Variation in foraging activity of Acanthochitona garnoti (Mollusca: Polyplacophora) from different habitats

Click on the link to view the abstract.S. Afc. J. Zool. 1997,32(3

Effect of the stacking fault energy on the mechanical properties of pure Cu and Cu-Al alloys subjected to severe plastic deformation

The effect of stacking fault energy (SFE) on the mechanical properties of pure Cu and alloys of Cu-2.2%Al and Cu-4.5%Al subjected to severe plastic deformation (SPD) was investigated. SPD was performed by equal channel angular pressing (ECAP) at room and cryogenic temperatures. It is established that the increase in the weight concentration of Al in the Cu matrix (a reduction of SFE) and decreasing the ECAP temperature leads to an increase of the strength characteristics. The observed tendency is caused by increasing of the role of deformation twinning

Alemtuzumab pre-conditioning with tacrolimus monotherapy in pediatric renal transplantation

We employed antibody pre-conditioning with alemtuzumab and posttransplant immunosuppression with low-dose tacrolimus monotherapy in 26 consecutive pediatric kidney transplant recipients between January 2004 and December 2005. Mean recipient age was 10.7 ± 5.8 years, 7.7% were undergoing retransplantation, and 3.8% were sensitized, with a PRA >20%. Mean donor age was 32.8 ± 9.2 years. Living donors were utilized in 65% of the transplants. Mean cold ischemia time was 27.6 ± 6.4 h. The mean number of HLA mismatches was 3.3 ± 1.3. Mean follow-up was 25 ± 8 months. One and 2 year patient survival was 100% and 96%. One and 2 year graft survival was 96% and 88%. Mean serum creatinine was 1.1 ± 0.6 mg/dL, and calculated creatinine clearance was 82.3 ± 29.4 mL/min/1.73 m 2. The incidence of pre-weaning acute rejection was 11.5%; the incidence of delayed graft function was 7.7%. Eighteen (69%) of the children were tapered to spaced tacrolimus monotherapy, 10.5 ± 2.2 months after transplantation. The incidence of CMV, PTLD and BK virus was 0%; the incidence of posttransplant diabetes was 7.7%. Although more follow-up is clearly needed, antibody pre-conditioning with alemtuzumab and tacrolimus monotherapy may be a safe and effective regimen in pediatric renal transplantation. © 2007 The Authors

Controller design for synchronization of an array of delayed neural networks using a controllable

This is the post-print version of the Article - Copyright @ 2011 ElsevierIn this paper, a controllable probabilistic particle swarm optimization (CPPSO) algorithm is introduced based on Bernoulli stochastic variables and a competitive penalized method. The CPPSO algorithm is proposed to solve optimization problems and is then applied to design the memoryless feedback controller, which is used in the synchronization of an array of delayed neural networks (DNNs). The learning strategies occur in a random way governed by Bernoulli stochastic variables. The expectations of Bernoulli stochastic variables are automatically updated by the search environment. The proposed method not only keeps the diversity of the swarm, but also maintains the rapid convergence of the CPPSO algorithm according to the competitive penalized mechanism. In addition, the convergence rate is improved because the inertia weight of each particle is automatically computed according to the feedback of fitness value. The efficiency of the proposed CPPSO algorithm is demonstrated by comparing it with some well-known PSO algorithms on benchmark test functions with and without rotations. In the end, the proposed CPPSO algorithm is used to design the controller for the synchronization of an array of continuous-time delayed neural networks.This research was partially supported by the National Natural Science Foundation of PR China (Grant No 60874113), the Research Fund for the Doctoral Program of Higher Education (Grant No 200802550007), the Key Creative Project of Shanghai Education Community (Grant No 09ZZ66), the Key Foundation Project of Shanghai(Grant No 09JC1400700), the Engineering and Physical Sciences Research Council EPSRC of the U.K. under Grant No. GR/S27658/01, an International Joint Project sponsored by the Royal Society of the U.K., and the Alexander von Humboldt Foundation of Germany

Graph Neural Networks for Particle Reconstruction in High Energy Physics detectors

Pattern recognition problems in high energy physics are notably different from traditional machine learning applications in computer vision. Reconstruction algorithms identify and measure the kinematic properties of particles produced in high energy collisions and recorded with complex detector systems. Two critical applications are the reconstruction of charged particle trajectories in tracking detectors and the reconstruction of particle showers in calorimeters. These two problems have unique challenges and characteristics, but both have high dimensionality, high degree of sparsity, and complex geometric layouts. Graph Neural Networks (GNNs) are a relatively new class of deep learning architectures which can deal with such data effectively, allowing scientists to incorporate domain knowledge in a graph structure and learn powerful representations leveraging that structure to identify patterns of interest. In this work we demonstrate the applicability of GNNs to these two diverse particle reconstruction problems

Graph Neural Networks for Particle Reconstruction in High Energy Physics detectors

Pattern recognition problems in high energy physics are notably different from traditional machine learning applications in computer vision. Reconstruction algorithms identify and measure the kinematic properties of particles produced in high energy collisions and recorded with complex detector systems. Two critical applications are the reconstruction of charged particle trajectories in tracking detectors and the reconstruction of particle showers in calorimeters. These two problems have unique challenges and characteristics, but both have high dimensionality, high degree of sparsity, and complex geometric layouts. Graph Neural Networks (GNNs) are a relatively new class of deep learning architectures which can deal with such data effectively, allowing scientists to incorporate domain knowledge in a graph structure and learn powerful representations leveraging that structure to identify patterns of interest. In this work we demonstrate the applicability of GNNs to these two diverse particle reconstruction problems.Comment: Presented at NeurIPS 2019 Workshop "Machine Learning and the Physical Sciences

Absence of charge backscattering in the nonequilibrium current of normal-superconductor structures

We study the nonequilibrium transport properties of a normal-superconductor-normal structure, focussing on the effect of adding an impurity in the superconducting region. Current conservation requires the superfluid velocity to be nonzero, causing a distortion of the quasiparticle dispersion relation within the superconductor. For weakly reflecting interfaces we find a regime of intermediate voltages in which Andreev transmission is the only permitted mechanism for quasiparticles to enter the superconductor. Impurities in the superconductor can only cause Andreev reflection of these quasiparticles and thus cannot degrade the current. At higher voltages, a state of gapless superconductivity develops which is sensitive to the presence of impurities.Comment: Latex file, 11 pages, 2 figures available upon request [email protected], to be published in Journal of Physics: Condensed Matte

Selenium digestibility and bioactivity in dogs : what the can can, the kibble can't

There is a growing concern for the long-term health effects of selenium (Se) over-or underfeeding. The efficiency of utilization of dietary Se is subject to many factors. Our study in dogs evaluated the effect of diet type (canned versus kibble) and dietary protein concentration on Se digestibility and bioactivity. Canned and kibble diets are commonly used formats of dog food, widely ranging in protein concentration. Twenty-four Labrador retrievers were used and four canned and four kibble diets were selected with crude protein concentrations ranging from 10.1 to 27.5 g/MJ. Crude protein concentration had no influence on the digestibility of Se in either canned or kibble diets, but a lower Se digestibility was observed in canned compared to kibble diets. However, the biological activity of Se, as measured by whole blood glutathione peroxidase, was higher in dogs fed the canned diets than in dogs fed the kibble diets and decreased with increasing crude protein intake. These results indicate that selenium recommendations in dog foods need to take diet type into account