Device for in-situ cleaving of hard crystals

Cleaving crystals in a vacuum chamber is a simple method for obtaining atomically flat and clean surfaces for materials that have a preferential cleaving plane. Most in-situ cleavers use parallel cutting edges that are applied from two sides on the sample. We found in ambient experiments that diagonal cutting pliers, where the cleavage force is introduced in a single point instead of a line work very well also for hard materials. Here, we incorporate the diagonal cutting plier principle in a design compatible with ultra-high vacuum requirements. We show optical microscopy (mm scale) and atomic force microscopy (atomic scale) images of NiO(001) surfaces cleaved with this device.Comment: 7 pages, 3 figures Submitted to Review of Scientific Instruments (2005

Looking for Design in Materials Design

Despite great advances in computation, materials design is still science fiction. The construction of structure-property relations on the quantum scale will turn computational empiricism into true design.Comment: 3 pages, 1 figur

A Multiobjective Optimization Approach for Market Timing

The introduction of electronic exchanges was a crucial point in history as it heralded the arrival of algorithmic trading. Designers of such systems face a number of issues, one of which is deciding when to buy or sell a given security on a financial market. Although Genetic Algorithms (GA) have been the most widely used to tackle this issue, Particle Swarm Optimization (PSO) has seen much lower adoption within the domain. In two previous works, the authors adapted PSO algorithms to tackle market timing and address the shortcomings of the previous approaches both with GA and PSO. The majority of work done to date on market timing tackled it as a single objective optimization problem, which limits its suitability to live trading as designers of such strategies will realistically pursue multiple objectives such as maximizing profits, minimizing exposure to risk and using the shortest strategies to improve execution speed. In this paper, we adapt both a GA and PSO to tackle market timing as a multiobjective optimization problem and provide an in depth discussion of our results and avenues of future research

Resolution of the stochastic strategy spatial prisoner's dilemma by means of particle swarm optimization

We study the evolution of cooperation among selfish individuals in the stochastic strategy spatial prisoner's dilemma game. We equip players with the particle swarm optimization technique, and find that it may lead to highly cooperative states even if the temptations to defect are strong. The concept of particle swarm optimization was originally introduced within a simple model of social dynamics that can describe the formation of a swarm, i.e., analogous to a swarm of bees searching for a food source. Essentially, particle swarm optimization foresees changes in the velocity profile of each player, such that the best locations are targeted and eventually occupied. In our case, each player keeps track of the highest payoff attained within a local topological neighborhood and its individual highest payoff. Thus, players make use of their own memory that keeps score of the most profitable strategy in previous actions, as well as use of the knowledge gained by the swarm as a whole, to find the best available strategy for themselves and the society. Following extensive simulations of this setup, we find a significant increase in the level of cooperation for a wide range of parameters, and also a full resolution of the prisoner's dilemma. We also demonstrate extreme efficiency of the optimization algorithm when dealing with environments that strongly favor the proliferation of defection, which in turn suggests that swarming could be an important phenomenon by means of which cooperation can be sustained even under highly unfavorable conditions. We thus present an alternative way of understanding the evolution of cooperative behavior and its ubiquitous presence in nature, and we hope that this study will be inspirational for future efforts aimed in this direction.Comment: 12 pages, 4 figures; accepted for publication in PLoS ON

Eph-Dependent Tyrosine Phosphorylation of Ephexin1 Modulates Growth Cone Collapse

SummaryEphs regulate growth cone repulsion, a process controlled by the actin cytoskeleton. The guanine nucleotide exchange factor (GEF) ephexin1 interacts with EphA4 and has been suggested to mediate the effect of EphA on the activity of Rho GTPases, key regulators of the cytoskeleton and axon guidance. Using cultured ephexin1−/− mouse neurons and RNA interference in the chick, we report that ephexin1 is required for normal axon outgrowth and ephrin-dependent axon repulsion. Ephexin1 becomes tyrosine phosphorylated in response to EphA signaling in neurons, and this phosphorylation event is required for growth cone collapse. Tyrosine phosphorylation of ephexin1 enhances ephexin1’s GEF activity toward RhoA while not altering its activity toward Rac1 or Cdc42, thus changing the balance of GTPase activities. These findings reveal that ephexin1 plays a role in axon guidance and is regulated by a switch mechanism that is specifically tailored to control Eph-mediated growth cone collapse

Childhood loneliness as a predictor of adolescent depressive symptoms: an 8-year longitudinal study

Childhood loneliness is characterised by children’s perceived dissatisfaction with aspects of their social relationships. This 8-year prospective study investigates whether loneliness in childhood predicts depressive symptoms in adolescence, controlling for early childhood indicators of emotional problems and a sociometric measure of peer social preference. 296 children were tested in the infant years of primary school (T1 5 years of age), in the upper primary school (T2 9 years of age) and in secondary school (T3 13 years of age). At T1, children completed the loneliness assessment and sociometric interview. Their teachers completed externalisation and internalisation rating scales for each child. At T2, children completed a loneliness assessment, a measure of depressive symptoms, and the sociometric interview. At T3, children completed the depressive symptom assessment. An SEM analysis showed that depressive symptoms in early adolescence (age 13) were predicted by reports of depressive symptoms at age 8, which were themselves predicted by internalisation in the infant school (5 years). The interactive effect of loneliness at 5 and 9, indicative of prolonged loneliness in childhood, also predicted depressive symptoms at age 13. Parent and peer-related loneliness at age 5 and 9, peer acceptance variables, and duration of parent loneliness did not predict depression. Our results suggest that enduring peer-related loneliness during childhood constitutes an interpersonal stressor that predisposes children to adolescent depressive symptoms. Possible mediators are discussed

Atmospheric band fitting coefficients derived from a self-consistent rocket-borne experiment

Based on self-consistent rocket-borne measurements of temperature, the densities of atomic oxygen and neutral air, and the volume emission of the atmospheric band (762&thinsp;nm), we examined the one-step and two-step excitation mechanism of O2b1Σg+ for nighttime conditions. Following McDade et al. (1986), we derived the empirical fitting coefficients, which parameterize the atmospheric band emission O2b1Σg+-X3Σg-0,0. This allows us to derive the atomic oxygen concentration from nighttime observations of atmospheric band emission O2b1Σg+-X3Σg-0,0. The derived empirical parameters can also be utilized for atmospheric band modeling. Additionally, we derived the fit function and corresponding coefficients for the combined (one- and two-step) mechanism. The simultaneous common volume measurements of all the parameters involved in the theoretical calculation of the observed O2b1Σg+-X3Σg-0,0 emission, i.e., temperature and density of the background air, atomic oxygen density, and volume emission rate, is the novelty and the advantage of this work.</p