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

Effect of a Novel Nonviral Gene Delivery of BMP-2 on Bone Healing

Background. Gene therapeutic drug delivery approaches have been introduced to improve the efficiency of growth factors at the site of interest. This study investigated the efficacy and safety of a new nonviral copolymer-protected gene vector (COPROG) for the stimulation of bone healing. Methods. In vitro, rat osteoblasts were transfected with COPROG + luciferase plasmid or COPROG + hBMP-2 plasmid. In vivo, rat tibial fractures were intramedullary stabilized with uncoated versus COPROG+hBMP-2-plasmid-coated titanium K-wires. The tibiae were prepared for biomechanical and histological analyses at days 28 and 42 and for transfection/safety study at days 2, 4, 7, 28, and 42. Results. In vitro results showed luciferase expression until day 21, and hBMP-2-protein was measured from day 2 – day 10. In vivo, the local application of hBMP-2-plasmid showed a significantly higher maximum load after 42 days compared to that in the control. The histomorphometric analysis revealed a significantly less mineralized periosteal callus area in the BMP-2 group compared to the control at day 28. The rt-PCR showed no systemic biodistribution of luciferase RNA. Conclusion. A positive effect on fracture healing by nonviral BMP-2 plasmid application from COPROG-coated implants could be shown in this study; however, the effect of the vector may be improved with higher plasmid concentrations. Transfection showed no biodistribution to distant organs and was considered to be safe

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

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

Studying the Effect of Measured Solar Power on Evolutionary Multi-objective Prediction Intervals

This paper has been presented at: 19th Intelligent Data Engineering and Automated Learning (IDEAL 2018)While it is common to make point forecasts for solar energy generation, estimating the forecast uncertainty has received less attention. In this article, prediction intervals are computed within a multi-objective approach in order to obtain an optimal coverage/width tradeoff. In particular, it is studied whether using measured power as an another input, additionally to the meteorological forecast variables, is able to improve the properties of prediction intervals for short time horizons (up to three hours). Results show that they tend to be narrower (i.e. less uncertain), and the ratio between coverage and width is larger. The method has shown to obtain intervals with better properties than baseline Quantile Regression.This work has been funded by the Spanish Ministry of Science under contract ENE2014-56126-C2-2-R (AOPRIN-SOL project)

Escherichia coli induces apoptosis and proliferation of mammary cells

Mammary cell apoptosis and proliferation were assessed after injection of Escherichia coli into the left mammary quarters of six cows. Bacteriological analysis of foremilk samples revealed coliform infection in the injected quarters of four cows. Milk somatic cell counts increased in these quarters and peaked at 24 h after bacterial injection. Body temperature also increased, peaking at 12 h postinjection, The number of apoptotic cells was significantly higher in the mastitic tissue than in the uninfected control. Expression of Bax and interleukin-1 beta converting enzyme increased in the mastitic tissue at 24 h and 72 h postinfection, whereas Bcl-2 expression decreased at 24 h but did not differ significantly from the control at 72 h postinfection, Induction of matrix metalloproteinase-g, stromelysin-1 and urokinase-type plasminogen activator was also observed in the mastitic tissue. Moreover, cell proliferation increased in the infected tissue, These results demonstrate that Escherichia coli-induced mastitis promotes apoptosis and cell proliferation

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

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