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.
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.
Agency: NSF | Branch: Standard Grant | Program: | Phase: ARCHAEOLOGY | Award Amount: 19.32K | Year: 2017
Understanding human population levels over time is fundamentally important for answering numerous questions about society?s deep past. For instance, were rising population levels responsible for spurring major social transformations, like the adoption of agriculture, more complex social and political hierarchies, or intensified warfare? How were population levels affected by major events like the colonization of new areas, environmental crises, or the collapse of states? Traditionally, archaeologists arrive at estimates of past demography through archaeological survey, but this is not always possible (e.g., for phases of human settlement with low population densities and high mobility, or in regions with poor surface visibility of sites due to thick vegetation or alluvial deposition). Paleolimnology, the study of lakes and other bodies of water , has the potential to provide relevant information. This interdisciplinary project aims to establish whether recently defined biochemical markers in ancient lake sediment can serve as reliable proxies for human population levels over time. P.I. Dr. Elizabeth Arkush, Co-P.I. Dr. Aubrey Hillman, Co-PI Dr. Josef Werne, and Co-PI Dr. Mark Abbott will use National Science Foundation support to analyze sediments in lake cores recovered in 2015 from small lakes in the Titicaca Basin of southern Peru. If successful, this technique will hold major potential for reconstructing the demographic histories of regions. In addition, because lake core sequences also have other significant information such as precipitation levels, this technique holds promise for investigating the relationship between human populations and environmental change by reducing the chronological uncertainty that comes from matching separate climate and population records.
Recent research shows that fecal ?stanols?, a class organic compounds deriving from feces of higher mammals, are present in measurable amounts in lake sediments. The presence and quantity should reflect human population levels in the lake watershed, particularly coprostanol, which is the major stanol type in human feces. This project aims to evaluate and expand the utility of this biomarker for archaeological research by comparing stanols in cores from two lakes in the south-central Andes with sequences of population levels derived from two completed full-coverage archaeological surveys. In addition to advancing new methodology for the reconstruction of past demography, the resulting dataset has the potential to make significant advances on questions about the connection between pre-Columbian sociopolitical change and paleoclimate. The project?s broader impacts include significant training and research opportunities for undergraduate and graduate students.
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.
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.
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
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
Agency: NSF | Branch: Standard Grant | Program: | Phase: RES IN NETWORKING TECH & SYS | Award Amount: 385.02K | Year: 2015
The vast majority of todays wireless communication systems operate in the microwave spectrum below 3 GHz, which is experiencing severe shortage and has become a crowded resource. To meet the 1000x growth challenge in mobile broadband traffic, the millimeter wave (mmWave) band, operating at frequencies between 20 and 300 GHz, has been identified for next-generation (5G) cellular systems. While the use of mmWave band addresses the pressing needs of more wireless spectrum, it brings a new set of unique technical challenges such as severe path loss and undesired coverage holes. To this end, Device-to-Device (D2D) networks are proposed to employ short-range wireless links to establish opportunistic connections between mobile users. In this project, the researchers will explore a diversity of application-oriented problems in D2D, culminating in the formulation of both new fundamental theories and advanced technologies that contribute to the development of next-generation mobile communication systems. This project will effectively stimulate multi-disciplinary collaboration across a broad spectrum of fields, including anthropology, communications, computer science, economics, public health, demography, and sociology. It will also effectively enrich courses by implementation and experimental activities, providing students with hands-on experience.
The proposed research includes two research thrusts to design, implement, evaluate, and prototype new protocols and algorithms, in support of efficient data gathering and dissemination in D2D. First, a class of applications involve large-scale data gathering from mobile devices. Although crowdsourcing has been discussed in recent years, the marriage of crowdsourcing and D2D creates new, interesting research problems, due to the unique non-deterministic network paradigm. The researchers will investigate several dimensions in support of D2D-based crowdsourcing, including a competition-based participant recruitment scheme for delay-sensitive applications and an effective quest algorithm to deliver crowdsourcing requests. Second, efficient data dissemination is indispensable in many D2D applications. In contrast to the prior work that focuses on classical multicasting from a source to a given set of receivers, the researchers propose to investigate a unique and interesting problem where the receivers are not explicitly known. In such settings, a natural approach is to distribute data at some depositories, that further deliver the content to interested data consumers upon requests. Under this framework, the researchers will devise algorithms to choose optimal depositories for maximizing the total profit and develop new incentive schemes to enable efficient dissemination. Complementing these research thrusts is an experimental prototyping and validation track, with various design choices and alternatives experimentally studied, evaluated and refined.
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.
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.
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.
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.