Butt Joint Reinforcement in Parallel-Laminated Veneer (PLV) Lumber

Parallel-laminated veneer (PLV) is a high-strength structural material consisting of thin parallel-laminated wood veneers. The use of graphite-cloth reinforcement, placed on either side of a butt joint in 1 1/2- by 3 1/2- by 32-inch Douglas-fir PLV tensile members, was assessed. The finite-element method of analysis was used to predict the behavior in different unreinforced and reinforced butt-jointed PLV tensile members. Relationships between the reinforcing parameters—length, modulus of elasticity, and thickness—and the stresses in the wood and reinforcement components were developed by regression analysis techniques. The reinforcing mechanism reduced the peak stresses at the butt joint and hence increased the ultimate strength of the member. Design of PLV material whose strength is limited by shear stresses that develop at the butt joint is facilitated by use of the proposed relationships.Experimental testing confirmed the predictions of the finite-element analysis. Failure initiated at the unreinforced joint in the specimens. Average tensile strength increased and variability decreased in reinforced specimens. Application of a small amount of reinforcement at the butt joint has been shown to enhance PLV performance

Domain wall propagation in Permalloy nanowires with a thickness gradient

The domain wall nucleation and motion processes in Permalloy nanowires with a thickness gradient along the nanowire axis have been studied. Nanowires with widths, w = 250 nm to 3 um and a base thickness of t = 10 nm were fabricated by electron-beam lithography. The magnetization hysteresis loops measured on individual nanowires are compared to corresponding nanowires without a thickness gradient. The Hc vs. t/w curves of wires with and without a thickness gradient are discussed and compared to micromagnetic simulations. We find a metastability regime at values of w, where a transformation from transverse to vortex domain wall type is expected

Estimating population extinction thresholds with categorical classification trees for Louisiana black bears.

Monitoring vulnerable species is critical for their conservation. Thresholds or tipping points are commonly used to indicate when populations become vulnerable to extinction and to trigger changes in conservation actions. However, quantitative methods to determine such thresholds have not been well explored. The Louisiana black bear (Ursus americanus luteolus) was removed from the list of threatened and endangered species under the U.S. Endangered Species Act in 2016 and our objectives were to determine the most appropriate parameters and thresholds for monitoring and management action. Capture mark recapture (CMR) data from 2006 to 2012 were used to estimate population parameters and variances. We used stochastic population simulations and conditional classification trees to identify demographic rates for monitoring that would be most indicative of heighted extinction risk. We then identified thresholds that would be reliable predictors of population viability. Conditional classification trees indicated that annual apparent survival rates for adult females averaged over 5 years ([Formula: see text]) was the best predictor of population persistence. Specifically, population persistence was estimated to be ≥95% over 100 years when [Formula: see text], suggesting that this statistic can be used as threshold to trigger management intervention. Our evaluation produced monitoring protocols that reliably predicted population persistence and was cost-effective. We conclude that population projections and conditional classification trees can be valuable tools for identifying extinction thresholds used in monitoring programs

Revue des architectes français : organe corporatif paraissant les 1er et 15 de chaque mois

15 août 19411941/08/15 (A1,N4)

Estimating population extinction thresholds with categorical classification trees for Louisiana black bears - Fig 1

<p>Variable importance scores (on the x axes) estimated from random forests of conditional classification trees based on stochastic population simulations for females in the Tensas River Basin (panels A and C) and Upper Atchafalaya River Basin (panels B and D) subpopulations. Explanatory variables corresponded to long-term demographic rates used to generate population trajectories (panels A and B) or averages derived from the first 5 years (panels C and D). Variables were temporal variation in per-capita recruitment (σ_<i>f</i>), the intercept and slope coefficients describing log-linear density-dependence in per-capita recruitment (<i>β</i>_<i>0f</i> and <i>β</i>_<i>1f</i>), temporal variation in apparent survival (σ_<i>φ</i>), mean apparent survival (), average abundance over a 5-year period (), average population growth rates over a 5-year period (), and average apparent survival probabilities over 5 years ().</p