Natural regeneration of trees in selectively logged forest in western Amazonia.

We evaluated the impacts of selective logging on tree regeneration one, four, and eight years after harvests in Antimary State Forest in the State of Acre, Brazil. We inventoried tree seedlings, saplings, and poles (>50 cm tall to <10 cm DBH) on secondary roads, log landing, and skid trails, as well as in the bole and crown zones of canopy gaps created by felling; for comparison we also sampled areas not affected directly by logging. We compared these habitats on the basis soil (physical) characteristics, canopy cover, and floristic composition. For areas one and four years after logging, we supplemented the ground-based information with aerial LiDAR data. By eight years post-logging the size class distributions of tree regeneration in all habitat types resembled those in unlogged areas, and densities were only lower in crown gaps. Eight years after logging, relative densities of pioneer trees were highest on secondary roads and log landings; no among habitat differences were observed in the relative densities of non-pioneer species at any time along the chronosequence. Tree species diversity (Fisher's alpha) converged on unlogged values on skid trails, bole gaps, and crown gaps at 8-years post-logging, but values remained lower on secondary roads and log landings. Canopy openness was greatest one year after logging, especially in log landings (mean 45.4 ± SE 4.5%) whereas four and eight years post-logging it did not exceed 10% and no differences were found among habitats. Soil bulk density was elevated relative to un-logged areas only on log landings one and four years after logging, and this difference disappeared by eight years postlogging. The total area disturbed by logging varied from 7.0% to 8.6% with nearly half of the totals in felling gaps (3.0-3.7%)

Computed tomography-osteoabsorptiometry for assessing the density distribution of subchondral bone as a measure of long-term mechanical adaptation in individual joints

To estimate subchondral mineralisation patterns which represent the long-term loading history of individual joints, a method has been developed employing computed tomography (CT) which permits repeated examination of living joints. The method was tested on 5 knee, 3 sacroiliac, 3 ankle and 5 shoulder joints and then investigated with X-ray densitometry. A CT absorptiometric presentation and maps of the area distribution of the subchondral bone density areas were derived using an image analyser. Comparison of the results from both X-ray densitometry and CT-absorptiometry revealed almost identical pictures of distribution of the subchondral bone density. The method may be used to examine subchondral mineralisation as a measure of the mechanical adaptability of joints in the living subject

Charge pairing, superconducting transition and supersymmetry in high-temperature cuprate superconductors

We propose a model for high-T$_{c}$ superconductors, valid for $0\leq\delta\leq\delta_{SC}$, that includes both the spin fluctuations of the Cu$^{++}$ magnetic ions and of the O$^{--}$ doped holes. Spin-charge separation is taken into account with the charge of the doped holes being associated to quantum skyrmion excitations (holons) of the Cu$^{++}$ spin background. The holon effective interaction potential is evaluated as a function of doping, indicating that Cooper pair formation is determined by the competition between the spin fluctuations of the Cu$^{++}$ background and of spins of the O$^{--}$ doped holes (spinons). The superconducting transition occurs when the spinon fluctuations dominate, thereby reversing the sign of the interaction. At this point ($\delta = \delta_{SC}$), the theory is supersymmetric at short distances and, as a consequence, the leading order results are not modified by radiative corrections. The critical doping parameter for the onset of superconductivity at T=0 is obtained and found to be a universal constant determined by the shape of the Fermi surface. Our theoretical values for $\delta_{SC}$ are in good agreement with the experiment for both LSCO and YBCO.Comment: RevTex, 4 pages, no figure

Composite quasiparticle formation and the low-energy effective Hamiltonians of the one- and two-dimensional Hubbard Model

We investigate the effect of hole doping on the strong-coupling Hubbard model at half-filling in spatial dimensions $D\ge 1$. We start with an antiferromagnetic mean-field description of the insulating state, and show that doping creates solitons in the antiferromagnetic background. In one dimension, the soliton is topological, spinless, and decoupled from the background antiferromagnetic fluctuations at low energies. In two dimensions and above, the soliton is non-topological, has spin quantum number 1/2, and is strongly coupled to the antiferromagnetic fluctuations. We derive the effective action governing the quasiparticle motion, study the properties of a single carrier, and comment on a possible description at finite concentration.Comment: REVTEX 3.0, 22 pages with 14 figures in the PostScript format compressed using uufile. Submitted to Phys. Rev. B. The complete PostScript file including figures can be obtained via ftp at ftp://serval.berkeley.edu/hubbard.ps . It is also available via www at http://roemer.fys.ku.dk/recent.ht

Theory of Spin Fluctuation-Induced Superconductivity Based on a d-p Model. II. -Superconducting State-

