Louisiana State University

About: Louisiana State University is a education organization based out in Baton Rouge, Louisiana, United States. It is known for research contribution in the topics: Population & Poison control. The organization has 40206 authors who have published 76587 publications receiving 2566076 citations. The organization is also known as: LSU & Louisiana State University and Agricultural and Mechanical College.

Grid coverage for surveillance and target location in distributed sensor networks

Abstract: We present novel grid coverage strategies for effective surveillance and target location in distributed sensor networks. We represent the sensor field as a grid (two or three-dimensional) of points (coordinates) and use the term target location to refer to the problem of locating a target at a grid point at any instant in time. We first present an integer linear programming (ILP) solution for minimizing the cost of sensors for complete coverage of the sensor field. We solve the ILP model using a representative public-domain solver and present a divide-and-conquer approach for solving large problem instances. We then use the framework of identifying codes to determine sensor placement for unique target location, We provide coding-theoretic bounds on the number of sensors and present methods for determining their placement in the sensor field. We also show that grid-based sensor placement for single targets provides asymptotically complete (unambiguous) location of multiple targets in the grid.

REVEL: A model for Recent plate velocities from space geodesy

Abstract: [1] We present a new global model for Recent plate velocities, REVEL, describing the relative velocities of 19 plates and continental blocks. The model is derived from publicly available space geodetic (primarily GPS) data for the period 1993–2000. We include an independent and rigorous estimate for GPS velocity uncertainties to assess plate rigidity and propagate these uncertainties to the velocity estimates. The velocity fields for North America, Eurasia, and Antarctica clearly show the effects of glacial isostatic adjustment, and Australia appears to depart from rigid plate behavior in a manner consistent with the mapped intraplate stress field. Two thirds of tested plate pairs agree with the NUVEL-1A geologic (3 Myr average) velocities within uncertainties. Three plate pairs (Caribbean–North America, Caribbean–South America, and North America–Pacific) exhibit significant differences between the geodetic and geologic model that may reflect systematic errors in NUVEL-1A due to the use of seafloor magnetic rate data that do not reflect the full plate rate because of tectonic complexities. Most other differences probably reflect real velocity changes over the last few million years. Several plate pairs (Arabia–Eurasia, Arabia–Nubia, Eurasia–India) move more slowly than the 3 Myr NUVEL-1A average, perhaps reflecting long-term deceleration associated with continental collision. Several other plate pairs, including Nazca–Pacific, Nazca–South America and Nubia–South America, are experiencing slowing that began ∼25 Ma, the beginning of the current phase of Andean crustal shortening.

Distinguishing coupon proneness from value consciousness: An acquisition-transaction utility theory perspective.

Abstract: Previous research on coupon proneness has measured the construct only in behavioral terms (i.e., consumers who are more responsive to coupon promotions are coupon prone). On the basis of the study ...

GGE Biplot vs. AMMI Analysis of Genotype-by-Environment Data

Abstract: The use of genotype main effect (G) plus genotype-by-environment (GE) interaction (G+GE) biplot analysis by plant breeders and other agricultural researchers has increased dramatically during the past 5 yr for analyzing multi-environment trial (MET) data. Recently, however, its legitimacy was questioned by a proponent of Additive Main Effect and Multiplicative Interaction (AMMI) analysis. The objectives of this review are: (i) to compare GGE biplot analysis and AMMI analysis on three aspects of genotype-by-environment data (GED) analysis, namely mega-environment analysis, genotype evaluation, and test-environment evaluation; (ii) to discuss whether G and GE should be combined or separated in these three aspects of GED analysis; and (iii) to discuss the role and importance of model diagnosis in biplot analysis of GED. Our main conclusions are: (i) both GGE biplot analysis and AMMI analysis combine rather than separate G and GE in mega-environment analysis and genotype evaluation, (ii) the GGE biplot is superior to the AMMI1 graph in mega-environment analysis and genotype evaluation because it explains more G+GE and has the inner-product property of the biplot, (iii) the discriminating power vs. representativeness view of the GGE biplot is effective in evaluating test environments, which is not possible in AMMI analysis, and (iv) model diagnosis for each dataset is useful, but accuracy gain from model diagnosis should not be overstated.