Boise State University

About: Boise State University is a education organization based out in Boise, Idaho, United States. It is known for research contribution in the topics: Population & Poison control. The organization has 3698 authors who have published 8664 publications receiving 210163 citations. The organization is also known as: BSU & Boise State.

First application of titanium-in-zircon thermometry to ultrahigh-temperature metamorphism

Abstract: Titanium-in-zircon thermometry has been applied to evaluate the temperatures of metamorphism for two ultrahigh-temperature (UHT) granulite localities, the Neoproterozoic Anapolis-Itaucu Complex in central Brazil, and lower crustal xenoliths from the Neo-archean Kaapvaal craton in South Africa. UHT metamorphism in the Anapolis-Itaucu Complex occurred ca. 640 Ma. Ti-in-zircon thermometry on metamorphic zircon yields temperatures of 965–811 °C; a population of late zircon that formed from ilmenite and rutile breakdown records temperatures of ∼780 °C. Maximum temperatures are less than those inferred from quantitative phase equilibria modeling. Based on textural setting (inclusions in peak phases and as matrix grains), these zircons likely record growth during the pro-grade and/or postpeak evolution. In contrast, the Kaapvaal xenoliths record two phases of zircon growth: during UHT metamorphism at 2720–2715 Ma, followed by growth during isobaric cooling in kyanite-bearing leucosomes ca. 2690 Ma. Temperatures recorded by the two phases of growth are 1024–878 °C and 936–839 °C, respectively, suggesting that zircon growth occurred during UHT conditions and continued throughout the postpeak evolution over a 200 °C interval. These examples demonstrate that the Ti-in-zircon thermometer is a powerful new tool with the potential to determine crystallization temperatures of zircon during granulite facies metamorphism, throughout the prograde to postpeak evolution of such terranes.

Adaptive learning of Byzantines' behavior in cooperative spectrum sensing

Abstract: This paper considers the problem of Byzantine attacks on cooperative spectrum sensing in cognitive radio networks. Our major contribution is a technique to learn about the cognitive radio (CR) potential malicious behavior over time and thereby identifies the Byzantines and then estimates their probabilities of false alarm (Pf a ) and detection (Pd). We show that for a given set of data over time, the Byzantines can be identified for any a (percentage of Byzantines). It has also been shown that these estimates of Pf a and Pn of the Byzantines are asymptotically unbiased and converge to their true values at the rate of 0(T–1/2). We then use these probabilities to adaptively design the fusion rule. We calculate the Probability of error (Q e ) and compare it with the minimum probability of error possible.

Using problem-based learning to develop skills in solving unstructured problems

Abstract: This article describes my efforts to improve student skills in solving unstructured problems in a junior-level undergraduate business course by employing a problem-based learning (PBL) design. A rationale for adopting a PBL approach for this course is articulated. A 7-step problem-solving model is then presented. The course’s design is described, including its learning outcomes, PBL projects, associated learning activities, and methods of assessing learning. Finally, student reactions and evidence of learning are discussed. The article concludes by raising a related issue: If graduates possess skills in solving unstructured problems, will businesses be receptive to their use?

Aboveground Total and Green Biomass of Dryland Shrub Derived from Terrestrial Laser Scanning

Abstract: Sagebrush ( Artemisia tridentata ), a dominant shrub species in the sagebrush-steppe ecosystem of the western US, is declining from its historical distribution due to feedbacks between climate and land use change, fire, and invasive species. Quantifying aboveground biomass of sagebrush is important for assessing carbon storage and monitoring the presence and distribution of this rapidly changing dryland ecosystem. Models of shrub canopy volume, derived from terrestrial laser scanning (TLS) point clouds, were used to accurately estimate aboveground sagebrush biomass. Ninety-one sagebrush plants were scanned and sampled across three study sites in the Great Basin, USA. Half of the plants were scanned and destructively sampled in the spring ( n = 46), while the other half were scanned again in the fall before destructive sampling ( n = 45). The latter set of sagebrush plants was scanned during both spring and fall to further test the ability of the TLS to quantify seasonal changes in green biomass. Sagebrush biomass was estimated using both a voxel and a 3-D convex hull approach applied to TLS point cloud data. The 3-D convex hull model estimated total and green biomass more accurately ( R 2 = 0.92 and R 2 = 0.83, respectively) than the voxel-based method ( R 2 = 0.86 and R 2 = 0.73, respectively). Seasonal differences in TLS-predicted green biomass were detected at two of the sites ( p p = 0.029), elucidating the amount of ephemeral leaf loss in the face of summer drought. The methods presented herein are directly transferable to other dryland shrubs, and implementation of the convex hull model with similar sagebrush species is straightforward.