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

An Efficient Algorithm for Optimizing Adaptive Quantum Metrology Processes

Quantum-enhanced metrology infers an unknown quantity with accuracy beyond the standard quantum limit (SQL). Feedback-based metrological techniques are promising for beating the SQL but devising the feedback procedures is difficult and inefficient. Here we introduce an efficient self-learning swarm-intelligence algorithm for devising feedback-based quantum metrological procedures. Our algorithm can be trained with simulated or real-world trials and accommodates experimental imperfections, losses, and decoherence

Micromachined Nanoporous Membranes For Blood Oxygenation Systems

Nanostructured membranes with precisely engineered nanopores were fabricated on a thin silicon nitride membrane, using a combination of bulk micromachining and focused-ion-beam drilling. These membranes are designed to preserve microscale blood channel dimensions, thereby permitting the red cell shape change that enhances gas exchange in the pulmonary capillary. The membranes were tested for their mechanical stability and the results were verified with finite element analysis. Initial studies have proven the membranes to be robust, and capable of withstanding pressures typically experienced in blood oxygenator channels. A novel MEMS-based blood oxygenation system employing the nanoporous membranes is also presented. The oxygenation system is designed to have controlled blood and gas volumes for efficient blood oxygenation. © 2008 IEEE

The continuity of the inversion and the structure of maximal subgroups in countably compact topological semigroups

In this paper we search for conditions on a countably compact (pseudo-compact) topological semigroup under which: (i) each maximal subgroup $H(e)$ in $S$ is a (closed) topological subgroup in $S$; (ii) the Clifford part $H(S)$(i.e. the union of all maximal subgroups) of the semigroup $S$ is a closed subset in $S$; (iii) the inversion $\operatorname{inv}\colon H(S)\to H(S)$ is continuous; and (iv) the projection $\pi\colon H(S)\to E(S)$, $\pi\colon x\longmapsto xx^{-1}$, onto the subset of idempotents $E(S)$ of $S$, is continuous

Increased 5-hydroxymethylcytosine and decreased 5-methylcytosine are indicators of global epigenetic dysregulation in diffuse intrinsic pontine glioma

Introduction Diffuse intrinsic pontine glioma (DIPG) is a malignant pediatric brain tumor associated with dismal outcome. Recent high-throughput molecular studies have shown a high frequency of mutations in histone-encoding genes (H3F3A and HIST1B) and distinctive epigenetic alterations in these tumors. Epigenetic alterations described in DIPG include global DNA hypomethylation. In addition to the generally repressive methylcytosine DNA alteration, 5-hydroxymethylation of cytosine (5hmC) is recognized as an epigenetic mark associated with active chromatin. We hypothesized that in addition to alterations in DNA methylation, that there would be changes in 5hmC. To test this hypothesis, we performed immunohistochemical studies to compare epigenetic alterations in DIPG to extrapontine adult and pediatric glioblastoma (GBM) and normal brain. A total of 124 tumors were scored for histone 3 lysine 27 trimethylation (H3K27me3) and histone 3 lysine 9 trimethylation (H3K9me3) and 104 for 5hmC and 5-methylcytosine (5mC). An H-score was derived by multiplying intensity (0–2) by percentage of positive tumor nuclei (0-100%). Results We identified decreased H3K27me3 in the DIPG cohort compared to pediatric GBM (p \u3c 0.01), adult GBM (p \u3c 0.0001) and normal brain (p \u3c 0.0001). H3K9me3 was not significantly different between tumor types. Global DNA methylation as measured by 5mC levels were significantly lower in DIPG compared to pediatric GBM (p \u3c 0.001), adult GBM (p \u3c 0.01), and normal brain (p \u3c 0.01). Conversely, 5hmC levels were significantly higher in DIPG compared to pediatric GBM (p \u3c 0.0001) and adult GBM (p \u3c 0.0001). Additionally, in an independent set of DIPG tumor samples, TET1 andTET3 mRNAs were found to be overexpressed relative to matched normal brain. Conclusions Our findings extend the immunohistochemical study of epigenetic alterations in archival tissue to DIPG specimens. Low H3K27me3, decreased 5mC and increased 5hmC are characteristic of DIPG in comparison with extrapontine GBM. In DIPG, the relative imbalance of 5mC compared to 5hmC may represent an opportunity for therapeutic intervention

State Transition Algorithm

In terms of the concepts of state and state transition, a new heuristic random search algorithm named state transition algorithm is proposed. For continuous function optimization problems, four special transformation operators called rotation, translation, expansion and axesion are designed. Adjusting measures of the transformations are mainly studied to keep the balance of exploration and exploitation. Convergence analysis is also discussed about the algorithm based on random search theory. In the meanwhile, to strengthen the search ability in high dimensional space, communication strategy is introduced into the basic algorithm and intermittent exchange is presented to prevent premature convergence. Finally, experiments are carried out for the algorithms. With 10 common benchmark unconstrained continuous functions used to test the performance, the results show that state transition algorithms are promising algorithms due to their good global search capability and convergence property when compared with some popular algorithms.Comment: 18 pages, 28 figure

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

Ab initio Random Structure Searching

It is essential to know the arrangement of the atoms in a material in order to compute and understand its properties. Searching for stable structures of materials using first-principles electronic structure methods, such as density functional theory (DFT), is a rapidly growing field. Here we describe our simple, elegant and powerful approach to searching for structures with DFT which we call ab initio random structure searching (AIRSS). Applications to discovering structures of solids, point defects, surfaces, and clusters are reviewed. New results for iron clusters on graphene, silicon clusters, polymeric nitrogen, hydrogen-rich lithium hydrides, and boron are presented.Comment: 44 pages, 23 figure

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