is a method for using collagen extracted from animal bones, hides, and flesh waste as a protein-based glue (Bone Glue) to create asphalt with a modified asphalt binder. The method comprises of mixing Bone Glue with water, adding it to an asphalt binder, evaporating the water, adding the modified binder to aggregate and mixing at an elevated temperature. The modified asphalt binder consists of a predetermined amount of Bone Glue and asphalt binder.


Patent
University of Louisiana at Lafayette | Date: 2016-10-28

A multi-functional open graded friction course and a method of treating highway water runoff using the multi-functional open graded friction course are described herein. Open graded friction course is treated with an additive or additives, such as, but not limited to, an adsorbent. After treatment with the additive, the additive remains in the void spaces in the open graded friction course, thus creating a multi-functional open graded friction course. When highway or roadway water runoff flows into the void spaces, pollutants, such as heavy metals, are adsorbed by the additives and the water then flows laterally out of the multi-functional open graded friction course.


Patent
University of Louisiana at Lafayette | Date: 2016-10-21

In compounding pharmaceuticals at the outset, the medications are typically arrive in their mass produced form (pills, creams, syrups, etc.) and are individually packaged. Some packaging forms, particularly tubes, take an excessive amount of time to empty for the medication to be used in later compound medications. Presently in this industry, human workers will squeeze the medication out of each individual tube into large bucket containers for storage until the medication is ready for mixing. The disclosed invention provides a novel apparatus and method for emptying multiple tubes at a time with limited human interaction. The apparatus uses a roller and cap-pressing method that can empty several tubes at one time, significantly increasing efficiency. The apparatus is then connected with a novel software package which controls the operation of the device and can provide a counting mechanism for contract monitoring and predicting the lifespan of the device.


Grant
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.


Grant
Agency: NSF | Branch: Standard Grant | Program: | Phase: S&CC: Smart & Connected Commun | Award Amount: 195.00K | Year: 2016

This EArly-Concept Grant for Exploratory Research (EAGER) project will design an analytic model for assessing a communitys resilience and analyzing the multi-dimensional effects of a crisis or disaster on the population. The research will provide new insights into network theory and how network characteristics affect transmission of hazard and risk warnings within communities. The outcomes of this effort will provide alternate approaches to planning and response, and develop the foundation for analyzing dynamic changes in social network structure that occur as crises unfold. Project findings will provide first responders, local, and state governments with the capacity to visualize and mobilize their communities and human capital in innovative and effective ways. The project also offers the potential to enhance public and private sector collaboration for disaster planning, build trusted communications networks, and improve coordination of resources across the private sector.

The mobilization of human capital is the most challenging facet of any response to a disaster. This research adopts a novel approach for analyzing civil emergencies by addressing the core question of the cost - broadly defined in terms of the negative social, economic and psychological impacts - of a single civil disaster event on a community. The research will employ a mixed-methods, multi-disciplinary approach to conduct a full spectrum of impact analyses on the economic, social, psychological, and security costs of a civil disaster. The impact analyses will be followed by an assessment of the response, resiliency, and adaptability of the community through the integration of the human capital database. Findings from this research could potentially transform analytical approaches in evaluating the response and economic cost analyses of disasters.


Grant
Agency: NSF | Branch: Standard Grant | Program: | Phase: INFO INTEGRATION & INFORMATICS | Award Amount: 497.86K | Year: 2016

With the advent of emerging massive datasets in image processing,
biology, finance, and so on, traditional data mining systems
face new challenges to induce knowledge and discover causal
relations in dynamic streaming feature environments, where new
features continuously stream in over time. These challenges include
(1) continuous growth of feature volumes over time, (2) a huge feature
space, even of unknown or infinite size, and (3) not all features
being available before learning begins. These challenges call for a
new learning paradigm with continuously increasing features. In this
project, we take the increasing feature volumes as streaming features,
and the corresponding learning problem is referred to as Online
Learning with Streaming Features (OLSF). Since existing online
learning efforts mostly deal with data with increasing observations
but fixed feature dimensions, OLSF provides a unique chance to unfold
and characterize pattern trends for dynamic systems with streaming
features.