The superconducting state of a two-dimensional d-p model is studied from the spin fluctuation point of view by using a strong coupling theory. The fluctuation exchange (FLEX) approximatoin is employed to calculate the spin fluctuations and the superconducting gap functions self-consistently in the optimal- and over-doped regions of hole concentration. The gap function has a symmetry of d_{x^2 - y^2} type and develops below the transition temperature T_c more rapidly than in the BCS model. Its saturation value at the maximum is about 10 T_c. When the spin fluctuation-induced superconductivity is well stabilized at low temperatures in the optimal regime, the imaginary part of the antiferromagnetic spin susceptibility shows a very sharp resonance peak reminiscent of the 41 meV peak observed in the neutron scattering experiment on YBCO. The one-particle spectral density around k=(pi,0) shows sharp quasi-particle peaks followed by dip and hump structures bearing resemblance to the features observed in the angle-resolved photoemission experiment. With increasing doping concentration these features gradually disappear.Comment: 13 pages(LaTeX), 20 eps figure

Non-commutative U(1) Super-Yang-Mills Theory: Perturbative Self-Energy Corrections

The quantization of the non-commutative N=1, U(1) super-Yang-Mills action is performed in the superfield formalism. We calculate the one-loop corrections to the self-energy of the vector superfield. Although the power-counting theorem predicts quadratic ultraviolet and infrared divergences, there are actually only logarithmic UV and IR divergences, which is a crucial feature of non-commutative supersymmetric field theories.Comment: 18 pages, latex, uses feynmf package; references added, Wess-Zumino gauge remove

Trade-Offs Between Carbon Stocks and Timber Recovery in Tropical Forests are Mediated by Logging Intensity

Forest degradation accounts for ~70% of total carbon losses from tropical forests. Substantial emissions are from selective logging, a land-use activity that decreases forest carbon density. To maintain carbon values in selectively logged forests, climate change mitigation policies and government agencies promote the adoption of reduced-impact logging (RIL) practices. However, whether RIL will maintain both carbon and timber values in managed tropical forests over time remains uncertain. In this study, we quantify the recovery of timber stocks and aboveground carbon at an experimental site where forests were subjected to different intensities of RIL (4, 8, and 16 trees/ha). Our census data span 20 years postlogging and 17 years after the liberation of future crop trees from competition in a tropical forest on the Guiana Shield, a globally important forest carbon reservoir. We model recovery of timber and carbon with a breakpoint regression that allowed us to capture elevated tree mortality immediately after logging. Recovery rates of timber and carbon were governed by the presence of residual trees (i.e., trees that persisted through the first harvest). The liberation treatment stimulated faster recovery of timber albeit at a carbon cost. Model results suggest a threshold logging intensity beyond which forests managed for timber and carbon derive few benefits from RIL, with recruitment and residual growth not sufficient to offset losses. Inclusion of the breakpoint at which carbon and timber gains outpaced postlogging mortality led to high predictive accuracy, including out-of-sample R2 values \u3e90%, and enabled inference on demographic changes postlogging. Our modeling framework is broadly applicable to studies that aim to quantify impacts of logging on forest recovery. Overall, we demonstrate that initial mortality drives variation in recovery rates, that the second harvest depends on old growth wood, and that timber intensification lowers carbon stocks

Predicting mental imagery based BCI performance from personality, cognitive profile and neurophysiological patterns

Mental-Imagery based Brain-Computer Interfaces (MI-BCIs) allow their users to send commands to a computer using their brain-activity alone (typically measured by ElectroEncephaloGraphy— EEG), which is processed while they perform specific mental tasks. While very promising, MI-BCIs remain barely used outside laboratories because of the difficulty encountered by users to control them. Indeed, although some users obtain good control performances after training, a substantial proportion remains unable to reliably control an MI-BCI. This huge variability in user-performance led the community to look for predictors of MI-BCI control ability. However, these predictors were only explored for motor-imagery based BCIs, and mostly for a single training session per subject. In this study, 18 participants were instructed to learn to control an EEG-based MI-BCI by performing 3 MI-tasks, 2 of which were non-motor tasks, across 6 training sessions, on 6 different days. Relationships between the participants’ BCI control performances and their personality, cognitive profile and neurophysiological markers were explored. While no relevant relationships with neurophysiological markers were found, strong correlations between MI-BCI performances and mental-rotation scores (reflecting spatial abilities) were revealed. Also, a predictive model of MI-BCI performance based on psychometric questionnaire scores was proposed. A leave-one-subject-out cross validation process revealed the stability and reliability of this model: it enabled to predict participants’ performance with a mean error of less than 3 points. This study determined how users’ profiles impact their MI-BCI control ability and thus clears the way for designing novel MI-BCI training protocols, adapted to the profile of each user