This project aims to address two fundamental issues for OLSF: (1)
causal discovery with sequentially increasing feature dimensions; and
(2) causal relations for feature selection. We design novel methods
and algorithms for causal discovery in OLSF and establish formal connections
between casual discovery and feature selection by investigating the
mutual benefits between them in the context of online stream feature
learning. To evaluate the proposed research, we conduct empirical
studies on a large body of benchmark datasets, as well as with a
domain-specific real-world case study in personalized news filtering
and summarization where the feature space changes over time. The
new algorithms and techniques in this project will advance our ability
to discover knowledge from dynamic systems using streaming features
with bounded resources. The spectrum of the methods from the project
will not only enrich our knowledge and understanding of pattern
discovery and machine learning for dynamic systems, but also provide a
new view to capture and characterize dynamic systems from a streaming
feature perspective.


Grant
Agency: NSF | Branch: Continuing grant | Program: | Phase: GLOBAL CHANGE | Award Amount: 225.28K | Year: 2016

Determination of Earth system climate sensitivity, the amount that global temperatures increase in response to a doubling of atmospheric carbon dioxide levels, is critical towards predicting the increase in global temperatures from rising carbon dioxide levels in the atmosphere. Much of our knowledge of this value is based on data from periods with atmospheric carbon dioxide levels no higher than today. This project will develop new high-resolution atmospheric carbon dioxide records for comparison with existing temperature data in order to better quantify the response between temperature and atmospheric carbon dioxide levels across the last 65 million years of Earth history. This approach will allow for improved quantification of climate sensitivity across a wide range of atmospheric carbon dioxide levels and climate states, including both icehouse and greenhouse conditions, and will provide better information for understanding how temperatures could increase as a result of future increases in atmospheric carbon dioxide from fossil fuel burning.

This research will use the large number of published carbon isotope measurements on fossil terrestrial organic matter and the known effects of pCO2 on C3-plant carbon isotope fractionation in order to provide a new, high-resolution pCO2 reconstruction using a Monte Carlo uncertainty analysis. Expansion of the available pCO2 proxy data to significantly higher resolution using the abundance of terrestrial carbon isotope data available in the literature will allow for improved estimates of Earth system climate sensitivity across different climate states. This work will focus on: 1) the late Cenozoic (30-0 Ma), which is characterized by relatively low pCO2, Antarctic ice sheets, and well-constrained estimates of the carbon isotope composition of atmospheric CO2, and 2) the early Cenozoic (66-50 Ma), which is characterized by elevated temperatures, moderate to high pCO2, a lack of polar ice sheets, and a series of geologically brief global warming events known as hyperthermals.


Patent
University of Louisiana at Lafayette | Date: 2016-06-24

The inventive method provides a mechanism for enhancing oil and gas production in shale wells in order to prevent re-Fracking of the wells. The invention discloses the effect that temperature has on creating micro-fractures in the shale and offers opportunities to apply temperature in a way that increases seismic activity, including through the application of low quality steam or by heating the fracturing fluid.


Patent
University of Louisiana at Lafayette | Date: 2016-06-24

This inventive method provides a novel way of modeling basins in planning the drilling of crude oil and natural gas wells by accounting for thermodynamic considerations in tracking the pore pressure of a location of interest. By plotting the energy gradients, heat flux, and thermal conductivity of the location of interest, the user can more accurately identify the location of the Top of Geopressure and additional pertinent information during the well drilling planning process that can reduce costs and increase the safety of the process.


Patent
University of Louisiana at Lafayette | Date: 2016-06-20

The method relates to the field of asymmetric allylic amination and comprises preparing a chiral N-substituted allylic amine compound from the corresponding allylic substrates and substituted hydroxylamines, in the presence of a catalyst, said catalyst comprising copper compounds and a chiral ligand. Examples of chiral amine compounds which can be made using the method include Vigabatrin, Ezetimibe Terbinafine, Naftifine 3-methylmorphine, Sertraline, Cinacalcet, Mefloquine hydrochloride, and Rivastigmine. There are over 20,000 known bioactive molecules with chiral N-substituted allylic amine substructure. The method may also be used to produce non-natural chiral -aminoacid esters, a sub-class of chiral N-substituted allylic amine compounds. Examples of -aminoacid ester which can be produced by the disclosed method, include, but are not limited to, N-(2-methylpent-1-en-3-yl)benzenamine and Ethyl 2-methylene-3-(phenylamino)butanoate. Further, the products of the method described herein can be used to produce chiral heterocycles and bioactive molecules or materials. A novel chiral copper-BINAM nitrosoarene complex is also set forth.

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